Micro-flock patterns and macro-clusters in chiral active Brownian disks

Science.gov (United States)

Levis, Demian; Liebchen, Benno

2018-02-01

Chiral active particles (or self-propelled circle swimmers) feature a rich collective behavior, comprising rotating macro-clusters and micro-flock patterns which consist of phase-synchronized rotating clusters with a characteristic self-limited size. These patterns emerge from the competition of alignment interactions and rotations suggesting that they might occur generically in many chiral active matter systems. However, although excluded volume interactions occur naturally among typical circle swimmers, it is not yet clear if macro-clusters and micro-flock patterns survive their presence. The present work shows that both types of pattern do survive but feature strongly enhance fluctuations regarding the size and shape of the individual clusters. Despite these fluctuations, we find that the average micro-flock size still follows the same characteristic scaling law as in the absence of excluded volume interactions, i.e. micro-flock sizes scale linearly with the single-swimmer radius.

Global Analysis of miRNA Gene Clusters and Gene Families Reveals Dynamic and Coordinated Expression

Directory of Open Access Journals (Sweden)

Li Guo

2014-01-01

Full Text Available To further understand the potential expression relationships of miRNAs in miRNA gene clusters and gene families, a global analysis was performed in 4 paired tumor (breast cancer and adjacent normal tissue samples using deep sequencing datasets. The compositions of miRNA gene clusters and families are not random, and clustered and homologous miRNAs may have close relationships with overlapped miRNA species. Members in the miRNA group always had various expression levels, and even some showed larger expression divergence. Despite the dynamic expression as well as individual difference, these miRNAs always indicated consistent or similar deregulation patterns. The consistent deregulation expression may contribute to dynamic and coordinated interaction between different miRNAs in regulatory network. Further, we found that those clustered or homologous miRNAs that were also identified as sense and antisense miRNAs showed larger expression divergence. miRNA gene clusters and families indicated important biological roles, and the specific distribution and expression further enrich and ensure the flexible and robust regulatory network.

The global kernel k-means algorithm for clustering in feature space.

Science.gov (United States)

Tzortzis, Grigorios F; Likas, Aristidis C

2009-07-01

Kernel k-means is an extension of the standard k -means clustering algorithm that identifies nonlinearly separable clusters. In order to overcome the cluster initialization problem associated with this method, we propose the global kernel k-means algorithm, a deterministic and incremental approach to kernel-based clustering. Our method adds one cluster at each stage, through a global search procedure consisting of several executions of kernel k-means from suitable initializations. This algorithm does not depend on cluster initialization, identifies nonlinearly separable clusters, and, due to its incremental nature and search procedure, locates near-optimal solutions avoiding poor local minima. Furthermore, two modifications are developed to reduce the computational cost that do not significantly affect the solution quality. The proposed methods are extended to handle weighted data points, which enables their application to graph partitioning. We experiment with several data sets and the proposed approach compares favorably to kernel k -means with random restarts.

Various oscillation patterns in phase models with locally attractive and globally repulsive couplings.

Science.gov (United States)

Sato, Katsuhiko; Shima, Shin-ichiro

2015-10-01

We investigate a phase model that includes both locally attractive and globally repulsive coupling in one dimension. This model exhibits nontrivial spatiotemporal patterns that have not been observed in systems that contain only local or global coupling. Depending on the relative strengths of the local and global coupling and on the form of global coupling, the system can show a spatially uniform state (in-phase synchronization), a monotonically increasing state (traveling wave), and three types of oscillations of relative phase difference. One of the oscillations of relative phase difference has the characteristic of being locally unstable but globally attractive. That is, any small perturbation to the periodic orbit in phase space destroys its periodic motion, but after a long time the system returns to the original periodic orbit. This behavior is closely related to the emergence of saddle two-cluster states for global coupling only, which are connected to each other by attractive heteroclinic orbits. The mechanism of occurrence of this type of oscillation is discussed.

Joint local and global consistency on interdocument and interword relationships for co-clustering.

