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  1. Selection of the Maximum Spatial Cluster Size of the Spatial Scan Statistic by Using the Maximum Clustering Set-Proportion Statistic.

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    Ma, Yue; Yin, Fei; Zhang, Tao; Zhou, Xiaohua Andrew; Li, Xiaosong

    2016-01-01

    Spatial scan statistics are widely used in various fields. The performance of these statistics is influenced by parameters, such as maximum spatial cluster size, and can be improved by parameter selection using performance measures. Current performance measures are based on the presence of clusters and are thus inapplicable to data sets without known clusters. In this work, we propose a novel overall performance measure called maximum clustering set-proportion (MCS-P), which is based on the likelihood of the union of detected clusters and the applied dataset. MCS-P was compared with existing performance measures in a simulation study to select the maximum spatial cluster size. Results of other performance measures, such as sensitivity and misclassification, suggest that the spatial scan statistic achieves accurate results in most scenarios with the maximum spatial cluster sizes selected using MCS-P. Given that previously known clusters are not required in the proposed strategy, selection of the optimal maximum cluster size with MCS-P can improve the performance of the scan statistic in applications without identified clusters.

  2. Comparison of Three Plot Selection Methods for Estimating Change in Temporally Variable, Spatially Clustered Populations.

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    Thompson, William L. [Bonneville Power Administration, Portland, OR (US). Environment, Fish and Wildlife

    2001-07-01

    Monitoring population numbers is important for assessing trends and meeting various legislative mandates. However, sampling across time introduces a temporal aspect to survey design in addition to the spatial one. For instance, a sample that is initially representative may lose this attribute if there is a shift in numbers and/or spatial distribution in the underlying population that is not reflected in later sampled plots. Plot selection methods that account for this temporal variability will produce the best trend estimates. Consequently, I used simulation to compare bias and relative precision of estimates of population change among stratified and unstratified sampling designs based on permanent, temporary, and partial replacement plots under varying levels of spatial clustering, density, and temporal shifting of populations. Permanent plots produced more precise estimates of change than temporary plots across all factors. Further, permanent plots performed better than partial replacement plots except for high density (5 and 10 individuals per plot) and 25% - 50% shifts in the population. Stratified designs always produced less precise estimates of population change for all three plot selection methods, and often produced biased change estimates and greatly inflated variance estimates under sampling with partial replacement. Hence, stratification that remains fixed across time should be avoided when monitoring populations that are likely to exhibit large changes in numbers and/or spatial distribution during the study period. Key words: bias; change estimation; monitoring; permanent plots; relative precision; sampling with partial replacement; temporary plots.

  3. Spatial cluster modelling

    CERN Document Server

    Lawson, Andrew B

    2002-01-01

    Research has generated a number of advances in methods for spatial cluster modelling in recent years, particularly in the area of Bayesian cluster modelling. Along with these advances has come an explosion of interest in the potential applications of this work, especially in epidemiology and genome research. In one integrated volume, this book reviews the state-of-the-art in spatial clustering and spatial cluster modelling, bringing together research and applications previously scattered throughout the literature. It begins with an overview of the field, then presents a series of chapters that illuminate the nature and purpose of cluster modelling within different application areas, including astrophysics, epidemiology, ecology, and imaging. The focus then shifts to methods, with discussions on point and object process modelling, perfect sampling of cluster processes, partitioning in space and space-time, spatial and spatio-temporal process modelling, nonparametric methods for clustering, and spatio-temporal ...

  4. Improved Density Based Spatial Clustering of Applications of Noise Clustering Algorithm for Knowledge Discovery in Spatial Data

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    Arvind Sharma

    2016-01-01

    Full Text Available There are many techniques available in the field of data mining and its subfield spatial data mining is to understand relationships between data objects. Data objects related with spatial features are called spatial databases. These relationships can be used for prediction and trend detection between spatial and nonspatial objects for social and scientific reasons. A huge data set may be collected from different sources as satellite images, X-rays, medical images, traffic cameras, and GIS system. To handle this large amount of data and set relationship between them in a certain manner with certain results is our primary purpose of this paper. This paper gives a complete process to understand how spatial data is different from other kinds of data sets and how it is refined to apply to get useful results and set trends to predict geographic information system and spatial data mining process. In this paper a new improved algorithm for clustering is designed because role of clustering is very indispensable in spatial data mining process. Clustering methods are useful in various fields of human life such as GIS (Geographic Information System, GPS (Global Positioning System, weather forecasting, air traffic controller, water treatment, area selection, cost estimation, planning of rural and urban areas, remote sensing, and VLSI designing. This paper presents study of various clustering methods and algorithms and an improved algorithm of DBSCAN as IDBSCAN (Improved Density Based Spatial Clustering of Application of Noise. The algorithm is designed by addition of some important attributes which are responsible for generation of better clusters from existing data sets in comparison of other methods.

  5. Selective memory generalization by spatial patterning of protein synthesis.

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    O'Donnell, Cian; Sejnowski, Terrence J

    2014-04-16

    Protein synthesis is crucial for both persistent synaptic plasticity and long-term memory. De novo protein expression can be restricted to specific neurons within a population, and to specific dendrites within a single neuron. Despite its ubiquity, the functional benefits of spatial protein regulation for learning are unknown. We used computational modeling to study this problem. We found that spatially patterned protein synthesis can enable selective consolidation of some memories but forgetting of others, even for simultaneous events that are represented by the same neural population. Key factors regulating selectivity include the functional clustering of synapses on dendrites, and the sparsity and overlap of neural activity patterns at the circuit level. Based on these findings, we proposed a two-step model for selective memory generalization during REM and slow-wave sleep. The pattern-matching framework we propose may be broadly applicable to spatial protein signaling throughout cortex and hippocampus. Copyright © 2014 Elsevier Inc. All rights reserved.

  6. Discovery of path nearby clusters in spatial networks

    KAUST Repository

    Shang, Shuo

    2015-06-01

    The discovery of regions of interest in large cities is an important challenge. We propose and investigate a novel query called the path nearby cluster (PNC) query that finds regions of potential interest (e.g., sightseeing places and commercial districts) with respect to a user-specified travel route. Given a set of spatial objects O (e.g., POIs, geo-tagged photos, or geo-tagged tweets) and a query route q , if a cluster c has high spatial-object density and is spatially close to q , it is returned by the query (a cluster is a circular region defined by a center and a radius). This query aims to bring important benefits to users in popular applications such as trip planning and location recommendation. Efficient computation of the PNC query faces two challenges: how to prune the search space during query processing, and how to identify clusters with high density effectively. To address these challenges, a novel collective search algorithm is developed. Conceptually, the search process is conducted in the spatial and density domains concurrently. In the spatial domain, network expansion is adopted, and a set of vertices are selected from the query route as expansion centers. In the density domain, clusters are sorted according to their density distributions and they are scanned from the maximum to the minimum. A pair of upper and lower bounds are defined to prune the search space in the two domains globally. The performance of the PNC query is studied in extensive experiments based on real and synthetic spatial data. © 2014 IEEE.

  7. Spatial cluster detection using dynamic programming

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    Sverchkov Yuriy

    2012-03-01

    Full Text Available Abstract Background The task of spatial cluster detection involves finding spatial regions where some property deviates from the norm or the expected value. In a probabilistic setting this task can be expressed as finding a region where some event is significantly more likely than usual. Spatial cluster detection is of interest in fields such as biosurveillance, mining of astronomical data, military surveillance, and analysis of fMRI images. In almost all such applications we are interested both in the question of whether a cluster exists in the data, and if it exists, we are interested in finding the most accurate characterization of the cluster. Methods We present a general dynamic programming algorithm for grid-based spatial cluster detection. The algorithm can be used for both Bayesian maximum a-posteriori (MAP estimation of the most likely spatial distribution of clusters and Bayesian model averaging over a large space of spatial cluster distributions to compute the posterior probability of an unusual spatial clustering. The algorithm is explained and evaluated in the context of a biosurveillance application, specifically the detection and identification of Influenza outbreaks based on emergency department visits. A relatively simple underlying model is constructed for the purpose of evaluating the algorithm, and the algorithm is evaluated using the model and semi-synthetic test data. Results When compared to baseline methods, tests indicate that the new algorithm can improve MAP estimates under certain conditions: the greedy algorithm we compared our method to was found to be more sensitive to smaller outbreaks, while as the size of the outbreaks increases, in terms of area affected and proportion of individuals affected, our method overtakes the greedy algorithm in spatial precision and recall. The new algorithm performs on-par with baseline methods in the task of Bayesian model averaging. Conclusions We conclude that the dynamic

  8. Micro-scale Spatial Clustering of Cholera Risk Factors in Urban Bangladesh.

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    Bi, Qifang; Azman, Andrew S; Satter, Syed Moinuddin; Khan, Azharul Islam; Ahmed, Dilruba; Riaj, Altaf Ahmed; Gurley, Emily S; Lessler, Justin

    2016-02-01

    Close interpersonal contact likely drives spatial clustering of cases of cholera and diarrhea, but spatial clustering of risk factors may also drive this pattern. Few studies have focused specifically on how exposures for disease cluster at small spatial scales. Improving our understanding of the micro-scale clustering of risk factors for cholera may help to target interventions and power studies with cluster designs. We selected sets of spatially matched households (matched-sets) near cholera case households between April and October 2013 in a cholera endemic urban neighborhood of Tongi Township in Bangladesh. We collected data on exposures to suspected cholera risk factors at the household and individual level. We used intra-class correlation coefficients (ICCs) to characterize clustering of exposures within matched-sets and households, and assessed if clustering depended on the geographical extent of the matched-sets. Clustering over larger spatial scales was explored by assessing the relationship between matched-sets. We also explored whether different exposures tended to appear together in individuals, households, and matched-sets. Household level exposures, including: drinking municipal supplied water (ICC = 0.97, 95%CI = 0.96, 0.98), type of latrine (ICC = 0.88, 95%CI = 0.71, 1.00), and intermittent access to drinking water (ICC = 0.96, 95%CI = 0.87, 1.00) exhibited strong clustering within matched-sets. As the geographic extent of matched-sets increased, the concordance of exposures within matched-sets decreased. Concordance between matched-sets of exposures related to water supply was elevated at distances of up to approximately 400 meters. Household level hygiene practices were correlated with infrastructure shown to increase cholera risk. Co-occurrence of different individual level exposures appeared to mostly reflect the differing domestic roles of study participants. Strong spatial clustering of exposures at a small spatial scale in a cholera endemic

  9. Micro-scale Spatial Clustering of Cholera Risk Factors in Urban Bangladesh.

    Directory of Open Access Journals (Sweden)

    Qifang Bi

    2016-02-01

    Full Text Available Close interpersonal contact likely drives spatial clustering of cases of cholera and diarrhea, but spatial clustering of risk factors may also drive this pattern. Few studies have focused specifically on how exposures for disease cluster at small spatial scales. Improving our understanding of the micro-scale clustering of risk factors for cholera may help to target interventions and power studies with cluster designs. We selected sets of spatially matched households (matched-sets near cholera case households between April and October 2013 in a cholera endemic urban neighborhood of Tongi Township in Bangladesh. We collected data on exposures to suspected cholera risk factors at the household and individual level. We used intra-class correlation coefficients (ICCs to characterize clustering of exposures within matched-sets and households, and assessed if clustering depended on the geographical extent of the matched-sets. Clustering over larger spatial scales was explored by assessing the relationship between matched-sets. We also explored whether different exposures tended to appear together in individuals, households, and matched-sets. Household level exposures, including: drinking municipal supplied water (ICC = 0.97, 95%CI = 0.96, 0.98, type of latrine (ICC = 0.88, 95%CI = 0.71, 1.00, and intermittent access to drinking water (ICC = 0.96, 95%CI = 0.87, 1.00 exhibited strong clustering within matched-sets. As the geographic extent of matched-sets increased, the concordance of exposures within matched-sets decreased. Concordance between matched-sets of exposures related to water supply was elevated at distances of up to approximately 400 meters. Household level hygiene practices were correlated with infrastructure shown to increase cholera risk. Co-occurrence of different individual level exposures appeared to mostly reflect the differing domestic roles of study participants. Strong spatial clustering of exposures at a small spatial scale in a

  10. Using Spatial Clustering in Forecasting Groundwater Quality Parameters by ANFIS

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    MohammadTaghi Alami

    2016-07-01

    Full Text Available Groundwater is a major source of water supply for domestic, agricultural, and industrial uses; hence, its quality modeling is an important task in hydro-environmental studies. While many data-based models have been developed for this purpose, the performance of such data-based models can be drastically enhanced if they are based on temporal and spatial pre-processing. In this study, geostatistics tools (e.g., Co-Kriging, as spatial estimators, and self-organizing map (SOM, as a clustering technique, were employed in conjunction with Adaptive Neuro-Fuzzy Inference System (ANFIS for the temporal forecasting of such quality parameters as electrical conductivity (EC and total dissolved solids (TDS of the groundwater in Ardabil Plain. Using the results thus obtained, the impact of spatial data clustering was also investigated on the same parameters. The results showed that, if propoer input data are selected, the proposed spatial clustering technique is capable of imporving groundwater quality forecasts made by ANFIS.

  11. Clustering with Obstacles in Spatial Databases

    OpenAIRE

    El-Zawawy, Mohamed A.; El-Sharkawi, Mohamed E.

    2009-01-01

    Clustering large spatial databases is an important problem, which tries to find the densely populated regions in a spatial area to be used in data mining, knowledge discovery, or efficient information retrieval. However most algorithms have ignored the fact that physical obstacles such as rivers, lakes, and highways exist in the real world and could thus affect the result of the clustering. In this paper, we propose CPO, an efficient clustering technique to solve the problem of clustering in ...

  12. Spatial clustering of pixels of a multispectral image

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    Conger, James Lynn

    2014-08-19

    A method and system for clustering the pixels of a multispectral image is provided. A clustering system computes a maximum spectral similarity score for each pixel that indicates the similarity between that pixel and the most similar neighboring. To determine the maximum similarity score for a pixel, the clustering system generates a similarity score between that pixel and each of its neighboring pixels and then selects the similarity score that represents the highest similarity as the maximum similarity score. The clustering system may apply a filtering criterion based on the maximum similarity score so that pixels with similarity scores below a minimum threshold are not clustered. The clustering system changes the current pixel values of the pixels in a cluster based on an averaging of the original pixel values of the pixels in the cluster.

  13. Mining Co-Location Patterns with Clustering Items from Spatial Data Sets

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    Zhou, G.; Li, Q.; Deng, G.; Yue, T.; Zhou, X.

    2018-05-01

    The explosive growth of spatial data and widespread use of spatial databases emphasize the need for the spatial data mining. Co-location patterns discovery is an important branch in spatial data mining. Spatial co-locations represent the subsets of features which are frequently located together in geographic space. However, the appearance of a spatial feature C is often not determined by a single spatial feature A or B but by the two spatial features A and B, that is to say where A and B appear together, C often appears. We note that this co-location pattern is different from the traditional co-location pattern. Thus, this paper presents a new concept called clustering terms, and this co-location pattern is called co-location patterns with clustering items. And the traditional algorithm cannot mine this co-location pattern, so we introduce the related concept in detail and propose a novel algorithm. This algorithm is extended by join-based approach proposed by Huang. Finally, we evaluate the performance of this algorithm.

  14. A novel artificial immune algorithm for spatial clustering with obstacle constraint and its applications.

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    Sun, Liping; Luo, Yonglong; Ding, Xintao; Zhang, Ji

    2014-01-01

    An important component of a spatial clustering algorithm is the distance measure between sample points in object space. In this paper, the traditional Euclidean distance measure is replaced with innovative obstacle distance measure for spatial clustering under obstacle constraints. Firstly, we present a path searching algorithm to approximate the obstacle distance between two points for dealing with obstacles and facilitators. Taking obstacle distance as similarity metric, we subsequently propose the artificial immune clustering with obstacle entity (AICOE) algorithm for clustering spatial point data in the presence of obstacles and facilitators. Finally, the paper presents a comparative analysis of AICOE algorithm and the classical clustering algorithms. Our clustering model based on artificial immune system is also applied to the case of public facility location problem in order to establish the practical applicability of our approach. By using the clone selection principle and updating the cluster centers based on the elite antibodies, the AICOE algorithm is able to achieve the global optimum and better clustering effect.

  15. A Novel Artificial Immune Algorithm for Spatial Clustering with Obstacle Constraint and Its Applications

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    Liping Sun

    2014-01-01

    Full Text Available An important component of a spatial clustering algorithm is the distance measure between sample points in object space. In this paper, the traditional Euclidean distance measure is replaced with innovative obstacle distance measure for spatial clustering under obstacle constraints. Firstly, we present a path searching algorithm to approximate the obstacle distance between two points for dealing with obstacles and facilitators. Taking obstacle distance as similarity metric, we subsequently propose the artificial immune clustering with obstacle entity (AICOE algorithm for clustering spatial point data in the presence of obstacles and facilitators. Finally, the paper presents a comparative analysis of AICOE algorithm and the classical clustering algorithms. Our clustering model based on artificial immune system is also applied to the case of public facility location problem in order to establish the practical applicability of our approach. By using the clone selection principle and updating the cluster centers based on the elite antibodies, the AICOE algorithm is able to achieve the global optimum and better clustering effect.

  16. Spatial clustering of porcine cysticercosis in Mbulu district, northern Tanzania.

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    Helena A Ngowi

    Full Text Available BACKGROUND: Porcine cysticercosis is caused by a zoonotic tapeworm, Taenia solium, which causes serious disease syndromes in human. Effective control of the parasite requires knowledge on the burden and pattern of the infections in order to properly direct limited resources. The objective of this study was to establish the spatial distribution of porcine cysticercosis in Mbulu district, northern Tanzania, to guide control strategies. METHODOLOGY/PRINCIPAL FINDINGS: This study is a secondary analysis of data collected during the baseline and follow-up periods of a randomized community trial aiming at reducing the incidence rate of porcine cysticercosis through an educational program. At baseline, 784 randomly selected pig-keeping households located in 42 villages in 14 wards were included. Lingual examination of indigenous pigs aged 2-12 (median 8 months, one randomly selected from each household, were conducted. Data from the control group of the randomized trial that included 21 of the 42 villages were used for the incidence study. A total of 295 pig-keeping households were provided with sentinel pigs (one each and reassessed for cysticercosis incidence once or twice for 2-9 (median 4 months using lingual examination and antigen ELISA. Prevalence of porcine cysticercosis was computed in Epi Info 3.5. The prevalence and incidence of porcine cysticercosis were mapped at household level using ArcView 3.2. K functions were computed in R software to assess general clustering of porcine cysticercosis. Spatial scan statistics were computed in SatScan to identify local clusters of the infection. The overall prevalence of porcine cysticercosis was 7.3% (95% CI: 5.6, 9.4; n = 784. The K functions revealed a significant overall clustering of porcine cysticercosis incidence for all distances between 600 m and 5 km from a randomly chosen case household based on Ag-ELISA. Lingual examination revealed clustering from 650 m to 6 km and between 7.5 and 10 km

  17. Spatial-Temporal Clustering of Tornadoes

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    Malamud, Bruce D.; Turcotte, Donald L.; Brooks, Harold E.

    2017-04-01

    The standard measure of the intensity of a tornado is the Enhanced Fujita scale, which is based qualitatively on the damage caused by a tornado. An alternative measure of tornado intensity is the tornado path length, L. Here we examine the spatial-temporal clustering of severe tornadoes, which we define as having path lengths L ≥ 10 km. Of particular concern are tornado outbreaks, when a large number of severe tornadoes occur in a day in a restricted region. We apply a spatial-temporal clustering analysis developed for earthquakes. We take all pairs of severe tornadoes in observed and modelled outbreaks, and for each pair plot the spatial lag (distance between touchdown points) against the temporal lag (time between touchdown points). We apply our spatial-temporal lag methodology to the intense tornado outbreaks in the central United States on 26 and 27 April 2011, which resulted in over 300 fatalities and produced 109 severe (L ≥ 10 km) tornadoes. The patterns of spatial-temporal lag correlations that we obtain for the 2 days are strikingly different. On 26 April 2011, there were 45 severe tornadoes and our clustering analysis is dominated by a complex sequence of linear features. We associate the linear patterns with the tornadoes generated in either a single cell thunderstorm or a closely spaced cluster of single cell thunderstorms moving at a near-constant velocity. Our study of a derecho tornado outbreak of six severe tornadoes on 4 April 2011 along with modelled outbreak scenarios confirms this association. On 27 April 2011, there were 64 severe tornadoes and our clustering analysis is predominantly random with virtually no embedded linear patterns. We associate this pattern with a large number of interacting supercell thunderstorms generating tornadoes randomly in space and time. In order to better understand these associations, we also applied our approach to the Great Plains tornado outbreak of 3 May 1999. Careful studies by others have associated

  18. Environment-based selection effects of Planck clusters

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    Kosyra, R.; Gruen, D.; Seitz, S.; Mana, A.; Rozo, E.; Rykoff, E.; Sanchez, A.; Bender, R.

    2015-07-24

    We investigate whether the large-scale structure environment of galaxy clusters imprints a selection bias on Sunyaev–Zel'dovich (SZ) catalogues. Such a selection effect might be caused by line of sight (LoS) structures that add to the SZ signal or contain point sources that disturb the signal extraction in the SZ survey. We use the Planck PSZ1 union catalogue in the Sloan Digital Sky Survey (SDSS) region as our sample of SZ-selected clusters. We calculate the angular two-point correlation function (2pcf) for physically correlated, foreground and background structure in the RedMaPPer SDSS DR8 catalogue with respect to each cluster. We compare our results with an optically selected comparison cluster sample and with theoretical predictions. In contrast to the hypothesis of no environment-based selection, we find a mean 2pcf for background structures of -0.049 on scales of ≲40 arcmin, significantly non-zero at ~4σ, which means that Planck clusters are more likely to be detected in regions of low background density. We hypothesize this effect arises either from background estimation in the SZ survey or from radio sources in the background. We estimate the defect in SZ signal caused by this effect to be negligibly small, of the order of ~10-4 of the signal of a typical Planck detection. Analogously, there are no implications on X-ray mass measurements. However, the environmental dependence has important consequences for weak lensing follow up of Planck galaxy clusters: we predict that projection effects account for half of the mass contained within a 15 arcmin radius of Planck galaxy clusters. We did not detect a background underdensity of CMASS LRGs, which also leaves a spatially varying redshift dependence of the Planck SZ selection function as a possible cause for our findings.

  19. An Adaptive Sweep-Circle Spatial Clustering Algorithm Based on Gestalt

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    Qingming Zhan

    2017-08-01

    Full Text Available An adaptive spatial clustering (ASC algorithm is proposed in this present study, which employs sweep-circle techniques and a dynamic threshold setting based on the Gestalt theory to detect spatial clusters. The proposed algorithm can automatically discover clusters in one pass, rather than through the modification of the initial model (for example, a minimal spanning tree, Delaunay triangulation, or Voronoi diagram. It can quickly identify arbitrarily-shaped clusters while adapting efficiently to non-homogeneous density characteristics of spatial data, without the need for prior knowledge or parameters. The proposed algorithm is also ideal for use in data streaming technology with dynamic characteristics flowing in the form of spatial clustering in large data sets.

  20. Spatial and kinematic distributions of transition populations in intermediate redshift galaxy clusters

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    Crawford, Steven M.; Wirth, Gregory D.; Bershady, Matthew A.

    2014-01-01

    We analyze the spatial and velocity distributions of confirmed members in five massive clusters of galaxies at intermediate redshift (0.5 < z < 0.9) to investigate the physical processes driving galaxy evolution. Based on spectral classifications derived from broad- and narrow-band photometry, we define four distinct galaxy populations representing different evolutionary stages: red sequence (RS) galaxies, blue cloud (BC) galaxies, green valley (GV) galaxies, and luminous compact blue galaxies (LCBGs). For each galaxy class, we derive the projected spatial and velocity distribution and characterize the degree of subclustering. We find that RS, BC, and GV galaxies in these clusters have similar velocity distributions, but that BC and GV galaxies tend to avoid the core of the two z ≈ 0.55 clusters. GV galaxies exhibit subclustering properties similar to RS galaxies, but their radial velocity distribution is significantly platykurtic compared to the RS galaxies. The absence of GV galaxies in the cluster cores may explain their somewhat prolonged star-formation history. The LCBGs appear to have recently fallen into the cluster based on their larger velocity dispersion, absence from the cores of the clusters, and different radial velocity distribution than the RS galaxies. Both LCBG and BC galaxies show a high degree of subclustering on the smallest scales, leading us to conclude that star formation is likely triggered by galaxy-galaxy interactions during infall into the cluster.

  1. Algorithm for Spatial Clustering with Obstacles

    OpenAIRE

    El-Sharkawi, Mohamed E.; El-Zawawy, Mohamed A.

    2009-01-01

    In this paper, we propose an efficient clustering technique to solve the problem of clustering in the presence of obstacles. The proposed algorithm divides the spatial area into rectangular cells. Each cell is associated with statistical information that enables us to label the cell as dense or non-dense. We also label each cell as obstructed (i.e. intersects any obstacle) or non-obstructed. Then the algorithm finds the regions (clusters) of connected, dense, non-obstructed cells. Finally, th...

  2. New Constraints on Spatial Variations of the Fine Structure Constant from Clusters of Galaxies

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    Ivan De Martino

    2016-12-01

    Full Text Available We have constrained the spatial variation of the fine structure constant using multi-frequency measurements of the thermal Sunyaev-Zeldovich effect of 618 X-ray selected clusters. Although our results are not competitive with the ones from quasar absorption lines, we improved by a factor 10 and ∼2.5 previous results from Cosmic Microwave Background power spectrum and from galaxy clusters, respectively.

  3. A spatial scan statistic for nonisotropic two-level risk cluster.

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    Li, Xiao-Zhou; Wang, Jin-Feng; Yang, Wei-Zhong; Li, Zhong-Jie; Lai, Sheng-Jie

    2012-01-30

    Spatial scan statistic methods are commonly used for geographical disease surveillance and cluster detection. The standard spatial scan statistic does not model any variability in the underlying risks of subregions belonging to a detected cluster. For a multilevel risk cluster, the isotonic spatial scan statistic could model a centralized high-risk kernel in the cluster. Because variations in disease risks are anisotropic owing to different social, economical, or transport factors, the real high-risk kernel will not necessarily take the central place in a whole cluster area. We propose a spatial scan statistic for a nonisotropic two-level risk cluster, which could be used to detect a whole cluster and a noncentralized high-risk kernel within the cluster simultaneously. The performance of the three methods was evaluated through an intensive simulation study. Our proposed nonisotropic two-level method showed better power and geographical precision with two-level risk cluster scenarios, especially for a noncentralized high-risk kernel. Our proposed method is illustrated using the hand-foot-mouth disease data in Pingdu City, Shandong, China in May 2009, compared with two other methods. In this practical study, the nonisotropic two-level method is the only way to precisely detect a high-risk area in a detected whole cluster. Copyright © 2011 John Wiley & Sons, Ltd.

  4. Comparison of combinatorial clustering methods on pharmacological data sets represented by machine learning-selected real molecular descriptors.

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    Rivera-Borroto, Oscar Miguel; Marrero-Ponce, Yovani; García-de la Vega, José Manuel; Grau-Ábalo, Ricardo del Corazón

    2011-12-27

    Cluster algorithms play an important role in diversity related tasks of modern chemoinformatics, with the widest applications being in pharmaceutical industry drug discovery programs. The performance of these grouping strategies depends on various factors such as molecular representation, mathematical method, algorithmical technique, and statistical distribution of data. For this reason, introduction and comparison of new methods are necessary in order to find the model that best fits the problem at hand. Earlier comparative studies report on Ward's algorithm using fingerprints for molecular description as generally superior in this field. However, problems still remain, i.e., other types of numerical descriptions have been little exploited, current descriptors selection strategy is trial and error-driven, and no previous comparative studies considering a broader domain of the combinatorial methods in grouping chemoinformatic data sets have been conducted. In this work, a comparison between combinatorial methods is performed,with five of them being novel in cheminformatics. The experiments are carried out using eight data sets that are well established and validated in the medical chemistry literature. Each drug data set was represented by real molecular descriptors selected by machine learning techniques, which are consistent with the neighborhood principle. Statistical analysis of the results demonstrates that pharmacological activities of the eight data sets can be modeled with a few of families with 2D and 3D molecular descriptors, avoiding classification problems associated with the presence of nonrelevant features. Three out of five of the proposed cluster algorithms show superior performance over most classical algorithms and are similar (or slightly superior in the most optimistic sense) to Ward's algorithm. The usefulness of these algorithms is also assessed in a comparative experiment to potent QSAR and machine learning classifiers, where they perform

  5. NMR studies of selective population inversion and spin clustering

    International Nuclear Information System (INIS)

    Baum, J.S.

    1986-02-01

    This work describes the development and application of selective excitation techniques in Nuclear Magnetic Resonance. Composite pulses and multiple-quantum methods are used to accomplish various goals, such as broadband and narrowband excitation in liquids, and collective excitation of groups of spins in solids. These methods are applied to a variety of problems, including non-invasive spatial localization, spin cluster size characterization in disordered solids and solid state NMR imaging

  6. Visualizing Confidence in Cluster-Based Ensemble Weather Forecast Analyses.

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    Kumpf, Alexander; Tost, Bianca; Baumgart, Marlene; Riemer, Michael; Westermann, Rudiger; Rautenhaus, Marc

    2018-01-01

    In meteorology, cluster analysis is frequently used to determine representative trends in ensemble weather predictions in a selected spatio-temporal region, e.g., to reduce a set of ensemble members to simplify and improve their analysis. Identified clusters (i.e., groups of similar members), however, can be very sensitive to small changes of the selected region, so that clustering results can be misleading and bias subsequent analyses. In this article, we - a team of visualization scientists and meteorologists-deliver visual analytics solutions to analyze the sensitivity of clustering results with respect to changes of a selected region. We propose an interactive visual interface that enables simultaneous visualization of a) the variation in composition of identified clusters (i.e., their robustness), b) the variability in cluster membership for individual ensemble members, and c) the uncertainty in the spatial locations of identified trends. We demonstrate that our solution shows meteorologists how representative a clustering result is, and with respect to which changes in the selected region it becomes unstable. Furthermore, our solution helps to identify those ensemble members which stably belong to a given cluster and can thus be considered similar. In a real-world application case we show how our approach is used to analyze the clustering behavior of different regions in a forecast of "Tropical Cyclone Karl", guiding the user towards the cluster robustness information required for subsequent ensemble analysis.

  7. An analysis of spatial representativeness of air temperature monitoring stations

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    Liu, Suhua; Su, Hongbo; Tian, Jing; Wang, Weizhen

    2018-05-01

    Surface air temperature is an essential variable for monitoring the atmosphere, and it is generally acquired at meteorological stations that can provide information about only a small area within an r m radius ( r-neighborhood) of the station, which is called the representable radius. In studies on a local scale, ground-based observations of surface air temperatures obtained from scattered stations are usually interpolated using a variety of methods without ascertaining their effectiveness. Thus, it is necessary to evaluate the spatial representativeness of ground-based observations of surface air temperature before conducting studies on a local scale. The present study used remote sensing data to estimate the spatial distribution of surface air temperature using the advection-energy balance for air temperature (ADEBAT) model. Two target stations in the study area were selected to conduct an analysis of spatial representativeness. The results showed that one station (AWS 7) had a representable radius of about 400 m with a possible error of less than 1 K, while the other station (AWS 16) had the radius of about 250 m. The representable radius was large when the heterogeneity of land cover around the station was small.

  8. Exploring spatial evolution of economic clusters: A case study of Beijing

    Science.gov (United States)

    Yang, Zhenshan; Sliuzas, Richard; Cai, Jianming; Ottens, Henk F. L.

    2012-10-01

    An identification of economic clusters and analysing their changing spatial patterns is important for understanding urban economic space dynamics. Previous studies, however, suffer from limitations as a consequence of using fixed geographically areas and not combining functional and spatial dynamics. The paper presents an approach, based on local spatial statistics and the case of Beijing to understand the spatial clustering of industries that are functionally interconnected by common or complementary patterns of demand or supply relations. Using register data of business establishments, it identifies economic clusters and analyses their pattern based on postcodes at different time slices during the period 1983-2002. The study shows how the advanced services occupy the urban centre and key sub centres. The Information and Communication Technology (ICT) cluster is mainly concentrated in the north part of the city and circles the urban centre, and the main manufacturing clusters are evolved in the key sub centers. This type of outcomes improves understanding of urban-economic dynamics, which can support spatial and economic planning.

  9. The spatial evaluation of neighborhood clusters of birth defects

    Energy Technology Data Exchange (ETDEWEB)

    Frisch, J.D.

    1990-04-16

    Spatial statistics have recently been applied in epidemiology to evaluate clusters of cancer and birth defects. Their use requires a comparison population, drawn from the population at risk for disease, that may not always be readily available. In this dissertation the plausibility of using data on all birth defects, available from birth defects registries, as a surrogate for the spatial distribution of all live births in the analysis of clusters is assessed. Three spatial statistics that have been applied in epidemiologic investigations of clusters, nearest neighbor distance, average interpoint distance, and average distance to a fixed point, were evaluated by computer simulation for their properties in a unit square, and in a zip code region. Comparison of spatial distributions of live births and birth defects was performed by drawing samples of live births and birth defects from Santa Clara County, determining the street address at birth, geocoding this address and evaluating the resultant maps using various statistical techniques. The proposed method was then demonstrated on a previously confirmed cluster of oral cleft cases. All live births for the neighborhood were geocoded, as were all birth defects. Evaluation of this cluster using the nearest neighbor and average interpoint distance statistics was performed using randomization techniques with both the live births population and the birth defect population as comparison groups. 113 refs., 36 figs., 16 tabs.

  10. Cluster analysis of activity-time series in motor learning

    DEFF Research Database (Denmark)

    Balslev, Daniela; Nielsen, Finn Å; Futiger, Sally A

    2002-01-01

    Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel......-time series. The optimal number of clusters was chosen using a cross-validated likelihood method, which highlights the clustering pattern that generalizes best over the subjects. Data were acquired with PET at different time points during practice of a visuomotor task. The results from cluster analysis show...

  11. Comparison of tests for spatial heterogeneity on data with global clustering patterns and outliers

    Directory of Open Access Journals (Sweden)

    Hachey Mark

    2009-10-01

    Full Text Available Abstract Background The ability to evaluate geographic heterogeneity of cancer incidence and mortality is important in cancer surveillance. Many statistical methods for evaluating global clustering and local cluster patterns are developed and have been examined by many simulation studies. However, the performance of these methods on two extreme cases (global clustering evaluation and local anomaly (outlier detection has not been thoroughly investigated. Methods We compare methods for global clustering evaluation including Tango's Index, Moran's I, and Oden's I*pop; and cluster detection methods such as local Moran's I and SaTScan elliptic version on simulated count data that mimic global clustering patterns and outliers for cancer cases in the continental United States. We examine the power and precision of the selected methods in the purely spatial analysis. We illustrate Tango's MEET and SaTScan elliptic version on a 1987-2004 HIV and a 1950-1969 lung cancer mortality data in the United States. Results For simulated data with outlier patterns, Tango's MEET, Moran's I and I*pop had powers less than 0.2, and SaTScan had powers around 0.97. For simulated data with global clustering patterns, Tango's MEET and I*pop (with 50% of total population as the maximum search window had powers close to 1. SaTScan had powers around 0.7-0.8 and Moran's I has powers around 0.2-0.3. In the real data example, Tango's MEET indicated the existence of global clustering patterns in both the HIV and lung cancer mortality data. SaTScan found a large cluster for HIV mortality rates, which is consistent with the finding from Tango's MEET. SaTScan also found clusters and outliers in the lung cancer mortality data. Conclusion SaTScan elliptic version is more efficient for outlier detection compared with the other methods evaluated in this article. Tango's MEET and Oden's I*pop perform best in global clustering scenarios among the selected methods. The use of SaTScan for

  12. Spatial Cluster Detection for Repeatedly Measured Outcomes while Accounting for Residential History

    OpenAIRE

    Cook, Andrea J.; Gold, Diane R.; Li, Yi

    2009-01-01

    Spatial cluster detection has become an important methodology in quantifying the effect of hazardous exposures. Previous methods have focused on cross-sectional outcomes that are binary or continuous. There are virtually no spatial cluster detection methods proposed for longitudinal outcomes. This paper proposes a new spatial cluster detection method for repeated outcomes using cumulative geographic residuals. A major advantage of this method is its ability to readily incorporate information ...

  13. Spatial clustering and local risk of leprosy in São Paulo, Brazil.

    Directory of Open Access Journals (Sweden)

    Antônio Carlos Vieira Ramos

    2017-02-01

    Full Text Available Although the detection rate is decreasing, the proportion of new cases with WHO grade 2 disability (G2D is increasing, creating concern among policy makers and the Brazilian government. This study aimed to identify spatial clustering of leprosy and classify high-risk areas in a major leprosy cluster using the SatScan method.Data were obtained including all leprosy cases diagnosed between January 2006 and December 2013. In addition to the clinical variable, information was also gathered regarding the G2D of the patient at diagnosis and after treatment. The Scan Spatial statistic test, developed by Kulldorff e Nagarwalla, was used to identify spatial clustering and to measure the local risk (Relative Risk-RR of leprosy. Maps considering these risks and their confidence intervals were constructed.A total of 434 cases were identified, including 188 (43.31% borderline leprosy and 101 (23.28% lepromatous leprosy cases. There was a predominance of males, with ages ranging from 15 to 59 years, and 51 patients (11.75% presented G2D. Two significant spatial clusters and three significant spatial-temporal clusters were also observed. The main spatial cluster (p = 0.000 contained 90 census tracts, a population of approximately 58,438 inhabitants, detection rate of 22.6 cases per 100,000 people and RR of approximately 3.41 (95%CI = 2.721-4.267. Regarding the spatial-temporal clusters, two clusters were observed, with RR ranging between 24.35 (95%CI = 11.133-52.984 and 15.24 (95%CI = 10.114-22.919.These findings could contribute to improvements in policies and programming, aiming for the eradication of leprosy in Brazil. The Spatial Scan statistic test was found to be an interesting resource for health managers and healthcare professionals to map the vulnerability of areas in terms of leprosy transmission risk and areas of underreporting.

  14. Spatial clustering and local risk of leprosy in São Paulo, Brazil.

    Science.gov (United States)

    Ramos, Antônio Carlos Vieira; Yamamura, Mellina; Arroyo, Luiz Henrique; Popolin, Marcela Paschoal; Chiaravalloti Neto, Francisco; Palha, Pedro Fredemir; Uchoa, Severina Alice da Costa; Pieri, Flávia Meneguetti; Pinto, Ione Carvalho; Fiorati, Regina Célia; Queiroz, Ana Angélica Rêgo de; Belchior, Aylana de Souza; Dos Santos, Danielle Talita; Garcia, Maria Concebida da Cunha; Crispim, Juliane de Almeida; Alves, Luana Seles; Berra, Thaís Zamboni; Arcêncio, Ricardo Alexandre

    2017-02-01

    Although the detection rate is decreasing, the proportion of new cases with WHO grade 2 disability (G2D) is increasing, creating concern among policy makers and the Brazilian government. This study aimed to identify spatial clustering of leprosy and classify high-risk areas in a major leprosy cluster using the SatScan method. Data were obtained including all leprosy cases diagnosed between January 2006 and December 2013. In addition to the clinical variable, information was also gathered regarding the G2D of the patient at diagnosis and after treatment. The Scan Spatial statistic test, developed by Kulldorff e Nagarwalla, was used to identify spatial clustering and to measure the local risk (Relative Risk-RR) of leprosy. Maps considering these risks and their confidence intervals were constructed. A total of 434 cases were identified, including 188 (43.31%) borderline leprosy and 101 (23.28%) lepromatous leprosy cases. There was a predominance of males, with ages ranging from 15 to 59 years, and 51 patients (11.75%) presented G2D. Two significant spatial clusters and three significant spatial-temporal clusters were also observed. The main spatial cluster (p = 0.000) contained 90 census tracts, a population of approximately 58,438 inhabitants, detection rate of 22.6 cases per 100,000 people and RR of approximately 3.41 (95%CI = 2.721-4.267). Regarding the spatial-temporal clusters, two clusters were observed, with RR ranging between 24.35 (95%CI = 11.133-52.984) and 15.24 (95%CI = 10.114-22.919). These findings could contribute to improvements in policies and programming, aiming for the eradication of leprosy in Brazil. The Spatial Scan statistic test was found to be an interesting resource for health managers and healthcare professionals to map the vulnerability of areas in terms of leprosy transmission risk and areas of underreporting.

  15. Spatial and space-time clustering of tuberculosis in Gurage Zone, Southern Ethiopia.

    Science.gov (United States)

    Tadesse, Sebsibe; Enqueselassie, Fikre; Hagos, Seifu

    2018-01-01

    Spatial targeting is advocated as an effective method that contributes for achieving tuberculosis control in high-burden countries. However, there is a paucity of studies clarifying the spatial nature of the disease in these countries. This study aims to identify the location, size and risk of purely spatial and space-time clusters for high occurrence of tuberculosis in Gurage Zone, Southern Ethiopia during 2007 to 2016. A total of 15,805 patient data that were retrieved from unit TB registers were included in the final analyses. The spatial and space-time cluster analyses were performed using the global Moran's I, Getis-Ord [Formula: see text] and Kulldorff's scan statistics. Eleven purely spatial and three space-time clusters were detected (P <0.001).The clusters were concentrated in border areas of the Gurage Zone. There were considerable spatial variations in the risk of tuberculosis by year during the study period. This study showed that tuberculosis clusters were mainly concentrated at border areas of the Gurage Zone during the study period, suggesting that there has been sustained transmission of the disease within these locations. The findings may help intensify the implementation of tuberculosis control activities in these locations. Further study is warranted to explore the roles of various ecological factors on the observed spatial distribution of tuberculosis.

  16. GGRaSP: A R-package for selecting representative genomes using Gaussian mixture models.

    Science.gov (United States)

    Clarke, Thomas H; Brinkac, Lauren M; Sutton, Granger; Fouts, Derrick E

    2018-04-14

    The vast number of available sequenced bacterial genomes occasionally exceeds the facilities of comparative genomic methods or is dominated by a single outbreak strain, and thus a diverse and representative subset is required. Generation of the reduced subset currently requires a priori supervised clustering and sequence-only selection of medoid genomic sequences, independent of any additional genome metrics or strain attributes. The GGRaSP R-package described below generates a reduced subset of genomes that prioritizes maintaining genomes of interest to the user as well as minimizing the loss of genetic variation. The package also allows for unsupervised clustering by modeling the genomic relationships using a Gaussian Mixture Model to select an appropriate cluster threshold. We demonstrate the capabilities of GGRaSP by generating a reduced list of 315 genomes from a genomic dataset of 4600 Escherichia coli genomes, prioritizing selection by type strain and by genome completeness. GGRaSP is available at https://github.com/JCVenterInstitute/ggrasp/. tclarke@jcvi.org. Supplementary data are available at the GitHub site.

  17. GEMINI/GMOS SPECTROSCOPY OF 26 STRONG-LENSING-SELECTED GALAXY CLUSTER CORES

    International Nuclear Information System (INIS)

    Bayliss, Matthew B.; Gladders, Michael D.; Koester, Benjamin P.; Hennawi, Joseph F.; Sharon, Keren; Dahle, Haakon; Oguri, Masamune

    2011-01-01

    We present results from a spectroscopic program targeting 26 strong-lensing cluster cores that were visually identified in the Sloan Digital Sky Survey (SDSS) and the Second Red-Sequence Cluster Survey (RCS-2). The 26 galaxy cluster lenses span a redshift range of 0.2 Vir = 7.84 x 10 14 M sun h -1 0.7 , which is somewhat higher than predictions for strong-lensing-selected clusters in simulations. The disagreement is not significant considering the large uncertainty in our dynamical data, systematic uncertainties in the velocity dispersion calibration, and limitations of the theoretical modeling. Nevertheless our study represents an important first step toward characterizing large samples of clusters that are identified in a systematic way as systems exhibiting dramatic strong-lensing features.

  18. Stochastic dynamics of spatial effects in fragmentation of clusters

    International Nuclear Information System (INIS)

    Salinas-Rodriguez, E.; Rodriguez, R.F.; Zamora, J.M.

    1991-01-01

    We use a stochastic approach to study the effects of spatial in homogeneities in the kinetics of a fragmentation model which occurs in cluster breakup and polymer degradation. The analytical form of the cluster size distribution function is obtained for both the discrete and continuous limits. From it we calculate numerically the average size and volume of the clusters, their total concentration and the total scattering of the dispersion in both limits. The influence of spatial effects is explicitly shown in the last two quantities. From our description the equations for the equal-time and the two times density correlation functions are also derived in the continuous limit. Finally, the perspectives and limitations of our approach are discussed (Author)

  19. Sejong Open Cluster Survey (SOS). 0. Target Selection and Data Analysis

    Science.gov (United States)

    Sung, Hwankyung; Lim, Beomdu; Bessell, Michael S.; Kim, Jinyoung S.; Hur, Hyeonoh; Chun, Moo-Young; Park, Byeong-Gon

    2013-06-01

    Star clusters are superb astrophysical laboratories containing cospatial and coeval samples of stars with similar chemical composition. We initiate the Sejong Open cluster Survey (SOS) - a project dedicated to providing homogeneous photometry of a large number of open clusters in the SAAO Johnson-Cousins' UBVI system. To achieve our main goal, we pay much attention to the observation of standard stars in order to reproduce the SAAO standard system. Many of our targets are relatively small sparse clusters that escaped previous observations. As clusters are considered building blocks of the Galactic disk, their physical properties such as the initial mass function, the pattern of mass segregation, etc. give valuable information on the formation and evolution of the Galactic disk. The spatial distribution of young open clusters will be used to revise the local spiral arm structure of the Galaxy. In addition, the homogeneous data can also be used to test stellar evolutionary theory, especially concerning rare massive stars. In this paper we present the target selection criteria, the observational strategy for accurate photometry, and the adopted calibrations for data analysis such as color-color relations, zero-age main sequence relations, Sp - M_V relations, Sp - T_{eff} relations, Sp - color relations, and T_{eff} - BC relations. Finally we provide some data analysis such as the determination of the reddening law, the membership selection criteria, and distance determination.

  20. Robust cluster analysis and variable selection

    CERN Document Server

    Ritter, Gunter

    2014-01-01

    Clustering remains a vibrant area of research in statistics. Although there are many books on this topic, there are relatively few that are well founded in the theoretical aspects. In Robust Cluster Analysis and Variable Selection, Gunter Ritter presents an overview of the theory and applications of probabilistic clustering and variable selection, synthesizing the key research results of the last 50 years. The author focuses on the robust clustering methods he found to be the most useful on simulated data and real-time applications. The book provides clear guidance for the varying needs of bot

  1. Methodology for Clustering High-Resolution Spatiotemporal Solar Resource Data

    Energy Technology Data Exchange (ETDEWEB)

    Getman, Dan [National Renewable Energy Lab. (NREL), Golden, CO (United States); Lopez, Anthony [National Renewable Energy Lab. (NREL), Golden, CO (United States); Mai, Trieu [National Renewable Energy Lab. (NREL), Golden, CO (United States); Dyson, Mark [National Renewable Energy Lab. (NREL), Golden, CO (United States)

    2015-09-01

    In this report, we introduce a methodology to achieve multiple levels of spatial resolution reduction of solar resource data, with minimal impact on data variability, for use in energy systems modeling. The selection of an appropriate clustering algorithm, parameter selection including cluster size, methods of temporal data segmentation, and methods of cluster evaluation are explored in the context of a repeatable process. In describing this process, we illustrate the steps in creating a reduced resolution, but still viable, dataset to support energy systems modeling, e.g. capacity expansion or production cost modeling. This process is demonstrated through the use of a solar resource dataset; however, the methods are applicable to other resource data represented through spatiotemporal grids, including wind data. In addition to energy modeling, the techniques demonstrated in this paper can be used in a novel top-down approach to assess renewable resources within many other contexts that leverage variability in resource data but require reduction in spatial resolution to accommodate modeling or computing constraints.

  2. Functional region prediction with a set of appropriate homologous sequences-an index for sequence selection by integrating structure and sequence information with spatial statistics

    Science.gov (United States)

    2012-01-01

    Background The detection of conserved residue clusters on a protein structure is one of the effective strategies for the prediction of functional protein regions. Various methods, such as Evolutionary Trace, have been developed based on this strategy. In such approaches, the conserved residues are identified through comparisons of homologous amino acid sequences. Therefore, the selection of homologous sequences is a critical step. It is empirically known that a certain degree of sequence divergence in the set of homologous sequences is required for the identification of conserved residues. However, the development of a method to select homologous sequences appropriate for the identification of conserved residues has not been sufficiently addressed. An objective and general method to select appropriate homologous sequences is desired for the efficient prediction of functional regions. Results We have developed a novel index to select the sequences appropriate for the identification of conserved residues, and implemented the index within our method to predict the functional regions of a protein. The implementation of the index improved the performance of the functional region prediction. The index represents the degree of conserved residue clustering on the tertiary structure of the protein. For this purpose, the structure and sequence information were integrated within the index by the application of spatial statistics. Spatial statistics is a field of statistics in which not only the attributes but also the geometrical coordinates of the data are considered simultaneously. Higher degrees of clustering generate larger index scores. We adopted the set of homologous sequences with the highest index score, under the assumption that the best prediction accuracy is obtained when the degree of clustering is the maximum. The set of sequences selected by the index led to higher functional region prediction performance than the sets of sequences selected by other sequence

  3. Harnessing Big Data to Represent 30-meter Spatial Heterogeneity in Earth System Models

    Science.gov (United States)

    Chaney, N.; Shevliakova, E.; Malyshev, S.; Van Huijgevoort, M.; Milly, C.; Sulman, B. N.

    2016-12-01

    Terrestrial land surface processes play a critical role in the Earth system; they have a profound impact on the global climate, food and energy production, freshwater resources, and biodiversity. One of the most fascinating yet challenging aspects of characterizing terrestrial ecosystems is their field-scale (˜30 m) spatial heterogeneity. It has been observed repeatedly that the water, energy, and biogeochemical cycles at multiple temporal and spatial scales have deep ties to an ecosystem's spatial structure. Current Earth system models largely disregard this important relationship leading to an inadequate representation of ecosystem dynamics. In this presentation, we will show how existing global environmental datasets can be harnessed to explicitly represent field-scale spatial heterogeneity in Earth system models. For each macroscale grid cell, these environmental data are clustered according to their field-scale soil and topographic attributes to define unique sub-grid tiles. The state-of-the-art Geophysical Fluid Dynamics Laboratory (GFDL) land model is then used to simulate these tiles and their spatial interactions via the exchange of water, energy, and nutrients along explicit topographic gradients. Using historical simulations over the contiguous United States, we will show how a robust representation of field-scale spatial heterogeneity impacts modeled ecosystem dynamics including the water, energy, and biogeochemical cycles as well as vegetation composition and distribution.

  4. Hierarchical clustering using correlation metric and spatial continuity constraint

    Science.gov (United States)

    Stork, Christopher L.; Brewer, Luke N.

    2012-10-02

    Large data sets are analyzed by hierarchical clustering using correlation as a similarity measure. This provides results that are superior to those obtained using a Euclidean distance similarity measure. A spatial continuity constraint may be applied in hierarchical clustering analysis of images.

  5. Spatial cluster detection for repeatedly measured outcomes while accounting for residential history.

    Science.gov (United States)

    Cook, Andrea J; Gold, Diane R; Li, Yi

    2009-10-01

    Spatial cluster detection has become an important methodology in quantifying the effect of hazardous exposures. Previous methods have focused on cross-sectional outcomes that are binary or continuous. There are virtually no spatial cluster detection methods proposed for longitudinal outcomes. This paper proposes a new spatial cluster detection method for repeated outcomes using cumulative geographic residuals. A major advantage of this method is its ability to readily incorporate information on study participants relocation, which most cluster detection statistics cannot. Application of these methods will be illustrated by the Home Allergens and Asthma prospective cohort study analyzing the relationship between environmental exposures and repeated measured outcome, occurrence of wheeze in the last 6 months, while taking into account mobile locations.

  6. An Efficient Data Compression Model Based on Spatial Clustering and Principal Component Analysis in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yihang Yin

    2015-08-01

    Full Text Available Wireless sensor networks (WSNs have been widely used to monitor the environment, and sensors in WSNs are usually power constrained. Because inner-node communication consumes most of the power, efficient data compression schemes are needed to reduce the data transmission to prolong the lifetime of WSNs. In this paper, we propose an efficient data compression model to aggregate data, which is based on spatial clustering and principal component analysis (PCA. First, sensors with a strong temporal-spatial correlation are grouped into one cluster for further processing with a novel similarity measure metric. Next, sensor data in one cluster are aggregated in the cluster head sensor node, and an efficient adaptive strategy is proposed for the selection of the cluster head to conserve energy. Finally, the proposed model applies principal component analysis with an error bound guarantee to compress the data and retain the definite variance at the same time. Computer simulations show that the proposed model can greatly reduce communication and obtain a lower mean square error than other PCA-based algorithms.

  7. An Efficient Data Compression Model Based on Spatial Clustering and Principal Component Analysis in Wireless Sensor Networks.

    Science.gov (United States)

    Yin, Yihang; Liu, Fengzheng; Zhou, Xiang; Li, Quanzhong

    2015-08-07

    Wireless sensor networks (WSNs) have been widely used to monitor the environment, and sensors in WSNs are usually power constrained. Because inner-node communication consumes most of the power, efficient data compression schemes are needed to reduce the data transmission to prolong the lifetime of WSNs. In this paper, we propose an efficient data compression model to aggregate data, which is based on spatial clustering and principal component analysis (PCA). First, sensors with a strong temporal-spatial correlation are grouped into one cluster for further processing with a novel similarity measure metric. Next, sensor data in one cluster are aggregated in the cluster head sensor node, and an efficient adaptive strategy is proposed for the selection of the cluster head to conserve energy. Finally, the proposed model applies principal component analysis with an error bound guarantee to compress the data and retain the definite variance at the same time. Computer simulations show that the proposed model can greatly reduce communication and obtain a lower mean square error than other PCA-based algorithms.

  8. Spatial correlations, clustering and percolation-like transitions in homicide crimes

    Science.gov (United States)

    Alves, L. G. A.; Lenzi, E. K.; Mendes, R. S.; Ribeiro, H. V.

    2015-07-01

    The spatial dynamics of criminal activities has been recently studied through statistical physics methods; however, models and results have been focusing on local scales (city level) and much less is known about these patterns at larger scales, e.g. at a country level. Here we report on a characterization of the spatial dynamics of the homicide crimes along the Brazilian territory using data from all cities (˜5000) in a period of more than thirty years. Our results show that the spatial correlation function in the per capita homicides decays exponentially with the distance between cities and that the characteristic correlation length displays an acute increasing trend in the latest years. We also investigate the formation of spatial clusters of cities via a percolation-like analysis, where clustering of cities and a phase-transition-like behavior describing the size of the largest cluster as a function of a homicide threshold are observed. This transition-like behavior presents evolutive features characterized by an increasing in the homicide threshold (where the transitions occur) and by a decreasing in the transition magnitudes (length of the jumps in the cluster size). We believe that our work sheds new light on the spatial patterns of criminal activities at large scales, which may contribute for better political decisions and resources allocation as well as opens new possibilities for modeling criminal activities by setting up fundamental empirical patterns at large scales.

  9. Spatial Field Variability Mapping of Rice Crop using Clustering Technique from Space Borne Hyperspectral Data

    Science.gov (United States)

    Moharana, S.; Dutta, S.

    2015-12-01

    Precision farming refers to field-specific management of an agricultural crop at a spatial scale with an aim to get the highest achievable yield and to achieve this spatial information on field variability is essential. The difficulty in mapping of spatial variability occurring within an agriculture field can be revealed by employing spectral techniques in hyperspectral imagery rather than multispectral imagery. However an advanced algorithm needs to be developed to fully make use of the rich information content in hyperspectral data. In the present study, potential of hyperspectral data acquired from space platform was examined to map the field variation of paddy crop and its species discrimination. This high dimensional data comprising 242 spectral narrow bands with 30m ground resolution Hyperion L1R product acquired for Assam, India (30th Sept and 3rd Oct, 2014) were allowed for necessary pre-processing steps followed by geometric correction using Hyperion L1GST product. Finally an atmospherically corrected and spatially deduced image consisting of 112 band was obtained. By employing an advanced clustering algorithm, 12 different clusters of spectral waveforms of the crop were generated from six paddy fields for each images. The findings showed that, some clusters were well discriminated representing specific rice genotypes and some clusters were mixed treating as a single rice genotype. As vegetation index (VI) is the best indicator of vegetation mapping, three ratio based VI maps were also generated and unsupervised classification was performed for it. The so obtained 12 clusters of paddy crop were mapped spatially to the derived VI maps. From these findings, the existence of heterogeneity was clearly captured in one of the 6 rice plots (rice plot no. 1) while heterogeneity was observed in rest of the 5 rice plots. The degree of heterogeneous was found more in rice plot no.6 as compared to other plots. Subsequently, spatial variability of paddy field was

  10. Analysis of the Spatial Variation of Network-Constrained Phenomena Represented by a Link Attribute Using a Hierarchical Bayesian Model

    Directory of Open Access Journals (Sweden)

    Zhensheng Wang

    2017-02-01

    Full Text Available The spatial variation of geographical phenomena is a classical problem in spatial data analysis and can provide insight into underlying processes. Traditional exploratory methods mostly depend on the planar distance assumption, but many spatial phenomena are constrained to a subset of Euclidean space. In this study, we apply a method based on a hierarchical Bayesian model to analyse the spatial variation of network-constrained phenomena represented by a link attribute in conjunction with two experiments based on a simplified hypothetical network and a complex road network in Shenzhen that includes 4212 urban facility points of interest (POIs for leisure activities. Then, the methods named local indicators of network-constrained clusters (LINCS are applied to explore local spatial patterns in the given network space. The proposed method is designed for phenomena that are represented by attribute values of network links and is capable of removing part of random variability resulting from small-sample estimation. The effects of spatial dependence and the base distribution are also considered in the proposed method, which could be applied in the fields of urban planning and safety research.

  11. Applying spatial clustering analysis to a township-level social vulnerability assessment in Taiwan

    Directory of Open Access Journals (Sweden)

    Wen-Yen Lin

    2016-09-01

    Full Text Available The degree of social vulnerability may vary according to the conditions and backgrounds of different locations, yet spatial clustering phenomena may exist when nearby spatial units exhibit similar characteristics. This study applied spatial autocorrelation statistics to analyze the spatial association of vulnerability among townships in Taiwan. The vulnerability was first assessed on the basis of a social vulnerability index that was constructed using Fuzzy Delphi and analytic hierarchy process methods. Subsequently, the corresponding indicator variables were applied to calculate standardized vulnerability assessment scores by using government data. According to the results of the vulnerability assessment in which T scores were normalized, the distribution of social vulnerabilities varied among the townships. The scores were further analyzed using spatial autocorrelation statistics for spatial clustering of vulnerability distribution. The Local G statistic identified 42 significant spatial association pockets, whereas the Global G statistic indicated no spatial phenomenon of clustering. This phenomenon was verified and explained by applying Moran's I statistics to examine the homogeneity and heterogeneity of spatial associations. Although both statistics were originally designed to identify the existence of spatial clustering, they serve diverse purposes, and the results can be compared to obtain additional insights into the distribution patterns of social vulnerability.

  12. Identifying clusters of active transportation using spatial scan statistics.

    Science.gov (United States)

    Huang, Lan; Stinchcomb, David G; Pickle, Linda W; Dill, Jennifer; Berrigan, David

    2009-08-01

    There is an intense interest in the possibility that neighborhood characteristics influence active transportation such as walking or biking. The purpose of this paper is to illustrate how a spatial cluster identification method can evaluate the geographic variation of active transportation and identify neighborhoods with unusually high/low levels of active transportation. Self-reported walking/biking prevalence, demographic characteristics, street connectivity variables, and neighborhood socioeconomic data were collected from respondents to the 2001 California Health Interview Survey (CHIS; N=10,688) in Los Angeles County (LAC) and San Diego County (SDC). Spatial scan statistics were used to identify clusters of high or low prevalence (with and without age-adjustment) and the quantity of time spent walking and biking. The data, a subset from the 2001 CHIS, were analyzed in 2007-2008. Geographic clusters of significantly high or low prevalence of walking and biking were detected in LAC and SDC. Structural variables such as street connectivity and shorter block lengths are consistently associated with higher levels of active transportation, but associations between active transportation and socioeconomic variables at the individual and neighborhood levels are mixed. Only one cluster with less time spent walking and biking among walkers/bikers was detected in LAC, and this was of borderline significance. Age-adjustment affects the clustering pattern of walking/biking prevalence in LAC, but not in SDC. The use of spatial scan statistics to identify significant clustering of health behaviors such as active transportation adds to the more traditional regression analysis that examines associations between behavior and environmental factors by identifying specific geographic areas with unusual levels of the behavior independent of predefined administrative units.

  13. A flexible spatial scan statistic with a restricted likelihood ratio for detecting disease clusters.

    Science.gov (United States)

    Tango, Toshiro; Takahashi, Kunihiko

    2012-12-30

    Spatial scan statistics are widely used tools for detection of disease clusters. Especially, the circular spatial scan statistic proposed by Kulldorff (1997) has been utilized in a wide variety of epidemiological studies and disease surveillance. However, as it cannot detect noncircular, irregularly shaped clusters, many authors have proposed different spatial scan statistics, including the elliptic version of Kulldorff's scan statistic. The flexible spatial scan statistic proposed by Tango and Takahashi (2005) has also been used for detecting irregularly shaped clusters. However, this method sets a feasible limitation of a maximum of 30 nearest neighbors for searching candidate clusters because of heavy computational load. In this paper, we show a flexible spatial scan statistic implemented with a restricted likelihood ratio proposed by Tango (2008) to (1) eliminate the limitation of 30 nearest neighbors and (2) to have surprisingly much less computational time than the original flexible spatial scan statistic. As a side effect, it is shown to be able to detect clusters with any shape reasonably well as the relative risk of the cluster becomes large via Monte Carlo simulation. We illustrate the proposed spatial scan statistic with data on mortality from cerebrovascular disease in the Tokyo Metropolitan area, Japan. Copyright © 2012 John Wiley & Sons, Ltd.

  14. Cluster: A New Application for Spatial Analysis of Pixelated Data for Epiphytotics.

    Science.gov (United States)

    Nelson, Scot C; Corcoja, Iulian; Pethybridge, Sarah J

    2017-12-01

    Spatial analysis of epiphytotics is essential to develop and test hypotheses about pathogen ecology, disease dynamics, and to optimize plant disease management strategies. Data collection for spatial analysis requires substantial investment in time to depict patterns in various frames and hierarchies. We developed a new approach for spatial analysis of pixelated data in digital imagery and incorporated the method in a stand-alone desktop application called Cluster. The user isolates target entities (clusters) by designating up to 24 pixel colors as nontargets and moves a threshold slider to visualize the targets. The app calculates the percent area occupied by targeted pixels, identifies the centroids of targeted clusters, and computes the relative compass angle of orientation for each cluster. Users can deselect anomalous clusters manually and/or automatically by specifying a size threshold value to exclude smaller targets from the analysis. Up to 1,000 stochastic simulations randomly place the centroids of each cluster in ranked order of size (largest to smallest) within each matrix while preserving their calculated angles of orientation for the long axes. A two-tailed probability t test compares the mean inter-cluster distances for the observed versus the values derived from randomly simulated maps. This is the basis for statistical testing of the null hypothesis that the clusters are randomly distributed within the frame of interest. These frames can assume any shape, from natural (e.g., leaf) to arbitrary (e.g., a rectangular or polygonal field). Cluster summarizes normalized attributes of clusters, including pixel number, axis length, axis width, compass orientation, and the length/width ratio, available to the user as a downloadable spreadsheet. Each simulated map may be saved as an image and inspected. Provided examples demonstrate the utility of Cluster to analyze patterns at various spatial scales in plant pathology and ecology and highlight the

  15. Automating an integrated spatial data-mining model for landfill site selection

    Science.gov (United States)

    Abujayyab, Sohaib K. M.; Ahamad, Mohd Sanusi S.; Yahya, Ahmad Shukri; Ahmad, Siti Zubaidah; Aziz, Hamidi Abdul

    2017-10-01

    An integrated programming environment represents a robust approach to building a valid model for landfill site selection. One of the main challenges in the integrated model is the complicated processing and modelling due to the programming stages and several limitations. An automation process helps avoid the limitations and improve the interoperability between integrated programming environments. This work targets the automation of a spatial data-mining model for landfill site selection by integrating between spatial programming environment (Python-ArcGIS) and non-spatial environment (MATLAB). The model was constructed using neural networks and is divided into nine stages distributed between Matlab and Python-ArcGIS. A case study was taken from the north part of Peninsular Malaysia. 22 criteria were selected to utilise as input data and to build the training and testing datasets. The outcomes show a high-performance accuracy percentage of 98.2% in the testing dataset using 10-fold cross validation. The automated spatial data mining model provides a solid platform for decision makers to performing landfill site selection and planning operations on a regional scale.

  16. Uniform deposition of size-selected clusters using Lissajous scanning

    International Nuclear Information System (INIS)

    Beniya, Atsushi; Watanabe, Yoshihide; Hirata, Hirohito

    2016-01-01

    Size-selected clusters can be deposited on the surface using size-selected cluster ion beams. However, because of the cross-sectional intensity distribution of the ion beam, it is difficult to define the coverage of the deposited clusters. The aggregation probability of the cluster depends on coverage, whereas cluster size on the surface depends on the position, despite the size-selected clusters are deposited. It is crucial, therefore, to deposit clusters uniformly on the surface. In this study, size-selected clusters were deposited uniformly on surfaces by scanning the cluster ions in the form of Lissajous pattern. Two sets of deflector electrodes set in orthogonal directions were placed in front of the sample surface. Triangular waves were applied to the electrodes with an irrational frequency ratio to ensure that the ion trajectory filled the sample surface. The advantages of this method are simplicity and low cost of setup compared with raster scanning method. The authors further investigated CO adsorption on size-selected Pt n (n = 7, 15, 20) clusters uniformly deposited on the Al 2 O 3 /NiAl(110) surface and demonstrated the importance of uniform deposition.

  17. Uniform deposition of size-selected clusters using Lissajous scanning

    Energy Technology Data Exchange (ETDEWEB)

    Beniya, Atsushi; Watanabe, Yoshihide, E-mail: e0827@mosk.tytlabs.co.jp [Toyota Central R& D Labs., Inc., 41-1 Yokomichi, Nagakute, Aichi 480-1192 (Japan); Hirata, Hirohito [Toyota Motor Corporation, 1200 Mishuku, Susono, Shizuoka 410-1193 (Japan)

    2016-05-15

    Size-selected clusters can be deposited on the surface using size-selected cluster ion beams. However, because of the cross-sectional intensity distribution of the ion beam, it is difficult to define the coverage of the deposited clusters. The aggregation probability of the cluster depends on coverage, whereas cluster size on the surface depends on the position, despite the size-selected clusters are deposited. It is crucial, therefore, to deposit clusters uniformly on the surface. In this study, size-selected clusters were deposited uniformly on surfaces by scanning the cluster ions in the form of Lissajous pattern. Two sets of deflector electrodes set in orthogonal directions were placed in front of the sample surface. Triangular waves were applied to the electrodes with an irrational frequency ratio to ensure that the ion trajectory filled the sample surface. The advantages of this method are simplicity and low cost of setup compared with raster scanning method. The authors further investigated CO adsorption on size-selected Pt{sub n} (n = 7, 15, 20) clusters uniformly deposited on the Al{sub 2}O{sub 3}/NiAl(110) surface and demonstrated the importance of uniform deposition.

  18. Perspective: Size selected clusters for catalysis and electrochemistry

    Science.gov (United States)

    Halder, Avik; Curtiss, Larry A.; Fortunelli, Alessandro; Vajda, Stefan

    2018-03-01

    Size-selected clusters containing a handful of atoms may possess noble catalytic properties different from nano-sized or bulk catalysts. Size- and composition-selected clusters can also serve as models of the catalytic active site, where an addition or removal of a single atom can have a dramatic effect on their activity and selectivity. In this perspective, we provide an overview of studies performed under both ultra-high vacuum and realistic reaction conditions aimed at the interrogation, characterization, and understanding of the performance of supported size-selected clusters in heterogeneous and electrochemical reactions, which address the effects of cluster size, cluster composition, cluster-support interactions, and reaction conditions, the key parameters for the understanding and control of catalyst functionality. Computational modeling based on density functional theory sampling of local minima and energy barriers or ab initio molecular dynamics simulations is an integral part of this research by providing fundamental understanding of the catalytic processes at the atomic level, as well as by predicting new materials compositions which can be validated in experiments. Finally, we discuss approaches which aim at the scale up of the production of well-defined clusters for use in real world applications.

  19. Spatial cluster analysis of nanoscopically mapped serotonin receptors for classification of fixed brain tissue

    Science.gov (United States)

    Sams, Michael; Silye, Rene; Göhring, Janett; Muresan, Leila; Schilcher, Kurt; Jacak, Jaroslaw

    2014-01-01

    We present a cluster spatial analysis method using nanoscopic dSTORM images to determine changes in protein cluster distributions within brain tissue. Such methods are suitable to investigate human brain tissue and will help to achieve a deeper understanding of brain disease along with aiding drug development. Human brain tissue samples are usually treated postmortem via standard fixation protocols, which are established in clinical laboratories. Therefore, our localization microscopy-based method was adapted to characterize protein density and protein cluster localization in samples fixed using different protocols followed by common fluorescent immunohistochemistry techniques. The localization microscopy allows nanoscopic mapping of serotonin 5-HT1A receptor groups within a two-dimensional image of a brain tissue slice. These nanoscopically mapped proteins can be confined to clusters by applying the proposed statistical spatial analysis. Selected features of such clusters were subsequently used to characterize and classify the tissue. Samples were obtained from different types of patients, fixed with different preparation methods, and finally stored in a human tissue bank. To verify the proposed method, samples of a cryopreserved healthy brain have been compared with epitope-retrieved and paraffin-fixed tissues. Furthermore, samples of healthy brain tissues were compared with data obtained from patients suffering from mental illnesses (e.g., major depressive disorder). Our work demonstrates the applicability of localization microscopy and image analysis methods for comparison and classification of human brain tissues at a nanoscopic level. Furthermore, the presented workflow marks a unique technological advance in the characterization of protein distributions in brain tissue sections.

  20. The spatial patterns of directional phenotypic selection.

    Science.gov (United States)

    Siepielski, Adam M; Gotanda, Kiyoko M; Morrissey, Michael B; Diamond, Sarah E; DiBattista, Joseph D; Carlson, Stephanie M

    2013-11-01

    Local adaptation, adaptive population divergence and speciation are often expected to result from populations evolving in response to spatial variation in selection. Yet, we lack a comprehensive understanding of the major features that characterise the spatial patterns of selection, namely the extent of variation among populations in the strength and direction of selection. Here, we analyse a data set of spatially replicated studies of directional phenotypic selection from natural populations. The data set includes 60 studies, consisting of 3937 estimates of selection across an average of five populations. We performed meta-analyses to explore features characterising spatial variation in directional selection. We found that selection tends to vary mainly in strength and less in direction among populations. Although differences in the direction of selection occur among populations they do so where selection is often weakest, which may limit the potential for ongoing adaptive population divergence. Overall, we also found that spatial variation in selection appears comparable to temporal (annual) variation in selection within populations; however, several deficiencies in available data currently complicate this comparison. We discuss future research needs to further advance our understanding of spatial variation in selection. © 2013 John Wiley & Sons Ltd/CNRS.

  1. The spatial patterns of directional phenotypic selection

    KAUST Repository

    Siepielski, Adam M.

    2013-09-12

    Local adaptation, adaptive population divergence and speciation are often expected to result from populations evolving in response to spatial variation in selection. Yet, we lack a comprehensive understanding of the major features that characterise the spatial patterns of selection, namely the extent of variation among populations in the strength and direction of selection. Here, we analyse a data set of spatially replicated studies of directional phenotypic selection from natural populations. The data set includes 60 studies, consisting of 3937 estimates of selection across an average of five populations. We performed meta-analyses to explore features characterising spatial variation in directional selection. We found that selection tends to vary mainly in strength and less in direction among populations. Although differences in the direction of selection occur among populations they do so where selection is often weakest, which may limit the potential for ongoing adaptive population divergence. Overall, we also found that spatial variation in selection appears comparable to temporal (annual) variation in selection within populations; however, several deficiencies in available data currently complicate this comparison. We discuss future research needs to further advance our understanding of spatial variation in selection. © 2013 John Wiley & Sons Ltd/CNRS.

  2. INTERSECTION DETECTION BASED ON QUALITATIVE SPATIAL REASONING ON STOPPING POINT CLUSTERS

    Directory of Open Access Journals (Sweden)

    S. Zourlidou

    2016-06-01

    Full Text Available The purpose of this research is to propose and test a method for detecting intersections by analysing collectively acquired trajectories of moving vehicles. Instead of solely relying on the geometric features of the trajectories, such as heading changes, which may indicate turning points and consequently intersections, we extract semantic features of the trajectories in form of sequences of stops and moves. Under this spatiotemporal prism, the extracted semantic information which indicates where vehicles stop can reveal important locations, such as junctions. The advantage of the proposed approach in comparison with existing turning-points oriented approaches is that it can detect intersections even when not all the crossing road segments are sampled and therefore no turning points are observed in the trajectories. The challenge with this approach is that first of all, not all vehicles stop at the same location – thus, the stop-location is blurred along the direction of the road; this, secondly, leads to the effect that nearby junctions can induce similar stop-locations. As a first step, a density-based clustering is applied on the layer of stop observations and clusters of stop events are found. Representative points of the clusters are determined (one per cluster and in a last step the existence of an intersection is clarified based on spatial relational cluster reasoning, with which less informative geospatial clusters, in terms of whether a junction exists and where its centre lies, are transformed in more informative ones. Relational reasoning criteria, based on the relative orientation of the clusters with their adjacent ones are discussed for making sense of the relation that connects them, and finally for forming groups of stop events that belong to the same junction.

  3. Beams of mass-selected clusters: realization and first experiments

    International Nuclear Information System (INIS)

    Kamalou, O.

    2007-04-01

    The main objective of this work concerns the production of beams of mass-selected clusters of metallic and semiconductor materials. Clusters are produced in magnetron sputtering source combined with a gas aggregation chamber, cooled by liquid nitrogen circulation. Downstream of the cluster source, a Wiley-McLaren time-of-flight setup allows to select a given cluster size or a narrow size range. The pulsed mass-selected cluster ion beam is separated from the continuous neutral one by an electrostatic 90-quadrupole deflector. After the deflector, the density of the pulsed beam amounts to about 10 3 particles/cm 3 . Preliminary deposition experiments of mass-selected copper clusters with a deposition energy of about 0.5 eV/atom have ben performed on highly oriented pyrolytic graphite (HOPG) substrates, indicating that copper clusters are evidently mobile on the HOPG-surface until they reach cleavage steps, dislocation lines or other surface defects. In order to lower the cluster mobility on the HOPG-surface, we have first irradiated HOPG samples with slow highly charged ions (high dose) in order to create superficial defects. In a second step we have deposited mass-selected copper clusters on these pre-irradiated samples. The first analysis by AFM (Atomic Force Microscopy) techniques showed that the copper clusters are trapped on the defects produced by the highly charged ions. (author)

  4. The spatial structure of habitat selection: A caribou's-eye-view

    Science.gov (United States)

    Mayor, Stephen J.; Schaefer, James A.; Schneider, David C.; Mahoney, Shane P.

    2009-03-01

    Greater understanding of habitat selection requires investigation at the scales at which organisms perceive and respond to their environment. Such knowledge could reveal the relative importance of factors limiting populations and the extent of response to habitat changes, and so guide conservation initiatives. We conducted a novel, spatially explicit analysis of winter habitat selection by caribou ( Rangifer tarandus) in Newfoundland, Canada, to elucidate the spatial scales of habitat selection. We combined conventional hierarchical habitat analysis with a newly developed geospatial approach that quantifies selection across scales as the difference in variance between available and used sites. We used both ordination and univariate analyses of lichen and plant cover, snow hardness and depth. This represents the first use of ordination with geostatistics for the assessment of habitat selection. Caribou habitat selection was driven by shallow, soft snow and high cover of Cladina lichens and was strongest at feeding microsites (craters) and broader feeding areas. Habitat selection was most evident at distance lags of up to 15 km, perhaps an indication of the perceptual abilities of caribou.

  5. Representing Degree Distributions, Clustering, and Homophily in Social Networks With Latent Cluster Random Effects Models.

    Science.gov (United States)

    Krivitsky, Pavel N; Handcock, Mark S; Raftery, Adrian E; Hoff, Peter D

    2009-07-01

    Social network data often involve transitivity, homophily on observed attributes, clustering, and heterogeneity of actor degrees. We propose a latent cluster random effects model to represent all of these features, and we describe a Bayesian estimation method for it. The model is applicable to both binary and non-binary network data. We illustrate the model using two real datasets. We also apply it to two simulated network datasets with the same, highly skewed, degree distribution, but very different network behavior: one unstructured and the other with transitivity and clustering. Models based on degree distributions, such as scale-free, preferential attachment and power-law models, cannot distinguish between these very different situations, but our model does.

  6. Spatial cluster analysis of human cases of Crimean Congo hemorrhagic fever reported in Pakistan.

    Science.gov (United States)

    Abbas, Tariq; Younus, Muhammad; Muhammad, Sayyad Aun

    2015-01-01

    Crimean Congo hemorrhagic fever (CCHF) is a tick-borne viral zoonotic disease that has been reported in almost all geographic regions in Pakistan. The aim of this study was to identify spatial clusters of human cases of CCHF reported in country. Kulldorff's spatial scan statisitc, Anselin's Local Moran's I and Getis Ord Gi* tests were applied on data (i.e. number of laboratory confirmed cases reported from each district during year 2013). The analyses revealed a large multi-district cluster of high CCHF incidence in the uplands of Balochistan province near it border with Afghanistan. The cluster comprised the following districts: Qilla Abdullah; Qilla Saifullah; Loralai, Quetta, Sibi, Chagai, and Mastung. Another cluster was detected in Punjab and included Rawalpindi district and a part of Islamabad. We provide empirical evidence of spatial clustering of human CCHF cases in the country. The districts in the clusters should be given priority in surveillance, control programs, and further research.

  7. Spatial clustering of childhood cancer in Great Britain during the period 1969-1993.

    Science.gov (United States)

    McNally, Richard J Q; Alexander, Freda E; Vincent, Tim J; Murphy, Michael F G

    2009-02-15

    The aetiology of childhood cancer is poorly understood. Both genetic and environmental factors are likely to be involved. The presence of spatial clustering is indicative of a very localized environmental component to aetiology. Spatial clustering is present when there are a small number of areas with greatly increased incidence or a large number of areas with moderately increased incidence. To determine whether localized environmental factors may play a part in childhood cancer aetiology, we analyzed for spatial clustering using a large set of national population-based data from Great Britain diagnosed 1969-1993. The Potthoff-Whittinghill method was used to test for extra-Poisson variation (EPV). Thirty-two thousand three hundred and twenty-three cases were allocated to 10,444 wards using diagnosis addresses. Analyses showed statistically significant evidence of clustering for acute lymphoblastic leukaemia (ALL) over the whole age range (estimate of EPV = 0.05, p = 0.002) and for ages 1-4 years (estimate of EPV = 0.03, p = 0.015). Soft-tissue sarcoma (estimate of EPV = 0.03, p = 0.04) and Wilms tumours (estimate of EPV = 0.04, p = 0.007) also showed significant clustering. Clustering tended to persist across different time periods for cases of ALL (estimate of between-time period EPV = 0.04, p =0.003). In conclusion, we observed low level spatial clustering that is attributable to a limited number of cases. This suggests that environmental factors, which in some locations display localized clustering, may be important aetiological agents in these diseases. For ALL and soft tissue sarcoma, but not Wilms tumour, common infectious agents may be likely candidates.