Science.gov (United States)

Bao, Bing-Kun; Min, Weiqing; Li, Teng; Xu, Changsheng

2015-01-01

Co-clustering has recently received a lot of attention due to its effectiveness in simultaneously partitioning words and documents by exploiting the relationships between them. However, most of the existing co-clustering methods neglect or only partially reveal the interword and interdocument relationships. To fully utilize those relationships, the local and global consistencies on both word and document spaces need to be considered, respectively. Local consistency indicates that the label of a word/document can be predicted from its neighbors, while global consistency enforces a smoothness constraint on words/documents labels over the whole data manifold. In this paper, we propose a novel co-clustering method, called co-clustering via local and global consistency, to not only make use of the relationship between word and document, but also jointly explore the local and global consistency on both word and document spaces, respectively. The proposed method has the following characteristics: 1) the word-document relationships is modeled by following information-theoretic co-clustering (ITCC); 2) the local consistency on both interword and interdocument relationships is revealed by a local predictor; and 3) the global consistency on both interword and interdocument relationships is explored by a global smoothness regularization. All the fitting errors from these three-folds are finally integrated together to formulate an objective function, which is iteratively optimized by a convergence provable updating procedure. The extensive experiments on two benchmark document datasets validate the effectiveness of the proposed co-clustering method.

Global Patterns in the Implementation of Payments for Environmental Services.

Directory of Open Access Journals (Sweden)

Driss Ezzine-de-Blas

Full Text Available Assessing global tendencies and impacts of conditional payments for environmental services (PES programs is challenging because of their heterogeneity, and scarcity of comparative studies. This meta-study systematizes 55 PES schemes worldwide in a quantitative database. Using categorical principal component analysis to highlight clustering patterns, we reconfirm frequently hypothesized differences between public and private PES schemes, but also identify diverging patterns between commercial and non-commercial private PES vis-à-vis their service focus, area size, and market orientation. When do these PES schemes likely achieve significant environmental additionality? Using binary logistical regression, we find additionality to be positively influenced by three theoretically recommended PES 'best design' features: spatial targeting, payment differentiation, and strong conditionality, alongside some contextual controls (activity paid for and implementation time elapsed. Our results thus stress the preeminence of customized design over operational characteristics when assessing what determines the outcomes of PES implementation.

Global Clusters as Laboratories for Stellar Evolution

Science.gov (United States)

Catelan, Marcio; Valcarce, Aldo A. R.; Sweigart, Allen V.

2010-01-01

Globular clusters have long been considered the closest approximation to a physicist's laboratory in astrophysics, and as such a near-ideal laboratory for (low-mass) stellar evolution, However, recent observations have cast a shadow on this long-standing paradigm, suggesting the presence of multiple populations with widely different abundance patterns, and - crucially - with widely different helium abundances as welL In this review we discuss which features of the Hertzsprung-Russell diagram may be used as helium abundance indicators, and present an overview of available constraints on the helium abundance in globular clusters,

Gene expression patterns of oxidative phosphorylation complex I subunits are organized in clusters.

Directory of Open Access Journals (Sweden)

Yael Garbian

Full Text Available After the radiation of eukaryotes, the NUO operon, controlling the transcription of the NADH dehydrogenase complex of the oxidative phosphorylation system (OXPHOS complex I, was broken down and genes encoding this protein complex were dispersed across the nuclear genome. Seven genes, however, were retained in the genome of the mitochondrion, the ancient symbiote of eukaryotes. This division, in combination with the three-fold increase in subunit number from bacteria (N = approximately 14 to man (N = 45, renders the transcription regulation of OXPHOS complex I a challenge. Recently bioinformatics analysis of the promoter regions of all OXPHOS genes in mammals supported patterns of co-regulation, suggesting that natural selection favored a mechanism facilitating the transcriptional regulatory control of genes encoding subunits of these large protein complexes. Here, using real time PCR of mitochondrial (mtDNA- and nuclear DNA (nDNA-encoded transcripts in a panel of 13 different human tissues, we show that the expression pattern of OXPHOS complex I genes is regulated in several clusters. Firstly, all mtDNA-encoded complex I subunits (N = 7 share a similar expression pattern, distinct from all tested nDNA-encoded subunits (N = 10. Secondly, two sub-clusters of nDNA-encoded transcripts with significantly different expression patterns were observed. Thirdly, the expression patterns of two nDNA-encoded genes, NDUFA4 and NDUFA5, notably diverged from the rest of the nDNA-encoded subunits, suggesting a certain degree of tissue specificity. Finally, the expression pattern of the mtDNA-encoded ND4L gene diverged from the rest of the tested mtDNA-encoded transcripts that are regulated by the same promoter, consistent with post-transcriptional regulation. These findings suggest, for the first time, that the regulation of complex I subunits expression in humans is complex rather than reflecting global co-regulation.

Peeking Network States with Clustered Patterns

Energy Technology Data Exchange (ETDEWEB)

Kim, Jinoh [Texas A & M Univ., Commerce, TX (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sim, Alex [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

2015-10-20

Network traffic monitoring has long been a core element for effec- tive network management and security. However, it is still a chal- lenging task with a high degree of complexity for comprehensive analysis when considering multiple variables and ever-increasing traffic volumes to monitor. For example, one of the widely con- sidered approaches is to scrutinize probabilistic distributions, but it poses a scalability concern and multivariate analysis is not gen- erally supported due to the exponential increase of the complexity. In this work, we propose a novel method for network traffic moni- toring based on clustering, one of the powerful deep-learning tech- niques. We show that the new approach enables us to recognize clustered results as patterns representing the network states, which can then be utilized to evaluate “similarity” of network states over time. In addition, we define a new quantitative measure for the similarity between two compared network states observed in dif- ferent time windows, as a supportive means for intuitive analysis. Finally, we demonstrate the clustering-based network monitoring with public traffic traces, and show that the proposed approach us- ing the clustering method has a great opportunity for feasible, cost- effective network monitoring.

Clustering Of Left Ventricular Wall Motion Patterns

Science.gov (United States)

Bjelogrlic, Z.; Jakopin, J.; Gyergyek, L.

1982-11-01

A method for detection of wall regions with similar motion was presented. A model based on local direction information was used to measure the left ventricular wall motion from cineangiographic sequence. Three time functions were used to define segmental motion patterns: distance of a ventricular contour segment from the mean contour, the velocity of a segment and its acceleration. Motion patterns were clustered by the UPGMA algorithm and by an algorithm based on K-nearest neighboor classification rule.

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

Robustness of cluster synchronous patterns in small-world networks with inter-cluster co-competition balance

International Nuclear Information System (INIS)

Zhang, Jianbao; Ma, Zhongjun; Chen, Guanrong

2014-01-01

All edges in the classical Watts and Strogatz's small-world network model are unweighted and cooperative (positive). By introducing competitive (negative) inter-cluster edges and assigning edge weights to mimic more realistic networks, this paper develops a modified model which possesses co-competitive weighted couplings and cluster structures while maintaining the common small-world network properties of small average shortest path lengths and large clustering coefficients. Based on theoretical analysis, it is proved that the new model with inter-cluster co-competition balance has an important dynamical property of robust cluster synchronous pattern formation. More precisely, clusters will neither merge nor split regardless of adding or deleting nodes and edges, under the condition of inter-cluster co-competition balance. Numerical simulations demonstrate the robustness of the model against the increase of the coupling strength and several topological variations

Robustness of cluster synchronous patterns in small-world networks with inter-cluster co-competition balance

Science.gov (United States)

Zhang, Jianbao; Ma, Zhongjun; Chen, Guanrong

2014-06-01

All edges in the classical Watts and Strogatz's small-world network model are unweighted and cooperative (positive). By introducing competitive (negative) inter-cluster edges and assigning edge weights to mimic more realistic networks, this paper develops a modified model which possesses co-competitive weighted couplings and cluster structures while maintaining the common small-world network properties of small average shortest path lengths and large clustering coefficients. Based on theoretical analysis, it is proved that the new model with inter-cluster co-competition balance has an important dynamical property of robust cluster synchronous pattern formation. More precisely, clusters will neither merge nor split regardless of adding or deleting nodes and edges, under the condition of inter-cluster co-competition balance. Numerical simulations demonstrate the robustness of the model against the increase of the coupling strength and several topological variations.

Robustness of cluster synchronous patterns in small-world networks with inter-cluster co-competition balance

Energy Technology Data Exchange (ETDEWEB)

Zhang, Jianbao [School of Science, Hangzhou Dianzi University, Hangzhou 310018 (China); Ma, Zhongjun, E-mail: mzj1234402@163.com [School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004 (China); Chen, Guanrong [Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong (China)

2014-06-15

All edges in the classical Watts and Strogatz's small-world network model are unweighted and cooperative (positive). By introducing competitive (negative) inter-cluster edges and assigning edge weights to mimic more realistic networks, this paper develops a modified model which possesses co-competitive weighted couplings and cluster structures while maintaining the common small-world network properties of small average shortest path lengths and large clustering coefficients. Based on theoretical analysis, it is proved that the new model with inter-cluster co-competition balance has an important dynamical property of robust cluster synchronous pattern formation. More precisely, clusters will neither merge nor split regardless of adding or deleting nodes and edges, under the condition of inter-cluster co-competition balance. Numerical simulations demonstrate the robustness of the model against the increase of the coupling strength and several topological variations.