  8. Optimizing the maximum reported cluster size in the spatial scan statistic for ordinal data.

    Science.gov (United States)

    Kim, Sehwi; Jung, Inkyung

    2017-01-01

    The spatial scan statistic is an important tool for spatial cluster detection. There have been numerous studies on scanning window shapes. However, little research has been done on the maximum scanning window size or maximum reported cluster size. Recently, Han et al. proposed to use the Gini coefficient to optimize the maximum reported cluster size. However, the method has been developed and evaluated only for the Poisson model. We adopt the Gini coefficient to be applicable to the spatial scan statistic for ordinal data to determine the optimal maximum reported cluster size. Through a simulation study and application to a real data example, we evaluate the performance of the proposed approach. With some sophisticated modification, the Gini coefficient can be effectively employed for the ordinal model. The Gini coefficient most often picked the optimal maximum reported cluster sizes that were the same as or smaller than the true cluster sizes with very high accuracy. It seems that we can obtain a more refined collection of clusters by using the Gini coefficient. The Gini coefficient developed specifically for the ordinal model can be useful for optimizing the maximum reported cluster size for ordinal data and helpful for properly and informatively discovering cluster patterns.

  9. A New Integrated Threshold Selection Methodology for Spatial Forecast Verification of Extreme Events

    Science.gov (United States)

    Kholodovsky, V.

    2017-12-01

    Extreme weather and climate events such as heavy precipitation, heat waves and strong winds can cause extensive damage to the society in terms of human lives and financial losses. As climate changes, it is important to understand how extreme weather events may change as a result. Climate and statistical models are often independently used to model those phenomena. To better assess performance of the climate models, a variety of spatial forecast verification methods have been developed. However, spatial verification metrics that are widely used in comparing mean states, in most cases, do not have an adequate theoretical justification to benchmark extreme weather events. We proposed a new integrated threshold selection methodology for spatial forecast verification of extreme events that couples existing pattern recognition indices with high threshold choices. This integrated approach has three main steps: 1) dimension reduction; 2) geometric domain mapping; and 3) thresholds clustering. We apply this approach to an observed precipitation dataset over CONUS. The results are evaluated by displaying threshold distribution seasonally, monthly and annually. The method offers user the flexibility of selecting a high threshold that is linked to desired geometrical properties. The proposed high threshold methodology could either complement existing spatial verification methods, where threshold selection is arbitrary, or be directly applicable in extreme value theory.

  10. Spatial and temporal clustering of dengue virus transmission in Thai villages.

    Science.gov (United States)

    Mammen, Mammen P; Pimgate, Chusak; Koenraadt, Constantianus J M; Rothman, Alan L; Aldstadt, Jared; Nisalak, Ananda; Jarman, Richard G; Jones, James W; Srikiatkhachorn, Anon; Ypil-Butac, Charity Ann; Getis, Arthur; Thammapalo, Suwich; Morrison, Amy C; Libraty, Daniel H; Green, Sharone; Scott, Thomas W

    2008-11-04

    Transmission of dengue viruses (DENV), the leading cause of arboviral disease worldwide, is known to vary through time and space, likely owing to a combination of factors related to the human host, virus, mosquito vector, and environment. An improved understanding of variation in transmission patterns is fundamental to conducting surveillance and implementing disease prevention strategies. To test the hypothesis that DENV transmission is spatially and temporally focal, we compared geographic and temporal characteristics within Thai villages where DENV are and are not being actively transmitted. Cluster investigations were conducted within 100 m of homes where febrile index children with (positive clusters) and without (negative clusters) acute dengue lived during two seasons of peak DENV transmission. Data on human infection and mosquito infection/density were examined to precisely (1) define the spatial and temporal dimensions of DENV transmission, (2) correlate these factors with variation in DENV transmission, and (3) determine the burden of inapparent and symptomatic infections. Among 556 village children enrolled as neighbors of 12 dengue-positive and 22 dengue-negative index cases, all 27 DENV infections (4.9% of enrollees) occurred in positive clusters (p availability of piped water in negative clusters (p < 0.01) and greater number of Ae. aegypti pupae per person in positive clusters (p = 0.04). During primarily DENV-4 transmission seasons, the ratio of inapparent to symptomatic infections was nearly 1:1 among child enrollees. Study limitations included inability to sample all children and mosquitoes within each cluster and our reliance on serologic rather than virologic evidence of interval infections in enrollees given restrictions on the frequency of blood collections in children. Our data reveal the remarkably focal nature of DENV transmission within a hyperendemic rural area of Thailand. These data suggest that active school-based dengue case detection

  11. Shifting patterns of Aedes aegypti fine scale spatial clustering in Iquitos, Peru.

    Science.gov (United States)

    LaCon, Genevieve; Morrison, Amy C; Astete, Helvio; Stoddard, Steven T; Paz-Soldan, Valerie A; Elder, John P; Halsey, Eric S; Scott, Thomas W; Kitron, Uriel; Vazquez-Prokopec, Gonzalo M

    2014-08-01

    Empiric evidence shows that Aedes aegypti abundance is spatially heterogeneous and that some areas and larval habitats produce more mosquitoes than others. There is a knowledge gap, however, with regards to the temporal persistence of such Ae. aegypti abundance hotspots. In this study, we used a longitudinal entomologic dataset from the city of Iquitos, Peru, to (1) quantify the spatial clustering patterns of adult Ae. aegypti and pupae counts per house, (2) determine overlap between clusters, (3) quantify the temporal stability of clusters over nine entomologic surveys spaced four months apart, and (4) quantify the extent of clustering at the household and neighborhood levels. Data from 13,662 household entomological visits performed in two Iquitos neighborhoods differing in Ae. aegypti abundance and dengue virus transmission was analyzed using global and local spatial statistics. The location and extent of Ae. aegypti pupae and adult hotspots (i.e., small groups of houses with significantly [pentomologic surveys. The extent of clustering was used to quantify the probability of finding spatially correlated populations. Our analyses indicate that Ae. aegypti distribution was highly focal (most clusters do not extend beyond 30 meters) and that hotspots of high vector abundance were common on every survey date, but they were temporally unstable over the period of study. Our findings have implications for understanding Ae. aegypti distribution and for the design of surveillance and control activities relying on household-level data. In settings like Iquitos, where there is a relatively low percentage of Ae. aegypti in permanent water-holding containers, identifying and targeting key premises will be significantly challenged by shifting hotspots of Ae. aegypti infestation. Focusing efforts in large geographic areas with historically high levels of transmission may be more effective than targeting Ae. aegypti hotspots.

  12. Shifting patterns of Aedes aegypti fine scale spatial clustering in Iquitos, Peru.

    Directory of Open Access Journals (Sweden)

    Genevieve LaCon

    2014-08-01

    Full Text Available Empiric evidence shows that Aedes aegypti abundance is spatially heterogeneous and that some areas and larval habitats produce more mosquitoes than others. There is a knowledge gap, however, with regards to the temporal persistence of such Ae. aegypti abundance hotspots. In this study, we used a longitudinal entomologic dataset from the city of Iquitos, Peru, to (1 quantify the spatial clustering patterns of adult Ae. aegypti and pupae counts per house, (2 determine overlap between clusters, (3 quantify the temporal stability of clusters over nine entomologic surveys spaced four months apart, and (4 quantify the extent of clustering at the household and neighborhood levels.Data from 13,662 household entomological visits performed in two Iquitos neighborhoods differing in Ae. aegypti abundance and dengue virus transmission was analyzed using global and local spatial statistics. The location and extent of Ae. aegypti pupae and adult hotspots (i.e., small groups of houses with significantly [p<0.05] high mosquito abundance were calculated for each of the 9 entomologic surveys. The extent of clustering was used to quantify the probability of finding spatially correlated populations. Our analyses indicate that Ae. aegypti distribution was highly focal (most clusters do not extend beyond 30 meters and that hotspots of high vector abundance were common on every survey date, but they were temporally unstable over the period of study.Our findings have implications for understanding Ae. aegypti distribution and for the design of surveillance and control activities relying on household-level data. In settings like Iquitos, where there is a relatively low percentage of Ae. aegypti in permanent water-holding containers, identifying and targeting key premises will be significantly challenged by shifting hotspots of Ae. aegypti infestation. Focusing efforts in large geographic areas with historically high levels of transmission may be more effective than

  13. GraphTeams: a method for discovering spatial gene clusters in Hi-C sequencing data.

    Science.gov (United States)

    Schulz, Tizian; Stoye, Jens; Doerr, Daniel

    2018-05-08

    Hi-C sequencing offers novel, cost-effective means to study the spatial conformation of chromosomes. We use data obtained from Hi-C experiments to provide new evidence for the existence of spatial gene clusters. These are sets of genes with associated functionality that exhibit close proximity to each other in the spatial conformation of chromosomes across several related species. We present the first gene cluster model capable of handling spatial data. Our model generalizes a popular computational model for gene cluster prediction, called δ-teams, from sequences to graphs. Following previous lines of research, we subsequently extend our model to allow for several vertices being associated with the same label. The model, called δ-teams with families, is particular suitable for our application as it enables handling of gene duplicates. We develop algorithmic solutions for both models. We implemented the algorithm for discovering δ-teams with families and integrated it into a fully automated workflow for discovering gene clusters in Hi-C data, called GraphTeams. We applied it to human and mouse data to find intra- and interchromosomal gene cluster candidates. The results include intrachromosomal clusters that seem to exhibit a closer proximity in space than on their chromosomal DNA sequence. We further discovered interchromosomal gene clusters that contain genes from different chromosomes within the human genome, but are located on a single chromosome in mouse. By identifying δ-teams with families, we provide a flexible model to discover gene cluster candidates in Hi-C data. Our analysis of Hi-C data from human and mouse reveals several known gene clusters (thus validating our approach), but also few sparsely studied or possibly unknown gene cluster candidates that could be the source of further experimental investigations.

  14. The spatial patterns of directional phenotypic selection

    KAUST Repository

    Siepielski, Adam M.; Gotanda, Kiyoko M.; Morrissey, Michael B.; Diamond, Sarah E.; DiBattista, Joseph; Carlson, Stephanie Marie

    2013-01-01

    the spatial patterns of selection, namely the extent of variation among populations in the strength and direction of selection. Here, we analyse a data set of spatially replicated studies of directional phenotypic selection from natural populations. The data

  15. An Examination of Three Spatial Event Cluster Detection Methods

    Directory of Open Access Journals (Sweden)

    Hensley H. Mariathas

    2015-03-01

    Full Text Available In spatial disease surveillance, geographic areas with large numbers of disease cases are to be identified, so that targeted investigations can be pursued. Geographic areas with high disease rates are called disease clusters and statistical cluster detection tests are used to identify geographic areas with higher disease rates than expected by chance alone. In some situations, disease-related events rather than individuals are of interest for geographical surveillance, and methods to detect clusters of disease-related events are called event cluster detection methods. In this paper, we examine three distributional assumptions for the events in cluster detection: compound Poisson, approximate normal and multiple hypergeometric (exact. The methods differ on the choice of distributional assumption for the potentially multiple correlated events per individual. The methods are illustrated on emergency department (ED presentations by children and youth (age < 18 years because of substance use in the province of Alberta, Canada, during 1 April 2007, to 31 March 2008. Simulation studies are conducted to investigate Type I error and the power of the clustering methods.

  16. A Self-Organizing Spatial Clustering Approach to Support Large-Scale Network RTK Systems.

    Science.gov (United States)

    Shen, Lili; Guo, Jiming; Wang, Lei

    2018-06-06

    The network real-time kinematic (RTK) technique can provide centimeter-level real time positioning solutions and play a key role in geo-spatial infrastructure. With ever-increasing popularity, network RTK systems will face issues in the support of large numbers of concurrent users. In the past, high-precision positioning services were oriented towards professionals and only supported a few concurrent users. Currently, precise positioning provides a spatial foundation for artificial intelligence (AI), and countless smart devices (autonomous cars, unmanned aerial-vehicles (UAVs), robotic equipment, etc.) require precise positioning services. Therefore, the development of approaches to support large-scale network RTK systems is urgent. In this study, we proposed a self-organizing spatial clustering (SOSC) approach which automatically clusters online users to reduce the computational load on the network RTK system server side. The experimental results indicate that both the SOSC algorithm and the grid algorithm can reduce the computational load efficiently, while the SOSC algorithm gives a more elastic and adaptive clustering solution with different datasets. The SOSC algorithm determines the cluster number and the mean distance to cluster center (MDTCC) according to the data set, while the grid approaches are all predefined. The side-effects of clustering algorithms on the user side are analyzed with real global navigation satellite system (GNSS) data sets. The experimental results indicate that 10 km can be safely used as the cluster radius threshold for the SOSC algorithm without significantly reducing the positioning precision and reliability on the user side.

  17. Spatial and temporal clustering of dengue virus transmission in Thai villages.

    Directory of Open Access Journals (Sweden)

    Mammen P Mammen

    2008-11-01

    Full Text Available Transmission of dengue viruses (DENV, the leading cause of arboviral disease worldwide, is known to vary through time and space, likely owing to a combination of factors related to the human host, virus, mosquito vector, and environment. An improved understanding of variation in transmission patterns is fundamental to conducting surveillance and implementing disease prevention strategies. To test the hypothesis that DENV transmission is spatially and temporally focal, we compared geographic and temporal characteristics within Thai villages where DENV are and are not being actively transmitted.Cluster investigations were conducted within 100 m of homes where febrile index children with (positive clusters and without (negative clusters acute dengue lived during two seasons of peak DENV transmission. Data on human infection and mosquito infection/density were examined to precisely (1 define the spatial and temporal dimensions of DENV transmission, (2 correlate these factors with variation in DENV transmission, and (3 determine the burden of inapparent and symptomatic infections. Among 556 village children enrolled as neighbors of 12 dengue-positive and 22 dengue-negative index cases, all 27 DENV infections (4.9% of enrollees occurred in positive clusters (p < 0.01; attributable risk [AR] = 10.4 per 100; 95% confidence interval 1-19.8 per 100]. In positive clusters, 12.4% of enrollees became infected in a 15-d period and DENV infections were aggregated centrally near homes of index cases. As only 1 of 217 pairs of serologic specimens tested in positive clusters revealed a recent DENV infection that occurred prior to cluster initiation, we attribute the observed DENV transmission subsequent to cluster investigation to recent DENV transmission activity. Of the 1,022 female adult Ae. aegypti collected, all eight (0.8% dengue-infected mosquitoes came from houses in positive clusters; none from control clusters or schools. Distinguishing features between

  18. The next generation Virgo cluster survey. VIII. The spatial distribution of globular clusters in the Virgo cluster

    Energy Technology Data Exchange (ETDEWEB)

    Durrell, Patrick R.; Accetta, Katharine [Department of Physics and Astronomy, Youngstown State University, Youngstown, OH 44555 (United States); Côté, Patrick; Blakeslee, John P.; Ferrarese, Laura; McConnachie, Alan; Gwyn, Stephen [Herzberg Astronomy and Astrophysics, National Research Council, 5071 West Saanich Road, Victoria, BC V9E 2E7 (Canada); Peng, Eric W.; Zhang, Hongxin [Department of Astronomy, Peking University, Beijing 100871 (China); Mihos, J. Christopher [Department of Astronomy, Case Western Reserve University, Cleveland, OH 44106 (United States); Puzia, Thomas H.; Jordán, Andrés [Institute of Astrophysics, Pontificia Universidad Catolica, Av. Vicu' a Mackenna 4860, Macul 7820436, Santiago (Chile); Lançon, Ariane [Observatoire astronomique de Strasbourg, Université de Strasbourg, CNRS, UMR 7550, 11 rue de l' Université, F-67000 Strasbourg (France); Liu, Chengze [Center for Astronomy and Astrophysics, Department of Physics and Astronomy, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240 (China); Cuillandre, Jean-Charles [Canada-France-Hawaii Telescope Corporation, Kamuela, HI 96743 (United States); Boissier, Samuel; Boselli, Alessandro [Aix Marseille Université, CNRS, LAM (Laboratoire d' Astrophysique de Marseille) UMR 7326, F-13388 Marseille (France); Courteau, Stéphane [Department of Physics, Engineering Physics and Astronomy, Queen' s University, Kingston, ON K7L 3N6 (Canada); Duc, Pierre-Alain [AIM Paris Saclay, CNRS/INSU, CEA/Irfu, Université Paris Diderot, Orme des Merisiers, F-91191 Gif sur Yvette cedex (France); Emsellem, Eric [Université de Lyon 1, CRAL, Observatoire de Lyon, 9 av. Charles André, F-69230 Saint-Genis Laval (France); CNRS, UMR 5574, ENS de Lyon (France); and others

    2014-10-20

    We report on a large-scale study of the distribution of globular clusters (GCs) throughout the Virgo cluster, based on photometry from the Next Generation Virgo Cluster Survey (NGVS), a large imaging survey covering Virgo's primary subclusters (Virgo A = M87 and Virgo B = M49) out to their virial radii. Using the g{sub o}{sup ′}, (g' – i') {sub o} color-magnitude diagram of unresolved and marginally resolved sources within the NGVS, we have constructed two-dimensional maps of the (irregular) GC distribution over 100 deg{sup 2} to a depth of g{sub o}{sup ′} = 24. We present the clearest evidence to date showing the difference in concentration between red and blue GCs over the full extent of the cluster, where the red (more metal-rich) GCs are largely located around the massive early-type galaxies in Virgo, while the blue (metal-poor) GCs have a much more extended spatial distribution with significant populations still present beyond 83' (∼215 kpc) along the major axes of both M49 and M87. A comparison of our GC maps to the diffuse light in the outermost regions of M49 and M87 show remarkable agreement in the shape, ellipticity, and boxiness of both luminous systems. We also find evidence for spatial enhancements of GCs surrounding M87 that may be indicative of recent interactions or an ongoing merger history. We compare the GC map to that of the locations of Virgo galaxies and the X-ray intracluster gas, and find generally good agreement between these various baryonic structures. We calculate the Virgo cluster contains a total population of N {sub GC} = 67, 300 ± 14, 400, of which 35% are located in M87 and M49 alone. For the first time, we compute a cluster-wide specific frequency S {sub N,} {sub CL} = 2.8 ± 0.7, after correcting for Virgo's diffuse light. We also find a GC-to-baryonic mass fraction ε {sub b} = 5.7 ± 1.1 × 10{sup –4} and a GC-to-total cluster mass formation efficiency ε {sub t} = 2.9 ± 0.5 × 10{sup –5

  19. A spatial hazard model for cluster detection on continuous indicators of disease: application to somatic cell score.

    Science.gov (United States)

    Gay, Emilie; Senoussi, Rachid; Barnouin, Jacques

    2007-01-01

    Methods for spatial cluster detection dealing with diseases quantified by continuous variables are few, whereas several diseases are better approached by continuous indicators. For example, subclinical mastitis of the dairy cow is evaluated using a continuous marker of udder inflammation, the somatic cell score (SCS). Consequently, this study proposed to analyze spatialized risk and cluster components of herd SCS through a new method based on a spatial hazard model. The dataset included annual SCS for 34 142 French dairy herds for the year 2000, and important SCS risk factors: mean parity, percentage of winter and spring calvings, and herd size. The model allowed the simultaneous estimation of the effects of known risk factors and of potential spatial clusters on SCS, and the mapping of the estimated clusters and their range. Mean parity and winter and spring calvings were significantly associated with subclinical mastitis risk. The model with the presence of 3 clusters was highly significant, and the 3 clusters were attractive, i.e. closeness to cluster center increased the occurrence of high SCS. The three localizations were the following: close to the city of Troyes in the northeast of France; around the city of Limoges in the center-west; and in the southwest close to the city of Tarbes. The semi-parametric method based on spatial hazard modeling applies to continuous variables, and takes account of both risk factors and potential heterogeneity of the background population. This tool allows a quantitative detection but assumes a spatially specified form for clusters.

  20. Time takes space: selective effects of multitasking on concurrent spatial processing.

    Science.gov (United States)

    Mäntylä, Timo; Coni, Valentina; Kubik, Veit; Todorov, Ivo; Del Missier, Fabio

    2017-08-01

    Many everyday activities require coordination and monitoring of complex relations of future goals and deadlines. Cognitive offloading may provide an efficient strategy for reducing control demands by representing future goals and deadlines as a pattern of spatial relations. We tested the hypothesis that multiple-task monitoring involves time-to-space transformational processes, and that these spatial effects are selective with greater demands on coordinate (metric) than categorical (nonmetric) spatial relation processing. Participants completed a multitasking session in which they monitored four series of deadlines, running on different time scales, while making concurrent coordinate or categorical spatial judgments. We expected and found that multitasking taxes concurrent coordinate, but not categorical, spatial processing. Furthermore, males showed a better multitasking performance than females. These findings provide novel experimental evidence for the hypothesis that efficient multitasking involves metric relational processing.

  1. A Self-Organizing Spatial Clustering Approach to Support Large-Scale Network RTK Systems

    Directory of Open Access Journals (Sweden)

    Lili Shen

    2018-06-01

    Full Text Available The network real-time kinematic (RTK technique can provide centimeter-level real time positioning solutions and play a key role in geo-spatial infrastructure. With ever-increasing popularity, network RTK systems will face issues in the support of large numbers of concurrent users. In the past, high-precision positioning services were oriented towards professionals and only supported a few concurrent users. Currently, precise positioning provides a spatial foundation for artificial intelligence (AI, and countless smart devices (autonomous cars, unmanned aerial-vehicles (UAVs, robotic equipment, etc. require precise positioning services. Therefore, the development of approaches to support large-scale network RTK systems is urgent. In this study, we proposed a self-organizing spatial clustering (SOSC approach which automatically clusters online users to reduce the computational load on the network RTK system server side. The experimental results indicate that both the SOSC algorithm and the grid algorithm can reduce the computational load efficiently, while the SOSC algorithm gives a more elastic and adaptive clustering solution with different datasets. The SOSC algorithm determines the cluster number and the mean distance to cluster center (MDTCC according to the data set, while the grid approaches are all predefined. The side-effects of clustering algorithms on the user side are analyzed with real global navigation satellite system (GNSS data sets. The experimental results indicate that 10 km can be safely used as the cluster radius threshold for the SOSC algorithm without significantly reducing the positioning precision and reliability on the user side.

  2. Galaxy clusters in the cosmic web

    Science.gov (United States)

    Acebrón, A.; Durret, F.; Martinet, N.; Adami, C.; Guennou, L.

    2014-12-01

    Simulations of large scale structure formation in the universe predict that matter is essentially distributed along filaments at the intersection of which lie galaxy clusters. We have analysed 9 clusters in the redshift range 0.4DAFT/FADA survey, which combines deep large field multi-band imaging and spectroscopic data, in order to detect filaments and/or structures around these clusters. Based on colour-magnitude diagrams, we have selected the galaxies likely to be in the cluster redshift range and studied their spatial distribution. We detect a number of structures and filaments around several clusters, proving that colour-magnitude diagrams are a reliable method to detect filaments around galaxy clusters. Since this method excludes blue (spiral) galaxies at the cluster redshift, we also apply the LePhare software to compute photometric redshifts from BVRIZ images to select galaxy cluster members and study their spatial distribution. We then find that, if only galaxies classified as early-type by LePhare are considered, we obtain the same distribution than with a red sequence selection, while taking into account late-type galaxies just pollutes the background level and deteriorates our detections. The photometric redshift based method therefore does not provide any additional information.

  3. Decreasing child mortality, spatial clustering and decreasing disparity in North-Western Burkina Faso.

    Science.gov (United States)

    Becher, Heiko; Müller, Olaf; Dambach, Peter; Gabrysch, Sabine; Niamba, Louis; Sankoh, Osman; Simboro, Seraphin; Schoeps, Anja; Stieglbauer, Gabriele; Yé, Yazoume; Sié, Ali

    2016-04-01

    Within relatively small areas, there exist high spatial variations of mortality between villages. In rural Burkina Faso, with data from 1993 to 1998, clusters of particularly high child mortality were identified in the population of the Nouna Health and Demographic Surveillance System (HDSS), a member of the INDEPTH Network. In this paper, we report child mortality with respect to temporal trends, spatial clustering and disparity in this HDSS from 1993 to 2012. Poisson regression was used to describe village-specific child mortality rates and time trends in mortality. The spatial scan statistic was used to identify villages or village clusters with higher child mortality. Clustering of mortality in the area is still present, but not as strong as before. The disparity of child mortality between villages has decreased. The decrease occurred in the context of an overall halving of child mortality in the rural area of Nouna HDSS between 1993 and 2012. Extrapolated to the Millennium Development Goals target period 1990-2015, this yields an estimated reduction of 54%, which is not too far off the aim of a two-thirds reduction. © 2016 John Wiley & Sons Ltd.

  4. Representing Spatial Layout According to Intrinsic Frames of Reference.

    Science.gov (United States)

    Xie, Chaoxiang; Li, Shiyi; Tao, Weidong; Wei, Yiping; Sun, Hong-Jin

    2017-01-01

    Mou and McNamara have suggested that object locations are represented according to intrinsic reference frames. In three experiments, we investigated the limitations of intrinsic reference frames as a mean to represent object locations in spatial memory. Participants learned the locations of seven or eight common objects in a rectangular room and then made judgments of relative direction based on their memory of the layout. The results of all experiments showed that when all objects were positioned regularly, judgments of relative direction were faster or more accurate for novel headings that were aligned with the primary intrinsic structure than for other novel headings; however, when one irregularly positioned object was added to the layout, this advantage was eliminated. The experiments further indicated that with a single view at study, participants could represent the layout from either an egocentric orientation or a different orientation, according to experimental instructions. Together, these results suggest that environmental reference frames and intrinsic axes can influence performance for novel headings, but their role in spatial memory depends on egocentric experience, layout regularity, and instructions.

  5. POLYMER COMPOSITE FILMS WITH SIZE-SELECTED METAL NANOPARTICLES FABRICATED BY CLUSTER BEAM TECHNIQUE

    DEFF Research Database (Denmark)

    Ceynowa, F. A.; Chirumamilla, Manohar; Popok, Vladimir

    2017-01-01

    Formation of polymer films with size-selected silver and copper nanoparticles (NPs) is studied. Polymers are prepared by spin coating while NPs are fabricated and deposited utilizing a magnetron sputtering cluster apparatus. The particle embedding into the films is provided by thermal annealing...... after the deposition. The degree of immersion can be controlled by the annealing temperature and time. Together with control of cluster coverage the described approach represents an efficient method for the synthesis of thin polymer composite layers with either partially or fully embedded metal NPs....... Combining electron beam lithography, cluster beam deposition and thermal annealing allows to form ordered arrays of metal NPs on polymer films. Plasticity and flexibility of polymer host and specific properties added by coinage metal NPs open a way for different applications of such composite materials...

  6. New conception of the spatial structure of the galactic clusters: Pleiades, Praesepe and Coma Berenices

    International Nuclear Information System (INIS)

    Pejkov, Z.I.

    1990-01-01

    The spatial structure of the galactic cluster Pleiads, Praesepe and Coma Berenices in the dependence on different star magnitude intervals and on different limiting star magnitudes is investigated on the basis of the star density distribution functions which were published by Kholopov and Artyukhina. It is shown that the spatial structure of these clusters, similarly to the globular ones, systematically changes with the star magnitude of the included stars, starting from the brightest stars of the upper part of the main sequance and descending along the 'V, B-V' diagram for the clusters. This change consists in an increase of the spatial zones radii, following the same law, whith the transition to the fainter stars

  7. Academic Clustering and Major Selection of Intercollegiate Student-Athletes

    Science.gov (United States)

    Schneider, Ray G.; Ross, Sally R.; Fisher, Morgan

    2010-01-01

    Although journalists and reporters have written about academic clustering among college student-athletes, there has been a dearth of scholarly analysis devoted to the subject. This study explored football players' academic major selections to determine if academic clustering actually existed. The seasons 1996, 2001, and 2006 were selected for…

  8. Deadlines in space: Selective effects of coordinate spatial processing in multitasking.

    Science.gov (United States)

    Todorov, Ivo; Del Missier, Fabio; Konke, Linn Andersson; Mäntylä, Timo

    2015-11-01

    Many everyday activities require coordination and monitoring of multiple deadlines. One way to handle these temporal demands might be to represent future goals and deadlines as a pattern of spatial relations. We examined the hypothesis that spatial ability, in addition to executive functioning, contributes to individual differences in multitasking. In two studies, participants completed a multitasking session in which they monitored four digital clocks running at different rates. In Study 1, we found that individual differences in spatial ability and executive functions were independent predictors of multiple-task performance. In Study 2, we found that individual differences in specific spatial abilities were selectively related to multiple-task performance, as only coordinate spatial processing, but not categorical, predicted multitasking, even beyond executive functioning and numeracy. In both studies, males outperformed females in spatial ability and multitasking and in Study 2 these sex differences generalized to a simulation of everyday multitasking. Menstrual changes moderated the effects on multitasking, in that sex differences in coordinate spatial processing and multitasking were observed between males and females in the luteal phase of the menstrual cycle, but not between males and females at menses. Overall, these findings suggest that multiple-task performance reflects independent contributions of spatial ability and executive functioning. Furthermore, our results support the distinction of categorical versus coordinate spatial processing, and suggest that these two basic relational processes are selectively affected by female sex hormones and differentially effective in transforming and handling temporal patterns as spatial relations in the context of multitasking.

  9. The MODIS (Collection V005) BRDF/albedo product: Assessment of spatial representativeness over forested landscapes

    Energy Technology Data Exchange (ETDEWEB)

    Roman, Miguel O. [NASA Goddard Space Flight Center; Schaaf, Crystal [Boston University; Woodcock, Curtis E. [Boston University; Strahler, Alan [Boston University; Yang, Xiaoyuan [Boston University; Braswell, Rob H. [Complex Systems Research Center, Durham, NH; Curtis, Peter [Ohio State University, The, Columbus; Davis, Kenneth J. [Pennsylvania State University; Dragoni, Danilo [Indiana University; Goulden, Michael L. [University of California, Irvine; Gu, Lianhong [ORNL; Hollinger, David Y [ORNL; Meyers, Tilden P. [NOAA, Oak Ridge, TN; Wilson, Tim B. [NOAA; Munger, J. William [Harvard University; Wofsy, Steve [Harvard University; Privette, Jeffrey L. [NOAA; Richardson, Andrew D. [Harvard University

    2009-11-01

    A new methodology for establishing the spatial representativeness of tower albedo measurements that are routinely used in validation of satellite retrievals from global land surface albedo and reflectance anisotropy products is presented. This method brings together knowledge of the intrinsic biophysical properties of a measurement site, and the surrounding landscape to produce a number of geostatistical attributes that describe the overall variability, spatial extent, strength of the spatial correlation, and spatial structure of surface albedo patterns at separate seasonal periods throughout the year. Variogram functions extracted from Enhanced Thematic Mapper Plus (ETM+) retrievals of surface albedo using multiple spatial and temporal thresholds were used to assess the degree to which a given point (tower) measurement is able to capture the intrinsic variability of the immediate landscape extending to a satellite pixel. A validation scheme was implemented over a wide range of forested landscapes, looking at both deciduous and coniferous sites, from tropical to boreal ecosystems. The experiment focused on comparisons between tower measurements of surface albedo acquired at local solar noon and matching retrievals from the MODerate Resolution Imaging Spectroradiometer (MODIS) (Collection V005) Bidirectional Reflectance Distribution Function (BRDF)/albedo algorithm. Assessments over a select group of field stations with comparable landscape features and daily retrieval scenarios further demonstrate the ability of this technique to identify measurement sites that contain the intrinsic spatial and seasonal features of surface albedo over sufficiently large enough footprints for use in modeling and remote sensing studies. This approach, therefore, improves our understanding of product uncertainty both in terms of the representativeness of the field data and its relationship to the larger satellite pixel.

  10. Identifying spatial clustering properties of the 1997-2003 Liguria (Northern Italy) forest-fire sequence

    International Nuclear Information System (INIS)

    Telesca, Luciano; Amatulli, Giuseppe; Lasaponara, Rosa; Lovallo, Michele; Santulli, Adriano

    2007-01-01

    The spatial clustering of the forest-fire sequence (1997-2003) of Liguria Region (Northern Italy) has been analysed using the correlation dimension D C , calculated by means of the correlation integral method. Studying the variations of this parameter, we recognize the presence of a strong variability of the spatial clusterization, modulated by seasonal cycles. Furthermore, we found that the larger fires (size >400 ha) mark the cyclic behaviour of the correlation dimension

  11. Clustering Vehicle Temporal and Spatial Travel Behavior Using License Plate Recognition Data

    Directory of Open Access Journals (Sweden)

    Huiyu Chen

    2017-01-01

    Full Text Available Understanding travel patterns of vehicle can support the planning and design of better services. In addition, vehicle clustering can improve management efficiency through more targeted access to groups of interest and facilitate planning by more specific survey design. This paper clustered 854,712 vehicles in a week using K-means clustering algorithm based on license plate recognition (LPR data obtained in Shenzhen, China. Firstly, several travel characteristics related to temporal and spatial variability and activity patterns are used to identify homogeneous clusters. Then, Davies-Bouldin index (DBI and Silhouette Coefficient (SC are applied to capture the optimal number of groups and, consequently, six groups are classified in weekdays and three groups are sorted in weekends, including commuting vehicles and some other occasional leisure travel vehicles. Moreover, a detailed analysis of the characteristics of each group in terms of spatial travel patterns and temporal changes are presented. This study highlights the possibility of applying LPR data for discovering the underlying factor in vehicle travel patterns and examining the characteristic of some groups specifically.

  12. A Comparative Study of Spatially Clustered Distribution of Jumbo Flying Squid (Dosidicus gigas) Offshore Peru

    Institute of Scientific and Technical Information of China (English)

    FENG Yongjiu; CUI Li; CHEN Xinjun; LIU Yu

    2017-01-01

    We examined spatially clustered distribution of jumbo flying squid (Dosidicus gigas) in the offshore waters of Peru bounded by 78°-86°W and 8°-20°S under 0.5°×0.5° fishing grid.The study is based on the catch-per-unit-effort (CPUE) and fishing effort from Chinese mainland squid jigging fleet in 2003-2004 and 2006-2013.The data for all years as well as the eight years (excluding E1 Ni(n)o events) were studied to examine the effect of climate variation on the spatial distribution of D.gigas.Five spatial clusters reflecting the spatial distribution were computed using K-means and Getis-Ord Gi* for a detailed comparative study.Our results showed that clusters identified by the two methods were quite different in terms of their spatial patterns,and K-means was not as accurate as Getis-Ord Gi*,as inferred from the agreement degree and receiver operating characteristic.There were more areas of hot and cold spots in years without the impact of El Ni(n)o,suggesting that such large-scale climate variations could reduce the clustering level ofD.gigas.The catches also showed that warm E1 Ni(n)o conditions and high water temperature were less favorable for D.gigas offshore Peru.The results suggested that the use of K-means is preferable if the aim is to discover the spatial distribution of each sub-region (cluster) of the study area,while Getis-Ord Gi* is preferable if the aim is to identify statistically significant hot spots that may indicate the central fishing ground.

  13. A comparative study of spatially clustered distribution of jumbo flying squid ( Dosidicus gigas) offshore Peru

    Science.gov (United States)

    Feng, Yongjiu; Cui, Li; Chen, Xinjun; Liu, Yu

    2017-06-01

    We examined spatially clustered distribution of jumbo flying squid ( Dosidicus gigas) in the offshore waters of Peru bounded by 78°-86°W and 8°-20°S under 0.5°×0.5° fishing grid. The study is based on the catch-per-unit-effort (CPUE) and fishing effort from Chinese mainland squid jigging fleet in 2003-2004 and 2006-2013. The data for all years as well as the eight years (excluding El Niño events) were studied to examine the effect of climate variation on the spatial distribution of D. gigas. Five spatial clusters reflecting the spatial distribution were computed using K-means and Getis-Ord Gi* for a detailed comparative study. Our results showed that clusters identified by the two methods were quite different in terms of their spatial patterns, and K-means was not as accurate as Getis-Ord Gi*, as inferred from the agreement degree and receiver operating characteristic. There were more areas of hot and cold spots in years without the impact of El Niño, suggesting that such large-scale climate variations could reduce the clustering level of D. gigas. The catches also showed that warm El Niño conditions and high water temperature were less favorable for D. gigas offshore Peru. The results suggested that the use of K-means is preferable if the aim is to discover the spatial distribution of each sub-region (cluster) of the study area, while Getis-Ord Gi* is preferable if the aim is to identify statistically significant hot spots that may indicate the central fishing ground.

  14. Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters

    Directory of Open Access Journals (Sweden)

    Fonseca Carlos M

    2010-10-01

    Full Text Available Abstract Background Irregularly shaped spatial clusters are difficult to delineate. A cluster found by an algorithm often spreads through large portions of the map, impacting its geographical meaning. Penalized likelihood methods for Kulldorff's spatial scan statistics have been used to control the excessive freedom of the shape of clusters. Penalty functions based on cluster geometry and non-connectivity have been proposed recently. Another approach involves the use of a multi-objective algorithm to maximize two objectives: the spatial scan statistics and the geometric penalty function. Results & Discussion We present a novel scan statistic algorithm employing a function based on the graph topology to penalize the presence of under-populated disconnection nodes in candidate clusters, the disconnection nodes cohesion function. A disconnection node is defined as a region within a cluster, such that its removal disconnects the cluster. By applying this function, the most geographically meaningful clusters are sifted through the immense set of possible irregularly shaped candidate cluster solutions. To evaluate the statistical significance of solutions for multi-objective scans, a statistical approach based on the concept of attainment function is used. In this paper we compared different penalized likelihoods employing the geometric and non-connectivity regularity functions and the novel disconnection nodes cohesion function. We also build multi-objective scans using those three functions and compare them with the previous penalized likelihood scans. An application is presented using comprehensive state-wide data for Chagas' disease in puerperal women in Minas Gerais state, Brazil. Conclusions We show that, compared to the other single-objective algorithms, multi-objective scans present better performance, regarding power, sensitivity and positive predicted value. The multi-objective non-connectivity scan is faster and better suited for the

  15. Shortest triplet clustering: reconstructing large phylogenies using representative sets

    Directory of Open Access Journals (Sweden)

    Sy Vinh Le

    2005-04-01

    Full Text Available Abstract Background Understanding the evolutionary relationships among species based on their genetic information is one of the primary objectives in phylogenetic analysis. Reconstructing phylogenies for large data sets is still a challenging task in Bioinformatics. Results We propose a new distance-based clustering method, the shortest triplet clustering algorithm (STC, to reconstruct phylogenies. The main idea is the introduction of a natural definition of so-called k-representative sets. Based on k-representative sets, shortest triplets are reconstructed and serve as building blocks for the STC algorithm to agglomerate sequences for tree reconstruction in O(n2 time for n sequences. Simulations show that STC gives better topological accuracy than other tested methods that also build a first starting tree. STC appears as a very good method to start the tree reconstruction. However, all tested methods give similar results if balanced nearest neighbor interchange (BNNI is applied as a post-processing step. BNNI leads to an improvement in all instances. The program is available at http://www.bi.uni-duesseldorf.de/software/stc/. Conclusion The results demonstrate that the new approach efficiently reconstructs phylogenies for large data sets. We found that BNNI boosts the topological accuracy of all methods including STC, therefore, one should use BNNI as a post-processing step to get better topological accuracy.

  16. MODEL EVALUATION OF THE SPATIAL DEVELOPMENT OF THE REGION THROUGH THE DEVELOPMENT OF REGIONAL ECONOMIC CLUSTERS

    Directory of Open Access Journals (Sweden)

    Чингис Дашидалаевич Дашицыренов

    2013-12-01

    Full Text Available The article describes a model of evaluation of effectiveness of spatial development of a region. Main approaches and criteria to assess effectiveness of socio-economic development of a region based on use of regional economic cluster are identified.The author believes that clusterization allows to eliminate or localize mentioned above restrictions which are characteristic of specific activity of entities. Effect in this case can be measured by increase in productivity obtained from cluster’s resources use  in regard to specific form of enterprises’ existence.The article also focused on definition of idea of synergic effect and the model of effectiveness of clusters. Cluster integration’s essence is considered – it is pointed out that a new structure is formed, which has emergent characteristics.Thus, main approach to spatial socio-economic development of a region proposed by the author is diversification of organizational and economic forms into regional economic clusters.Proposed by the author model allows to assess effectiveness of clusterization for spatial socio-economic development of any region. DOI: http://dx.doi.org/10.12731/2218-7405-2013-10-14

  17. Spatial clustering of amyotrophic lateral sclerosis in Finland at place of birth and place of death

    DEFF Research Database (Denmark)

    Sabel, Clive E.; Boyle, P. J.; Löytönen, M.

    2003-01-01

    location at the time of death as the basis for cluster detection, rather than exploring clusters at other points in the life cycle. In this study, the authors examine 1,000 cases of amyotrophic lateral sclerosis distributed throughout Finland who died between June 1985 and December 1995. Using a spatial......Previous evidence for spatial clustering of amyotrophic lateral sclerosis is inconclusive. Studies that have identified apparent clusters have often been based on a small number of cases, which means the results may have occurred by chance processes. Also, most studies have used the geographic...... stages of the cases' life cycle, different conclusions about where potential risk factors may exist might result....

  18. A Variable-Selection Heuristic for K-Means Clustering.

    Science.gov (United States)

    Brusco, Michael J.; Cradit, J. Dennis

    2001-01-01

    Presents a variable selection heuristic for nonhierarchical (K-means) cluster analysis based on the adjusted Rand index for measuring cluster recovery. Subjected the heuristic to Monte Carlo testing across more than 2,200 datasets. Results indicate that the heuristic is extremely effective at eliminating masking variables. (SLD)

  19. Variable selection in multivariate calibration based on clustering of variable concept.

    Science.gov (United States)

    Farrokhnia, Maryam; Karimi, Sadegh

    2016-01-01

    Recently we have proposed a new variable selection algorithm, based on clustering of variable concept (CLoVA) in classification problem. With the same idea, this new concept has been applied to a regression problem and then the obtained results have been compared with conventional variable selection strategies for PLS. The basic idea behind the clustering of variable is that, the instrument channels are clustered into different clusters via clustering algorithms. Then, the spectral data of each cluster are subjected to PLS regression. Different real data sets (Cargill corn, Biscuit dough, ACE QSAR, Soy, and Tablet) have been used to evaluate the influence of the clustering of variables on the prediction performances of PLS. Almost in the all cases, the statistical parameter especially in prediction error shows the superiority of CLoVA-PLS respect to other variable selection strategies. Finally the synergy clustering of variable (sCLoVA-PLS), which is used the combination of cluster, has been proposed as an efficient and modification of CLoVA algorithm. The obtained statistical parameter indicates that variable clustering can split useful part from redundant ones, and then based on informative cluster; stable model can be reached. Copyright © 2015 Elsevier B.V. All rights reserved.

  20. A Socio-spatial Dimension of Local Creative Industry Development in Semarang and Kudus Batik Clusters

    Science.gov (United States)

    Nugroho, P.

    2018-02-01

    Creative industries existence is inseparable from the underlying social construct which provides sources for creativity and innovation. The working of social capital in a society facilitates information exchange, knowledge transfer and technology acquisition within the industry through social networks. As a result, a socio-spatial divide exists in directing the growth of the creative industries. This paper aims to examine how such a socio-spatial divide contributes to the local creative industry development in Semarang and Kudus batik clusters. Explanatory sequential mixed methods approach covering a quantitative approach followed by a qualitative approach is chosen to understand better the interplay between tangible and intangible variables in the local batik clusters. Surveys on secondary data taken from the government statistics and reports, previous studies, and media exposures are completed in the former approach to identify clustering pattern of the local batik industry and the local embeddedness factors which have shaped the existing business environment. In-depth interviews, content analysis, and field observations are engaged in the latter approach to explore reciprocal relationships between the elements of social capital and the local batik cluster development. The result demonstrates that particular social ties have determined the forms of spatial proximity manifested in forward and backward business linkages. Trust, shared norms, and inherited traditions are the key social capital attributes that lead to such a socio-spatial divide. Therefore, the intermediating roles of the bridging actors are necessary to encouraging cooperation among the participating stakeholders for a better cluster development.

  1. Regionalizing Aquatic Ecosystems Based on the River Subbasin Taxonomy Concept and Spatial Clustering Techniques

    Directory of Open Access Journals (Sweden)

    Jiahu Zhao

    2011-11-01

    Full Text Available Aquatic ecoregions were increasingly used as spatial units for aquatic ecosystem management at the watershed scale. In this paper, the principle of including land area, comprehensiveness and dominance, conjugation and hierarchy were selected as regionalizing principles. Elevation and drainage density were selected as the regionalizing indicators for the delineation of level I aquatic ecoregions, and percent of construction land area, percent of cultivated land area, soil type and slope for the level II. Under the support of GIS technology, the spatial distribution maps of the two indicators for level I and the four indicators for level II aquatic ecoregion delineation were generated from the raster data based on the 1,107 subwatersheds. River subbasin taxonomy concept, two-step spatial clustering analysis approach and manual-assisted method were used to regionalize aquatic ecosystems in the Taihu Lake watershed. Then the Taihu Lake watershed was divided into two level I aquatic ecoregions, including Ecoregion I1 and Ecoregion I2, and five level II aquatic subecoregions, including Subecoregion II11, Subecoregion II12, Subecoregion II21, Subecoregion II22 and Subecoregion II23. Moreover, the characteristics of the two level I aquatic ecoregions and five level II aquatic subecoregions in the Taihu Lake watershed were summarized, showing that there were significant differences in topography, socio-economic development, water quality and aquatic ecology, etc. The results of quantitative comparison of aquatic life also indicated that the dominant species of fish, benthic density, biomass, dominant species, Shannon-Wiener diversity index, Margalef species richness index, Pielou evenness index and ecological dominance showed great spatial variability between the two level I aquatic ecoregions and five level II aquatic subecoregions. It reflected the spatial heterogeneities and the uneven natures of aquatic ecosystems in the Taihu Lake watershed.

  2. SPATIAL CLUSTER AND OUTLIER IDENTIFICATION OF GEOCHEMICAL ASSOCIATION OF ELEMENTS: A CASE STUDY IN JUIRUI COPPER MINING AREA

    Directory of Open Access Journals (Sweden)

    Tien Thanh NGUYEN

    2016-12-01

    Full Text Available Spatial clusters and spatial outliers play an important role in the study of the spatial distribution patterns of geochemical data. They characterize the fundamental properties of mineralization processes, the spatial distribution of mineral deposits, and ore element concentrations in mineral districts. In this study, a new method for the study of spatial distribution patterns of multivariate data is proposed based on a combination of robust Mahalanobis distance and local Moran’s Ii. In order to construct the spatial matrix, the Moran's I spatial correlogram was first used to determine the range. The robust Mahalanobis distances were then computed for an association of elements. Finally, local Moran’s Ii statistics was used to measure the degree of spatial association and discover the spatial distribution patterns of associations of Cu, Au, Mo, Ag, Pb, Zn, As, and Sb elements including spatial clusters and spatial outliers. Spatial patterns were analyzed at six different spatial scales (2km, 4 km, 6 km, 8 km, 10 km and 12 km for both the raw data and Box-Cox transformed data. The results show that identified spatial cluster and spatial outlier areas using local Moran’s Ii and the robust Mahalanobis accord the objective reality and have a good conformity with known deposits in the study area.

  3. A spatial cluster analysis of tractor overturns in Kentucky from 1960 to 2002.

    Directory of Open Access Journals (Sweden)

    Daniel M Saman

    Full Text Available Agricultural tractor overturns without rollover protective structures are the leading cause of farm fatalities in the United States. To our knowledge, no studies have incorporated the spatial scan statistic in identifying high-risk areas for tractor overturns. The aim of this study was to determine whether tractor overturns cluster in certain parts of Kentucky and identify factors associated with tractor overturns.A spatial statistical analysis using Kulldorff's spatial scan statistic was performed to identify county clusters at greatest risk for tractor overturns. A regression analysis was then performed to identify factors associated with tractor overturns.The spatial analysis revealed a cluster of higher than expected tractor overturns in four counties in northern Kentucky (RR = 2.55 and 10 counties in eastern Kentucky (RR = 1.97. Higher rates of tractor overturns were associated with steeper average percent slope of pasture land by county (p = 0.0002 and a greater percent of total tractors with less than 40 horsepower by county (p<0.0001.This study reveals that geographic hotspots of tractor overturns exist in Kentucky and identifies factors associated with overturns. This study provides policymakers a guide to targeted county-level interventions (e.g., roll-over protective structures promotion interventions with the intention of reducing tractor overturns in the highest risk counties in Kentucky.

  4. Cluster analysis of activity-time series in motor learning

    DEFF Research Database (Denmark)

    Balslev, Daniela; Nielsen, Finn Årup; Frutiger, Sally A.

    2002-01-01

    Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel...... practice-related activity in a fronto-parieto-cerebellar network, in agreement with previous studies of motor learning. These voxels were separated from a group of voxels showing an unspecific time-effect and another group of voxels, whose activation was an artifact from smoothing. Hum. Brain Mapping 15...

  5. Chemisorption on size-selected metal clusters: activation barriers and chemical reactions for deuterium and aluminum cluster ions

    International Nuclear Information System (INIS)

    Jarrold, M.F.; Bower, J.E.

    1988-01-01

    The authors describe a new approach to investigating chemisorption on size-selected metal clusters. This approach involves investigating the collision-energy dependence of chemisorption using low-energy ion beam techniques. The method provides a direct measure of the activation barrier for chemisorption and in some cases an estimate of the desorption energy as well. They describe the application of this technique to chemisorption of deuterium on size-selected aluminum clusters. The activation barriers increase with cluster size (from a little over 1 eV for Al 10 + to around 2 eV for Al 27 + ) and show significant odd-even oscillations. The activation barriers for the clusters with an odd number of atoms are larger than those for the even-numbered clusters. In addition to chemisorption of deuterium onto the clusters, chemical reactions were observed, often resulting in cluster fragmentation. The main products observed were Al/sub n-1/D + , Al/sub n-2/ + , and Al + for clusters with n + and Al/sub n-1/D + for the larger clusters

  6. Big Data GPU-Driven Parallel Processing Spatial and Spatio-Temporal Clustering Algorithms

    Science.gov (United States)

    Konstantaras, Antonios; Skounakis, Emmanouil; Kilty, James-Alexander; Frantzeskakis, Theofanis; Maravelakis, Emmanuel

    2016-04-01

    Diamantaras, K.: 'Programming and architecture of parallel processing systems', 1st Edition, Eds. Kleidarithmos, 2011 [4] NVIDIA.: 'NVidia CUDA C Programming Guide', version 5.0, NVidia (reference book) [5] Konstantaras, A.: 'Classification of Distinct Seismic Regions and Regional Temporal Modelling of Seismicity in the Vicinity of the Hellenic Seismic Arc', IEEE Selected Topics in Applied Earth Observations and Remote Sensing, vol. 6 (4), pp. 1857-1863, 2013 [6] Konstantaras, A. Varley, M.R.,. Valianatos, F., Collins, G. and Holifield, P.: 'Recognition of electric earthquake precursors using neuro-fuzzy models: methodology and simulation results', Proc. IASTED International Conference on Signal Processing Pattern Recognition and Applications (SPPRA 2002), Crete, Greece, 2002, pp 303-308, 2002 [7] Konstantaras, A., Katsifarakis, E., Maravelakis, E., Skounakis, E., Kokkinos, E. and Karapidakis, E.: 'Intelligent Spatial-Clustering of Seismicity in the Vicinity of the Hellenic Seismic Arc', Earth Science Research, vol. 1 (2), pp. 1-10, 2012 [8] Georgoulas, G., Konstantaras, A., Katsifarakis, E., Stylios, C.D., Maravelakis, E. and Vachtsevanos, G.: '"Seismic-Mass" Density-based Algorithm for Spatio-Temporal Clustering', Expert Systems with Applications, vol. 40 (10), pp. 4183-4189, 2013 [9] Konstantaras, A. J.: 'Expert knowledge-based algorithm for the dynamic discrimination of interactive natural clusters', Earth Science Informatics, 2015 (In Press, see: www.scopus.com) [10] Drakatos, G. and Latoussakis, J.: 'A catalog of aftershock sequences in Greece (1971-1997): Their spatial and temporal characteristics', Journal of Seismology, vol. 5, pp. 137-145, 2001

  7. Spatially explicit population estimates for black bears based on cluster sampling

    Science.gov (United States)

    Humm, J.; McCown, J. Walter; Scheick, B.K.; Clark, Joseph D.

    2017-01-01

    We estimated abundance and density of the 5 major black bear (Ursus americanus) subpopulations (i.e., Eglin, Apalachicola, Osceola, Ocala-St. Johns, Big Cypress) in Florida, USA with spatially explicit capture-mark-recapture (SCR) by extracting DNA from hair samples collected at barbed-wire hair sampling sites. We employed a clustered sampling configuration with sampling sites arranged in 3 × 3 clusters spaced 2 km apart within each cluster and cluster centers spaced 16 km apart (center to center). We surveyed all 5 subpopulations encompassing 38,960 km2 during 2014 and 2015. Several landscape variables, most associated with forest cover, helped refine density estimates for the 5 subpopulations we sampled. Detection probabilities were affected by site-specific behavioral responses coupled with individual capture heterogeneity associated with sex. Model-averaged bear population estimates ranged from 120 (95% CI = 59–276) bears or a mean 0.025 bears/km2 (95% CI = 0.011–0.44) for the Eglin subpopulation to 1,198 bears (95% CI = 949–1,537) or 0.127 bears/km2 (95% CI = 0.101–0.163) for the Ocala-St. Johns subpopulation. The total population estimate for our 5 study areas was 3,916 bears (95% CI = 2,914–5,451). The clustered sampling method coupled with information on land cover was efficient and allowed us to estimate abundance across extensive areas that would not have been possible otherwise. Clustered sampling combined with spatially explicit capture-recapture methods has the potential to provide rigorous population estimates for a wide array of species that are extensive and heterogeneous in their distribution.

  8. Uncertainty of a detected spatial cluster in 1D: quantification and visualization

    KAUST Repository

    Lee, Junho; Gangnon, Ronald E.; Zhu, Jun; Liang, Jingjing

    2017-01-01

    Spatial cluster detection is an important problem in a variety of scientific disciplines such as environmental sciences, epidemiology and sociology. However, there appears to be very limited statistical methodology for quantifying the uncertainty of a detected cluster. In this paper, we develop a new method for the quantification and visualization of uncertainty associated with a detected cluster. Our approach is defining a confidence set for the true cluster and visualizing the confidence set, based on the maximum likelihood, in time or in one-dimensional space. We evaluate the pivotal property of the statistic used to construct the confidence set and the coverage rate for the true cluster via empirical distributions. For illustration, our methodology is applied to both simulated data and an Alaska boreal forest dataset. Copyright © 2017 John Wiley & Sons, Ltd.

  9. Uncertainty of a detected spatial cluster in 1D: quantification and visualization

    KAUST Repository

    Lee, Junho

    2017-10-19

    Spatial cluster detection is an important problem in a variety of scientific disciplines such as environmental sciences, epidemiology and sociology. However, there appears to be very limited statistical methodology for quantifying the uncertainty of a detected cluster. In this paper, we develop a new method for the quantification and visualization of uncertainty associated with a detected cluster. Our approach is defining a confidence set for the true cluster and visualizing the confidence set, based on the maximum likelihood, in time or in one-dimensional space. We evaluate the pivotal property of the statistic used to construct the confidence set and the coverage rate for the true cluster via empirical distributions. For illustration, our methodology is applied to both simulated data and an Alaska boreal forest dataset. Copyright © 2017 John Wiley & Sons, Ltd.

  10. THE REDSHIFT EVOLUTION OF THE MEAN TEMPERATURE, PRESSURE, AND ENTROPY PROFILES IN 80 SPT-SELECTED GALAXY CLUSTERS

    Energy Technology Data Exchange (ETDEWEB)

    McDonald, M.; Benson, B. A.; Vikhlinin, A.; Aird, K. A.; Allen, S. W.; Bautz, M.; Bayliss, M.; Bleem, L. E.; Bocquet, S.; Brodwin, M.; Carlstrom, J. E.; Chang, C. L.; Cho, H. M.; Clocchiatti, A.; Crawford, T. M.; Crites, A. T.; de Haan, T.; Dobbs, M. A.; Foley, R. J.; Forman, W. R.; George, E. M.; Gladders, M. D.; Gonzalez, A. H.; Halverson, N. W.; Hlavacek-Larrondo, J.; Holder, G. P.; Holzapfel, W. L.; Hrubes, J. D.; Jones, C.; Keisler, R.; Knox, L.; Lee, A. T.; Leitch, E. M.; Liu, J.; Lueker, M.; Luong-Van, D.; Mantz, A.; Marrone, D. P.; McMahon, J. J.; Meyer, S. S.; Miller, E. D.; Mocanu, L.; Mohr, J. J.; Murray, S. S.; Padin, S.; Pryke, C.; Reichardt, C. L.; Rest, A.; Ruhl, J. E.; Saliwanchik, B. R.; Saro, A.; Sayre, J. T.; Schaffer, K. K.; Shirokoff, E.; Spieler, H. G.; Stalder, B.; Stanford, S. A.; Staniszewski, Z.; Stark, A. A.; Story, K. T.; Stubbs, C. W.; Vanderlinde, K.; Vieira, J. D.; Williamson, R.; Zahn, O.; Zenteno, A.

    2014-09-24

    We present the results of an X-ray analysis of 80 galaxy clusters selected in the 2500 deg(2) South Pole Telescope survey and observed with the Chandra X-ray Observatory. We divide the full sample into subsamples of ~20 clusters based on redshift and central density, performing a joint X-ray spectral fit to all clusters in a subsample simultaneously, assuming self-similarity of the temperature profile. This approach allows us to constrain the shape of the temperature profile over 0 < r < 1.5R (500), which would be impossible on a per-cluster basis, since the observations of individual clusters have, on average, 2000 X-ray counts. The results presented here represent the first constraints on the evolution of the average temperature profile from z = 0 to z = 1.2. We find that high-z (0.6 < z < 1.2) clusters are slightly (~30%) cooler both in the inner (r < 0.1R (500)) and outer (r > R (500)) regions than their low-z (0.3 < z < 0.6) counterparts. Combining the average temperature profile with measured gas density profiles from our earlier work, we infer the average pressure and entropy profiles for each subsample. Confirming earlier results from this data set, we find an absence of strong cool cores at high z, manifested in this analysis as a significantly lower observed pressure in the central 0.1R (500) of the high-z cool-core subset of clusters compared to the low-z cool-core subset. Overall, our observed pressure profiles agree well with earlier lower-redshift measurements, suggesting minimal redshift evolution in the pressure profile outside of the core. We find no measurable redshift evolution in the entropy profile at r lsim 0.7R (500)—this may reflect a long-standing balance between cooling and feedback over long timescales and large physical scales. We observe a slight flattening of the entropy profile at r gsim R (500) in our high-z subsample. This flattening is consistent with a temperature bias due to the enhanced (~3×) rate at which group-mass (~2

  11. Soft landing of size selected clusters in rare gas matrices

    International Nuclear Information System (INIS)

    Lau, J.T; Wurth, W.; Ehrke, H-U.; Achleitner, A.

    2003-01-01

    Soft landing of mass selected clusters in rare gas matrices is a technique used to preserve mass selection in cluster deposition. To prevent fragmentation upon deposition, the substrate is covered with rare gas matrices to dissipate the cluster kinetic energy upon impact. Theoretical and experimental studies demonstrate the power of this technique. Besides STM, optical absorption, excitation, and fluorescence experiments, x-ray absorption at core levels can be used as a tool to study soft landing conditions, as will be shown here. X-ray absorption spectroscopy is also well suited to follow diffusion and agglomeration of clusters on surfaces via energy shifts in core level absorption

  12. Analysing the spatial patterns of livestock anthrax in Kazakhstan in relation to environmental factors: a comparison of local (Gi* and morphology cluster statistics

    Directory of Open Access Journals (Sweden)

    Ian T. Kracalik

    2012-11-01

    Full Text Available We compared a local clustering and a cluster morphology statistic using anthrax outbreaks in large (cattle and small (sheep and goats domestic ruminants across Kazakhstan. The Getis-Ord (Gi* statistic and a multidirectional optimal ecotope algorithm (AMOEBA were compared using 1st, 2nd and 3rd order Rook contiguity matrices. Multivariate statistical tests were used to evaluate the environmental signatures between clusters and non-clusters from the AMOEBA and Gi* tests. A logistic regression was used to define a risk surface for anthrax outbreaks and to compare agreement between clustering methodologies. Tests revealed differences in the spatial distribution of clusters as well as the total number of clusters in large ruminants for AMOEBA (n = 149 and for small ruminants (n = 9. In contrast, Gi* revealed fewer large ruminant clusters (n = 122 and more small ruminant clusters (n = 61. Significant environmental differences were found between groups using the Kruskall-Wallis and Mann- Whitney U tests. Logistic regression was used to model the presence/absence of anthrax outbreaks and define a risk surface for large ruminants to compare with cluster analyses. The model predicted 32.2% of the landscape as high risk. Approximately 75% of AMOEBA clusters corresponded to predicted high risk, compared with ~64% of Gi* clusters. In general, AMOEBA predicted more irregularly shaped clusters of outbreaks in both livestock groups, while Gi* tended to predict larger, circular clusters. Here we provide an evaluation of both tests and a discussion of the use of each to detect environmental conditions associated with anthrax outbreak clusters in domestic livestock. These findings illustrate important differences in spatial statistical methods for defining local clusters and highlight the importance of selecting appropriate levels of data aggregation.

  13. Spatial clustering of fatal, and non-fatal, suicide in new South Wales, Australia: implications for evidence-based prevention.

    Science.gov (United States)

    Torok, Michelle; Konings, Paul; Batterham, Philip J; Christensen, Helen

    2017-10-06

    Rates of suicide appear to be increasing, indicating a critical need for more effective prevention initiatives. To increase the efficacy of future prevention initiatives, we examined the spatial distribution of suicide deaths and suicide attempts in New South Wales (NSW), Australia, to identify where high incidence 'suicide clusters' were occurring. Such clusters represent candidate regions where intervention is critically needed, and likely to have the greatest impact, thus providing an evidence-base for the targeted prioritisation of resources. Analysis is based on official suicide mortality statistics for NSW, provided by the Australian Bureau of Statistics, and hospital separations for non-fatal intentional self-harm, provided through the NSW Health Admitted Patient Data Collection at a Statistical Area 2 (SA2) geography. Geographical Information System (GIS) techniques were applied to detect suicide clusters occurring between 2005 and 2013 (aggregated), for persons aged over 5 years. The final dataset contained 5466 mortality and 86,017 non-fatal intentional self-harm cases. In total, 25 Local Government Areas were identified as primary or secondary likely candidate regions for intervention. Together, these regions contained approximately 200 SA2 level suicide clusters, which represented 46% (n = 39,869) of hospital separations and 43% (n = 2330) of suicide deaths between 2005 and 2013. These clusters primarily converged on the Eastern coastal fringe of NSW. Crude rates of suicide deaths and intentional self-harm differed at the Local Government Areas (LGA) level in NSW. There was a tendency for primary suicide clusters to occur within metropolitan and coastal regions, rather than rural areas. The findings demonstrate the importance of taking geographical variation of suicidal behaviour into account, prior to development and implementation of prevention initiatives, so that such initiatives can target key problem areas where they are likely to have

  14. Cluster Detection Tests in Spatial Epidemiology: A Global Indicator for Performance Assessment.

    Directory of Open Access Journals (Sweden)

    Aline Guttmann

    Full Text Available In cluster detection of disease, the use of local cluster detection tests (CDTs is current. These methods aim both at locating likely clusters and testing for their statistical significance. New or improved CDTs are regularly proposed to epidemiologists and must be subjected to performance assessment. Because location accuracy has to be considered, performance assessment goes beyond the raw estimation of type I or II errors. As no consensus exists for performance evaluations, heterogeneous methods are used, and therefore studies are rarely comparable. A global indicator of performance, which assesses both spatial accuracy and usual power, would facilitate the exploration of CDTs behaviour and help between-studies comparisons. The Tanimoto coefficient (TC is a well-known measure of similarity that can assess location accuracy but only for one detected cluster. In a simulation study, performance is measured for many tests. From the TC, we here propose two statistics, the averaged TC and the cumulated TC, as indicators able to provide a global overview of CDTs performance for both usual power and location accuracy. We evidence the properties of these two indicators and the superiority of the cumulated TC to assess performance. We tested these indicators to conduct a systematic spatial assessment displayed through performance maps.

  15. Cluster Detection Tests in Spatial Epidemiology: A Global Indicator for Performance Assessment

    Science.gov (United States)

    Guttmann, Aline; Li, Xinran; Feschet, Fabien; Gaudart, Jean; Demongeot, Jacques; Boire, Jean-Yves; Ouchchane, Lemlih

    2015-01-01

    In cluster detection of disease, the use of local cluster detection tests (CDTs) is current. These methods aim both at locating likely clusters and testing for their statistical significance. New or improved CDTs are regularly proposed to epidemiologists and must be subjected to performance assessment. Because location accuracy has to be considered, performance assessment goes beyond the raw estimation of type I or II errors. As no consensus exists for performance evaluations, heterogeneous methods are used, and therefore studies are rarely comparable. A global indicator of performance, which assesses both spatial accuracy and usual power, would facilitate the exploration of CDTs behaviour and help between-studies comparisons. The Tanimoto coefficient (TC) is a well-known measure of similarity that can assess location accuracy but only for one detected cluster. In a simulation study, performance is measured for many tests. From the TC, we here propose two statistics, the averaged TC and the cumulated TC, as indicators able to provide a global overview of CDTs performance for both usual power and location accuracy. We evidence the properties of these two indicators and the superiority of the cumulated TC to assess performance. We tested these indicators to conduct a systematic spatial assessment displayed through performance maps. PMID:26086911

  16. The precision of spatial selection into the focus of attention in working memory.

    Science.gov (United States)

    Souza, Alessandra S; Thalmann, Mirko; Oberauer, Klaus

    2018-04-23

    Attention helps manage the information held in visual working memory (vWM). Perceptual attention selects the stimuli to be represented in vWM, whereas internal attention prioritizes information already in vWM. In the present study we assessed the spatial precision of perceptual and internal attention in vWM. Participants encoded eight colored dots for a local-recognition test. To manipulate attention, a cue indicated the item most likely to be tested (~65% validity). The cue appeared either before the onset of the memory array (precue) or during the retention interval (retrocue). The precue guides perceptual attention to gate encoding into vWM, whereas the retrocue guides internal attention to prioritize the cued item within vWM. If attentional selection is spatially imprecise, attention should be preferentially allocated to the cued location, with a gradual drop-off of attention over space to nearby uncued locations. In this case, memory for uncued locations should vary as a function of their distance from the cued location. As compared to a no-cue condition, memory was better for validly cued items but worse for uncued items. The spatial distance between the uncued and cued locations modulated the cuing costs: Items close in space to the cued location were insulated from cuing costs. The extension of this spatial proximity effect was larger for precues than for retrocues, mostly because the benefits of attention were larger for precues. These results point to similar selection principles between perceptual and internal attention and to a critical role of spatial distance in the selection of visual representations.

  17. New method for reconstruction of star spatial distribution in globular clusters and its application to flare stars in Pleiades

    International Nuclear Information System (INIS)

    Kosarev, E.L.

    1980-01-01

    A new method to reconstruct spatial star distribution in globular clusters is presented. The method gives both the estimation of unknown spatial distribution and the probable reconstruction error. This error has statistical origin and depends only on the number of stars in a cluster. The method is applied to reconstruct the spatial density of 441 flare stars in Pleiades. The spatial density has a maximum in the centre of the cluster of about 1.6-2.5 pc -3 and with increasing distance from the center smoothly falls down to zero approximately with the Gaussian law with a scale parameter of 3.5 pc

  18. Cluster policy in Europe and Asia: A comparison using selected cluster policy characteristics

    Directory of Open Access Journals (Sweden)

    Martina Sopoligová

    2017-10-01

    Full Text Available Currently, cluster concept is one of the most important tools for governments to enhance competitiveness and innovations through sectoral specialization and cooperation. The paper focuses on applications of the cluster policy in the distinct territorial context of Europe and Asia so that to perform a comparison between different approaches to the cluster concept application in real practice. The paper introduces a comparative study of the cluster policy concepts based on the characteristics defined by the authors, such as scope, approach, targeting, autonomy, institutional coordination, policy instruments and evaluation system studied for the selected European and Asian countries such as Denmark, France, Germany, China, Japan, and South Korea. The research draws upon processing the secondary data obtained through content analysis of the related literature, government documents and strategies, and also cluster funding programmes. The findings demonstrate the diversity of cluster policies implemented in the context of European and Asian conditions at the current stage of their development.

  19. Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clustering

    KAUST Repository

    Xu, Zhiqiang

    2017-02-16

    Attributed graph clustering, also known as community detection on attributed graphs, attracts much interests recently due to the ubiquity of attributed graphs in real life. Many existing algorithms have been proposed for this problem, which are either distance based or model based. However, model selection in attributed graph clustering has not been well addressed, that is, most existing algorithms assume the cluster number to be known a priori. In this paper, we propose two efficient approaches for attributed graph clustering with automatic model selection. The first approach is a popular Bayesian nonparametric method, while the second approach is an asymptotic method based on a recently proposed model selection criterion, factorized information criterion. Experimental results on both synthetic and real datasets demonstrate that our approaches for attributed graph clustering with automatic model selection significantly outperform the state-of-the-art algorithm.

  20. Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clustering

    KAUST Repository

    Xu, Zhiqiang; Cheng, James; Xiao, Xiaokui; Fujimaki, Ryohei; Muraoka, Yusuke

    2017-01-01

    Attributed graph clustering, also known as community detection on attributed graphs, attracts much interests recently due to the ubiquity of attributed graphs in real life. Many existing algorithms have been proposed for this problem, which are either distance based or model based. However, model selection in attributed graph clustering has not been well addressed, that is, most existing algorithms assume the cluster number to be known a priori. In this paper, we propose two efficient approaches for attributed graph clustering with automatic model selection. The first approach is a popular Bayesian nonparametric method, while the second approach is an asymptotic method based on a recently proposed model selection criterion, factorized information criterion. Experimental results on both synthetic and real datasets demonstrate that our approaches for attributed graph clustering with automatic model selection significantly outperform the state-of-the-art algorithm.

  1. Spatially Compact Neural Clusters in the Dorsal Striatum Encode Locomotion Relevant Information.

    Science.gov (United States)

    Barbera, Giovanni; Liang, Bo; Zhang, Lifeng; Gerfen, Charles R; Culurciello, Eugenio; Chen, Rong; Li, Yun; Lin, Da-Ting

    2016-10-05

    An influential striatal model postulates that neural activities in the striatal direct and indirect pathways promote and inhibit movement, respectively. Normal behavior requires coordinated activity in the direct pathway to facilitate intended locomotion and indirect pathway to inhibit unwanted locomotion. In this striatal model, neuronal population activity is assumed to encode locomotion relevant information. Here, we propose a novel encoding mechanism for the dorsal striatum. We identified spatially compact neural clusters in both the direct and indirect pathways. Detailed characterization revealed similar cluster organization between the direct and indirect pathways, and cluster activities from both pathways were correlated with mouse locomotion velocities. Using machine-learning algorithms, cluster activities could be used to decode locomotion relevant behavioral states and locomotion velocity. We propose that neural clusters in the dorsal striatum encode locomotion relevant information and that coordinated activities of direct and indirect pathway neural clusters are required for normal striatal controlled behavior. VIDEO ABSTRACT. Published by Elsevier Inc.

  2. Model selection for semiparametric marginal mean regression accounting for within-cluster subsampling variability and informative cluster size.

    Science.gov (United States)

    Shen, Chung-Wei; Chen, Yi-Hau

    2018-03-13

    We propose a model selection criterion for semiparametric marginal mean regression based on generalized estimating equations. The work is motivated by a longitudinal study on the physical frailty outcome in the elderly, where the cluster size, that is, the number of the observed outcomes in each subject, is "informative" in the sense that it is related to the frailty outcome itself. The new proposal, called Resampling Cluster Information Criterion (RCIC), is based on the resampling idea utilized in the within-cluster resampling method (Hoffman, Sen, and Weinberg, 2001, Biometrika 88, 1121-1134) and accommodates informative cluster size. The implementation of RCIC, however, is free of performing actual resampling of the data and hence is computationally convenient. Compared with the existing model selection methods for marginal mean regression, the RCIC method incorporates an additional component accounting for variability of the model over within-cluster subsampling, and leads to remarkable improvements in selecting the correct model, regardless of whether the cluster size is informative or not. Applying the RCIC method to the longitudinal frailty study, we identify being female, old age, low income and life satisfaction, and chronic health conditions as significant risk factors for physical frailty in the elderly. © 2018, The International Biometric Society.

  3. Spatial clustering of knowledge-based industries in the Helsinki Metropolitan Area

    Directory of Open Access Journals (Sweden)

    Juan Eduardo Chica

    2016-01-01

    Full Text Available The central locations of metropolitan areas have some specific attributes, leading to an accumulation of large knowledge exchanges and extensive knowledge externalities, which encourage the concentration of various economic activities, especially knowledge-based industries (KBI. Other agglomeration economies found in metropolitan areas – such as telecommunications and transport infrastructures connected to global productive circuits and complementary labour markets – are key factors for KBI employment growth. This paper explores the Helsinki Metropolitan Area’s (HMA spatial clustering of KBI at the sub-district level, and the role played by agglomeration economies (both specialization and diversity economies in fostering this process. The results reveal that KBI employment shows patterns of concentration in the core and adjacent areas. The specialization and diversity economies found in the metropolitan core and the specialization economies found in others areas lead to KBI spatial clustering in the HMA. Public policies regarding the promotion of science parks have also played a decisive role.

  4. A Novel Cluster Head Selection Algorithm Based on Fuzzy Clustering and Particle Swarm Optimization.

    Science.gov (United States)

    Ni, Qingjian; Pan, Qianqian; Du, Huimin; Cao, Cen; Zhai, Yuqing

    2017-01-01

    An important objective of wireless sensor network is to prolong the network life cycle, and topology control is of great significance for extending the network life cycle. Based on previous work, for cluster head selection in hierarchical topology control, we propose a solution based on fuzzy clustering preprocessing and particle swarm optimization. More specifically, first, fuzzy clustering algorithm is used to initial clustering for sensor nodes according to geographical locations, where a sensor node belongs to a cluster with a determined probability, and the number of initial clusters is analyzed and discussed. Furthermore, the fitness function is designed considering both the energy consumption and distance factors of wireless sensor network. Finally, the cluster head nodes in hierarchical topology are determined based on the improved particle swarm optimization. Experimental results show that, compared with traditional methods, the proposed method achieved the purpose of reducing the mortality rate of nodes and extending the network life cycle.

  5. Analytical network process based optimum cluster head selection in wireless sensor network.

    Science.gov (United States)

    Farman, Haleem; Javed, Huma; Jan, Bilal; Ahmad, Jamil; Ali, Shaukat; Khalil, Falak Naz; Khan, Murad

    2017-01-01

    Wireless Sensor Networks (WSNs) are becoming ubiquitous in everyday life due to their applications in weather forecasting, surveillance, implantable sensors for health monitoring and other plethora of applications. WSN is equipped with hundreds and thousands of small sensor nodes. As the size of a sensor node decreases, critical issues such as limited energy, computation time and limited memory become even more highlighted. In such a case, network lifetime mainly depends on efficient use of available resources. Organizing nearby nodes into clusters make it convenient to efficiently manage each cluster as well as the overall network. In this paper, we extend our previous work of grid-based hybrid network deployment approach, in which merge and split technique has been proposed to construct network topology. Constructing topology through our proposed technique, in this paper we have used analytical network process (ANP) model for cluster head selection in WSN. Five distinct parameters: distance from nodes (DistNode), residual energy level (REL), distance from centroid (DistCent), number of times the node has been selected as cluster head (TCH) and merged node (MN) are considered for CH selection. The problem of CH selection based on these parameters is tackled as a multi criteria decision system, for which ANP method is used for optimum cluster head selection. Main contribution of this work is to check the applicability of ANP model for cluster head selection in WSN. In addition, sensitivity analysis is carried out to check the stability of alternatives (available candidate nodes) and their ranking for different scenarios. The simulation results show that the proposed method outperforms existing energy efficient clustering protocols in terms of optimum CH selection and minimizing CH reselection process that results in extending overall network lifetime. This paper analyzes that ANP method used for CH selection with better understanding of the dependencies of

  6. An unsupervised technique for optimal feature selection in attribute profiles for spectral-spatial classification of hyperspectral images

    Science.gov (United States)

    Bhardwaj, Kaushal; Patra, Swarnajyoti

    2018-04-01

    Inclusion of spatial information along with spectral features play a significant role in classification of remote sensing images. Attribute profiles have already proved their ability to represent spatial information. In order to incorporate proper spatial information, multiple attributes are required and for each attribute large profiles need to be constructed by varying the filter parameter values within a wide range. Thus, the constructed profiles that represent spectral-spatial information of an hyperspectral image have huge dimension which leads to Hughes phenomenon and increases computational burden. To mitigate these problems, this work presents an unsupervised feature selection technique that selects a subset of filtered image from the constructed high dimensional multi-attribute profile which are sufficiently informative to discriminate well among classes. In this regard the proposed technique exploits genetic algorithms (GAs). The fitness function of GAs are defined in an unsupervised way with the help of mutual information. The effectiveness of the proposed technique is assessed using one-against-all support vector machine classifier. The experiments conducted on three hyperspectral data sets show the robustness of the proposed method in terms of computation time and classification accuracy.

  7. Sample size estimation and sampling techniques for selecting a representative sample

    Directory of Open Access Journals (Sweden)

    Aamir Omair

    2014-01-01

    Full Text Available Introduction: The purpose of this article is to provide a general understanding of the concepts of sampling as applied to health-related research. Sample Size Estimation: It is important to select a representative sample in quantitative research in order to be able to generalize the results to the target population. The sample should be of the required sample size and must be selected using an appropriate probability sampling technique. There are many hidden biases which can adversely affect the outcome of the study. Important factors to consider for estimating the sample size include the size of the study population, confidence level, expected proportion of the outcome variable (for categorical variables/standard deviation of the outcome variable (for numerical variables, and the required precision (margin of accuracy from the study. The more the precision required, the greater is the required sample size. Sampling Techniques: The probability sampling techniques applied for health related research include simple random sampling, systematic random sampling, stratified random sampling, cluster sampling, and multistage sampling. These are more recommended than the nonprobability sampling techniques, because the results of the study can be generalized to the target population.

  8. Challenges in microarray class discovery: a comprehensive examination of normalization, gene selection and clustering

    Directory of Open Access Journals (Sweden)

    Landfors Mattias

    2010-10-01

    Full Text Available Abstract Background Cluster analysis, and in particular hierarchical clustering, is widely used to extract information from gene expression data. The aim is to discover new classes, or sub-classes, of either individuals or genes. Performing a cluster analysis commonly involve decisions on how to; handle missing values, standardize the data and select genes. In addition, pre-processing, involving various types of filtration and normalization procedures, can have an effect on the ability to discover biologically relevant classes. Here we consider cluster analysis in a broad sense and perform a comprehensive evaluation that covers several aspects of cluster analyses, including normalization. Result We evaluated 2780 cluster analysis methods on seven publicly available 2-channel microarray data sets with common reference designs. Each cluster analysis method differed in data normalization (5 normalizations were considered, missing value imputation (2, standardization of data (2, gene selection (19 or clustering method (11. The cluster analyses are evaluated using known classes, such as cancer types, and the adjusted Rand index. The performances of the different analyses vary between the data sets and it is difficult to give general recommendations. However, normalization, gene selection and clustering method are all variables that have a significant impact on the performance. In particular, gene selection is important and it is generally necessary to include a relatively large number of genes in order to get good performance. Selecting genes with high standard deviation or using principal component analysis are shown to be the preferred gene selection methods. Hierarchical clustering using Ward's method, k-means clustering and Mclust are the clustering methods considered in this paper that achieves the highest adjusted Rand. Normalization can have a significant positive impact on the ability to cluster individuals, and there are indications that

  9. Challenges in microarray class discovery: a comprehensive examination of normalization, gene selection and clustering

    Science.gov (United States)

    2010-01-01

    Background Cluster analysis, and in particular hierarchical clustering, is widely used to extract information from gene expression data. The aim is to discover new classes, or sub-classes, of either individuals or genes. Performing a cluster analysis commonly involve decisions on how to; handle missing values, standardize the data and select genes. In addition, pre-processing, involving various types of filtration and normalization procedures, can have an effect on the ability to discover biologically relevant classes. Here we consider cluster analysis in a broad sense and perform a comprehensive evaluation that covers several aspects of cluster analyses, including normalization. Result We evaluated 2780 cluster analysis methods on seven publicly available 2-channel microarray data sets with common reference designs. Each cluster analysis method differed in data normalization (5 normalizations were considered), missing value imputation (2), standardization of data (2), gene selection (19) or clustering method (11). The cluster analyses are evaluated using known classes, such as cancer types, and the adjusted Rand index. The performances of the different analyses vary between the data sets and it is difficult to give general recommendations. However, normalization, gene selection and clustering method are all variables that have a significant impact on the performance. In particular, gene selection is important and it is generally necessary to include a relatively large number of genes in order to get good performance. Selecting genes with high standard deviation or using principal component analysis are shown to be the preferred gene selection methods. Hierarchical clustering using Ward's method, k-means clustering and Mclust are the clustering methods considered in this paper that achieves the highest adjusted Rand. Normalization can have a significant positive impact on the ability to cluster individuals, and there are indications that background correction is

  10. Data Stream Clustering With Affinity Propagation

    KAUST Repository

    Zhang, Xiangliang

    2014-07-09

    Data stream clustering provides insights into the underlying patterns of data flows. This paper focuses on selecting the best representatives from clusters of streaming data. There are two main challenges: how to cluster with the best representatives and how to handle the evolving patterns that are important characteristics of streaming data with dynamic distributions. We employ the Affinity Propagation (AP) algorithm presented in 2007 by Frey and Dueck for the first challenge, as it offers good guarantees of clustering optimality for selecting exemplars. The second challenging problem is solved by change detection. The presented StrAP algorithm combines AP with a statistical change point detection test; the clustering model is rebuilt whenever the test detects a change in the underlying data distribution. Besides the validation on two benchmark data sets, the presented algorithm is validated on a real-world application, monitoring the data flow of jobs submitted to the EGEE grid.

  11. Data Stream Clustering With Affinity Propagation

    KAUST Repository

    Zhang, Xiangliang; Furtlehner, Cyril; Germain-Renaud, Cecile; Sebag, Michele

    2014-01-01

    Data stream clustering provides insights into the underlying patterns of data flows. This paper focuses on selecting the best representatives from clusters of streaming data. There are two main challenges: how to cluster with the best representatives and how to handle the evolving patterns that are important characteristics of streaming data with dynamic distributions. We employ the Affinity Propagation (AP) algorithm presented in 2007 by Frey and Dueck for the first challenge, as it offers good guarantees of clustering optimality for selecting exemplars. The second challenging problem is solved by change detection. The presented StrAP algorithm combines AP with a statistical change point detection test; the clustering model is rebuilt whenever the test detects a change in the underlying data distribution. Besides the validation on two benchmark data sets, the presented algorithm is validated on a real-world application, monitoring the data flow of jobs submitted to the EGEE grid.

  12. A COMPARISON STUDY OF DIFFERENT MARKER SELECTION METHODS FOR SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL IMAGES

    Directory of Open Access Journals (Sweden)

    D. Akbari

    2015-12-01

    Full Text Available An effective approach based on the Minimum Spanning Forest (MSF, grown from automatically selected markers using Support Vector Machines (SVM, has been proposed for spectral-spatial classification of hyperspectral images by Tarabalka et al. This paper aims at improving this approach by using image segmentation to integrate the spatial information into marker selection process. In this study, the markers are extracted from the classification maps, obtained by both SVM and segmentation algorithms, and then are used to build the MSF. The segmentation algorithms are the watershed, expectation maximization (EM and hierarchical clustering. These algorithms are used in parallel and independently to segment the image. Moreover, the pixels of each class, with the largest population in the classification map, are kept for each region of the segmentation map. Lastly, the most reliable classified pixels are chosen from among the exiting pixels as markers. Two benchmark urban hyperspectral datasets are used for evaluation: Washington DC Mall and Berlin. The results of our experiments indicate that, compared to the original MSF approach, the marker selection using segmentation algorithms leads in more accurate classification maps.

  13. Analyzing the Effects of Spatial Interaction among City Clusters on Urban Growth—Case of Wuhan Urban Agglomeration

    Directory of Open Access Journals (Sweden)

    Ronghui Tan

    2016-08-01

    Full Text Available For the past two decades, China’s urbanization has attracted increasing attention from scholars around the world. Numerous insightful studies have attempted to determine the socioeconomic causes of the rapid urban growth in Chinese cities. However, most of these studies regarded each city as a single entity, with few considering inter-city relationships. The present study uses a gravity-based model to measure the spatial interaction among city clusters in the Wuhan urban agglomeration (WUA, which is one of China’s most rapidly urbanizing regions. The effects of spatial interaction on urban growth area were also analyzed. Empirical results indicate that, similar to urban population or employment in secondary and tertiary industries in the WUA from 2000 to 2005, the spatial interaction among city clusters is one of the main drivers of urban growth. In fact, this study finds the effects of spatial interaction as the only socioeconomic factor that affected the spatial expansion from 2005 to 2010. This finding suggests that population migration and information and commodity flows showed greater influence than the socioeconomic drivers of each city did on promoting urbanization in the WUA during this period. We thus argue that spatial interaction among city clusters should be a consideration in future regional planning.

  14. Evaluating patterns of a white-band disease (WBD outbreak in Acropora palmata using spatial analysis: a comparison of transect and colony clustering.

    Directory of Open Access Journals (Sweden)

    Jennifer A Lentz

    Full Text Available BACKGROUND: Despite being one of the first documented, there is little known of the causative agent or environmental stressors that promote white-band disease (WBD, a major disease of Caribbean Acropora palmata. Likewise, there is little known about the spatiality of outbreaks. We examined the spatial patterns of WBD during a 2004 outbreak at Buck Island Reef National Monument in the US Virgin Islands. METHODOLOGY/PRINCIPAL FINDINGS: Ripley's K statistic was used to measure spatial dependence of WBD across scales. Localized clusters of WBD were identified using the DMAP spatial filtering technique. Statistics were calculated for colony- (number of A. palmata colonies with and without WBD within each transect and transect-level (presence/absence of WBD within transects data to evaluate differences in spatial patterns at each resolution of coral sampling. The Ripley's K plots suggest WBD does cluster within the study area, and approached statistical significance (p = 0.1 at spatial scales of 1100 m or less. Comparisons of DMAP results suggest the transect-level overestimated the prevalence and spatial extent of the outbreak. In contrast, more realistic prevalence estimates and spatial patterns were found by weighting each transect by the number of individual A. palmata colonies with and without WBD. CONCLUSIONS: As the search for causation continues, surveillance and proper documentation of the spatial patterns may inform etiology, and at the same time assist reef managers in allocating resources to tracking the disease. Our results indicate that the spatial scale of data collected can drastically affect the calculation of prevalence and spatial distribution of WBD outbreaks. Specifically, we illustrate that higher resolution sampling resulted in more realistic disease estimates. This should assist in selecting appropriate sampling designs for future outbreak investigations. The spatial techniques used here can be used to facilitate other

  15. Evaluating patterns of a white-band disease (WBD) outbreak in Acropora palmata using spatial analysis: a comparison of transect and colony clustering.

    Science.gov (United States)

    Lentz, Jennifer A; Blackburn, Jason K; Curtis, Andrew J

    2011-01-01

    Despite being one of the first documented, there is little known of the causative agent or environmental stressors that promote white-band disease (WBD), a major disease of Caribbean Acropora palmata. Likewise, there is little known about the spatiality of outbreaks. We examined the spatial patterns of WBD during a 2004 outbreak at Buck Island Reef National Monument in the US Virgin Islands. Ripley's K statistic was used to measure spatial dependence of WBD across scales. Localized clusters of WBD were identified using the DMAP spatial filtering technique. Statistics were calculated for colony- (number of A. palmata colonies with and without WBD within each transect) and transect-level (presence/absence of WBD within transects) data to evaluate differences in spatial patterns at each resolution of coral sampling. The Ripley's K plots suggest WBD does cluster within the study area, and approached statistical significance (p = 0.1) at spatial scales of 1100 m or less. Comparisons of DMAP results suggest the transect-level overestimated the prevalence and spatial extent of the outbreak. In contrast, more realistic prevalence estimates and spatial patterns were found by weighting each transect by the number of individual A. palmata colonies with and without WBD. As the search for causation continues, surveillance and proper documentation of the spatial patterns may inform etiology, and at the same time assist reef managers in allocating resources to tracking the disease. Our results indicate that the spatial scale of data collected can drastically affect the calculation of prevalence and spatial distribution of WBD outbreaks. Specifically, we illustrate that higher resolution sampling resulted in more realistic disease estimates. This should assist in selecting appropriate sampling designs for future outbreak investigations. The spatial techniques used here can be used to facilitate other coral disease studies, as well as, improve reef conservation and management.

  16. Spatial abundance and clustering of Culicoides (Diptera: Ceratopogonidae) on a local scale

    DEFF Research Database (Denmark)

    Kirkeby, Carsten; Bødker, Rene; Stockmarr, Anders

    2013-01-01

    , and cluster locations shifted between catch nights. No significant temporal autocorrelation was detected. CAR models for both species groups identified a significant positive impact of humidity and significant negative impacts of precipitation and wind turbulence. Temperature was also found to be significant...... abundance pattern of these two species groups in the field by intensive sampling with a grid of light traps on 16 catch nights. Neighboring trap catches can be spatially dependent on each other, hence we developed a conditional autoregressive (CAR) model framework to test a number of spatial and non...... of Culicoides moved around in a dynamic pattern varying between catch nights. This conforms with the modeling but was not explained by any of the tested covariates. The mean abundance within these clusters was up to 11 times higher for the Obsoletus group and 4 times higher for the Pulicaris group compared...

  17. Persistent Spatial Clusters of Prescribed Antimicrobials among Danish Pig Farms - A Register-Based Study

    DEFF Research Database (Denmark)

    Fertner, Mette Ely; Sanchez, Javier; Boklund, Anette

    2015-01-01

    The emergence of pathogens resistant to antimicrobials has prompted political initiatives targeting a reduction in the use of veterinary antimicrobials in Denmark, especially for pigs. This study elucidates the tendency of pig farms with a significantly higher antimicrobial use to remain...... in clusters in certain geographical regions of Denmark. Animal Daily Doses/100 pigs/day were calculated for all three age groups of pigs (weaners, finishers and sows) for each quarter during 2012-13 in 6,143 commercial indoor pig producing farms. The data were split into four time periods of six months....... Repeated spatial cluster analyses were performed to identify persistent clusters, i.e. areas included in a significant cluster throughout all four time periods. Antimicrobials prescribed for weaners did not result in any persistent clusters. In contrast, antimicrobial use in finishers clustered...

  18. Race, deprivation, and immigrant isolation: The spatial demography of air-toxic clusters in the continental United States.

    Science.gov (United States)

    Liévanos, Raoul S

    2015-11-01

    This article contributes to environmental inequality outcomes research on the spatial and demographic factors associated with cumulative air-toxic health risks at multiple geographic scales across the United States. It employs a rigorous spatial cluster analysis of census tract-level 2005 estimated lifetime cancer risk (LCR) of ambient air-toxic emissions from stationary (e.g., facility) and mobile (e.g., vehicular) sources to locate spatial clusters of air-toxic LCR risk in the continental United States. It then tests intersectional environmental inequality hypotheses on the predictors of tract presence in air-toxic LCR clusters with tract-level principal component factor measures of economic deprivation by race and immigrant status. Logistic regression analyses show that net of controls, isolated Latino immigrant-economic deprivation is the strongest positive demographic predictor of tract presence in air-toxic LCR clusters, followed by black-economic deprivation and isolated Asian/Pacific Islander immigrant-economic deprivation. Findings suggest scholarly and practical implications for future research, advocacy, and policy. Copyright © 2015 Elsevier Inc. All rights reserved.

  19. THE MASSIVE DISTANT CLUSTERS OF WISE SURVEY: THE FIRST DISTANT GALAXY CLUSTER DISCOVERED BY WISE

    International Nuclear Information System (INIS)

    Gettings, Daniel P.; Gonzalez, Anthony H.; Mancone, Conor; Stanford, S. Adam; Eisenhardt, Peter R. M.; Stern, Daniel; Brodwin, Mark; Zeimann, Gregory R.; Masci, Frank J.; Papovich, Casey; Tanaka, Ichi; Wright, Edward L.

    2012-01-01

    We present spectroscopic confirmation of a z = 0.99 galaxy cluster discovered using data from the Wide-field Infrared Survey Explorer (WISE). This is the first z ∼ 1 cluster candidate from the Massive Distant Clusters of WISE Survey to be confirmed. It was selected as an overdensity of probable z ∼> 1 sources using a combination of WISE and Sloan Digital Sky Survey DR8 photometric catalogs. Deeper follow-up imaging data from Subaru and WIYN reveal the cluster to be a rich system of galaxies, and multi-object spectroscopic observations from Keck confirm five cluster members at z = 0.99. The detection and confirmation of this cluster represents a first step toward constructing a uniformly selected sample of distant, high-mass galaxy clusters over the full extragalactic sky using WISE data.

  20. Spatial expression of Hox cluster genes in the ontogeny of a sea urchin

    Science.gov (United States)

    Arenas-Mena, C.; Cameron, A. R.; Davidson, E. H.

    2000-01-01

    The Hox cluster of the sea urchin Strongylocentrous purpuratus contains ten genes in a 500 kb span of the genome. Only two of these genes are expressed during embryogenesis, while all of eight genes tested are expressed during development of the adult body plan in the larval stage. We report the spatial expression during larval development of the five 'posterior' genes of the cluster: SpHox7, SpHox8, SpHox9/10, SpHox11/13a and SpHox11/13b. The five genes exhibit a dynamic, largely mesodermal program of expression. Only SpHox7 displays extensive expression within the pentameral rudiment itself. A spatially sequential and colinear arrangement of expression domains is found in the somatocoels, the paired posterior mesodermal structures that will become the adult perivisceral coeloms. No such sequential expression pattern is observed in endodermal, epidermal or neural tissues of either the larva or the presumptive juvenile sea urchin. The spatial expression patterns of the Hox genes illuminate the evolutionary process by which the pentameral echinoderm body plan emerged from a bilateral ancestor.

  1. Attosecond extreme ultraviolet generation in cluster by using spatially inhomogeneous field

    Energy Technology Data Exchange (ETDEWEB)

    Feng, Liqiang, E-mail: lqfeng-lngy@126.com [College of Science, Liaoning University of Technology, Jinzhou, 121000 (China); State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics Chinese Academy of Sciences, Dalian 116023 (China); Liu, Hang [School of Chemical and Environmental Engineering, Liaoning University of Technology, Jinzhou 121000 (China)

    2015-01-15

    A promising method to generate the attosecond extreme ultraviolet (XUV) sources has been theoretically investigated emerging from the two-dimensional Ar{sup +} cluster driven by the spatially inhomogeneous field. The results show that with the introduction of the Ar{sup +} cluster model, not only the harmonic cutoffs are enhanced, but also the harmonic yields are reinforced. Furthermore, by properly moderating the inhomogeneity as well as the laser parameters of the inhomogeneous field, the harmonic cutoff can be further extended. As a result, three almost linearly polarized XUV pulses with durations of 40 as, 42 as, and 45 as can be obtained.

  2. Nearest Neighbor Networks: clustering expression data based on gene neighborhoods

    Directory of Open Access Journals (Sweden)

    Olszewski Kellen L

    2007-07-01

    Full Text Available Abstract Background The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both individual biological pathways and the integrated workings of the cell. However, translating this amount of data into biological insight remains a daunting task. An important initial step in the analysis of microarray data is clustering of genes with similar behavior. A number of classical techniques are commonly used to perform this task, particularly hierarchical and K-means clustering, and many novel approaches have been suggested recently. While these approaches are useful, they are not without drawbacks; these methods can find clusters in purely random data, and even clusters enriched for biological functions can be skewed towards a small number of processes (e.g. ribosomes. Results We developed Nearest Neighbor Networks (NNN, a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods. Conclusion The Nearest Neighbor Networks algorithm is a valuable clustering method that effectively groups genes that are likely to be functionally related. It is particularly attractive due to its simplicity, its success in the

  3. Evaluating spatial and temporal relationships between an earthquake cluster near Entiat, central Washington, and the large December 1872 Entiat earthquake

    Science.gov (United States)

    Brocher, Thomas M.; Blakely, Richard J.; Sherrod, Brian

    2017-01-01

    We investigate spatial and temporal relations between an ongoing and prolific seismicity cluster in central Washington, near Entiat, and the 14 December 1872 Entiat earthquake, the largest historic crustal earthquake in Washington. A fault scarp produced by the 1872 earthquake lies within the Entiat cluster; the locations and areas of both the cluster and the estimated 1872 rupture surface are comparable. Seismic intensities and the 1–2 m of coseismic displacement suggest a magnitude range between 6.5 and 7.0 for the 1872 earthquake. Aftershock forecast models for (1) the first several hours following the 1872 earthquake, (2) the largest felt earthquakes from 1900 to 1974, and (3) the seismicity within the Entiat cluster from 1976 through 2016 are also consistent with this magnitude range. Based on this aftershock modeling, most of the current seismicity in the Entiat cluster could represent aftershocks of the 1872 earthquake. Other earthquakes, especially those with long recurrence intervals, have long‐lived aftershock sequences, including the Mw">MwMw 7.5 1891 Nobi earthquake in Japan, with aftershocks continuing 100 yrs after the mainshock. Although we do not rule out ongoing tectonic deformation in this region, a long‐lived aftershock sequence can account for these observations.

  4. Managing distance and covariate information with point-based clustering

    Directory of Open Access Journals (Sweden)

    Peter A. Whigham

    2016-09-01

    Full Text Available Abstract Background Geographic perspectives of disease and the human condition often involve point-based observations and questions of clustering or dispersion within a spatial context. These problems involve a finite set of point observations and are constrained by a larger, but finite, set of locations where the observations could occur. Developing a rigorous method for pattern analysis in this context requires handling spatial covariates, a method for constrained finite spatial clustering, and addressing bias in geographic distance measures. An approach, based on Ripley’s K and applied to the problem of clustering with deliberate self-harm (DSH, is presented. Methods Point-based Monte-Carlo simulation of Ripley’s K, accounting for socio-economic deprivation and sources of distance measurement bias, was developed to estimate clustering of DSH at a range of spatial scales. A rotated Minkowski L1 distance metric allowed variation in physical distance and clustering to be assessed. Self-harm data was derived from an audit of 2 years’ emergency hospital presentations (n = 136 in a New Zealand town (population ~50,000. Study area was defined by residential (housing land parcels representing a finite set of possible point addresses. Results Area-based deprivation was spatially correlated. Accounting for deprivation and distance bias showed evidence for clustering of DSH for spatial scales up to 500 m with a one-sided 95 % CI, suggesting that social contagion may be present for this urban cohort. Conclusions Many problems involve finite locations in geographic space that require estimates of distance-based clustering at many scales. A Monte-Carlo approach to Ripley’s K, incorporating covariates and models for distance bias, are crucial when assessing health-related clustering. The case study showed that social network structure defined at the neighbourhood level may account for aspects of neighbourhood clustering of DSH. Accounting for

  5. A two-stage approach to estimate spatial and spatio-temporal disease risks in the presence of local discontinuities and clusters.

    Science.gov (United States)

    Adin, A; Lee, D; Goicoa, T; Ugarte, María Dolores

    2018-01-01

    Disease risk maps for areal unit data are often estimated from Poisson mixed models with local spatial smoothing, for example by incorporating random effects with a conditional autoregressive prior distribution. However, one of the limitations is that local discontinuities in the spatial pattern are not usually modelled, leading to over-smoothing of the risk maps and a masking of clusters of hot/coldspot areas. In this paper, we propose a novel two-stage approach to estimate and map disease risk in the presence of such local discontinuities and clusters. We propose approaches in both spatial and spatio-temporal domains, where for the latter the clusters can either be fixed or allowed to vary over time. In the first stage, we apply an agglomerative hierarchical clustering algorithm to training data to provide sets of potential clusters, and in the second stage, a two-level spatial or spatio-temporal model is applied to each potential cluster configuration. The superiority of the proposed approach with regard to a previous proposal is shown by simulation, and the methodology is applied to two important public health problems in Spain, namely stomach cancer mortality across Spain and brain cancer incidence in the Navarre and Basque Country regions of Spain.

  6. Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations.

    Science.gov (United States)

    Bragg, Elise M; Briggs, Farran

    2017-02-15

    This protocol outlines large-scale reconstructions of neurons combined with the use of independent and unbiased clustering analyses to create a comprehensive survey of the morphological characteristics observed among a selective neuronal population. Combination of these techniques constitutes a novel approach for the collection and analysis of neuroanatomical data. Together, these techniques enable large-scale, and therefore more comprehensive, sampling of selective neuronal populations and establish unbiased quantitative methods for describing morphologically unique neuronal classes within a population. The protocol outlines the use of modified rabies virus to selectively label neurons. G-deleted rabies virus acts like a retrograde tracer following stereotaxic injection into a target brain structure of interest and serves as a vehicle for the delivery and expression of EGFP in neurons. Large numbers of neurons are infected using this technique and express GFP throughout their dendrites, producing "Golgi-like" complete fills of individual neurons. Accordingly, the virus-mediated retrograde tracing method improves upon traditional dye-based retrograde tracing techniques by producing complete intracellular fills. Individual well-isolated neurons spanning all regions of the brain area under study are selected for reconstruction in order to obtain a representative sample of neurons. The protocol outlines procedures to reconstruct cell bodies and complete dendritic arborization patterns of labeled neurons spanning multiple tissue sections. Morphological data, including positions of each neuron within the brain structure, are extracted for further analysis. Standard programming functions were utilized to perform independent cluster analyses and cluster evaluations based on morphological metrics. To verify the utility of these analyses, statistical evaluation of a cluster analysis performed on 160 neurons reconstructed in the thalamic reticular nucleus of the thalamus

  7. The Atacama Cosmology Telescope: Physical Properties and Purity of a Galaxy Cluster Sample Selected Via the Sunyaev-Zel'Dovich Effect

    Science.gov (United States)

    Menanteau, Felipe; Gonzalez, Jorge; Juin, Jean-Baptiste; Marriage, Tobias; Reese, Erik D.; Acquaviva, Viviana; Aguirre, Paula; Appel, John Willam; Baker, Andrew J.; Barrientos, L. Felipe; hide

    2010-01-01

    We present optical and X-ray properties for the first confirmed galaxy cluster sample selected by the Sunyaev-Zel'dovich Effect from 148 GHz maps over 455 square degrees of sky made with the Atacama Cosmology Telescope. These maps. coupled with multi-band imaging on 4-meter-class optical telescopes, have yielded a sample of 23 galaxy clusters with redshifts between 0.118 and 1.066. Of these 23 clusters, 10 are newly discovered. The selection of this sample is approximately mass limited and essentially independent of redshift. We provide optical positions, images, redshifts and X-ray fluxes and luminosities for the full sample, and X-ray temperatures of an important subset. The mass limit of the full sample is around 8.0 x 10(exp 14) Stellar Mass. with a number distribution that peaks around a redshift of 0.4. For the 10 highest significance SZE-selected cluster candidates, all of which are optically confirmed, the mass threshold is 1 x 10(exp 15) Stellar Mass and the redshift range is 0.167 to 1.066. Archival observations from Chandra, XMM-Newton. and ROSAT provide X-ray luminosities and temperatures that are broadly consistent with this mass threshold. Our optical follow-up procedure also allowed us to assess the purity of the ACT cluster sample. Eighty (one hundred) percent of the 148 GHz candidates with signal-to-noise ratios greater than 5.1 (5.7) are confirmed as massive clusters. The reported sample represents one of the largest SZE-selected sample of massive clusters over all redshifts within a cosmologically-significant survey volume, which will enable cosmological studies as well as future studies on the evolution, morphology, and stellar populations in the most massive clusters in the Universe.

  8. THE CLUSTERING CHARACTERISTICS OF H I-SELECTED GALAXIES FROM THE 40% ALFALFA SURVEY

    Energy Technology Data Exchange (ETDEWEB)

    Martin, Ann M. [NASA Postdoctoral Program, NASA Langley Research Center, Hampton, VA 23618 (United States); Giovanelli, Riccardo; Haynes, Martha P. [Center for Radiophysics and Space Research, Space Sciences Building, Cornell University, Ithaca, NY 14853 (United States); Guzzo, Luigi, E-mail: ann.m.martin@nasa.gov, E-mail: riccardo@astro.cornell.edu, E-mail: haynes@astro.cornell.edu, E-mail: luigi.guzzo@brera.inaf.it [INAF Osservatorio Astronomico di Brera, Milan (Italy)

    2012-05-01

    The 40% Arecibo Legacy Fast ALFA survey catalog ({alpha}.40) of {approx}10,150 H I-selected galaxies is used to analyze the clustering properties of gas-rich galaxies. By employing the Landy-Szalay estimator and a full covariance analysis for the two-point galaxy-galaxy correlation function, we obtain the real-space correlation function and model it as a power law, {xi}(r) = (r/r{sub 0}){sup -{gamma}}, on scales <10 h{sup -1} Mpc. As the largest sample of blindly H I-selected galaxies to date, {alpha}.40 provides detailed understanding of the clustering of this population. We find {gamma} = 1.51 {+-} 0.09 and r{sub 0} = 3.3 + 0.3, -0.2 h{sup -1} Mpc, reinforcing the understanding that gas-rich galaxies represent the most weakly clustered galaxy population known; we also observe a departure from a pure power-law shape at intermediate scales, as predicted in {Lambda}CDM halo occupation distribution models. Furthermore, we measure the bias parameter for the {alpha}.40 galaxy sample and find that H I galaxies are severely antibiased on small scales, but only weakly antibiased on large scales. The robust measurement of the correlation function for gas-rich galaxies obtained via the {alpha}.40 sample constrains models of the distribution of H I in simulated galaxies, and will be employed to better understand the role of gas in environmentally dependent galaxy evolution.

  9. Can we adequately represent the spatial interplay between humans and nature?

    Science.gov (United States)

    One of the challenges remaining before ecosystem services assessments can become part of mainstream decision making is how to spatially represent the interplay of nature as a whole and humans. Nature’s ecosystems act as natural capital by producing things (i.e. stocks and ...

  10. Merger and Acquisition Activity as Driver of Spatial Clustering : The Spatial Evolution of the Dutch Banking Industry, 1850-1993

    NARCIS (Netherlands)

    Boschma, Ron; Hartog, Matté

    2014-01-01

    This article investigates the extent to which merger and acquisition (M&A) activity contributed to the spatial clustering of the Dutch banking industry in Amsterdam. This analysis is based on a unique database of all banks in the Netherlands that existed in the period 1850-1993. We found that

  11. The Not So Simple Globular Cluster ω Cen. I. Spatial Distribution of the Multiple Stellar Populations

    Energy Technology Data Exchange (ETDEWEB)

    Calamida, A.; Saha, A. [National Optical Astronomy Observatory—AURA, 950 N Cherry Avenue, Tucson, AZ, 85719 (United States); Strampelli, G.; Rest, A. [Space Telescope Science Institute—AURA, 3700 San Martin Drive, Baltimore, MD 21218 (United States); Bono, G.; Ferraro, I.; Iannicola, G. [INAF—Osservatorio Astronomico di Roma—Via Frascati 33, I-00040, Monteporzio Catone, Rome (Italy); Scolnic, D. [The University of Chicago, The Kavli Institute for Cosmological Physics, William Eckhardt Research Center—Suite 499, 5640 South Ellis Avenue, Chicago, IL 60637 (United States); James, D.; Smith, C.; Zenteno, A., E-mail: calamida@noao.edu [Cerro Tololo Inter-American Observatory, Casilla 603, La Serena (Chile)

    2017-04-01

    We present a multi-band photometric catalog of ≈1.7 million cluster members for a field of view of ≈2° × 2° across ω Cen. Photometry is based on images collected with the Dark Energy Camera on the 4 m Blanco telescope and the Advanced Camera for Surveys on the Hubble Space Telescope . The unprecedented photometric accuracy and field coverage allowed us, for the first time, to investigate the spatial distribution of ω Cen multiple populations from the core to the tidal radius, confirming its very complex structure. We found that the frequency of blue main-sequence stars is increasing compared to red main-sequence stars starting from a distance of ≈25′ from the cluster center. Blue main-sequence stars also show a clumpy spatial distribution, with an excess in the northeast quadrant of the cluster pointing toward the direction of the Galactic center. Stars belonging to the reddest and faintest red-giant branch also show a more extended spatial distribution in the outskirts of ω Cen, a region never explored before. Both these stellar sub-populations, according to spectroscopic measurements, are more metal-rich compared to the cluster main stellar population. These findings, once confirmed, make ω Cen the only stellar system currently known where metal-rich stars have a more extended spatial distribution compared to metal-poor stars. Kinematic and chemical abundance measurements are now needed for stars in the external regions of ω Cen to better characterize the properties of these sub-populations.

  12. How to detect trap cluster systems?

    International Nuclear Information System (INIS)

    Mandowski, Arkadiusz

    2008-01-01

    Spatially correlated traps and recombination centres (trap-recombination centre pairs and larger clusters) are responsible for many anomalous phenomena that are difficult to explain in the framework of both classical models, i.e. model of localized transitions (LT) and the simple trap model (STM), even with a number of discrete energy levels. However, these 'anomalous' effects may provide a good platform for identifying trap cluster systems. This paper considers selected cluster-type effects, mainly relating to an anomalous dependence of TL on absorbed dose in the system of isolated clusters (ICs). Some consequences for interacting cluster (IAC) systems, involving both localized and delocalized transitions occurring simultaneously, are also discussed

  13. Machine learning etudes in astrophysics: selection functions for mock cluster catalogs

    Energy Technology Data Exchange (ETDEWEB)

    Hajian, Amir; Alvarez, Marcelo A.; Bond, J. Richard, E-mail: ahajian@cita.utoronto.ca, E-mail: malvarez@cita.utoronto.ca, E-mail: bond@cita.utoronto.ca [Canadian Institute for Theoretical Astrophysics, University of Toronto, Toronto, ON M5S 3H8 (Canada)

    2015-01-01

    Making mock simulated catalogs is an important component of astrophysical data analysis. Selection criteria for observed astronomical objects are often too complicated to be derived from first principles. However the existence of an observed group of objects is a well-suited problem for machine learning classification. In this paper we use one-class classifiers to learn the properties of an observed catalog of clusters of galaxies from ROSAT and to pick clusters from mock simulations that resemble the observed ROSAT catalog. We show how this method can be used to study the cross-correlations of thermal Sunya'ev-Zeldovich signals with number density maps of X-ray selected cluster catalogs. The method reduces the bias due to hand-tuning the selection function and is readily scalable to large catalogs with a high-dimensional space of astrophysical features.

  14. Machine learning etudes in astrophysics: selection functions for mock cluster catalogs

    International Nuclear Information System (INIS)

    Hajian, Amir; Alvarez, Marcelo A.; Bond, J. Richard

    2015-01-01

    Making mock simulated catalogs is an important component of astrophysical data analysis. Selection criteria for observed astronomical objects are often too complicated to be derived from first principles. However the existence of an observed group of objects is a well-suited problem for machine learning classification. In this paper we use one-class classifiers to learn the properties of an observed catalog of clusters of galaxies from ROSAT and to pick clusters from mock simulations that resemble the observed ROSAT catalog. We show how this method can be used to study the cross-correlations of thermal Sunya'ev-Zeldovich signals with number density maps of X-ray selected cluster catalogs. The method reduces the bias due to hand-tuning the selection function and is readily scalable to large catalogs with a high-dimensional space of astrophysical features

  15. Integrating resource selection information with spatial capture--recapture

    Science.gov (United States)

    Royle, J. Andrew; Chandler, Richard B.; Sun, Catherine C.; Fuller, Angela K.

    2013-01-01

    1. Understanding space usage and resource selection is a primary focus of many studies of animal populations. Usually, such studies are based on location data obtained from telemetry, and resource selection functions (RSFs) are used for inference. Another important focus of wildlife research is estimation and modeling population size and density. Recently developed spatial capture–recapture (SCR) models accomplish this objective using individual encounter history data with auxiliary spatial information on location of capture. SCR models include encounter probability functions that are intuitively related to RSFs, but to date, no one has extended SCR models to allow for explicit inference about space usage and resource selection.

  16. When teacher clusters work: selected experiences of South African teachers with the cluster approach to professional development

    Directory of Open Access Journals (Sweden)

    Loyiso C Jita

    2014-06-01

    Full Text Available Recent scholarship on teacher professional development has shown renewed interest in collaborative forms of teacher learning. Networks, communities of practice and clusters are related concepts that describe forms of collaboration between schools and/or teachers that encourage such learning. In South Africa, teacher clusters represent a relatively recent and popular experiment in teacher professional development. However, there is no verdict yet about their effectiveness. While the utility of such collaborative structures for teacher learning is fairly well established in many developed countries, we still know very little about how the intended beneficiaries (the teachers experience these non-traditional structures of professional development. Using qualitative data from a large-scale research project, we explore teachers' perspectives on what constitutes a successful clustering experience, and the kinds of professional development benefits they derive from their participation therein. Our major findings are twofold: First, clusters seem to enhance teachers' content knowledge and pedagogical content knowledge. Second, and somewhat unexpectedly, the teachers identified another set of benefits, the so-called "process benefits" that include collaboration, instructional guidance and teacher leadership. In a context where teachers have tended to work solo and insulated their classroom practices from influence, the presence of the "process benefits" represents a significant finding. We conclude the paper by exploring several possible directions for further research on these process benefits of clusters for teachers in South Africa and elsewhere.

  17. Source selection for cluster weak lensing measurements in the Hyper Suprime-Cam survey

    Science.gov (United States)

    Medezinski, Elinor; Oguri, Masamune; Nishizawa, Atsushi J.; Speagle, Joshua S.; Miyatake, Hironao; Umetsu, Keiichi; Leauthaud, Alexie; Murata, Ryoma; Mandelbaum, Rachel; Sifón, Cristóbal; Strauss, Michael A.; Huang, Song; Simet, Melanie; Okabe, Nobuhiro; Tanaka, Masayuki; Komiyama, Yutaka

    2018-03-01

    We present optimized source galaxy selection schemes for measuring cluster weak lensing (WL) mass profiles unaffected by cluster member dilution from the Subaru Hyper Suprime-Cam Strategic Survey Program (HSC-SSP). The ongoing HSC-SSP survey will uncover thousands of galaxy clusters to z ≲ 1.5. In deriving cluster masses via WL, a critical source of systematics is contamination and dilution of the lensing signal by cluster members, and by foreground galaxies whose photometric redshifts are biased. Using the first-year CAMIRA catalog of ˜900 clusters with richness larger than 20 found in ˜140 deg2 of HSC-SSP data, we devise and compare several source selection methods, including selection in color-color space (CC-cut), and selection of robust photometric redshifts by applying constraints on their cumulative probability distribution function (P-cut). We examine the dependence of the contamination on the chosen limits adopted for each method. Using the proper limits, these methods give mass profiles with minimal dilution in agreement with one another. We find that not adopting either the CC-cut or P-cut methods results in an underestimation of the total cluster mass (13% ± 4%) and the concentration of the profile (24% ± 11%). The level of cluster contamination can reach as high as ˜10% at R ≈ 0.24 Mpc/h for low-z clusters without cuts, while employing either the P-cut or CC-cut results in cluster contamination consistent with zero to within the 0.5% uncertainties. Our robust methods yield a ˜60 σ detection of the stacked CAMIRA surface mass density profile, with a mean mass of M200c = [1.67 ± 0.05(stat)] × 1014 M⊙/h.

  18. Accounting for regional background and population size in the detection of spatial clusters and outliers using geostatistical filtering and spatial neutral models: the case of lung cancer in Long Island, New York

    Directory of Open Access Journals (Sweden)

    Goovaerts Pierre

    2004-07-01

    Full Text Available Abstract Background Complete Spatial Randomness (CSR is the null hypothesis employed by many statistical tests for spatial pattern, such as local cluster or boundary analysis. CSR is however not a relevant null hypothesis for highly complex and organized systems such as those encountered in the environmental and health sciences in which underlying spatial pattern is present. This paper presents a geostatistical approach to filter the noise caused by spatially varying population size and to generate spatially correlated neutral models that account for regional background obtained by geostatistical smoothing of observed mortality rates. These neutral models were used in conjunction with the local Moran statistics to identify spatial clusters and outliers in the geographical distribution of male and female lung cancer in Nassau, Queens, and Suffolk counties, New York, USA. Results We developed a typology of neutral models that progressively relaxes the assumptions of null hypotheses, allowing for the presence of spatial autocorrelation, non-uniform risk, and incorporation of spatially heterogeneous population sizes. Incorporation of spatial autocorrelation led to fewer significant ZIP codes than found in previous studies, confirming earlier claims that CSR can lead to over-identification of the number of significant spatial clusters or outliers. Accounting for population size through geostatistical filtering increased the size of clusters while removing most of the spatial outliers. Integration of regional background into the neutral models yielded substantially different spatial clusters and outliers, leading to the identification of ZIP codes where SMR values significantly depart from their regional background. Conclusion The approach presented in this paper enables researchers to assess geographic relationships using appropriate null hypotheses that account for the background variation extant in real-world systems. In particular, this new

  19. A ROBUST CLUSTER HEAD SELECTION BASED ON NEIGHBORHOOD CONTRIBUTION AND AVERAGE MINIMUM POWER FOR MANETs

    Directory of Open Access Journals (Sweden)

    S.Balaji

    2015-06-01

    Full Text Available Mobile Adhoc network is an instantaneous wireless network that is dynamic in nature. It supports single hop and multihop communication. In this infrastructure less network, clustering is a significant model to maintain the topology of the network. The clustering process includes different phases like cluster formation, cluster head selection, cluster maintenance. Choosing cluster head is important as the stability of the network depends on well-organized and resourceful cluster head. When the node has increased number of neighbors it can act as a link between the neighbor nodes which in further reduces the number of hops in multihop communication. Promisingly the node with more number of neighbors should also be available with enough energy to provide stability in the network. Hence these aspects demand the focus. In weight based cluster head selection, closeness and average minimum power required is considered for purging the ineligible nodes. The optimal set of nodes selected after purging will compete to become cluster head. The node with maximum weight selected as cluster head. Mathematical formulation is developed to show the proposed method provides optimum result. It is also suggested that weight factor in calculating the node weight should give precise importance to energy and node stability.

  20. Testing the accuracy of clustering redshifts with simulations

    Science.gov (United States)

    Scottez, V.; Benoit-Lévy, A.; Coupon, J.; Ilbert, O.; Mellier, Y.

    2018-03-01

    We explore the accuracy of clustering-based redshift inference within the MICE2 simulation. This method uses the spatial clustering of galaxies between a spectroscopic reference sample and an unknown sample. This study give an estimate of the reachable accuracy of this method. First, we discuss the requirements for the number objects in the two samples, confirming that this method does not require a representative spectroscopic sample for calibration. In the context of next generation of cosmological surveys, we estimated that the density of the Quasi Stellar Objects in BOSS allows us to reach 0.2 per cent accuracy in the mean redshift. Secondly, we estimate individual redshifts for galaxies in the densest regions of colour space ( ˜ 30 per cent of the galaxies) without using the photometric redshifts procedure. The advantage of this procedure is threefold. It allows: (i) the use of cluster-zs for any field in astronomy, (ii) the possibility to combine photo-zs and cluster-zs to get an improved redshift estimation, (iii) the use of cluster-z to define tomographic bins for weak lensing. Finally, we explore this last option and build five cluster-z selected tomographic bins from redshift 0.2 to 1. We found a bias on the mean redshift estimate of 0.002 per bin. We conclude that cluster-z could be used as a primary redshift estimator by next generation of cosmological surveys.

  1. Threshold selection for classification of MR brain images by clustering method

    Energy Technology Data Exchange (ETDEWEB)

    Moldovanu, Simona [Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, Dunărea de Jos University of Galaţi, 47 Domnească St., 800008, Romania, Phone: +40 236 460 780 (Romania); Dumitru Moţoc High School, 15 Milcov St., 800509, Galaţi (Romania); Obreja, Cristian; Moraru, Luminita, E-mail: luminita.moraru@ugal.ro [Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, Dunărea de Jos University of Galaţi, 47 Domnească St., 800008, Romania, Phone: +40 236 460 780 (Romania)

    2015-12-07

    Given a grey-intensity image, our method detects the optimal threshold for a suitable binarization of MR brain images. In MR brain image processing, the grey levels of pixels belonging to the object are not substantially different from the grey levels belonging to the background. Threshold optimization is an effective tool to separate objects from the background and further, in classification applications. This paper gives a detailed investigation on the selection of thresholds. Our method does not use the well-known method for binarization. Instead, we perform a simple threshold optimization which, in turn, will allow the best classification of the analyzed images into healthy and multiple sclerosis disease. The dissimilarity (or the distance between classes) has been established using the clustering method based on dendrograms. We tested our method using two classes of images: the first consists of 20 T2-weighted and 20 proton density PD-weighted scans from two healthy subjects and from two patients with multiple sclerosis. For each image and for each threshold, the number of the white pixels (or the area of white objects in binary image) has been determined. These pixel numbers represent the objects in clustering operation. The following optimum threshold values are obtained, T = 80 for PD images and T = 30 for T2w images. Each mentioned threshold separate clearly the clusters that belonging of the studied groups, healthy patient and multiple sclerosis disease.

  2. The Fornax Cluster VLT Spectroscopic Survey II - Planetary Nebulae kinematics within 200 kpc of the cluster core

    Science.gov (United States)

    Spiniello, C.; Napolitano, N. R.; Arnaboldi, M.; Tortora, C.; Coccato, L.; Capaccioli, M.; Gerhard, O.; Iodice, E.; Spavone, M.; Cantiello, M.; Peletier, R.; Paolillo, M.; Schipani, P.

    2018-06-01

    We present the largest and most spatially extended planetary nebulae (PNe) catalogue ever obtained for the Fornax cluster. We measured velocities of 1452 PNe out to 200 kpc in the cluster core using a counter-dispersed slitless spectroscopic technique with data from FORS2 on the Very Large Telescope (VLT). With such an extended spatial coverage, we can study separately the stellar haloes of some of the cluster main galaxies and the intracluster light. In this second paper of the Fornax Cluster VLT Spectroscopic Survey, we identify and classify the emission-line sources, describe the method to select PNe, and calculate their coordinates and velocities from the dispersed slitless images. From the PN 2D velocity map, we identify stellar streams that are possibly tracing the gravitational interaction of NGC 1399 with NGC 1404 and NGC 1387. We also present the velocity dispersion profile out to ˜200 kpc radii, which shows signatures of a superposition of the bright central galaxy and the cluster potential, with the latter clearly dominating the regions outside R ˜ 1000 arcsec (˜100 kpc).

  3. A spatio-temporal nonparametric Bayesian variable selection model of fMRI data for clustering correlated time courses.

    Science.gov (United States)

    Zhang, Linlin; Guindani, Michele; Versace, Francesco; Vannucci, Marina

    2014-07-15

    In this paper we present a novel wavelet-based Bayesian nonparametric regression model for the analysis of functional magnetic resonance imaging (fMRI) data. Our goal is to provide a joint analytical framework that allows to detect regions of the brain which exhibit neuronal activity in response to a stimulus and, simultaneously, infer the association, or clustering, of spatially remote voxels that exhibit fMRI time series with similar characteristics. We start by modeling the data with a hemodynamic response function (HRF) with a voxel-dependent shape parameter. We detect regions of the brain activated in response to a given stimulus by using mixture priors with a spike at zero on the coefficients of the regression model. We account for the complex spatial correlation structure of the brain by using a Markov random field (MRF) prior on the parameters guiding the selection of the activated voxels, therefore capturing correlation among nearby voxels. In order to infer association of the voxel time courses, we assume correlated errors, in particular long memory, and exploit the whitening properties of discrete wavelet transforms. Furthermore, we achieve clustering of the voxels by imposing a Dirichlet process (DP) prior on the parameters of the long memory process. For inference, we use Markov Chain Monte Carlo (MCMC) sampling techniques that combine Metropolis-Hastings schemes employed in Bayesian variable selection with sampling algorithms for nonparametric DP models. We explore the performance of the proposed model on simulated data, with both block- and event-related design, and on real fMRI data. Copyright © 2014 Elsevier Inc. All rights reserved.

  4. Cluster size selectivity in the product distribution of ethene dehydrogenation on niobium clusters.

    Science.gov (United States)

    Parnis, J Mark; Escobar-Cabrera, Eric; Thompson, Matthew G K; Jacula, J Paul; Lafleur, Rick D; Guevara-García, Alfredo; Martínez, Ana; Rayner, David M

    2005-08-18

    Ethene reactions with niobium atoms and clusters containing up to 25 constituent atoms have been studied in a fast-flow metal cluster reactor. The clusters react with ethene at about the gas-kinetic collision rate, indicating a barrierless association process as the cluster removal step. Exceptions are Nb8 and Nb10, for which a significantly diminished rate is observed, reflecting some cluster size selectivity. Analysis of the experimental primary product masses indicates dehydrogenation of ethene for all clusters save Nb10, yielding either Nb(n)C2H2 or Nb(n)C2. Over the range Nb-Nb6, the extent of dehydrogenation increases with cluster size, then decreases for larger clusters. For many clusters, secondary and tertiary product masses are also observed, showing varying degrees of dehydrogenation corresponding to net addition of C2H4, C2H2, or C2. With Nb atoms and several small clusters, formal addition of at least six ethene molecules is observed, suggesting a polymerization process may be active. Kinetic analysis of the Nb atom and several Nb(n) cluster reactions with ethene shows that the process is consistent with sequential addition of ethene units at rates corresponding approximately to the gas-kinetic collision frequency for several consecutive reacting ethene molecules. Some variation in the rate of ethene pick up is found, which likely reflects small energy barriers or steric constraints associated with individual mechanistic steps. Density functional calculations of structures of Nb clusters up to Nb(6), and the reaction products Nb(n)C2H2 and Nb(n)C2 (n = 1...6) are presented. Investigation of the thermochemistry for the dehydrogenation of ethene to form molecular hydrogen, for the Nb atom and clusters up to Nb6, demonstrates that the exergonicity of the formation of Nb(n)C2 species increases with cluster size over this range, which supports the proposal that the extent of dehydrogenation is determined primarily by thermodynamic constraints. Analysis of

  5. Parental Action and Referral Patterns in Spatial Clusters of Childhood Autism Spectrum Disorder

    Science.gov (United States)

    Schelly, David; Jiménez González, Patricia; Solís, Pedro J.

    2018-01-01

    Sociodemographic factors have long been associated with disparities in autism spectrum disorder (ASD) diagnosis. Studies that identified spatial clustering of cases have suggested the importance of information about ASD moving through social networks of parents. Yet there is no direct evidence of this mechanism. This study explores the…

  6. Spatial Region Estimation for Autonomous CoT Clustering Using Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Joon‐young Jung

    2018-02-01

    Full Text Available This paper proposes a hierarchical dual filtering (HDF algorithm to estimate the spatial region between a Cloud of Things (CoT gateway and an Internet of Things (IoT device. The accuracy of the spatial region estimation is important for autonomous CoT clustering. We conduct spatial region estimation using a hidden Markov model (HMM with a raw Bluetooth received signal strength indicator (RSSI. However, the accuracy of the region estimation using the validation data is only 53.8%. To increase the accuracy of the spatial region estimation, the HDF algorithm removes the high‐frequency signals hierarchically, and alters the parameters according to whether the IoT device moves. The accuracy of spatial region estimation using a raw RSSI, Kalman filter, and HDF are compared to evaluate the effectiveness of the HDF algorithm. The success rate and root mean square error (RMSE of all regions are 0.538, 0.622, and 0.75, and 0.997, 0.812, and 0.5 when raw RSSI, a Kalman filter, and HDF are used, respectively. The HDF algorithm attains the best results in terms of the success rate and RMSE of spatial region estimation using HMM.

  7. Optically-Selected Cluster Catalogs As a Precision Cosmology Tool

    Energy Technology Data Exchange (ETDEWEB)

    Rozo, Eduardo; /Ohio State U. /Chicago U. /KICP, Chicago; Wechsler, Risa H.; /KICP, Chicago /KIPAC, Menlo Park; Koester, Benjamin P.; /Michigan U. /Chicago U., Astron.; Evrard, August E.; McKay, Timothy A.; /Michigan U.

    2007-03-26

    We introduce a framework for describing the halo selection function of optical cluster finders. We treat the problem as being separable into a term that describes the intrinsic galaxy content of a halo (the Halo Occupation Distribution, or HOD) and a term that captures the effects of projection and selection by the particular cluster finding algorithm. Using mock galaxy catalogs tuned to reproduce the luminosity dependent correlation function and the empirical color-density relation measured in the SDSS, we characterize the maxBCG algorithm applied by Koester et al. to the SDSS galaxy catalog. We define and calibrate measures of completeness and purity for this algorithm, and demonstrate successful recovery of the underlying cosmology and HOD when applied to the mock catalogs. We identify principal components--combinations of cosmology and HOD parameters--that are recovered by survey counts as a function of richness, and demonstrate that percent-level accuracies are possible in the first two components, if the selection function can be understood to {approx} 15% accuracy.

  8. Spatial clustering of metal and metalloid mixtures in unregulated water sources on the Navajo Nation - Arizona, New Mexico, and Utah, USA.

    Science.gov (United States)

    Hoover, Joseph H; Coker, Eric; Barney, Yolanda; Shuey, Chris; Lewis, Johnnye

    2018-08-15

    Contaminant mixtures are identified regularly in public and private drinking water supplies throughout the United States; however, the complex and often correlated nature of mixtures makes identification of relevant combinations challenging. This study employed a Bayesian clustering method to identify subgroups of water sources with similar metal and metalloid profiles. Additionally, a spatial scan statistic assessed spatial clustering of these subgroups and a human health metric was applied to investigate potential for human toxicity. These methods were applied to a dataset comprised of metal and metalloid measurements from unregulated water sources located on the Navajo Nation, in the southwest United States. Results indicated distinct subgroups of water sources with similar contaminant profiles and that some of these subgroups were spatially clustered. Several profiles had metal and metalloid concentrations that may have potential for human toxicity including arsenic, uranium, lead, manganese, and selenium. This approach may be useful for identifying mixtures in water sources, spatially evaluating the clusters, and help inform toxicological research investigating mixtures. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

  9. Dynamics of spatial clustering of schistosomiasis in the Yangtze River Valley at the end of and following the World Bank Loan Project.

    Science.gov (United States)

    Hu, Yi; Xiong, Chenglong; Zhang, Zhijie; Luo, Can; Ward, Michael; Gao, Jie; Zhang, Lijuan; Jiang, Qingwu

    2014-06-01

    The 10-year (1992-2001) World Bank Loan Project (WBLP) contributed greatly to schistosomiasis control in China. However, the re-emergence of schistosomiasis in recent years challenged the long-term progress of the WBLP strategy. In order to gain insight in the long-term progress of the WBLP, the spatial pattern of the epidemic was investigated in the Yangtze River Valley between 1999-2001 and 2007-2008. Two spatial cluster methods were jointly used to identify spatial clusters of cases. The magnitude and number of clusters varied during 1999-2001. It was found that prevalence of schistosomiasis had been greatly reduced and maintained at a low level during 2007-2008, with little change. Besides, spatial clusters most frequently occurred within 16 counties in the Dongting Lake region and within 5 counties in the Poyang Lake region. These findings precisely pointed out the prior places for future public health planning and resource allocation of schistosomiasis. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  10. Spatial and temporal characteristics of poloidal waves in the terrestrial plasmasphere: a CLUSTER case study

    Directory of Open Access Journals (Sweden)

    S. Schäfer

    2007-05-01

    Full Text Available Oscillating magnetic field lines are frequently observed by spacecraft in the terrestrial and other planetary magnetospheres. The CLUSTER mission is a very suitable tool to further study these Alfvén waves as the four CLUSTER spacecraft provide for an opportunity to separate spatial and temporal structures in the terrestrial magnetosphere. Using a large scaled configuration formed by the four spacecraft we are able to detect a poloidal Ultra-Low-Frequency (ULF pulsation of the magnetic and electric field in order to analyze its temporal and spatial structures. For this purpose the measurements are transformed into a specific field line related coordinate system to investigate their specific amplitude pattern depending on the path of the CLUSTER spacecraft across oscillating field lines. These measurements are then compared with modeled spacecraft observations across a localized poloidal wave resonator in the dayside plasmasphere. A detailed investigation of theoretically expected poloidal eigenfrequencies allows us to specify the observed 16 mHz pulsation as a third harmonic oscillation. Based on this we perform a case study providing a clear identification of wave properties such as an spatial scale structure of about 0.67 RE, the azimuthal wave number m≈30, temporal evolution, and energy transport in the detected ULF pulsations.

  11. Deficits of spatial and task-related attentional selection in mild cognitive impairment and Alzheimer's disease.

    Science.gov (United States)

    Redel, P; Bublak, P; Sorg, C; Kurz, A; Förstl, H; Müller, H J; Schneider, W X; Perneczky, R; Finke, K

    2012-01-01

    Visual selective attention was assessed with a partial-report task in patients with probable Alzheimer's disease (AD), amnestic mild cognitive impairment (MCI), and healthy elderly controls. Based on Bundesen's "theory of visual attention" (TVA), two parameters were derived: top-down control of attentional selection, representing task-related attentional weighting for prioritizing relevant visual objects, and spatial distribution of attentional weights across the left and the right hemifield. Compared with controls, MCI patients showed significantly reduced top-down controlled selection, which was further deteriorated in AD subjects. Moreover, attentional weighting was significantly unbalanced across hemifields in MCI and tended to be more lateralized in AD. Across MCI and AD patients, carriers of the apolipoprotein E ε4 allele (ApoE4) displayed a leftward spatial bias, which was the more pronounced the younger the ApoE4-positive patients and the earlier disease onset. These results indicate that impaired top-down control may be linked to early dysfunction of fronto-parietal networks. An early temporo-parietal interhemispheric asymmetry might cause a pathological spatial bias which is associated with ApoE4 genotype and may therefore function as early cognitive marker of upcoming AD. Copyright © 2012 Elsevier Inc. All rights reserved.

  12. Evidence-based case selection: An innovative knowledge management method to cluster public technical and vocational education and training colleges in South Africa

    Directory of Open Access Journals (Sweden)

    Margaretha M. Visser

    2017-03-01

    Full Text Available Background: Case studies are core constructs used in information management research. A persistent challenge for business, information management and social science researchers is how to select a representative sample of cases among a population with diverse characteristics when convenient or purposive sampling is not considered rigorous enough. The context of the study is post-school education, and it involves an investigation of quantitative methods of clustering the population of public technical and vocational education and training (TVET colleges in South Africa into groups with a similar level of maturity in terms of their information systems. Objectives: The aim of the study was to propose an evidence-based quantitative method for the selection of cases for case study research and to demonstrate the use and usefulness thereof by clustering public TVET colleges. Method: The clustering method was based on the use of a representative characteristic of the context, as a proxy. In this context of management information systems (MISs, website maturity was used as a proxy and website maturity model theory was used in the development of an evaluation questionnaire. The questionnaire was used for capturing data on website characteristics, which was used to determine website maturity. The websites of the 50 public TVET colleges were evaluated by nine evaluators. Multiple statistical techniques were applied to establish inter-rater reliability and to produce clusters of colleges. Results: The analyses revealed three clusters of public TVET colleges based on their website maturity levels. The first cluster includes three colleges with no websites or websites at a low maturity level. The second cluster consists of 30 colleges with websites at an average maturity level. The third cluster contains 17 colleges with websites at a high maturity level. Conclusion: The main contribution to the knowledge domain is an innovative quantitative method employing a

  13. Advanced analysis of forest fire clustering

    Science.gov (United States)

    Kanevski, Mikhail; Pereira, Mario; Golay, Jean

    2017-04-01

    Analysis of point pattern clustering is an important topic in spatial statistics and for many applications: biodiversity, epidemiology, natural hazards, geomarketing, etc. There are several fundamental approaches used to quantify spatial data clustering using topological, statistical and fractal measures. In the present research, the recently introduced multi-point Morisita index (mMI) is applied to study the spatial clustering of forest fires in Portugal. The data set consists of more than 30000 fire events covering the time period from 1975 to 2013. The distribution of forest fires is very complex and highly variable in space. mMI is a multi-point extension of the classical two-point Morisita index. In essence, mMI is estimated by covering the region under study by a grid and by computing how many times more likely it is that m points selected at random will be from the same grid cell than it would be in the case of a complete random Poisson process. By changing the number of grid cells (size of the grid cells), mMI characterizes the scaling properties of spatial clustering. From mMI, the data intrinsic dimension (fractal dimension) of the point distribution can be estimated as well. In this study, the mMI of forest fires is compared with the mMI of random patterns (RPs) generated within the validity domain defined as the forest area of Portugal. It turns out that the forest fires are highly clustered inside the validity domain in comparison with the RPs. Moreover, they demonstrate different scaling properties at different spatial scales. The results obtained from the mMI analysis are also compared with those of fractal measures of clustering - box counting and sand box counting approaches. REFERENCES Golay J., Kanevski M., Vega Orozco C., Leuenberger M., 2014: The multipoint Morisita index for the analysis of spatial patterns. Physica A, 406, 191-202. Golay J., Kanevski M. 2015: A new estimator of intrinsic dimension based on the multipoint Morisita index

  14. Spatial distribution of ion energy related on electron density in a plasma channel generated in gas clusters by a femtosecond laser

    International Nuclear Information System (INIS)

    Nam, S. M.; Han, J. M.; Cha, Y. H.; Lee, Y. W.; Rhee, Y. J.; Cha, H. K.

    2008-01-01

    Neutron generation through Coulomb explosion of deuterium contained gas clusters is known as one of the very effective methods to produce fusion neutrons using a table top terawatt laser. The energy of ions produced through Coulomb explosions is very important factor to generate neutrons efficiently. Until the ion energy reaches around∼MeV level, the D D fusion reaction probability increases exponentially. The understanding of laser beam propagation and laser energy deposition in clusters is very important to improve neutron yields. As the laser beam propagates through clusters medium, laser energy is absorbed in clusters by ionization of molecules consisting clusters. When the backing pressure of gas increases, the average size of clusters increases and which results in higher energy absorption and earlier termination of laser propagation. We first installed a Michelson interferometer to view laser beam traces in a cluster plume and to measure spatial electron density profiles of a plasma channel which was produced by a laser beam. And then we measured the energy of ions distributed along the plasma channel with a translating slit to select ions from narrow parts of a plasma channel. In our experiments, methane gas was used to produce gas clusters at a room temperature and the energy distribution of proton ions for different gas backing pressure were measured by the time of flight method using dual micro channel plates. By comparing the distribution of ion energies and electron densities, we could understand the condition for effective laser energy delivery to clusters

  15. Spatial clustering of toxic trace elements in adolescents around the Torreón, Mexico lead–zinc smelter

    Science.gov (United States)

    Garcia-Vargas, Gonzalo G.; Rothenberg, Stephen J.; Silbergeld, Ellen K.; Weaver, Virginia; Zamoiski, Rachel; Resnick, Carol; Rubio-Andrade, Marisela; Parsons, Patrick J.; Steuerwald, Amy J.; Navas-Acién, Ana; Guallar, Eliseo

    2016-01-01

    High blood lead (BPb) levels in children and elevated soil and dust arsenic, cadmium, and lead were previously found in Torreón, northern Mexico, host to the world’s fourth largest lead–zinc metal smelter. The objectives of this study were to determine spatial distributions of adolescents with higher BPb and creatinine-corrected urine total arsenic, cadmium, molybdenum, thallium, and uranium around the smelter. Cross-sectional study of 512 male and female subjects 12–15 years of age was conducted. We measured BPb by graphite furnace atomic absorption spectrometry and urine trace elements by inductively coupled plasma-mass spectrometry, with dynamic reaction cell mode for arsenic. We constructed multiple regression models including sociodemographic variables and adjusted for subject residence spatial correlation with spatial lag or error terms. We applied local indicators of spatial association statistics to model residuals to identify hot spots of significant spatial clusters of subjects with higher trace elements. We found spatial clusters of subjects with elevated BPb (range 3.6–14.7 µg/dl) and urine cadmium (0.18–1.14 µg/g creatinine) adjacent to and downwind of the smelter and elevated urine thallium (0.28–0.93 µg/g creatinine) and uranium (0.07–0.13 µg/g creatinine) near ore transport routes, former waste, and industrial discharge sites. The conclusion derived from this study was that spatial clustering of adolescents with high BPb and urine cadmium adjacent to and downwind of the smelter and residual waste pile, areas identified over a decade ago with high lead and cadmium in soil and dust, suggests that past and/or present plant operations continue to present health risks to children in those neighborhoods. PMID:24549228

  16. HIV Clustering in Mississippi: Spatial Epidemiological Study to Inform Implementation Science in the Deep South.

    Science.gov (United States)

    Stopka, Thomas J; Brinkley-Rubinstein, Lauren; Johnson, Kendra; Chan, Philip A; Hutcheson, Marga; Crosby, Richard; Burke, Deirdre; Mena, Leandro; Nunn, Amy

    2018-04-03

    In recent years, more than half of new HIV infections in the United States occur among African Americans in the Southeastern United States. Spatial epidemiological analyses can inform public health responses in the Deep South by identifying HIV hotspots and community-level factors associated with clustering. The goal of this study was to identify and characterize HIV clusters in Mississippi through analysis of state-level HIV surveillance data. We used a combination of spatial epidemiology and statistical modeling to identify and characterize HIV hotspots in Mississippi census tracts (n=658) from 2008 to 2014. We conducted spatial analyses of all HIV infections, infections among men who have sex with men (MSM), and infections among African Americans. Multivariable logistic regression analyses identified community-level sociodemographic factors associated with HIV hotspots considering all cases. There were HIV hotspots for the entire population, MSM, and African American MSM identified in the Mississippi Delta region, Southern Mississippi, and in greater Jackson, including surrounding rural counties (PHIV cases, HIV hotspots were significantly more likely to include urban census tracts (adjusted odds ratio [AOR] 2.01, 95% CI 1.20-3.37) and census tracts that had a higher proportion of African Americans (AOR 3.85, 95% CI 2.23-6.65). The HIV hotspots were less likely to include census tracts with residents who had less than a high school education (AOR 0.95, 95% CI 0.92-0.98), census tracts with residents belonging to two or more racial/ethnic groups (AOR 0.46, 95% CI 0.30-0.70), and census tracts that had a higher percentage of the population living below the poverty level (AOR 0.51, 95% CI 0.28-0.92). We used spatial epidemiology and statistical modeling to identify and characterize HIV hotspots for the general population, MSM, and African Americans. HIV clusters concentrated in Jackson and the Mississippi Delta. African American race and urban location were

  17. Synchronous Firefly Algorithm for Cluster Head Selection in WSN

    Directory of Open Access Journals (Sweden)

    Madhusudhanan Baskaran

    2015-01-01

    Full Text Available Wireless Sensor Network (WSN consists of small low-cost, low-power multifunctional nodes interconnected to efficiently aggregate and transmit data to sink. Cluster-based approaches use some nodes as Cluster Heads (CHs and organize WSNs efficiently for aggregation of data and energy saving. A CH conveys information gathered by cluster nodes and aggregates/compresses data before transmitting it to a sink. However, this additional responsibility of the node results in a higher energy drain leading to uneven network degradation. Low Energy Adaptive Clustering Hierarchy (LEACH offsets this by probabilistically rotating cluster heads role among nodes with energy above a set threshold. CH selection in WSN is NP-Hard as optimal data aggregation with efficient energy savings cannot be solved in polynomial time. In this work, a modified firefly heuristic, synchronous firefly algorithm, is proposed to improve the network performance. Extensive simulation shows the proposed technique to perform well compared to LEACH and energy-efficient hierarchical clustering. Simulations show the effectiveness of the proposed method in decreasing the packet loss ratio by an average of 9.63% and improving the energy efficiency of the network when compared to LEACH and EEHC.

  18. Size dependent magnetism of mass selected deposited transition metal clusters

    International Nuclear Information System (INIS)

    Lau, T.

    2002-05-01

    The size dependent magnetic properties of small iron clusters deposited on ultrathin Ni/Cu(100) films have been studied with circularly polarised synchrotron radiation. For X-ray magnetic circular dichroism studies, the magnetic moments of size selected clusters were aligned perpendicular to the sample surface. Exchange coupling of the clusters to the ultrathin Ni/Cu(100) film determines the orientation of their magnetic moments. All clusters are coupled ferromagnetically to the underlayer. With the use of sum rules, orbital and spin magnetic moments as well as their ratios have been extracted from X-ray magnetic circular dichroism spectra. The ratio of orbital to spin magnetic moments varies considerably as a function of cluster size, reflecting the dependence of magnetic properties on cluster size and geometry. These variations can be explained in terms of a strongly size dependent orbital moment. Both orbital and spin magnetic moments are significantly enhanced in small clusters as compared to bulk iron, although this effect is more pronounced for the spin moment. Magnetic properties of deposited clusters are governed by the interplay of cluster specific properties on the one hand and cluster-substrate interactions on the other hand. Size dependent variations of magnetic moments are modified upon contact with the substrate. (orig.)

  19. A scan statistic for binary outcome based on hypergeometric probability model, with an application to detecting spatial clusters of Japanese encephalitis.

    Science.gov (United States)

    Zhao, Xing; Zhou, Xiao-Hua; Feng, Zijian; Guo, Pengfei; He, Hongyan; Zhang, Tao; Duan, Lei; Li, Xiaosong

    2013-01-01

    As a useful tool for geographical cluster detection of events, the spatial scan statistic is widely applied in many fields and plays an increasingly important role. The classic version of the spatial scan statistic for the binary outcome is developed by Kulldorff, based on the Bernoulli or the Poisson probability model. In this paper, we apply the Hypergeometric probability model to construct the likelihood function under the null hypothesis. Compared with existing methods, the likelihood function under the null hypothesis is an alternative and indirect method to identify the potential cluster, and the test statistic is the extreme value of the likelihood function. Similar with Kulldorff's methods, we adopt Monte Carlo test for the test of significance. Both methods are applied for detecting spatial clusters of Japanese encephalitis in Sichuan province, China, in 2009, and the detected clusters are identical. Through a simulation to independent benchmark data, it is indicated that the test statistic based on the Hypergeometric model outweighs Kulldorff's statistics for clusters of high population density or large size; otherwise Kulldorff's statistics are superior.

  20. Investigation of psychophysical similarity measures for selection of similar images in the diagnosis of clustered microcalcifications on mammograms

    International Nuclear Information System (INIS)

    Muramatsu, Chisako; Li Qiang; Schmidt, Robert; Shiraishi, Junji; Doi, Kunio

    2008-01-01

    The presentation of images with lesions of known pathology that are similar to an unknown lesion may be helpful to radiologists in the diagnosis of challenging cases for improving the diagnostic accuracy and also for reducing variation among different radiologists. The authors have been developing a computerized scheme for automatically selecting similar images with clustered microcalcifications on mammograms from a large database. For similar images to be useful, they must be similar from the point of view of the diagnosing radiologists. In order to select such images, subjective similarity ratings were obtained for a number of pairs of clustered microcalcifications by breast radiologists for establishment of a ''gold standard'' of image similarity, and the gold standard was employed for determination and evaluation of the selection of similar images. The images used in this study were obtained from the Digital Database for Screening Mammography developed by the University of South Florida. The subjective similarity ratings for 300 pairs of images with clustered microcalcifications were determined by ten breast radiologists. The authors determined a number of image features which represent the characteristics of clustered microcalcifications that radiologists would use in their diagnosis. For determination of objective similarity measures, an artificial neural network (ANN) was employed. The ANN was trained with the average subjective similarity ratings as teacher and selected image features as input data. The ANN was trained to learn the relationship between the image features and the radiologists' similarity ratings; therefore, once the training was completed, the ANN was able to determine the similarity, called a psychophysical similarity measure, which was expected to be close to radiologists' impressions, for an unknown pair of clustered microcalcifications. By use of a leave-one-out test method, the best combination of features was selected. The correlation

  1. Investigation of psychophysical similarity measures for selection of similar images in the diagnosis of clustered microcalcifications on mammograms

    Energy Technology Data Exchange (ETDEWEB)

    Muramatsu, Chisako; Li Qiang; Schmidt, Robert; Shiraishi, Junji; Doi, Kunio [Department of Radiology, University of Chicago, 5841 South Maryland Avenue, Chicago, Illinois 60637 (United States) and Department of Intelligent Image Information, Gifu University, 1-1 Yanagido, Gifu (Japan); Department of Radiology, Duke Advanced Imaging Labs, Duke University, 2424 Erwin Road, Suite 302, Durham, North Carolina 27705 (United States); Department of Radiology, University of Chicago, 5841 South Maryland Avenue, Chicago, Illinois 60637 (United States)

    2008-12-15

    The presentation of images with lesions of known pathology that are similar to an unknown lesion may be helpful to radiologists in the diagnosis of challenging cases for improving the diagnostic accuracy and also for reducing variation among different radiologists. The authors have been developing a computerized scheme for automatically selecting similar images with clustered microcalcifications on mammograms from a large database. For similar images to be useful, they must be similar from the point of view of the diagnosing radiologists. In order to select such images, subjective similarity ratings were obtained for a number of pairs of clustered microcalcifications by breast radiologists for establishment of a ''gold standard'' of image similarity, and the gold standard was employed for determination and evaluation of the selection of similar images. The images used in this study were obtained from the Digital Database for Screening Mammography developed by the University of South Florida. The subjective similarity ratings for 300 pairs of images with clustered microcalcifications were determined by ten breast radiologists. The authors determined a number of image features which represent the characteristics of clustered microcalcifications that radiologists would use in their diagnosis. For determination of objective similarity measures, an artificial neural network (ANN) was employed. The ANN was trained with the average subjective similarity ratings as teacher and selected image features as input data. The ANN was trained to learn the relationship between the image features and the radiologists' similarity ratings; therefore, once the training was completed, the ANN was able to determine the similarity, called a psychophysical similarity measure, which was expected to be close to radiologists' impressions, for an unknown pair of clustered microcalcifications. By use of a leave-one-out test method, the best combination of features

  2. Size selected metal clusters

    Indian Academy of Sciences (India)

    First page Back Continue Last page Overview Graphics. The Optical Absorption Spectra of Small Silver Clusters (5-11) ... Soft Landing and Fragmentation of Small Clusters Deposited in Noble-Gas Films. Harbich, W.; Fedrigo, S.; Buttet, J. Phys. Rev. B 1998, 58, 7428. CO combustion on supported gold clusters. Arenz M ...

  3. Spatial memory deficits in patients after unilateral selective amygdalohippocampectomy

    NARCIS (Netherlands)

    Kessels, R.P.C.; Hendriks, M.P.H.; Schouten, J.A.; Asselen, M. van; Postma, A.

    2004-01-01

    The present study investigated the differential involvement of the right and left hippocampus in various forms of spatial memory: spatial search, positional memory versus object-location binding, and coordinate versus categorical processing. Twenty-five epilepsy patients with selective

  4. Spatial and temporal structure of typhoid outbreaks in Washington, D.C., 1906–1909: evaluating local clustering with the Gi* statistic

    Directory of Open Access Journals (Sweden)

    Curtis Andrew

    2006-03-01

    Full Text Available Abstract Background To better understand the distribution of typhoid outbreaks in Washington, D.C., the U.S. Public Health Service (PHS conducted four investigations of typhoid fever. These studies included maps of cases reported between 1 May – 31 October 1906 – 1909. These data were entered into a GIS database and analyzed using Ripley's K-function followed by the Gi* statistic in yearly intervals to evaluate spatial clustering, the scale of clustering, and the temporal stability of these clusters. Results The Ripley's K-function indicated no global spatial autocorrelation. The Gi* statistic indicated clustering of typhoid at multiple scales across the four year time period, refuting the conclusions drawn in all four PHS reports concerning the distribution of cases. While the PHS reports suggested an even distribution of the disease, this study quantified both areas of localized disease clustering, as well as mobile larger regions of clustering. Thus, indicating both highly localized and periodic generalized sources of infection within the city. Conclusion The methodology applied in this study was useful for evaluating the spatial distribution and annual-level temporal patterns of typhoid outbreaks in Washington, D.C. from 1906 to 1909. While advanced spatial analyses of historical data sets must be interpreted with caution, this study does suggest that there is utility in these types of analyses and that they provide new insights into the urban patterns of typhoid outbreaks during the early part of the twentieth century.

  5. Reduced sintering of mass-selected Au clusters on SiO2 by alloying with Ti: an aberration-corrected STEM and computational study

    DEFF Research Database (Denmark)

    Niu, Yubiao; Schlexer, Philomena; Sebök, Béla

    2018-01-01

    Au nanoparticles represent the most remarkable example of a size effect in heterogeneous catalysis. However, a major issue hindering the use of Au nanoparticles in technological applications is their rapid sintering. We explore the potential of stabilizing Au nanoclusters on SiO2 by alloying them...... in the Au/Ti clusters, but in line with the model computational investigation, Au atoms were still present on the surface. Thus size-selected, deposited nanoalloy Au/Ti clusters appear to be promising candidates for sustainable gold-based nanocatalysis....

  6. Temporal Data-Driven Sleep Scheduling and Spatial Data-Driven Anomaly Detection for Clustered Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Gang Li

    2016-09-01

    Full Text Available The spatial–temporal correlation is an important feature of sensor data in wireless sensor networks (WSNs. Most of the existing works based on the spatial–temporal correlation can be divided into two parts: redundancy reduction and anomaly detection. These two parts are pursued separately in existing works. In this work, the combination of temporal data-driven sleep scheduling (TDSS and spatial data-driven anomaly detection is proposed, where TDSS can reduce data redundancy. The TDSS model is inspired by transmission control protocol (TCP congestion control. Based on long and linear cluster structure in the tunnel monitoring system, cooperative TDSS and spatial data-driven anomaly detection are then proposed. To realize synchronous acquisition in the same ring for analyzing the situation of every ring, TDSS is implemented in a cooperative way in the cluster. To keep the precision of sensor data, spatial data-driven anomaly detection based on the spatial correlation and Kriging method is realized to generate an anomaly indicator. The experiment results show that cooperative TDSS can realize non-uniform sensing effectively to reduce the energy consumption. In addition, spatial data-driven anomaly detection is quite significant for maintaining and improving the precision of sensor data.

  7. A Novel Joint Spatial-Code Clustered Interference Alignment Scheme for Large-Scale Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Zhilu Wu

    2015-01-01

    Full Text Available Interference alignment (IA has been put forward as a promising technique which can mitigate interference and effectively increase the throughput of wireless sensor networks (WSNs. However, the number of users is strictly restricted by the IA feasibility condition, and the interference leakage will become so strong that the quality of service will degrade significantly when there are more users than that IA can support. In this paper, a novel joint spatial-code clustered (JSCC-IA scheme is proposed to solve this problem. In the proposed scheme, the users are clustered into several groups so that feasible IA can be achieved within each group. In addition, each group is assigned a pseudo noise (PN code in order to suppress the inter-group interference via the code dimension. The analytical bit error rate (BER expressions of the proposed JSCC-IA scheme are formulated for the systems with identical and different propagation delays, respectively. To further improve the performance of the JSCC-IA scheme in asymmetric networks, a random grouping selection (RGS algorithm is developed to search for better grouping combinations. Numerical results demonstrate that the proposed JSCC-IA scheme is capable of accommodating many more users to communicate simultaneously in the same frequency band with better performance.

  8. Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Gulnaz Ahmed

    2017-02-01

    Full Text Available The longer network lifetime of Wireless Sensor Networks (WSNs is a goal which is directly related to energy consumption. This energy consumption issue becomes more challenging when the energy load is not properly distributed in the sensing area. The hierarchal clustering architecture is the best choice for these kind of issues. In this paper, we introduce a novel clustering protocol called Markov chain model-based optimal cluster heads (MOCHs selection for WSNs. In our proposed model, we introduce a simple strategy for the optimal number of cluster heads selection to overcome the problem of uneven energy distribution in the network. The attractiveness of our model is that the BS controls the number of cluster heads while the cluster heads control the cluster members in each cluster in such a restricted manner that a uniform and even load is ensured in each cluster. We perform an extensive range of simulation using five quality measures, namely: the lifetime of the network, stable and unstable region in the lifetime of the network, throughput of the network, the number of cluster heads in the network, and the transmission time of the network to analyze the proposed model. We compare MOCHs against Sleep-awake Energy Efficient Distributed (SEED clustering, Artificial Bee Colony (ABC, Zone Based Routing (ZBR, and Centralized Energy Efficient Clustering (CEEC using the above-discussed quality metrics and found that the lifetime of the proposed model is almost 1095, 2630, 3599, and 2045 rounds (time steps greater than SEED, ABC, ZBR, and CEEC, respectively. The obtained results demonstrate that the MOCHs is better than SEED, ABC, ZBR, and CEEC in terms of energy efficiency and the network throughput.