Identifying clinical course patterns in SMS data using cluster analysis

DEFF Research Database (Denmark)

Kent, Peter; Kongsted, Alice

2012-01-01

ABSTRACT: BACKGROUND: Recently, there has been interest in using the short message service (SMS or text messaging), to gather frequent information on the clinical course of individual patients. One possible role for identifying clinical course patterns is to assist in exploring clinically important...... showed that clinical course patterns can be identified by cluster analysis using all SMS time points as cluster variables. This method is simple, intuitive and does not require a high level of statistical skill. However, there are alternative ways of managing SMS data and many different methods...

Message Passing Framework for Globally Interconnected Clusters

International Nuclear Information System (INIS)

Hafeez, M; Riaz, N; Asghar, S; Malik, U A; Rehman, A

2011-01-01

In prevailing technology trends it is apparent that the network requirements and technologies will advance in future. Therefore the need of High Performance Computing (HPC) based implementation for interconnecting clusters is comprehensible for scalability of clusters. Grid computing provides global infrastructure of interconnecting clusters consisting of dispersed computing resources over Internet. On the other hand the leading model for HPC programming is Message Passing Interface (MPI). As compared to Grid computing, MPI is better suited for solving most of the complex computational problems. MPI itself is restricted to a single cluster. It does not support message passing over the internet to use the computing resources of different clusters in an optimal way. We propose a model that provides message passing capabilities between parallel applications over the internet. The proposed model is based on Architecture for Java Universal Message Passing (A-JUMP) framework and Enterprise Service Bus (ESB) named as High Performance Computing Bus. The HPC Bus is built using ActiveMQ. HPC Bus is responsible for communication and message passing in an asynchronous manner. Asynchronous mode of communication offers an assurance for message delivery as well as a fault tolerance mechanism for message passing. The idea presented in this paper effectively utilizes wide-area intercluster networks. It also provides scheduling, dynamic resource discovery and allocation, and sub-clustering of resources for different jobs. Performance analysis and comparison study of the proposed framework with P2P-MPI are also presented in this paper.

A multi-Poisson dynamic mixture model to cluster developmental patterns of gene expression by RNA-seq.

Science.gov (United States)

Ye, Meixia; Wang, Zhong; Wang, Yaqun; Wu, Rongling

2015-03-01

Dynamic changes of gene expression reflect an intrinsic mechanism of how an organism responds to developmental and environmental signals. With the increasing availability of expression data across a time-space scale by RNA-seq, the classification of genes as per their biological function using RNA-seq data has become one of the most significant challenges in contemporary biology. Here we develop a clustering mixture model to discover distinct groups of genes expressed during a period of organ development. By integrating the density function of multivariate Poisson distribution, the model accommodates the discrete property of read counts characteristic of RNA-seq data. The temporal dependence of gene expression is modeled by the first-order autoregressive process. The model is implemented with the Expectation-Maximization algorithm and model selection to determine the optimal number of gene clusters and obtain the estimates of Poisson parameters that describe the pattern of time-dependent expression of genes from each cluster. The model has been demonstrated by analyzing a real data from an experiment aimed to link the pattern of gene expression to catkin development in white poplar. The usefulness of the model has been validated through computer simulation. The model provides a valuable tool for clustering RNA-seq data, facilitating our global view of expression dynamics and understanding of gene regulation mechanisms. © The Author 2014. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

A Collaboration Service Model for a Global Port Cluster

Directory of Open Access Journals (Sweden)

Keith K.T. Toh

2010-03-01

Full Text Available The importance of port clusters to a global city may be viewed from a number of perspectives. The development of port clusters and economies of agglomeration and their contribution to a regional economy is underpinned by information and physical infrastructure that facilitates collaboration between business entities within the cluster. The maturity of technologies providing portals, web and middleware services provides an opportunity to push the boundaries of contemporary service reference models and service catalogues to what the authors propose to be "collaboration services". Servicing port clusters, portal engineers of the future must consider collaboration services to benefit a region. Particularly, service orchestration through a "public user portal" must gain better utilisation of publically owned infrastructure, to share knowledge and collaborate among organisations through information systems.

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.