  9. Spatial clustering of all-cause and HIV-related mortality in a rural South African population (2000-2006.

    Directory of Open Access Journals (Sweden)

    Elias Namosha

    Full Text Available Sub-Saharan Africa bears a disproportionate burden of HIV infection. Knowledge of the spatial distribution of HIV outcomes is vital so that appropriate public health interventions can be directed at locations most in need. In this regard, spatial clustering analysis of HIV-related mortality events has not been performed in a rural sub-Saharan African setting.Kulldorff's spatial scan statistic was used to identify HIV-related and all-cause mortality clusters (p<0.05 in a population-based demographic surveillance survey in rural KwaZulu Natal, South Africa (2000-2006. The analysis was split pre (2000-2003 and post (2004-2006 rollout of antiretroviral therapy, respectively. Between 2000-2006 a total of 86,175 resident individuals ≥15 years of age were under surveillance and 5,875 deaths were recorded (of which 2,938 were HIV-related over 343,060 person-years of observation (crude all-cause mortality rate 17.1/1000. During both time periods a cluster of high HIV-related (RR = 1.46/1.51, p = 0.001 and high all-cause mortality (RR = 1.35/1.38, p = 0.001 was identified in peri-urban communities near the National Road. A consistent low-risk cluster was detected in the urban township in both time periods (RR = 0.60/0.39, p = 0.003/0.005 and in the first time period (2000-2003 a large cluster of low HIV-related and all-cause mortality in a remote rural area was identified.HIV-related and all-cause mortality exhibit strong spatial clustering tendencies in this population. Highest HIV-related mortality and all-cause mortality occurred in the peri-urban communities along the National Road and was lowest in the urban township and remote rural communities. The geography of HIV-related mortality corresponded closely to the geography of HIV prevalence, with the notable exception of the urban township where high HIV-related mortality would have been expected on the basis of the high HIV prevalence. Our results suggest that HIV treatment

  10. Beyond time and space: The effect of a lateralized sustained attention task and brain stimulation on spatial and selective attention.

    Science.gov (United States)

    Shalev, Nir; De Wandel, Linde; Dockree, Paul; Demeyere, Nele; Chechlacz, Magdalena

    2017-10-03

    The Theory of Visual Attention (TVA) provides a mathematical formalisation of the "biased competition" account of visual attention. Applying this model to individual performance in a free recall task allows the estimation of 5 independent attentional parameters: visual short-term memory (VSTM) capacity, speed of information processing, perceptual threshold of visual detection; attentional weights representing spatial distribution of attention (spatial bias), and the top-down selectivity index. While the TVA focuses on selection in space, complementary accounts of attention describe how attention is maintained over time, and how temporal processes interact with selection. A growing body of evidence indicates that different facets of attention interact and share common neural substrates. The aim of the current study was to modulate a spatial attentional bias via transfer effects, based on a mechanistic understanding of the interplay between spatial, selective and temporal aspects of attention. Specifically, we examined here: (i) whether a single administration of a lateralized sustained attention task could prime spatial orienting and lead to transferable changes in attentional weights (assigned to the left vs right hemi-field) and/or other attentional parameters assessed within the framework of TVA (Experiment 1); (ii) whether the effects of such spatial-priming on TVA parameters could be further enhanced by bi-parietal high frequency transcranial random noise stimulation (tRNS) (Experiment 2). Our results demonstrate that spatial attentional bias, as assessed within the TVA framework, was primed by sustaining attention towards the right hemi-field, but this spatial-priming effect did not occur when sustaining attention towards the left. Furthermore, we show that bi-parietal high-frequency tRNS combined with the rightward spatial-priming resulted in an increased attentional selectivity. To conclude, we present a novel, theory-driven method for attentional modulation

  11. Fischer–Tropsch Synthesis at a Low Pressure on Subnanometer Cobalt Oxide Clusters: The Effect of Cluster Size and Support on Activity and Selectivity

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Sungsik; Lee, Byeongdu; Seifert, Sönke; Winans, Randall E.; Vajda, Stefan

    2015-05-21

    In this study, the catalytic activity and changes in the oxidation state during the Fischer Tropsch (FT) reaction was investigated on subnanometer size-selected cobalt clusters deposited on oxide (Al2O3, MgO) and carbon-based (ultrananocrystalline diamond UNCD) supports by temperature programmed reaction (TPRx) combined with in-situ grazing-incidence X-ray absorption characterization (GIXAS). The activity and selectivity of ultrasmall cobalt clusters exhibits a very strong dependence on cluster size and support. The evolution of the oxidation state of metal cluster during the reaction reveals that metal-support interaction plays a key role in the reaction.

  12. Interplay between experiments and calculations for organometallic clusters and caged clusters

    International Nuclear Information System (INIS)

    Nakajima, Atsushi

    2015-01-01

    Clusters consisting of 10-1000 atoms exhibit size-dependent electronic and geometric properties. In particular, composite clusters consisting of several elements and/or components provide a promising way for a bottom-up approach for designing functional advanced materials, because the functionality of the composite clusters can be optimized not only by the cluster size but also by their compositions. In the formation of composite clusters, their geometric symmetry and dimensionality are emphasized to control the physical and chemical properties, because selective and anisotropic enhancements for optical, chemical, and magnetic properties can be expected. Organometallic clusters and caged clusters are demonstrated as a representative example of designing the functionality of the composite clusters. Organometallic vanadium-benzene forms a one dimensional sandwich structure showing ferromagnetic behaviors and anomalously large HOMO-LUMO gap differences of two spin orbitals, which can be regarded as spin-filter components for cluster-based spintronic devices. Caged clusters of aluminum (Al) are well stabilized both geometrically and electronically at Al 12 X, behaving as a “superatom”

  13. Selection in spatial working memory is independent of perceptual selective attention, but they interact in a shared spatial priority map.

    Science.gov (United States)

    Hedge, Craig; Oberauer, Klaus; Leonards, Ute

    2015-11-01

    We examined the relationship between the attentional selection of perceptual information and of information in working memory (WM) through four experiments, using a spatial WM-updating task. Participants remembered the locations of two objects in a matrix and worked through a sequence of updating operations, each mentally shifting one dot to a new location according to an arrow cue. Repeatedly updating the same object in two successive steps is typically faster than switching to the other object; this object switch cost reflects the shifting of attention in WM. In Experiment 1, the arrows were presented in random peripheral locations, drawing perceptual attention away from the selected object in WM. This manipulation did not eliminate the object switch cost, indicating that the mechanisms of perceptual selection do not underlie selection in WM. Experiments 2a and 2b corroborated the independence of selection observed in Experiment 1, but showed a benefit to reaction times when the placement of the arrow cue was aligned with the locations of relevant objects in WM. Experiment 2c showed that the same benefit also occurs when participants are not able to mark an updating location through eye fixations. Together, these data can be accounted for by a framework in which perceptual selection and selection in WM are separate mechanisms that interact through a shared spatial priority map.

  14. Sunlight Modulates Fruit Metabolic Profile and Shapes the Spatial Pattern of Compound Accumulation within the Grape Cluster.

    Science.gov (United States)

    Reshef, Noam; Walbaum, Natasha; Agam, Nurit; Fait, Aaron

    2017-01-01

    Vineyards are characterized by their large spatial variability of solar irradiance (SI) and temperature, known to effectively modulate grape metabolism. To explore the role of sunlight in shaping fruit composition and cluster uniformity, we studied the spatial pattern of incoming irradiance, fruit temperature and metabolic profile within individual grape clusters under three levels of sunlight exposure. The experiment was conducted in a vineyard of Cabernet Sauvignon cv. located in the Negev Highlands, Israel, where excess SI and midday temperatures are known to degrade grape quality. Filtering SI lowered the surface temperature of exposed fruits and increased the uniformity of irradiance and temperature in the cluster zone. SI affected the overall levels and patterns of accumulation of sugars, organic acids, amino acids and phenylpropanoids, across the grape cluster. Increased exposure to sunlight was associated with lower accumulation levels of malate, aspartate, and maleate but with higher levels of valine, leucine, and serine, in addition to the stress-related proline and GABA. Flavan-3-ols metabolites showed a negative response to SI, whereas flavonols were highly induced. The overall levels of anthocyanins decreased with increased sunlight exposure; however, a hierarchical cluster analysis revealed that the members of this family were grouped into three distinct accumulation patterns, with malvidin anthocyanins and cyanidin-glucoside showing contrasting trends. The flavonol-glucosides, quercetin and kaempferol, exhibited a logarithmic response to SI, leading to improved cluster uniformity under high-light conditions. Comparing the within-cluster variability of metabolite accumulation highlighted the stability of sugars, flavan-3-ols, and cinnamic acid metabolites to SI, in contrast to the plasticity of flavonols. A correlation-based network analysis revealed that extended exposure to SI modified metabolic coordination, increasing the number of negative

  15. Selection of Representative Models for Decision Analysis Under Uncertainty

    Science.gov (United States)

    Meira, Luis A. A.; Coelho, Guilherme P.; Santos, Antonio Alberto S.; Schiozer, Denis J.

    2016-03-01

    The decision-making process in oil fields includes a step of risk analysis associated with the uncertainties present in the variables of the problem. Such uncertainties lead to hundreds, even thousands, of possible scenarios that are supposed to be analyzed so an effective production strategy can be selected. Given this high number of scenarios, a technique to reduce this set to a smaller, feasible subset of representative scenarios is imperative. The selected scenarios must be representative of the original set and also free of optimistic and pessimistic bias. This paper is devoted to propose an assisted methodology to identify representative models in oil fields. To do so, first a mathematical function was developed to model the representativeness of a subset of models with respect to the full set that characterizes the problem. Then, an optimization tool was implemented to identify the representative models of any problem, considering not only the cross-plots of the main output variables, but also the risk curves and the probability distribution of the attribute-levels of the problem. The proposed technique was applied to two benchmark cases and the results, evaluated by experts in the field, indicate that the obtained solutions are richer than those identified by previously adopted manual approaches. The program bytecode is available under request.

  16. Computational Aspects of Nuclear Coupled-Cluster Theory

    International Nuclear Information System (INIS)

    Dean, David Jarvis; Hagen, Gaute; Hjorth-Jensen, M.; Papenbrock, T.F.

    2008-01-01

    Coupled-cluster theory represents an important theoretical tool that we use to solve the quantum many-body problem. Coupled-cluster theory also lends itself to computation in a parallel computing environment. In this article, we present selected results from ab initio studies of stable and weakly bound nuclei utilizing computational techniques that we employ to solve coupled-cluster theory. We also outline several perspectives for future research directions in this area.

  17. An Initial Seed Selection Algorithm for K-means Clustering of Georeferenced Data to Improve Replicability of Cluster Assignments for Mapping Application

    OpenAIRE

    Khan, Fouad

    2016-01-01

    K-means is one of the most widely used clustering algorithms in various disciplines, especially for large datasets. However the method is known to be highly sensitive to initial seed selection of cluster centers. K-means++ has been proposed to overcome this problem and has been shown to have better accuracy and computational efficiency than k-means. In many clustering problems though -such as when classifying georeferenced data for mapping applications- standardization of clustering methodolo...

  18. Use of a spatial scan statistic to identify clusters of births occurring outside Ghanaian health facilities for targeted intervention.

    Science.gov (United States)

    Bosomprah, Samuel; Dotse-Gborgbortsi, Winfred; Aboagye, Patrick; Matthews, Zoe

    2016-11-01

    To identify and evaluate clusters of births that occurred outside health facilities in Ghana for targeted intervention. A retrospective study was conducted using a convenience sample of live births registered in Ghanaian health facilities from January 1 to December 31, 2014. Data were extracted from the district health information system. A spatial scan statistic was used to investigate clusters of home births through a discrete Poisson probability model. Scanning with a circular spatial window was conducted only for clusters with high rates of such deliveries. The district was used as the geographic unit of analysis. The likelihood P value was estimated using Monte Carlo simulations. Ten statistically significant clusters with a high rate of home birth were identified. The relative risks ranged from 1.43 ("least likely" cluster; P=0.001) to 1.95 ("most likely" cluster; P=0.001). The relative risks of the top five "most likely" clusters ranged from 1.68 to 1.95; these clusters were located in Ashanti, Brong Ahafo, and the Western, Eastern, and Greater regions of Accra. Health facility records, geospatial techniques, and geographic information systems provided locally relevant information to assist policy makers in delivering targeted interventions to small geographic areas. Copyright © 2016 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.

  19. Conjugation of colloidal clusters and chains by capillary condensation.

    Science.gov (United States)

    Li, Fan; Stein, Andreas

    2009-07-29

    Capillary condensation was used to establish connections in colloidal clusters and 1D colloidal chains with high regional selectivity. This vapor-phase process produced conjugated clusters and chains with anisotropic functionality. The capillary condensation method is simple and can be applied to a wide range of materials. It can tolerate geometric variations and even permits conjugation of spatially separated particles. The selective deposition was also used to modulate the functionality on the colloid surfaces, producing tip-tethered nanosized building blocks that may be suitable for further assembly via directional interactions.

  20. A supplier selection using a hybrid grey based hierarchical clustering and artificial bee colony

    Directory of Open Access Journals (Sweden)

    Farshad Faezy Razi

    2014-06-01

    Full Text Available Selection of one or a combination of the most suitable potential providers and outsourcing problem is the most important strategies in logistics and supply chain management. In this paper, selection of an optimal combination of suppliers in inventory and supply chain management are studied and analyzed via multiple attribute decision making approach, data mining and evolutionary optimization algorithms. For supplier selection in supply chain, hierarchical clustering according to the studied indexes first clusters suppliers. Then, according to its cluster, each supplier is evaluated through Grey Relational Analysis. Then the combination of suppliers’ Pareto optimal rank and costs are obtained using Artificial Bee Colony meta-heuristic algorithm. A case study is conducted for a better description of a new algorithm to select a multiple source of suppliers.

  1. Patch-based visual tracking with online representative sample selection

    Science.gov (United States)

    Ou, Weihua; Yuan, Di; Li, Donghao; Liu, Bin; Xia, Daoxun; Zeng, Wu

    2017-05-01

    Occlusion is one of the most challenging problems in visual object tracking. Recently, a lot of discriminative methods have been proposed to deal with this problem. For the discriminative methods, it is difficult to select the representative samples for the target template updating. In general, the holistic bounding boxes that contain tracked results are selected as the positive samples. However, when the objects are occluded, this simple strategy easily introduces the noises into the training data set and the target template and then leads the tracker to drift away from the target seriously. To address this problem, we propose a robust patch-based visual tracker with online representative sample selection. Different from previous works, we divide the object and the candidates into several patches uniformly and propose a score function to calculate the score of each patch independently. Then, the average score is adopted to determine the optimal candidate. Finally, we utilize the non-negative least square method to find the representative samples, which are used to update the target template. The experimental results on the object tracking benchmark 2013 and on the 13 challenging sequences show that the proposed method is robust to the occlusion and achieves promising results.

  2. Multitarget Tracking with Spatial Nonmaximum Suppressed Sensor Selection

    Directory of Open Access Journals (Sweden)

    Liang Ma

    2015-01-01

    Full Text Available Multitarget tracking is one of the most important applications of sensor networks, yet it is an extremely challenging problem since multisensor multitarget tracking itself is nontrivial and the difficulty is further compounded by sensor management. Recently, random finite set based Bayesian framework has opened doors for multitarget tracking with sensor management, which is modelled in the framework of partially observed Markov decision process (POMDP. However, sensor management posed as a POMDP is in essence a combinatorial optimization problem which is NP-hard and computationally unacceptable. In this paper, we propose a novel sensor selection method for multitarget tracking. We first present the sequential multi-Bernoulli filter as a centralized multisensor fusion scheme for multitarget tracking. In order to perform sensor selection, we define the hypothesis information gain (HIG of a sensor to measure its information quantity when the sensor is selected alone. Then, we propose spatial nonmaximum suppression approach to select sensors with respect to their locations and HIGs. Two distinguished implementations have been provided using the greedy spatial nonmaximum suppression. Simulation results verify the effectiveness of proposed sensor selection approach for multitarget tracking.

  3. Selection bias and subject refusal in a cluster-randomized controlled trial

    Directory of Open Access Journals (Sweden)

    Rochelle Yang

    2017-07-01

    Full Text Available Abstract Background Selection bias and non-participation bias are major methodological concerns which impact external validity. Cluster-randomized controlled trials are especially prone to selection bias as it is impractical to blind clusters to their allocation into intervention or control. This study assessed the impact of selection bias in a large cluster-randomized controlled trial. Methods The Improved Cardiovascular Risk Reduction to Enhance Rural Primary Care (ICARE study examined the impact of a remote pharmacist-led intervention in twelve medical offices. To assess eligibility, a standardized form containing patient demographics and medical information was completed for each screened patient. Eligible patients were approached by the study coordinator for recruitment. Both the study coordinator and the patient were aware of the site’s allocation prior to consent. Patients who consented or declined to participate were compared across control and intervention arms for differing characteristics. Statistical significance was determined using a two-tailed, equal variance t-test and a chi-square test with adjusted Bonferroni p-values. Results were adjusted for random cluster variation. Results There were 2749 completed screening forms returned to research staff with 461 subjects who had either consented or declined participation. Patients with poorly controlled diabetes were found to be significantly more likely to decline participation in intervention sites compared to those in control sites. A higher mean diastolic blood pressure was seen in patients with uncontrolled hypertension who declined in the control sites compared to those who declined in the intervention sites. However, these findings were no longer significant after adjustment for random variation among the sites. After this adjustment, females were now found to be significantly more likely to consent than males (odds ratio = 1.41; 95% confidence interval = 1.03, 1

  4. [Spatial and temporal clustering characteristics of typhoid and paratyphoid fever and its change pattern in 3 provinces in southwestern China, 2001-2012].

    Science.gov (United States)

    Wang, L X; Yang, B; Yan, M Y; Tang, Y Q; Liu, Z C; Wang, R Q; Li, S; Ma, L; Kan, B

    2017-11-10

    Objective: To analyze the spatial and temporal clustering characteristics of typhoid and paratyphoid fever and its change pattern in Yunnan, Guizhou and Guangxi provinces in southwestern China in recent years. Methods: The incidence data of typhoid and paratyphoid fever cases at county level in 3 provinces during 2001-2012 were collected from China Information System for Diseases Control and Prevention and analyzed by the methods of descriptive epidemiology and geographic informatics. And the map showing the spatial and temporal clustering characters of typhoid and paratyphoid fever cases in three provinces was drawn. SaTScan statistics was used to identify the typhoid and paratyphoid fever clustering areas of three provinces in each year from 2001 to 2012. Results: During the study period, the reported cases of typhoid and paratyphoid fever declined with year. The reported incidence decreased from 30.15 per 100 000 in 2001 to 10.83 per 100 000 in 2006(annual incidence 21.12 per 100 000); while during 2007-2012, the incidence became stable, ranging from 4.75 per 100 000 to 6.83 per 100 000 (annual incidence 5.73 per 100 000). The seasonal variation of the incidence was consistent in three provinces, with majority of cases occurred in summer and autumn. The spatial and temporal clustering of typhoid and paratyphoid fever was demonstrated by the incidence map. Most high-incidence counties were located in a zonal area extending from Yuxi of Yunnan to Guiyang of Guizhou, but were concentrated in Guilin in Guangxi. Temporal and spatial scan statistics identified the positional shifting of class Ⅰ clustering area from Guizhou to Yunnan. Class Ⅰ clustering area was located around the central and western areas (Zunyi and Anshun) of Guizhou during 2001-2003, and moved to the central area of Yunnan during 2004-2012. Conclusion: Spatial and temporal clustering of typhoid and paratyphoid fever existed in the endemic areas of southwestern China, and the clustering area

  5. Selected spatial effects of the global financial and economic crisis in Ljubljana, Slovenia

    Directory of Open Access Journals (Sweden)

    Simon Kušar

    2012-01-01

    Full Text Available In 2007, the United States witnessed a financial crisis that gradually developed into one of the most serious global financial and economic recessions in the history of (postmodern society. Its effects are numerous. This article studies one of its spatial effects; that is, newly built (after 2005 residential and office buildings that are either unfinished or already built but not fully occupied. In Ljubljana in November 2011, there were ninety-seven locations with unoccupied or partly occupied residential houses and office buildings or groups of houses and office buildings together with abandoned or active construction sites. The majority of the structures studied were predominately represented by several blocks of flats and groups of dwellings. The others are office buildings or buildings and complexes with distinctively mixed residential-business functions. There are more than 1,500 empty flats and almost 75,000 m² of office area in the buildings surveyed. Spatial analysis showed that the structures surveyed are relatively scattered throughout Ljubljana. However, there were some clusters of buildings, especially in areas with the best accessibility. This article analyses the causes of this phenomenon, which is creating a new morphological element in Ljubljana. The article concludes by stating possible directions for future research.

  6. Melting of size-selected gallium clusters with 60-183 atoms.

    Science.gov (United States)

    Pyfer, Katheryne L; Kafader, Jared O; Yalamanchali, Anirudh; Jarrold, Martin F

    2014-07-10

    Heat capacities have been measured as a function of temperature for size-selected gallium cluster cations with between 60 and 183 atoms. Almost all clusters studied show a single peak in the heat capacity that is attributed to a melting transition. The peaks can be fit by a two-state model incorporating only fully solid-like and fully liquid-like species, and hence no partially melted intermediates. The exceptions are Ga90(+), which does not show a peak, and Ga80(+) and Ga81(+), which show two peaks. For the clusters with two peaks, the lower temperature peak is attributed to a structural transition. The melting temperatures for clusters with less than 50 atoms have previously been shown to be hundreds of degrees above the bulk melting point. For clusters with more than 60 atoms the melting temperatures decrease, approaching the bulk value (303 K) at around 95 atoms, and then show several small upward excursions with increasing cluster size. A plot of the latent heat against the entropy change for melting reveals two groups of clusters: the latent heats and entropy changes for clusters with less than 94 atoms are distinct from those for clusters with more than 93 atoms. This observation suggests that a significant change in the nature of the bonding or the structure of the clusters occurs at 93-94 atoms. Even though the melting temperatures are close to the bulk value for the larger clusters studied here, the latent heats and entropies of melting are still far from the bulk values.

  7. Recursive Cluster Elimination (RCE for classification and feature selection from gene expression data

    Directory of Open Access Journals (Sweden)

    Showe Louise C

    2007-05-01

    Full Text Available Abstract Background Classification studies using gene expression datasets are usually based on small numbers of samples and tens of thousands of genes. The selection of those genes that are important for distinguishing the different sample classes being compared, poses a challenging problem in high dimensional data analysis. We describe a new procedure for selecting significant genes as recursive cluster elimination (RCE rather than recursive feature elimination (RFE. We have tested this algorithm on six datasets and compared its performance with that of two related classification procedures with RFE. Results We have developed a novel method for selecting significant genes in comparative gene expression studies. This method, which we refer to as SVM-RCE, combines K-means, a clustering method, to identify correlated gene clusters, and Support Vector Machines (SVMs, a supervised machine learning classification method, to identify and score (rank those gene clusters for the purpose of classification. K-means is used initially to group genes into clusters. Recursive cluster elimination (RCE is then applied to iteratively remove those clusters of genes that contribute the least to the classification performance. SVM-RCE identifies the clusters of correlated genes that are most significantly differentially expressed between the sample classes. Utilization of gene clusters, rather than individual genes, enhances the supervised classification accuracy of the same data as compared to the accuracy when either SVM or Penalized Discriminant Analysis (PDA with recursive feature elimination (SVM-RFE and PDA-RFE are used to remove genes based on their individual discriminant weights. Conclusion SVM-RCE provides improved classification accuracy with complex microarray data sets when it is compared to the classification accuracy of the same datasets using either SVM-RFE or PDA-RFE. SVM-RCE identifies clusters of correlated genes that when considered together

  8. Spatial clusters of suicide in the municipality of São Paulo 1996-2005: an ecological study.

    Science.gov (United States)

    Bando, Daniel H; Moreira, Rafael S; Pereira, Julio C R; Barrozo, Ligia V

    2012-08-23

    In a classical study, Durkheim mapped suicide rates, wealth, and low family density and realized that they clustered in northern France. Assessing others variables, such as religious society, he constructed a framework for the analysis of the suicide, which still allows international comparisons using the same basic methodology. The present study aims to identify possible significantly clusters of suicide in the city of São Paulo, and then, verify their statistical associations with socio-economic and cultural characteristics. A spatial scan statistical test was performed to analyze the geographical pattern of suicide deaths of residents in the city of São Paulo by Administrative District, from 1996 to 2005. Relative risks and high and/or low clusters were calculated accounting for gender and age as co-variates, were analyzed using spatial scan statistics to identify geographical patterns. Logistic regression was used to estimate associations with socioeconomic variables, considering, the spatial cluster of high suicide rates as the response variable. Drawing from Durkheim's original work, current World Health Organization (WHO) reports and recent reviews, the following independent variables were considered: marital status, income, education, religion, and migration. The mean suicide rate was 4.1/100,000 inhabitant-years. Against this baseline, two clusters were identified: the first, of increased risk (RR=1.66), comprising 18 districts in the central region; the second, of decreased risk (RR=0.78), including 14 districts in the southern region. The downtown area toward the southwestern region of the city displayed the highest risk for suicide, and though the overall risk may be considered low, the rate climbs up to an intermediate level in this region. One logistic regression analysis contrasted the risk cluster (18 districts) against the other remaining 78 districts, testing the effects of socioeconomic-cultural variables. The following categories of proportion

  9. Changing patterns of spatial clustering of schistosomiasis in Southwest China between 1999-2001 and 2007-2008: assessing progress toward eradication after the World Bank Loan Project.

    Science.gov (United States)

    Hu, Yi; Xiong, Chenglong; Zhang, Zhijie; Luo, Can; Cohen, Ted; Gao, Jie; Zhang, Lijuan; Jiang, Qingwu

    2014-01-03

    We compared changes in the spatial clustering of schistosomiasis in Southwest China at the conclusion of and six years following the end of the World Bank Loan Project (WBLP), the control strategy of which was focused on the large-scale use of chemotherapy. Parasitological data were obtained through standardized surveys conducted in 1999-2001 and again in 2007-2008. Two alternate spatial cluster methods were used to identify spatial clusters of cases: Anselin's Local Moran's I test and Kulldorff's spatial scan statistic. Substantial reductions in the burden of schistosomiasis were found after the end of the WBLP, but the spatial extent of schistosomiasis was not reduced across the study area. Spatial clusters continued to occur in three regions: Chengdu Plain, Yangtze River Valley, and Lancang River Valley during the two periods, and regularly involved five counties. These findings suggest that despite impressive reductions in burden, the hilly and mountainous regions of Southwest China remain at risk of schistosome re-emergence. Our results help to highlight specific locations where integrated control programs can focus to speed the elimination of schistosomiasis in China.

  10. Mass media image of selected instruments of economic develepment

    Directory of Open Access Journals (Sweden)

    Kruliš Ladislav

    2016-07-01

    Full Text Available The goal of this paper is twofold. Firstly, two instruments of economic development – investment incentives and cluster initiatives – were compared according to the frequency of their occurrence in selected mass media sources in the Czech Republic in the periods 2004-2005 and 2011-2012. Secondly, the mass media image of these two instruments of economic development was evaluated with respect to the frames deductively constructed from literature review. The findings pointed out a higher occurrence of the mass media articles/news dealing with investment incentives. These articles/news were, additionally, more controversial and covered a wider spectrum of frames. Politicians were a relatively more frequent type of actors who created the media message from the articles/news. On the contrary, the mass media articles/news concerning cluster initiatives typically created the frame of positive effects of clusters. The messages were told either by economic experts or by public authority representatives who were closely connected with cluster initiatives. Spatial origin of these messages was rather limited. The definitional vagueness, intangible and uncontroversial nature of cluster initiatives restrained their media appeal.

  11. Persistent Spatial Clusters of Prescribed Antimicrobials among Danish Pig Farms – A Register-Based Study

    Science.gov (United States)

    Fertner, Mette; Sanchez, Javier; Boklund, Anette; Stryhn, Henrik; Dupont, Nana; Toft, Nils

    2015-01-01

    The emergence of pathogens resistant to antimicrobials has prompted political initiatives targeting a reduction in the use of veterinary antimicrobials in Denmark, especially for pigs. This study elucidates the tendency of pig farms with a significantly higher antimicrobial use to remain in clusters in certain geographical regions of Denmark. Animal Daily Doses/100 pigs/day were calculated for all three age groups of pigs (weaners, finishers and sows) for each quarter during 2012–13 in 6,143 commercial indoor pig producing farms. The data were split into four time periods of six months. Repeated spatial cluster analyses were performed to identify persistent clusters, i.e. areas included in a significant cluster throughout all four time periods. Antimicrobials prescribed for weaners did not result in any persistent clusters. In contrast, antimicrobial use in finishers clustered persistently in two areas (157 farms), while those issued for sows clustered in one area (51 farms). A multivariate analysis including data on antimicrobial use for weaners, finishers and sows as three separate outcomes resulted in three persistent clusters (551 farms). Compared to farms outside the clusters during this period, weaners, finishers and sows on farms within these clusters had 19%, 104% and 4% higher use of antimicrobials, respectively. Production type, farm type and farm size seemed to have some bearing on the clustering effect. Adding these factors as categorical covariates one at a time in the multivariate analysis reduced the persistent clusters by 24.3%, 30.5% and 34.1%, respectively. PMID:26317206

  12. A new experimental setup for high-pressure catalytic activity measurements on surface deposited mass-selected Pt clusters

    International Nuclear Information System (INIS)

    Watanabe, Yoshihide; Isomura, Noritake

    2009-01-01

    A new experimental setup to study catalytic and electronic properties of size-selected clusters on metal oxide substrates from the viewpoint of cluster-support interaction and to formulate a method for the development of heterogeneous catalysts such as automotive exhaust catalysts has been developed. The apparatus consists of a size-selected cluster source, a photoemission spectrometer, a scanning tunneling microscope (STM), and a high-pressure reaction cell. The high-pressure reaction cell measurements provided information on catalytic properties in conditions close to practical use. The authors investigated size-selected platinum clusters deposited on a TiO 2 (110) surface using a reaction cell and STM. Catalytic activity measurements showed that the catalytic activities have a cluster-size dependency.

  13. Selection of spatial reference frames depends on task's demands

    Directory of Open Access Journals (Sweden)

    Greeshma Sharma

    2016-12-01

    Full Text Available Spatial reference frames (SRF are the means of representing spatial relations or locations either in an egocentric coordinate system (centred on navigator or in an allocentric coordinate system (Centred on object. It is necessary to understand when and how spatial representation switches between allocentric and egocentric reference frames in context to spatial tasks. The objective of this study was to explore if the elementary spatial representation does exist, whether it would remain consistent or change under the influence of a task's demand. Also, we explored how the SRF would assist if the environment is enriched with landmarks, having multiple routes for wayfinding. The results showed that the switching of SRF depends not only on the default representation but also on a task's demand. They also demonstrated that participants who were using allocentric representation performed better in the presence of landmarks.

  14. Selecting locations for landing of various formations of helicopters using spatial modelling

    International Nuclear Information System (INIS)

    Kovarik, V; Rybansky, M

    2014-01-01

    During crisis situations such as floods, landslides, humanitarian crisis and even military clashes there are situations when it is necessary to send helicopters to the crisis areas. To facilitate the process of searching for the sites suitable for landing, it is possible to use the tools of spatial modelling. The paper describes a procedure of selecting areas potentially suitable for landing of particular formations of helicopters. It lists natural and man-made terrain features that represent the obstacles that can prevent helicopters from landing. It also states specific requirements of the NATO documents that have to be respected when selecting the areas for landing. These requirements relate to a slope of ground and an obstruction angle on approach and exit paths. Creating the knowledge base and graphical models in ERDAS IMAGINE is then described. In the first step of the procedure the areas generally suitable for landing are selected. Then the different configurations of landing points that form the landing sites are created and corresponding outputs are generated. Finally, several tactical requirements are incorporated

  15. Spatial evolutionary games with weak selection.

    Science.gov (United States)

    Nanda, Mridu; Durrett, Richard

    2017-06-06

    Recently, a rigorous mathematical theory has been developed for spatial games with weak selection, i.e., when the payoff differences between strategies are small. The key to the analysis is that when space and time are suitably rescaled, the spatial model converges to the solution of a partial differential equation (PDE). This approach can be used to analyze all [Formula: see text] games, but there are a number of [Formula: see text] games for which the behavior of the limiting PDE is not known. In this paper, we give rules for determining the behavior of a large class of [Formula: see text] games and check their validity using simulation. In words, the effect of space is equivalent to making changes in the payoff matrix, and once this is done, the behavior of the spatial game can be predicted from the behavior of the replicator equation for the modified game. We say predicted here because in some cases the behavior of the spatial game is different from that of the replicator equation for the modified game. For example, if a rock-paper-scissors game has a replicator equation that spirals out to the boundary, space stabilizes the system and produces an equilibrium.

  16. Core Business Selection Based on Ant Colony Clustering Algorithm

    Directory of Open Access Journals (Sweden)

    Yu Lan

    2014-01-01

    Full Text Available Core business is the most important business to the enterprise in diversified business. In this paper, we first introduce the definition and characteristics of the core business and then descript the ant colony clustering algorithm. In order to test the effectiveness of the proposed method, Tianjin Port Logistics Development Co., Ltd. is selected as the research object. Based on the current situation of the development of the company, the core business of the company can be acquired by ant colony clustering algorithm. Thus, the results indicate that the proposed method is an effective way to determine the core business for company.

  17. PHAT STELLAR CLUSTER SURVEY. I. YEAR 1 CATALOG AND INTEGRATED PHOTOMETRY

    International Nuclear Information System (INIS)

    Johnson, L. Clifton; Dalcanton, Julianne J.; Fouesneau, Morgan; Hodge, Paul W.; Weisz, Daniel R.; Williams, Benjamin F.; Beerman, Lori C.; Seth, Anil C.; Caldwell, Nelson; Gouliermis, Dimitrios A.; Larsen, Søren S.; Olsen, Knut A. G.; San Roman, Izaskun; Sarajedini, Ata; Bianchi, Luciana; Dolphin, Andrew E.; Girardi, Léo; Guhathakurta, Puragra; Kalirai, Jason; Lang, Dustin

    2012-01-01

    The Panchromatic Hubble Andromeda Treasury (PHAT) survey is an ongoing Hubble Space Telescope (HST) multi-cycle program to obtain high spatial resolution imaging of one-third of the M31 disk at ultraviolet through near-infrared wavelengths. In this paper, we present the first installment of the PHAT stellar cluster catalog. When completed, the PHAT cluster catalog will be among the largest and most comprehensive surveys of resolved star clusters in any galaxy. The exquisite spatial resolution achieved with HST has allowed us to identify hundreds of new clusters that were previously inaccessible with existing ground-based surveys. We identify 601 clusters in the Year 1 sample, representing more than a factor of four increase over previous catalogs within the current survey area (390 arcmin 2 ). This work presents results derived from the first ∼25% of the survey data; we estimate that the final sample will include ∼2500 clusters. For the Year 1 objects, we present a catalog with positions, radii, and six-band integrated photometry. Along with a general characterization of the cluster luminosities and colors, we discuss the cluster luminosity function, the cluster size distributions, and highlight a number of individually interesting clusters found in the Year 1 search.

  18. PHAT STELLAR CLUSTER SURVEY. I. YEAR 1 CATALOG AND INTEGRATED PHOTOMETRY

    Energy Technology Data Exchange (ETDEWEB)

    Johnson, L. Clifton; Dalcanton, Julianne J.; Fouesneau, Morgan; Hodge, Paul W.; Weisz, Daniel R.; Williams, Benjamin F.; Beerman, Lori C. [Department of Astronomy, University of Washington, Box 351580, Seattle, WA 98195 (United States); Seth, Anil C. [Department of Physics and Astronomy, University of Utah, Salt Lake City, UT 84112 (United States); Caldwell, Nelson [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Gouliermis, Dimitrios A. [Institut fuer Theoretische Astrophysik, Zentrum fuer Astronomie der Universitaet Heidelberg, Albert-Ueberle-Strasse 2, D-69120 Heidelberg (Germany); Larsen, Soren S. [Department of Astrophysics, IMAPP, Radboud University Nijmegen, P.O. Box 9010, 6500 GL Nijmegen (Netherlands); Olsen, Knut A. G. [National Optical Astronomy Observatory, 950 North Cherry Avenue, Tucson, AZ 85719 (United States); San Roman, Izaskun; Sarajedini, Ata [Department of Astronomy, University of Florida, 211 Bryant Space Science Center, Gainesville, FL 32611-2055 (United States); Bianchi, Luciana [Department of Physics and Astronomy, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218 (United States); Dolphin, Andrew E. [Raytheon Company, 1151 East Hermans Road, Tucson, AZ 85756 (United States); Girardi, Leo [Osservatorio Astronomico di Padova-INAF, Vicolo dell' Osservatorio 5, I-35122 Padova (Italy); Guhathakurta, Puragra [Department of Astronomy and Astrophysics, University of California Observatories/Lick Observatory, University of California, 1156 High Street, Santa Cruz, CA 95064 (United States); Kalirai, Jason [Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218 (United States); Lang, Dustin, E-mail: lcjohnso@astro.washington.edu [Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544 (United States); and others

    2012-06-20

    The Panchromatic Hubble Andromeda Treasury (PHAT) survey is an ongoing Hubble Space Telescope (HST) multi-cycle program to obtain high spatial resolution imaging of one-third of the M31 disk at ultraviolet through near-infrared wavelengths. In this paper, we present the first installment of the PHAT stellar cluster catalog. When completed, the PHAT cluster catalog will be among the largest and most comprehensive surveys of resolved star clusters in any galaxy. The exquisite spatial resolution achieved with HST has allowed us to identify hundreds of new clusters that were previously inaccessible with existing ground-based surveys. We identify 601 clusters in the Year 1 sample, representing more than a factor of four increase over previous catalogs within the current survey area (390 arcmin{sup 2}). This work presents results derived from the first {approx}25% of the survey data; we estimate that the final sample will include {approx}2500 clusters. For the Year 1 objects, we present a catalog with positions, radii, and six-band integrated photometry. Along with a general characterization of the cluster luminosities and colors, we discuss the cluster luminosity function, the cluster size distributions, and highlight a number of individually interesting clusters found in the Year 1 search.

  19. Spatial clustering by disease severity among reported Rocky Mountain spotted fever cases in the United States, 2001-2005.

    Science.gov (United States)

    Adjemian, Jennifer Zipser; Krebs, John; Mandel, Eric; McQuiston, Jennifer

    2009-01-01

    Rocky Mountain spotted fever (RMSF) occurs throughout much of the United States, ranging in clinical severity from moderate to fatal infection. Yet, little is known about possible differences among severity levels across geographic locations. To identify significant spatial clusters of severe and non-severe disease, RMSF cases reported to Centers for Disease Control and Prevention (CDC) were geocoded by county and classified by severity level. The statistical software program SaTScan was used to detect significant spatial clusters. Of 4,533 RMSF cases reported, 1,089 hospitalizations (168 with complications) and 23 deaths occurred. Significant clusters of 6 deaths (P = 0.05, RR = 11.4) and 19 hospitalizations with complications (P = 0.02, RR = 3.45) were detected in southwestern Tennessee. Two geographic areas were identified in north-central North Carolina with unusually low rates of severity (P = 0.001, RR = 0.62 and P = 0.001, RR = 0.45, respectively). Of all hospitalizations, 20% were clustered in central Oklahoma (P = 0.02, RR = 1.43). Significant geographic differences in severity were observed, suggesting that biologic and/or anthropogenic factors may be impacting RMSF epidemiology in the United States.

  20. Substructure in clusters of galaxies

    International Nuclear Information System (INIS)

    Fitchett, M.J.

    1988-01-01

    Optical observations suggesting the existence of substructure in clusters of galaxies are examined. Models of cluster formation and methods used to detect substructure in clusters are reviewed. Consideration is given to classification schemes based on a departure of bright cluster galaxies from a spherically symmetric distribution, evidence for statistically significant substructure, and various types of substructure, including velocity, spatial, and spatial-velocity substructure. The substructure observed in the galaxy distribution in clusters is discussed, focusing on observations from general cluster samples, the Virgo cluster, the Hydra cluster, Centaurus, the Coma cluster, and the Cancer cluster. 88 refs

  1. Spatial Representativeness of Surface-Measured Variations of Downward Solar Radiation

    Science.gov (United States)

    Schwarz, M.; Folini, D.; Hakuba, M. Z.; Wild, M.

    2017-12-01

    When using time series of ground-based surface solar radiation (SSR) measurements in combination with gridded data, the spatial and temporal representativeness of the point observations must be considered. We use SSR data from surface observations and high-resolution (0.05°) satellite-derived data to infer the spatiotemporal representativeness of observations for monthly and longer time scales in Europe. The correlation analysis shows that the squared correlation coefficients (R2) between SSR times series decrease linearly with increasing distance between the surface observations. For deseasonalized monthly mean time series, R2 ranges from 0.85 for distances up to 25 km between the stations to 0.25 at distances of 500 km. A decorrelation length (i.e., the e-folding distance of R2) on the order of 400 km (with spread of 100-600 km) was found. R2 from correlations between point observations and colocated grid box area means determined from satellite data were found to be 0.80 for a 1° grid. To quantify the error which arises when using a point observation as a surrogate for the area mean SSR of larger surroundings, we calculated a spatial sampling error (SSE) for a 1° grid of 8 (3) W/m2 for monthly (annual) time series. The SSE based on a 1° grid, therefore, is of the same magnitude as the measurement uncertainty. The analysis generally reveals that monthly mean (or longer temporally aggregated) point observations of SSR capture the larger-scale variability well. This finding shows that comparing time series of SSR measurements with gridded data is feasible for those time scales.

  2. Attention to Hierarchical Level Influences Attentional Selection of Spatial Scale

    Science.gov (United States)

    Flevaris, Anastasia V.; Bentin, Shlomo; Robertson, Lynn C.

    2011-01-01

    Ample evidence suggests that global perception may involve low spatial frequency (LSF) processing and that local perception may involve high spatial frequency (HSF) processing (Shulman, Sullivan, Gish, & Sakoda, 1986; Shulman & Wilson, 1987; Robertson, 1996). It is debated whether SF selection is a low-level mechanism associating global…

  3. Arrays of Size-Selected Metal Nanoparticles Formed by Cluster Ion Beam Technique

    DEFF Research Database (Denmark)

    Ceynowa, F. A.; Chirumamilla, Manohar; Zenin, Volodymyr

    2018-01-01

    Deposition of size-selected copper and silver nanoparticles (NPs) on polymers using cluster beam technique is studied. It is shown that ratio of particle embedment in the film can be controlled by simple thermal annealing. Combining electron beam lithography, cluster beam deposition, and heat...... with required configurations which can be applied for wave-guiding, resonators, in sensor technologies, and surface enhanced Raman scattering....

  4. Variable selection based on clustering analysis for improvement of polyphenols prediction in green tea using synchronous fluorescence spectra

    Science.gov (United States)

    Shan, Jiajia; Wang, Xue; Zhou, Hao; Han, Shuqing; Riza, Dimas Firmanda Al; Kondo, Naoshi

    2018-04-01

    Synchronous fluorescence spectra, combined with multivariate analysis were used to predict flavonoids content in green tea rapidly and nondestructively. This paper presented a new and efficient spectral intervals selection method called clustering based partial least square (CL-PLS), which selected informative wavelengths by combining clustering concept and partial least square (PLS) methods to improve models’ performance by synchronous fluorescence spectra. The fluorescence spectra of tea samples were obtained and k-means and kohonen-self organizing map clustering algorithms were carried out to cluster full spectra into several clusters, and sub-PLS regression model was developed on each cluster. Finally, CL-PLS models consisting of gradually selected clusters were built. Correlation coefficient (R) was used to evaluate the effect on prediction performance of PLS models. In addition, variable influence on projection partial least square (VIP-PLS), selectivity ratio partial least square (SR-PLS), interval partial least square (iPLS) models and full spectra PLS model were investigated and the results were compared. The results showed that CL-PLS presented the best result for flavonoids prediction using synchronous fluorescence spectra.

  5. a Three-Step Spatial-Temporal Clustering Method for Human Activity Pattern Analysis

    Science.gov (United States)

    Huang, W.; Li, S.; Xu, S.

    2016-06-01

    How people move in cities and what they do in various locations at different times form human activity patterns. Human activity pattern plays a key role in in urban planning, traffic forecasting, public health and safety, emergency response, friend recommendation, and so on. Therefore, scholars from different fields, such as social science, geography, transportation, physics and computer science, have made great efforts in modelling and analysing human activity patterns or human mobility patterns. One of the essential tasks in such studies is to find the locations or places where individuals stay to perform some kind of activities before further activity pattern analysis. In the era of Big Data, the emerging of social media along with wearable devices enables human activity data to be collected more easily and efficiently. Furthermore, the dimension of the accessible human activity data has been extended from two to three (space or space-time) to four dimensions (space, time and semantics). More specifically, not only a location and time that people stay and spend are collected, but also what people "say" for in a location at a time can be obtained. The characteristics of these datasets shed new light on the analysis of human mobility, where some of new methodologies should be accordingly developed to handle them. Traditional methods such as neural networks, statistics and clustering have been applied to study human activity patterns using geosocial media data. Among them, clustering methods have been widely used to analyse spatiotemporal patterns. However, to our best knowledge, few of clustering algorithms are specifically developed for handling the datasets that contain spatial, temporal and semantic aspects all together. In this work, we propose a three-step human activity clustering method based on space, time and semantics to fill this gap. One-year Twitter data, posted in Toronto, Canada, is used to test the clustering-based method. The results show that the

  6. Multivariate analysis of nutritional information of foodstuff of plant origin for the selection of representative matrices for the analysis of pesticide residues

    International Nuclear Information System (INIS)

    Neves Bettencourt da Silva, Ricardo Jorge; Gomes Ferreira Crujo Camoes, Maria Filomena

    2010-01-01

    Testing safety of foodstuffs of plant origin involves the analysis of hundreds of pesticide residues. This control is only cost-effective through the use of methods validated for the analysis of many thousands of analyte/matrix combinations. Several documents propose representative matrices of groups of matrices from which the validity of the analytical method can be extrapolated to the represented matrices after summarised experimental check of within group method performance homogeneity. Those groups are based on an evolved expert consensus based on the empirical knowledge on the current analytical procedures; they are not exhaustive, they are not objectively defined and they propose a large list of representative matrices which makes their application difficult. This work proposes grouping 240 matrices, based on the nutritional composition pattern equivalence of the analytical portion right after hydration and before solvent extraction, aiming at defining groups that observe method performance homogeneity. This grouping was based on the combined outcome of three multivariate tools, namely: Principal Component Analysis, Hierarchical Cluster Analysis and K-Mean Cluster Analysis. These tools allowed the selection of eight groups for which representative matrices with average characteristics and objective criteria to test inclusion of new matrices were established. The proposed matrices groups are homogeneous to nutritional data not considered in their definition but correlated with the studied multivariate nutritional pattern. The developed grouping that must be checked with experimental test before use was tested against small deviations in food composition and for the integration of new matrices.

  7. Microseismic Monitoring of Stimulating Shale Gas Reservoir in SW China: 2. Spatial Clustering Controlled by the Preexisting Faults and Fractures

    Science.gov (United States)

    Chen, Haichao; Meng, Xiaobo; Niu, Fenglin; Tang, Youcai; Yin, Chen; Wu, Furong

    2018-02-01

    Microseismic monitoring is crucial to improving stimulation efficiency of hydraulic fracturing treatment, as well as to mitigating potential induced seismic hazard. We applied an improved matching and locating technique to the downhole microseismic data set during one treatment stage along a horizontal well within the Weiyuan shale gas play inside Sichuan Basin in SW China, resulting in 3,052 well-located microseismic events. We employed this expanded catalog to investigate the spatiotemporal evolution of the microseismicity in order to constrain migration of the injected fluids and the associated dynamic processes. The microseismicity is generally characterized by two distinctly different clusters, both of which are highly correlated with the injection activity spatially and temporarily. The distant and well-confined cluster (cluster A) is featured by relatively large-magnitude events, with 40 events of M -1 or greater, whereas the cluster in the immediate vicinity of the wellbore (cluster B) includes two apparent lineations of seismicity with a NE-SW trending, consistent with the predominant orientation of natural fractures. We calculated the b-value and D-value, an index of fracture complexity, and found significant differences between the two seismicity clusters. Particularly, the distant cluster showed an extremely low b-value ( 0.47) and D-value ( 1.35). We speculate that the distant cluster is triggered by reactivation of a preexisting critically stressed fault, whereas the two lineations are induced by shear failures of optimally oriented natural fractures associated with fluid diffusion. In both cases, the spatially clustered microseismicity related to hydraulic stimulation is strongly controlled by the preexisting faults and fractures.

  8. AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns

    Directory of Open Access Journals (Sweden)

    Vishwambhar Pathak

    2012-11-01

    Full Text Available Recent efforts in modeling of dynamics of the natural immune cells leading to artificial immune systems (AIS have ignited contemporary research interest in finding out its analogies to real world problems. The AIS models have been vastly exploited to develop dependable robust
    solutions to clustering. Most of the traditional clustering methods bear limitations in their capability to detect clusters of arbitrary shapes in a fully unsupervised manner. In this paper the recognition and communication dynamics of T Cell Receptors, the recognizing elements in innate immune
    system, has been modeled with a kernel density estimation method. The model has been shown to successfully discover non spherical clusters in spatial patterns. Modeling the cohesion of the antibodies and pathogens with ‘local influence’ measure inducts comprehensive extension of the
    antibody representation ball (ARB, which in turn corresponds to controlled expansion of clusters and prevents overfitting.

  9. The Pattern of Spatially Concentrated Industries in East Germany - A Contribution to the Discussion on Economic "Clusters"

    OpenAIRE

    Rosenfeld, Martin T.W.; Franz, Peter; Heimpold, Gerhard

    2005-01-01

    Throughout the literature in regional economics, most authors agree that spatially concentrated industrial activities are important for regional economic growth. Agglomeration economies, which may occur in the context of spatial concentration and "clusters", may lead to lower costs of production and may reduce transaction costs of all kind, e. g. information costs, including the costs for R&D activities. There is much less agreement on (and: knowledge about) the empirical identification of ex...

  10. THE EXTENDED VIRGO CLUSTER CATALOG

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Suk; Rey, Soo-Chang; Lee, Youngdae; Chung, Jiwon; Pak, Mina; Yi, Wonhyeong; Lee, Woong [Department of Astronomy and Space Science, Chungnam National University, 99 Daehak-ro, Daejeon 305-764 (Korea, Republic of); Jerjen, Helmut [Research School of Astronomy and Astrophysics, The Australian National University, Cotter Road, Weston, ACT 2611 (Australia); Lisker, Thorsten [Astronomisches Rechen-Institut, Zentrum für Astronomie der Universität Heidelberg (ZAH), Mönchhofstraße 12-14, D-69120 Heidelberg (Germany); Sung, Eon-Chang [Korea Astronomy and Space Science institute, 776 Daedeokdae-ro, Daejeon 305-348 (Korea, Republic of)

    2015-01-01

    We present a new catalog of galaxies in the wider region of the Virgo cluster, based on the Sloan Digital Sky Survey (SDSS) Data Release 7. The Extended Virgo Cluster Catalog (EVCC) covers an area of 725 deg{sup 2} or 60.1 Mpc{sup 2}. It is 5.2 times larger than the footprint of the classical Virgo Cluster Catalog (VCC) and reaches out to 3.5 times the virial radius of the Virgo cluster. We selected 1324 spectroscopically targeted galaxies with radial velocities less than 3000 km s{sup –1}. In addition, 265 galaxies that have been overlooked in the SDSS spectroscopic survey but have available redshifts in the NASA Extragalactic Database are also included. Our selection process secured a total of 1589 galaxies, 676 of which are not included in the VCC. The certain and possible cluster members are defined by means of redshift comparison with a cluster infall model. We employed two independent and complementary galaxy classification schemes: the traditional morphological classification based on the visual inspection of optical images and a characterization of galaxies from their spectroscopic features. SDSS u, g, r, i, and z passband photometry of all EVCC galaxies was performed using Source Extractor. We compare the EVCC galaxies with the VCC in terms of morphology, spatial distribution, and luminosity function. The EVCC defines a comprehensive galaxy sample covering a wider range in galaxy density that is significantly different from the inner region of the Virgo cluster. It will be the foundation for forthcoming galaxy evolution studies in the extended Virgo cluster region, complementing ongoing and planned Virgo cluster surveys at various wavelengths.

  11. Clustering of HIV-1 Subtypes Based on gp120 V3 Loop electrostatic properties

    International Nuclear Information System (INIS)

    López de Victoria, Aliana; Kieslich, Chris A; Rizos, Apostolos K; Krambovitis, Elias; Morikis, Dimitrios

    2012-01-01

    The V3 loop of the glycoprotein gp120 of HIV-1 plays an important role in viral entry into cells by utilizing as coreceptor CCR5 or CXCR4, and is implicated in the phenotypic tropisms of HIV viruses. It has been hypothesized that the interaction between the V3 loop and CCR5 or CXCR4 is mediated by electrostatics. We have performed hierarchical clustering analysis of the spatial distributions of electrostatic potentials and charges of V3 loop structures containing consensus sequences of HIV-1 subtypes. Although the majority of consensus sequences have a net charge of +3, the spatial distribution of their electrostatic potentials and charges may be a discriminating factor for binding and infectivity. This is demonstrated by the formation of several small subclusters, within major clusters, which indicates common origin but distinct spatial details of electrostatic properties. Some of this information may be present, in a coarse manner, in clustering of sequences, but the spatial details are largely lost. We show the effect of ionic strength on clustering of electrostatic potentials, information that is not present in clustering of charges or sequences. We also make correlations between clustering of electrostatic potentials and net charge, coreceptor selectivity, global prevalence, and geographic distribution. Finally, we interpret coreceptor selectivity based on the N 6 X 7 T 8 |S 8 X 9 sequence glycosylation motif, the specific positive charge location according to the 11/24/25 rule, and the overall charge and electrostatic potential distribution. We propose that in addition to the sequence and the net charge of the V3 loop of each subtype, the spatial distributions of electrostatic potentials and charges may also be important factors for receptor recognition and binding and subsequent viral entry into cells. This implies that the overall electrostatic potential is responsible for long-range recognition of the V3 loop with coreceptors CCR5/CXCR4, whereas the charge

  12. Clustering of HIV-1 Subtypes Based on gp120 V3 Loop electrostatic properties

    Directory of Open Access Journals (Sweden)

    López de Victoria Aliana

    2012-02-01

    Full Text Available Abstract Background The V3 loop of the glycoprotein gp120 of HIV-1 plays an important role in viral entry into cells by utilizing as coreceptor CCR5 or CXCR4, and is implicated in the phenotypic tropisms of HIV viruses. It has been hypothesized that the interaction between the V3 loop and CCR5 or CXCR4 is mediated by electrostatics. We have performed hierarchical clustering analysis of the spatial distributions of electrostatic potentials and charges of V3 loop structures containing consensus sequences of HIV-1 subtypes. Results Although the majority of consensus sequences have a net charge of +3, the spatial distribution of their electrostatic potentials and charges may be a discriminating factor for binding and infectivity. This is demonstrated by the formation of several small subclusters, within major clusters, which indicates common origin but distinct spatial details of electrostatic properties. Some of this information may be present, in a coarse manner, in clustering of sequences, but the spatial details are largely lost. We show the effect of ionic strength on clustering of electrostatic potentials, information that is not present in clustering of charges or sequences. We also make correlations between clustering of electrostatic potentials and net charge, coreceptor selectivity, global prevalence, and geographic distribution. Finally, we interpret coreceptor selectivity based on the N6X7T8|S8X9 sequence glycosylation motif, the specific positive charge location according to the 11/24/25 rule, and the overall charge and electrostatic potential distribution. Conclusions We propose that in addition to the sequence and the net charge of the V3 loop of each subtype, the spatial distributions of electrostatic potentials and charges may also be important factors for receptor recognition and binding and subsequent viral entry into cells. This implies that the overall electrostatic potential is responsible for long-range recognition of the V3

  13. Location Aggregation of Spatial Population CTMC Models

    Directory of Open Access Journals (Sweden)

    Luca Bortolussi

    2016-10-01

    Full Text Available In this paper we focus on spatial Markov population models, describing the stochastic evolution of populations of agents, explicitly modelling their spatial distribution, representing space as a discrete, finite graph. More specifically, we present a heuristic approach to aggregating spatial locations, which is designed to preserve the dynamical behaviour of the model whilst reducing the computational cost of analysis. Our approach combines stochastic approximation ideas (moment closure, linear noise, with computational statistics (spectral clustering to obtain an efficient aggregation, which is experimentally shown to be reasonably accurate on two case studies: an instance of epidemic spreading and a London bike sharing scenario.

  14. FRCA: A Fuzzy Relevance-Based Cluster Head Selection Algorithm for Wireless Mobile Ad-Hoc Sensor Networks

    Directory of Open Access Journals (Sweden)

    Taegwon Jeong

    2011-05-01

    Full Text Available Clustering is an important mechanism that efficiently provides information for mobile nodes and improves the processing capacity of routing, bandwidth allocation, and resource management and sharing. Clustering algorithms can be based on such criteria as the battery power of nodes, mobility, network size, distance, speed and direction. Above all, in order to achieve good clustering performance, overhead should be minimized, allowing mobile nodes to join and leave without perturbing the membership of the cluster while preserving current cluster structure as much as possible. This paper proposes a Fuzzy Relevance-based Cluster head selection Algorithm (FRCA to solve problems found in existing wireless mobile ad hoc sensor networks, such as the node distribution found in dynamic properties due to mobility and flat structures and disturbance of the cluster formation. The proposed mechanism uses fuzzy relevance to select the cluster head for clustering in wireless mobile ad hoc sensor networks. In the simulation implemented on the NS-2 simulator, the proposed FRCA is compared with algorithms such as the Cluster-based Routing Protocol (CBRP, the Weighted-based Adaptive Clustering Algorithm (WACA, and the Scenario-based Clustering Algorithm for Mobile ad hoc networks (SCAM. The simulation results showed that the proposed FRCA achieves better performance than that of the other existing mechanisms.

  15. FRCA: a fuzzy relevance-based cluster head selection algorithm for wireless mobile ad-hoc sensor networks.

    Science.gov (United States)

    Lee, Chongdeuk; Jeong, Taegwon

    2011-01-01

    Clustering is an important mechanism that efficiently provides information for mobile nodes and improves the processing capacity of routing, bandwidth allocation, and resource management and sharing. Clustering algorithms can be based on such criteria as the battery power of nodes, mobility, network size, distance, speed and direction. Above all, in order to achieve good clustering performance, overhead should be minimized, allowing mobile nodes to join and leave without perturbing the membership of the cluster while preserving current cluster structure as much as possible. This paper proposes a Fuzzy Relevance-based Cluster head selection Algorithm (FRCA) to solve problems found in existing wireless mobile ad hoc sensor networks, such as the node distribution found in dynamic properties due to mobility and flat structures and disturbance of the cluster formation. The proposed mechanism uses fuzzy relevance to select the cluster head for clustering in wireless mobile ad hoc sensor networks. In the simulation implemented on the NS-2 simulator, the proposed FRCA is compared with algorithms such as the Cluster-based Routing Protocol (CBRP), the Weighted-based Adaptive Clustering Algorithm (WACA), and the Scenario-based Clustering Algorithm for Mobile ad hoc networks (SCAM). The simulation results showed that the proposed FRCA achieves better performance than that of the other existing mechanisms.

  16. Feature Selection and Kernel Learning for Local Learning-Based Clustering.

    Science.gov (United States)

    Zeng, Hong; Cheung, Yiu-ming

    2011-08-01

    The performance of the most clustering algorithms highly relies on the representation of data in the input space or the Hilbert space of kernel methods. This paper is to obtain an appropriate data representation through feature selection or kernel learning within the framework of the Local Learning-Based Clustering (LLC) (Wu and Schölkopf 2006) method, which can outperform the global learning-based ones when dealing with the high-dimensional data lying on manifold. Specifically, we associate a weight to each feature or kernel and incorporate it into the built-in regularization of the LLC algorithm to take into account the relevance of each feature or kernel for the clustering. Accordingly, the weights are estimated iteratively in the clustering process. We show that the resulting weighted regularization with an additional constraint on the weights is equivalent to a known sparse-promoting penalty. Hence, the weights of those irrelevant features or kernels can be shrunk toward zero. Extensive experiments show the efficacy of the proposed methods on the benchmark data sets.

  17. Elucidating the underlying causes of oral cancer through spatial clustering in high-risk areas of Taiwan with a distinct gender ratio of incidence

    Directory of Open Access Journals (Sweden)

    Chi-Ting Chiang

    2010-05-01

    Full Text Available This study aimed to elucidate whether or not high-risk clusters of oral cancer (OC incidence spatially correlate with the prevalence rates of betel quid chewing (BQC and cigarette smoking (CS in Taiwan. The spatial autocorrelation and potential clusters of OC incidence among the 307 townships and heavy metal content of soil throughout Taiwan were identified using the Anselin’s local Moran test. Additionally, the spatial correlations among the incidence of OC, the prevalence of BQC and CS and heavy metal content of soil were determined based on a comparison of spatial clusters. High-risk OC (Moran’s I = 0.638, P <0.001 clusters were located in central and eastern Taiwan, while “hot spots” of BQC and CS prevalence were located mainly in eastern Taiwan. The distributions of BQC and CS lifestyle factors (P <0.001 were spatially autocorrelated. The “hot spots” of OC largely coincided with the “hot spots” of BQC, except for the Changhua and Yunlin counties, which are located in central Taiwan. However, high soil contents of nickel and chromium (P <0.001 in central Taiwan also coincided with the high-risk areas of OC incidence. In particular, Changhua county has incurred several decades of serious heavy-metal pollution, with inhabitants living in polluted areas having high-risk exposure to these metals. Results of this study suggest that, in addition to BQC and CS, anthropogenic pollution may profoundly impact the complexity of OC aetiology in central Taiwan.

  18. Clustering Effect on the Dynamics in a Spatial Rock-Paper-Scissors System

    Science.gov (United States)

    Hashimoto, Tsuyoshi; Sato, Kazunori; Ichinose, Genki; Miyazaki, Rinko; Tainaka, Kei-ichi

    2018-01-01

    The lattice dynamics for rock-paper-scissors games is related to population theories in ecology. In most cases, simulations are performed by local and global interactions. It is known in the former case that the dynamics is usually stable. We find two types of non-random distributions in the stationary state. One is a cluster formation of endangered species: when the density of a species approaches zero, its clumping degree diverges to infinity. The other is the strong aggregations of high-density species. Such spatial pattern formations play important roles in population dynamics.

  19. Clustering and training set selection methods for improving the accuracy of quantitative laser induced breakdown spectroscopy

    International Nuclear Information System (INIS)

    Anderson, Ryan B.; Bell, James F.; Wiens, Roger C.; Morris, Richard V.; Clegg, Samuel M.

    2012-01-01

    We investigated five clustering and training set selection methods to improve the accuracy of quantitative chemical analysis of geologic samples by laser induced breakdown spectroscopy (LIBS) using partial least squares (PLS) regression. The LIBS spectra were previously acquired for 195 rock slabs and 31 pressed powder geostandards under 7 Torr CO 2 at a stand-off distance of 7 m at 17 mJ per pulse to simulate the operational conditions of the ChemCam LIBS instrument on the Mars Science Laboratory Curiosity rover. The clustering and training set selection methods, which do not require prior knowledge of the chemical composition of the test-set samples, are based on grouping similar spectra and selecting appropriate training spectra for the partial least squares (PLS2) model. These methods were: (1) hierarchical clustering of the full set of training spectra and selection of a subset for use in training; (2) k-means clustering of all spectra and generation of PLS2 models based on the training samples within each cluster; (3) iterative use of PLS2 to predict sample composition and k-means clustering of the predicted compositions to subdivide the groups of spectra; (4) soft independent modeling of class analogy (SIMCA) classification of spectra, and generation of PLS2 models based on the training samples within each class; (5) use of Bayesian information criteria (BIC) to determine an optimal number of clusters and generation of PLS2 models based on the training samples within each cluster. The iterative method and the k-means method using 5 clusters showed the best performance, improving the absolute quadrature root mean squared error (RMSE) by ∼ 3 wt.%. The statistical significance of these improvements was ∼ 85%. Our results show that although clustering methods can modestly improve results, a large and diverse training set is the most reliable way to improve the accuracy of quantitative LIBS. In particular, additional sulfate standards and specifically

  20. Multivariate spatial Gaussian mixture modeling for statistical clustering of hemodynamic parameters in functional MRI

    International Nuclear Information System (INIS)

    Fouque, A.L.; Ciuciu, Ph.; Risser, L.; Fouque, A.L.; Ciuciu, Ph.; Risser, L.

    2009-01-01

    In this paper, a novel statistical parcellation of intra-subject functional MRI (fMRI) data is proposed. The key idea is to identify functionally homogenous regions of interest from their hemodynamic parameters. To this end, a non-parametric voxel-based estimation of hemodynamic response function is performed as a prerequisite. Then, the extracted hemodynamic features are entered as the input data of a Multivariate Spatial Gaussian Mixture Model (MSGMM) to be fitted. The goal of the spatial aspect is to favor the recovery of connected components in the mixture. Our statistical clustering approach is original in the sense that it extends existing works done on univariate spatially regularized Gaussian mixtures. A specific Gibbs sampler is derived to account for different covariance structures in the feature space. On realistic artificial fMRI datasets, it is shown that our algorithm is helpful for identifying a parsimonious functional parcellation required in the context of joint detection estimation of brain activity. This allows us to overcome the classical assumption of spatial stationarity of the BOLD signal model. (authors)

  1. m-BIRCH: an online clustering approach for computer vision applications

    Science.gov (United States)

    Madan, Siddharth K.; Dana, Kristin J.

    2015-03-01

    We adapt a classic online clustering algorithm called Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), to incrementally cluster large datasets of features commonly used in multimedia and computer vision. We call the adapted version modified-BIRCH (m-BIRCH). The algorithm uses only a fraction of the dataset memory to perform clustering, and updates the clustering decisions when new data comes in. Modifications made in m-BIRCH enable data driven parameter selection and effectively handle varying density regions in the feature space. Data driven parameter selection automatically controls the level of coarseness of the data summarization. Effective handling of varying density regions is necessary to well represent the different density regions in data summarization. We use m-BIRCH to cluster 840K color SIFT descriptors, and 60K outlier corrupted grayscale patches. We use the algorithm to cluster datasets consisting of challenging non-convex clustering patterns. Our implementation of the algorithm provides an useful clustering tool and is made publicly available.

  2. Statistical analysis of the spatial distribution of galaxies and clusters

    International Nuclear Information System (INIS)

    Cappi, Alberto

    1993-01-01

    This thesis deals with the analysis of the distribution of galaxies and clusters, describing some observational problems and statistical results. First chapter gives a theoretical introduction, aiming to describe the framework of the formation of structures, tracing the history of the Universe from the Planck time, t_p = 10"-"4"3 sec and temperature corresponding to 10"1"9 GeV, to the present epoch. The most usual statistical tools and models of the galaxy distribution, with their advantages and limitations, are described in chapter two. A study of the main observed properties of galaxy clustering, together with a detailed statistical analysis of the effects of selecting galaxies according to apparent magnitude or diameter, is reported in chapter three. Chapter four delineates some properties of groups of galaxies, explaining the reasons of discrepant results on group distributions. Chapter five is a study of the distribution of galaxy clusters, with different statistical tools, like correlations, percolation, void probability function and counts in cells; it is found the same scaling-invariant behaviour of galaxies. Chapter six describes our finding that rich galaxy clusters too belong to the fundamental plane of elliptical galaxies, and gives a discussion of its possible implications. Finally chapter seven reviews the possibilities offered by multi-slit and multi-fibre spectrographs, and I present some observational work on nearby and distant galaxy clusters. In particular, I show the opportunities offered by ongoing surveys of galaxies coupled with multi-object fibre spectrographs, focusing on the ESO Key Programme A galaxy redshift survey in the south galactic pole region to which I collaborate and on MEFOS, a multi-fibre instrument with automatic positioning. Published papers related to the work described in this thesis are reported in the last appendix. (author) [fr

  3. A new multidimensional population health indicator for policy makers: absolute level, inequality and spatial clustering - an empirical application using global sub-national infant mortality data

    Directory of Open Access Journals (Sweden)

    Benn K.D. Sartorius

    2014-11-01

    Full Text Available The need for a multidimensional measure of population health that accounts for its distribution remains a central problem to guide the allocation of limited resources. Absolute proxy measures, like the infant mortality rate (IMR, are limi- ted because they ignore inequality and spatial clustering. We propose a novel, three-part, multidimensional mortality indi- cator that can be used as the first step to differentiate interventions in a region or country. The three-part indicator (MortalityABC index combines absolute mortality rate, the Theil Index to calculate mortality inequality and the Getis-Ord G statistic to determine the degree of spatial clustering. The analysis utilises global sub-national IMR data to empirically illu- strate the proposed indicator. The three-part indicator is mapped globally to display regional/country variation and further highlight its potential application. Developing countries (e.g. in sub-Saharan Africa display high levels of absolute mortality as well as variable mortality inequality with evidence of spatial clustering within certain sub-national units (“hotspots”. Although greater inequality is observed outside developed regions, high mortality inequality and spatial clustering are com- mon in both developed and developing countries. Significant positive correlation was observed between the degree of spatial clustering and absolute mortality. The proposed multidimensional indicator should prove useful for spatial allocation of healthcare resources within a country, because it can prompt a wide range of policy options and prioritise high-risk areas. The new indicator demonstrates the inadequacy of IMR as a single measure of population health, and it can also be adapted to lower administrative levels within a country and other population health measures.

  4. Spatial attention enhances the selective integration of activity from area MT.

    Science.gov (United States)

    Masse, Nicolas Y; Herrington, Todd M; Cook, Erik P

    2012-09-01

    Distinguishing which of the many proposed neural mechanisms of spatial attention actually underlies behavioral improvements in visually guided tasks has been difficult. One attractive hypothesis is that attention allows downstream neural circuits to selectively integrate responses from the most informative sensory neurons. This would allow behavioral performance to be based on the highest-quality signals available in visual cortex. We examined this hypothesis by asking how spatial attention affects both the stimulus sensitivity of middle temporal (MT) neurons and their corresponding correlation with behavior. Analyzing a data set pooled from two experiments involving four monkeys, we found that spatial attention did not appreciably affect either the stimulus sensitivity of the neurons or the correlation between their activity and behavior. However, for those sessions in which there was a robust behavioral effect of attention, focusing attention inside the neuron's receptive field significantly increased the correlation between these two metrics, an indication of selective integration. These results suggest that, similar to mechanisms proposed for the neural basis of perceptual learning, the behavioral benefits of focusing spatial attention are attributable to selective integration of neural activity from visual cortical areas by their downstream targets.

  5. POTENTIAL CLUSTERS IN BANAT AND THEIR ROLE IN REGIONAL ECONOMIC DEVELOPMENT

    Directory of Open Access Journals (Sweden)

    Ramona IŞFĂNESCU

    2010-06-01

    Full Text Available The interest in the study of clusters and their role in the economic development of certain regions has constantly grown in the past years. This interest has also been emphasized by the emergence of successful clusters in many regions; these are clusters that have visibly and they have determined the increase in competitiveness of those particular regions. Clusters are geographic gatherings of firms and institutions, connected to each other and specialized in certain fields of activity. In Romania, due to the low cooperation level among enterprises we cannot say that proper clusters exists, but just some “spatial gatherings” of firms activating in certain domains, connected by the need of using certain natural resources and the existence of a specialized workforce in that particular domain. Natural “clusters” can be identified by means of quantitative analyses, these indicating the possibility to identify certain spatial assemblies of firms in a certain economic sector. Starting from these quantitative analyses, for Banat region have been identified some important spatial gatherings of firms activating in certain domains which could represent potential clusters in this area. As clusters function on the principle of cooperation among enterprises, a strong point of the region is the presence of foreign investors which promoted the model of enterprise cooperation through sub-contracting local enterprises. Among these, we mention the Italian investors which brought to Banat, especially to Timiş County the Italian cluster model. Are there in Banat premises for the emergence of clusters? Which are the fields of activity in which these clusters can emerge? What role will these clusters play in the economic development of the region? These are just some of the questions that we aim answering to through this study.

  6. The VISTA Carina Nebula Survey. II. Spatial distribution of the infrared-excess-selected young stellar population

    Science.gov (United States)

    Zeidler, P.; Preibisch, T.; Ratzka, T.; Roccatagliata, V.; Petr-Gotzens, M. G.

    2016-01-01

    We performed a deep wide-field (6.76 sq. deg) near-infrared survey with the VISTA telescope that covers the entire extent of the Carina nebula complex (CNC). The point-source catalog created from these data contains around four million individual objects down to masses of 0.1 M⊙. We present a statistical study of the large-scale spatial distribution and an investigation of the clustering properties of infrared-excesses objects, which are used to trace disk-bearing young stellar objects (YSOs). A selection based on a near-infrared (J-H) versus (H-Ks) color-color diagram shows an almost uniform distribution over the entire observed area. We interpret this as a result of the very high degree of background contamination that arises from the Carina Nebula's location close to the Galactic plane. Complementing the VISTA near-infrared catalog with Spitzer IRAC mid-infrared photometry improves the situation of the background contamination considerably. We find that a (J-H) versus (Ks- [4.5]) color-color diagram is well suited to tracing the population of YSO-candidates (cYSOs) by their infrared excess. We identify 8781 sources with strong infrared excess, which we consider as cYSOs. This sample is used to investigate the spatial distribution of the cYSOs with a nearest-neighbor analysis. The surface density distribution of cYSOs agrees well with the shape of the clouds as seen in our Herschel far-infrared survey. The strong decline in the surface density of excess sources outside the area of the clouds supports the hypothesis that our excess-selected sample consists predominantly of cYSOs with a low level of background contamination. This analysis allows us to identify 14 groups of cYSOs outside the central area.Our results suggest that the total population of cYSOs in the CNC comprises about 164 000 objects, with a substantial fraction (~35%) located in the northern, still not well studied parts. Our cluster analysis suggests that roughly half of the cYSOs constitute a

  7. Spatial clusters of suicide in the municipality of São Paulo 1996–2005: an ecological study

    Directory of Open Access Journals (Sweden)

    Bando Daniel H

    2012-08-01

    Full Text Available Abstract Background In a classical study, Durkheim mapped suicide rates, wealth, and low family density and realized that they clustered in northern France. Assessing others variables, such as religious society, he constructed a framework for the analysis of the suicide, which still allows international comparisons using the same basic methodology. The present study aims to identify possible significantly clusters of suicide in the city of São Paulo, and then, verify their statistical associations with socio-economic and cultural characteristics. Methods A spatial scan statistical test was performed to analyze the geographical pattern of suicide deaths of residents in the city of São Paulo by Administrative District, from 1996 to 2005. Relative risks and high and/or low clusters were calculated accounting for gender and age as co-variates, were analyzed using spatial scan statistics to identify geographical patterns. Logistic regression was used to estimate associations with socioeconomic variables, considering, the spatial cluster of high suicide rates as the response variable. Drawing from Durkheim’s original work, current World Health Organization (WHO reports and recent reviews, the following independent variables were considered: marital status, income, education, religion, and migration. Results The mean suicide rate was 4.1/100,000 inhabitant-years. Against this baseline, two clusters were identified: the first, of increased risk (RR = 1.66, comprising 18 districts in the central region; the second, of decreased risk (RR = 0.78, including 14 districts in the southern region. The downtown area toward the southwestern region of the city displayed the highest risk for suicide, and though the overall risk may be considered low, the rate climbs up to an intermediate level in this region. One logistic regression analysis contrasted the risk cluster (18 districts against the other remaining 78 districts, testing the effects of

  8. Changing Patterns of Spatial Clustering of Schistosomiasis in Southwest China between 1999–2001 and 2007–2008: Assessing Progress toward Eradication after the World Bank Loan Project

    Science.gov (United States)

    Hu, Yi; Xiong, Chenglong; Zhang, Zhijie; Luo, Can; Cohen, Ted; Gao, Jie; Zhang, Lijuan; Jiang, Qingwu

    2014-01-01

    We compared changes in the spatial clustering of schistosomiasis in Southwest China at the conclusion of and six years following the end of the World Bank Loan Project (WBLP), the control strategy of which was focused on the large-scale use of chemotherapy. Parasitological data were obtained through standardized surveys conducted in 1999–2001 and again in 2007–2008. Two alternate spatial cluster methods were used to identify spatial clusters of cases: Anselin’s Local Moran’s I test and Kulldorff’s spatial scan statistic. Substantial reductions in the burden of schistosomiasis were found after the end of the WBLP, but the spatial extent of schistosomiasis was not reduced across the study area. Spatial clusters continued to occur in three regions: Chengdu Plain, Yangtze River Valley, and Lancang River Valley during the two periods, and regularly involved five counties. These findings suggest that despite impressive reductions in burden, the hilly and mountainous regions of Southwest China remain at risk of schistosome re-emergence. Our results help to highlight specific locations where integrated control programs can focus to speed the elimination of schistosomiasis in China. PMID:24394217

  9. Different cortical mechanisms for spatial vs. feature-based attentional selection in visual working memory

    Directory of Open Access Journals (Sweden)

    Anna Heuer

    2016-08-01

    Full Text Available The limited capacity of visual working memory necessitates attentional mechanisms that selectively update and maintain only the most task-relevant content. Psychophysical experiments have shown that the retroactive selection of memory content can be based on visual properties such as location or shape, but the neural basis for such differential selection is unknown. For example, it is not known if there are different cortical modules specialized for spatial versus feature-based mnemonic attention, in the same way that has been demonstrated for attention to perceptual input. Here, we used transcranial magnetic stimulation (TMS to identify areas in human parietal and occipital cortex involved in the selection of objects from memory based on cues to their location (spatial information or their shape (featural information. We found that TMS over the supramarginal gyrus (SMG selectively facilitated spatial selection, whereas TMS over the lateral occipital cortex selectively enhanced feature-based selection for remembered objects in the contralateral visual field. Thus, different cortical regions are responsible for spatial vs. feature-based selection of working memory representations. Since the same regions are involved in attention to external events, these new findings indicate overlapping mechanisms for attentional control over perceptual input and mnemonic representations.

  10. Spatially Selective Functionalization of Conducting Polymers by "Electroclick" Chemistry

    DEFF Research Database (Denmark)

    Hansen, Thomas Steen; Daugaard, Anders Egede; Hvilsted, Søren

    2009-01-01

    Conducting polymer microelectrodes can electrochemically generate the catalyst required for their own functionalization by "click chemistry" with high spatial resolution. Interdigitated microelectrodes prepared from an azide-containing conducting polymer are selectively functionalized in sequence...

  11. A high-significance measurement of correlation between unresolved IRAS sources and optically-selected galaxy clusters

    Energy Technology Data Exchange (ETDEWEB)

    Hincks, Adam D.; Hajian, Amir [Canadian Institute for Theoretical Astrophysics, University of Toronto, Toronto, ON M5S 3H8 (Canada); Addison, Graeme E., E-mail: hincks@cita.utoronto.ca, E-mail: ahajian@cita.utoronto.ca, E-mail: gaddison@phas.ubc.ca [Department of Physics and Astronomy, University of British Columbia, Vancouver, BC V6T 1Z4 (Canada)

    2013-05-01

    We cross-correlate the 100 μm Improved Reprocessing of the IRAS Survey (IRIS) map and galaxy clusters at 0.1 < z < 0.3 in the maxBCG catalogue taken from the Sloan Digital Sky Survey, measuring an angular cross-power spectrum over multipole moments 150 < l < 3000 at a total significance of over 40σ. The cross-spectrum, which arises from the spatial correlation between unresolved dusty galaxies that make up the cosmic infrared background (CIB) in the IRIS map and the galaxy clusters, is well-fit by a single power law with an index of −1.28±0.12, similar to the clustering of unresolved galaxies from cross-correlating far-infrared and submillimetre maps at longer wavelengths. Using a recent, phenomenological model for the spectral and clustering properties of the IRIS galaxies, we constrain the large-scale bias of the maxBCG clusters to be 2.6±1.4, consistent with existing analyses of the real-space cluster correlation function. The success of our method suggests that future CIB-optical cross-correlations using Planck and Herschel data will significantly improve our understanding of the clustering and redshift distribution of the faint CIB sources.