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

Study on Global GIS architecture and its key technologies

Science.gov (United States)

Cheng, Chengqi; Guan, Li; Lv, Xuefeng

2010-11-01

Global GIS (G2IS) is a system, which supports the huge data process and the global direct manipulation on global grid based on spheroid or ellipsoid surface. Based on global subdivision grid (GSG), Global GIS architecture is presented in this paper, taking advantage of computer cluster theory, the space-time integration technology and the virtual reality technology. Global GIS system architecture is composed of five layers, including data storage layer, data representation layer, network and cluster layer, data management layer and data application layer. Thereinto, it is designed that functions of four-level protocol framework and three-layer data management pattern of Global GIS based on organization, management and publication of spatial information in this architecture. Three kinds of core supportive technologies, which are computer cluster theory, the space-time integration technology and the virtual reality technology, and its application pattern in the Global GIS are introduced in detail. The primary ideas of Global GIS in this paper will be an important development tendency of GIS.

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

[Measurement of plasma parameters in cluster hexagon pattern discharge by optical emission spectrum].

Science.gov (United States)

Dong, Li-Fang; Shen, Zhong-Kai; Li, Xin-Chun; Liu, Liang; Lu, Ning; Shang, Jie

2012-09-01

The cluster hexagon pattern was obtained in a dielectric barrier discharge in air/argon for the first time. Three plasma parameters, i. e. the molecular vibrational temperature, the molecular rotational temperature and the average electron energy of individual cluster in cluster hexagon pattern discharge, were studied by changing the air content. The molecular vibrational temperature and the molecular rotational temperature were calculated using the second positive band system of nitrogen molecules (C 3IIu --> B 3IIg) and the first negative band system of nitrogen molecular ions (B 2Sigma(u)+ --> Chi2 Sigma(g)+). The relative intensities of the first negative system of nitrogen molecular ions (391. 4 nm) and nitrogen molecules emission spectrum line (337.1 nm) were analyzed for studying the variations of the electron energy. It was found that the three plasma parameters of individual cluster in cluster hexagon pattern increase with air content increasing from 16% to 24%.

Clustering in Ethiopia

African Journals Online (AJOL)

Background: The importance of local variations in patterns of health and disease are increasingly recognised, but, particularly in the case of tropical infections, available methods and resources for characterising disease clusters in time and space are limited. Whilst the Global Positioning System. (GPS) allows accurate and ...

Automatic classification of canine PRG neuronal discharge patterns using K-means clustering.

Science.gov (United States)

Zuperku, Edward J; Prkic, Ivana; Stucke, Astrid G; Miller, Justin R; Hopp, Francis A; Stuth, Eckehard A

2015-02-01

Respiratory-related neurons in the parabrachial-Kölliker-Fuse (PB-KF) region of the pons play a key role in the control of breathing. The neuronal activities of these pontine respiratory group (PRG) neurons exhibit a variety of inspiratory (I), expiratory (E), phase spanning and non-respiratory related (NRM) discharge patterns. Due to the variety of patterns, it can be difficult to classify them into distinct subgroups according to their discharge contours. This report presents a method that automatically classifies neurons according to their discharge patterns and derives an average subgroup contour of each class. It is based on the K-means clustering technique and it is implemented via SigmaPlot User-Defined transform scripts. The discharge patterns of 135 canine PRG neurons were classified into seven distinct subgroups. Additional methods for choosing the optimal number of clusters are described. Analysis of the results suggests that the K-means clustering method offers a robust objective means of both automatically categorizing neuron patterns and establishing the underlying archetypical contours of subtypes based on the discharge patterns of group of neurons. Published by Elsevier B.V.

Clustering of housing and household patterns using 2011 population census

CSIR Research Space (South Africa)

Dudeni-Tlhone, N

2013-11-01

Full Text Available the clusters to measure the effects of urban growth boundaries on land development patterns. Vickers and Rees (2007) clustered the 2001 census data from the UK National Statistics Office to provide area classifications that could be distributed by the Office... of National Statistics via their website. Cluster analysis is also commonly applied inter- nationally to land-use data for many different types of studies, including: to derive landscape ty- pologies (Van Eetvelde and Antrop, 2009), to differentiate between...

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.

ABCluster: the artificial bee colony algorithm for cluster global optimization.

Science.gov (United States)

Zhang, Jun; Dolg, Michael

2015-10-07

Global optimization of cluster geometries is of fundamental importance in chemistry and an interesting problem in applied mathematics. In this work, we introduce a relatively new swarm intelligence algorithm, i.e. the artificial bee colony (ABC) algorithm proposed in 2005, to this field. It is inspired by the foraging behavior of a bee colony, and only three parameters are needed to control it. We applied it to several potential functions of quite different nature, i.e., the Coulomb-Born-Mayer, Lennard-Jones, Morse, Z and Gupta potentials. The benchmarks reveal that for long-ranged potentials the ABC algorithm is very efficient in locating the global minimum, while for short-ranged ones it is sometimes trapped into a local minimum funnel on a potential energy surface of large clusters. We have released an efficient, user-friendly, and free program "ABCluster" to realize the ABC algorithm. It is a black-box program for non-experts as well as experts and might become a useful tool for chemists to study clusters.

Melodic pattern discovery by structural analysis via wavelets and clustering techniques

DEFF Research Database (Denmark)

Velarde, Gissel; Meredith, David

We present an automatic method to support melodic pattern discovery by structural analysis of symbolic representations by means of wavelet analysis and clustering techniques. In previous work, we used the method to recognize the parent works of melodic segments, or to classify tunes into tune......-means to cluster melodic segments into groups of measured similarity and obtain a raking of the most prototypical melodic segments or patterns and their occurrences. We test the method on the JKU Patterns Development Database and evaluate it based on the ground truth defined by the MIREX 2013 Discovery of Repeated...... Themes & Sections task. We compare the results of our method to the output of geometric approaches. Finally, we discuss about the relevance of our wavelet-based analysis in relation to structure, pattern discovery, similarity and variation, and comment about the considerations of the method when used...

A Comparison between Standard and Functional Clustering Methodologies: Application to Agricultural Fields for Yield Pattern Assessment

Directory of Open Access Journals (Sweden)

Simone Pascucci

2018-04-01

Full Text Available The recognition of spatial patterns within agricultural fields, presenting similar yield potential areas, stable through time, is very important for optimizing agricultural practices. This study proposes the evaluation of different clustering methodologies applied to multispectral satellite time series for retrieving temporally stable (constant patterns in agricultural fields, related to within-field yield spatial distribution. The ability of different clustering procedures for the recognition and mapping of constant patterns in fields of cereal crops was assessed. Crop vigor patterns, considered to be related to soils characteristics, and possibly indicative of yield potential, were derived by applying the different clustering algorithms to time series of Landsat images acquired on 94 agricultural fields near Rome (Italy. Two different approaches were applied and validated using Landsat 7 and 8 archived imagery. The first approach automatically extracts and calculates for each field of interest (FOI the Normalized Difference Vegetation Index (NDVI, then exploits the standard K-means clustering algorithm to derive constant patterns at the field level. The second approach applies novel clustering procedures directly to spectral reflectance time series, in particular: (1 standard K-means; (2 functional K-means; (3 multivariate functional principal components clustering analysis; (4 hierarchical clustering. The different approaches were validated through cluster accuracy estimates on a reference set of FOIs for which yield maps were available for some years. Results show that multivariate functional principal components clustering, with an a priori determination of the optimal number of classes for each FOI, provides a better accuracy than those of standard clustering algorithms. The proposed novel functional clustering methodologies are effective and efficient for constant pattern retrieval and can be used for a sustainable management of

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.

Clustering of Dietary Patterns, Lifestyles, and Overweight among Spanish Children and Adolescents in the ANIBES Study

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Carmen Pérez-Rodrigo

2015-12-01

Full Text Available Weight gain has been associated with behaviors related to diet, sedentary lifestyle, and physical activity. We investigated dietary patterns and possible meaningful clustering of physical activity, sedentary behavior, and sleep time in Spanish children and adolescents and whether the identified clusters could be associated with overweight. Analysis was based on a subsample (n = 415 of the cross-sectional ANIBES study in Spain. We performed exploratory factor analysis and subsequent cluster analysis of dietary patterns, physical activity, sedentary behaviors, and sleep time. Logistic regression analysis was used to explore the association between the cluster solutions and overweight. Factor analysis identified four dietary patterns, one reflecting a profile closer to the traditional Mediterranean diet. Dietary patterns, physical activity behaviors, sedentary behaviors and sleep time on weekdays in Spanish children and adolescents clustered into two different groups. A low physical activity-poorer diet lifestyle pattern, which included a higher proportion of girls, and a high physical activity, low sedentary behavior, longer sleep duration, healthier diet lifestyle pattern. Although increased risk of being overweight was not significant, the Prevalence Ratios (PRs for the low physical activity-poorer diet lifestyle pattern were >1 in children and in adolescents. The healthier lifestyle pattern included lower proportions of children and adolescents from low socioeconomic status backgrounds.