  12. The occipital place area represents the local elements of scenes.

    Science.gov (United States)

    Kamps, Frederik S; Julian, Joshua B; Kubilius, Jonas; Kanwisher, Nancy; Dilks, Daniel D

    2016-05-15

    Neuroimaging studies have identified three scene-selective regions in human cortex: parahippocampal place area (PPA), retrosplenial complex (RSC), and occipital place area (OPA). However, precisely what scene information each region represents is not clear, especially for the least studied, more posterior OPA. Here we hypothesized that OPA represents local elements of scenes within two independent, yet complementary scene descriptors: spatial boundary (i.e., the layout of external surfaces) and scene content (e.g., internal objects). If OPA processes the local elements of spatial boundary information, then it should respond to these local elements (e.g., walls) themselves, regardless of their spatial arrangement. Indeed, we found that OPA, but not PPA or RSC, responded similarly to images of intact rooms and these same rooms in which the surfaces were fractured and rearranged, disrupting the spatial boundary. Next, if OPA represents the local elements of scene content information, then it should respond more when more such local elements (e.g., furniture) are present. Indeed, we found that OPA, but not PPA or RSC, responded more to multiple than single pieces of furniture. Taken together, these findings reveal that OPA analyzes local scene elements - both in spatial boundary and scene content representation - while PPA and RSC represent global scene properties. Copyright © 2016 Elsevier Inc. All rights reserved.

  13. Clustering and training set selection methods for improving the accuracy of quantitative laser induced breakdown spectroscopy

    Energy Technology Data Exchange (ETDEWEB)

    Anderson, Ryan B., E-mail: randerson@astro.cornell.edu [Cornell University Department of Astronomy, 406 Space Sciences Building, Ithaca, NY 14853 (United States); Bell, James F., E-mail: Jim.Bell@asu.edu [Arizona State University School of Earth and Space Exploration, Bldg.: INTDS-A, Room: 115B, Box 871404, Tempe, AZ 85287 (United States); Wiens, Roger C., E-mail: rwiens@lanl.gov [Los Alamos National Laboratory, P.O. Box 1663 MS J565, Los Alamos, NM 87545 (United States); Morris, Richard V., E-mail: richard.v.morris@nasa.gov [NASA Johnson Space Center, 2101 NASA Parkway, Houston, TX 77058 (United States); Clegg, Samuel M., E-mail: sclegg@lanl.gov [Los Alamos National Laboratory, P.O. Box 1663 MS J565, Los Alamos, NM 87545 (United States)

    2012-04-15

    We investigated five clustering and training set selection methods to improve the accuracy of quantitative chemical analysis of geologic samples by laser induced breakdown spectroscopy (LIBS) using partial least squares (PLS) regression. The LIBS spectra were previously acquired for 195 rock slabs and 31 pressed powder geostandards under 7 Torr CO{sub 2} at a stand-off distance of 7 m at 17 mJ per pulse to simulate the operational conditions of the ChemCam LIBS instrument on the Mars Science Laboratory Curiosity rover. The clustering and training set selection methods, which do not require prior knowledge of the chemical composition of the test-set samples, are based on grouping similar spectra and selecting appropriate training spectra for the partial least squares (PLS2) model. These methods were: (1) hierarchical clustering of the full set of training spectra and selection of a subset for use in training; (2) k-means clustering of all spectra and generation of PLS2 models based on the training samples within each cluster; (3) iterative use of PLS2 to predict sample composition and k-means clustering of the predicted compositions to subdivide the groups of spectra; (4) soft independent modeling of class analogy (SIMCA) classification of spectra, and generation of PLS2 models based on the training samples within each class; (5) use of Bayesian information criteria (BIC) to determine an optimal number of clusters and generation of PLS2 models based on the training samples within each cluster. The iterative method and the k-means method using 5 clusters showed the best performance, improving the absolute quadrature root mean squared error (RMSE) by {approx} 3 wt.%. The statistical significance of these improvements was {approx} 85%. Our results show that although clustering methods can modestly improve results, a large and diverse training set is the most reliable way to improve the accuracy of quantitative LIBS. In particular, additional sulfate standards and

  14. Modeling Temporal-Spatial Earthquake and Volcano Clustering at Yucca Mountain, Nevada

    International Nuclear Information System (INIS)

    T. Parsons; G.A. Thompson; A.H. Cogbill

    2006-01-01

    The proposed national high-level nuclear repository at Yucca Mountain is close to Quaternary faults and cinder cones. The frequency of these events is low, with indications of spatial and temporal clustering, making probabilistic assessments difficult. In an effort to identify the most likely intrusion sites, we based a 3D finite element model on the expectation that faulting and basalt intrusions are primarily sensitive to the magnitude and orientation of the least principal stress in extensional terranes. We found that in the absence of fault slip, variation in overburden pressure caused a stress state that preferentially favored intrusions at Crater Flat. However, when we allowed central Yucca Mountain faults to slip in the model, we found that magmatic clustering was not favored at Crater Flat or in the central Yucca Mountain block. Instead, we calculated that the stress field was most encouraging to intrusions near fault terminations, consistent with the location of the most recent volcanism at Yucca Mountain, the Lathrop Wells cone. We found this linked fault and magmatic system to be mutually reinforcing in the model in that dike inflation favored renewed fault slip

  15. A THREE-STEP SPATIAL-TEMPORAL-SEMANTIC CLUSTERING METHOD FOR HUMAN ACTIVITY PATTERN ANALYSIS

    Directory of Open Access Journals (Sweden)

    W. Huang

    2016-06-01

    Full Text Available How people move in cities and what they do in various locations at different times form human activity patterns. Human activity pattern plays a key role in in urban planning, traffic forecasting, public health and safety, emergency response, friend recommendation, and so on. Therefore, scholars from different fields, such as social science, geography, transportation, physics and computer science, have made great efforts in modelling and analysing human activity patterns or human mobility patterns. One of the essential tasks in such studies is to find the locations or places where individuals stay to perform some kind of activities before further activity pattern analysis. In the era of Big Data, the emerging of social media along with wearable devices enables human activity data to be collected more easily and efficiently. Furthermore, the dimension of the accessible human activity data has been extended from two to three (space or space-time to four dimensions (space, time and semantics. More specifically, not only a location and time that people stay and spend are collected, but also what people “say” for in a location at a time can be obtained. The characteristics of these datasets shed new light on the analysis of human mobility, where some of new methodologies should be accordingly developed to handle them. Traditional methods such as neural networks, statistics and clustering have been applied to study human activity patterns using geosocial media data. Among them, clustering methods have been widely used to analyse spatiotemporal patterns. However, to our best knowledge, few of clustering algorithms are specifically developed for handling the datasets that contain spatial, temporal and semantic aspects all together. In this work, we propose a three-step human activity clustering method based on space, time and semantics to fill this gap. One-year Twitter data, posted in Toronto, Canada, is used to test the clustering-based method. The

  16. Risk Factors and Spatial Clusters of Cryptosporidium Infection among School-Age Children in a Rural Region of Eastern China.

    Science.gov (United States)

    Zheng, Hao; He, Jianfeng; Wang, Li; Zhang, Rong; Ding, Zhen; Hu, Wenbiao

    2018-05-06

    The epidemiological features of Cryptosporidium infection among school-age children in China still remain unclear. Hereby, a cross-sectional study of 1637 children aged 3⁻9 years was designed to investigate the risk factors and spatial clusters of Cryptosporidium infection in a rural region of Eastern China. Stool specimens collected from participants were examined using the auramine-phenol and modified acid-fast staining. Univariable and multivariable analyses were performed to identify the risk factors of Cryptospordium infection. The spatial clusters were analyzed by a discrete Poisson model using SaTScan software. Our results showed that the overall prevalence of Cryptosporidium infection was 11‰ in the research region. At the age of 3⁻6 years (odds ratios (OR) = 3.072, 95% confidence intervals (CI) : 1.001⁻9.427), not washing hands before eating and after defecation (OR = 3.003, 95% CI: 1.060⁻8.511) were recognized as risk factors. Furthermore, a high-risk spatial cluster (relative risk = 4.220, p = 0.025) was identified. These findings call for effective sustainable interventions including family and school-based hygienic education to reduce the prevalence of Cryptosporidium infection. Therefore, an early warning system based spatiotemporal models with risk factors is required to further improve the effectiveness and efficiency of cryptosporidiosis control in the future.

  17. The influence of caffeine on spatial-selective attention: an event-related potential study.

    Science.gov (United States)

    Ruijter, J; de Ruiter, M B; Snel, J; Lorist, M M

    2000-12-01

    Following the indications of previous studies that caffeine might have a specific effect on the processing of spatial information compared with other types of information, the present study investigated the influence of caffeine on an often used spatial-selective attention task. Event-related potentials (ERPs) were recorded from 11 participants under conditions of caffeine (250 mg) and placebo. Spatial-selective attention effects were reflected in the ERPs as more positive going occipital P1 and broadly distributed P2 components, and more negative going occipital-temporal N1 and broadly distributed N2 components. A treatment effect was found as a more positive going frontal P2 component in the caffeine condition, whereas interactions between treatment and attention were observed for P2 and N2 components, but not for P1 and N1 components. This pattern of results suggests that caffeine has no specific influence on spatial-selective attention, but rather, has a more general facilitating effect on perceptual processing, as well as a possible effect on the frontal control mechanisms, i.e. focusing attention and increasing selectivity.

  18. X-Ray Temperatures, Luminosities, and Masses from XMM-Newton Follow-up of the First Shear-selected Galaxy Cluster Sample

    Energy Technology Data Exchange (ETDEWEB)

    Deshpande, Amruta J.; Hughes, John P. [Department of Physics and Astronomy, Rutgers the State University of New Jersey, 136 Frelinghuysen Road, Piscataway, NJ 08854 (United States); Wittman, David, E-mail: amrejd@physics.rutgers.edu, E-mail: jph@physics.rutgers.edu, E-mail: dwittman@physics.ucdavis.edu [Department of Physics, University of California, Davis, One Shields Avenue, Davis, CA 95616 (United States)

    2017-04-20

    We continue the study of the first sample of shear-selected clusters from the initial 8.6 square degrees of the Deep Lens Survey (DLS); a sample with well-defined selection criteria corresponding to the highest ranked shear peaks in the survey area. We aim to characterize the weak lensing selection by examining the sample’s X-ray properties. There are multiple X-ray clusters associated with nearly all the shear peaks: 14 X-ray clusters corresponding to seven DLS shear peaks. An additional three X-ray clusters cannot be definitively associated with shear peaks, mainly due to large positional offsets between the X-ray centroid and the shear peak. Here we report on the XMM-Newton properties of the 17 X-ray clusters. The X-ray clusters display a wide range of luminosities and temperatures; the L {sub X} − T {sub X} relation we determine for the shear-associated X-ray clusters is consistent with X-ray cluster samples selected without regard to dynamical state, while it is inconsistent with self-similarity. For a subset of the sample, we measure X-ray masses using temperature as a proxy, and compare to weak lensing masses determined by the DLS team. The resulting mass comparison is consistent with equality. The X-ray and weak lensing masses show considerable intrinsic scatter (∼48%), which is consistent with X-ray selected samples when their X-ray and weak lensing masses are independently determined.

  19. [Research on K-means clustering segmentation method for MRI brain image based on selecting multi-peaks in gray histogram].

    Science.gov (United States)

    Chen, Zhaoxue; Yu, Haizhong; Chen, Hao

    2013-12-01

    To solve the problem of traditional K-means clustering in which initial clustering centers are selected randomly, we proposed a new K-means segmentation algorithm based on robustly selecting 'peaks' standing for White Matter, Gray Matter and Cerebrospinal Fluid in multi-peaks gray histogram of MRI brain image. The new algorithm takes gray value of selected histogram 'peaks' as the initial K-means clustering center and can segment the MRI brain image into three parts of tissue more effectively, accurately, steadily and successfully. Massive experiments have proved that the proposed algorithm can overcome many shortcomings caused by traditional K-means clustering method such as low efficiency, veracity, robustness and time consuming. The histogram 'peak' selecting idea of the proposed segmentootion method is of more universal availability.

  20. Spatially Resolved Analysis of Bragg Selectivity

    Directory of Open Access Journals (Sweden)

    Tina Sabel

    2015-11-01

    Full Text Available This paper targets an inherent control of optical shrinkage in photosensitive polymers, contributing by means of spatially resolved analysis of volume holographic phase gratings. Point by point scanning of the local material response to the Gaussian intensity distribution of the recording beams is accomplished. Derived information on the local grating period and grating slant is evaluated by mapping of optical shrinkage in the lateral plane as well as through the depth of the layer. The influence of recording intensity, exposure duration and the material viscosity on the Bragg selectivity is investigated.

  1. Hanseniaspora uvarum from winemaking environments show spatial and temporal genetic clustering

    Directory of Open Access Journals (Sweden)

    Warren eAlbertin

    2016-01-01

    Full Text Available Hanseniaspora uvarum is one of the most abundant yeast species found on grapes and in grape must, at least before the onset of alcoholic fermentation which is usually performed by Saccharomyces species. The aim of this study was to characterise the genetic and phenotypic variability within the H. uvarum species. One hundred and fifteen strains isolated from winemaking environments in different geographical origins were analysed using 11 microsatellite markers and a subset of 47 strains were analysed by AFLP. H. uvarum isolates clustered mainly on the basis of their geographical localisation as revealed by microsatellites. In addition, a strong clustering based on year of isolation was evidenced, indicating that the genetic diversity of Hanseniaspora uvarum isolates was related to both spatial and temporal variations. Conversely, clustering analysis based on AFLP data provided a different picture with groups showing no particular characteristics, but provided higher strain discrimination. This result indicated that AFLP approaches are inadequate to establish the genetic relationship between individuals, but allowed good strain discrimination. At the phenotypic level, several extracellular enzymatic activities of enological relevance (pectinase, chitinase, protease, β-glucosidase were measured but showed low diversity. The impact of environmental factors of enological interest (temperature, anaerobia and copper addition on growth was also assessed and showed poor variation. Altogether, this work provided both new analytical tool (microsatellites and new insights into the genetic and phenotypic diversity of H. uvarum, a yeast species that has previously been identified as a potential candidate for co-inoculation in grape must, but whose intraspecific variability had never been fully assessed.

  2. Effects of spatial and selective attention on basic multisensory integration

    DEFF Research Database (Denmark)

    Gondan, Matthias; Blurton, Steven Paul; Hughes, F.

    2011-01-01

    underlying the RSE. We investigated the role of spatial and selective attention on the RSE in audiovisual redundant signals tasks. In Experiment 1, stimuli were presented either centrally (narrow attentional focus) or at 1 of 3 unpredictable locations (wide focus). The RSE was accurately described...... task) or to central stimuli only (selective attention task). The RSE was consistent with task-specific coactivation models; accumulation of evidence, however, differed between the 2 tasks....... by a coactivation model assuming linear superposition of modality-specific activation. Effects of spatial attention were explained by a shift of the evidence criterion. In Experiment 2, stimuli were presented at 3 locations; participants had to respond either to all signals regardless of location (simple response...

  3. An active learning representative subset selection method using net analyte signal

    Science.gov (United States)

    He, Zhonghai; Ma, Zhenhe; Luan, Jingmin; Cai, Xi

    2018-05-01

    To guarantee accurate predictions, representative samples are needed when building a calibration model for spectroscopic measurements. However, in general, it is not known whether a sample is representative prior to measuring its concentration, which is both time-consuming and expensive. In this paper, a method to determine whether a sample should be selected into a calibration set is presented. The selection is based on the difference of Euclidean norm of net analyte signal (NAS) vector between the candidate and existing samples. First, the concentrations and spectra of a group of samples are used to compute the projection matrix, NAS vector, and scalar values. Next, the NAS vectors of candidate samples are computed by multiplying projection matrix with spectra of samples. Scalar value of NAS is obtained by norm computation. The distance between the candidate set and the selected set is computed, and samples with the largest distance are added to selected set sequentially. Last, the concentration of the analyte is measured such that the sample can be used as a calibration sample. Using a validation test, it is shown that the presented method is more efficient than random selection. As a result, the amount of time and money spent on reference measurements is greatly reduced.

  4. Use of CFD modeling for estimating spatial representativeness of urban air pollution monitoring sites and suitability of their locations

    International Nuclear Information System (INIS)

    Santiago, J. L.; Martin, F.

    2015-01-01

    A methodology to estimate the spatial representativeness of air pollution monitoring sites is applied to two urban districts. This methodology is based on high resolution maps of air pollution computed by using Computational Fluid Dynamics (CFD) modelling tools. Traffic-emitted NO 2 dispersion is simulated for several meteorological conditions taking into account the effect of the buildings on air flow and pollutant dispersion and using a steady state CFD-RANS approach. From these results, maps of average pollutant concentrations for January -May 2011 are computed as a combination of the simulated scenarios. Two urban districts of Madrid City were simulated. Spatial representativeness areas for 32 different sites within the same district (including the site of the operative air quality stations) have been estimated by computing the portion of the domains with average NO 2 concentration differing less than a 20% of the concentration at each candidate monitoring site. New parameters such as the ratio AR between the representativeness area and the whole domain area or the representativeness index (IR) has been proposed to discuss and compare the representativeness areas. Significant differences between the spatial representativeness of the candidate sites of both studied districts have been found. The sites of the Escuelas Aguirre district have generally smaller representativeness areas than those of the Plaza de Castilla. More stations are needed to cover the Escuelas Aguirre district than for the Plaza de Castilla one. The operative air quality station of the Escuelas Aguirre district is less representative than the station of the Plaza de Castilla district. The cause of these differences seems to be the differences in urban structure of both districts prompting different ventilation. (Author)

  5. Use of CFD modeling for estimating spatial representativeness of urban air pollution monitoring sites and suitability of their locations

    Energy Technology Data Exchange (ETDEWEB)

    Santiago, J. L.; Martin, F.

    2015-07-01

    A methodology to estimate the spatial representativeness of air pollution monitoring sites is applied to two urban districts. This methodology is based on high resolution maps of air pollution computed by using Computational Fluid Dynamics (CFD) modelling tools. Traffic-emitted NO{sub 2} dispersion is simulated for several meteorological conditions taking into account the effect of the buildings on air flow and pollutant dispersion and using a steady state CFD-RANS approach. From these results, maps of average pollutant concentrations for January -May 2011 are computed as a combination of the simulated scenarios. Two urban districts of Madrid City were simulated. Spatial representativeness areas for 32 different sites within the same district (including the site of the operative air quality stations) have been estimated by computing the portion of the domains with average NO{sub 2} concentration differing less than a 20% of the concentration at each candidate monitoring site. New parameters such as the ratio AR between the representativeness area and the whole domain area or the representativeness index (IR) has been proposed to discuss and compare the representativeness areas. Significant differences between the spatial representativeness of the candidate sites of both studied districts have been found. The sites of the Escuelas Aguirre district have generally smaller representativeness areas than those of the Plaza de Castilla. More stations are needed to cover the Escuelas Aguirre district than for the Plaza de Castilla one. The operative air quality station of the Escuelas Aguirre district is less representative than the station of the Plaza de Castilla district. The cause of these differences seems to be the differences in urban structure of both districts prompting different ventilation. (Author)

  6. Use of CFD modeling for estimating spatial representativeness of urban air pollution monitoring sites and suitability of their locations

    Energy Technology Data Exchange (ETDEWEB)

    Santiago, J.L.; Martin, F.

    2015-07-01

    A methodology to estimate the spatial representativeness of air pollution monitoring sites is applied to two urban districts. This methodology is based on high resolution maps of air pollution computed by using Computational Fluid Dynamics (CFD) modelling tools. Traffic-emitted NO2 dispersion is simulated for several meteorological conditions taking into account the effect of the buildings on air flow and pollutant dispersion and using a steady state CFD-RANS approach. From these results, maps of average pollutant concentrations for January–May 2011 are computed as a combination of the simulated scenarios. Two urban districts of Madrid City were simulated. Spatial representativeness areas for 32 different sites within the same district (including the site of the operative air quality stations) have been estimated by computing the portion of the domains with average NO2 concentration differing less than a 20% of the concentration at each candidate monitoring site. New parameters such as the ratio AR between the representativeness area and the whole domain area or the representativeness index (IR) has been proposed to discuss and compare the representativeness areas. Significant differences between the spatial representativeness of the candidate sites of both studied districts have been found. The sites of the Escuelas Aguirre district have generally smaller representativeness areas than those of the Plaza de Castilla. More stations are needed to cover the Escuelas Aguirre district than for the Plaza de Castilla one. The operative air quality station of the Escuelas Aguirre district is less representative than the station of the Plaza de Castilla district. The cause of these differences seems to be the differences in urban structure of both districts prompting different ventilation. (Author)

  7. Cross-Industry Spatially Localized Innovation Networks

    Directory of Open Access Journals (Sweden)

    Aleksandr Evseevich Karlik

    2016-12-01

    Full Text Available This article’s objective is to develop conceptual approach to the study of key decision-making factors of cross-industry spatially localized innovation networks regularities by the application of quantitative and qualitative data of St. Petersburg Innovation and Technology Cluster of Machinery Manufacturing and Metalworking. The paper is based on the previous research findings which conclude that such networks have a set of opportunities and constraints for innovation. The hypothesis is that in the clusters, representing a special type of these networks, the spatial proximity partly offsets the negative impact of industrial distance. The authors propose a structural and logical model of strategic decision-making to analyze these effects on innovation. They specify network’s influences on performance: cognitive diversity; knowledge and expertise; structural autonomy and equivalence. The model is applied to spatially localized cross-industry cluster and then improved in accordance with the obtained results for accounting resource flows. It allowed to take into account the dynamics of innovation activity and to develop the practical implications in the particular business context. The analysis identified the peculiarities of spatially localized crossindustry innovation cooperation in perspective of the combinations of tangible resources, information and other intangible resources for the renewal of mature industries. The research results can be used in business as well as in industrial and regional economic policy. In the conclusion, the article outlines future research directions: a comprehensive empirical study with the analysis of data on the factors of cross-industry cooperation which were identified in this paper with testing of causal relations; the developing an approach to the study of spatially localized networks based on the exchange of primary resources in the economic system stability framework.

  8. Selective Propene Epoxidation on Immobilized Au6-10 Clusters: The Effect of Hydrogen and Water on Activity and Selectivity

    DEFF Research Database (Denmark)

    Lee, Sungsik; Molina, Luis M.; López, María J.

    2009-01-01

    Epoxidation made easy: Subnanometer gold clusters immobilized on amorphous alumina result in a highly active and selective catalyst for propene epoxidation. The highest selectivity is found for gas mixtures involving oxygen and water, thus avoiding the use of hydrogen. Ab initio DFT calculations ...

  9. Dual-task results and the lateralization of spatial orientation: artifact of test selection?

    Science.gov (United States)

    Bowers, C A; Milham, L M; Price, C

    1998-01-01

    An investigation was conducted to identify the degree to which results regarding the lateralization of spatial orientation among men and women are artifacts of test selection. A dual-task design was used to study possible lateralization differences, providing baseline and dual-task measures of spatial-orientation performance, right- and left-hand tapping, and vocalization of "cat, dog, horse." The Guilford-Zimmerman Test (Guilford & Zimmerman, 1953), the Eliot-Price Test (Eliot & Price, 1976), and the Stumpf-Fay Cube Perspectives Test (Stumpf & Fay, 1983) were the three spatial-orientation tests used to investigate possible artifacts of test selection. Twenty-eight right-handed male and 39 right-handed female undergraduates completed random baseline and dual-task sessions. Analyses indicated no significant sex-related differences in spatial-orientation ability for all three tests. Furthermore, there was no evidence of differential lateralization of spatial orientation between the sexes.

  10. Model catalysis by size-selected cluster deposition

    Energy Technology Data Exchange (ETDEWEB)

    Anderson, Scott [Univ. of Utah, Salt Lake City, UT (United States)

    2015-11-20

    This report summarizes the accomplishments during the last four years of the subject grant. Results are presented for experiments in which size-selected model catalysts were studied under surface science and aqueous electrochemical conditions. Strong effects of cluster size were found, and by correlating the size effects with size-dependent physical properties of the samples measured by surface science methods, it was possible to deduce mechanistic insights, such as the factors that control the rate-limiting step in the reactions. Results are presented for CO oxidation, CO binding energetics and geometries, and electronic effects under surface science conditions, and for the electrochemical oxygen reduction reaction, ethanol oxidation reaction, and for oxidation of carbon by water.

  11. Altering spatial priority maps via statistical learning of target selection and distractor filtering.

    Science.gov (United States)

    Ferrante, Oscar; Patacca, Alessia; Di Caro, Valeria; Della Libera, Chiara; Santandrea, Elisa; Chelazzi, Leonardo

    2018-05-01

    The cognitive system has the capacity to learn and make use of environmental regularities - known as statistical learning (SL), including for the implicit guidance of attention. For instance, it is known that attentional selection is biased according to the spatial probability of targets; similarly, changes in distractor filtering can be triggered by the unequal spatial distribution of distractors. Open questions remain regarding the cognitive/neuronal mechanisms underlying SL of target selection and distractor filtering. Crucially, it is unclear whether the two processes rely on shared neuronal machinery, with unavoidable cross-talk, or they are fully independent, an issue that we directly addressed here. In a series of visual search experiments, participants had to discriminate a target stimulus, while ignoring a task-irrelevant salient distractor (when present). We systematically manipulated spatial probabilities of either one or the other stimulus, or both. We then measured performance to evaluate the direct effects of the applied contingent probability distribution (e.g., effects on target selection of the spatial imbalance in target occurrence across locations) as well as its indirect or "transfer" effects (e.g., effects of the same spatial imbalance on distractor filtering across locations). By this approach, we confirmed that SL of both target and distractor location implicitly bias attention. Most importantly, we described substantial indirect effects, with the unequal spatial probability of the target affecting filtering efficiency and, vice versa, the unequal spatial probability of the distractor affecting target selection efficiency across locations. The observed cross-talk demonstrates that SL of target selection and distractor filtering are instantiated via (at least partly) shared neuronal machinery, as further corroborated by strong correlations between direct and indirect effects at the level of individual participants. Our findings are compatible

  12. MEASURING THE ULTIMATE HALO MASS OF GALAXY CLUSTERS: REDSHIFTS AND MASS PROFILES FROM THE HECTOSPEC CLUSTER SURVEY (HeCS)

    International Nuclear Information System (INIS)

    Rines, Kenneth; Geller, Margaret J.; Kurtz, Michael J.; Diaferio, Antonaldo

    2013-01-01

    The infall regions of galaxy clusters represent the largest gravitationally bound structures in a ΛCDM universe. Measuring cluster mass profiles into the infall regions provides an estimate of the ultimate mass of these halos. We use the caustic technique to measure cluster mass profiles from galaxy redshifts obtained with the Hectospec Cluster Survey (HeCS), an extensive spectroscopic survey of galaxy clusters with MMT/Hectospec. We survey 58 clusters selected by X-ray flux at 0.1 200 , a new observational cosmological test in essential agreement with simulations. Summed profiles binned in M 200 and in L X demonstrate that the predicted Navarro-Frenk-White form of the density profile is a remarkably good representation of the data in agreement with weak lensing results extending to large radius. The concentration of these summed profiles is also consistent with theoretical predictions.

  13. Data Clustering

    Science.gov (United States)

    Wagstaff, Kiri L.

    2012-03-01

    On obtaining a new data set, the researcher is immediately faced with the challenge of obtaining a high-level understanding from the observations. What does a typical item look like? What are the dominant trends? How many distinct groups are included in the data set, and how is each one characterized? Which observable values are common, and which rarely occur? Which items stand out as anomalies or outliers from the rest of the data? This challenge is exacerbated by the steady growth in data set size [11] as new instruments push into new frontiers of parameter space, via improvements in temporal, spatial, and spectral resolution, or by the desire to "fuse" observations from different modalities and instruments into a larger-picture understanding of the same underlying phenomenon. Data clustering algorithms provide a variety of solutions for this task. They can generate summaries, locate outliers, compress data, identify dense or sparse regions of feature space, and build data models. It is useful to note up front that "clusters" in this context refer to groups of items within some descriptive feature space, not (necessarily) to "galaxy clusters" which are dense regions in physical space. The goal of this chapter is to survey a variety of data clustering methods, with an eye toward their applicability to astronomical data analysis. In addition to improving the individual researcher’s understanding of a given data set, clustering has led directly to scientific advances, such as the discovery of new subclasses of stars [14] and gamma-ray bursts (GRBs) [38]. All clustering algorithms seek to identify groups within a data set that reflect some observed, quantifiable structure. Clustering is traditionally an unsupervised approach to data analysis, in the sense that it operates without any direct guidance about which items should be assigned to which clusters. There has been a recent trend in the clustering literature toward supporting semisupervised or constrained

  14. Segmentation and clustering as complementary sources of information

    Science.gov (United States)

    Dale, Michael B.; Allison, Lloyd; Dale, Patricia E. R.

    2007-03-01

    This paper examines the effects of using a segmentation method to identify change-points or edges in vegetation. It identifies coherence (spatial or temporal) in place of unconstrained clustering. The segmentation method involves change-point detection along a sequence of observations so that each cluster formed is composed of adjacent samples; this is a form of constrained clustering. The protocol identifies one or more models, one for each section identified, and the quality of each is assessed using a minimum message length criterion, which provides a rational basis for selecting an appropriate model. Although the segmentation is less efficient than clustering, it does provide other information because it incorporates textural similarity as well as homogeneity. In addition it can be useful in determining various scales of variation that may apply to the data, providing a general method of small-scale pattern analysis.

  15. Two novel species representing a new clade and cluster of Phytophthora.

    Science.gov (United States)

    Yang, Xiao; Copes, Warren E; Hong, Chuanxue

    2014-01-01

    Phytophthora stricta sp. nov. and Phytophthora macilentosa sp. nov. are described based on morphological, physiological and molecular characters in this study. Phytophthora stricta represents a previously unknown clade in the rRNA internal transcribed spacer (ITS)-based phylogeny. Phytophthora macilentosa, along with nine other species, consistently forms a high temperature-tolerant cluster within ITS clade 9. These observations are supported by the sequence analysis of the mitochondrial cytochrome c oxidase 1 gene. Both species are heterothallic and all examined isolates are A1 mating type. Phytophthora stricta produces nonpapillate and slightly caducous sporangia. This species is named after its characteristic constrictions on sporangiophores. Phytophthora macilentosa produces nonpapillate and noncaducous sporangia, which are mostly elongated obpyriform with a high length to breadth ratio. Both species were recovered from irrigation water of an ornamental plant nursery in Mississippi, USA and P. stricta was also recovered from stream water in Virginia, USA. Copyright © 2013 The British Mycological Society. All rights reserved.

  16. Statistical measures of galaxy clustering

    International Nuclear Information System (INIS)

    Porter, D.H.

    1988-01-01

    Consideration is given to the large-scale distribution of galaxies and ways in which this distribution may be statistically measured. Galaxy clustering is hierarchical in nature, so that the positions of clusters of galaxies are themselves spatially clustered. A simple identification of groups of galaxies would be an inadequate description of the true richness of galaxy clustering. Current observations of the large-scale structure of the universe and modern theories of cosmology may be studied with a statistical description of the spatial and velocity distributions of galaxies. 8 refs

  17. Poly(methyl methacrylate) Composites with Size-selected Silver Nanoparticles Fabricated Using Cluster Beam Technique

    DEFF Research Database (Denmark)

    Muhammad, Hanif; Juluri, Raghavendra R.; Chirumamilla, Manohar

    2016-01-01

    based on cluster beam technique allowing the formation of monocrystalline size-selected silver nanoparticles with a ±5–7% precision of diameter and controllable embedment into poly (methyl methacrylate). It is shown that the soft-landed silver clusters preserve almost spherical shape with a slight...... tendency to flattening upon impact. By controlling the polymer hardness (from viscous to soft state) prior the cluster deposition and annealing conditions after the deposition the degree of immersion of the nanoparticles into polymer can be tuned, thus, making it possible to create composites with either...

  18. Characterizing China's energy consumption with selective economic factors and energy-resource endowment: a spatial econometric approach

    Science.gov (United States)

    Jiang, Lei; Ji, Minhe; Bai, Ling

    2015-06-01

    Coupled with intricate regional interactions, the provincial disparity of energy-resource endowment and other economic conditions in China have created spatially complex energy consumption patterns that require analyses beyond the traditional ones. To distill the spatial effect out of the resource and economic factors on China's energy consumption, this study recast the traditional econometric model in a spatial context. Several analytic steps were taken to reveal different aspects of the issue. Per capita energy consumption (AVEC) at the provincial level was first mapped to reveal spatial clusters of high energy consumption being located in either well developed or energy resourceful regions. This visual spatial autocorrelation pattern of AVEC was quantitatively tested to confirm its existence among Chinese provinces. A Moran scatterplot was employed to further display a relatively centralized trend occurring in those provinces that had parallel AVEC, revealing a spatial structure with attraction among high-high or low-low regions and repellency among high-low or low-high regions. By a comparison between the ordinary least square (OLS) model and its spatial econometric counterparts, a spatial error model (SEM) was selected to analyze the impact of major economic determinants on AVEC. While the analytic results revealed a significant positive correlation between AVEC and economic development, other determinants showed some intricate influential patterns. The provinces endowed with rich energy reserves were inclined to consume much more energy than those otherwise, whereas changing the economic structure by increasing the proportion of secondary and tertiary industries also tended to consume more energy. Both situations seem to underpin the fact that these provinces were largely trapped in the economies that were supported by technologies of low energy efficiency during the period, while other parts of the country were rapidly modernized by adopting advanced

  19. Event Streams Clustering Using Machine Learning Techniques

    Directory of Open Access Journals (Sweden)

    Hanen Bouali

    2015-10-01

    Full Text Available Data streams are usually of unbounded lengths which push users to consider only recent observations by focusing on a time window, and ignore past data. However, in many real world applications, past data must be taken in consideration to guarantee the efficiency, the performance of decision making and to handle data streams evolution over time. In order to build a selectively history to track the underlying event streams changes, we opt for the continuously data of the sliding window which increases the time window based on changes over historical data. In this paper, to have the ability to access to historical data without requiring any significant storage or multiple passes over the data. In this paper, we propose a new algorithm for clustering multiple data streams using incremental support vector machine and data representative points’ technique. The algorithm uses a sliding window model for the most recent clustering results and data representative points to model the old data clustering results. Our experimental results on electromyography signal show a better clustering than other present in the literature

  20. Voronoi-based spatial analysis reveals selective interneuron changes in the cortex of FALS mice.

    Science.gov (United States)

    Minciacchi, Diego; Kassa, Roman M; Del Tongo, Claudia; Mariotti, Raffaella; Bentivoglio, Marina

    2009-01-01

    The neurodegenerative disease amyotrophic lateral sclerosis affects lower motoneurons and corticospinal cells. Mice expressing human mutant superoxide dismutase (SOD)1 provide widely investigated models of the familial form of disease, but information on cortical changes in these mice is still limited. We here analyzed the spatial organization of interneurons characterized by parvalbumin immunoreactivity in the motor, somatosensory, and visual cortical areas of SOD1(G93A) mice. Cell number and sociological spatial behavior were assessed by digital charts of cell location in cortical samples, cell counts, and generation of two-dimensional Voronoi diagrams. In end-stage SOD1-mutant mice, an increase of parvalbumin-containing cortical interneurons was found in the motor and somatosensory areas (about 35% and 20%, respectively) with respect to wild-type littermates. Changes in cell spatial distribution, as documented by Voronoi-derived coefficients of variation, indicated increased tendency of parvalbumin cells to aggregate into clusters in the same areas of the SOD1-mutant cortex. Counts and coefficients of variation of parvalbumin cells in the visual cortex gave instead similar results in SOD1-mutant and wild-type mice. Analyses of motor and somatosensory areas in presymptomatic SOD1-mutant mice provided findings very similar to those obtained at end-stage, indicating early changes of interneurons in these cortical areas during the pathology. Altogether the data reveal in the SOD1-mutant mouse cortex an altered architectonic pattern of interneurons, which selectively affects areas involved in motor control. The findings, which can be interpreted as pathogenic factors or early disease-related adaptations, point to changes in the cortical regulation and modulation of the motor circuit during motoneuron disease.

  1. Chemical library subset selection algorithms: a unified derivation using spatial statistics.

    Science.gov (United States)

    Hamprecht, Fred A; Thiel, Walter; van Gunsteren, Wilfred F

    2002-01-01

    If similar compounds have similar activity, rational subset selection becomes superior to random selection in screening for pharmacological lead discovery programs. Traditional approaches to this experimental design problem fall into two classes: (i) a linear or quadratic response function is assumed (ii) some space filling criterion is optimized. The assumptions underlying the first approach are clear but not always defendable; the second approach yields more intuitive designs but lacks a clear theoretical foundation. We model activity in a bioassay as realization of a stochastic process and use the best linear unbiased estimator to construct spatial sampling designs that optimize the integrated mean square prediction error, the maximum mean square prediction error, or the entropy. We argue that our approach constitutes a unifying framework encompassing most proposed techniques as limiting cases and sheds light on their underlying assumptions. In particular, vector quantization is obtained, in dimensions up to eight, in the limiting case of very smooth response surfaces for the integrated mean square error criterion. Closest packing is obtained for very rough surfaces under the integrated mean square error and entropy criteria. We suggest to use either the integrated mean square prediction error or the entropy as optimization criteria rather than approximations thereof and propose a scheme for direct iterative minimization of the integrated mean square prediction error. Finally, we discuss how the quality of chemical descriptors manifests itself and clarify the assumptions underlying the selection of diverse or representative subsets.

  2. Assessment of tuberculosis spatial hotspot areas in Antananarivo, Madagascar, by combining spatial analysis and genotyping.

    Science.gov (United States)

    Ratovonirina, Noël Harijaona; Rakotosamimanana, Niaina; Razafimahatratra, Solohery Lalaina; Raherison, Mamy Serge; Refrégier, Guislaine; Sola, Christophe; Rakotomanana, Fanjasoa; Rasolofo Razanamparany, Voahangy

    2017-08-14

    Tuberculosis (TB) remains a public health problem in Madagascar. A crucial element of TB control is the development of an easy and rapid method for the orientation of TB control strategies in the country. Our main objective was to develop a TB spatial hotspot identification method by combining spatial analysis and TB genotyping method in Antananarivo. Sputa of new pulmonary TB cases from 20 TB diagnosis and treatment centers (DTCs) in Antananarivo were collected from August 2013 to May 2014 for culture. Mycobacterium tuberculosis complex (MTBC) clinical isolates were typed by spoligotyping on a Luminex® 200 platform. All TB patients were respectively localized according to their neighborhood residence and the spatial distribution of all pulmonary TB patients and patients with genotypic clustered isolates were scanned respectively by the Kulldorff spatial scanning method for identification of significant spatial clustering. Areas exhibiting spatial clustering of patients with genotypic clustered isolates were considered as hotspot TB areas for transmission. Overall, 467 new cases were included in the study, and 394 spoligotypes were obtained (84.4%). New TB cases were distributed in 133 of the 192 Fokontany (administrative neighborhoods) of Antananarivo (1 to 15 clinical patients per Fokontany) and patients with genotypic clustered isolates were distributed in 127 of the 192 Fokontany (1 to 13 per Fokontany). A single spatial focal point of epidemics was detected when ignoring genotypic data (p = 0.039). One Fokontany of this focal point and three additional ones were detected to be spatially clustered when taking genotypes into account (p Madagascar and will allow better TB control strategies by public health authorities.

  3. Selecting Appropriate Spatial Scale for Mapping Plastic-Mulched Farmland with Satellite Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Hasituya

    2017-03-01

    Full Text Available In recent years, the area of plastic-mulched farmland (PMF has undergone rapid growth and raised remarkable environmental problems. Therefore, mapping the PMF plays a crucial role in agricultural production, environmental protection and resource management. However, appropriate data selection criteria are currently lacking. Thus, this study was carried out in two main plastic-mulching practice regions, Jizhou and Guyuan, to look for an appropriate spatial scale for mapping PMF with remote sensing. The average local variance (ALV function was used to obtain the appropriate spatial scale for mapping PMF based on the GaoFen-1 (GF-1 satellite imagery. Afterwards, in order to validate the effectiveness of the selected method and to interpret the relationship between the appropriate spatial scale derived from the ALV and the spatial scale with the highest classification accuracy, we classified the imagery with varying spatial resolution by the Support Vector Machine (SVM algorithm using the spectral features, textural features and the combined spectral and textural features respectively. The results indicated that the appropriate spatial scales from the ALV lie between 8 m and 20 m for mapping the PMF both in Jizhou and Guyuan. However, there is a proportional relation: the spatial scale with the highest classification accuracy is at the 1/2 location of the appropriate spatial scale generated from the ALV in Jizhou and at the 2/3 location of the appropriate spatial scale generated from the ALV in Guyuan. Therefore, the ALV method for quantitatively selecting the appropriate spatial scale for mapping PMF with remote sensing imagery has theoretical and practical significance.

  4. Spatial Fleming-Viot models with selection and mutation

    CERN Document Server

    Dawson, Donald A

    2014-01-01

    This book constructs a rigorous framework for analysing selected phenomena in evolutionary theory of populations arising due to the combined effects of migration, selection and mutation in a spatial stochastic population model, namely the evolution towards fitter and fitter types through punctuated equilibria. The discussion is based on a number of new methods, in particular multiple scale analysis, nonlinear Markov processes and their entrance laws, atomic measure-valued evolutions and new forms of duality (for state-dependent mutation and multitype selection) which are used to prove ergodic theorems in this context and are applicable for many other questions and renormalization analysis for a variety of phenomena (stasis, punctuated equilibrium, failure of naive branching approximations, biodiversity) which occur due to the combination of rare mutation, mutation, resampling, migration and selection and make it necessary to mathematically bridge the gap (in the limit) between time and space scales.

  5. THE MASSIVE AND DISTANT CLUSTERS OF WISE SURVEY. II. INITIAL SPECTROSCOPIC CONFIRMATION OF z ∼ 1 GALAXY CLUSTERS SELECTED FROM 10,000 deg2

    International Nuclear Information System (INIS)

    Stanford, S. A.; Gonzalez, Anthony H.; Gettings, Daniel P.; Brodwin, Mark; Eisenhardt, Peter R. M.; Stern, Daniel; Wylezalek, Dominika

    2014-01-01

    We present optical and infrared imaging and optical spectroscopy of galaxy clusters which were identified as part of an all-sky search for high-redshift galaxy clusters, the Massive and Distant Clusters of WISE Survey (MaDCoWS). The initial phase of MaDCoWS combined infrared data from the all-sky data release of the Wide-field Infrared Survey Explorer (WISE) with optical data from the Sloan Digital Sky Survey to select probable z ∼ 1 clusters of galaxies over an area of 10,000 deg 2 . Our spectroscopy confirms 19 new clusters at 0.7 < z < 1.3, half of which are at z > 1, demonstrating the viability of using WISE to identify high-redshift galaxy clusters. The next phase of MaDCoWS will use the greater depth of the AllWISE data release to identify even higher redshift cluster candidates

  6. Novel Ordered Stepped-Wedge Cluster Trial Designs for Detecting Ebola Vaccine Efficacy Using a Spatially Structured Mathematical Model.

    Directory of Open Access Journals (Sweden)

    Ibrahim Diakite

    2016-08-01

    Full Text Available During the 2014 Ebola virus disease (EVD outbreak, policy-makers were confronted with difficult decisions on how best to test the efficacy of EVD vaccines. On one hand, many were reluctant to withhold a vaccine that might prevent a fatal disease from study participants randomized to a control arm. On the other, regulatory bodies called for rigorous placebo-controlled trials to permit direct measurement of vaccine efficacy prior to approval of the products. A stepped-wedge cluster study (SWCT was proposed as an alternative to a more traditional randomized controlled vaccine trial to address these concerns. Here, we propose novel "ordered stepped-wedge cluster trial" (OSWCT designs to further mitigate tradeoffs between ethical concerns, logistics, and statistical rigor.We constructed a spatially structured mathematical model of the EVD outbreak in Sierra Leone. We used the output of this model to simulate and compare a series of stepped-wedge cluster vaccine studies. Our model reproduced the observed order of first case occurrence within districts of Sierra Leone. Depending on the infection risk within the trial population and the trial start dates, the statistical power to detect a vaccine efficacy of 90% varied from 14% to 32% for standard SWCT, and from 67% to 91% for OSWCTs for an alpha error of 5%. The model's projection of first case occurrence was robust to changes in disease natural history parameters.Ordering clusters in a step-wedge trial based on the cluster's underlying risk of infection as predicted by a spatial model can increase the statistical power of a SWCT. In the event of another hemorrhagic fever outbreak, implementation of our proposed OSWCT designs could improve statistical power when a step-wedge study is desirable based on either ethical concerns or logistical constraints.

  7. Malaria prevalence, spatial clustering and risk factors in a low endemic area of Eastern Rwanda: a cross sectional study

    NARCIS (Netherlands)

    Rulisa, Stephen; Kateera, Fredrick; Bizimana, Jean Pierre; Agaba, Steven; Dukuzumuremyi, Javier; Baas, Lisette; de Dieu Harelimana, Jean; Mens, Petra F.; Boer, Kimberly R.; de Vries, Peter J.

    2013-01-01

    Rwanda reported significant reductions in malaria burden following scale up of control intervention from 2005 to 2010. This study sought to; measure malaria prevalence, describe spatial malaria clustering and investigate for malaria risk factors among health-centre-presumed malaria cases and their

  8. SPECTROSCOPIC CONFIRMATION OF A MASSIVE RED-SEQUENCE-SELECTED GALAXY CLUSTER AT z = 1.34 IN THE SpARCS-SOUTH CLUSTER SURVEY

    International Nuclear Information System (INIS)

    Wilson, Gillian; Demarco, Ricardo; Muzzin, Adam; Yee, H. K. C.; Lacy, Mark; Surace, Jason; Gilbank, David; Blindert, Kris; Hoekstra, Henk; Majumdar, Subhabrata; Gardner, Jonathan P.; Gladders, Michael D.; Lonsdale, Carol

    2009-01-01

    The Spitzer Adaptation of the Red-sequence Cluster Survey (SpARCS) is a z'-passband imaging survey, consisting of deep (z' ≅ 24 AB) observations made from both hemispheres using the CFHT 3.6 m and CTIO 4 m telescopes. The survey was designed with the primary aim of detecting galaxy clusters at z > 1. In tandem with pre-existing 3.6 μm observations from the Spitzer Space Telescope SWIRE Legacy Survey, SpARCS detects clusters using an infrared adaptation of the two-filter red-sequence cluster technique. The total effective area of the SpARCS cluster survey is 41.9 deg 2 . In this paper, we provide an overview of the 13.6 deg 2 Southern CTIO/MOSAIC II observations. The 28.3 deg 2 Northern CFHT/MegaCam observations are summarized in a companion paper by Muzzin et al. In this paper, we also report spectroscopic confirmation of SpARCS J003550-431224, a very rich galaxy cluster at z = 1.335, discovered in the ELAIS-S1 field. To date, this is the highest spectroscopically confirmed redshift for a galaxy cluster discovered using the red-sequence technique. Based on nine confirmed members, SpARCS J003550-431224 has a preliminary velocity dispersion of 1050 ± 230 km s -1 . With its proven capability for efficient cluster detection, SpARCS is a demonstration that we have entered an era of large, homogeneously selected z > 1 cluster surveys.

  9. Magnetron sputtering cluster apparatus for formation and deposition of size-selected metal nanoparticles

    DEFF Research Database (Denmark)

    Hanif, Muhammad; Popok, Vladimir

    2015-01-01

    selection is achieved using an electrostatic quadrupole mass selector. The deposited silver clusters are studied using atomic force microscopy. The height distributions show typical relative standard size deviation of 9-13% for given sizes in the range between 5-23 nm. Thus, the apparatus demonstrates good...... capability in formation of supported size-selected metal nanoparticles with controllable coverage for various practical applications....

  10. Constraints on the Richness-Mass Relation and the Optical-SZE Positional Offset Distribution for SZE-Selected Clusters

    CERN Document Server

    Saro, A; Rozo, E; Benson, B A; Mohr, J; Rykoff, E S; Soares-Santos, M; Bleem, L; Dodelson, S; Melchior, P; Sobreira, F; Upadhyay, V; Weller, J; Abbott, T; Abdalla, F B; Allam, S; Armstrong, R; Banerji, M; Bauer, A H; Bayliss, M; Benoit-Lévy, A; Bernstein, G M; Bertin, E; Brodwin, M; Brooks, D; Buckley-Geer, E; Burke, D L; Carlstrom, J E; Capasso, R; Capozzi, D; Carnero Rosell, A; Carrasco Kind, M; Chiu, I; Covarrubias, R; Crawford, T M; Crocce, M; D'Andrea, C B; da Costa, L N; DePoy, D L; Desai, S; de Haan, T; Diehl, H T; Dietrich, J P; Doel, P; Cunha, C E; Eifler, T F; Evrard, A E; Fausti Neto, A; Fernandez, E; Flaugher, B; Fosalba, P; Frieman, J; Gangkofner, C; Gaztanaga, E; Gerdes, D; Gruen, D; Gruendl, R A; Gupta, N; Hennig, C; Holzapfel, W L; Honscheid, K; Jain, B; James, D; Kuehn, K; Kuropatkin, N; Lahav, O; Li, T S; Lin, H; Maia, M A G; March, M; Marshall, J L; Martini, Paul; McDonald, M; Miller, C J; Miquel, R; Nord, B; Ogando, R; Plazas, A A; Reichardt, C L; Romer, A K; Roodman, A; Sako, M; Sanchez, E; Schubnell, M; Sevilla, I; Smith, R C; Stalder, B; Stark, A A; Strazzullo, V; Suchyta, E; Swanson, M E C; Tarle, G; Thaler, J; Thomas, D; Tucker, D; Vikram, V; von der Linden, A; Walker, A R; Wechsler, R H; Wester, W; Zenteno, A; Ziegler, K E

    2015-01-01

    We cross-match galaxy cluster candidates selected via their Sunyaev-Zel'dovich effect (SZE) signatures in 129.1 deg$^2$ of the South Pole Telescope 2500d SPT-SZ survey with optically identified clusters selected from the Dark Energy Survey (DES) science verification data. We identify 25 clusters between $0.1\\lesssim z\\lesssim 0.8$ in the union of the SPT-SZ and redMaPPer (RM) samples. RM is an optical cluster finding algorithm that also returns a richness estimate for each cluster. We model the richness $\\lambda$-mass relation with the following function $\\langle\\ln\\lambda|M_{500}\\rangle\\propto B_\\lambda\\ln M_{500}+C_\\lambda\\ln E(z)$ and use SPT-SZ cluster masses and RM richnesses $\\lambda$ to constrain the parameters. We find $B_\\lambda= 1.14^{+0.21}_{-0.18}$ and $C_\\lambda=0.73^{+0.77}_{-0.75}$. The associated scatter in mass at fixed richness is $\\sigma_{\\ln M|\\lambda} = 0.18^{+0.08}_{-0.05}$ at a characteristic richness $\\lambda=70$. We demonstrate that our model provides an adequate description of the ma...

  11. TRMM Satellite Algorithm Estimates to Represent the Spatial Distribution of Rainstorms

    Directory of Open Access Journals (Sweden)

    Patrick Marina

    2017-01-01

    Full Text Available On-site measurements from rain gauge provide important information for the design, construction, and operation of water resources engineering projects, groundwater potentials, and the water supply and irrigation systems. A dense gauging network is needed to accurately characterize the variation of rainfall over a region, unfitting for conditions with limited networks, such as in Sarawak, Malaysia. Hence, satellite-based algorithm estimates are introduced as an innovative solution to these challenges. With accessibility to dataset retrievals from public domain websites, it has become a useful source to measure rainfall for a wider coverage area at finer temporal resolution. This paper aims to investigate the rainfall estimates prepared by Tropical Rainfall Measuring Mission (TRMM to explain whether it is suitable to represent the distribution of extreme rainfall in Sungai Sarawak Basin. Based on the findings, more uniform correlations for the investigated storms can be observed for low to medium altitude (>40 MASL. It is found for the investigated events of Jan 05-11, 2009: the normalized root mean square error (NRMSE = 36.7 %; and good correlation (CC = 0.9. These findings suggest that satellite algorithm estimations from TRMM are suitable to represent the spatial distribution of extreme rainfall.

  12. Variation in sequence and location of the fumonisin mycotoxin niosynthetic gene cluster in Fusarium

    NARCIS (Netherlands)

    Proctor, R.H.; Hove, van F.; Susca, A.; Stea, A.; Busman, M.; Lee, van der T.A.J.; Waalwijk, C.; Moretti, A.

    2010-01-01

    In Fusarium, the ability to produce fumonisins is governed by a 17-gene fumonisin biosynthetic gene (FUM) cluster. Here, we examined the cluster in F. oxysporum strain O-1890 and nine other species selected to represent a wide range of the genetic diversity within the GFSC.

  13. fMR-adaptation indicates selectivity to audiovisual content congruency in distributed clusters in human superior temporal cortex.

    Science.gov (United States)

    van Atteveldt, Nienke M; Blau, Vera C; Blomert, Leo; Goebel, Rainer

    2010-02-02

    Efficient multisensory integration is of vital importance for adequate interaction with the environment. In addition to basic binding cues like temporal and spatial coherence, meaningful multisensory information is also bound together by content-based associations. Many functional Magnetic Resonance Imaging (fMRI) studies propose the (posterior) superior temporal cortex (STC) as the key structure for integrating meaningful multisensory information. However, a still unanswered question is how superior temporal cortex encodes content-based associations, especially in light of inconsistent results from studies comparing brain activation to semantically matching (congruent) versus nonmatching (incongruent) multisensory inputs. Here, we used fMR-adaptation (fMR-A) in order to circumvent potential problems with standard fMRI approaches, including spatial averaging and amplitude saturation confounds. We presented repetitions of audiovisual stimuli (letter-speech sound pairs) and manipulated the associative relation between the auditory and visual inputs (congruent/incongruent pairs). We predicted that if multisensory neuronal populations exist in STC and encode audiovisual content relatedness, adaptation should be affected by the manipulated audiovisual relation. The results revealed an occipital-temporal network that adapted independently of the audiovisual relation. Interestingly, several smaller clusters distributed over superior temporal cortex within that network, adapted stronger to congruent than to incongruent audiovisual repetitions, indicating sensitivity to content congruency. These results suggest that the revealed clusters contain multisensory neuronal populations that encode content relatedness by selectively responding to congruent audiovisual inputs, since unisensory neuronal populations are assumed to be insensitive to the audiovisual relation. These findings extend our previously revealed mechanism for the integration of letters and speech sounds and

  14. fMR-adaptation indicates selectivity to audiovisual content congruency in distributed clusters in human superior temporal cortex

    Directory of Open Access Journals (Sweden)

    Blomert Leo

    2010-02-01

    Full Text Available Abstract Background Efficient multisensory integration is of vital importance for adequate interaction with the environment. In addition to basic binding cues like temporal and spatial coherence, meaningful multisensory information is also bound together by content-based associations. Many functional Magnetic Resonance Imaging (fMRI studies propose the (posterior superior temporal cortex (STC as the key structure for integrating meaningful multisensory information. However, a still unanswered question is how superior temporal cortex encodes content-based associations, especially in light of inconsistent results from studies comparing brain activation to semantically matching (congruent versus nonmatching (incongruent multisensory inputs. Here, we used fMR-adaptation (fMR-A in order to circumvent potential problems with standard fMRI approaches, including spatial averaging and amplitude saturation confounds. We presented repetitions of audiovisual stimuli (letter-speech sound pairs and manipulated the associative relation between the auditory and visual inputs (congruent/incongruent pairs. We predicted that if multisensory neuronal populations exist in STC and encode audiovisual content relatedness, adaptation should be affected by the manipulated audiovisual relation. Results The results revealed an occipital-temporal network that adapted independently of the audiovisual relation. Interestingly, several smaller clusters distributed over superior temporal cortex within that network, adapted stronger to congruent than to incongruent audiovisual repetitions, indicating sensitivity to content congruency. Conclusions These results suggest that the revealed clusters contain multisensory neuronal populations that encode content relatedness by selectively responding to congruent audiovisual inputs, since unisensory neuronal populations are assumed to be insensitive to the audiovisual relation. These findings extend our previously revealed mechanism for

  15. Selective deficit of spatial short-term memory: Role of storage and rehearsal mechanisms.

    Science.gov (United States)

    Bonnì, Sonia; Perri, Roberta; Fadda, Lucia; Tomaiuolo, Francesco; Koch, Giacomo; Caltagirone, Carlo; Carlesimo, Giovanni Augusto

    2014-10-01

    We report the neuropsychological and MRI investigation of a patient (GP) who developed a selective impairment of spatial short-term memory (STM) following damage to the dorso-mesial areas of the right frontal lobe. We assessed in this patient spatial STM with an experimental procedure that evaluated immediate and 5-20 s delayed recall of verbal, visual and spatial stimuli. The patient scored significantly worse than normal controls on tests that required delayed recall of spatial data. This could not be ascribed to a deficit of spatial episodic long-term memory because amnesic patients performed normally on these tests. Conversely, the patient scored in the normal range on tests of immediate recall of verbal, visual and spatial data and tests of delayed recall of verbal and visual data. Comparison with a previously described patient who had a selective deficit in immediate spatial recall and an ischemic lesion that affected frontal and parietal dorso-mesial areas in the right hemisphere (Carlesimo GA, Perri R, Turriziani P, Tomaiuolo F, Caltagirone C. Remembering what but not where: independence of spatial and visual working memory in the human brain. Cortex. 2001 Sep; 37(4):519-34) suggests that the right parietal areas are involved in the short-term storage of spatial information and that the dorso-mesial regions of the right frontal underlie mechanisms for the delayed maintenance of the same data.

  16. Using visual language to represent interdisciplinary content in urban development: Selected findings

    Directory of Open Access Journals (Sweden)

    Špela Verovšek

    2013-01-01

    Full Text Available This article addresses visual language in architecture and spatial disciplines, using it as a means of communicating and conveying information, knowledge and ideas about space that are permeated by their interdisciplinary character. We focus in particular on the transmission of messages between professionals and the general public, arguing that this process aids the long-term formation of a responsible and critical public, which is then able to take an active part in sustainable planning and design practices. The article highlights some findings of an empirical study of 245 people that tested the effectiveness of selected presentation techniques in communicating spatial messages to the general public and placing them in the framework of existing knowledge.

  17. Understanding Boron through Size-Selected Clusters: Structure, Chemical Bonding, and Fluxionality

    Energy Technology Data Exchange (ETDEWEB)

    Sergeeva, Alina P.; Popov, Ivan A.; Piazza, Zachary A.; Li, Wei-Li; Romanescu, Constantin; Wang, Lai S.; Boldyrev, Alexander I.

    2014-04-15

    /C analogy. It is believed that the electronic transmutation concept will be effective and valuable in aiding the design of new boride materials with predictable properties. The study of boron clusters with intermediate properties between those of individual atoms and bulk solids has given rise to a unique opportunity to broaden the frontier of boron chemistry. Understanding boron clusters has spurred experimentalists and theoreticians to find new boron-based nanomaterials, such as boron fullerenes, nanotubes, two-dimensional boron, and new compounds containing boron clusters as building blocks. Here, a brief and timely overview is presented addressing the recent progress made on boron clusters and the approaches used in the authors’ laboratories to determine the structure, stability, and chemical bonding of size-selected boron clusters by joint photoelectron spectroscopy and theoretical studies. Specifically, key findings on all-boron hydrocarbon analogues, metal-centered boron wheels, and electronic transmutation in boron clusters are summarized.

  18. Feature selection model based on clustering and ranking in pipeline for microarray data

    Directory of Open Access Journals (Sweden)

    Barnali Sahu

    2017-01-01

    Full Text Available Most of the available feature selection techniques in the literature are classifier bound. It means a group of features tied to the performance of a specific classifier as applied in wrapper and hybrid approach. Our objective in this study is to select a set of generic features not tied to any classifier based on the proposed framework. This framework uses attribute clustering and feature ranking techniques in pipeline in order to remove redundant features. On each uncovered cluster, signal-to-noise ratio, t-statistics and significance analysis of microarray are independently applied to select the top ranked features. Both filter and evolutionary wrapper approaches have been considered for feature selection and the data set with selected features are given to ensemble of predefined statistically different classifiers. The class labels of the test data are determined using majority voting technique. Moreover, with the aforesaid objectives, this paper focuses on obtaining a stable result out of various classification models. Further, a comparative analysis has been performed to study the classification accuracy and computational time of the current approach and evolutionary wrapper techniques. It gives a better insight into the features and further enhancing the classification accuracy with less computational time.

  19. Foot and mouth disease in Zambia: Spatial and temporal distributions of outbreaks, assessment of clusters and implications for control

    Directory of Open Access Journals (Sweden)

    Yona Sinkala

    2014-04-01

    Full Text Available Zambia has been experiencing low livestock productivity as well as trade restrictions owing to the occurrence of foot and mouth disease (FMD, but little is known about the epidemiology of the disease in these endemic settings. The fundamental questions relate to the spatio-temporal distribution of FMD cases and what determines their occurrence. A retrospective review of FMD cases in Zambia from 1981 to 2012 was conducted using geographical information systems and the SaTScan software package. Information was collected from peer-reviewed journal articles, conference proceedings, laboratory reports, unpublished scientific reports and grey literature. A space–time permutation probability model using a varying time window of one year was used to scan for areas with high infection rates. The spatial scan statistic detected a significant purely spatial cluster around the Mbala–Isoka area between 2009 and 2012, with secondary clusters in Sesheke–Kazungula in 2007 and 2008, the Kafue flats in 2004 and 2005 and Livingstone in 2012. This study provides evidence of the existence of statistically significant FMD clusters and an increase in occurrence in Zambia between 2004 and 2012. The identified clusters agree with areas known to be at high risk of FMD. The FMD virus transmission dynamics and the heterogeneous variability in risk within these locations may need further investigation.

  20. Spike timing-dependent plasticity as the origin of the formation of clustered synaptic efficacy engrams

    Directory of Open Access Journals (Sweden)

    Nicolangelo L Iannella

    2010-07-01

    Full Text Available Synapse location, dendritic active properties and synaptic plasticity are all known to play some role in shaping the different input streams impinging onto a neuron. It remains unclear however, how the magnitude and spatial distribution of synaptic efficacies emerge from this interplay. Here, we investigate this interplay using a biophysically detailed neuron model of a reconstructed layer 2/3 pyramidal cell and spike timing-dependent plasticity (STDP. Specifically, we focus on the issue of how the efficacy of synapses contributed by different input streams are spatially represented in dendrites after STDP learning. We construct a simple feed forward network where a detailed model neuron receives synaptic inputs independently from multiple yet equally sized groups of afferent fibres with correlated activity, mimicking the spike activity from different neuronal populations encoding, for example, different sensory modalities. Interestingly, ensuing STDP learning, we observe that for all afferent groups, STDP leads to synaptic efficacies arranged into spatially segregated clusters effectively partitioning the dendritic tree. These segregated clusters possess a characteristic global organisation in space, where they form a tessellation in which each group dominates mutually exclusive regions of the dendrite.Put simply, the dendritic imprint from different input streams left after STDP learning effectively forms what we term a dendritic efficacy mosaic. Furthermore, we show how variations of the inputs and STDP rule affect such an organization. Our model suggests that STDP may be an important mechanism for creating a clustered plasticity engram, which shapes how different input streams are spatially represented in dendrite.

  1. Exploring the relation between spatial configuration of buildings and remotely sensed temperatures

    Science.gov (United States)

    Myint, S. W.; Zheng, B.; Kaplan, S.; Huang, H.

    2013-12-01

    While the relationship between fractional cover of buildings and the UHI has been well studied, relationships of how spatial arrangements (e.g., clustered, dispersed) of buildings influence urban warming are not well understood. Since a diversity of spatial patterns can be observed under the same percentage of buildings cover, it is of great interest and importance to investigate the amount of variation in certain urban thermal feature such as surface temperature that is accounted for by the inclusion of spatial arrangement component. The various spatial arrangements of buildings cover can give rise to different urban thermal behaviors that may not be uncovered with the information of buildings fraction only, but can be captured to some extent using spatial analysis. The goal of this study is to examine how spatial arrangements of buildings influence and shape surface temperature in different urban settings. The study area selected is the Las-Vegas metropolitan area in Nevada, located in the Mojave Desert. An object-oriented approach was used to identify buildings using a Geoeye-1 image acquired on October 12, 2011. A spatial autocorrelation technique (i.e., Moran's I) that can measure spatial pattern (clustered, dispersed) was used to determine spatial configuration of buildings. A daytime temperature layer in degree Celsius, generated from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image, was integrated with Moran's I values of building cover and building fractions to achieve the goals set in the study. To avoid uncertainty and properly evaluate if spatial pattern of buildings has an impact on urban warming, the relation between Moran's I values and surface temperatures was observed at different levels according to their fractions (e.g., 0-0.1, 0.5-0.6, 0.9-1). There is a negative correlation exists between spatial pattern of buildings and surface temperatures implying that dispersed building arrangements elevate surface temperatures

  2. Undergraduate ALFALFA Team: Analysis of Spatially-Resolved Star-Formation in Nearby Galaxy Groups and Clusters

    Science.gov (United States)

    Finn, Rose; Collova, Natasha; Spicer, Sandy; Whalen, Kelly; Koopmann, Rebecca A.; Durbala, Adriana; Haynes, Martha P.; Undergraduate ALFALFA Team

    2017-01-01

    As part of the Undergraduate ALFALFA Team, we are conducting a survey of the gas and star-formation properties of galaxies in 36 groups and clusters in the local universe. The galaxies in our sample span a large range of galactic environments, from the centers of galaxy groups and clusters to the surrounding infall regions. One goal of the project is to map the spatial distribution of star-formation; the relative extent of the star-forming and stellar disks provides important information about the internal and external processes that deplete gas and thus drive galaxy evolution. We obtained wide-field H-alpha observations with the WIYN 0.9m telescope at Kitt Peak National Observatory for galaxies in the vicinity of the MKW11 and NRGb004 galaxy groups and the Abell 1367 cluster. We present a preliminary analysis of the relative size of the star-forming and stellar disks as a function of galaxy morphology and local galaxy density, and we calculate gas depletion times using star-formation rates and HI gas mass. We will combine these results with those from other UAT members to determine if and how environmentally-driven gas depletion varies with the mass and X-ray properties of the host group or cluster. This work has supported by NSF grants AST-0847430, AST-1211005 and AST-1637339.

  3. Hierarchical modeling of cluster size in wildlife surveys

    Science.gov (United States)

    Royle, J. Andrew

    2008-01-01

    Clusters or groups of individuals are the fundamental unit of observation in many wildlife sampling problems, including aerial surveys of waterfowl, marine mammals, and ungulates. Explicit accounting of cluster size in models for estimating abundance is necessary because detection of individuals within clusters is not independent and detectability of clusters is likely to increase with cluster size. This induces a cluster size bias in which the average cluster size in the sample is larger than in the population at large. Thus, failure to account for the relationship between delectability and cluster size will tend to yield a positive bias in estimates of abundance or density. I describe a hierarchical modeling framework for accounting for cluster-size bias in animal sampling. The hierarchical model consists of models for the observation process conditional on the cluster size distribution and the cluster size distribution conditional on the total number of clusters. Optionally, a spatial model can be specified that describes variation in the total number of clusters per sample unit. Parameter estimation, model selection, and criticism may be carried out using conventional likelihood-based methods. An extension of the model is described for the situation where measurable covariates at the level of the sample unit are available. Several candidate models within the proposed class are evaluated for aerial survey data on mallard ducks (Anas platyrhynchos).

  4. Spatial Substructure in the M87 Globular Cluster System

    Science.gov (United States)

    Feng, Yuting; Zhang, Yunhao; Guhathakurta, Puragra; Peng, Eric; Lim, Sungsoon

    2018-01-01

    Based on the observation of Next Generation Virgo Cluster Survey (NGVS) project, we obtained the u,g,r,i,z and Ks band photometric information of all the objects in the 2 degree × 2 degree area (Pilot Region) around M87, the major subcluster of Virgo. By adapting an Extreme Deconvolution method, which classifies objects into Globular Clusters (GCs), galaxies and foreground stars with their color and morphology data, we got a purer-than-ever GC distribution map with a depth to gmag=25 in Pilot Region. After masking galaxy GCs, smoothing with a 10arcmin Gaussian kernel and performing a flat field correction, we show the GC density map of M87, and got a good sersic fitting of GC radial distribution with a sersic index~2.2 in the central ellipse part (45arcmin semi major axis area of M87). We quantitatively compared our GC sample with a substructure-free mock data set, which was generated from the smoothed density map as well as the sersic fitting, by calculating the 2 point correlation function (TPCF) value in different parts of the map. After separately performing such comparison with mocks based on different galaxy masking radii which vary from 4 times g band effective radius to 10, we found signals of remarkable spatial enhancement in certain directions in the central ellipse of M87, as well as halo substructures shown as lumpiness and holes in the outer region. We present the estimated scales of these substructures from the TPCF results, and, managed to locate them with a statistical analysis of the pixelized GC map. Apart from all results listed above, we discuss the constant, extra-galactic substructure signal at a scale of ~3kpc, which does not diminish with masking sizes, as the evidence of merging and accretion history of M87.

  5. Topic modeling for cluster analysis of large biological and medical datasets.

    Science.gov (United States)

    Zhao, Weizhong; Zou, Wen; Chen, James J

    2014-01-01

    The big data moniker is nowhere better deserved than to describe the ever-increasing prodigiousness and complexity of biological and medical datasets. New methods are needed to generate and test hypotheses, foster biological interpretation, and build validated predictors. Although multivariate techniques such as cluster analysis may allow researchers to identify groups, or clusters, of related variables, the accuracies and effectiveness of traditional clustering methods diminish for large and hyper dimensional datasets. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. Its ability to reduce high dimensionality to a small number of latent variables makes it suitable as a means for clustering or overcoming clustering difficulties in large biological and medical datasets. In this study, three topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, are proposed and tested on the cluster analysis of three large datasets: Salmonella pulsed-field gel electrophoresis (PFGE) dataset, lung cancer dataset, and breast cancer dataset, which represent various types of large biological or medical datasets. All three various methods are shown to improve the efficacy/effectiveness of clustering results on the three datasets in comparison to traditional methods. A preferable cluster analysis method emerged for each of the three datasets on the basis of replicating known biological truths. Topic modeling could be advantageously applied to the large datasets of biological or medical research. The three proposed topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, yield clustering improvements for the three different data types. Clusters more efficaciously represent truthful groupings and subgroupings in the data than traditional methods, suggesting

  6. Deposition of size-selected atomic clusters on surfaces

    International Nuclear Information System (INIS)

    Carroll, S.J.

    1999-06-01

    This dissertation presents technical developments and experimental and computational investigations concerned with the deposition of atomic clusters onto surfaces. It consists of a collection of papers, in which the main body of results are contained, and four chapters presenting a subject review, computational and experimental techniques and a summary of the results presented in full within the papers. Technical work includes the optimization of an existing gas condensation cluster source based on evaporation, and the design, construction and optimization of a new gas condensation cluster source based on RF magnetron sputtering (detailed in Paper 1). The result of cluster deposition onto surfaces is found to depend on the cluster deposition energy; three impact energy regimes are explored in this work. (1) Low energy: n clusters create a defect in the surface, which pins the cluster in place, inhibiting cluster diffusion at room temperature (Paper V). (3) High energy: > 50 eV/atom. The clusters implant into the surface. For Ag 20 -Ag 200 clusters, the implantation depth is found to scale linearly with the impact energy and inversely with the cross-sectional area of the cluster, with an offset due to energy lost to the elastic compression of the surface (Paper VI). For smaller (Ag 3 ) clusters the orientation of the cluster with respect to the surface and the precise impact site play an important role; the impact energy has to be 'focused' in order for cluster implantation to occur (Paper VII). The application of deposited clusters for the creation of Si nanostructures by plasma etching is explored in Paper VIII. (author)

  7. Likelihood-based inference for clustered line transect data

    DEFF Research Database (Denmark)

    Waagepetersen, Rasmus Plenge; Schweder, Tore

    The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference...

  8. Likelihood-based inference for clustered line transect data

    DEFF Research Database (Denmark)

    Waagepetersen, Rasmus; Schweder, Tore

    2006-01-01

    The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference...

  9. Deriving spatial patterns from a novel database of volcanic rock geochemistry in the Virunga Volcanic Province, East African Rift

    Science.gov (United States)

    Poppe, Sam; Barette, Florian; Smets, Benoît; Benbakkar, Mhammed; Kervyn, Matthieu

    2016-04-01

    The Virunga Volcanic Province (VVP) is situated within the western branch of the East-African Rift. The geochemistry and petrology of its' volcanic products has been studied extensively in a fragmented manner. They represent a unique collection of silica-undersaturated, ultra-alkaline and ultra-potassic compositions, displaying marked geochemical variations over the area occupied by the VVP. We present a novel spatially-explicit database of existing whole-rock geochemical analyses of the VVP volcanics, compiled from international publications, (post-)colonial scientific reports and PhD theses. In the database, a total of 703 geochemical analyses of whole-rock samples collected from the 1950s until recently have been characterised with a geographical location, eruption source location, analytical results and uncertainty estimates for each of these categories. Comparative box plots and Kruskal-Wallis H tests on subsets of analyses with contrasting ages or analytical methods suggest that the overall database accuracy is consistent. We demonstrate how statistical techniques such as Principal Component Analysis (PCA) and subsequent cluster analysis allow the identification of clusters of samples with similar major-element compositions. The spatial patterns represented by the contrasting clusters show that both the historically active volcanoes represent compositional clusters which can be identified based on their contrasted silica and alkali contents. Furthermore, two sample clusters are interpreted to represent the most primitive, deep magma source within the VVP, different from the shallow magma reservoirs that feed the eight dominant large volcanoes. The samples from these two clusters systematically originate from locations which 1. are distal compared to the eight large volcanoes and 2. mostly coincide with the surface expressions of rift faults or NE-SW-oriented inherited Precambrian structures which were reactivated during rifting. The lava from the Mugogo

  10. The influence of caffeine on spatial-selective attention : an event-related potential study

    NARCIS (Netherlands)

    de Ruiter, MB; Snel, J; Lorist, MM; Ruijter, J

    2000-01-01

    Objectives: Following the indications of previous studies that caffeine might have a specific effect on the processing of spatial information compared with other types of information, the present study investigated the influence of caffeine on an often used spatial-selective attention task. Methods:

  11. Determination of geostatistically representative sampling locations in Porsuk Dam Reservoir (Turkey)

    Science.gov (United States)

    Aksoy, A.; Yenilmez, F.; Duzgun, S.

    2013-12-01

    Several factors such as wind action, bathymetry and shape of a lake/reservoir, inflows, outflows, point and diffuse pollution sources result in spatial and temporal variations in water quality of lakes and reservoirs. The guides by the United Nations Environment Programme and the World Health Organization to design and implement water quality monitoring programs suggest that even a single monitoring station near the center or at the deepest part of a lake will be sufficient to observe long-term trends if there is good horizontal mixing. In stratified water bodies, several samples can be required. According to the guide of sampling and analysis under the Turkish Water Pollution Control Regulation, a minimum of five sampling locations should be employed to characterize the water quality in a reservoir or a lake. The European Union Water Framework Directive (2000/60/EC) states to select a sufficient number of monitoring sites to assess the magnitude and impact of point and diffuse sources and hydromorphological pressures in designing a monitoring program. Although existing regulations and guidelines include frameworks for the determination of sampling locations in surface waters, most of them do not specify a procedure in establishment of monitoring aims with representative sampling locations in lakes and reservoirs. In this study, geostatistical tools are used to determine the representative sampling locations in the Porsuk Dam Reservoir (PDR). Kernel density estimation and kriging were used in combination to select the representative sampling locations. Dissolved oxygen and specific conductivity were measured at 81 points. Sixteen of them were used for validation. In selection of the representative sampling locations, care was given to keep similar spatial structure in distributions of measured parameters. A procedure was proposed for that purpose. Results indicated that spatial structure was lost under 30 sampling points. This was as a result of varying water

  12. Manual hierarchical clustering of regional geochemical data using a Bayesian finite mixture model

    International Nuclear Information System (INIS)

    Ellefsen, Karl J.; Smith, David B.

    2016-01-01

    Interpretation of regional scale, multivariate geochemical data is aided by a statistical technique called “clustering.” We investigate a particular clustering procedure by applying it to geochemical data collected in the State of Colorado, United States of America. The clustering procedure partitions the field samples for the entire survey area into two clusters. The field samples in each cluster are partitioned again to create two subclusters, and so on. This manual procedure generates a hierarchy of clusters, and the different levels of the hierarchy show geochemical and geological processes occurring at different spatial scales. Although there are many different clustering methods, we use Bayesian finite mixture modeling with two probability distributions, which yields two clusters. The model parameters are estimated with Hamiltonian Monte Carlo sampling of the posterior probability density function, which usually has multiple modes. Each mode has its own set of model parameters; each set is checked to ensure that it is consistent both with the data and with independent geologic knowledge. The set of model parameters that is most consistent with the independent geologic knowledge is selected for detailed interpretation and partitioning of the field samples. - Highlights: • We evaluate a clustering procedure by applying it to geochemical data. • The procedure generates a hierarchy of clusters. • Different levels of the hierarchy show geochemical processes at different spatial scales. • The clustering method is Bayesian finite mixture modeling. • Model parameters are estimated with Hamiltonian Monte Carlo sampling.

  13. Applying spatial analysis tools in public health: an example using SaTScan to detect geographic targets for colorectal cancer screening interventions.

    Science.gov (United States)

    Sherman, Recinda L; Henry, Kevin A; Tannenbaum, Stacey L; Feaster, Daniel J; Kobetz, Erin; Lee, David J

    2014-03-20

    Epidemiologists are gradually incorporating spatial analysis into health-related research as geocoded cases of disease become widely available and health-focused geospatial computer applications are developed. One health-focused application of spatial analysis is cluster detection. Using cluster detection to identify geographic areas with high-risk populations and then screening those populations for disease can improve cancer control. SaTScan is a free cluster-detection software application used by epidemiologists around the world to describe spatial clusters of infectious and chronic disease, as well as disease vectors and risk factors. The objectives of this article are to describe how spatial analysis can be used in cancer control to detect geographic areas in need of colorectal cancer screening intervention, identify issues commonly encountered by SaTScan users, detail how to select the appropriate methods for using SaTScan, and explain how method selection can affect results. As an example, we used various methods to detect areas in Florida where the population is at high risk for late-stage diagnosis of colorectal cancer. We found that much of our analysis was underpowered and that no single method detected all clusters of statistical or public health significance. However, all methods detected 1 area as high risk; this area is potentially a priority area for a screening intervention. Cluster detection can be incorporated into routine public health operations, but the challenge is to identify areas in which the burden of disease can be alleviated through public health intervention. Reliance on SaTScan's default settings does not always produce pertinent results.

  14. Spatial clustering of Borrelia burgdorferi sensu lato within populations of Allen's chipmunks and dusky-footed woodrats in northwestern California.

    Science.gov (United States)

    Hacker, Gregory M; Brown, Richard N; Fedorova, Natalia; Girard, Yvette A; Higley, Mark; Clueit, Bernadette; Lane, Robert S

    2018-01-01

    The ecology of Lyme borreliosis is complex in northwestern California, with several potential reservoir hosts, tick vectors, and genospecies of Borrelia burgdorferi sensu lato. The primary objective of this study was to determine the fine-scale spatial distribution of different genospecies in four rodent species, the California ground squirrel (Otospermophilus beecheyi), northern flying squirrel (Glaucomys sabrinus), dusky-footed woodrat (Neotoma fuscipes), and Allen's chipmunk (Neotamias senex). Rodents were live-trapped between June 2004 and May 2005 at the Hoopa Valley Tribal Reservation (HVTR) in Humboldt County, California. Ear-punch biopsies obtained from each rodent were tested by polymerase chain reaction (PCR) and sequencing analysis. The programs ArcGIS and SaTScan were used to examine the spatial distribution of genospecies. Multinomial log-linear models were used to model habitat and host-specific characteristics and their effect on the presence of each borrelial genospecies. The Akaike information criterion (AICc) was used to compare models and determine model fit. Borrelia burgdorferi sensu stricto was primarily associated with chipmunks and B. bissettiae largely with woodrats. The top model included the variables "host species", "month", and "elevation" (weight = 0.84). Spatial clustering of B. bissettiae was detected in the northwestern section of the HVTR, whereas B. burgdorferi sensu stricto was clustered in the southeastern section. We conclude that the spatial distribution of these borreliae are driven at least in part by host species, time-of-year, and elevation.

  15. Spatial representations are specific to different domains of knowledge.

    Directory of Open Access Journals (Sweden)

    Rowena Beecham

    Full Text Available There is evidence that many abstract concepts are represented cognitively in a spatial format. However, it is unknown whether similar spatial processes are employed in different knowledge domains, or whether individuals exhibit similar spatial profiles within and across domains. This research investigated similarities in spatial representation in two knowledge domains--mathematics and music. Sixty-one adults completed analogous number magnitude and pitch discrimination tasks: the Spatial-Numerical Association of Response Codes and Spatial-Musical Association of Response Codes tasks. Subgroups of individuals with different response patterns were identified through cluster analyses. For both the mathematical and musical tasks, approximately half of the participants showed the expected spatial judgment effect when explicitly cued to focus on the spatial properties of the stimuli. Despite this, performances on the two tasks were largely independent. Consistent with previous research, the study provides evidence for the spatial representation of number and pitch in the majority of individuals. However, there was little evidence to support the claim that the same spatial representation processes underpin mathematical and musical judgments.

  16. A New Variable Weighting and Selection Procedure for K-Means Cluster Analysis

    Science.gov (United States)

    Steinley, Douglas; Brusco, Michael J.

    2008-01-01

    A variance-to-range ratio variable weighting procedure is proposed. We show how this weighting method is theoretically grounded in the inherent variability found in data exhibiting cluster structure. In addition, a variable selection procedure is proposed to operate in conjunction with the variable weighting technique. The performances of these…

  17. Secure and Fair Cluster Head Selection Protocol for Enhancing Security in Mobile Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    B. Paramasivan

    2014-01-01

    Full Text Available Mobile ad hoc networks (MANETs are wireless networks consisting of number of autonomous mobile devices temporarily interconnected into a network by wireless media. MANETs become one of the most prevalent areas of research in the recent years. Resource limitations, energy efficiency, scalability, and security are the great challenging issues in MANETs. Due to its deployment nature, MANETs are more vulnerable to malicious attack. The secure routing protocols perform very basic security related functions which are not sufficient to protect the network. In this paper, a secure and fair cluster head selection protocol (SFCP is proposed which integrates security factors into the clustering approach for achieving attacker identification and classification. Byzantine agreement based cooperative technique is used for attacker identification and classification to make the network more attack resistant. SFCP used to solve this issue by making the nodes that are totally surrounded by malicious neighbors adjust dynamically their belief and disbelief thresholds. The proposed protocol selects the secure and energy efficient cluster head which acts as a local detector without imposing overhead to the clustering performance. SFCP is simulated in network simulator 2 and compared with two protocols including AODV and CBRP.

  18. Secure and fair cluster head selection protocol for enhancing security in mobile ad hoc networks.

    Science.gov (United States)

    Paramasivan, B; Kaliappan, M

    2014-01-01

    Mobile ad hoc networks (MANETs) are wireless networks consisting of number of autonomous mobile devices temporarily interconnected into a network by wireless media. MANETs become one of the most prevalent areas of research in the recent years. Resource limitations, energy efficiency, scalability, and security are the great challenging issues in MANETs. Due to its deployment nature, MANETs are more vulnerable to malicious attack. The secure routing protocols perform very basic security related functions which are not sufficient to protect the network. In this paper, a secure and fair cluster head selection protocol (SFCP) is proposed which integrates security factors into the clustering approach for achieving attacker identification and classification. Byzantine agreement based cooperative technique is used for attacker identification and classification to make the network more attack resistant. SFCP used to solve this issue by making the nodes that are totally surrounded by malicious neighbors adjust dynamically their belief and disbelief thresholds. The proposed protocol selects the secure and energy efficient cluster head which acts as a local detector without imposing overhead to the clustering performance. SFCP is simulated in network simulator 2 and compared with two protocols including AODV and CBRP.

  19. Spatial and simultaneous representative seroprevalence of anti-Toxoplasma gondii antibodies in owners and their domiciled dogs in a major city of southern Brazil.

    Directory of Open Access Journals (Sweden)

    Aline do Nascimento Benitez

    Full Text Available Toxoplasmosis, caused by Toxoplasma gondii, has traditionally been considered an important water and foodborne protozoonosis with important public health considerations. Although felids play a well-established role as definitive hosts, canine epidemiological involvement in the parasite's life cycle remains questionable and controversial. The increasing closeness of the human-dog bond, particularly seen in urban settings, has been recognized as a historically unprecedented worldwide movement. Sharing daily lives in the same households, dogs may be exposed to similar associated risks of T. gondii infection as their owners. Thus, epidemiological assessment of the intra-domiciled environment, especially among socio-economically different human populations, may provide novel information regarding the actual role of dogs in animal and human toxoplasmosis. Despite spatial approaches being recently used for other water and foodborne diseases, no study has been conducted on the simultaneous spatial seroprevalence of both human and animal IgG anti-T. gondii antibodies in urban areas of major cities. Accordingly, the aim of the present study was to assess the seroprevalence and associated variables of Toxoplasma infection in owners and their domiciled dogs in Londrina, southern Brazil. Human and canine seroprevalence rates and variables associated with seroprevalence were investigated through representative random sampling among 564 households, which included 597 owners and 729 dogs. Overall, statistically significant differences between the seroprevalence of human and dog anti-T. gondii antibodies were found by Immunofluorescence Antibody Testing in 248/597 (41.54% owners and 119/729 (16.32% dogs. Through multiple analysis, significant concomitant variables for seropositivity of household individuals (people and dogs were determined, including public sewer service, yard cleaning frequency, and having a dirty yard. Although no statistically significant

  20. The Next Generation Virgo Cluster Survey (NGVS). XXXII. A Search for Globular Cluster Substructures in the Virgo Galaxy Cluster Core

    Science.gov (United States)

    Powalka, Mathieu; Puzia, Thomas H.; Lançon, Ariane; Longobardi, Alessia; Peng, Eric W.; Duc, Pierre-Alain; Alamo-Martínez, Karla; Blakeslee, John P.; Côté, Patrick; Cuillandre, Jean-Charles; Durrell, Patrick; Eigenthaler, Paul; Ferrarese, Laura; Guhathakurta, Puragra; Gwyn, S. D. J.; Hudelot, Patrick; Liu, Chengze; Mei, Simona; Muñoz, Roberto P.; Roediger, Joel; Sánchez-Janssen, Rubén; Toloba, Elisa; Zhang, Hongxin

    2018-03-01

    Substructure in globular cluster (GC) populations around large galaxies is expected in galaxy formation scenarios that involve accretion or merger events, and it has been searched for using direct associations between GCs and structure in the diffuse galaxy light, or with GC kinematics. Here, we present a search for candidate substructures in the GC population around the Virgo cD galaxy M87 through the analysis of the spatial distribution of the GC colors. The study is based on a sample of ∼1800 bright GCs with high-quality u, g, r, i, z, K s photometry, selected to ensure a low contamination by foreground stars or background galaxies. The spectral energy distributions of the GCs are associated with formal estimates of age and metallicity, which are representative of its position in a 4D color space relative to standard single stellar population models. Dividing the sample into broad bins based on the relative formal ages, we observe inhomogeneities that reveal signatures of GC substructures. The most significant of these is a spatial overdensity of GCs with relatively young age labels, of diameter ∼0.°1 (∼30 kpc), located to the south of M87. The significance of this detection is larger than about 5σ after accounting for estimates of random and systematic errors. Surprisingly, no large Virgo galaxy is present in this area that could potentially host these GCs. But candidate substructures in the M87 halo with equally elusive hosts have been described based on kinematic studies in the past. The number of GC spectra available around M87 is currently insufficient to clarify the nature of the new candidate substructure.

  1. 20 CFR 266.4 - Information considered in selecting a representative payee.

    Science.gov (United States)

    2010-04-01

    ..., including the type of relationship, e.g., family or legal guardianship; degree of relationship, if the... 20 Employees' Benefits 1 2010-04-01 2010-04-01 false Information considered in selecting a representative payee. 266.4 Section 266.4 Employees' Benefits RAILROAD RETIREMENT BOARD REGULATIONS UNDER THE...

  2. SPATIALLY RESOLVED SPECTROSCOPY AND CHEMICAL HISTORY OF STAR-FORMING GALAXIES IN THE HERCULES CLUSTER: THE EFFECTS OF THE ENVIRONMENT

    International Nuclear Information System (INIS)

    Petropoulou, V.; Vilchez, J.; Iglesias-Paramo, J.; Cedres, B.; Papaderos, P.; Magrini, L.; Reverte, D.

    2011-01-01

    Spatially resolved spectroscopy has been obtained for a sample of 27 star-forming (SF) galaxies selected from our deep Hα survey of the Hercules cluster. We have applied spectral synthesis models to all emission-line spectra of this sample using the population synthesis code STARLIGHT and have obtained fundamental parameters of stellar components such as mean metallicity and age. The emission-line spectra were corrected for underlying stellar absorption using these spectral synthesis models. Line fluxes were measured and O/H and N/O gas chemical abundances were obtained using the latest empirical calibrations. We have derived the masses and total luminosities of the galaxies using available Sloan Digital Sky Survey broadband photometry. The effects of cluster environment on the chemical evolution of galaxies and on their mass-metallicity (MZ) and luminosity-metallicity (LZ) relations were studied by combining the derived gas metallicities, the mean stellar metallicities and ages, the masses and luminosities of the galaxies, and their existing H I data. Our Hercules SF galaxies are divided into three main subgroups: (1) chemically evolved spirals with truncated ionized-gas disks and nearly flat oxygen gradients, demonstrating the effect of ram-pressure stripping; (2) chemically evolved dwarfs/irregulars populating the highest local densities, possible products of tidal interactions in preprocessing events; and (3) less metallic dwarf galaxies that appear to be 'newcomers' to the cluster and are experiencing pressure-triggered star formation. Most Hercules SF galaxies follow well-defined MZ and LZ sequences (for both O/H and N/O), though the dwarf/irregular galaxies located at the densest regions appear to be outliers to these global relations, suggesting a physical reason for the dispersion in these fundamental relations. The Hercules cluster appears to be currently assembling via the merger of smaller substructures, providing an ideal laboratory where the local

  3. Analyzing Dynamic Probabilistic Risk Assessment Data through Topology-Based Clustering

    Energy Technology Data Exchange (ETDEWEB)

    Diego Mandelli; Dan Maljovec; BeiWang; Valerio Pascucci; Peer-Timo Bremer

    2013-09-01

    We investigate the use of a topology-based clustering technique on the data generated by dynamic event tree methodologies. The clustering technique we utilizes focuses on a domain-partitioning algorithm based on topological structures known as the Morse-Smale complex, which partitions the data points into clusters based on their uniform gradient flow behavior. We perform both end state analysis and transient analysis to classify the set of nuclear scenarios. We demonstrate our methodology on a dataset generated for a sodium-cooled fast reactor during an aircraft crash scenario. The simulation tracks the temperature of the reactor as well as the time for a recovery team to fix the passive cooling system. Combined with clustering results obtained previously through mean shift methodology, we present the user with complementary views of the data that help illuminate key features that may be otherwise hidden using a single methodology. By clustering the data, the number of relevant test cases to be selected for further analysis can be drastically reduced by selecting a representative from each cluster. Identifying the similarities of simulations within a cluster can also aid in the drawing of important conclusions with respect to safety analysis.

  4. Implications for gravitational lensing and the dark matter content in clusters of galaxies from spatially resolved x-ray spectra

    Science.gov (United States)

    Loewenstein, M.

    1994-01-01

    A simple method for deriving well-behaved temperature solutions to the equation of hydrostatic equilibrium for intracluster media with X-ray imaging observations is presented and applied to a series of generalized models as well as to observations of the Perseus cluster and Abell 2256. In these applications the allowed range in the ratio of nonbaryons to baryons as a function of radius is derived, taking into account the uncertainties and crude spatial resolution of the X-ray spectra and considering a range of physically reasonable mass models with various scale heights. Particular attention is paid to the central regions of the cluster, and it is found that the dark matter can be sufficiently concentrated to be consistent with the high central mass surface densities for moderate-redshift clusters from their gravitational lensing properties.

  5. Spatial distribution and cluster analysis of retail drug shop characteristics and antimalarial behaviors as reported by private medicine retailers in western Kenya: informing future interventions.

    Science.gov (United States)

    Rusk, Andria; Highfield, Linda; Wilkerson, J Michael; Harrell, Melissa; Obala, Andrew; Amick, Benjamin

    2016-02-19

    Efforts to improve malaria case management in sub-Saharan Africa have shifted focus to private antimalarial retailers to increase access to appropriate treatment. Demands to decrease intervention cost while increasing efficacy requires interventions tailored to geographic regions with demonstrated need. Cluster analysis presents an opportunity to meet this demand, but has not been applied to the retail sector or antimalarial retailer behaviors. This research conducted cluster analysis on medicine retailer behaviors in Kenya, to improve malaria case management and inform future interventions. Ninety-seven surveys were collected from medicine retailers working in the Webuye Health and Demographic Surveillance Site. Survey items included retailer training, education, antimalarial drug knowledge, recommending behavior, sales, and shop characteristics, and were analyzed using Kulldorff's spatial scan statistic. The Bernoulli purely spatial model for binomial data was used, comparing cases to controls. Statistical significance of found clusters was tested with a likelihood ratio test, using the null hypothesis of no clustering, and a p value based on 999 Monte Carlo simulations. The null hypothesis was rejected with p values of 0.05 or less. A statistically significant cluster of fewer than expected pharmacy-trained retailers was found (RR = .09, p = .001) when compared to the expected random distribution. Drug recommending behavior also yielded a statistically significant cluster, with fewer than expected retailers recommending the correct antimalarial medication to adults (RR = .018, p = .01), and fewer than expected shops selling that medication more often than outdated antimalarials when compared to random distribution (RR = 0.23, p = .007). All three of these clusters were co-located, overlapping in the northwest of the study area. Spatial clustering was found in the data. A concerning amount of correlation was found in one specific region in the study area where

  6. Global survey of star clusters in the Milky Way. VI. Age distribution and cluster formation history

    Science.gov (United States)

    Piskunov, A. E.; Just, A.; Kharchenko, N. V.; Berczik, P.; Scholz, R.-D.; Reffert, S.; Yen, S. X.

    2018-06-01

    Context. The all-sky Milky Way Star Clusters (MWSC) survey provides uniform and precise ages, along with other relevant parameters, for a wide variety of clusters in the extended solar neighbourhood. Aims: In this study we aim to construct the cluster age distribution, investigate its spatial variations, and discuss constraints on cluster formation scenarios of the Galactic disk during the last 5 Gyrs. Methods: Due to the spatial extent of the MWSC, we have considered spatial variations of the age distribution along galactocentric radius RG, and along Z-axis. For the analysis of the age distribution we used 2242 clusters, which all lie within roughly 2.5 kpc of the Sun. To connect the observed age distribution to the cluster formation history we built an analytical model based on simple assumptions on the cluster initial mass function and on the cluster mass-lifetime relation, fit it to the observations, and determined the parameters of the cluster formation law. Results: Comparison with the literature shows that earlier results strongly underestimated the number of evolved clusters with ages t ≳ 100 Myr. Recent studies based on all-sky catalogues agree better with our data, but still lack the oldest clusters with ages t ≳ 1 Gyr. We do not observe a strong variation in the age distribution along RG, though we find an enhanced fraction of older clusters (t > 1 Gyr) in the inner disk. In contrast, the distribution strongly varies along Z. The high altitude distribution practically does not contain clusters with t < 1 Gyr. With simple assumptions on the cluster formation history, the cluster initial mass function and the cluster lifetime we can reproduce the observations. The cluster formation rate and the cluster lifetime are strongly degenerate, which does not allow us to disentangle different formation scenarios. In all cases the cluster formation rate is strongly declining with time, and the cluster initial mass function is very shallow at the high mass end.

  7. Genome based analysis of type-I polyketide synthase and nonribosomal peptide synthetase gene clusters in seven strains of five representative Nocardia species.

    Science.gov (United States)

    Komaki, Hisayuki; Ichikawa, Natsuko; Hosoyama, Akira; Takahashi-Nakaguchi, Azusa; Matsuzawa, Tetsuhiro; Suzuki, Ken-ichiro; Fujita, Nobuyuki; Gonoi, Tohru

    2014-04-30

    Actinobacteria of the genus Nocardia usually live in soil or water and play saprophytic roles, but they also opportunistically infect the respiratory system, skin, and other organs of humans and animals. Primarily because of the clinical importance of the strains, some Nocardia genomes have been sequenced, and genome sequences have accumulated. Genome sizes of Nocardia strains are similar to those of Streptomyces strains, the producers of most antibiotics. In the present work, we compared secondary metabolite biosynthesis gene clusters of type-I polyketide synthase (PKS-I) and nonribosomal peptide synthetase (NRPS) among genomes of representative Nocardia species/strains based on domain organization and amino acid sequence homology. Draft genome sequences of Nocardia asteroides NBRC 15531(T), Nocardia otitidiscaviarum IFM 11049, Nocardia brasiliensis NBRC 14402(T), and N. brasiliensis IFM 10847 were read and compared with published complete genome sequences of Nocardia farcinica IFM 10152, Nocardia cyriacigeorgica GUH-2, and N. brasiliensis HUJEG-1. Genome sizes are as follows: N. farcinica, 6.0 Mb; N. cyriacigeorgica, 6.2 Mb; N. asteroides, 7.0 Mb; N. otitidiscaviarum, 7.8 Mb; and N. brasiliensis, 8.9 - 9.4 Mb. Predicted numbers of PKS-I, NRPS, and PKS-I/NRPS hybrid clusters ranged between 4-11, 7-13, and 1-6, respectively, depending on strains, and tended to increase with increasing genome size. Domain and module structures of representative or unique clusters are discussed in the text. We conclude the following: 1) genomes of Nocardia strains carry as many PKS-I and NRPS gene clusters as those of Streptomyces strains, 2) the number of PKS-I and NRPS gene clusters in Nocardia strains varies substantially depending on species, and N. brasiliensis strains carry the largest numbers of clusters among the species studied, 3) the seven Nocardia strains studied in the present work have seven common PKS-I and/or NRPS clusters, some of whose products are yet to be studied

  8. Spatial access method for urban geospatial database management: An efficient approach of 3D vector data clustering technique

    DEFF Research Database (Denmark)

    Azri, Suhaibah; Ujang, Uznir; Rahman, Alias Abdul

    2014-01-01

    In the last few years, 3D urban data and its information are rapidly increased due to the growth of urban area and urbanization phenomenon. These datasets are then maintain and manage in 3D spatial database system. However, performance deterioration is likely to happen due to the massiveness of 3D...... datasets. As a solution, 3D spatial index structure is used as a booster to increase the performance of data retrieval. In commercial database, commonly and widely used index structure for 3D spatial database is 3D R-Tree. This is due to its simplicity and promising method in handling spatial data. However......D geospatial data clustering to be used in the construction of 3D R-Tree and respectively could reduce the overlapping among nodes. The proposed method is tested on 3D urban dataset for the application of urban infill development. By using several cases of data updating operations such as building...

  9. Cluster-cluster correlations in the two-dimensional stationary Ising-model

    International Nuclear Information System (INIS)

    Klassmann, A.

    1997-01-01

    In numerical integration of the Cahn-Hillard equation, which describes Oswald rising in a two-phase matrix, N. Masbaum showed that spatial correlations between clusters scale with respect to the mean cluster size (itself a function of time). T. B. Liverpool showed by Monte Carlo simulations for the Ising model that the analogous correlations have a similar form. Both demonstrated that immediately around each cluster there is some depletion area followed by something like a ring of clusters of the same size as the original one. More precisely, it has been shown that the distribution of clusters around a given cluster looks like a sinus-curve decaying exponentially with respect to the distance to a constant value

  10. Spatial-area selective retrieval of multiple object-place associations in a hierarchical cognitive map formed by theta phase coding.

    Science.gov (United States)

    Sato, Naoyuki; Yamaguchi, Yoko

    2009-06-01

    The human cognitive map is known to be hierarchically organized consisting of a set of perceptually clustered landmarks. Patient studies have demonstrated that these cognitive maps are maintained by the hippocampus, while the neural dynamics are still poorly understood. The authors have shown that the neural dynamic "theta phase precession" observed in the rodent hippocampus may be capable of forming hierarchical cognitive maps in humans. In the model, a visual input sequence consisting of object and scene features in the central and peripheral visual fields, respectively, results in the formation of a hierarchical cognitive map for object-place associations. Surprisingly, it is possible for such a complex memory structure to be formed in a few seconds. In this paper, we evaluate the memory retrieval of object-place associations in the hierarchical network formed by theta phase precession. The results show that multiple object-place associations can be retrieved with the initial cue of a scene input. Importantly, according to the wide-to-narrow unidirectional connections among scene units, the spatial area for object-place retrieval can be controlled by the spatial area of the initial cue input. These results indicate that the hierarchical cognitive maps have computational advantages on a spatial-area selective retrieval of multiple object-place associations. Theta phase precession dynamics is suggested as a fundamental neural mechanism of the human cognitive map.

  11. Extensive polycistronism and antisense transcription in the mammalian Hox clusters.

    Directory of Open Access Journals (Sweden)

    Gaëll Mainguy

    Full Text Available The Hox clusters play a crucial role in body patterning during animal development. They encode both Hox transcription factor and micro-RNA genes that are activated in a precise temporal and spatial sequence that follows their chromosomal order. These remarkable collinear properties confer functional unit status for Hox clusters. We developed the TranscriptView platform to establish high resolution transcriptional profiling and report here that transcription in the Hox clusters is far more complex than previously described in both human and mouse. Unannotated transcripts can represent up to 60% of the total transcriptional output of a cluster. In particular, we identified 14 non-coding Transcriptional Units antisense to Hox genes, 10 of which (70% have a detectable mouse homolog. Most of these Transcriptional Units in both human and mouse present conserved sizeable sequences (>40 bp overlapping Hox transcripts, suggesting that these Hox antisense transcripts are functional. Hox clusters also display at least seven polycistronic clusters, i.e., different genes being co-transcribed on long isoforms (up to 30 kb. This work provides a reevaluated framework for understanding Hox gene function and dys-function. Such extensive transcriptions may provide a structural explanation for Hox clustering.

  12. Selection and Penalty Strategies for Genetic Algorithms Designed to Solve Spatial Forest Planning Problems

    International Nuclear Information System (INIS)

    Thompson, M.P.; Sessions, J.; Hamann, J.D.

    2009-01-01

    Genetic algorithms (GAs) have demonstrated success in solving spatial forest planning problems. We present an adaptive GA that incorporates population-level statistics to dynamically update penalty functions, a process analogous to strategic oscillation from the tabu search literature. We also explore performance of various selection strategies. The GA identified feasible solutions within 96%, 98%, and 93% of a non spatial relaxed upper bound calculated for landscapes of 100, 500, and 1000 units, respectively. The problem solved includes forest structure constraints limiting harvest opening sizes and requiring minimally sized patches of mature forest. Results suggest that the dynamic penalty strategy is superior to the more standard static penalty implementation. Results also suggest that tournament selection can be superior to the more standard implementation of proportional selection for smaller problems, but becomes susceptible to premature convergence as problem size increases. It is therefore important to balance selection pressure with appropriate disruption. We conclude that integrating intelligent search strategies into the context of genetic algorithms can yield improvements and should be investigated for future use in spatial planning with ecological goals.

  13. Towards Clustering of Mobile and Smartwatch Accelerometer Data for Physical Activity Recognition

    Directory of Open Access Journals (Sweden)

    Chelsea Dobbins

    2018-06-01

    Full Text Available Mobile and wearable devices now have a greater capability of sensing human activity ubiquitously and unobtrusively through advancements in miniaturization and sensing abilities. However, outstanding issues remain around the energy restrictions of these devices when processing large sets of data. This paper presents our approach that uses feature selection to refine the clustering of accelerometer data to detect physical activity. This also has a positive effect on the computational burden that is associated with processing large sets of data, as energy efficiency and resource use is decreased because less data is processed by the clustering algorithms. Raw accelerometer data, obtained from smartphones and smartwatches, have been preprocessed to extract both time and frequency domain features. Principle component analysis feature selection (PCAFS and correlation feature selection (CFS have been used to remove redundant features. The reduced feature sets have then been evaluated against three widely used clustering algorithms, including hierarchical clustering analysis (HCA, k-means, and density-based spatial clustering of applications with noise (DBSCAN. Using the reduced feature sets resulted in improved separability, reduced uncertainty, and improved efficiency compared with the baseline, which utilized all features. Overall, the CFS approach in conjunction with HCA produced higher Dunn Index results of 9.7001 for the phone and 5.1438 for the watch features, which is an improvement over the baseline. The results of this comparative study of feature selection and clustering, with the specific algorithms used, has not been performed previously and provides an optimistic and usable approach to recognize activities using either a smartphone or smartwatch.

  14. Evolution of phenotypic clusters through competition and local adaptation along an environmental gradient.

    Science.gov (United States)

    Leimar, Olof; Doebeli, Michael; Dieckmann, Ulf

    2008-04-01

    We have analyzed the evolution of a quantitative trait in populations that are spatially extended along an environmental gradient, with gene flow between nearby locations. In the absence of competition, there is stabilizing selection toward a locally best-adapted trait that changes gradually along the gradient. According to traditional ideas, gradual spatial variation in environmental conditions is expected to lead to gradual variation in the evolved trait. A contrasting possibility is that the trait distribution instead breaks up into discrete clusters. Doebeli and Dieckmann (2003) argued that competition acting locally in trait space and geographical space can promote such clustering. We have investigated this possibility using deterministic population dynamics for asexual populations, analyzing our model numerically and through an analytical approximation. We examined how the evolution of clusters is affected by the shape of competition kernels, by the presence of Allee effects, and by the strength of gene flow along the gradient. For certain parameter ranges clustering was a robust outcome, and for other ranges there was no clustering. Our analysis shows that the shape of competition kernels is important for clustering: the sign structure of the Fourier transform of a competition kernel determines whether the kernel promotes clustering. Also, we found that Allee effects promote clustering, whereas gene flow can have a counteracting influence. In line with earlier findings, we could demonstrate that phenotypic clustering was favored by gradients of intermediate slope.

  15. Spatial-temporal cluster analysis of mortality from road traffic injuries using geographic information systems in West of Iran during 2009-2014.

    Science.gov (United States)

    Zangeneh, Alireza; Najafi, Farid; Karimi, Saeed; Saeidi, Shahram; Izadi, Neda

    2018-04-01

    Road traffic injuries (RTIs) are considered as one of the most important health problems endangering people's life. The examination of the geographical distribution of RTIs could help policymakers in better planning to reduce RTIs. This study, therefore, aimed to determine the spatial-temporal clustering of mortality from RTIs in West of Iran. Deaths from RTIs, registered in Forensic Medicine Organization of Kermanshah province over a period of six years (2009-2014), were used. Using negative binomial regression, the mortality trend was investigated. In order to investigate the spatial distribution of RTIs, we used ArcGIS. (Version 10.3). The median age of the 3231 people died in RTIs was 37 (IQR = 31) year, 78.4% were male. The 6-year average mortality rate from RTIs was 27.8/100,000 deaths, and the average rate had a declining trend. The dispersion of RTIs showed that most deaths occurred in Kermanshah, Islamabad, Bisotun, and Harsin road axes, respectively. The mean center of all deaths from RTIs occurred in Kermanshah province, the central area of Kermanshah district. The spatial trend of such deaths has moved to the northeast-southwest, and such deaths were geographically centralized. Results of Moran's I with respect to cluster analysis also indicated positive spatial autocorrelations. The results showed that the mortality rate from RTIs, despite the decline in recent years, is still high when compared with other countries. The clustering of accidents raises the concern that road infrastructure in certain locations may also be a factor. Regarding the results related to the temporal analysis, it is suggested that the enforcement of traffic rules be stricter at rush hours. Copyright © 2018 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

  16. Cluster analysis of typhoid cases in Kota Bharu, Kelantan, Malaysia

    Directory of Open Access Journals (Sweden)

    Nazarudin Safian

    2008-09-01

    Full Text Available Typhoid fever is still a major public health problem globally as well as in Malaysia. This study was done to identify the spatial epidemiology of typhoid fever in the Kota Bharu District of Malaysia as a first step to developing more advanced analysis of the whole country. The main characteristic of the epidemiological pattern that interested us was whether typhoid cases occurred in clusters or whether they were evenly distributed throughout the area. We also wanted to know at what spatial distances they were clustered. All confirmed typhoid cases that were reported to the Kota Bharu District Health Department from the year 2001 to June of 2005 were taken as the samples. From the home address of the cases, the location of the house was traced and a coordinate was taken using handheld GPS devices. Spatial statistical analysis was done to determine the distribution of typhoid cases, whether clustered, random or dispersed. The spatial statistical analysis was done using CrimeStat III software to determine whether typhoid cases occur in clusters, and later on to determine at what distances it clustered. From 736 cases involved in the study there was significant clustering for cases occurring in the years 2001, 2002, 2003 and 2005. There was no significant clustering in year 2004. Typhoid clustering also occurred strongly for distances up to 6 km. This study shows that typhoid cases occur in clusters, and this method could be applicable to describe spatial epidemiology for a specific area. (Med J Indones 2008; 17: 175-82Keywords: typhoid, clustering, spatial epidemiology, GIS

  17. The Pattern of Spatially Concentrated Industries in East Germany - A Contribution to the Discussion on Economic “Clusters“

    OpenAIRE

    Martin T.W. Rosenfeld; Peter Franz; Gerhard Heimpold

    2005-01-01

    Throughout the literature in regional economics, most authors agree that spatially concentrated industrial activities are important for regional economic growth. Agglomeration economies, which may occur in the context of spatial concentration and “clusters“, may lead to lower costs of production and may reduce transaction costs of all kind, e. g. information costs, including the costs for R&D activities. There is much less agreement on (and: knowledge about) the empirical identification of ...

  18. Geovisual analytics to enhance spatial scan statistic interpretation: an analysis of U.S. cervical cancer mortality.

    Science.gov (United States)

    Chen, Jin; Roth, Robert E; Naito, Adam T; Lengerich, Eugene J; Maceachren, Alan M

    2008-11-07

    Kulldorff's spatial scan statistic and its software implementation - SaTScan - are widely used for detecting and evaluating geographic clusters. However, two issues make using the method and interpreting its results non-trivial: (1) the method lacks cartographic support for understanding the clusters in geographic context and (2) results from the method are sensitive to parameter choices related to cluster scaling (abbreviated as scaling parameters), but the system provides no direct support for making these choices. We employ both established and novel geovisual analytics methods to address these issues and to enhance the interpretation of SaTScan results. We demonstrate our geovisual analytics approach in a case study analysis of cervical cancer mortality in the U.S. We address the first issue by providing an interactive visual interface to support the interpretation of SaTScan results. Our research to address the second issue prompted a broader discussion about the sensitivity of SaTScan results to parameter choices. Sensitivity has two components: (1) the method can identify clusters that, while being statistically significant, have heterogeneous contents comprised of both high-risk and low-risk locations and (2) the method can identify clusters that are unstable in location and size as the spatial scan scaling parameter is varied. To investigate cluster result stability, we conducted multiple SaTScan runs with systematically selected parameters. The results, when scanning a large spatial dataset (e.g., U.S. data aggregated by county), demonstrate that no single spatial scan scaling value is known to be optimal to identify clusters that exist at different scales; instead, multiple scans that vary the parameters are necessary. We introduce a novel method of measuring and visualizing reliability that facilitates identification of homogeneous clusters that are stable across analysis scales. Finally, we propose a logical approach to proceed through the analysis of

  19. Segmentation of confocal Raman microspectroscopic imaging data using edge-preserving denoising and clustering.

    Science.gov (United States)

    Alexandrov, Theodore; Lasch, Peter

    2013-06-18

    Over the past decade, confocal Raman microspectroscopic (CRM) imaging has matured into a useful analytical tool to obtain spatially resolved chemical information on the molecular composition of biological samples and has found its way into histopathology, cytology, and microbiology. A CRM imaging data set is a hyperspectral image in which Raman intensities are represented as a function of three coordinates: a spectral coordinate λ encoding the wavelength and two spatial coordinates x and y. Understanding CRM imaging data is challenging because of its complexity, size, and moderate signal-to-noise ratio. Spatial segmentation of CRM imaging data is a way to reveal regions of interest and is traditionally performed using nonsupervised clustering which relies on spectral domain-only information with the main drawback being the high sensitivity to noise. We present a new pipeline for spatial segmentation of CRM imaging data which combines preprocessing in the spectral and spatial domains with k-means clustering. Its core is the preprocessing routine in the spatial domain, edge-preserving denoising (EPD), which exploits the spatial relationships between Raman intensities acquired at neighboring pixels. Additionally, we propose to use both spatial correlation to identify Raman spectral features colocalized with defined spatial regions and confidence maps to assess the quality of spatial segmentation. For CRM data acquired from midsagittal Syrian hamster ( Mesocricetus auratus ) brain cryosections, we show how our pipeline benefits from the complex spatial-spectral relationships inherent in the CRM imaging data. EPD significantly improves the quality of spatial segmentation that allows us to extract the underlying structural and compositional information contained in the Raman microspectra.

  20. Document clustering methods, document cluster label disambiguation methods, document clustering apparatuses, and articles of manufacture

    Science.gov (United States)

    Sanfilippo, Antonio [Richland, WA; Calapristi, Augustin J [West Richland, WA; Crow, Vernon L [Richland, WA; Hetzler, Elizabeth G [Kennewick, WA; Turner, Alan E [Kennewick, WA

    2009-12-22

    Document clustering methods, document cluster label disambiguation methods, document clustering apparatuses, and articles of manufacture are described. In one aspect, a document clustering method includes providing a document set comprising a plurality of documents, providing a cluster comprising a subset of the documents of the document set, using a plurality of terms of the documents, providing a cluster label indicative of subject matter content of the documents of the cluster, wherein the cluster label comprises a plurality of word senses, and selecting one of the word senses of the cluster label.

  1. A spatial scan statistic for compound Poisson data.

    Science.gov (United States)

    Rosychuk, Rhonda J; Chang, Hsing-Ming

    2013-12-20

    The topic of spatial cluster detection gained attention in statistics during the late 1980s and early 1990s. Effort has been devoted to the development of methods for detecting spatial clustering of cases and events in the biological sciences, astronomy and epidemiology. More recently, research has examined detecting clusters of correlated count data associated with health conditions of individuals. Such a method allows researchers to examine spatial relationships of disease-related events rather than just incident or prevalent cases. We introduce a spatial scan test that identifies clusters of events in a study region. Because an individual case may have multiple (repeated) events, we base the test on a compound Poisson model. We illustrate our method for cluster detection on emergency department visits, where individuals may make multiple disease-related visits. Copyright © 2013 John Wiley & Sons, Ltd.

  2. Seismogenic structure behaviour revealed by spatial clustering of seismicity in the Umbria-Marche Region (Central Italy

    Directory of Open Access Journals (Sweden)

    P. Tosi

    1998-06-01

    Full Text Available Time variations in the spatial distribution of earthquake epicentres are analyzed by application of the fractal correlation dimension method. The zone under investigation is located in Central Italy, bounded in longitude by 12.0 and 14.4 degrees east and in latitude by 42.0 and 43.6 degrees north. From 1st January 1978 to 5th October 1997, 2028 events with a magnitude above Ml= 2.5 constitute the database.Evolution of the spatial fractal dimension Ds permits the identification of seismic cycles that are connected to the occurrence of main earthquakes.In particular, it is possible to recognize a division within each cycle, between a period of random background seismicity and a spatial clustering of events where shocks of magnitude Ml ³occur. Moreover, the decrease in Ds prior to such events, evidences a structural relationship between foreshocks and the occurrence of a main shock, even if not in close territorial proximity.This feature indicates a new, more extensive definition of seismogenic structure which can includes several interconnected structures within a large area.

  3. Brightest Cluster Galaxies in REXCESS Clusters

    Science.gov (United States)

    Haarsma, Deborah B.; Leisman, L.; Bruch, S.; Donahue, M.

    2009-01-01

    Most galaxy clusters contain a Brightest Cluster Galaxy (BCG) which is larger than the other cluster ellipticals and has a more extended profile. In the hierarchical model, the BCG forms through many galaxy mergers in the crowded center of the cluster, and thus its properties give insight into the assembly of the cluster as a whole. In this project, we are working with the Representative XMM-Newton Cluster Structure Survey (REXCESS) team (Boehringer et al 2007) to study BCGs in 33 X-ray luminous galaxy clusters, 0.055 < z < 0.183. We are imaging the BCGs in R band at the Southern Observatory for Astrophysical Research (SOAR) in Chile. In this poster, we discuss our methods and give preliminary measurements of the BCG magnitudes, morphology, and stellar mass. We compare these BCG properties with the properties of their host clusters, particularly of the X-ray emitting gas.

  4. Spatial Arrangement of Colonies in Intact Biofilms from a Model Cooling Water System

    Directory of Open Access Journals (Sweden)

    Michael Taylor

    2013-01-01

    Full Text Available There is disagreement among microbiologists about whether Legionella requires a protozoan host in order to replicate. This research sought to determine where in biofilm Legionellae are found and whether all biofilm associated Legionella would be located within protozoan hosts. While it is accepted that Legionella colonizes biofilm, its life cycle and nutritional fastidiousness suggest that Legionella employs multiple survival strategies to persist within microbial systems. Fluorescent in situ hybridization (FISH and confocal laser scanning microscopy (CLSM demonstrated an undulating biofilm surface architecture and a roughly homogenous distribution of heterotrophic bacteria with clusters of protozoa. Legionella displayed 3 distinct spatial arrangements either contained within or directly associated with protozoa, or dispersed in loosely associated clusters or in tightly packed aggregations of cells forming dense colonial clusters. The formation of discreet clusters of tightly packed Legionella suggests that colony formation is influenced by specific environmental conditions allowing for limited extracellular replication. This work represents the first time that an environmentally representative, multispecies biofilm containing Legionella has been fluorescently tagged and Legionella colony morphology noted within a complex microbial system.

  5. Does self-selection affect samples' representativeness in online surveys? An investigation in online video game research.

    Science.gov (United States)

    Khazaal, Yasser; van Singer, Mathias; Chatton, Anne; Achab, Sophia; Zullino, Daniele; Rothen, Stephane; Khan, Riaz; Billieux, Joel; Thorens, Gabriel

    2014-07-07

    The number of medical studies performed through online surveys has increased dramatically in recent years. Despite their numerous advantages (eg, sample size, facilitated access to individuals presenting stigmatizing issues), selection bias may exist in online surveys. However, evidence on the representativeness of self-selected samples in online studies is patchy. Our objective was to explore the representativeness of a self-selected sample of online gamers using online players' virtual characters (avatars). All avatars belonged to individuals playing World of Warcraft (WoW), currently the most widely used online game. Avatars' characteristics were defined using various games' scores, reported on the WoW's official website, and two self-selected samples from previous studies were compared with a randomly selected sample of avatars. We used scores linked to 1240 avatars (762 from the self-selected samples and 478 from the random sample). The two self-selected samples of avatars had higher scores on most of the assessed variables (except for guild membership and exploration). Furthermore, some guilds were overrepresented in the self-selected samples. Our results suggest that more proficient players or players more involved in the game may be more likely to participate in online surveys. Caution is needed in the interpretation of studies based on online surveys that used a self-selection recruitment procedure. Epidemiological evidence on the reduced representativeness of sample of online surveys is warranted.

  6. Cluster Head Selection in a Homogeneous Wireless Sensor Network Ensuring Full Connectivity with Minimum Isolated Nodes

    Directory of Open Access Journals (Sweden)

    Tapan Kumar Jain

    2014-01-01

    Full Text Available The research work proposes a cluster head selection algorithm for a wireless sensor network. A node can be a cluster head if it is connected to at least one unique neighbor node where the unique neighbor is the one that is not connected to any other node. If there is no connected unique node then the CH is selected on the basis of residual energy and the number of neighbor nodes. With the increase in number of clusters, the processing energy of the network increases; hence, this algorithm proposes minimum number of clusters which further leads to increased network lifetime. The major novel contribution of the proposed work is an algorithm that ensures a completely connected network with minimum number of isolated nodes. An isolated node will remain only if it is not within the transmission range of any other node. With the maximum connectivity, the coverage of the network is automatically maximized. The superiority of the proposed design is verified by simulation results done in MATLAB, where it clearly depicts that the total numbers of rounds before the network dies out are maximum compared to other existing protocols.

  7. Targeted capture and heterologous expression of the Pseudoalteromonas alterochromide gene cluster in Escherichia coli represents a promising natural product exploratory platform.

    Science.gov (United States)

    Ross, Avena C; Gulland, Lauren E S; Dorrestein, Pieter C; Moore, Bradley S

    2015-04-17

    Marine pseudoalteromonads represent a very promising source of biologically important natural product molecules. To access and exploit the full chemical capacity of these cosmopolitan Gram-(-) bacteria, we sought to apply universal synthetic biology tools to capture, refactor, and express biosynthetic gene clusters for the production of complex organic compounds in reliable host organisms. Here, we report a platform for the capture of proteobacterial gene clusters using a transformation-associated recombination (TAR) strategy coupled with direct pathway manipulation and expression in Escherichia coli. The ~34 kb pathway for production of alterochromide lipopeptides by Pseudoalteromonas piscicida JCM 20779 was captured and heterologously expressed in E. coli utilizing native and E. coli-based T7 promoter sequences. Our approach enabled both facile production of the alterochromides and in vivo interrogation of gene function associated with alterochromide's unusual brominated lipid side chain. This platform represents a simple but effective strategy for the discovery and biosynthetic characterization of natural products from marine proteobacteria.

  8. Climate Change and Agricultural Productivity in Sub-Saharan Africa: A Spatial Sample Selection Model

    NARCIS (Netherlands)

    Ward, P.S.; Florax, R.J.G.M.; Flores-Lagunes, A.

    2014-01-01

    Using spatially explicit data, we estimate a cereal yield response function using a recently developed estimator for spatial error models when endogenous sample selection is of concern. Our results suggest that yields across Sub-Saharan Africa will decline with projected climatic changes, and that

  9. Additive effects of emotional content and spatial selective attention on electrocortical facilitation.

    Science.gov (United States)

    Keil, Andreas; Moratti, Stephan; Sabatinelli, Dean; Bradley, Margaret M; Lang, Peter J

    2005-08-01

    Affectively arousing visual stimuli have been suggested to automatically attract attentional resources in order to optimize sensory processing. The present study crosses the factors of spatial selective attention and affective content, and examines the relationship between instructed (spatial) and automatic attention to affective stimuli. In addition to response times and error rate, electroencephalographic data from 129 electrodes were recorded during a covert spatial attention task. This task required silent counting of random-dot targets embedded in a 10 Hz flicker of colored pictures presented to both hemifields. Steady-state visual evoked potentials (ssVEPs) were obtained to determine amplitude and phase of electrocortical responses to pictures. An increase of ssVEP amplitude was observed as an additive function of spatial attention and emotional content. Statistical parametric mapping of this effect indicated occipito-temporal and parietal cortex activation contralateral to the attended visual hemifield in ssVEP amplitude modulation. This difference was most pronounced during selection of the left visual hemifield, at right temporal electrodes. In line with this finding, phase information revealed accelerated processing of aversive arousing, compared to affectively neutral pictures. The data suggest that affective stimulus properties modulate the spatiotemporal process along the ventral stream, encompassing amplitude amplification and timing changes of posterior and temporal cortex.

  10. Chemistry and kinetics of size-selected cobalt cluster cations at thermal energies. I. Reactions with CO

    Science.gov (United States)

    Guo, B. C.; Kerns, K. P.; Castleman, A. W., Jr.

    1992-06-01

    The chemistry and kinetics of size-selected Co+n cluster-ion (n=2-8) reactions with CO are studied using a selected ion drift tube affixed with a laser vaporization source operated under well-defined thermal conditions. All reactions studied in the present work are found to be association reactions. Their absolute rate constants, which are determined quantitatively, are found to have a strong dependence on cluster size. Similar to the cases of reactions with many other reactants such as H2 and CH4, Co+4 and Co+5 display a higher reactivity toward the CO molecule than do clusters of neighboring size. The multiple-collision conditions employed in the present work have enabled a determination of the maximum coordination number of CO molecules bound onto each Co+n cluster. It is found that the tetramer tends to bond 12 CO molecules, the pentamer 14 CO, hexamer 16 CO, and so on. The results are interpreted in terms of Lauher's calculation and the polyhedral skeletal electron pair theory. All the measured maximum coordination numbers correlate extremely well with the predictions of these theories, except for the trimer where the measured number is one CO less than the predicted value. The good agreement between experiment and theory enables one to gain some insight into the geometric structure of the clusters. Based on the present findings, the cobalt tetramer cation is interpreted to have a tetrahedral structure, the pentamer a trigonal bipyramid, and the hexamer an octahedral structure. Other cluster structures are also discussed.

  11. Spatial-Temporal Correlation Properties of the 3GPP Spatial Channel Model and the Kronecker MIMO Channel Model

    Directory of Open Access Journals (Sweden)

    Cheng-Xiang Wang

    2007-02-01

    Full Text Available The performance of multiple-input multiple-output (MIMO systems is greatly influenced by the spatial-temporal correlation properties of the underlying MIMO channels. This paper investigates the spatial-temporal correlation characteristics of the spatial channel model (SCM in the Third Generation Partnership Project (3GPP and the Kronecker-based stochastic model (KBSM at three levels, namely, the cluster level, link level, and system level. The KBSM has both the spatial separability and spatial-temporal separability at all the three levels. The spatial-temporal separability is observed for the SCM only at the system level, but not at the cluster and link levels. The SCM shows the spatial separability at the link and system levels, but not at the cluster level since its spatial correlation is related to the joint distribution of the angle of arrival (AoA and angle of departure (AoD. The KBSM with the Gaussian-shaped power azimuth spectrum (PAS is found to fit best the 3GPP SCM in terms of the spatial correlations. Despite its simplicity and analytical tractability, the KBSM is restricted to model only the average spatial-temporal behavior of MIMO channels. The SCM provides more insights of the variations of different MIMO channel realizations, but the implementation complexity is relatively high.

  12. Comparing Residue Clusters from Thermophilic and Mesophilic Enzymes Reveals Adaptive Mechanisms.

    Science.gov (United States)

    Sammond, Deanne W; Kastelowitz, Noah; Himmel, Michael E; Yin, Hang; Crowley, Michael F; Bomble, Yannick J

    2016-01-01

    Understanding how proteins adapt to function at high temperatures is important for deciphering the energetics that dictate protein stability and folding. While multiple principles important for thermostability have been identified, we lack a unified understanding of how internal protein structural and chemical environment determine qualitative or quantitative impact of evolutionary mutations. In this work we compare equivalent clusters of spatially neighboring residues between paired thermophilic and mesophilic homologues to evaluate adaptations under the selective pressure of high temperature. We find the residue clusters in thermophilic enzymes generally display improved atomic packing compared to mesophilic enzymes, in agreement with previous research. Unlike residue clusters from mesophilic enzymes, however, thermophilic residue clusters do not have significant cavities. In addition, anchor residues found in many clusters are highly conserved with respect to atomic packing between both thermophilic and mesophilic enzymes. Thus the improvements in atomic packing observed in thermophilic homologues are not derived from these anchor residues but from neighboring positions, which may serve to expand optimized protein core regions.

  13. Space-Time Analysis of Testicular Cancer Clusters Using Residential Histories: A Case-Control Study in Denmark

    Science.gov (United States)

    Sloan, Chantel D.; Nordsborg, Rikke B.; Jacquez, Geoffrey M.; Raaschou-Nielsen, Ole; Meliker, Jaymie R.

    2015-01-01

    Though the etiology is largely unknown, testicular cancer incidence has seen recent significant increases in northern Europe and throughout many Western regions. The most common cancer in males under age 40, age period cohort models have posited exposures in the in utero environment or in early childhood as possible causes of increased risk of testicular cancer. Some of these factors may be tied to geography through being associated with behavioral, cultural, sociodemographic or built environment characteristics. If so, this could result in detectable geographic clusters of cases that could lead to hypotheses regarding environmental targets for intervention. Given a latency period between exposure to an environmental carcinogen and testicular cancer diagnosis, mobility histories are beneficial for spatial cluster analyses. Nearest-neighbor based Q-statistics allow for the incorporation of changes in residency in spatial disease cluster detection. Using these methods, a space-time cluster analysis was conducted on a population-wide case-control population selected from the Danish Cancer Registry with mobility histories since 1971 extracted from the Danish Civil Registration System. Cases (N=3297) were diagnosed between 1991 and 2003, and two sets of controls (N=3297 for each set) matched on sex and date of birth were included in the study. We also examined spatial patterns in maternal residential history for those cases and controls born in 1971 or later (N= 589 case-control pairs). Several small clusters were detected when aligning individuals by year prior to diagnosis, age at diagnosis and calendar year of diagnosis. However, the largest of these clusters contained only 2 statistically significant individuals at their center, and were not replicated in SaTScan spatial-only analyses which are less susceptible to multiple testing bias. We found little evidence of local clusters in residential histories of testicular cancer cases in this Danish population. PMID

  14. Space-time analysis of testicular cancer clusters using residential histories: a case-control study in Denmark.

    Directory of Open Access Journals (Sweden)

    Chantel D Sloan

    Full Text Available Though the etiology is largely unknown, testicular cancer incidence has seen recent significant increases in northern Europe and throughout many Western regions. The most common cancer in males under age 40, age period cohort models have posited exposures in the in utero environment or in early childhood as possible causes of increased risk of testicular cancer. Some of these factors may be tied to geography through being associated with behavioral, cultural, sociodemographic or built environment characteristics. If so, this could result in detectable geographic clusters of cases that could lead to hypotheses regarding environmental targets for intervention. Given a latency period between exposure to an environmental carcinogen and testicular cancer diagnosis, mobility histories are beneficial for spatial cluster analyses. Nearest-neighbor based Q-statistics allow for the incorporation of changes in residency in spatial disease cluster detection. Using these methods, a space-time cluster analysis was conducted on a population-wide case-control population selected from the Danish Cancer Registry with mobility histories since 1971 extracted from the Danish Civil Registration System. Cases (N=3297 were diagnosed between 1991 and 2003, and two sets of controls (N=3297 for each set matched on sex and date of birth were included in the study. We also examined spatial patterns in maternal residential history for those cases and controls born in 1971 or later (N= 589 case-control pairs. Several small clusters were detected when aligning individuals by year prior to diagnosis, age at diagnosis and calendar year of diagnosis. However, the largest of these clusters contained only 2 statistically significant individuals at their center, and were not replicated in SaTScan spatial-only analyses which are less susceptible to multiple testing bias. We found little evidence of local clusters in residential histories of testicular cancer cases in this Danish

  15. Space-time analysis of testicular cancer clusters using residential histories: a case-control study in Denmark.

    Science.gov (United States)

    Sloan, Chantel D; Nordsborg, Rikke B; Jacquez, Geoffrey M; Raaschou-Nielsen, Ole; Meliker, Jaymie R

    2015-01-01

    Though the etiology is largely unknown, testicular cancer incidence has seen recent significant increases in northern Europe and throughout many Western regions. The most common cancer in males under age 40, age period cohort models have posited exposures in the in utero environment or in early childhood as possible causes of increased risk of testicular cancer. Some of these factors may be tied to geography through being associated with behavioral, cultural, sociodemographic or built environment characteristics. If so, this could result in detectable geographic clusters of cases that could lead to hypotheses regarding environmental targets for intervention. Given a latency period between exposure to an environmental carcinogen and testicular cancer diagnosis, mobility histories are beneficial for spatial cluster analyses. Nearest-neighbor based Q-statistics allow for the incorporation of changes in residency in spatial disease cluster detection. Using these methods, a space-time cluster analysis was conducted on a population-wide case-control population selected from the Danish Cancer Registry with mobility histories since 1971 extracted from the Danish Civil Registration System. Cases (N=3297) were diagnosed between 1991 and 2003, and two sets of controls (N=3297 for each set) matched on sex and date of birth were included in the study. We also examined spatial patterns in maternal residential history for those cases and controls born in 1971 or later (N= 589 case-control pairs). Several small clusters were detected when aligning individuals by year prior to diagnosis, age at diagnosis and calendar year of diagnosis. However, the largest of these clusters contained only 2 statistically significant individuals at their center, and were not replicated in SaTScan spatial-only analyses which are less susceptible to multiple testing bias. We found little evidence of local clusters in residential histories of testicular cancer cases in this Danish population.

  16. Identification and characterization of earthquake clusters: a comparative analysis for selected sequences in Italy

    Science.gov (United States)

    Peresan, Antonella; Gentili, Stefania

    2017-04-01

    Identification and statistical characterization of seismic clusters may provide useful insights about the features of seismic energy release and their relation to physical properties of the crust within a given region. Moreover, a number of studies based on spatio-temporal analysis of main-shocks occurrence require preliminary declustering of the earthquake catalogs. Since various methods, relying on different physical/statistical assumptions, may lead to diverse classifications of earthquakes into main events and related events, we aim to investigate the classification differences among different declustering techniques. Accordingly, a formal selection and comparative analysis of earthquake clusters is carried out for the most relevant earthquakes in North-Eastern Italy, as reported in the local OGS-CRS bulletins, compiled at the National Institute of Oceanography and Experimental Geophysics since 1977. The comparison is then extended to selected earthquake sequences associated with a different seismotectonic setting, namely to events that occurred in the region struck by the recent Central Italy destructive earthquakes, making use of INGV data. Various techniques, ranging from classical space-time windows methods to ad hoc manual identification of aftershocks, are applied for detection of earthquake clusters. In particular, a statistical method based on nearest-neighbor distances of events in space-time-energy domain, is considered. Results from clusters identification by the nearest-neighbor method turn out quite robust with respect to the time span of the input catalogue, as well as to minimum magnitude cutoff. The identified clusters for the largest events reported in North-Eastern Italy since 1977 are well consistent with those reported in earlier studies, which were aimed at detailed manual aftershocks identification. The study shows that the data-driven approach, based on the nearest-neighbor distances, can be satisfactorily applied to decompose the seismic

  17. Spatial Scaling of the Profile of Selective Attention in the Visual Field.

    Science.gov (United States)

    Gannon, Matthew A; Knapp, Ashley A; Adams, Thomas G; Long, Stephanie M; Parks, Nathan A

    2016-01-01

    Neural mechanisms of selective attention must be capable of adapting to variation in the absolute size of an attended stimulus in the ever-changing visual environment. To date, little is known regarding how attentional selection interacts with fluctuations in the spatial expanse of an attended object. Here, we use event-related potentials (ERPs) to investigate the scaling of attentional enhancement and suppression across the visual field. We measured ERPs while participants performed a task at fixation that varied in its attentional demands (attentional load) and visual angle (1.0° or 2.5°). Observers were presented with a stream of task-relevant stimuli while foveal, parafoveal, and peripheral visual locations were probed by irrelevant distractor stimuli. We found two important effects in the N1 component of visual ERPs. First, N1 modulations to task-relevant stimuli indexed attentional selection of stimuli during the load task and further correlated with task performance. Second, with increased task size, attentional modulation of the N1 to distractor stimuli showed a differential pattern that was consistent with a scaling of attentional selection. Together, these results demonstrate that the size of an attended stimulus scales the profile of attentional selection across the visual field and provides insights into the attentional mechanisms associated with such spatial scaling.

  18. STAR CLUSTER DISRUPTION IN THE STARBURST GALAXY MESSIER 82

    International Nuclear Information System (INIS)

    Li, Shuo; Li, Chengyuan; De Grijs, Richard; Anders, Peter

    2015-01-01

    Using high-resolution, multiple-passband Hubble Space Telescope images spanning the entire optical/near-infrared wavelength range, we obtained a statistically complete U-band-selected sample of 846 extended star clusters across the disk of the nearby starburst galaxy M82. Based on a careful analysis of the clusters' spectral energy distributions, we determined their galaxy-wide age and mass distributions. The M82 clusters exhibit three clear peaks in their age distribution, thus defining relatively young, log (t yr –1 ) ≤ 7.5, intermediate-age, log (t yr –1 ) in [7.5, 8.5], and old samples, log (t yr –1 ) ≥ 8.5. Comparison of the completeness-corrected mass distributions offers a firm handle on the galaxy's star cluster disruption history. The most massive star clusters in the young and old samples are (almost) all concentrated in the most densely populated central region, while the intermediate-age sample's most massive clusters are more spatially dispersed, which may reflect the distribution of the highest-density gas throughout the galaxy's evolutionary history, combined with the solid-body nature of the galaxy's central region

  19. Multivariate Spatio-Temporal Clustering: A Framework for Integrating Disparate Data to Understand Network Representativeness and Scaling Up Sparse Ecosystem Measurements

    Science.gov (United States)

    Hoffman, F. M.; Kumar, J.; Maddalena, D. M.; Langford, Z.; Hargrove, W. W.

    2014-12-01

    Disparate in situ and remote sensing time series data are being collected to understand the structure and function of ecosystems and how they may be affected by climate change. However, resource and logistical constraints limit the frequency and extent of observations, particularly in the harsh environments of the arctic and the tropics, necessitating the development of a systematic sampling strategy to maximize coverage and objectively represent variability at desired scales. These regions host large areas of potentially vulnerable ecosystems that are poorly represented in Earth system models (ESMs), motivating two new field campaigns, called Next Generation Ecosystem Experiments (NGEE) for the Arctic and Tropics, funded by the U.S. Department of Energy. Multivariate Spatio-Temporal Clustering (MSTC) provides a quantitative methodology for stratifying sampling domains, informing site selection, and determining the representativeness of measurement sites and networks. We applied MSTC to down-scaled general circulation model results and data for the State of Alaska at a 4 km2 resolution to define maps of ecoregions for the present (2000-2009) and future (2090-2099), showing how combinations of 37 bioclimatic characteristics are distributed and how they may shift in the future. Optimal representative sampling locations were identified on present and future ecoregion maps, and representativeness maps for candidate sampling locations were produced. We also applied MSTC to remotely sensed LiDAR measurements and multi-spectral imagery from the WorldView-2 satellite at a resolution of about 5 m2 within the Barrow Environmental Observatory (BEO) in Alaska. At this resolution, polygonal ground features—such as centers, edges, rims, and troughs—can be distinguished. Using these remote sensing data, we up-scaled vegetation distribution data collected on these polygonal ground features to a large area of the BEO to provide distributions of plant functional types that can

  20. A scale invariant clustering of genes on human chromosome 7

    Directory of Open Access Journals (Sweden)

    Kendal Wayne S

    2004-01-01

    Full Text Available Abstract Background Vertebrate genes often appear to cluster within the background of nontranscribed genomic DNA. Here an analysis of the physical distribution of gene structures on human chromosome 7 was performed to confirm the presence of clustering, and to elucidate possible underlying statistical and biological mechanisms. Results Clustering of genes was confirmed by virtue of a variance of the number of genes per unit physical length that exceeded the respective mean. Further evidence for clustering came from a power function relationship between the variance and mean that possessed an exponent of 1.51. This power function implied that the spatial distribution of genes on chromosome 7 was scale invariant, and that the underlying statistical distribution had a Poisson-gamma (PG form. A PG distribution for the spatial scattering of genes was validated by stringent comparisons of both the predicted variance to mean power function and its cumulative distribution function to data derived from chromosome 7. Conclusion The PG distribution was consistent with at least two different biological models: In the microrearrangement model, the number of genes per unit length of chromosome represented the contribution of a random number of smaller chromosomal segments that had originated by random breakage and reconstruction of more primitive chromosomes. Each of these smaller segments would have necessarily contained (on average a gamma distributed number of genes. In the gene cluster model, genes would be scattered randomly to begin with. Over evolutionary timescales, tandem duplication, mutation, insertion, deletion and rearrangement could act at these gene sites through a stochastic birth death and immigration process to yield a PG distribution. On the basis of the gene position data alone it was not possible to identify the biological model which best explained the observed clustering. However, the underlying PG statistical model implicated neutral

  1. All-dielectric metamaterial frequency selective surface based on spatial arrangement ceramic resonators

    Science.gov (United States)

    Li, Liyang; Wang, Jun; Feng, Mingde; Ma, Hua; Wang, Jiafu; Du, Hongliang; Qu, Shaobo

    In this paper, we demonstrate a method of designing all-dielectric metamaterial frequency selective surface (FSS) with ceramic resonators in spatial arrangement. Compared with the traditional way, spatial arrangement provides a flexible way to handle the permutation and combination of different ceramic resonators. With this method, the resonance response can be adjusted easily to achieve pass/stop band effects. As an example, a stop band spatial arrangement all-dielectric metamaterial FSS is designed. Its working band is in 11.65-12.23GHz. By adjusting permittivity and geometrical parameters of ceramic resonators, we can easily modulate the resonances, band pass or band stop characteristic, as well as the working band.

  2. Time course of spatial and feature selective attention for partly-occluded objects.

    Science.gov (United States)

    Kasai, Tetsuko; Takeya, Ryuji

    2012-07-01

    Attention selects objects/groups as the most fundamental units, and this may be achieved by an attention-spreading mechanism. Previous event-related potential (ERP) studies have found that attention-spreading is reflected by a decrease in the N1 spatial attention effect. The present study tested whether the electrophysiological attention effect is associated with the perception of object unity or amodal completion through the use of partly-occluded objects. ERPs were recorded in 14 participants who were required to pay attention to their left or right visual field and to press a button for a target shape in the attended field. Bilateral stimuli were presented rapidly, and were separated, connected, or connected behind an occluder. Behavioral performance in the connected and occluded conditions was worse than that in the separated condition, indicating that attention spread over perceptual object representations after amodal completion. Consistently, the late N1 spatial attention effect (180-220 ms post-stimulus) and the early phase (230-280 ms) of feature selection effects (target N2) at contralateral sites decreased, equally for the occluded and connected conditions, while the attention effect in the early N1 latency (140-180 ms) shifted most positively for the occluded condition. These results suggest that perceptual organization processes for object recognition transiently modulate spatial and feature selection processes in the visual cortex. Copyright © 2012 Elsevier Ltd. All rights reserved.

  3. Spatial Frequency Selectivity Is Impaired in Dopamine D2 Receptor Knockout Mice

    Science.gov (United States)

    Souza, Bruno Oliveira Ferreira; Abou Rjeili, Mira; Quintana, Clémentine; Beaulieu, Jean M.; Casanova, Christian

    2018-01-01

    Dopamine is a neurotransmitter implicated in several brain functions, including vision. In the present study, we investigated the impacts of the lack of D2 dopamine receptors on the structure and function of the primary visual cortex (V1) of D2-KO mice using optical imaging of intrinsic signals. Retinotopic maps were generated in order to measure anatomo-functional parameters such as V1 shape, cortical magnification factor, scatter, and ocular dominance. Contrast sensitivity and spatial frequency selectivity (SF) functions were computed from responses to drifting gratings. When compared to control mice, none of the parameters of the retinotopic maps were affected by D2 receptor loss of function. While the contrast sensitivity function of D2-KO mice did not differ from their wild-type counterparts, SF selectivity function was significantly affected as the optimal SF and the high cut-off frequency (p D2-KO than in WT mice. These findings show that the lack of function of D2 dopamine receptors had no influence on cortical structure whereas it had a significant impact on the spatial frequency selectivity and high cut-off. Taken together, our results suggest that D2 receptors play a specific role on the processing of spatial features in early visual cortex while they do not seem to participate in its development. PMID:29379422

  4. Subaru Weak-Lensing Survey II: Multi-Object Spectroscopy and Cluster Masses

    Science.gov (United States)

    Hamana, Takashi; Miyazaki, Satoshi; Kashikawa, Nobunari; Ellis, Richard S.; Massey, Richard J.; Refregier, Alexandre; Taylor, James E.

    2009-08-01

    We present the first results of a multi-object spectroscopic campaign to follow up cluster candidates located via weak lensing. Our main goals are to search for spatial concentrations of galaxies that are plausible optical counterparts of the weak-lensing signals, and to determine the cluster redshifts from those of member galaxies. Around each of 36 targeted cluster candidates, we obtained 15-32 galaxy redshifts. For 28 of these targets, we confirmed a secure cluster identification, with more than five spectroscopic galaxies within a velocity of ±3000km s-1. This includes three cases where two clusters at different redshifts are projected along the same line-of-sight. In 6 of the 8 unconfirmed targets, we found multiple small galaxy concentrations at different redshifts, each containing at least three spectroscopic galaxies. The weak-lensing signal around those systems was thus probably created by the projection of groups or small clusters along the same line-of-sight. In both of the remaining two targets, a single small galaxy concentration was found. In some candidate super-cluster systems, we found additional evidence of filaments connecting the main density peak to an additional nearby structure. For a subsample of our most cleanly measured clusters, we investigated the statistical relation between their weak-lensing mass (MNFW, σSIS) and the velocity dispersion of their member galaxies (σv), comparing our sample with optically and X-ray selected samples from the literature. Our lensing-selected clusters are consistent with σv = σSIS, with a similar scatter to that of optically and X-ray selected clusters. We also derived an empirical relation between the cluster mass and the galaxy velocity dispersion, M200E(z) = 11.0 × 1014 × (σv/1000km s-1)3.0 h-1 Modot, which is in reasonable agreement with predictions of N-body simulations in the Λ CDM cosmology.

  5. Clustering based gene expression feature selection method: A computational approach to enrich the classifier efficiency of differentially expressed genes

    KAUST Repository

    Abusamra, Heba

    2016-07-20

    The native nature of high dimension low sample size of gene expression data make the classification task more challenging. Therefore, feature (gene) selection become an apparent need. Selecting a meaningful and relevant genes for classifier not only decrease the computational time and cost, but also improve the classification performance. Among different approaches of feature selection methods, however most of them suffer from several problems such as lack of robustness, validation issues etc. Here, we present a new feature selection technique that takes advantage of clustering both samples and genes. Materials and methods We used leukemia gene expression dataset [1]. The effectiveness of the selected features were evaluated by four different classification methods; support vector machines, k-nearest neighbor, random forest, and linear discriminate analysis. The method evaluate the importance and relevance of each gene cluster by summing the expression level for each gene belongs to this cluster. The gene cluster consider important, if it satisfies conditions depend on thresholds and percentage otherwise eliminated. Results Initial analysis identified 7120 differentially expressed genes of leukemia (Fig. 15a), after applying our feature selection methodology we end up with specific 1117 genes discriminating two classes of leukemia (Fig. 15b). Further applying the same method with more stringent higher positive and lower negative threshold condition, number reduced to 58 genes have be tested to evaluate the effectiveness of the method (Fig. 15c). The results of the four classification methods are summarized in Table 11. Conclusions The feature selection method gave good results with minimum classification error. Our heat-map result shows distinct pattern of refines genes discriminating between two classes of leukemia.

  6. A comparison of performance of automatic cloud coverage assessment algorithm for Formosat-2 image using clustering-based and spatial thresholding methods

    Science.gov (United States)

    Hsu, Kuo-Hsien

    2012-11-01

    Formosat-2 image is a kind of high-spatial-resolution (2 meters GSD) remote sensing satellite data, which includes one panchromatic band and four multispectral bands (Blue, Green, Red, near-infrared). An essential sector in the daily processing of received Formosat-2 image is to estimate the cloud statistic of image using Automatic Cloud Coverage Assessment (ACCA) algorithm. The information of cloud statistic of image is subsequently recorded as an important metadata for image product catalog. In this paper, we propose an ACCA method with two consecutive stages: preprocessing and post-processing analysis. For pre-processing analysis, the un-supervised K-means classification, Sobel's method, thresholding method, non-cloudy pixels reexamination, and cross-band filter method are implemented in sequence for cloud statistic determination. For post-processing analysis, Box-Counting fractal method is implemented. In other words, the cloud statistic is firstly determined via pre-processing analysis, the correctness of cloud statistic of image of different spectral band is eventually cross-examined qualitatively and quantitatively via post-processing analysis. The selection of an appropriate thresholding method is very critical to the result of ACCA method. Therefore, in this work, We firstly conduct a series of experiments of the clustering-based and spatial thresholding methods that include Otsu's, Local Entropy(LE), Joint Entropy(JE), Global Entropy(GE), and Global Relative Entropy(GRE) method, for performance comparison. The result shows that Otsu's and GE methods both perform better than others for Formosat-2 image. Additionally, our proposed ACCA method by selecting Otsu's method as the threshoding method has successfully extracted the cloudy pixels of Formosat-2 image for accurate cloud statistic estimation.

  7. Does Self-Selection Affect Samples’ Representativeness in Online Surveys? An Investigation in Online Video Game Research

    Science.gov (United States)

    van Singer, Mathias; Chatton, Anne; Achab, Sophia; Zullino, Daniele; Rothen, Stephane; Khan, Riaz; Billieux, Joel; Thorens, Gabriel

    2014-01-01

    Background The number of medical studies performed through online surveys has increased dramatically in recent years. Despite their numerous advantages (eg, sample size, facilitated access to individuals presenting stigmatizing issues), selection bias may exist in online surveys. However, evidence on the representativeness of self-selected samples in online studies is patchy. Objective Our objective was to explore the representativeness of a self-selected sample of online gamers using online players’ virtual characters (avatars). Methods All avatars belonged to individuals playing World of Warcraft (WoW), currently the most widely used online game. Avatars’ characteristics were defined using various games’ scores, reported on the WoW’s official website, and two self-selected samples from previous studies were compared with a randomly selected sample of avatars. Results We used scores linked to 1240 avatars (762 from the self-selected samples and 478 from the random sample). The two self-selected samples of avatars had higher scores on most of the assessed variables (except for guild membership and exploration). Furthermore, some guilds were overrepresented in the self-selected samples. Conclusions Our results suggest that more proficient players or players more involved in the game may be more likely to participate in online surveys. Caution is needed in the interpretation of studies based on online surveys that used a self-selection recruitment procedure. Epidemiological evidence on the reduced representativeness of sample of online surveys is warranted. PMID:25001007

  8. A large sample of shear-selected clusters from the Hyper Suprime-Cam Subaru Strategic Program S16A Wide field mass maps

    Science.gov (United States)

    Miyazaki, Satoshi; Oguri, Masamune; Hamana, Takashi; Shirasaki, Masato; Koike, Michitaro; Komiyama, Yutaka; Umetsu, Keiichi; Utsumi, Yousuke; Okabe, Nobuhiro; More, Surhud; Medezinski, Elinor; Lin, Yen-Ting; Miyatake, Hironao; Murayama, Hitoshi; Ota, Naomi; Mitsuishi, Ikuyuki

    2018-01-01

    We present the result of searching for clusters of galaxies based on weak gravitational lensing analysis of the ˜160 deg2 area surveyed by Hyper Suprime-Cam (HSC) as a Subaru Strategic Program. HSC is a new prime focus optical imager with a 1.5°-diameter field of view on the 8.2 m Subaru telescope. The superb median seeing on the HSC i-band images of 0.56" allows the reconstruction of high angular resolution mass maps via weak lensing, which is crucial for the weak lensing cluster search. We identify 65 mass map peaks with a signal-to-noise (S/N) ratio larger than 4.7, and carefully examine their properties by cross-matching the clusters with optical and X-ray cluster catalogs. We find that all the 39 peaks with S/N > 5.1 have counterparts in the optical cluster catalogs, and only 2 out of the 65 peaks are probably false positives. The upper limits of X-ray luminosities from the ROSAT All Sky Survey (RASS) imply the existence of an X-ray underluminous cluster population. We show that the X-rays from the shear-selected clusters can be statistically detected by stacking the RASS images. The inferred average X-ray luminosity is about half that of the X-ray-selected clusters of the same mass. The radial profile of the dark matter distribution derived from the stacking analysis is well modeled by the Navarro-Frenk-White profile with a small concentration parameter value of c500 ˜ 2.5, which suggests that the selection bias on the orientation or the internal structure for our shear-selected cluster sample is not strong.

  9. Preferential selection based on degree difference in the spatial prisoner's dilemma games

    Science.gov (United States)

    Huang, Changwei; Dai, Qionglin; Cheng, Hongyan; Li, Haihong

    2017-10-01

    Strategy evolution in spatial evolutionary games is generally implemented through imitation processes between individuals. In most previous studies, it is assumed that individuals pick up one of their neighbors randomly to learn from. However, by considering the heterogeneity of individuals' influence in the real society, preferential selection is more realistic. Here, we introduce a preferential selection mechanism based on degree difference into spatial prisoner's dilemma games on Erdös-Rényi networks and Barabási-Albert scale-free networks and investigate the effects of the preferential selection on cooperation. The results show that, when the individuals prefer to choose the neighbors who have small degree difference with themselves to imitate, cooperation is hurt by the preferential selection. In contrast, when the individuals prefer to choose those large degree difference neighbors to learn from, there exists optimal preference strength resulting in the maximal cooperation level no matter what the network structure is. In addition, we investigate the robustness of the results against variations of the noise, the average degree and the size of network in the model, and find that the qualitative features of the results are unchanged.

  10. Stellar Kinematics and Structural Properties of Virgo Cluster Dwarf Early-Type Galaxies from the SMAKCED Project

    NARCIS (Netherlands)

    Toloba, Elisa; Guhathakurta, Puragra; Peletier, Reynier; Boselli, Alessandro; Lisker, Thorsten; Emsellem, Eric; Simon, Joshua D.; van de Ven, Glenn; Smakced Collaboration, [Unknown

    2015-01-01

    We analyze the stellar kinematics of 39 dwarf early-type galaxies (dEs) in the Virgo cluster. This is the largest survey conducted so far on spatially resolved kinematics of dEs. This sample is representative of the early-type population in the absolute magnitude range -19.0

  11. Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks.

    Science.gov (United States)

    Aadil, Farhan; Raza, Ali; Khan, Muhammad Fahad; Maqsood, Muazzam; Mehmood, Irfan; Rho, Seungmin

    2018-05-03

    Flying ad-hoc networks (FANETs) are a very vibrant research area nowadays. They have many military and civil applications. Limited battery energy and the high mobility of micro unmanned aerial vehicles (UAVs) represent their two main problems, i.e., short flight time and inefficient routing. In this paper, we try to address both of these problems by means of efficient clustering. First, we adjust the transmission power of the UAVs by anticipating their operational requirements. Optimal transmission range will have minimum packet loss ratio (PLR) and better link quality, which ultimately save the energy consumed during communication. Second, we use a variant of the K-Means Density clustering algorithm for selection of cluster heads. Optimal cluster heads enhance the cluster lifetime and reduce the routing overhead. The proposed model outperforms the state of the art artificial intelligence techniques such as Ant Colony Optimization-based clustering algorithm and Grey Wolf Optimization-based clustering algorithm. The performance of the proposed algorithm is evaluated in term of number of clusters, cluster building time, cluster lifetime and energy consumption.

  12. Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks

    Directory of Open Access Journals (Sweden)

    Farhan Aadil

    2018-05-01

    Full Text Available Flying ad-hoc networks (FANETs are a very vibrant research area nowadays. They have many military and civil applications. Limited battery energy and the high mobility of micro unmanned aerial vehicles (UAVs represent their two main problems, i.e., short flight time and inefficient routing. In this paper, we try to address both of these problems by means of efficient clustering. First, we adjust the transmission power of the UAVs by anticipating their operational requirements. Optimal transmission range will have minimum packet loss ratio (PLR and better link quality, which ultimately save the energy consumed during communication. Second, we use a variant of the K-Means Density clustering algorithm for selection of cluster heads. Optimal cluster heads enhance the cluster lifetime and reduce the routing overhead. The proposed model outperforms the state of the art artificial intelligence techniques such as Ant Colony Optimization-based clustering algorithm and Grey Wolf Optimization-based clustering algorithm. The performance of the proposed algorithm is evaluated in term of number of clusters, cluster building time, cluster lifetime and energy consumption.

  13. Stability-integrated Fuzzy C means segmentation for spatial ...

    Indian Academy of Sciences (India)

    V ROYNA DAISY

    2018-03-16

    Mar 16, 2018 ... clusters and including spatial information to basic Fuzzy C Means clustering are done in .... modify the objective function with Kernel distance function .... spatial information, thus making it sensitive to noise and outliers.

  14. Diversity among galaxy clusters

    International Nuclear Information System (INIS)

    Struble, M.F.; Rood, H.J.

    1988-01-01

    The classification of galaxy clusters is discussed. Consideration is given to the classification scheme of Abell (1950's), Zwicky (1950's), Morgan, Matthews, and Schmidt (1964), and Morgan-Bautz (1970). Galaxies can be classified based on morphology, chemical composition, spatial distribution, and motion. The correlation between a galaxy's environment and morphology is examined. The classification scheme of Rood-Sastry (1971), which is based on clusters's morphology and galaxy population, is described. The six types of clusters they define include: (1) a cD-cluster dominated by a single large galaxy, (2) a cluster dominated by a binary, (3) a core-halo cluster, (4) a cluster dominated by several bright galaxies, (5) a cluster appearing flattened, and (6) an irregularly shaped cluster. Attention is also given to the evolution of cluster structures, which is related to initial density and cluster motion

  15. Spatial distribution of juvenile and adult stages of limnetic Cladocera in relation to selected environmental factors

    Directory of Open Access Journals (Sweden)

    Małgorzata Adamczuk

    2012-01-01

    Full Text Available Environmental factors have a varied impact on the development of juvenile and adult Cladocera, depending on their different physiological conditions and body size. The values of these factors alter spatially and temporarily, thus implying that they play a role in the spatial distribution of the pre-reproductive and potentially reproductive stages of cladocerans. The aim of the study was to determine seasonal and spatial variations in the distribution of juvenile and adult individuals of limnetic Cladocera in relation to selected physicochemical factors (temperature, conductivity, pH, concentration of dissolved oxygen, total organic carbon, total suspended solids and fish predation pressure (measured by Chesson’s coefficient λ in deep Lake Piaseczno (eastern Poland. Adult Cladocera displayed spatial distribution related to fish predation pressure. The species selectively eaten, B. coregoni and D. longispina, and non-selectively eaten, D. cucullata, selected the pelagic zone to exist, whereas those avoided by fish, D. brachyurum and B. longirostris, were evenly distributed in the littoral and pelagic zone. Juvenile cladocerans were strongly impacted by physico-chemical factors. Juvenile Daphnia, Diaphanosoma and B. longirostris showed preferences to biotic zones similar to the adults but differed in their habitat choices. Juvenile and adult stages of B. coregoni differed in their distribution, indicating that adult individuals impacted by high predation pressure alternatively modified their habitat selection. Principal component analysis (PCA ordination showed a seasonal tendency for the spatial segregation of the cladocerans, suggesting that possible competitive interactions between the studied cladocerans may also influence their distribution patterns.