Accident patterns for construction-related workers: a cluster analysis

Science.gov (United States)

Liao, Chia-Wen; Tyan, Yaw-Yauan

2012-01-01

The construction industry has been identified as one of the most hazardous industries. The risk of constructionrelated workers is far greater than that in a manufacturing based industry. However, some steps can be taken to reduce worker risk through effective injury prevention strategies. In this article, k-means clustering methodology is employed in specifying the factors related to different worker types and in identifying the patterns of industrial occupational accidents. Accident reports during the period 1998 to 2008 are extracted from case reports of the Northern Region Inspection Office of the Council of Labor Affairs of Taiwan. The results show that the cluster analysis can indicate some patterns of occupational injuries in the construction industry. Inspection plans should be proposed according to the type of construction-related workers. The findings provide a direction for more effective inspection strategies and injury prevention programs.

Formation of global energy minimim structures in the growth process of Lennard-Jones clusters

DEFF Research Database (Denmark)

Solov'yov, Ilia; Koshelev, Andrey; Shutovich, Andrey

2003-01-01

that in this way all known global minimum structures of the Lennard-Jones (LJ) clusters can be found. Our method provides an efficient tool for the calculation and analysis of atomic cluster structure. With its use we justify the magic numbers sequence for the clusters of noble gases atoms and compare...

The Role of National Clusters in the Development of Global Value Chains

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Ostrovskaya Elena, Y.

2016-06-01

Full Text Available In modern economic literature there are many publications concerning innovative development of clusters and global value chains (GVCs. But still the interaction between these two structures is not studied completely. What are the main features and mechanisms of their interaction? Can clusters stimulate companies’ involvement in GVCs? Do they facilitate competition between participating companies and countries? These are just a few of those issues that are covered in the article. The efficiency of incorporation to GVCs depends both on the type of embedded cluster, and the management of particular links within the chain. In order to explain the cooperation between companies, the institutional approach is used, paying much attention to the issues of moral risks in the context of multiply goals, the effect of gauge, and the development of long-term relational contracts among agents. Thus, the recommendations concerning the role of clusters and GVCs for the raise of competitiveness of countries are provided with regard to the integrated research of global suppliers and customers’ motives.

A comparison of heuristic and model-based clustering methods for dietary pattern analysis.

Science.gov (United States)

Greve, Benjamin; Pigeot, Iris; Huybrechts, Inge; Pala, Valeria; Börnhorst, Claudia

2016-02-01

Cluster analysis is widely applied to identify dietary patterns. A new method based on Gaussian mixture models (GMM) seems to be more flexible compared with the commonly applied k-means and Ward's method. In the present paper, these clustering approaches are compared to find the most appropriate one for clustering dietary data. The clustering methods were applied to simulated data sets with different cluster structures to compare their performance knowing the true cluster membership of observations. Furthermore, the three methods were applied to FFQ data assessed in 1791 children participating in the IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) Study to explore their performance in practice. The GMM outperformed the other methods in the simulation study in 72 % up to 100 % of cases, depending on the simulated cluster structure. Comparing the computationally less complex k-means and Ward's methods, the performance of k-means was better in 64-100 % of cases. Applied to real data, all methods identified three similar dietary patterns which may be roughly characterized as a 'non-processed' cluster with a high consumption of fruits, vegetables and wholemeal bread, a 'balanced' cluster with only slight preferences of single foods and a 'junk food' cluster. The simulation study suggests that clustering via GMM should be preferred due to its higher flexibility regarding cluster volume, shape and orientation. The k-means seems to be a good alternative, being easier to use while giving similar results when applied to real data.

Delay-induced cluster patterns in coupled Cayley tree networks

Science.gov (United States)

Singh, A.; Jalan, S.

2013-07-01

We study effects of delay in diffusively coupled logistic maps on the Cayley tree networks. We find that smaller coupling values exhibit sensitiveness to value of delay, and lead to different cluster patterns of self-organized and driven types. Whereas larger coupling strengths exhibit robustness against change in delay values, and lead to stable driven clusters comprising nodes from last generation of the Cayley tree. Furthermore, introduction of delay exhibits suppression as well as enhancement of synchronization depending upon coupling strength values. To the end we discuss the importance of results to understand conflicts and cooperations observed in family business.