  16. Suzaku observations of low surface brightness cluster Abell 1631

    Science.gov (United States)

    Babazaki, Yasunori; Mitsuishi, Ikuyuki; Ota, Naomi; Sasaki, Shin; Böhringer, Hans; Chon, Gayoung; Pratt, Gabriel W.; Matsumoto, Hironori

    2018-06-01

    We present analysis results for a nearby galaxy cluster Abell 1631 at z = 0.046 using the X-ray observatory Suzaku. This cluster is categorized as a low X-ray surface brightness cluster. To study the dynamical state of the cluster, we conduct four-pointed Suzaku observations and investigate physical properties of the Mpc-scale hot gas associated with the A 1631 cluster for the first time. Unlike relaxed clusters, the X-ray image shows no strong peak at the center and an irregular morphology. We perform spectral analysis and investigate the radial profiles of the gas temperature, density, and entropy out to approximately 1.5 Mpc in the east, north, west, and south directions by combining with the XMM-Newton data archive. The measured gas density in the central region is relatively low (a few ×10-4 cm-3) at the given temperature (˜2.9 keV) compared with X-ray-selected clusters. The entropy profile and value within the central region (r clusters. These features are also observed in another low surface brightness cluster, Abell 76. The spatial distributions of galaxies and the hot gas appear to be different. The X-ray luminosity is relatively lower than that expected from the velocity dispersion. A post-merger scenario may explain the observed results.

  17. Interpreting semantic clustering effects in free recall.

    Science.gov (United States)

    Manning, Jeremy R; Kahana, Michael J

    2012-07-01

    The order in which participants choose to recall words from a studied list of randomly selected words provides insights into how memories of the words are represented, organised, and retrieved. One pervasive finding is that when a pair of semantically related words (e.g., "cat" and "dog") is embedded in the studied list, the related words are often recalled successively. This tendency to successively recall semantically related words is termed semantic clustering (Bousfield, 1953; Bousfield & Sedgewick, 1944; Cofer, Bruce, & Reicher, 1966). Measuring semantic clustering effects requires making assumptions about which words participants consider to be similar in meaning. However, it is often difficult to gain insights into individual participants' internal semantic models, and for this reason researchers typically rely on standardised semantic similarity metrics. Here we use simulations to gain insights into the expected magnitudes of semantic clustering effects given systematic differences between participants' internal similarity models and the similarity metric used to quantify the degree of semantic clustering. Our results provide a number of useful insights into the interpretation of semantic clustering effects in free recall.

  18. Exploring the Structure of Spatial Representations

    Science.gov (United States)

    Madl, Tamas; Franklin, Stan; Chen, Ke; Trappl, Robert; Montaldi, Daniela

    2016-01-01

    It has been suggested that the map-like representations that support human spatial memory are fragmented into sub-maps with local reference frames, rather than being unitary and global. However, the principles underlying the structure of these ‘cognitive maps’ are not well understood. We propose that the structure of the representations of navigation space arises from clustering within individual psychological spaces, i.e. from a process that groups together objects that are close in these spaces. Building on the ideas of representational geometry and similarity-based representations in cognitive science, we formulate methods for learning dissimilarity functions (metrics) characterizing participants’ psychological spaces. We show that these learned metrics, together with a probabilistic model of clustering based on the Bayesian cognition paradigm, allow prediction of participants’ cognitive map structures in advance. Apart from insights into spatial representation learning in human cognition, these methods could facilitate novel computational tools capable of using human-like spatial concepts. We also compare several features influencing spatial memory structure, including spatial distance, visual similarity and functional similarity, and report strong correlations between these dimensions and the grouping probability in participants’ spatial representations, providing further support for clustering in spatial memory. PMID:27347681

  19. Locating irregularly shaped clusters of infection intensity

    Directory of Open Access Journals (Sweden)

    Niko Yiannakoulias

    2010-05-01

    Full Text Available Patterns of disease may take on irregular geographic shapes, especially when features of the physical environment influence risk. Identifying these patterns can be important for planning, and also identifying new environmental or social factors associated with high or low risk of illness. Until recently, cluster detection methods were limited in their ability to detect irregular spatial patterns, and limited to finding clusters that were roughly circular in shape. This approach has less power to detect irregularly-shaped, yet important spatial anomalies, particularly at high spatial resolutions. We employ a new method of finding irregularly-shaped spatial clusters at micro-geographical scales using both simulated and real data on Schistosoma mansoni and hookworm infection intensities. This method, which we refer to as the “greedy growth scan”, is a modification of the spatial scan method for cluster detection. Real data are based on samples of hookworm and S. mansoni from Kitengei, Makueni district, Kenya. Our analysis of simulated data shows how methods able to find irregular shapes are more likely to identify clusters along rivers than methods constrained to fixed geometries. Our analysis of infection intensity identifies two small areas within the study region in which infection intensity is elevated, possibly due to local features of the physical or social environment. Collectively, our results show that the “greedy growth scan” is a suitable method for exploratory geographical analysis of infection intensity data when irregular shapes are suspected, especially at micro-geographical scales.

  20. Don't spin the pen: two alternative methods for second-stage sampling in urban cluster surveys

    Directory of Open Access Journals (Sweden)

    Rose Angela MC

    2007-06-01

    Full Text Available Abstract In two-stage cluster surveys, the traditional method used in second-stage sampling (in which the first household in a cluster is selected is time-consuming and may result in biased estimates of the indicator of interest. Firstly, a random direction from the center of the cluster is selected, usually by spinning a pen. The houses along that direction are then counted out to the boundary of the cluster, and one is then selected at random to be the first household surveyed. This process favors households towards the center of the cluster, but it could easily be improved. During a recent meningitis vaccination coverage survey in Maradi, Niger, we compared this method of first household selection to two alternatives in urban zones: 1 using a superimposed grid on the map of the cluster area and randomly selecting an intersection; and 2 drawing the perimeter of the cluster area using a Global Positioning System (GPS and randomly selecting one point within the perimeter. Although we only compared a limited number of clusters using each method, we found the sampling grid method to be the fastest and easiest for field survey teams, although it does require a map of the area. Selecting a random GPS point was also found to be a good method, once adequate training can be provided. Spinning the pen and counting households to the boundary was the most complicated and time-consuming. The two methods tested here represent simpler, quicker and potentially more robust alternatives to spinning the pen for cluster surveys in urban areas. However, in rural areas, these alternatives would favor initial household selection from lower density (or even potentially empty areas. Bearing in mind these limitations, as well as available resources and feasibility, investigators should choose the most appropriate method for their particular survey context.

  1. An improved initialization center k-means clustering algorithm based on distance and density

    Science.gov (United States)

    Duan, Yanling; Liu, Qun; Xia, Shuyin

    2018-04-01

    Aiming at the problem of the random initial clustering center of k means algorithm that the clustering results are influenced by outlier data sample and are unstable in multiple clustering, a method of central point initialization method based on larger distance and higher density is proposed. The reciprocal of the weighted average of distance is used to represent the sample density, and the data sample with the larger distance and the higher density are selected as the initial clustering centers to optimize the clustering results. Then, a clustering evaluation method based on distance and density is designed to verify the feasibility of the algorithm and the practicality, the experimental results on UCI data sets show that the algorithm has a certain stability and practicality.

  2. The Relationship between Utilization of Computer Games and Spatial Abilities among High School Students

    Science.gov (United States)

    Motamedi, Vahid; Yaghoubi, Razeyah Mohagheghyan

    2015-01-01

    This study aimed at investigating the relationship between computer game use and spatial abilities among high school students. The sample consisted of 300 high school male students selected through multi-stage cluster sampling. Data gathering tools consisted of a researcher made questionnaire (to collect information on computer game usage) and the…

  3. Clusters - Tourism Activity Increase Competitiveness Support

    Directory of Open Access Journals (Sweden)

    Carmen IORDACHE

    2010-05-01

    Full Text Available Tourism represents one of those areas with the greatest potential of global expansion. Tourism development strategy in terms of maximizing its positive effects on regional economic increase and implicitly on the national one starts from the premise that in global economy value is created in regions which are defined as particular geographical entities, separated by geographical reasons and not as political-administrative structures, and economic increase is centrally cumulated and valued according to the economic policy and the national legal system.Regional economic system approach based on “cluster” concept is explained by the fact that the regional activities portfolio is based on an inter and intra-industry networking grouped by cluster, in which is created the value that increases as the activity results are leading to the final consumers.This type of communication aims to highlight the tourism role as a factor in regional development, the clustering process significance in obtaining some competitiveness advantages, clusters development in tourism beginnings, and also the identification methodology used to select one touristic area to create the cluster.

  4. Visualization techniques for spatial probability density function data

    Directory of Open Access Journals (Sweden)

    Udeepta D Bordoloi

    2006-01-01

    Full Text Available Novel visualization methods are presented for spatial probability density function data. These are spatial datasets, where each pixel is a random variable, and has multiple samples which are the results of experiments on that random variable. We use clustering as a means to reduce the information contained in these datasets; and present two different ways of interpreting and clustering the data. The clustering methods are used on two datasets, and the results are discussed with the help of visualization techniques designed for the spatial probability data.

  5. Spatial Scaling of the Profile of Selective Attention in the Visual Field.

    Directory of Open Access Journals (Sweden)

    Matthew A Gannon

    Full Text Available Neural mechanisms of selective attention must be capable of adapting to variation in the absolute size of an attended stimulus in the ever-changing visual environment. To date, little is known regarding how attentional selection interacts with fluctuations in the spatial expanse of an attended object. Here, we use event-related potentials (ERPs to investigate the scaling of attentional enhancement and suppression across the visual field. We measured ERPs while participants performed a task at fixation that varied in its attentional demands (attentional load and visual angle (1.0° or 2.5°. Observers were presented with a stream of task-relevant stimuli while foveal, parafoveal, and peripheral visual locations were probed by irrelevant distractor stimuli. We found two important effects in the N1 component of visual ERPs. First, N1 modulations to task-relevant stimuli indexed attentional selection of stimuli during the load task and further correlated with task performance. Second, with increased task size, attentional modulation of the N1 to distractor stimuli showed a differential pattern that was consistent with a scaling of attentional selection. Together, these results demonstrate that the size of an attended stimulus scales the profile of attentional selection across the visual field and provides insights into the attentional mechanisms associated with such spatial scaling.

  6. A Negative Selection Algorithm Based on Hierarchical Clustering of Self Set and its Application in Anomaly Detection

    Directory of Open Access Journals (Sweden)

    Wen Chen

    2011-08-01

    Full Text Available A negative selection algorithm based on the hierarchical clustering of self set HC-RNSA is introduced in this paper. Several strategies are applied to improve the algorithm performance. First, the self data set is replaced by the self cluster centers to compare with the detector candidates in each cluster level. As the number of self clusters is much less than the self set size, the detector generation efficiency is improved. Second, during the detector generation process, the detector candidates are restricted to the lower coverage space to reduce detector redundancy. In the article, the problem that the distances between antigens coverage to a constant value in the high dimensional space is analyzed, accordingly the Principle Component Analysis (PCA method is used to reduce the data dimension, and the fractional distance function is employed to enhance the distinctiveness between the self and non-self antigens. The detector generation procedure is terminated when the expected non-self coverage is reached. The theory analysis and experimental results demonstrate that the detection rate of HC-RNSA is higher than that of the traditional negative selection algorithms while the false alarm rate and time cost are reduced.

  7. Radio Selection of the Most Distant Galaxy Clusters

    Science.gov (United States)

    Daddi, E.; Jin, S.; Strazzullo, V.; Sargent, M. T.; Wang, T.; Ferrari, C.; Schinnerer, E.; Smolčić, V.; Calabró, A.; Coogan, R.; Delhaize, J.; Delvecchio, I.; Elbaz, D.; Gobat, R.; Gu, Q.; Liu, D.; Novak, M.; Valentino, F.

    2017-09-01

    We show that the most distant X-ray-detected cluster known to date, Cl J1001 at {z}{spec}=2.506, hosts a strong overdensity of radio sources. Six of them are individually detected (within 10\\prime\\prime ) in deep 0\\buildrel{\\prime\\prime}\\over{.} 75 resolution VLA 3 GHz imaging, with {S}3{GHz}> 8 μ {Jy}. Of the six, an active galactic nucleus (AGN) likely affects the radio emission in two galaxies, while star formation is the dominant source powering the remaining four. We searched for cluster candidates over the full COSMOS 2 deg2 field using radio-detected 3 GHz sources and looking for peaks in {{{Σ }}}5 density maps. Cl J1001 is the strongest overdensity by far with > 10σ , with a simple {z}{phot}> 1.5 preselection. A cruder photometric rejection of zsamples of the first generation of forming galaxy clusters. In these remarkable structures, widespread star formation and AGN activity of massive galaxy cluster members, residing within the inner cluster core, will ultimately lead to radio continuum as one of the most effective means for their identification, with detection rates expected in the ballpark of 0.1-1 per square degree at z≳ 2.5. Samples of hundreds such high-redshift clusters could potentially constrain cosmological parameters and test cluster and galaxy formation models.

  8. Data-driven inference for the spatial scan statistic.

    Science.gov (United States)

    Almeida, Alexandre C L; Duarte, Anderson R; Duczmal, Luiz H; Oliveira, Fernando L P; Takahashi, Ricardo H C

    2011-08-02

    Kulldorff's spatial scan statistic for aggregated area maps searches for clusters of cases without specifying their size (number of areas) or geographic location in advance. Their statistical significance is tested while adjusting for the multiple testing inherent in such a procedure. However, as is shown in this work, this adjustment is not done in an even manner for all possible cluster sizes. A modification is proposed to the usual inference test of the spatial scan statistic, incorporating additional information about the size of the most likely cluster found. A new interpretation of the results of the spatial scan statistic is done, posing a modified inference question: what is the probability that the null hypothesis is rejected for the original observed cases map with a most likely cluster of size k, taking into account only those most likely clusters of size k found under null hypothesis for comparison? This question is especially important when the p-value computed by the usual inference process is near the alpha significance level, regarding the correctness of the decision based in this inference. A practical procedure is provided to make more accurate inferences about the most likely cluster found by the spatial scan statistic.

  9. Young star clusters in nearby molecular clouds

    Science.gov (United States)

    Getman, K. V.; Kuhn, M. A.; Feigelson, E. D.; Broos, P. S.; Bate, M. R.; Garmire, G. P.

    2018-06-01

    The SFiNCs (Star Formation in Nearby Clouds) project is an X-ray/infrared study of the young stellar populations in 22 star-forming regions with distances ≲ 1 kpc designed to extend our earlier MYStIX (Massive Young Star-Forming Complex Study in Infrared and X-ray) survey of more distant clusters. Our central goal is to give empirical constraints on cluster formation mechanisms. Using parametric mixture models applied homogeneously to the catalogue of SFiNCs young stars, we identify 52 SFiNCs clusters and 19 unclustered stellar structures. The procedure gives cluster properties including location, population, morphology, association with molecular clouds, absorption, age (AgeJX), and infrared spectral energy distribution (SED) slope. Absorption, SED slope, and AgeJX are age indicators. SFiNCs clusters are examined individually, and collectively with MYStIX clusters, to give the following results. (1) SFiNCs is dominated by smaller, younger, and more heavily obscured clusters than MYStIX. (2) SFiNCs cloud-associated clusters have the high ellipticities aligned with their host molecular filaments indicating morphology inherited from their parental clouds. (3) The effect of cluster expansion is evident from the radius-age, radius-absorption, and radius-SED correlations. Core radii increase dramatically from ˜0.08 to ˜0.9 pc over the age range 1-3.5 Myr. Inferred gas removal time-scales are longer than 1 Myr. (4) Rich, spatially distributed stellar populations are present in SFiNCs clouds representing early generations of star formation. An appendix compares the performance of the mixture models and non-parametric minimum spanning tree to identify clusters. This work is a foundation for future SFiNCs/MYStIX studies including disc longevity, age gradients, and dynamical modelling.

  10. Observations of High-Redshift X-Ray Selected Clusters with the Sunyaev-Zel'dovich Array

    Science.gov (United States)

    Muchovej, Stephen; Carlstrom, John E.; Cartwright, John; Greer, Christopher; Hawkins, David; Hennessey, Ryan; Joy, Marshall; Lamb, James; Leitch, Erik M.; Loh, Michael; hide

    2006-01-01

    We report measurements of the Sunyaev-Zel'dovich (SZ) effect in three high redshift (0.89 less than or equal to z less than or equal to 1.03), X-ray selected galaxy clusters. The observations were obtained at 30 GHz during the commissioning period of a new, eight-element interferometer - the Sunyaev-Zel'dovich Array (SZA) - built for dedicated SZ effect observations. The SZA observations are sensitive to angular scales larger than those subtended by the virial radii of the clusters. Assuming isothermality and hydrostatic equilibrium for the intracluster medium, and gas-mass fractions consistent with those for clusters at moderate redshift, we calculate electron temperatures, gas masses, and total cluster masses from the SZ data. The SZ-derived masses, integrated approximately to the virial radii, are 1.9 (sup +0.5)(sub -0.4) x 10(exp 14) solar mass for Cl J1415.1+3612, 3.4 (sup +0.6)(sub -0.5) x 10(exp 14) solar mass for Cl J1429.0+4241 and 7.2 (sup +1.3)(sub -0.9) x 10(exp 14) solar mass for Cl J1226.9+3332. The SZ-derived quantities are in good agreement with the cluster properties derived from X-ray measurements.

  11. Two-stage clustering (TSC: a pipeline for selecting operational taxonomic units for the high-throughput sequencing of PCR amplicons.

    Directory of Open Access Journals (Sweden)

    Xiao-Tao Jiang

    Full Text Available Clustering 16S/18S rRNA amplicon sequences into operational taxonomic units (OTUs is a critical step for the bioinformatic analysis of microbial diversity. Here, we report a pipeline for selecting OTUs with a relatively low computational demand and a high degree of accuracy. This pipeline is referred to as two-stage clustering (TSC because it divides tags into two groups according to their abundance and clusters them sequentially. The more abundant group is clustered using a hierarchical algorithm similar to that in ESPRIT, which has a high degree of accuracy but is computationally costly for large datasets. The rarer group, which includes the majority of tags, is then heuristically clustered to improve efficiency. To further improve the computational efficiency and accuracy, two preclustering steps are implemented. To maintain clustering accuracy, all tags are grouped into an OTU depending on their pairwise Needleman-Wunsch distance. This method not only improved the computational efficiency but also mitigated the spurious OTU estimation from 'noise' sequences. In addition, OTUs clustered using TSC showed comparable or improved performance in beta-diversity comparisons compared to existing OTU selection methods. This study suggests that the distribution of sequencing datasets is a useful property for improving the computational efficiency and increasing the clustering accuracy of the high-throughput sequencing of PCR amplicons. The software and user guide are freely available at http://hwzhoulab.smu.edu.cn/paperdata/.

  12. Galaxy Kinematics and Mass Calibration in Massive SZE Selected Galaxy Clusters to z=1.3

    Energy Technology Data Exchange (ETDEWEB)

    Capasso, R.; et al.

    2017-11-27

    The galaxy phase-space distribution in galaxy clusters provides insights into the formation and evolution of cluster galaxies, and it can also be used to measure cluster mass profiles. We present a dynamical study based on $\\sim$3000 passive, non-emission line cluster galaxies drawn from 110 galaxy clusters. The galaxy clusters were selected using the Sunyaev-Zel'dovich effect (SZE) in the 2500 deg$^2$ SPT-SZ survey and cover the redshift range $0.2 < z < 1.3$. We model the clusters using the Jeans equation, while adopting NFW mass profiles and a broad range of velocity dispersion anisotropy profiles. The data prefer velocity dispersion anisotropy profiles that are approximately isotropic near the center and increasingly radial toward the cluster virial radius, and this is true for all redshifts and masses we study. The pseudo-phase-space density profile of the passive galaxies is consistent with expectations for dark matter particles and subhalos from cosmological $N$-body simulations. The dynamical mass constraints are in good agreement with external mass estimates of the SPT cluster sample from either weak lensing, velocity dispersions, or X-ray $Y_X$ measurements. However, the dynamical masses are lower (at the 2.2$\\sigma$ level) when compared to the mass calibration favored when fitting the SPT cluster data to a LCDM model with external cosmological priors, including CMB anisotropy data from Planck. The tension grows with redshift, where in the highest redshift bin the ratio of dynamical to SPT+Planck masses is $\\eta=0.63^{+0.13}_{-0.08}\\pm0.05$ (statistical and systematic), corresponding to 2.6$\\sigma$ tension.

  13. Two- and three-cluster decays of light nuclei within a hyperspherical harmonics approach

    Science.gov (United States)

    Vasilevsky, V. S.; Lashko, Yu. A.; Filippov, G. F.

    2018-06-01

    We consider a set of three-cluster systems (4He, 7Li, 7Be, 8Be, 10Be) within a microscopic model which involves hyperspherical harmonics to represent intercluster motion. We selected three-cluster systems which have at least one binary channel. Our aim is to study whether hyperspherical harmonics are able, and under what conditions, to describe two-body channel(s) (nondemocratic motion) or if they are suitable for describing the three-cluster continuum only (democratic motion). It is demonstrated that a rather restricted number of hyperspherical harmonics allows us to describe bound states and scattering states in the two-body continuum for a three-cluster system.

  14. Cochlear Implant Spatial Selectivity with Monopolar, Bipolar and Tripolar Stimulation

    Science.gov (United States)

    Zhu, Ziyan; Tang, Qing; Zeng, Fan-Gang; Guan, Tian; Ye, Datian

    2011-01-01

    Sharp spatial selectivity is critical to auditory performance, particularly in pitch related tasks. Most contemporary cochlear implants have employed monopolar stimulation that produces broad electric fields, which presumably contribute to poor pitch and pitch-related performance by implant users. Bipolar or tripolar stimulation can generate focused electric fields but requires higher current to reach threshold and, more interestingly, has not produced any apparent improvement in cochlear implant performance. The present study addressed this dilemma by measuring psychophysical and physiological spatial selectivity with both broad and focused stimulations in the same cohort of subjects. Different current levels were adjusted by systematically measuring loudness growth for each stimulus, each stimulation mode, and in each subject. Both psychophysical and physiological measures showed that, although focused stimulation produced significantly sharper spatial tuning than monopolar stimulation, it could shift the tuning position or even split the tuning tips. The altered tuning with focused stimulation is interpreted as a result of poor electrode-to-neuron interface in the cochlea, and is suggested to be mainly responsible for the lack of consistent improvement in implant performance. A linear model could satisfactorily quantify the psychophysical and physiological data and derive the tuning width. Significant correlation was found between the individual physiological and psychophysical tuning widths, and the correlation was improved by log-linearly transforming the physiological data to predict the psychophysical data. Because the physiological measure took only one-tenth of the time of the psychophysical measure, the present model is of high clinical significance in terms of predicting and improving cochlear implant performance. PMID:22138630

  15. Spatial distribution of 12 class B notifiable infectious diseases in China: A retrospective study.

    Science.gov (United States)

    Zhu, Bin; Fu, Yang; Liu, Jinlin; Mao, Ying

    2018-01-01

    China is the largest developing country with a relatively developed public health system. To further prevent and eliminate the spread of infectious diseases, China has listed 39 notifiable infectious diseases characterized by wide prevalence or great harm, and classified them into classes A, B, and C, with severity decreasing across classes. Class A diseases have been almost eradicated in China, thus making class B diseases a priority in infectious disease prevention and control. In this retrospective study, we analyze the spatial distribution patterns of 12 class B notifiable infectious diseases that remain active all over China. Global and local Moran's I and corresponding graphic tools are adopted to explore and visualize the global and local spatial distribution of the incidence of the selected epidemics, respectively. Inter-correlations of clustering patterns of each pair of diseases and a cumulative summary of the high/low cluster frequency of the provincial units are also provided by means of figures and maps. Of the 12 most commonly notifiable class B infectious diseases, viral hepatitis and tuberculosis show high incidence rates and account for more than half of the reported cases. Almost all the diseases, except pertussis, exhibit positive spatial autocorrelation at the provincial level. All diseases feature varying spatial concentrations. Nevertheless, associations exist between spatial distribution patterns, with some provincial units displaying the same type of cluster features for two or more infectious diseases. Overall, high-low (unit with high incidence surrounded by units with high incidence, the same below) and high-high spatial cluster areas tend to be prevalent in the provincial units located in western and southwest China, whereas low-low and low-high spatial cluster areas abound in provincial units in north and east China. Despite the various distribution patterns of 12 class B notifiable infectious diseases, certain similarities between

  16. Do satellite galaxies trace matter in galaxy clusters?

    Science.gov (United States)

    Wang, Chunxiang; Li, Ran; Gao, Liang; Shan, Huanyuan; Kneib, Jean-Paul; Wang, Wenting; Chen, Gang; Makler, Martin; Pereira, Maria E. S.; Wang, Lin; Maia, Marcio A. G.; Erben, Thomas

    2018-04-01

    The spatial distribution of satellite galaxies encodes rich information of the structure and assembly history of galaxy clusters. In this paper, we select a red-sequence Matched-filter Probabilistic Percolation cluster sample in SDSS Stripe 82 region with 0.1 ≤ z ≤ 0.33, 20 0.7. Using the high-quality weak lensing data from CS82 Survey, we constrain the mass profile of this sample. Then we compare directly the mass density profile with the satellite number density profile. We find that the total mass and number density profiles have the same shape, both well fitted by an NFW profile. The scale radii agree with each other within a 1σ error (r_s,gal=0.34_{-0.03}^{+0.04} Mpc versus r_s=0.37_{-0.10}^{+0.15} Mpc).

  17. Cluster Physics with Merging Galaxy Clusters

    Directory of Open Access Journals (Sweden)

    Sandor M. Molnar

    2016-02-01

    Full Text Available Collisions between galaxy clusters provide a unique opportunity to study matter in a parameter space which cannot be explored in our laboratories on Earth. In the standard LCDM model, where the total density is dominated by the cosmological constant ($Lambda$ and the matter density by cold dark matter (CDM, structure formation is hierarchical, and clusters grow mostly by merging.Mergers of two massive clusters are the most energetic events in the universe after the Big Bang,hence they provide a unique laboratory to study cluster physics.The two main mass components in clusters behave differently during collisions:the dark matter is nearly collisionless, responding only to gravity, while the gas is subject to pressure forces and dissipation, and shocks and turbulenceare developed during collisions. In the present contribution we review the different methods used to derive the physical properties of merging clusters. Different physical processes leave their signatures on different wavelengths, thusour review is based on a multifrequency analysis. In principle, the best way to analyze multifrequency observations of merging clustersis to model them using N-body/HYDRO numerical simulations. We discuss the results of such detailed analyses.New high spatial and spectral resolution ground and space based telescopeswill come online in the near future. Motivated by these new opportunities,we briefly discuss methods which will be feasible in the near future in studying merging clusters.

  18. The Atacama Cosmology Telescope: High-Resolution Sunyaev-Zeldovich Array Observations of ACT SZE-Selected Clusters from the Equatorial Strip

    Science.gov (United States)

    Reese, Erik; Mroczkowski, Tony; Menateau, Felipe; Hilton, Matt; Sievers, Jonathan; Aguirre, Paula; Appel, John William; Baker, Andrew J.; Bond, J. Richard; Das, Sudeep; hide

    2011-01-01

    We present follow-up observations with the Sunyaev-Zel'dovich Array (SZA) of optically-confirmed galaxy clusters found in the equatorial survey region of the Atacama Cosmology Telescope (ACT): ACT-CL J0022-0036, ACT-CL J2051+0057, and ACT-CL J2337+0016. ACT-CL J0022-0036 is a newly-discovered, massive ( approximately equals 10(exp 15) Solar M), high-redshift (z = 0.81) cluster revealed by ACT through the Sunyaev-Zeldovich effect (SZE). Deep, targeted observations with the SZA allow us to probe a broader range of cluster spatial scales, better disentangle cluster decrements from radio point source emission, and derive more robust integrated SZE flux and mass estimates than we can with ACT data alone. For the two clusters we detect with the SZA we compute integrated SZE signal and derive masses from the SZA data only. ACT-CL J2337+0016, also known as Abell 2631, has archival Chandra data that allow an additional X-ray-based mass estimate. Optical richness is also used to estimate cluster masses and shows good agreement with the SZE and X-ray-based estimates. Based on the point sources detected by the SZA in these three cluster fields and an extrapolation to ACT's frequency, we estimate that point sources could be contaminating the SZE decrement at the approx < 20% level for some fraction of clusters.

  19. Weighted Clustering

    DEFF Research Database (Denmark)

    Ackerman, Margareta; Ben-David, Shai; Branzei, Simina

    2012-01-01

    We investigate a natural generalization of the classical clustering problem, considering clustering tasks in which different instances may have different weights.We conduct the first extensive theoretical analysis on the influence of weighted data on standard clustering algorithms in both...... the partitional and hierarchical settings, characterizing the conditions under which algorithms react to weights. Extending a recent framework for clustering algorithm selection, we propose intuitive properties that would allow users to choose between clustering algorithms in the weighted setting and classify...

  20. Seismic clusters analysis in Northeastern Italy by the nearest-neighbor approach

    Science.gov (United States)

    Peresan, Antonella; Gentili, Stefania

    2018-01-01

    The main features of earthquake clusters in Northeastern Italy are explored, with the aim to get new insights on local scale patterns of seismicity in the area. The study is based on a systematic analysis of robustly and uniformly detected seismic clusters, which are identified by a statistical method, based on nearest-neighbor distances of events in the space-time-energy domain. The method permits us to highlight and investigate the internal structure of earthquake sequences, and to differentiate the spatial properties of seismicity according to the different topological features of the clusters structure. To analyze seismicity of Northeastern Italy, we use information from local OGS bulletins, compiled at the National Institute of Oceanography and Experimental Geophysics since 1977. A preliminary reappraisal of the earthquake bulletins is carried out and the area of sufficient completeness is outlined. Various techniques are considered to estimate the scaling parameters that characterize earthquakes occurrence in the region, namely the b-value and the fractal dimension of epicenters distribution, required for the application of the nearest-neighbor technique. Specifically, average robust estimates of the parameters of the Unified Scaling Law for Earthquakes, USLE, are assessed for the whole outlined region and are used to compute the nearest-neighbor distances. Clusters identification by the nearest-neighbor method turn out quite reliable and robust with respect to the minimum magnitude cutoff of the input catalog; the identified clusters are well consistent with those obtained from manual aftershocks identification of selected sequences. We demonstrate that the earthquake clusters have distinct preferred geographic locations, and we identify two areas that differ substantially in the examined clustering properties. Specifically, burst-like sequences are associated with the north-western part and swarm-like sequences with the south-eastern part of the study

  1. Evolution of the spherical clusters

    International Nuclear Information System (INIS)

    Surdin, V.G.

    1978-01-01

    The possible processes of the Galaxy spherical clusters formation and evolution are described on a popular level. The orbits of spherical cluster motion and their spatial velocities are determined. Given are the distrbutions of spherical cluster stars according to their velocities and the observed distribution of spherical clusters in the area of the Galaxy slow evolution. The dissipation and dynamic friction processes destructing clusters with the mass less than 10 4 of solar mass and bringing about the reduction of clusters in the Galaxy are considered. The paradox of forming mainly X-ray sources in spherical clusters is explained. The schematic image of possible ways of forming X-ray sources in spherical clusters is given

  2. Dark matter searches with Cherenkov telescopes: nearby dwarf galaxies or local galaxy clusters?

    Energy Technology Data Exchange (ETDEWEB)

    Sánchez-Conde, Miguel A. [SLAC National Laboratory and Kavli Institute for Particle Astrophysics and Cosmology, 2575 Sand Hill Road, Menlo Park, CA 94025 (United States); Cannoni, Mirco; Gómez, Mario E. [Dpto. Física Aplicada, Facultad de Ciencias Experimentales, Universidad de Huelva, 21071 Huelva (Spain); Zandanel, Fabio; Prada, Francisco, E-mail: masc@stanford.edu, E-mail: mirco.cannoni@dfa.uhu.es, E-mail: fabio@iaa.es, E-mail: mario.gomez@dfa.uhu.es, E-mail: fprada@iaa.es [Instituto de Astrofísica de Andalucía (CSIC), E-18008, Granada (Spain)

    2011-12-01

    In this paper, we compare dwarf galaxies and galaxy clusters in order to elucidate which object class is the best target for gamma-ray DM searches with imaging atmospheric Cherenkov telescopes (IACTs). We have built a mixed dwarfs+clusters sample containing some of the most promising nearby dwarf galaxies (Draco, Ursa Minor, Wilman 1 and Segue 1) and local galaxy clusters (Perseus, Coma, Ophiuchus, Virgo, Fornax, NGC 5813 and NGC 5846), and then compute their DM annihilation flux profiles by making use of the latest modeling of their DM density profiles. We also include in our calculations the effect of DM substructure. Willman 1 appears as the best candidate in the sample. However, its mass modeling is still rather uncertain, so probably other candidates with less uncertainties and quite similar fluxes, namely Ursa Minor and Segue 1, might be better options. As for galaxy clusters, Virgo represents the one with the highest flux. However, its large spatial extension can be a serious handicap for IACT observations and posterior data analysis. Yet, other local galaxy cluster candidates with more moderate emission regions, such as Perseus, may represent good alternatives. After comparing dwarfs and clusters, we found that the former exhibit annihilation flux profiles that, at the center, are roughly one order of magnitude higher than those of clusters, although galaxy clusters can yield similar, or even higher, integrated fluxes for the whole object once substructure is taken into account. Even when any of these objects are strictly point-like according to the properties of their annihilation signals, we conclude that dwarf galaxies are best suited for observational strategies based on the search of point-like sources, while galaxy clusters represent best targets for analyses that can deal with rather extended emissions. Finally, we study the detection prospects for present and future IACTs in the framework of the constrained minimal supersymmetric standard model. We

  3. Dark Matter Searches with Cherenkov Telescopes: Nearby Dwarf Galaxies or Local Galaxy Clusters?

    Energy Technology Data Exchange (ETDEWEB)

    Sanchez-Conde, Miguel A.; /KIPAC, Menlo Park /SLAC /IAC, La Laguna /Laguna U., Tenerife; Cannoni, Mirco; /Huelva U.; Zandanel, Fabio; /IAA, Granada; Gomez, Mario E.; /Huelva U.; Prada, Francisco; /IAA, Granada

    2012-06-06

    In this paper, we compare dwarf galaxies and galaxy clusters in order to elucidate which object class is the best target for gamma-ray DM searches with imaging atmospheric Cherenkov telescopes (IACTs). We have built a mixed dwarfs+clusters sample containing some of the most promising nearby dwarf galaxies (Draco, Ursa Minor, Wilman 1 and Segue 1) and local galaxy clusters (Perseus, Coma, Ophiuchus, Virgo, Fornax, NGC 5813 and NGC 5846), and then compute their DM annihilation flux profiles by making use of the latest modeling of their DM density profiles. We also include in our calculations the effect of DM substructure. Willman 1 appears as the best candidate in the sample. However, its mass modeling is still rather uncertain, so probably other candidates with less uncertainties and quite similar fluxes, namely Ursa Minor and Segue 1, might be better options. As for galaxy clusters, Virgo represents the one with the highest flux. However, its large spatial extension can be a serious handicap for IACT observations and posterior data analysis. Yet, other local galaxy cluster candidates with more moderate emission regions, such as Perseus, may represent good alternatives. After comparing dwarfs and clusters, we found that the former exhibit annihilation flux profiles that, at the center, are roughly one order of magnitude higher than those of clusters, although galaxy clusters can yield similar, or even higher, integrated fluxes for the whole object once substructure is taken into account. Even when any of these objects are strictly point-like according to the properties of their annihilation signals, we conclude that dwarf galaxies are best suited for observational strategies based on the search of point-like sources, while galaxy clusters represent best targets for analyses that can deal with rather extended emissions. Finally, we study the detection prospects for present and future IACTs in the framework of the constrained minimal supersymmetric standard model. We

  4. The smart cluster method. Adaptive earthquake cluster identification and analysis in strong seismic regions

    Science.gov (United States)

    Schaefer, Andreas M.; Daniell, James E.; Wenzel, Friedemann

    2017-07-01

    Earthquake clustering is an essential part of almost any statistical analysis of spatial and temporal properties of seismic activity. The nature of earthquake clusters and subsequent declustering of earthquake catalogues plays a crucial role in determining the magnitude-dependent earthquake return period and its respective spatial variation for probabilistic seismic hazard assessment. This study introduces the Smart Cluster Method (SCM), a new methodology to identify earthquake clusters, which uses an adaptive point process for spatio-temporal cluster identification. It utilises the magnitude-dependent spatio-temporal earthquake density to adjust the search properties, subsequently analyses the identified clusters to determine directional variation and adjusts its search space with respect to directional properties. In the case of rapid subsequent ruptures like the 1992 Landers sequence or the 2010-2011 Darfield-Christchurch sequence, a reclassification procedure is applied to disassemble subsequent ruptures using near-field searches, nearest neighbour classification and temporal splitting. The method is capable of identifying and classifying earthquake clusters in space and time. It has been tested and validated using earthquake data from California and New Zealand. A total of more than 1500 clusters have been found in both regions since 1980 with M m i n = 2.0. Utilising the knowledge of cluster classification, the method has been adjusted to provide an earthquake declustering algorithm, which has been compared to existing methods. Its performance is comparable to established methodologies. The analysis of earthquake clustering statistics lead to various new and updated correlation functions, e.g. for ratios between mainshock and strongest aftershock and general aftershock activity metrics.

  5. Selective spatial attention modulates bottom-up informational masking of speech.

    Science.gov (United States)

    Carlile, Simon; Corkhill, Caitlin

    2015-03-02

    To hear out a conversation against other talkers listeners overcome energetic and informational masking. Largely attributed to top-down processes, information masking has also been demonstrated using unintelligible speech and amplitude-modulated maskers suggesting bottom-up processes. We examined the role of speech-like amplitude modulations in information masking using a spatial masking release paradigm. Separating a target talker from two masker talkers produced a 20 dB improvement in speech reception threshold; 40% of which was attributed to a release from informational masking. When across frequency temporal modulations in the masker talkers are decorrelated the speech is unintelligible, although the within frequency modulation characteristics remains identical. Used as a masker as above, the information masking accounted for 37% of the spatial unmasking seen with this masker. This unintelligible and highly differentiable masker is unlikely to involve top-down processes. These data provides strong evidence of bottom-up masking involving speech-like, within-frequency modulations and that this, presumably low level process, can be modulated by selective spatial attention.

  6. Helmintic infections in water buffaloes on Italian farms: a spatial analysis

    Directory of Open Access Journals (Sweden)

    Laura Rinaldi

    2009-05-01

    Full Text Available The present paper reports the results of a cross-sectional survey aimed at obtaining up-to-date information on the spatial distribution of different groups and/or species of helminths in water buffaloes from central Italy. Geographical information systems (GIS and spatial analysis were used to plan the sampling procedures, to display the results as maps and to detect spatial clusters of helminths in the study area. The survey was conducted on 127 water buffalo farms, which were selected in the study area using a grid sampling approach, followed by proportional allocation. Faecal samples (n. = 1,883 collected from the 127 farms were examined using the Flotac dual technique. Gastrointestinal strongyles were the most frequent helminths (33.1% on the tested farms, followed by the liver fluke Fasciola hepatica (7.1%, the rumen fluke Calicophoron daubneyi (7.1%, the nematode Strongyloides spp. (3.1%, the lancet liver fluke Dicrocoelium dendriticum (2.4% and the tapeworm Moniezia spp. (2.4%. In order to display the spatial distribution of the various helminths detected on the water buffalo farms (used as epidemiological unit in our study, point maps were drawn within the GIS. In addition, for each helminth, clustering of test-positive farms were investigated based on location determined by exact coordinates. Using spatial scan statistic, spatial clusters were found for the flukes F. hepatica and C. daubneyi and the cestode Moniezia spp.; these findings are consistent with the life cycle of these parasites, which have strong environmental determinants. In conclusion, the present study demonstrated that, with the appropriate survey-based data at hand, GIS is a useful tool to study epidemiological patterns of infections in veterinary health, in particular in water buffaloes.

  7. Mass-selected iron-cobalt alloy clusters. Correlation of magnetic and structural properties; Massenselektierte Eisen-Kobalt-Legierungscluster. Korrelation magnetischer und struktureller Eigenschaften

    Energy Technology Data Exchange (ETDEWEB)

    Bulut, Furkan

    2008-10-13

    In this work, I present results concerning structural and magnetic properties of massselected iron-cobalt alloy clusters with diameters between 5 and 15 nm. I have studied the structure of FeCo alloy clusters with high resolution transmission electron microscopy (HRTEM) and scanning tunneling microscopy (STM). I have also investigated the crystalline structure of pure iron and pure cobalt clusters with HRTEM to ensure a reliable determination of the lattice parameter for the alloy clusters. The FeCo nanoparticles have a truncated dodecahedral shape with a CsCl-structure. The clusters were produced with a continuously working arc cluster ion source and subsequently mass-selected with an electrostatic quadrupole deflector. The composition of the alloy clusters was checked with energy dispersive x-ray spectroscopy (EDX). The lateral size distribution was investigated by TEM and the height of the deposited FeCo clusters on the (110) surface of tungsten was determined by STM. Comparing the results I have observed that the supported clusters were flattened due to the high surface energy of W(110). The decrease in height of the mass-selected supported clusters amounts to about 1 nm. Furthermore, element specific magnetic studies performed by means of X-ray magnetic circular dichroism (XMCD) have shown that magnetic moments of Fe{sub 50}Co{sub 50} alloy clusters are in good agreement with the theoretically expected values in the bulk. I have also examined the behavior of the alloy clusters at elevated temperatures. The clusters exhibit an anisotropic melting on the W(110) surface. (orig.)

  8. A Missing Link in Galaxy Evolution: The Mysteries of Dissolving Star Clusters

    Science.gov (United States)

    Pellerin, Anne; Meyer, Martin; Harris, Jason; Calzetti, Daniela

    2007-05-01

    Star-forming events in starbursts and normal galaxies have a direct impact on the global stellar content of galaxies. These events create numerous compact clusters where stars are produced in great number. These stars eventually end up in the star field background where they are smoothly distributed. However, due to instrumental limitations such as spatial resolution and sensitivity, the processes involved during the transition phase from the compact clusters to the star field background as well as the impact of the environment (spiral waves, bars, starburst) on the lifetime of clusters are still poorly constrained observationally. I will present our latest results on the physical properties of dissolving clusters directly detected in HST/ACS archival images of the three nearby galaxies IC 2574, NGC 1313, and IC 10 (D detect and spatially resolve individual stars in nearby galaxies within a large field-of-view. For all ACS images obtained in three filters (F435W, F555W or F606W, and F814W), we performed PSF stellar photometry in crowded field. Color-magnitude diagrams (CMD) allow us to identify the most massive stars more likely to be part of dissolving clusters (A-type and earlier), and to isolate them from the star field background. We then adapt and use a clustering algorithm on the selected stars to find groups of stars to reveal and quantify the properties of all star clusters (compactness, size, age, mass). With this algorithm, even the less compact clusters are revealed while they are being destroyed. Our sample of three galaxies covers an interesting range in gravitational potential well and explores a variety of galaxy morphological types, which allows us to discuss the dissolving cluster properties as a function of the host galaxy characteristics. The properties of the star field background will also be discussed.

  9. Comparing Residue Clusters from Thermophilic and Mesophilic Enzymes Reveals Adaptive Mechanisms.

    Directory of Open Access Journals (Sweden)

    Deanne W Sammond

    Full Text Available Understanding how proteins adapt to function at high temperatures is important for deciphering the energetics that dictate protein stability and folding. While multiple principles important for thermostability have been identified, we lack a unified understanding of how internal protein structural and chemical environment determine qualitative or quantitative impact of evolutionary mutations. In this work we compare equivalent clusters of spatially neighboring residues between paired thermophilic and mesophilic homologues to evaluate adaptations under the selective pressure of high temperature. We find the residue clusters in thermophilic enzymes generally display improved atomic packing compared to mesophilic enzymes, in agreement with previous research. Unlike residue clusters from mesophilic enzymes, however, thermophilic residue clusters do not have significant cavities. In addition, anchor residues found in many clusters are highly conserved with respect to atomic packing between both thermophilic and mesophilic enzymes. Thus the improvements in atomic packing observed in thermophilic homologues are not derived from these anchor residues but from neighboring positions, which may serve to expand optimized protein core regions.

  10. Procedural Factors That Affect Psychophysical Measures of Spatial Selectivity in Cochlear Implant Users

    Directory of Open Access Journals (Sweden)

    Stefano Cosentino

    2015-09-01

    Full Text Available Behavioral measures of spatial selectivity in cochlear implants are important both for guiding the programing of individual users’ implants and for the evaluation of different stimulation methods. However, the methods used are subject to a number of confounding factors that can contaminate estimates of spatial selectivity. These factors include off-site listening, charge interactions between masker and probe pulses in interleaved masking paradigms, and confusion effects in forward masking. We review the effects of these confounds and discuss methods for minimizing them. We describe one such method in which the level of a 125-pps masker is adjusted so as to mask a 125-pps probe, and where the masker and probe pulses are temporally interleaved. Five experiments describe the method and evaluate the potential roles of the different potential confounding factors. No evidence was obtained for off-site listening of the type observed in acoustic hearing. The choice of the masking paradigm was shown to alter the measured spatial selectivity. For short gaps between masker and probe pulses, both facilitation and refractory mechanisms had an effect on masking; this finding should inform the choice of stimulation rate in interleaved masking experiments. No evidence for confusion effects in forward masking was revealed. It is concluded that the proposed method avoids many potential confounds but that the choice of method should depend on the research question under investigation.

  11. The Munich Near-Infrared Cluster Survey - IX. Galaxy evolution to z ~ 2 from optically selected catalogues†‡

    Science.gov (United States)

    Feulner, Georg; Goranova, Yuliana; Hopp, Ulrich; Gabasch, Armin; Bender, Ralf; Botzler, Christine S.; Drory, Niv

    2007-06-01

    We present B-, R- and I-band-selected galaxy catalogues based on the Munich Near-Infrared Cluster Survey (MUNICS) which, together with the previously used K-selected sample, serve as an important probe of galaxy evolution in the redshift range 0 Karl-Schwarzschild Strasse 2, D-85748, Garching bei München, Germany.

  12. Readout from iconic memory and selective spatial attention involve similar neural processes.

    Science.gov (United States)

    Ruff, Christian C; Kristjánsson, Arni; Driver, Jon

    2007-10-01

    Iconic memory and spatial attention are often considered separately, but they may have functional similarities. Here we provide functional magnetic resonance imaging evidence for some common underlying neural effects. Subjects judged three visual stimuli in one hemifield of a bilateral array comprising six stimuli. The relevant hemifield for partial report was indicated by an auditory cue, administered either before the visual array (precue, spatial attention) or shortly after the array (postcue, iconic memory). Pre- and postcues led to similar activity modulations in lateral occipital cortex contralateral to the cued side. This finding indicates that readout from iconic memory can have some neural effects similar to those of spatial attention. We also found common bilateral activation of a fronto-parietal network for postcue and precue trials. These neuroimaging data suggest that some common neural mechanisms underlie selective spatial attention and readout from iconic memory. Some differences were also found; compared with precues, postcues led to higher activity in the right middle frontal gyrus.

  13. VizieR Online Data Catalog: 44 SZ-selected galaxy clusters ACT observations (Sifon+, 2016)

    Science.gov (United States)

    Sifon, C.; Battaglia, N.; Hasselfield, M.; Menanteau, F.; Barrientos, L. F.; Bond, J. R.; Crichton, D.; Devlin, M. J.; Dunner, R.; Hilton, M.; Hincks, A. D.; Hlozek, R.; Huffenberger, K. M.; Hughes, J. P.; Infante, L.; Kosowsky, A.; Marsden, D.; Marriage, T. A.; Moodley, K.; Niemack, M. D.; Page, L. A.; Spergel, D. N.; Staggs, S. T.; Trac, H.; Wollack, E. J.

    2017-11-01

    ACT is a 6-metre off-axis Gregorian telescope located at an altitude of 5200um in the Atacama desert in Chile, designed to observe the CMB at arcminute resolution. Galaxy clusters were detected in the 148GHz band by matched-filtering the maps with the pressure profile suggested by Arnaud et al. (2010A&A...517A..92A), fit to X-ray selected local (zGMOS) on the Gemini-South telescope, split in semesters 2011B (ObsID:GS-2011B-C-1, PI:Barrientos/Menanteau) and 2012A (ObsID:GS-2012A-C-1, PI:Menanteau), prioritizing clusters in the cosmological sample at 0.3clusters in S82 with the Robert Stobie Spectrograph (RSS) on the Southern African Large Telescope (SALT), using MOS. Details of these observations are given in Kirk et al. (2015, Cat. J/MNRAS/449/4010). In order to enlarge the sample of studied clusters and member galaxies, we also compiled archival data for the equatorial sample. (1 data file).

  14. Data-driven inference for the spatial scan statistic

    Directory of Open Access Journals (Sweden)

    Duczmal Luiz H

    2011-08-01

    Full Text Available Abstract Background Kulldorff's spatial scan statistic for aggregated area maps searches for clusters of cases without specifying their size (number of areas or geographic location in advance. Their statistical significance is tested while adjusting for the multiple testing inherent in such a procedure. However, as is shown in this work, this adjustment is not done in an even manner for all possible cluster sizes. Results A modification is proposed to the usual inference test of the spatial scan statistic, incorporating additional information about the size of the most likely cluster found. A new interpretation of the results of the spatial scan statistic is done, posing a modified inference question: what is the probability that the null hypothesis is rejected for the original observed cases map with a most likely cluster of size k, taking into account only those most likely clusters of size k found under null hypothesis for comparison? This question is especially important when the p-value computed by the usual inference process is near the alpha significance level, regarding the correctness of the decision based in this inference. Conclusions A practical procedure is provided to make more accurate inferences about the most likely cluster found by the spatial scan statistic.

  15. Composition dependent selectivity in the coadsorption of H2O and CO on pure and binary silver-gold clusters

    Science.gov (United States)

    Fleischer, Irene; Popolan, Denisia M.; Krstić, Marjan; Bonačić-Koutecký, Vlasta; Bernhardt, Thorsten M.

    2013-04-01

    Small cationic gold clusters exhibit a strong affinity toward carbon monoxide. This prevents the coadsorption of water which would be the first step of a catalytic water gas shift chemistry on these clusters. In a gas phase ion trap experiment with mass selected AgnAum+ it was however possible to demonstrate that the replacement of gold by silver atoms in triatomic cluster ions liberates sites for H2O adsorption. The resulting observed coadsorption effect occurs at a cross-over in the molecular binding energies of carbon monoxide and water to these clusters determined by reaction kinetics measurements and first principles calculations.

  16. Transverse tripolar stimulation of peripheral nerve: a modelling study of spatial selectivity

    NARCIS (Netherlands)

    Deurloo, K.E.I.; Holsheimer, J.; Boom, H.B.K.

    1998-01-01

    Various anode-cathode configurations in a nerve cuff are modelled to predict their spatial selectivity characteristics for functional nerve stimulation. A 3D volume conductor model of a monofascicular nerve is used for the computation of stimulation-induced field potentials, whereas a cable model of

  17. Cochlear-implant spatial selectivity with monopolar, bipolar and tripolar stimulation.

    Science.gov (United States)

    Zhu, Ziyan; Tang, Qing; Zeng, Fan-Gang; Guan, Tian; Ye, Datian

    2012-01-01

    Sharp spatial selectivity is critical to auditory performance, particularly in pitch-related tasks. Most contemporary cochlear implants have employed monopolar stimulation that produces broad electric fields, which presumably contribute to poor pitch and pitch-related performance by implant users. Bipolar or tripolar stimulation can generate focused electric fields but requires higher current to reach threshold and, more interestingly, has not produced any apparent improvement in cochlear-implant performance. The present study addressed this dilemma by measuring psychophysical and physiological spatial selectivity with both broad and focused stimulations in the same cohort of subjects. Different current levels were adjusted by systematically measuring loudness growth for each stimulus, each stimulation mode, and in each subject. Both psychophysical and physiological measures showed that, although focused stimulation produced significantly sharper spatial tuning than monopolar stimulation, it could shift the tuning position or even split the tuning tips. The altered tuning with focused stimulation is interpreted as a result of poor electrode-to-neuron interface in the cochlea, and is suggested to be mainly responsible for the lack of consistent improvement in implant performance. A linear model could satisfactorily quantify the psychophysical and physiological data and derive the tuning width. Significant correlation was found between the individual physiological and psychophysical tuning widths, and the correlation was improved by log-linearly transforming the physiological data to predict the psychophysical data. Because the physiological measure took only one-tenth of the time of the psychophysical measure, the present model is of high clinical significance in terms of predicting and improving cochlear-implant performance. Copyright © 2011 Elsevier B.V. All rights reserved.

  18. Spatial Tuning of a RF Frequency Selective Surface through Origami (Postprint)

    Science.gov (United States)

    2016-05-12

    computational tools to systematically predict optimal folds. 15. SUBJECT TERMS origami, frequency selective surface, tuning, radio frequency 16...experimental study and motivates the development of computational tools to systematically predict optimal fold patterns for targeted frequency response...folding motions. The precise mapping of origami presents a novel method to spatially tune radio frequency (RF) devices, including adaptive antennas

  19. Spatial-simultaneous working memory and selective interference in Down syndrome.

    Science.gov (United States)

    Lanfranchi, Silvia; Mammarella, Irene C; Carretti, Barbara

    2015-01-01

    Several studies have suggested that individuals with Down syndrome (DS) have impairments in some aspects of the visuospatial domain. It has been reported that they are particularly impaired in the spatial-simultaneous working memory (WM) even in advantageous conditions such as when information is grouped to form a configuration. This study aimed to assess the performance of individuals with DS carrying out a spatial-simultaneous WM task in single and dual selective interference conditions in order to better explore the characteristics of their impairment in this area. Groups of individuals with DS and mentally age-matched typically developing (TD) children were asked to carry out a spatial-simultaneous WM task in a single- and in two dual-task conditions. In the single condition, the participants were required to recall an increasing number of positions of red squares presented simultaneously in a matrix. In the dual-task conditions, together with the spatial-simultaneous WM task, the participants were asked to carry out an articulatory suppression task or a tapping task. As has already been shown in other studies, individuals with DS were found to be impaired in carrying out a spatial-simultaneous WM task and showed a worse performance with respect to the TD group in both the conditions. These findings indicate that individuals with DS use the same coding modality as TD children of the same mental age. Just as the TD children, they performed lower in the dual- than in the single-task condition and there was no difference between the verbal and visuospatial conditions.

  20. Iridium Clusters Encapsulated in Carbon Nanospheres as Nanocatalysts for Methylation of (Bio)Alcohols.

    Science.gov (United States)

    Liu, Qiang; Xu, Guoqiang; Wang, Zhendong; Liu, Xiaoran; Wang, Xicheng; Dong, Linlin; Mu, Xindong; Liu, Huizhou

    2017-12-08

    C-H methylation is an attractive chemical transformation for C-C bonds construction in organic chemistry, yet efficient methylation of readily available (bio)alcohols in water using methanol as sustainable C1 feedstock is limited. Herein, iridium nanocatalysts encapsulated in yolk-shell-structured mesoporous carbon nanospheres (Ir@YSMCNs) were synthesized for this transformation. Monodispersed Ir clusters (ca. 1.0 nm) were encapsulated in situ and spatially isolated within YSMCNs by a silica-assisted sol-gel emulsion strategy. A selection of (bio)alcohols (19 examples) was selectively methylated in aqueous phase with good-to-high yields over the developed Ir@YSMCNs. The improved catalytic efficiencies in terms of activity and selectivity together with the good stability and recyclability were contributable to the ultrasmall Ir clusters with oxidation chemical state as a consequence of the confinement effect of YSMCNs with interconnected nanostructures. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  1. Tools for Material Design and Selection

    Science.gov (United States)

    Wehage, Kristopher

    The present thesis focuses on applications of numerical methods to create tools for material characterization, design and selection. The tools generated in this work incorporate a variety of programming concepts, from digital image analysis, geometry, optimization, and parallel programming to data-mining, databases and web design. The first portion of the thesis focuses on methods for characterizing clustering in bimodal 5083 Aluminum alloys created by cryomilling and powder metallurgy. The bimodal samples analyzed in the present work contain a mixture of a coarse grain phase, with a grain size on the order of several microns, and an ultra-fine grain phase, with a grain size on the order of 200 nm. The mixing of the two phases is not homogeneous and clustering is observed. To investigate clustering in these bimodal materials, various microstructures were created experimentally by conventional cryomilling, Hot Isostatic Pressing (HIP), Extrusion, Dual-Mode Dynamic Forging (DMDF) and a new 'Gradient' cryomilling process. Two techniques for quantitative clustering analysis are presented, formulated and implemented. The first technique, the Area Disorder function, provides a metric of the quality of coarse grain dispersion in an ultra-fine grain matrix and the second technique, the Two-Point Correlation function, provides a metric of long and short range spatial arrangements of the two phases, as well as an indication of the mean feature size in any direction. The two techniques are implemented on digital images created by Scanning Electron Microscopy (SEM) and Electron Backscatter Detection (EBSD) of the microstructures. To investigate structure--property relationships through modeling and simulation, strategies for generating synthetic microstructures are discussed and a computer program that generates randomized microstructures with desired configurations of clustering described by the Area Disorder Function is formulated and presented. In the computer program, two

  2. Canonical PSO Based K-Means Clustering Approach for Real Datasets.

    Science.gov (United States)

    Dey, Lopamudra; Chakraborty, Sanjay

    2014-01-01

    "Clustering" the significance and application of this technique is spread over various fields. Clustering is an unsupervised process in data mining, that is why the proper evaluation of the results and measuring the compactness and separability of the clusters are important issues. The procedure of evaluating the results of a clustering algorithm is known as cluster validity measure. Different types of indexes are used to solve different types of problems and indices selection depends on the kind of available data. This paper first proposes Canonical PSO based K-means clustering algorithm and also analyses some important clustering indices (intercluster, intracluster) and then evaluates the effects of those indices on real-time air pollution database, wholesale customer, wine, and vehicle datasets using typical K-means, Canonical PSO based K-means, simple PSO based K-means, DBSCAN, and Hierarchical clustering algorithms. This paper also describes the nature of the clusters and finally compares the performances of these clustering algorithms according to the validity assessment. It also defines which algorithm will be more desirable among all these algorithms to make proper compact clusters on this particular real life datasets. It actually deals with the behaviour of these clustering algorithms with respect to validation indexes and represents their results of evaluation in terms of mathematical and graphical forms.

  3. A Cluster of CO2 Change Characteristics with GOSAT Observations for Viewing the Spatial Pattern of CO2 Emission and Absorption

    Directory of Open Access Journals (Sweden)

    Da Liu

    2015-11-01

    Full Text Available Satellite observations can be used to detect the changes of CO2 concentration at global and regional scales. With the column-averaged CO2 dry-air mole fraction (Xco2 data derived from satellite observations, the issue is how to extract and assess these changes, which are related to anthropogenic emissions and biosphere absorptions. We propose a k-means cluster analysis to extract the temporally changing features of Xco2 in the Central-Eastern Asia using the data from 2009 to 2013 obtained by Greenhouse Gases Observing Satellite (GOSAT, and assess the effects of anthropogenic emissions and biosphere absorptions on CO2 changes combining with the data of emission and vegetation net primary production (NPP. As a result, 14 clusters, which are 14 types of Xco2 seasonal changing patterns, are obtained in the study area by using the optimal clustering parameters. These clusters are generally in agreement with the spatial pattern of underlying anthropogenic emissions and vegetation absorptions. According to correlation analysis with emission and NPP, these 14 clusters are divided into three groups: strong emission, strong absorption, and a tendency of balancing between emission and absorption. The proposed clustering approach in this study provides us with a potential way to better understand how the seasonal changes of CO2 concentration depend on underlying anthropogenic emissions and vegetation absorptions.

  4. Hierarchical clustering of gene expression patterns in the Eomes + lineage of excitatory neurons during early neocortical development

    Directory of Open Access Journals (Sweden)

    Cameron David A

    2012-08-01

    Full Text Available Abstract Background Cortical neurons display dynamic patterns of gene expression during the coincident processes of differentiation and migration through the developing cerebrum. To identify genes selectively expressed by the Eomes + (Tbr2 lineage of excitatory cortical neurons, GFP-expressing cells from Tg(Eomes::eGFP Gsat embryos were isolated to > 99% purity and profiled. Results We report the identification, validation and spatial grouping of genes selectively expressed within the Eomes + cortical excitatory neuron lineage during early cortical development. In these neurons 475 genes were expressed ≥ 3-fold, and 534 genes ≤ 3-fold, compared to the reference population of neuronal precursors. Of the up-regulated genes, 328 were represented at the Genepaint in situ hybridization database and 317 (97% were validated as having spatial expression patterns consistent with the lineage of differentiating excitatory neurons. A novel approach for quantifying in situ hybridization patterns (QISP across the cerebral wall was developed that allowed the hierarchical clustering of genes into putative co-regulated groups. Forty four candidate genes were identified that show spatial expression with Intermediate Precursor Cells, 49 candidate genes show spatial expression with Multipolar Neurons, while the remaining 224 genes achieved peak expression in the developing cortical plate. Conclusions This analysis of differentiating excitatory neurons revealed the expression patterns of 37 transcription factors, many chemotropic signaling molecules (including the Semaphorin, Netrin and Slit signaling pathways, and unexpected evidence for non-canonical neurotransmitter signaling and changes in mechanisms of glucose metabolism. Over half of the 317 identified genes are associated with neuronal disease making these findings a valuable resource for studies of neurological development and disease.

  5. Enhancing spatial detection accuracy for syndromic surveillance with street level incidence data

    Directory of Open Access Journals (Sweden)

    Alemi Farrokh

    2010-01-01

    Full Text Available Abstract Background The Department of Defense Military Health System operates a syndromic surveillance system that monitors medical records at more than 450 non-combat Military Treatment Facilities (MTF worldwide. The Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE uses both temporal and spatial algorithms to detect disease outbreaks. This study focuses on spatial detection and attempts to improve the effectiveness of the ESSENCE implementation of the spatial scan statistic by increasing the spatial resolution of incidence data from zip codes to street address level. Methods Influenza-Like Illness (ILI was used as a test syndrome to develop methods to improve the spatial accuracy of detected alerts. Simulated incident clusters of various sizes were superimposed on real ILI incidents from the 2008/2009 influenza season. Clusters were detected using the spatial scan statistic and their displacement from simulated loci was measured. Detected cluster size distributions were also evaluated for compliance with simulated cluster sizes. Results Relative to the ESSENCE zip code based method, clusters detected using street level incidents were displaced on average 65% less for 2 and 5 mile radius clusters and 31% less for 10 mile radius clusters. Detected cluster size distributions for the street address method were quasi normal and sizes tended to slightly exceed simulated radii. ESSENCE methods yielded fragmented distributions and had high rates of zero radius and oversized clusters. Conclusions Spatial detection accuracy improved notably with regard to both location and size when incidents were geocoded to street addresses rather than zip code centroids. Since street address geocoding success rates were only 73.5%, zip codes were still used for more than one quarter of ILI cases. Thus, further advances in spatial detection accuracy are dependant on systematic improvements in the collection of individual

  6. The Atacama Cosmology Telescope: High-Resolution Sunyaev-Zel'dovich Array Observations of ACT SZE-Selected Clusters from the Equatorial Strip

    Science.gov (United States)

    Reese, Erik D.; Mroczkowski, Tony; Menanteau, Felipe; Hilton, Matt; Sievers, Jonathan; Aguirre, Paula; Appel, John William; Baker, Andrew J.; Bond, J. Richard; Das, Sudeep; hide

    2011-01-01

    We present follow-up observations with the Sunyaev-Zel'dovich Array (SZA) of optically-confirmed galaxy clusters found in the equatorial survey region of the Atacama Cosmology Telescope (ACT): ACT-CL J0022-0036, ACT-CL J2051+0057, and ACT-CL J2337+0016. ACT-CL J0022-0036 is a newly-discovered, massive (10(exp 15) Msun), high-redshift (z=0.81) cluster revealed by ACT through the Sunyaev-Zel'dovich effect (SZE). Deep, targeted observations with the SZA allow us to probe a broader range of cluster spatial scales, better disentangle cluster decrements from radio point source emission, and derive more robust integrated SZE flux and mass estimates than we can with ACT data alone. For the two clusters we detect with the SZA we compute integrated SZE signal and derive masses from the SZA data only. ACT-CL J2337+0016, also known as Abell 2631, has archival Chandra data that allow an additional X-ray-based mass estimate. Optical richness is also used to estimate cluster masses and shows good agreement with the SZE and X-ray-based estimates. Based on the point sources detected by the SZA in these three cluster fields and an extrapolation to ACT's frequency, we estimate that point sources could be contaminating the SZE decrement at the less than = 20% level for some fraction of clusters.

  7. Triggered cluster formation in the RMC

    Science.gov (United States)

    Li, Jin Zeng; Smith, Michael D.

    An investigation based on data from the spatially complete 2MASS Survey reveals that a remarkable burst of clustered star formation is taking place throughout the south-east quadrant of the Rosette Molecular Cloud. Compact clusters are forming in a multi-seeded mode, in parallel and at various places. In addition, sparse aggregates of embedded young stars are extensively distributed. Here we present the primary results and implications for high-mass and clustered star formation in this giant molecular cloud. In particular, we incorporate for the first time the birth of medium to low-mass stars into the scenario of sequential formation of OB clusters. Following the emergence of the young OB cluster NGC 2244, a variety of manifestations of forming clusters of medium to high mass appear in the vicinity of the swept-up layer of the H II region as well as further into the molecular cloud. The embedded clusters appear to form in a structured manner, which suggests they follow tracks laid out by the decay of macroturbulence. We address the possible origins of the turbulence. This leads us to propose a tree model to interpret the neat spatial distribution of clusters within a large section of the Rosette complex. Prominent new generation OB clusters are identified at the root of the tree pattern.

  8. A qualitative multi-attribute model for the selection of the private hydropower plant investments in Turkey: By foundation of the search results clustering engine (Carrot2, hydropower plant clustering, DEXi and DEXiTree

    Directory of Open Access Journals (Sweden)

    Burak Omer Saracoglu

    2016-03-01

    Full Text Available Purpose: The electricity demand in Turkey has been increasing for a while. Hydropower is one of the major electricity generation types to compensate this electricity demand in Turkey. Private investors (domestic and foreign in the hydropower electricity generation sector have been looking for the most appropriate and satisfactory new private hydropower investment (PHPI options and opportunities in Turkey. This study aims to present a qualitative multi-attribute decision making (MADM model, that is easy, straightforward, and fast for the selection of the most satisfactory reasonable PHPI options during the very early investment stages (data and information poorness on projects. Design/methodology/approach: The data and information of the PHPI options was gathered from the official records on the official websites. A wide and deep literature review was conducted for the MADM models and for the hydropower industry. The attributes of the model were identified, selected, clustered and evaluated by the expert decision maker (EDM opinion and by help of an open source search results clustering engine (Carrot2 (helpful for also comprehension. The PHPI options were clustered according to their installed capacities main property to analyze the options in the most appropriate, decidable, informative, understandable and meaningful way. A simple clustering algorithm for the PHPI options was executed in the current study. A template model for the selection of the most satisfactory PHPI options was built in the DEXi (Decision EXpert for Education and the DEXiTree software. Findings: The basic attributes for the selection of the PHPI options were presented and afterwards the aggregate attributes were defined by the bottom-up structuring for the early investment stages. The attributes were also analyzed by help of Carrot2. The most satisfactory PHPI options in Turkey in the big options data set were selected for each PHPI options cluster by the EDM evaluations in

  9. Weighted voting-based consensus clustering for chemical structure databases

    Science.gov (United States)

    Saeed, Faisal; Ahmed, Ali; Shamsir, Mohd Shahir; Salim, Naomie

    2014-06-01

    The cluster-based compound selection is used in the lead identification process of drug discovery and design. Many clustering methods have been used for chemical databases, but there is no clustering method that can obtain the best results under all circumstances. However, little attention has been focused on the use of combination methods for chemical structure clustering, which is known as consensus clustering. Recently, consensus clustering has been used in many areas including bioinformatics, machine learning and information theory. This process can improve the robustness, stability, consistency and novelty of clustering. For chemical databases, different consensus clustering methods have been used including the co-association matrix-based, graph-based, hypergraph-based and voting-based methods. In this paper, a weighted cumulative voting-based aggregation algorithm (W-CVAA) was developed. The MDL Drug Data Report (MDDR) benchmark chemical dataset was used in the experiments and represented by the AlogP and ECPF_4 descriptors. The results from the clustering methods were evaluated by the ability of the clustering to separate biologically active molecules in each cluster from inactive ones using different criteria, and the effectiveness of the consensus clustering was compared to that of Ward's method, which is the current standard clustering method in chemoinformatics. This study indicated that weighted voting-based consensus clustering can overcome the limitations of the existing voting-based methods and improve the effectiveness of combining multiple clusterings of chemical structures.

  10. Multi-scale Clustering of Points Synthetically Considering Lines and Polygons Distribution

    Directory of Open Access Journals (Sweden)

    YU Li

    2015-10-01

    Full Text Available Considering the complexity and discontinuity of spatial data distribution, a clustering algorithm of points was proposed. To accurately identify and express the spatial correlation among points,lines and polygons, a Voronoi diagram that is generated by all spatial features is introduced. According to the distribution characteristics of point's position, an area threshold used to control clustering granularity was calculated. Meanwhile, judging scale convergence by constant area threshold, the algorithm classifies spatial features based on multi-scale, with an O(n log n running time.Results indicate that spatial scale converges self-adaptively according with distribution of points.Without the custom parameters, the algorithm capable to discover arbitrary shape clusters which be bound by lines and polygons, and is robust for outliers.

  11. Observation of the inhomogeneous spatial distribution of MeV ions accelerated by the hydrodynamic ambipolar expansion of clusters

    International Nuclear Information System (INIS)

    Kanasaki, Masato; Jinno, Satoshi; Sakaki, Hironao; Faenov, Anatoly Ya.; Pikuz, Tatiana A.; Nishiuchi, Mamiko; Kiriyama, Hiromitsu; Kando, Masaki; Sugiyama, Akira; Kondo, Kiminori; Matsui, Ryutaro; Kishimoto, Yasuaki; Morishima, Kunihiro; Watanabe, Yukinobu; Scullion, Clare; Smyth, Ashley G.; Alejo, Aaron; Doria, Domenico; Kar, Satyabrata; Borghesi, Marco

    2015-01-01

    An inhomogeneous spatial distribution of laser accelerated carbon/oxygen ions produced via the hydrodynamic ambipolar expansion of CO_2 clusters has been measured by using CR-39 detectors. An inhomogeneous etch pits spatial distribution has appeared on the etched CR-39 detector installed on the laser propagation direction, while homogeneous ones are appeared on those installed at 45° and 90° from the laser propagation direction. From the range of ions in CR-39 obtained by using the multi-step etching technique, the averaged energies of carbon/oxygen ions for all directions are determined as 0.78 ± 0.09 MeV/n. The number of ions in the laser propagation direction is about 1.5 times larger than those in other directions. The inhomogeneous etch pits spatial distribution in the laser propagation direction could originate from an ion beam collimation and modulation by the effect of electromagnetic structures created in the laser plasma. - Highlights: • A spatial distribution of ions due to hydrodynamic ambipolar expansion is measured. • The homogeneous ion energy distribution of 0.78 ± 0.09 MeV/n is measured by CR-39. • The number of ions in the laser axis is about 1.5 times larger than other directions.

  12. Spatial scan statistics using elliptic windows

    DEFF Research Database (Denmark)

    Christiansen, Lasse Engbo; Andersen, Jens Strodl; Wegener, Henrik Caspar

    The spatial scan statistic is widely used to search for clusters in epidemiologic data. This paper shows that the usually applied elimination of secondary clusters as implemented in SatScan is sensitive to smooth changes in the shape of the clusters. We present an algorithm for generation of set...

  13. Cluster Optimization and Parallelization of Simulations with Dynamically Adaptive Grids

    KAUST Repository

    Schreiber, Martin; Weinzierl, Tobias; Bungartz, Hans-Joachim

    2013-01-01

    The present paper studies solvers for partial differential equations that work on dynamically adaptive grids stemming from spacetrees. Due to the underlying tree formalism, such grids efficiently can be decomposed into connected grid regions (clusters) on-the-fly. A graph on those clusters classified according to their grid invariancy, workload, multi-core affinity, and further meta data represents the inter-cluster communication. While stationary clusters already can be handled more efficiently than their dynamic counterparts, we propose to treat them as atomic grid entities and introduce a skip mechanism that allows the grid traversal to omit those regions completely. The communication graph ensures that the cluster data nevertheless are kept consistent, and several shared memory parallelization strategies are feasible. A hyperbolic benchmark that has to remesh selected mesh regions iteratively to preserve conforming tessellations acts as benchmark for the present work. We discuss runtime improvements resulting from the skip mechanism and the implications on shared memory performance and load balancing. © 2013 Springer-Verlag.

  14. Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments

    Directory of Open Access Journals (Sweden)

    Renata De Paris

    2015-01-01

    Full Text Available Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR model containing a 20 ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for the k-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand.

  15. Cluster analysis of rural, urban, and curbside atmospheric particle size data.

    Science.gov (United States)

    Beddows, David C S; Dall'Osto, Manuel; Harrison, Roy M

    2009-07-01

    Particle size is a key determinant of the hazard posed by airborne particles. Continuous multivariate particle size data have been collected using aerosol particle size spectrometers sited at four locations within the UK: Harwell (Oxfordshire); Regents Park (London); British Telecom Tower (London); and Marylebone Road (London). These data have been analyzed using k-means cluster analysis, deduced to be the preferred cluster analysis technique, selected from an option of four partitional cluster packages, namelythe following: Fuzzy; k-means; k-median; and Model-Based clustering. Using cluster validation indices k-means clustering was shown to produce clusters with the smallest size, furthest separation, and importantly the highest degree of similarity between the elements within each partition. Using k-means clustering, the complexity of the data set is reduced allowing characterization of the data according to the temporal and spatial trends of the clusters. At Harwell, the rural background measurement site, the cluster analysis showed that the spectra may be differentiated by their modal-diameters and average temporal trends showing either high counts during the day-time or night-time hours. Likewise for the urban sites, the cluster analysis differentiated the spectra into a small number of size distributions according their modal-diameter, the location of the measurement site, and time of day. The responsible aerosol emission, formation, and dynamic processes can be inferred according to the cluster characteristics and correlation to concurrently measured meteorological, gas phase, and particle phase measurements.

  16. Hot spots, cluster detection and spatial outlier analysis of teen birth rates in the U.S., 2003-2012.

    Science.gov (United States)

    Khan, Diba; Rossen, Lauren M; Hamilton, Brady E; He, Yulei; Wei, Rong; Dienes, Erin

    2017-06-01

    Teen birth rates have evidenced a significant decline in the United States over the past few decades. Most of the states in the US have mirrored this national decline, though some reports have illustrated substantial variation in the magnitude of these decreases across the U.S. Importantly, geographic variation at the county level has largely not been explored. We used National Vital Statistics Births data and Hierarchical Bayesian space-time interaction models to produce smoothed estimates of teen birth rates at the county level from 2003-2012. Results indicate that teen birth rates show evidence of clustering, where hot and cold spots occur, and identify spatial outliers. Findings from this analysis may help inform efforts targeting the prevention efforts by illustrating how geographic patterns of teen birth rates have changed over the past decade and where clusters of high or low teen birth rates are evident. Published by Elsevier Ltd.

  17. A qualitative multi-attribute model for the selection of the private hydropower plant investments in Turkey: By foundation of the search results clustering engine (Carrot2), hydropower plant clustering, DEXi and DEXiTree

    Energy Technology Data Exchange (ETDEWEB)

    Saracoglu, B.O.

    2016-07-01

    The electricity demand in Turkey has been increasing for a while. Hydropower is one of the major electricity generation types to compensate this electricity demand in Turkey. Private investors (domestic and foreign) in the hydropower electricity generation sector have been looking for the most appropriate and satisfactory new private hydropower investment (PHPI) options and opportunities in Turkey. This study aims to present a qualitative multi-attribute decision making (MADM) model, that is easy, straightforward, and fast for the selection of the most satisfactory reasonable PHPI options during the very early investment stages (data and information poorness on projects). The data and information of the PHPI options was gathered from the official records on the official websites. A wide and deep literature review was conducted for the MADM models and for the hydropower industry. The attributes of the model were identified, selected, clustered and evaluated by the expert decision maker (EDM) opinion and by help of an open source search results clustering engine (Carrot2) (helpful for also comprehension). The PHPI options were clustered according to their installed capacities main property to analyze the options in the most appropriate, decidable, informative, understandable and meaningful way. A simple clustering algorithm for the PHPI options was executed in the current study. A template model for the selection of the most satisfactory PHPI options was built in the DEXi (Decision EXpert for Education) and the DEXiTree software. The basic attributes for the selection of the PHPI options were presented and afterwards the aggregate attributes were defined by the bottom-up structuring for the early investment stages. The attributes were also analyzed by help of Carrot2. The most satisfactory PHPI options in Turkey in the big options data set were selected for each PHPI options cluster by the EDM evaluations in the DEXi. (Author)

  18. Spatial scan statistics using elliptic windows

    DEFF Research Database (Denmark)

    Christiansen, Lasse Engbo; Andersen, Jens Strodl; Wegener, Henrik Caspar

    2006-01-01

    The spatial scan statistic is widely used to search for clusters. This article shows that the usually applied elimination of secondary clusters as implemented in SatScan is sensitive to smooth changes in the shape of the clusters. We present an algorithm for generation of a set of confocal elliptic...

  19. Automated three-dimensional morphology-based clustering of human erythrocytes with regular shapes: stomatocytes, discocytes, and echinocytes

    Science.gov (United States)

    Ahmadzadeh, Ezat; Jaferzadeh, Keyvan; Lee, Jieun; Moon, Inkyu

    2017-07-01

    We present unsupervised clustering methods for automatic grouping of human red blood cells (RBCs) extracted from RBC quantitative phase images obtained by digital holographic microscopy into three RBC clusters with regular shapes, including biconcave, stomatocyte, and sphero-echinocyte. We select some good features related to the RBC profile and morphology, such as RBC average thickness, sphericity coefficient, and mean corpuscular volume, and clustering methods, including density-based spatial clustering applications with noise, k-medoids, and k-means, are applied to the set of morphological features. The clustering results of RBCs using a set of three-dimensional features are compared against a set of two-dimensional features. Our experimental results indicate that by utilizing the introduced set of features, two groups of biconcave RBCs and old RBCs (suffering from the sphero-echinocyte process) can be perfectly clustered. In addition, by increasing the number of clusters, the three RBC types can be effectively clustered in an automated unsupervised manner with high accuracy. The performance evaluation of the clustering techniques reveals that they can assist hematologists in further diagnosis.

  20. Quantitative radiomic profiling of glioblastoma represents transcriptomic expression.

    Science.gov (United States)

    Kong, Doo-Sik; Kim, Junhyung; Ryu, Gyuha; You, Hye-Jin; Sung, Joon Kyung; Han, Yong Hee; Shin, Hye-Mi; Lee, In-Hee; Kim, Sung-Tae; Park, Chul-Kee; Choi, Seung Hong; Choi, Jeong Won; Seol, Ho Jun; Lee, Jung-Il; Nam, Do-Hyun

    2018-01-19

    Quantitative imaging biomarkers have increasingly emerged in the field of research utilizing available imaging modalities. We aimed to identify good surrogate radiomic features that can represent genetic changes of tumors, thereby establishing noninvasive means for predicting treatment outcome. From May 2012 to June 2014, we retrospectively identified 65 patients with treatment-naïve glioblastoma with available clinical information from the Samsung Medical Center data registry. Preoperative MR imaging data were obtained for all 65 patients with primary glioblastoma. A total of 82 imaging features including first-order statistics, volume, and size features, were semi-automatically extracted from structural and physiologic images such as apparent diffusion coefficient and perfusion images. Using commercially available software, NordicICE, we performed quantitative imaging analysis and collected the dataset composed of radiophenotypic parameters. Unsupervised clustering methods revealed that the radiophenotypic dataset was composed of three clusters. Each cluster represented a distinct molecular classification of glioblastoma; classical type, proneural and neural types, and mesenchymal type. These clusters also reflected differential clinical outcomes. We found that extracted imaging signatures does not represent copy number variation and somatic mutation. Quantitative radiomic features provide a potential evidence to predict molecular phenotype and treatment outcome. Radiomic profiles represents transcriptomic phenotypes more well.

  1. Self-selection in size and structure in argon clusters formed on amorphous carbon

    Energy Technology Data Exchange (ETDEWEB)

    Krainyukova, Nina V.; Waal, Benjamin W. van de

    2004-07-01

    Argon clusters formed on an amorphous carbon substrate as deposited from the vapor phase were studied by means of transmission high energy electron diffraction using the liquid helium cryostat. Electron diffractograms were analysed on the basis of assumption that there exist a cluster size distribution in samples formed on substrate and multi-shell structures such as icosahedra, decahedra, fcc and hcp were probed for different sizes up to {approx}15 000 atoms. The experimental data were considered as a result of a superposition of diffracted intensities from clusters of different sizes and structures. The comparative analysis was based on the R-factor minimization that was found to be equal to 0.014 for the best fit between experiment and modelling. The total size and structure distribution function shows the presence of 'non-crystallographic' structures such as icosahedra and decahedra with five-fold symmetry that was found to prevail and a smaller amount of fcc and hcp structures. Possible growth mechanisms as well as observed general tendency to self-selection in sizes and structures are presumably governed by confined pore-like geometry in an amorphous carbon substrate.

  2. CLUSTERING OF γ-RAY-SELECTED 2LAC FERMI BLAZARS

    Energy Technology Data Exchange (ETDEWEB)

    Allevato, V.; Finoguenov, A. [Department of Physics, University of Helsinki, Gustaf Hällströmin katu 2a, FI-00014 Helsinki (Finland); Cappelluti, N. [University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250 (United States)

    2014-12-20

    We present the first measurement of the projected correlation function of 485 γ-ray-selected blazars, divided into 175 BL Lacertae (BL Lacs) and 310 flat-spectrum radio quasars (FSRQs) detected in the 2 year all-sky survey by the Fermi-Large Area Telescope. We find that Fermi BL Lacs and FSRQs reside in massive dark matter halos (DMHs) with log M{sub h} = 13.35{sub −0.14}{sup +0.20} and log M{sub h} = 13.40{sub −0.19}{sup +0.15} h {sup –1} M {sub ☉}, respectively, at low (z ∼ 0.4) and high (z ∼ 1.2) redshift. In terms of clustering properties, these results suggest that BL Lacs and FSRQs are similar objects residing in the same dense environment typical of galaxy groups, despite their different spectral energy distributions, power, and accretion rates. We find no difference in the typical bias and hosting halo mass between Fermi blazars and radio-loud active galactic nuclei (AGNs), supporting the unification scheme simply equating radio-loud objects with misaligned blazar counterparts. This similarity in terms of the typical environment they preferentially live in, suggests that blazars tend to occupy the center of DMHs, as already pointed out for radio-loud AGNs. This implies, in light of several projects looking for the γ-ray emission from DM annihilation in galaxy clusters, a strong contamination from blazars to the expected signal from DM annihilation.

  3. Regulation of spatial selectivity by crossover inhibition.

    Science.gov (United States)

    Cafaro, Jon; Rieke, Fred

    2013-04-10

    Signals throughout the nervous system diverge into parallel excitatory and inhibitory pathways that later converge on downstream neurons to control their spike output. Converging excitatory and inhibitory synaptic inputs can exhibit a variety of temporal relationships. A common motif is feedforward inhibition, in which an increase (decrease) in excitatory input precedes a corresponding increase (decrease) in inhibitory input. The delay of inhibitory input relative to excitatory input originates from an extra synapse in the circuit shaping inhibitory input. Another common motif is push-pull or "crossover" inhibition, in which increases (decreases) in excitatory input occur together with decreases (increases) in inhibitory input. Primate On midget ganglion cells receive primarily feedforward inhibition and On parasol cells receive primarily crossover inhibition; this difference provides an opportunity to study how each motif shapes the light responses of cell types that play a key role in visual perception. For full-field stimuli, feedforward inhibition abbreviated and attenuated responses of On midget cells, while crossover inhibition, though plentiful, had surprisingly little impact on the responses of On parasol cells. Spatially structured stimuli, however, could cause excitatory and inhibitory inputs to On parasol cells to increase together, adopting a temporal relation very much like that for feedforward inhibition. In this case, inhibitory inputs substantially abbreviated a cell's spike output. Thus inhibitory input shapes the temporal stimulus selectivity of both midget and parasol ganglion cells, but its impact on responses of parasol cells depends strongly on the spatial structure of the light inputs.

  4. Socio-Spatial Typology In Karanrang Island

    Directory of Open Access Journals (Sweden)

    Amin Ishak Rahmi

    2018-01-01

    Full Text Available The phenomenon of community life on the small island is influenced by the stimulating factor of harmonious social interaction system through cooperation, kinship, economic activity, children playing, transportation system, religion and other social activities. The social dynamics of small island communities appear in the layout and environment in which they live, how they manage and utilize space, both indoors and outdoors. The purpose of this paper is to describe the socio-spatial typology of settlements on Karanrang Island, including a description of the spatial pattern of communalenvironments. Research approaches through spatial similarities and differences in the classification of behavioral setting, including physical, non-physical, socio-spatial arrangements. Karanrang Island as a research focus which has an area of 7.8 Ha is one of small islands inhabited in cluster PangkajeneIslands (Pangkep South Sulawesi, with characteristic of dense settlement, and diversity of tribe, also inhabited by 434 families. The method of this research is observation, data collection through field survey with descriptive analysis based on empirical data on meso / environment which is divided into:1 inter building space; 2 Space in the building; 3 Open space, and; 4 Environmental facilities. The results showed that classification of socio-spatial typology of communal environment is divided into four types of socio-spatial models based on the configuration of social interaction activities, namely:1 Type of Linear Centripetal, at the inter buildings space; 2 Type of Centripetal Cluster, space on the building; 3 Type of Centrifugal Cluster, at green open space/field; 4 Type of cluster Centripetal, at environmental facilities. The socio-spatial type based on actor’s activities, occupancy, and territory, can be distinguished on: 1 Type of children’s activity; 2 Type of mother’sactivity; 3 Type of father’s activity, and 4 Type of combination activity.

  5. Spatial-Temporal Event Detection from Geo-Tagged Tweets

    Directory of Open Access Journals (Sweden)

    Yuqian Huang

    2018-04-01

    Full Text Available As one of the most popular social networking services in the world, Twitter allows users to post messages along with their current geographic locations. Such georeferenced or geo-tagged Twitter datasets can benefit location-based services, targeted advertising and geosocial studies. Our study focused on the detection of small-scale spatial-temporal events and their textual content. First, we used Spatial-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN to spatially-temporally cluster the tweets. Then, the word frequencies were summarized for each cluster and the potential topics were modeled by the Latent Dirichlet Allocation (LDA algorithm. Using two years of Twitter data from four college cities in the U.S., we were able to determine the spatial-temporal patterns of two known events, two unknown events and one recurring event, which then were further explored and modeled to identify the semantic content about the events. This paper presents our process and recommendations for both finding event-related tweets as well as understanding the spatial-temporal behaviors and semantic natures of the detected events.

  6. Simulations of Fractal Star Cluster Formation. I. New Insights for Measuring Mass Segregation of Star Clusters with Substructure

    International Nuclear Information System (INIS)

    Yu, Jincheng; Puzia, Thomas H.; Lin, Congping; Zhang, Yiwei

    2017-01-01

    We compare the existent methods, including the minimum spanning tree based method and the local stellar density based method, in measuring mass segregation of star clusters. We find that the minimum spanning tree method reflects more the compactness, which represents the global spatial distribution of massive stars, while the local stellar density method reflects more the crowdedness, which provides the local gravitational potential information. It is suggested to measure the local and the global mass segregation simultaneously. We also develop a hybrid method that takes both aspects into account. This hybrid method balances the local and the global mass segregation in the sense that the predominant one is either caused by dynamical evolution or purely accidental, especially when such information is unknown a priori. In addition, we test our prescriptions with numerical models and show the impact of binaries in estimating the mass segregation value. As an application, we use these methods on the Orion Nebula Cluster (ONC) observations and the Taurus cluster. We find that the ONC is significantly mass segregated down to the 20th most massive stars. In contrast, the massive stars of the Taurus cluster are sparsely distributed in many different subclusters, showing a low degree of compactness. The massive stars of Taurus are also found to be distributed in the high-density region of the subclusters, showing significant mass segregation at subcluster scales. Meanwhile, we also apply these methods to discuss the possible mechanisms of the dynamical evolution of the simulated substructured star clusters.

  7. Simulations of Fractal Star Cluster Formation. I. New Insights for Measuring Mass Segregation of Star Clusters with Substructure

    Energy Technology Data Exchange (ETDEWEB)

    Yu, Jincheng; Puzia, Thomas H. [Institute of Astrophysics, Pontificia Universidad Católica, Av. Vicuña Mackenna 4860, Casilla 306, Santiago 22 (Chile); Lin, Congping; Zhang, Yiwei, E-mail: yujc.astro@gmail.com, E-mail: tpuzia@gmail.com, E-mail: congpinglin@gmail.com, E-mail: yiweizhang831129@gmail.com [Center for Mathematical Science, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 4370074 (China)

    2017-05-10

    We compare the existent methods, including the minimum spanning tree based method and the local stellar density based method, in measuring mass segregation of star clusters. We find that the minimum spanning tree method reflects more the compactness, which represents the global spatial distribution of massive stars, while the local stellar density method reflects more the crowdedness, which provides the local gravitational potential information. It is suggested to measure the local and the global mass segregation simultaneously. We also develop a hybrid method that takes both aspects into account. This hybrid method balances the local and the global mass segregation in the sense that the predominant one is either caused by dynamical evolution or purely accidental, especially when such information is unknown a priori. In addition, we test our prescriptions with numerical models and show the impact of binaries in estimating the mass segregation value. As an application, we use these methods on the Orion Nebula Cluster (ONC) observations and the Taurus cluster. We find that the ONC is significantly mass segregated down to the 20th most massive stars. In contrast, the massive stars of the Taurus cluster are sparsely distributed in many different subclusters, showing a low degree of compactness. The massive stars of Taurus are also found to be distributed in the high-density region of the subclusters, showing significant mass segregation at subcluster scales. Meanwhile, we also apply these methods to discuss the possible mechanisms of the dynamical evolution of the simulated substructured star clusters.

  8. Suzaku observations of low surface brightness cluster Abell 1631

    Science.gov (United States)

    Babazaki, Yasunori; Mitsuishi, Ikuyuki; Ota, Naomi; Sasaki, Shin; Böhringer, Hans; Chon, Gayoung; Pratt, Gabriel W.; Matsumoto, Hironori

    2018-04-01

    We present analysis results for a nearby galaxy cluster Abell 1631 at z = 0.046 using the X-ray observatory Suzaku. This cluster is categorized as a low X-ray surface brightness cluster. To study the dynamical state of the cluster, we conduct four-pointed Suzaku observations and investigate physical properties of the Mpc-scale hot gas associated with the A 1631 cluster for the first time. Unlike relaxed clusters, the X-ray image shows no strong peak at the center and an irregular morphology. We perform spectral analysis and investigate the radial profiles of the gas temperature, density, and entropy out to approximately 1.5 Mpc in the east, north, west, and south directions by combining with the XMM-Newton data archive. The measured gas density in the central region is relatively low (a few ×10-4 cm-3) at the given temperature (˜2.9 keV) compared with X-ray-selected clusters. The entropy profile and value within the central region (r < 0.1 r200) are found to be flatter and higher (≳400 keV cm2). The observed bolometric luminosity is approximately three times lower than that expected from the luminosity-temperature relation in previous studies of relaxed clusters. These features are also observed in another low surface brightness cluster, Abell 76. The spatial distributions of galaxies and the hot gas appear to be different. The X-ray luminosity is relatively lower than that expected from the velocity dispersion. A post-merger scenario may explain the observed results.

  9. Cluster Mass Calibration at High Redshift: HST Weak Lensing Analysis of 13 Distant Galaxy Clusters from the South Pole Telescope Sunyaev-Zel'dovich Survey

    Energy Technology Data Exchange (ETDEWEB)

    Schrabback, T.; et al.

    2016-11-11

    We present an HST/ACS weak gravitational lensing analysis of 13 massive high-redshift (z_median=0.88) galaxy clusters discovered in the South Pole Telescope (SPT) Sunyaev-Zel'dovich Survey. This study is part of a larger campaign that aims to robustly calibrate mass-observable scaling relations over a wide range in redshift to enable improved cosmological constraints from the SPT cluster sample. We introduce new strategies to ensure that systematics in the lensing analysis do not degrade constraints on cluster scaling relations significantly. First, we efficiently remove cluster members from the source sample by selecting very blue galaxies in V-I colour. Our estimate of the source redshift distribution is based on CANDELS data, where we carefully mimic the source selection criteria of the cluster fields. We apply a statistical correction for systematic photometric redshift errors as derived from Hubble Ultra Deep Field data and verified through spatial cross-correlations. We account for the impact of lensing magnification on the source redshift distribution, finding that this is particularly relevant for shallower surveys. Finally, we account for biases in the mass modelling caused by miscentring and uncertainties in the mass-concentration relation using simulations. In combination with temperature estimates from Chandra we constrain the normalisation of the mass-temperature scaling relation ln(E(z) M_500c/10^14 M_sun)=A+1.5 ln(kT/7.2keV) to A=1.81^{+0.24}_{-0.14}(stat.) +/- 0.09(sys.), consistent with self-similar redshift evolution when compared to lower redshift samples. Additionally, the lensing data constrain the average concentration of the clusters to c_200c=5.6^{+3.7}_{-1.8}.

  10. Reduced sintering of mass-selected Au clusters on SiO2 by alloying with Ti: an aberration-corrected STEM and computational study

    DEFF Research Database (Denmark)

    Niu, Yubiao; Schlexer, Philomena; Sebök, Béla

    2018-01-01

    with a reactive metal, Ti. Mass-selected Au/Ti clusters (400 000 amu) and Au2057 clusters (405 229 amu) were produced with a magnetron sputtering, gas condensation cluster beam source in conjunction with a lateral time-of-flight mass filter, deposited onto a silica support and characterised by XPS and LEIS....... The sintering dynamics of mass-selected Au and Au/Ti alloy nanoclusters were investigated in real space and real time with atomic resolution aberration-corrected HAADF-STEM imaging, supported by model DFT calculations. A strong anchoring effect was revealed in the case of the Au/Ti clusters, because of a much...

  11. SPATIAL ANISOTROPY OF GALAXY KINEMATICS IN SLOAN DIGITAL SKY SURVEY GALAXY CLUSTERS

    International Nuclear Information System (INIS)

    Skielboe, Andreas; Wojtak, Radosław; Pedersen, Kristian; Rozo, Eduardo; Rykoff, Eli S.

    2012-01-01

    Measurements of galaxy cluster kinematics are important in understanding the dynamical state and evolution of clusters of galaxies, as well as constraining cosmological models. While it is well established that clusters exhibit non-spherical geometries, evident in the distribution of galaxies on the sky, azimuthal variations of galaxy kinematics within clusters have yet to be observed. Here we measure the azimuthal dependence of the line-of-sight velocity dispersion profile in a stacked sample of 1743 galaxy clusters from the Sloan Digital Sky Survey (SDSS). The clusters are drawn from the SDSS DR8 redMaPPer catalog. We find that the line-of-sight velocity dispersion of galaxies lying along the major axis of the central galaxy is larger than those that lie along the minor axis. This is the first observational detection of anisotropic kinematics of galaxies in clusters. We show that the result is consistent with predictions from numerical simulations. Furthermore, we find that the degree of projected anisotropy is strongly dependent on the line-of-sight orientation of the galaxy cluster, opening new possibilities for assessing systematics in optical cluster finding.

  12. Site-specific fragmentation of polystyrene molecule using size-selected Ar gas cluster ion beam

    International Nuclear Information System (INIS)

    Moritani, Kousuke; Mukai, Gen; Hashinokuchi, Michihiro; Mochiji, Kozo

    2009-01-01

    The secondary ion mass spectrum (SIMS) of a polystyrene thin film was investigated using a size-selected Ar gas cluster ion beam (GCIB). The fragmentation in the SIM spectrum varied by kinetic energy per atom (E atom ); the E atom dependence of the secondary ion intensity of the fragment species of polystyrene can be essentially classified into three types based on the relationship between E atom and the dissociation energy of a specific bonding site in the molecule. These results indicate that adjusting E atom of size-selected GCIB may realize site-specific bond breaking within a molecule. (author)

  13. Temporal and Spatial Scales Matter: Circannual Habitat Selection by Bird Communities in Vineyards.

    Directory of Open Access Journals (Sweden)

    Claire Guyot

    Full Text Available Vineyards are likely to be regionally important for wildlife, but we lack biodiversity studies in this agroecosystem which is undergoing a rapid management revolution. As vine cultivation is restricted to arid and warm climatic regions, biodiversity-friendly management would promote species typical of southern biomes. Vineyards are often intensively cultivated, mostly surrounded by few natural features and offering a fairly mineral appearance with little ground vegetation cover. Ground vegetation cover and composition may further strongly vary with respect to season, influencing patterns of habitat selection by ecological communities. We investigated season-specific bird-habitat associations to highlight the importance of semi-natural habitat features and vineyard ground vegetation cover throughout the year. Given that avian habitat selection varies according to taxa, guilds and spatial scale, we modelled bird-habitat associations in all months at two spatial scales using mixed effects regression models. At the landscape scale, birds were recorded along 10 1-km long transects in Southwestern Switzerland (February 2014 -January 2015. At the field scale, we compared the characteristics of visited and unvisited vineyard fields (hereafter called parcels. Bird abundance in vineyards tripled in winter compared to summer. Vineyards surrounded by a greater amount of hedges and small woods harboured higher bird abundance, species richness and diversity, especially during the winter season. Regarding ground vegetation, birds showed a season-specific habitat selection pattern, notably a marked preference for ground-vegetated parcels in winter and for intermediate vegetation cover in spring and summer. These season-specific preferences might be related to species-specific life histories: more insectivorous, ground-foraging species occur during the breeding season whereas granivores predominate in winter. These results highlight the importance of

  14. Sensorineural hearing loss degrades behavioral and physiological measures of human spatial selective auditory attention

    Science.gov (United States)

    Dai, Lengshi; Best, Virginia; Shinn-Cunningham, Barbara G.

    2018-01-01

    Listeners with sensorineural hearing loss often have trouble understanding speech amid other voices. While poor spatial hearing is often implicated, direct evidence is weak; moreover, studies suggest that reduced audibility and degraded spectrotemporal coding may explain such problems. We hypothesized that poor spatial acuity leads to difficulty deploying selective attention, which normally filters out distracting sounds. In listeners with normal hearing, selective attention causes changes in the neural responses evoked by competing sounds, which can be used to quantify the effectiveness of attentional control. Here, we used behavior and electroencephalography to explore whether control of selective auditory attention is degraded in hearing-impaired (HI) listeners. Normal-hearing (NH) and HI listeners identified a simple melody presented simultaneously with two competing melodies, each simulated from different lateral angles. We quantified performance and attentional modulation of cortical responses evoked by these competing streams. Compared with NH listeners, HI listeners had poorer sensitivity to spatial cues, performed more poorly on the selective attention task, and showed less robust attentional modulation of cortical responses. Moreover, across NH and HI individuals, these measures were correlated. While both groups showed cortical suppression of distracting streams, this modulation was weaker in HI listeners, especially when attending to a target at midline, surrounded by competing streams. These findings suggest that hearing loss interferes with the ability to filter out sound sources based on location, contributing to communication difficulties in social situations. These findings also have implications for technologies aiming to use neural signals to guide hearing aid processing. PMID:29555752

  15. Implications of multiple high-redshift galaxy clusters

    International Nuclear Information System (INIS)

    Hoyle, Ben; Jimenez, Raul; Verde, Licia

    2011-01-01

    To date, 14 high-redshift (z>1.0) galaxy clusters with mass measurements have been observed, spectroscopically confirmed, and are reported in the literature. These objects should be exceedingly rare in the standard Λ cold dark matter (ΛCDM) model. We conservatively approximate the selection functions of these clusters' parent surveys and quantify the tension between the abundances of massive clusters as predicted by the standard ΛCDM model and the observed ones. We alleviate the tension, considering non-Gaussian primordial perturbations of the local type, characterized by the parameter f NL , and derive constraints on f NL arising from the mere existence of these clusters. At the 95% confidence level, f NL >467, with cosmological parameters fixed to their most likely WMAP5 values, or f NL > or approx. 123 (at 95% confidence) if we marginalize over prior WMAP5 parameters. In combination with f NL constraints from cosmic microwave background and halo bias, this determination implies a scale dependence of f NL at ≅3σ. Given the assumptions made in the analysis, we expect any future improvements to the modeling of the non-Gaussian mass function, survey volumes, or selection functions to increase the significance of f NL >0 found here. In order to reconcile these massive, high-z clusters with f NL =0, their masses would need to be systematically lowered by 1.5σ, or the σ 8 parameter should be ∼3σ higher than cosmic microwave background (and large-scale structure) constraints. The existence of these objects is a puzzle: it either represents a challenge to the ΛCDM paradigm or it is an indication that the mass estimates of clusters are dramatically more uncertain than we think.

  16. Quantifying spatial and temporal trends in beach-dune volumetric changes using spatial statistics

    Science.gov (United States)

    Eamer, Jordan B. R.; Walker, Ian J.

    2013-06-01

    Spatial statistics are generally underutilized in coastal geomorphology, despite offering great potential for identifying and quantifying spatial-temporal trends in landscape morphodynamics. In particular, local Moran's Ii provides a statistical framework for detecting clusters of significant change in an attribute (e.g., surface erosion or deposition) and quantifying how this changes over space and time. This study analyzes and interprets spatial-temporal patterns in sediment volume changes in a beach-foredune-transgressive dune complex following removal of invasive marram grass (Ammophila spp.). Results are derived by detecting significant changes in post-removal repeat DEMs derived from topographic surveys and airborne LiDAR. The study site was separated into discrete, linked geomorphic units (beach, foredune, transgressive dune complex) to facilitate sub-landscape scale analysis of volumetric change and sediment budget responses. Difference surfaces derived from a pixel-subtraction algorithm between interval DEMs and the LiDAR baseline DEM were filtered using the local Moran's Ii method and two different spatial weights (1.5 and 5 m) to detect statistically significant change. Moran's Ii results were compared with those derived from a more spatially uniform statistical method that uses a simpler student's t distribution threshold for change detection. Morphodynamic patterns and volumetric estimates were similar between the uniform geostatistical method and Moran's Ii at a spatial weight of 5 m while the smaller spatial weight (1.5 m) consistently indicated volumetric changes of less magnitude. The larger 5 m spatial weight was most representative of broader site morphodynamics and spatial patterns while the smaller spatial weight provided volumetric changes consistent with field observations. All methods showed foredune deflation immediately following removal with increased sediment volumes into the spring via deposition at the crest and on lobes in the lee

  17. Demonstrating the utility of boron based precursor molecules for selective area deposition in a scanning tunneling microscope

    International Nuclear Information System (INIS)

    Perkins, F.K.; Onellion, M.; Lee, S.; Bowben, T.A.

    1992-01-01

    The scanning tunneling microscope (STM) can be used to selectively deposit material from a gaseous precursor compound. Ultrasmall (less than a 100 nm across) spatial dimensions for selective area deposition may be achieved by this means. In this paper the authors outline a scheme foreselecting and designing main group cluster compounds and organometallics for this type of selective area deposition using nido-decaborane(14) as an example

  18. A Multi-Hop Clustering Mechanism for Scalable IoT Networks.

    Science.gov (United States)

    Sung, Yoonyoung; Lee, Sookyoung; Lee, Meejeong

    2018-03-23

    It is expected that up to 26 billion Internet of Things (IoT) equipped with sensors and wireless communication capabilities will be connected to the Internet by 2020 for various purposes. With a large scale IoT network, having each node connected to the Internet with an individual connection may face serious scalability issues. The scalability problem of the IoT network may be alleviated by grouping the nodes of the IoT network into clusters and having a representative node in each cluster connect to the Internet on behalf of the other nodes in the cluster instead of having a per-node Internet connection and communication. In this paper, we propose a multi-hop clustering mechanism for IoT networks to minimize the number of required Internet connections. Specifically, the objective of proposed mechanism is to select the minimum number of coordinators, which take the role of a representative node for the cluster, i.e., having the Internet connection on behalf of the rest of the nodes in the cluster and to map a partition of the IoT nodes onto the selected set of coordinators to minimize the total distance between the nodes and their respective coordinator under a certain constraint in terms of maximum hop count between the IoT nodes and their respective coordinator. Since this problem can be mapped into a set cover problem which is known as NP-hard, we pursue a heuristic approach to solve the problem and analyze the complexity of the proposed solution. Through a set of experiments with varying parameters, the proposed scheme shows 63-87.3% reduction of the Internet connections depending on the number of the IoT nodes while that of the optimal solution is 65.6-89.9% in a small scale network. Moreover, it is shown that the performance characteristics of the proposed mechanism coincide with expected performance characteristics of the optimal solution in a large-scale network.

  19. Selective attention modulates human auditory brainstem responses: relative contributions of frequency and spatial cues.

    Directory of Open Access Journals (Sweden)

    Alexandre Lehmann

    Full Text Available Selective attention is the mechanism that allows focusing one's attention on a particular stimulus while filtering out a range of other stimuli, for instance, on a single conversation in a noisy room. Attending to one sound source rather than another changes activity in the human auditory cortex, but it is unclear whether attention to different acoustic features, such as voice pitch and speaker location, modulates subcortical activity. Studies using a dichotic listening paradigm indicated that auditory brainstem processing may be modulated by the direction of attention. We investigated whether endogenous selective attention to one of two speech signals affects amplitude and phase locking in auditory brainstem responses when the signals were either discriminable by frequency content alone, or by frequency content and spatial location. Frequency-following responses to the speech sounds were significantly modulated in both conditions. The modulation was specific to the task-relevant frequency band. The effect was stronger when both frequency and spatial information were available. Patterns of response were variable between participants, and were correlated with psychophysical discriminability of the stimuli, suggesting that the modulation was biologically relevant. Our results demonstrate that auditory brainstem responses are susceptible to efferent modulation related to behavioral goals. Furthermore they suggest that mechanisms of selective attention actively shape activity at early subcortical processing stages according to task relevance and based on frequency and spatial cues.

  20. Effect of random inhomogeneities in the spatial distribution of radiation-induced defect clusters on carrier transport through the thin base of a heterojunction bipolar transistor upon neutron irradiation

    Energy Technology Data Exchange (ETDEWEB)

    Puzanov, A. S.; Obolenskiy, S. V., E-mail: obolensk@rf.unn.ru; Kozlov, V. A. [Lobachevsky State University of Nizhny Novgorod (NNSU) (Russian Federation)

    2016-12-15

    We analyze the electron transport through the thin base of a GaAs heterojunction bipolar transistor with regard to fluctuations in the spatial distribution of defect clusters induced by irradiation with a fissionspectrum fast neutron flux. We theoretically demonstrate that the homogeneous filling of the working region with radiation-induced defect clusters causes minimum degradation of the dc gain of the heterojunction bipolar transistor.

  1. HICOSMO - cosmology with a complete sample of galaxy clusters - I. Data analysis, sample selection and luminosity-mass scaling relation

    Science.gov (United States)

    Schellenberger, G.; Reiprich, T. H.

    2017-08-01

    The X-ray regime, where the most massive visible component of galaxy clusters, the intracluster medium, is visible, offers directly measured quantities, like the luminosity, and derived quantities, like the total mass, to characterize these objects. The aim of this project is to analyse a complete sample of galaxy clusters in detail and constrain cosmological parameters, like the matter density, Ωm, or the amplitude of initial density fluctuations, σ8. The purely X-ray flux-limited sample (HIFLUGCS) consists of the 64 X-ray brightest galaxy clusters, which are excellent targets to study the systematic effects, that can bias results. We analysed in total 196 Chandra observations of the 64 HIFLUGCS clusters, with a total exposure time of 7.7 Ms. Here, we present our data analysis procedure (including an automated substructure detection and an energy band optimization for surface brightness profile analysis) that gives individually determined, robust total mass estimates. These masses are tested against dynamical and Planck Sunyaev-Zeldovich (SZ) derived masses of the same clusters, where good overall agreement is found with the dynamical masses. The Planck SZ masses seem to show a mass-dependent bias to our hydrostatic masses; possible biases in this mass-mass comparison are discussed including the Planck selection function. Furthermore, we show the results for the (0.1-2.4) keV luminosity versus mass scaling relation. The overall slope of the sample (1.34) is in agreement with expectations and values from literature. Splitting the sample into galaxy groups and clusters reveals, even after a selection bias correction, that galaxy groups exhibit a significantly steeper slope (1.88) compared to clusters (1.06).

  2. Cluster analysis of track structure

    International Nuclear Information System (INIS)

    Michalik, V.

    1991-01-01

    One of the possibilities of classifying track structures is application of conventional partition techniques of analysis of multidimensional data to the track structure. Using these cluster algorithms this paper attempts to find characteristics of radiation reflecting the spatial distribution of ionizations in the primary particle track. An absolute frequency distribution of clusters of ionizations giving the mean number of clusters produced by radiation per unit of deposited energy can serve as this characteristic. General computation techniques used as well as methods of calculations of distributions of clusters for different radiations are discussed. 8 refs.; 5 figs

  3. Deposition of Size-Selected Cu Nanoparticles by Inert Gas Condensation

    Directory of Open Access Journals (Sweden)

    Martínez E

    2009-01-01

    Full Text Available Abstract Nanometer size-selected Cu clusters in the size range of 1–5 nm have been produced by a plasma-gas-condensation-type cluster deposition apparatus, which combines a grow-discharge sputtering with an inert gas condensation technique. With this method, by controlling the experimental conditions, it was possible to produce nanoparticles with a strict control in size. The structure and size of Cu nanoparticles were determined by mass spectroscopy and confirmed by atomic force microscopy (AFM and scanning electron transmission microscopy (STEM measurements. In order to preserve the structural and morphological properties, the energy of cluster impact was controlled; the energy of acceleration of the nanoparticles was in near values at 0.1 ev/atom for being in soft landing regime. From SEM measurements developed in STEM-HAADF mode, we found that nanoparticles are near sized to those values fixed experimentally also confirmed by AFM observations. The results are relevant, since it demonstrates that proper optimization of operation conditions can lead to desired cluster sizes as well as desired cluster size distributions. It was also demonstrated the efficiency of the method to obtain size-selected Cu clusters films, as a random stacking of nanometer-size crystallites assembly. The deposition of size-selected metal clusters represents a novel method of preparing Cu nanostructures, with high potential in optical and catalytic applications.

  4. Do North Atlantic eels show parallel patterns of spatially varying selection?

    DEFF Research Database (Denmark)

    Ulrik, Malene G.; Pujolar, Jose Martin; Ferchaud, Anne-Laure

    2014-01-01

    was used to genotype European eel individuals (glass eels) from 8 sampling locations across the species distribution. We tested for single-generation signatures of spatially varying selection in European eel by searching for elevated genetic differentiation using F-ST-based outlier tests and by testing...... for significant associations between allele frequencies and environmental variables. Results: We found signatures of possible selection at a total of 11 coding-gene SNPs. Candidate genes for local selection constituted mainly genes with a major role in metabolism as well as defense genes. Contrary to what has...... been found for American eel, only 2 SNPs in our study correlated with differences in temperature, which suggests that other explanatory variables may play a role. None of the genes found to be associated with explanatory variables in European eel showed any correlations with environmental factors...

  5. Cluster Mass Calibration at High Redshift: HST Weak Lensing Analysis of 13 Distant Galaxy Clusters from the South Pole Telescope Sunyaev-Zel’dovich Survey

    Energy Technology Data Exchange (ETDEWEB)

    Schrabback, T.; Applegate, D.; Dietrich, J. P.; Hoekstra, H.; Bocquet, S.; Gonzalez, A. H.; der Linden, A. von; McDonald, M.; Morrison, C. B.; Raihan, S. F.; Allen, S. W.; Bayliss, M.; Benson, B. A.; Bleem, L. E.; Chiu, I.; Desai, S.; Foley, R. J.; de Haan, T.; High, F. W.; Hilbert, S.; Mantz, A. B.; Massey, R.; Mohr, J.; Reichardt, C. L.; Saro, A.; Simon, P.; Stern, C.; Stubbs, C. W.; Zenteno, A.

    2017-10-14

    We present an HST/Advanced Camera for Surveys (ACS) weak gravitational lensing analysis of 13 massive high-redshift (z(median) = 0.88) galaxy clusters discovered in the South Pole Telescope (SPT) Sunyaev-Zel'dovich Survey. This study is part of a larger campaign that aims to robustly calibrate mass-observable scaling relations over a wide range in redshift to enable improved cosmological constraints from the SPT cluster sample. We introduce new strategies to ensure that systematics in the lensing analysis do not degrade constraints on cluster scaling relations significantly. First, we efficiently remove cluster members from the source sample by selecting very blue galaxies in V - I colour. Our estimate of the source redshift distribution is based on Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) data, where we carefully mimic the source selection criteria of the cluster fields. We apply a statistical correction for systematic photometric redshift errors as derived from Hubble Ultra Deep Field data and verified through spatial cross-correlations. We account for the impact of lensing magnification on the source redshift distribution, finding that this is particularly relevant for shallower surveys. Finally, we account for biases in the mass modelling caused by miscentring and uncertainties in the concentration-mass relation using simulations. In combination with temperature estimates from Chandra we constrain the normalization of the mass-temperature scaling relation ln (E(z) M-500c/10(14)M(circle dot)) = A + 1.5ln (kT/7.2 keV) to A = 1.81(-0.14)(+0.24)(stat.)+/- 0.09(sys.), consistent with self-similar redshift evolution when compared to lower redshift samples. Additionally, the lensing data constrain the average concentration of the clusters to c(200c) = 5.6(-1.8)(+3.7).

  6. Cluster mass calibration at high redshift: HST weak lensing analysis of 13 distant galaxy clusters from the South Pole Telescope Sunyaev-Zel'dovich Survey

    Science.gov (United States)

    Schrabback, T.; Applegate, D.; Dietrich, J. P.; Hoekstra, H.; Bocquet, S.; Gonzalez, A. H.; von der Linden, A.; McDonald, M.; Morrison, C. B.; Raihan, S. F.; Allen, S. W.; Bayliss, M.; Benson, B. A.; Bleem, L. E.; Chiu, I.; Desai, S.; Foley, R. J.; de Haan, T.; High, F. W.; Hilbert, S.; Mantz, A. B.; Massey, R.; Mohr, J.; Reichardt, C. L.; Saro, A.; Simon, P.; Stern, C.; Stubbs, C. W.; Zenteno, A.

    2018-02-01

    We present an HST/Advanced Camera for Surveys (ACS) weak gravitational lensing analysis of 13 massive high-redshift (zmedian = 0.88) galaxy clusters discovered in the South Pole Telescope (SPT) Sunyaev-Zel'dovich Survey. This study is part of a larger campaign that aims to robustly calibrate mass-observable scaling relations over a wide range in redshift to enable improved cosmological constraints from the SPT cluster sample. We introduce new strategies to ensure that systematics in the lensing analysis do not degrade constraints on cluster scaling relations significantly. First, we efficiently remove cluster members from the source sample by selecting very blue galaxies in V - I colour. Our estimate of the source redshift distribution is based on Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) data, where we carefully mimic the source selection criteria of the cluster fields. We apply a statistical correction for systematic photometric redshift errors as derived from Hubble Ultra Deep Field data and verified through spatial cross-correlations. We account for the impact of lensing magnification on the source redshift distribution, finding that this is particularly relevant for shallower surveys. Finally, we account for biases in the mass modelling caused by miscentring and uncertainties in the concentration-mass relation using simulations. In combination with temperature estimates from Chandra we constrain the normalization of the mass-temperature scaling relation ln (E(z)M500c/1014 M⊙) = A + 1.5ln (kT/7.2 keV) to A=1.81^{+0.24}_{-0.14}(stat.) {± } 0.09(sys.), consistent with self-similar redshift evolution when compared to lower redshift samples. Additionally, the lensing data constrain the average concentration of the clusters to c_200c=5.6^{+3.7}_{-1.8}.

  7. Space-time clustering characteristics of tuberculosis in China, 2005-2011.

    Directory of Open Access Journals (Sweden)

    Fei Zhao

    Full Text Available OBJECTIVES: China is one of the 22 tuberculosis (TB high-burden countries in the world. As TB is a major public health problem in China, spatial analysis could be applied to detect geographic distribution of TB clusters for targeted intervention on TB epidemics. METHODS: Spatial analysis was applied for detecting TB clusters on county-based TB notification data in the national notifiable infectious disease case reporting surveillance system from 2005 to 2011. Two indicators of TB epidemic were used including new sputum smear-positive (SS+ notification rate and total TB notification rate. Global Moran's I by ArcGIS was used to assess whether TB clustering and its trend were significant. SaTScan software that used the retrospective space-time analysis and Possion probability model was utilized to identify geographic areas and time period of potential clusters with notification rates on county-level from 2005 to 2011. RESULTS: Two indicators of TB notification had presented significant spatial autocorrelation globally each year (p<0.01. Global Moran's I of total TB notification rate had positive trend as time went by (t=6.87, p<0.01. The most likely clusters of two indicators had similar spatial distribution and size in the south-central regions of China from 2006 to 2008, and the secondary clusters in two regions: northeastern China and western China. Besides, the secondary clusters of total TB notification rate had two more large clustering centers in Inner Mongolia, Gansu and Qinghai provinces and several smaller clusters in Shanxi, Henan, Hebei and Jiangsu provinces. CONCLUSION: The total TB notification cases clustered significantly in some special areas each year and the clusters trended to aggregate with time. The most-likely and secondary clusters that overlapped among two TB indicators had higher TB burden and risks of TB transmission. These were the focused geographic areas where TB control efforts should be prioritized.

  8. Cluster Dynamics Modeling with Bubble Nucleation, Growth and Coalescence

    Energy Technology Data Exchange (ETDEWEB)

    de Almeida, Valmor F. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Blondel, Sophie [Univ. of Tennessee, Knoxville, TN (United States); Bernholdt, David E. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Wirth, Brian D. [Univ. of Tennessee, Knoxville, TN (United States)

    2017-06-01

    The topic of this communication pertains to defect formation in irradiated solids such as plasma-facing tungsten submitted to helium implantation in fusion reactor com- ponents, and nuclear fuel (metal and oxides) submitted to volatile ssion product generation in nuclear reactors. The purpose of this progress report is to describe ef- forts towards addressing the prediction of long-time evolution of defects via continuum cluster dynamics simulation. The di culties are twofold. First, realistic, long-time dynamics in reactor conditions leads to a non-dilute di usion regime which is not accommodated by the prevailing dilute, stressless cluster dynamics theory. Second, long-time dynamics calls for a large set of species (ideally an in nite set) to capture all possible emerging defects, and this represents a computational bottleneck. Extensions beyond the dilute limit is a signi cant undertaking since no model has been advanced to extend cluster dynamics to non-dilute, deformable conditions. Here our proposed approach to model the non-dilute limit is to monitor the appearance of a spatially localized void volume fraction in the solid matrix with a bell shape pro le and insert an explicit geometrical bubble onto the support of the bell function. The newly cre- ated internal moving boundary provides the means to account for the interfacial ux of mobile species into the bubble, and the growth of bubbles allows for coalescence phenomena which captures highly non-dilute interactions. We present a preliminary interfacial kinematic model with associated interfacial di usion transport to follow the evolution of the bubble in any number of spatial dimensions and any number of bubbles, which can be further extended to include a deformation theory. Finally we comment on a computational front-tracking method to be used in conjunction with conventional cluster dynamics simulations in the non-dilute model proposed.

  9. Gas load forecasting based on optimized fuzzy c-mean clustering analysis of selecting similar days

    Directory of Open Access Journals (Sweden)

    Qiu Jing

    2017-08-01

    Full Text Available Traditional fuzzy c-means (FCM clustering in short term load forecasting method is easy to fall into local optimum and is sensitive to the initial cluster center.In this paper,we propose to use global search feature of particle swarm optimization (PSO algorithm to avoid these shortcomings,and to use FCM optimization to select similar date of forecast as training sample of support vector machines.This will not only strengthen the data rule of training samples,but also ensure the consistency of data characteristics.Experimental results show that the prediction accuracy of this prediction model is better than that of BP neural network and support vector machine (SVM algorithms.

  10. Hot spots, cluster detection and spatial outlier analysis of teen birth rates in the U.S., 2003–2012

    Science.gov (United States)

    Khan, Diba; Rossen, Lauren M.; Hamilton, Brady E.; He, Yulei; Wei, Rong; Dienes, Erin

    2017-01-01

    Teen birth rates have evidenced a significant decline in the United States over the past few decades. Most of the states in the US have mirrored this national decline, though some reports have illustrated substantial variation in the magnitude of these decreases across the U.S. Importantly, geographic variation at the county level has largely not been explored. We used National Vital Statistics Births data and Hierarchical Bayesian space-time interaction models to produce smoothed estimates of teen birth rates at the county level from 2003–2012. Results indicate that teen birth rates show evidence of clustering, where hot and cold spots occur, and identify spatial outliers. Findings from this analysis may help inform efforts targeting the prevention efforts by illustrating how geographic patterns of teen birth rates have changed over the past decade and where clusters of high or low teen birth rates are evident. PMID:28552189

  11. Landsat TM band 431 combine on clustering analysis for pattern recognition land use using idrisi 4.2 software

    International Nuclear Information System (INIS)

    Wiweka, Arief H.; Izzawati, Tjahyaningsih A.

    1997-01-01

    The recognition of earth object's pattern which is recorded on remote sensing digital image can do by classification process based on the group of spectral pixel value. The spectral assessment on a spatial which represent the object characteristic can be helped through supervised or unsupervised. On certain case, there no media, such as maps, airborne, photo, the capability of field observation and the knowledge of object's location. Classification process can be done by clustering. The group of pixel based on the wide of the whole value interval of spectral image, then the class group base on the desired accuracy. The clustering method in Idris 4.2 software equipments are sequential method, statistic, iso data, and RGB. The clustering existence can help pre-process pattern recognition

  12. SPATIAL-SPECTRAL CLASSIFICATION BASED ON THE UNSUPERVISED CONVOLUTIONAL SPARSE AUTO-ENCODER FOR HYPERSPECTRAL REMOTE SENSING IMAGERY

    Directory of Open Access Journals (Sweden)

    X. Han

    2016-06-01

    Full Text Available Current hyperspectral remote sensing imagery spatial-spectral classification methods mainly consider concatenating the spectral information vectors and spatial information vectors together. However, the combined spatial-spectral information vectors may cause information loss and concatenation deficiency for the classification task. To efficiently represent the spatial-spectral feature information around the central pixel within a neighbourhood window, the unsupervised convolutional sparse auto-encoder (UCSAE with window-in-window selection strategy is proposed in this paper. Window-in-window selection strategy selects the sub-window spatial-spectral information for the spatial-spectral feature learning and extraction with the sparse auto-encoder (SAE. Convolution mechanism is applied after the SAE feature extraction stage with the SAE features upon the larger outer window. The UCSAE algorithm was validated by two common hyperspectral imagery (HSI datasets – Pavia University dataset and the Kennedy Space Centre (KSC dataset, which shows an improvement over the traditional hyperspectral spatial-spectral classification methods.

  13. Clustering, randomness, and regularity in cloud fields. 4: Stratocumulus cloud fields

    Science.gov (United States)

    Lee, J.; Chou, J.; Weger, R. C.; Welch, R. M.

    1994-01-01

    To complete the analysis of the spatial distribution of boundary layer cloudiness, the present study focuses on nine stratocumulus Landsat scenes. The results indicate many similarities between stratocumulus and cumulus spatial distributions. Most notably, at full spatial resolution all scenes exhibit a decidedly clustered distribution. The strength of the clustering signal decreases with increasing cloud size; the clusters themselves consist of a few clouds (less than 10), occupy a small percentage of the cloud field area (less than 5%), contain between 20% and 60% of the cloud field population, and are randomly located within the scene. In contrast, stratocumulus in almost every respect are more strongly clustered than are cumulus cloud fields. For instance, stratocumulus clusters contain more clouds per cluster, occupy a larger percentage of the total area, and have a larger percentage of clouds participating in clusters than the corresponding cumulus examples. To investigate clustering at intermediate spatial scales, the local dimensionality statistic is introduced. Results obtained from this statistic provide the first direct evidence for regularity among large (more than 900 m in diameter) clouds in stratocumulus and cumulus cloud fields, in support of the inhibition hypothesis of Ramirez and Bras (1990). Also, the size compensated point-to-cloud cumulative distribution function statistic is found to be necessary to obtain a consistent description of stratocumulus cloud distributions. A hypothesis regarding the underlying physical mechanisms responsible for cloud clustering is presented. It is suggested that cloud clusters often arise from 4 to 10 triggering events localized within regions less than 2 km in diameter and randomly distributed within the cloud field. As the size of the cloud surpasses the scale of the triggering region, the clustering signal weakens and the larger cloud locations become more random.

  14. Clustering, randomness, and regularity in cloud fields. 4. Stratocumulus cloud fields

    Science.gov (United States)

    Lee, J.; Chou, J.; Weger, R. C.; Welch, R. M.

    1994-07-01

    To complete the analysis of the spatial distribution of boundary layer cloudiness, the present study focuses on nine stratocumulus Landsat scenes. The results indicate many similarities between stratocumulus and cumulus spatial distributions. Most notably, at full spatial resolution all scenes exhibit a decidedly clustered distribution. The strength of the clustering signal decreases with increasing cloud size; the clusters themselves consist of a few clouds (less than 10), occupy a small percentage of the cloud field area (less than 5%), contain between 20% and 60% of the cloud field population, and are randomly located within the scene. In contrast, stratocumulus in almost every respect are more strongly clustered than are cumulus cloud fields. For instance, stratocumulus clusters contain more clouds per cluster, occupy a larger percentage of the total area, and have a larger percentage of clouds participating in clusters than the corresponding cumulus examples. To investigate clustering at intermediate spatial scales, the local dimensionality statistic is introduced. Results obtained from this statistic provide the first direct evidence for regularity among large (>900 m in diameter) clouds in stratocumulus and cumulus cloud fields, in support of the inhibition hypothesis of Ramirez and Bras (1990). Also, the size compensated point-to-cloud cumulative distribution function statistic is found to be necessary to obtain a consistent description of stratocumulus cloud distributions. A hypothesis regarding the underlying physical mechanisms responsible for cloud clustering is presented. It is suggested that cloud clusters often arise from 4 to 10 triggering events localized within regions less than 2 km in diameter and randomly distributed within the cloud field. As the size of the cloud surpasses the scale of the triggering region, the clustering signal weakens and the larger cloud locations become more random.

  15. Spatial and temporal estimation of soil loss for the sustainable management of a wet semi-arid watershed cluster.

    Science.gov (United States)

    Rejani, R; Rao, K V; Osman, M; Srinivasa Rao, Ch; Reddy, K Sammi; Chary, G R; Pushpanjali; Samuel, Josily

    2016-03-01

    The ungauged wet semi-arid watershed cluster, Seethagondi, lies in the Adilabad district of Telangana in India and is prone to severe erosion and water scarcity. The runoff and soil loss data at watershed, catchment, and field level are necessary for planning soil and water conservation interventions. In this study, an attempt was made to develop a spatial soil loss estimation model for Seethagondi cluster using RUSLE coupled with ARCGIS and was used to estimate the soil loss spatially and temporally. The daily rainfall data of Aphrodite for the period from 1951 to 2007 was used, and the annual rainfall varied from 508 to 1351 mm with a mean annual rainfall of 950 mm and a mean erosivity of 6789 MJ mm ha(-1) h(-1) year(-1). Considerable variation in land use land cover especially in crop land and fallow land was observed during normal and drought years, and corresponding variation in the erosivity, C factor, and soil loss was also noted. The mean value of C factor derived from NDVI for crop land was 0.42 and 0.22 in normal year and drought years, respectively. The topography is undulating and major portion of the cluster has slope less than 10°, and 85.3% of the cluster has soil loss below 20 t ha(-1) year(-1). The soil loss from crop land varied from 2.9 to 3.6 t ha(-1) year(-1) in low rainfall years to 31.8 to 34.7 t ha(-1) year(-1) in high rainfall years with a mean annual soil loss of 12.2 t ha(-1) year(-1). The soil loss from crop land was higher in the month of August with an annual soil loss of 13.1 and 2.9 t ha(-1) year(-1) in normal and drought year, respectively. Based on the soil loss in a normal year, the interventions recommended for 85.3% of area of the watershed includes agronomic measures such as contour cultivation, graded bunds, strip cropping, mixed cropping, crop rotations, mulching, summer plowing, vegetative bunds, agri-horticultural system, and management practices such as broad bed furrow, raised sunken beds, and harvesting available water

  16. Spatial patterns of fetal loss and infant death in an arsenic-affected area in Bangladesh

    Directory of Open Access Journals (Sweden)

    Streatfield Peter

    2010-10-01

    Full Text Available Abstract Background Arsenic exposure in pregnancy is associated with adverse pregnancy outcome and infant mortality. Knowledge of the spatial characteristics of the outcomes and their possible link to arsenic exposure are important for planning effective mitigation activities. The aim of this study was to identify spatial and spatiotemporal clustering of fetal loss and infant death, and spatial relationships between high and low clusters of fetal loss and infant death rates and high and low clusters of arsenic concentrations in tube-well water used for drinking. Methods Pregnant women from Matlab, Bangladesh, who used tube-well water for drinking while pregnant between 1991 and 2000, were included in this study. In total 29,134 pregnancies were identified. A spatial scan test was used to identify unique non-random spatial and spatiotemporal clusters of fetal loss and infant death using a retrospective spatial and spatiotemporal permutation and Poisson probability models. Results Two significant clusters of fetal loss and infant death were identified and these clusters remained stable after adjustment for covariates. One cluster of higher rates of fetal loss and infant death was in the vicinity of the Meghna River, and the other cluster of lower rates was in the center of Matlab. The average concentration of arsenic in the water differed between these clusters (319 μg/L for the high cluster and 174 μg/L for the low cluster. The spatial patterns of arsenic concentrations in tube-well water were found to be linked with the adverse pregnancy outcome clusters. In the spatiotemporal analysis, only one high fetal loss and infant death cluster was identified in the same high cluster area obtained from purely spatial analysis. However, the cluster was no longer significant after adjustment for the covariates. Conclusion The finding of this study suggests that given the geographical variation in tube-well water contamination, higher fetal loss and

  17. Clustering environments of BL Lac objects

    Science.gov (United States)

    Wurtz, Ronald; Ellingson, Erica; Stocke, John T.; Yee, H. K. C.

    1993-01-01

    We report measurements of the amplitude of the BL Lac galaxy spatial covariance function, B(gb), for the fields of five BL Lacertae objects. We present evidence for rich clusters around MS 1207+39 and MS 1407+59, and confirm high richness for the cluster containing H0414+009. We discuss the ease of 3C 66 A and find evidence for a poor cluster based on an uncertain redshift of z = 0.444. These data suggest that at least some BL Lac objects are consistent with being FR 1 radio galaxies in rich clusters.

  18. The Centaurus cluster of galaxies. Pt. 3

    International Nuclear Information System (INIS)

    Lucey, J.R.; Currie, M.J.; Dickens, R.J.

    1986-05-01

    Previous work by the authors has shown that the Centaurus cluster (α = 12sup(h) 47 delta = -41 0 ) is composed of two velocity components, Cen30 (mean velocity 3000 km s -1 ) and Cen45 (mean velocity 4500 km s -1 ), which very probably lie within one cluster. In this paper the internal structure of the cluster is described and the spatial and velocity distributions of the different galaxy types within the cluster are discussed. (author)

  19. Comparison of Bayesian clustering and edge detection methods for inferring boundaries in landscape genetics

    Science.gov (United States)

    Safner, T.; Miller, M.P.; McRae, B.H.; Fortin, M.-J.; Manel, S.

    2011-01-01

    Recently, techniques available for identifying clusters of individuals or boundaries between clusters using genetic data from natural populations have expanded rapidly. Consequently, there is a need to evaluate these different techniques. We used spatially-explicit simulation models to compare three spatial Bayesian clustering programs and two edge detection methods. Spatially-structured populations were simulated where a continuous population was subdivided by barriers. We evaluated the ability of each method to correctly identify boundary locations while varying: (i) time after divergence, (ii) strength of isolation by distance, (iii) level of genetic diversity, and (iv) amount of gene flow across barriers. To further evaluate the methods' effectiveness to detect genetic clusters in natural populations, we used previously published data on North American pumas and a European shrub. Our results show that with simulated and empirical data, the Bayesian spatial clustering algorithms outperformed direct edge detection methods. All methods incorrectly detected boundaries in the presence of strong patterns of isolation by distance. Based on this finding, we support the application of Bayesian spatial clustering algorithms for boundary detection in empirical datasets, with necessary tests for the influence of isolation by distance. ?? 2011 by the authors; licensee MDPI, Basel, Switzerland.

  20. Spatial epidemiology of eastern equine encephalitis in Florida.

    Science.gov (United States)

    Vander Kelen, Patrick T; Downs, Joni A; Stark, Lillian M; Loraamm, Rebecca W; Anderson, James H; Unnasch, Thomas R

    2012-11-05

    Eastern Equine Encephalitis virus (EEEV) is an alphavirus with high pathogenicity in both humans and horses. Florida continues to have the highest occurrence of human cases in the USA, with four fatalities recorded in 2010. Unlike other states, Florida supports year-round EEEV transmission. This research uses GIS to examine spatial patterns of documented horse cases during 2005-2010 in order to understand the relationships between habitat and transmission intensity of EEEV in Florida. Cumulative incidence rates of EEE in horses were calculated for each county. Two cluster analyses were performed using density-based spatial clustering of applications with noise (DBSCAN). The first analysis was based on regional clustering while the second focused on local clustering. Ecological associations of EEEV were examined using compositional analysis and Euclidean distance analysis to determine if the proportion or proximity of certain habitats played a role in transmission. The DBSCAN algorithm identified five distinct regional spatial clusters that contained 360 of the 438 horse cases. The local clustering resulted in 18 separate clusters containing 105 of the 438 cases. Both the compositional analysis and Euclidean distance analysis indicated that the top five habitats positively associated with horse cases were rural residential areas, crop and pastureland, upland hardwood forests, vegetated non-forested wetlands, and tree plantations. This study demonstrates that in Florida tree plantations are a focus for epizootic transmission of EEEV. It appears both the abundance and proximity of tree plantations are factors associated with increased risk of EEE in horses and therefore humans. This association helps to explain why there is are spatially distinct differences in the amount of EEE horse cases across Florida.

  1. Non-standard spatial statistics and spatial econometrics

    CERN Document Server

    Griffith, Daniel A

    2011-01-01

    Spatial statistics and spatial econometrics are recent sprouts of the tree "spatial analysis with measurement". Still, several general themes have emerged. Exploring selected fields of possible interest is tantalizing, and this is what the authors aim here.

  2. Selective spatial enhancement: Attentional spotlight size impacts spatial but not temporal perception.

    Science.gov (United States)

    Goodhew, Stephanie C; Shen, Elizabeth; Edwards, Mark

    2016-08-01

    An important but often neglected aspect of attention is how changes in the attentional spotlight size impact perception. The zoom-lens model predicts that a small ("focal") attentional spotlight enhances all aspects of perception relative to a larger ("diffuse" spotlight). However, based on the physiological properties of the two major classes of visual cells (magnocellular and parvocellular neurons) we predicted trade-offs in spatial and temporal acuity as a function of spotlight size. Contrary to both of these accounts, however, across two experiments we found that attentional spotlight size affected spatial acuity, such that spatial acuity was enhanced for a focal relative to a diffuse spotlight, whereas the same modulations in spotlight size had no impact on temporal acuity. This likely reflects the function of attention: to induce the high spatial resolution of the fovea in periphery, where spatial resolution is poor but temporal resolution is good. It is adaptive, therefore, for the attentional spotlight to enhance spatial acuity, whereas enhancing temporal acuity does not confer the same benefit.

  3. Dynamic nuclear polarization and optimal control spatial-selective 13C MRI and MRS

    DEFF Research Database (Denmark)

    Vinding, Mads Sloth; Laustsen, Christoffer; Maximov, Ivan I.

    2013-01-01

    . This is achieved through the development of spatial-selective single-shot spiral-readout MRI and MRS experiments combined with dynamic nuclear polarization hyperpolarized [1-13C]pyruvate on a 4.7T pre-clinical MR scanner. The method stands out from related techniques by facilitating anatomic shaped region...

  4. Spatial and color clustering on an FPGA-based computer system

    Science.gov (United States)

    Leeser, Miriam E.; Kitaryeva, Natalya V.; Crisman, Jill D.

    1998-10-01

    We are mapping an image clustering algorithm onto an FPGA- based computer system. Our approach processes raw pixel data in the red, green, blue color space and generates an output image where all pixels are assigned to classes. A class is a group of pixels with similar color and location. These classes are then used as the basis of further processing to generate tags. The tags, in turn, are used to generate queries for searching libraries of digital images. We run our image tagging approach on an FPGA-based computing machine. The image clustering algorithm is run on an FPGA board, and only the classified image is communicated to the host PC. Further processing is run on the host. Our experimental system consists of an Annapolis Wildforce board with four Xilinx XC4000 chips and a PCI connection to a host PC. Our implementation allows the raw image data to stay local to the FPGAs, and only the class image is communicated to the host PC. The classified pixels are then used to generate tags which can be used for searching a digital library. This approach allows us to parallelize the image processing on the FPGA board, and to minimize the data handled by the PC. FPGA platforms are ideally suited for this sort of initial processing of images. The large amount of image data can be preprocessed by exploiting the inherent parallelism available in FPGA architectures, keeping unnecessary data off the host processor. The result of our algorithm is a reduction by up to a factor of six in the number of bits required to represent each pixel. The output data is passed to the host PC, thus reducing the processing and memory resources needed compared to handling the raw data on the PC. The process of generating tags of images is simplified by first classifying pixels on an FPGA-based system, and digital library search is accelerated.

  5. Mutual information spectrum for selection of event-related spatial components. Application to eloquent motor cortex mapping.

    Directory of Open Access Journals (Sweden)

    Alexei eOssadtchi

    2014-01-01

    Full Text Available Spatial component analysis is often used to explore multidimensional time series data whose sources cannot be measured directly. Several methods may be used to decompose the data into a set of spatial components with temporal loadings. Component selection is of crucial importance, and should be supported by objective criteria. In some applications, the use of a well defined component selection criterion may provide for automation of the analysis.In this paper we describe a novel approach for ranking of spatial components calculated from the EEG or MEG data recorded within evoked response paradigm. Our method is called Mutual Information Spectrum and is based on gauging the amount of mutual information of spatial component temporal loadings with a synthetically created reference signal. We also describe the appropriate randomization based statistical assessment scheme that can be used for selection of components with statistically significant amount of mutual information. Using simulated data with realistic trial to trial variations and SNR corresponding to the real recordings we demonstrate the superior performance characteristics of the described mutual information based measure as compared to a more conventionally used power driven gauge. We also demonstrate the application of the Mutual Information Spectrum for the selection of task-related independent components from real MEG data. We show that the Mutual Information spectrum allows to identify task-related components reliably in a consistent fashion, yielding stable results even from a small number of trials. We conclude that the proposed method fits naturally the information driven nature of ICA and can be used for routine and automatic ranking of independent components calculated from the functional neuroimaging data collected within event-related paradigms.

  6. Discovery of path nearby clusters in spatial networks

    KAUST Repository

    Shang, Shuo; Zheng, Kai; Jensen, Christian S.; Yang, Bin; Kalnis, Panos; Li, Guohe; Wen, Ji Rong

    2015-01-01

    The discovery of regions of interest in large cities is an important challenge. We propose and investigate a novel query called the path nearby cluster (PNC) query that finds regions of potential interest (e.g., sightseeing places and commercial

  7. Neutrino dark matter in clusters of galaxies

    International Nuclear Information System (INIS)

    Treumann, R A; Kull, A; Boehringer, H

    2000-01-01

    We present a model calculation for the radial matter density and mass distribution in two clusters of galaxies (Coma and A119) including cold dark matter, massive though light (≅2 eV) neutrino dark matter and collisional intra-cluster gas which emits x-ray radiation. The calculation uses an extension of the Lynden-Bell statistics to the choice of constant masses instead of constant volume. This allows proper inclusion of mixtures of particles of various masses in the gravitational interaction. When it is applied to the matter in the galaxy cluster the radial ROSAT x-ray luminosity profiles can be nicely accounted for. The result is that the statistics identifies the neutrino dark matter in the cluster centre as being degenerate in the sense of Lynden-Bell's spatial degeneracy. This implies that it is distributed in a way different from the classical assumption. The best fits are obtained for the ≅2 eV neutrinos. The fraction of these and their spatial distribution are of interest for understanding cluster dynamics and may have cosmological implications

  8. Neutrino dark matter in clusters of galaxies

    International Nuclear Information System (INIS)

    Treumann, R.A.

    2000-01-01

    We present a model calculation for the radial matter density and mass distribution in two clusters of galaxies (Coma and A119) including cold dark matter, massive though light (approx. 2 eV) neutrino dark matter and collisional intra-cluster gas which emits x-ray radiation. The calculation uses an extension of the Lynden-Bell statistics to the choice of constant masses instead of constant volume. This allows proper inclusion of mixtures of particles of various masses in the gravitational interaction. When it is applied to the matter in the galaxy cluster the radial ROSAT x-ray luminosity profiles can be nicely accounted for. The result is that the statistics identifies the neutrino dark matter in the cluster centre as being degenerate in the sense of Lynden-Bell's spatial degeneracy. This implies that it is distributed in a way different from the classical assumption. The best fits are obtained for the approx. 2 eV neutrinos. The fraction of these and their spatial distribution are of interest for understanding cluster dynamics and may have cosmological implications. (author)

  9. THE ATACAMA COSMOLOGY TELESCOPE: HIGH-RESOLUTION SUNYAEV-ZEL'DOVICH ARRAY OBSERVATIONS OF ACT SZE-SELECTED CLUSTERS FROM THE EQUATORIAL STRIP

    Energy Technology Data Exchange (ETDEWEB)

    Reese, Erik D.; Mroczkowski, Tony; Devlin, Mark J.; Dicker, Simon R. [Department of Physics and Astronomy, University of Pennsylvania, 209 South 33rd Street, Philadelphia, PA 19104 (United States); Menanteau, Felipe; Baker, Andrew J. [Department of Physics and Astronomy, Rutgers, The State University of New Jersey, Piscataway, NJ 08854-8019 (United States); Hilton, Matt [School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD (United Kingdom); Sievers, Jonathan; Bond, J. Richard; Hajian, Amir [Canadian Institute for Theoretical Astrophysics, University of Toronto, Toronto, ON M5S 3H8 (Canada); Aguirre, Paula; Duenner, Rolando [Departamento de Astronomia y Astrofisica, Facultad de Fisica, Pontificia Universidad Catolica de Chile, Casilla 306, Santiago 22 (Chile); Appel, John William; Das, Sudeep; Essinger-Hileman, Thomas; Hincks, Adam D. [Joseph Henry Laboratories of Physics, Jadwin Hall, Princeton University, Princeton, NJ 08544 (United States); Fowler, Joseph W.; Hill, J. Colin [Department of Astrophysical Sciences, Peyton Hall, Princeton University, Princeton, NJ 08544 (United States); Halpern, Mark; Hasselfield, Matthew [Department of Physics and Astronomy, University of British Columbia, Vancouver, BC V6T 1Z4 (Canada); and others

    2012-05-20

    We present follow-up observations with the Sunyaev-Zel'dovich Array (SZA) of optically confirmed galaxy clusters found in the equatorial survey region of the Atacama Cosmology Telescope (ACT): ACT-CL J0022-0036, ACT-CL J2051+0057, and ACT-CL J2337+0016. ACT-CL J0022-0036 is a newly discovered, massive ({approx_equal} 10{sup 15} M{sub Sun }), high-redshift (z = 0.81) cluster revealed by ACT through the Sunyaev-Zel'dovich effect (SZE). Deep, targeted observations with the SZA allow us to probe a broader range of cluster spatial scales, better disentangle cluster decrements from radio point-source emission, and derive more robust integrated SZE flux and mass estimates than we can with ACT data alone. For the two clusters we detect with the SZA we compute integrated SZE signal and derive masses from the SZA data only. ACT-CL J2337+0016, also known as A2631, has archival Chandra data that allow an additional X-ray-based mass estimate. Optical richness is also used to estimate cluster masses and shows good agreement with the SZE and X-ray-based estimates. Based on the point sources detected by the SZA in these three cluster fields and an extrapolation to ACT's frequency, we estimate that point sources could be contaminating the SZE decrement at the {approx}< 20% level for some fraction of clusters.

  10. Temporal and spatial distribution characteristics in the natural plague foci of Chinese Mongolian gerbils based on spatial autocorrelation.

    Science.gov (United States)

    Du, Hai-Wen; Wang, Yong; Zhuang, Da-Fang; Jiang, Xiao-San

    2017-08-07

    The nest flea index of Meriones unguiculatus is a critical indicator for the prevention and control of plague, which can be used not only to detect the spatial and temporal distributions of Meriones unguiculatus, but also to reveal its cluster rule. This research detected the temporal and spatial distribution characteristics of the plague natural foci of Mongolian gerbils by body flea index from 2005 to 2014, in order to predict plague outbreaks. Global spatial autocorrelation was used to describe the entire spatial distribution pattern of the body flea index in the natural plague foci of typical Chinese Mongolian gerbils. Cluster and outlier analysis and hot spot analysis were also used to detect the intensity of clusters based on geographic information system methods. The quantity of M. unguiculatus nest fleas in the sentinel surveillance sites from 2005 to 2014 and host density data of the study area from 2005 to 2010 used in this study were provided by Chinese Center for Disease Control and Prevention. The epidemic focus regions of the Mongolian gerbils remain the same as the hot spot regions relating to the body flea index. High clustering areas possess a similar pattern as the distribution pattern of the body flea index indicating that the transmission risk of plague is relatively high. In terms of time series, the area of the epidemic focus gradually increased from 2005 to 2007, declined rapidly in 2008 and 2009, and then decreased slowly and began trending towards stability from 2009 to 2014. For the spatial change, the epidemic focus regions began moving northward from the southwest epidemic focus of the Mongolian gerbils from 2005 to 2007, and then moved from north to south in 2007 and 2008. The body flea index of Chinese gerbil foci reveals significant spatial and temporal aggregation characteristics through the employing of spatial autocorrelation. The diversity of temporary and spatial distribution is mainly affected by seasonal variation, the human

  11. Mode selectivity in cluster-molecule interactions: Ni13 + D2

    International Nuclear Information System (INIS)

    Jellinek, J.; Guevenc, Z.B.

    1991-01-01

    Results of a detailed quasiclassical simulation study of the Ni 13 + D 2 collision system are presented. The dissociative adsorption of the molecule as well as its scattering from the cluster are analyzed as functions of the initial rovibrational molecular state, collision energy and structure of the cluster. Mode-specific features of the reactive and nonreactive channels of the cluster-molecule interaction are displayed and discussed. Evidence for resonances and for a strong cluster structure-reactivity correlation is presented. 13 refs., 6 figs

  12. Spatial pattern recognition of seismic events in South West Colombia

    Science.gov (United States)

    Benítez, Hernán D.; Flórez, Juan F.; Duque, Diana P.; Benavides, Alberto; Lucía Baquero, Olga; Quintero, Jiber

    2013-09-01

    Recognition of seismogenic zones in geographical regions supports seismic hazard studies. This recognition is usually based on visual, qualitative and subjective analysis of data. Spatial pattern recognition provides a well founded means to obtain relevant information from large amounts of data. The purpose of this work is to identify and classify spatial patterns in instrumental data of the South West Colombian seismic database. In this research, clustering tendency analysis validates whether seismic database possesses a clustering structure. A non-supervised fuzzy clustering algorithm creates groups of seismic events. Given the sensitivity of fuzzy clustering algorithms to centroid initial positions, we proposed a methodology to initialize centroids that generates stable partitions with respect to centroid initialization. As a result of this work, a public software tool provides the user with the routines developed for clustering methodology. The analysis of the seismogenic zones obtained reveals meaningful spatial patterns in South-West Colombia. The clustering analysis provides a quantitative location and dispersion of seismogenic zones that facilitates seismological interpretations of seismic activities in South West Colombia.

  13. The C4 clustering algorithm: Clusters of galaxies in the Sloan Digital Sky Survey

    Energy Technology Data Exchange (ETDEWEB)

    Miller, Christopher J.; Nichol, Robert; Reichart, Dan; Wechsler, Risa H.; Evrard, August; Annis, James; McKay, Timothy; Bahcall, Neta; Bernardi, Mariangela; Boehringer,; Connolly, Andrew; Goto, Tomo; Kniazev, Alexie; Lamb, Donald; Postman, Marc; Schneider, Donald; Sheth, Ravi; Voges, Wolfgang; /Cerro-Tololo InterAmerican Obs. /Portsmouth U.,

    2005-03-01

    We present the ''C4 Cluster Catalog'', a new sample of 748 clusters of galaxies identified in the spectroscopic sample of the Second Data Release (DR2) of the Sloan Digital Sky Survey (SDSS). The C4 cluster-finding algorithm identifies clusters as overdensities in a seven-dimensional position and color space, thus minimizing projection effects that have plagued previous optical cluster selection. The present C4 catalog covers {approx}2600 square degrees of sky and ranges in redshift from z = 0.02 to z = 0.17. The mean cluster membership is 36 galaxies (with redshifts) brighter than r = 17.7, but the catalog includes a range of systems, from groups containing 10 members to massive clusters with over 200 cluster members with redshifts. The catalog provides a large number of measured cluster properties including sky location, mean redshift, galaxy membership, summed r-band optical luminosity (L{sub r}), velocity dispersion, as well as quantitative measures of substructure and the surrounding large-scale environment. We use new, multi-color mock SDSS galaxy catalogs, empirically constructed from the {Lambda}CDM Hubble Volume (HV) Sky Survey output, to investigate the sensitivity of the C4 catalog to the various algorithm parameters (detection threshold, choice of passbands and search aperture), as well as to quantify the purity and completeness of the C4 cluster catalog. These mock catalogs indicate that the C4 catalog is {approx_equal}90% complete and 95% pure above M{sub 200} = 1 x 10{sup 14} h{sup -1}M{sub {circle_dot}} and within 0.03 {le} z {le} 0.12. Using the SDSS DR2 data, we show that the C4 algorithm finds 98% of X-ray identified clusters and 90% of Abell clusters within 0.03 {le} z {le} 0.12. Using the mock galaxy catalogs and the full HV dark matter simulations, we show that the L{sub r} of a cluster is a more robust estimator of the halo mass (M{sub 200}) than the galaxy line-of-sight velocity dispersion or the richness of the cluster

  14. Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA for Load Profiling Applications

    Directory of Open Access Journals (Sweden)

    Ioannis P. Panapakidis

    2018-02-01

    Full Text Available Due to high implementation rates of smart meter systems, considerable amount of research is placed in machine learning tools for data handling and information retrieval. A key tool in load data processing is clustering. In recent years, a number of researches have proposed different clustering algorithms in the load profiling field. The present paper provides a methodology for addressing the aforementioned problem through Multi-Criteria Decision Analysis (MCDA and namely, using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS. A comparison of the algorithms is employed. Next, a single test case on the selection of an algorithm is examined. User specific weights are applied and based on these weight values, the optimal algorithm is drawn.

  15. Linear and nonlinear surface spectroscopy of supported size selected metal clusters and organic adsorbates

    Energy Technology Data Exchange (ETDEWEB)

    Thaemer, Martin Georg

    2012-03-08

    The spectroscopic investigation of supported size selected metal clusters over a wide wavelength range plays an important role for understanding their outstanding catalytic properties. The challenge which must be overcome to perform such measurements is the difficult detection of the weak spectroscopic signals from these samples. As a consequence, highly sensitive spectroscopic methods are applied, such as surface Cavity Ringdown Spectroscopy and surface Second Harmonic Generation Spectroscopy. The spectroscopic apparatus developed is shown to have a sensitivity which is high enough to detect sub-monolayer coverages of adsorbates on surfaces. In the measured spectra of small supported silver clusters of the sizes Ag{sub 4}2, Ag{sub 2}1, Ag{sub 9}, and Ag atoms a stepwise transition from particles with purely metallic character to particles with molecule-like properties can be observed within this size range.

  16. LOCFES-B: Solving the one-dimensional transport equation with user-selected spatial approximations

    International Nuclear Information System (INIS)

    Jarvis, R.D.; Nelson, P.

    1993-01-01

    Closed linear one-cell functional (CLOF) methods constitute an abstractly defined class of spatial approximations to the one-dimensional discrete ordinates equations of linear particle transport that encompass, as specific instances, the vast majority of the spatial approximations that have been either used or suggested in the computational solution of these equations. A specific instance of the class of CLOF methods is defined by a (typically small) number of functions of the cell width, total cross section, and direction cosine of particle motion. The LOCFES code takes advantage of the latter observation by permitting the use, within a more-or-less standard source iteration solution process, of an arbitrary CLOF method as defined by a user-supplied subroutine. The design objective of LOCFES was to provide automated determination of the order of accuracy (i.e., order of the discretization error) in the fine-mesh limit for an arbitrary user-selected CLOF method. This asymptotic order of accuracy is one widely used measure of the merit of a spatial approximation. This paper discusses LOCFES-B, which is a code that uses methods developed in LOCFES to solve one-dimensional linear particle transport problems with any user-selected CLOF method. LOCFES-B provides automatic solution of a given problem to within an accuracy specified by user input and provides comparison of the computational results against results from externally provided benchmark results

  17. Spatial and temporal dynamics of forest canopy gaps following selective logging in the eastern Amazon.

    Science.gov (United States)

    GREGORY P. ASNER; MICHAEL KELLER; JOSEN M. SILVA

    2004-01-01

    Selective logging is a dominant form of land use in the Amazon basin and throughout the humid tropics, yet little is known about the spatial variability of forest canopy gap formation and closure following timber harvests. We established chronosequences of large-area (14–158 ha) selective logging sites spanning a 3.5-year period of forest regeneration and two distinct...

  18. A spatial approach of magnitude-squared coherence applied to selective attention detection.

    Science.gov (United States)

    Bonato Felix, Leonardo; de Souza Ranaudo, Fernando; D'affonseca Netto, Aluizio; Ferreira Leite Miranda de Sá, Antonio Mauricio

    2014-05-30

    Auditory selective attention is the human ability of actively focusing in a certain sound stimulus while avoiding all other ones. This ability can be used, for example, in behavioral studies and brain-machine interface. In this work we developed an objective method - called Spatial Coherence - to detect the side where a subject is focusing attention to. This method takes into consideration the Magnitude Squared Coherence and the topographic distribution of responses among electroencephalogram electrodes. The individuals were stimulated with amplitude-modulated tones binaurally and were oriented to focus attention to only one of the stimuli. The results indicate a contralateral modulation of ASSR in the attention condition and are in agreement with prior studies. Furthermore, the best combination of electrodes led to a hit rate of 82% for 5.03 commands per minute. Using a similar paradigm, in a recent work, a maximum hit rate of 84.33% was achieved, but with a greater a classification time (20s, i.e. 3 commands per minute). It seems that Spatial Coherence is a useful technique for detecting focus of auditory selective attention. Copyright © 2014 Elsevier B.V. All rights reserved.

  19. Evaluating three proposals for testing independence in non linear spatial processes

    OpenAIRE

    López Hernández, Fernando Antonio; Mate Sánchez-Val, María Luz; Artal Tur, Andrés

    2013-01-01

    [ENG]This paper evaluates the behaviour of different families of tests when checking for spatial independence in the presence of nonlinearities. To reach this goal, we select three representative proposals. The usual parametric tests of I-Moran, the nonparametric proposal of Brett and Pinkse (1997), and the semiparametric Scan test. In order to study how they perform, we simulate different nonlinear spatial structures by Monte Carlo methods, hence conducting empirical tests on ...

  20. Sunyaev-Zel'dovich Effect and X-ray Scaling Relations from Weak-Lensing Mass Calibration of 32 SPT Selected Galaxy Clusters

    Energy Technology Data Exchange (ETDEWEB)

    Dietrich, J.P.; et al.

    2017-11-14

    Uncertainty in the mass-observable scaling relations is currently the limiting factor for galaxy cluster based cosmology. Weak gravitational lensing can provide a direct mass calibration and reduce the mass uncertainty. We present new ground-based weak lensing observations of 19 South Pole Telescope (SPT) selected clusters and combine them with previously reported space-based observations of 13 galaxy clusters to constrain the cluster mass scaling relations with the Sunyaev-Zel'dovich effect (SZE), the cluster gas mass $M_\\mathrm{gas}$, and $Y_\\mathrm{X}$, the product of $M_\\mathrm{gas}$ and X-ray temperature. We extend a previously used framework for the analysis of scaling relations and cosmological constraints obtained from SPT-selected clusters to make use of weak lensing information. We introduce a new approach to estimate the effective average redshift distribution of background galaxies and quantify a number of systematic errors affecting the weak lensing modelling. These errors include a calibration of the bias incurred by fitting a Navarro-Frenk-White profile to the reduced shear using $N$-body simulations. We blind the analysis to avoid confirmation bias. We are able to limit the systematic uncertainties to 6.4% in cluster mass (68% confidence). Our constraints on the mass-X-ray observable scaling relations parameters are consistent with those obtained by earlier studies, and our constraints for the mass-SZE scaling relation are consistent with the the simulation-based prior used in the most recent SPT-SZ cosmology analysis. We can now replace the external mass calibration priors used in previous SPT-SZ cosmology studies with a direct, internal calibration obtained on the same clusters.