Theoretical studies of the global minima and polarizabilities of small lithium clusters

Energy Technology Data Exchange (ETDEWEB)

Hu, Hanshi; Zhao, Ya-Fan; Hammond, Jeffrey R.; Bylaska, Eric J.; Apra, Edoardo; van Dam, Hubertus JJ; Li, Jun; Govind, Niranjan; Kowalski, Karol

2016-01-16

Lithium clusters Lin (n=1-20) have been investigated with density functional theory (DFT) and coupled—cluster (CC) methods. The global-minimum structures are located via an improved basin---hopping algorithm and the lowest energy Lin isomers are confirmed with DFT geometry optimizations, CCSD(T) energy calculations, and by comparing simulated and experimental polarizabilities. The tetrahedral Li4 structure is found to be the basic building block of lithium clusters Lin (n=6-20). Simulated polarizabilities, including thermal effects at room temperature, are in good agreement with measured isotropic polarizabilities.

Mapping global diversity patterns for migratory birds.

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Marius Somveille

Full Text Available Nearly one in five bird species has separate breeding and overwintering distributions, and the regular migrations of these species cause a substantial seasonal redistribution of avian diversity across the world. However, despite its ecological importance, bird migration has been largely ignored in studies of global avian biodiversity, with few studies having addressed it from a macroecological perspective. Here, we analyse a dataset on the global distribution of the world's birds in order to examine global spatial patterns in the diversity of migratory species, including: the seasonal variation in overall species diversity due to migration; the contribution of migratory birds to local bird diversity; and the distribution of narrow-range and threatened migratory birds. Our analyses reveal a striking asymmetry between the Northern and Southern hemispheres, evident in all of the patterns investigated. The highest migratory bird diversity was found in the Northern Hemisphere, with high inter-continental turnover in species composition between breeding and non-breeding seasons, and extensive regions (at high latitudes where migratory birds constitute the majority of the local avifauna. Threatened migratory birds are concentrated mainly in Central and Southern Asia, whereas narrow-range migratory species are mainly found in Central America, the Himalayas and Patagonia. Overall, global patterns in the diversity of migratory birds indicate that bird migration is mainly a Northern Hemisphere phenomenon. The asymmetry between the Northern and Southern hemispheres could not have easily been predicted from the combined results of regional scale studies, highlighting the importance of a global perspective.

Characterizing the Spatio-Temporal Pattern of Land Surface Temperature through Time Series Clustering: Based on the Latent Pattern and Morphology

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Huimin Liu

2018-04-01

Full Text Available Land Surface Temperature (LST is a critical component to understand the impact of urbanization on the urban thermal environment. Previous studies were inclined to apply only one snapshot to analyze the pattern and dynamics of LST without considering the non-stationarity in the temporal domain, or focus on the diurnal, seasonal, and annual pattern analysis of LST which has limited support for the understanding of how LST varies with the advancing of urbanization. This paper presents a workflow to extract the spatio-temporal pattern of LST through time series clustering by focusing on the LST of Wuhan, China, from 2002 to 2017 with a 3-year time interval with 8-day MODerate-resolution Imaging Spectroradiometer (MODIS satellite image products. The Latent pattern of LST (LLST generated by non-parametric Multi-Task Gaussian Process Modeling (MTGP and the Multi-Scale Shape Index (MSSI which characterizes the morphology of LLST are coupled for pattern recognition. Specifically, spatio-temporal patterns are discovered after the extraction of spatial patterns conducted by the incorporation of k -means and the Back-Propagation neural networks (BP-Net. The spatial patterns of the 6 years form a basic understanding about the corresponding temporal variances. For spatio-temporal pattern recognition, LLSTs and MSSIs of the 6 years are regarded as geo-referenced time series. Multiple algorithms including traditional k -means with Euclidean Distance (ED, shape-based k -means with the constrained Dynamic Time Warping ( c DTW distance measure, and the Dynamic Time Warping Barycenter Averaging (DBA centroid computation method ( k - c DBA and k -shape are applied. Ten external indexes are employed to evaluate the performance of the three algorithms and reveal k - c DBA as the optimal time series clustering algorithm for our study. The study area is divided into 17 geographical time series clusters which respectively illustrate heterogeneous temporal dynamics of LST

The evolution of the global stellar mass function of star clusters: an analytic description

NARCIS (Netherlands)

Lamers, H.J.G.L.M.; Baumgardt, H.; Gieles, M.

2013-01-01

The evolution of the global stellar mass function of star clusters is studied based on a large set of N-body simulations of clusters with a range of initial masses, initial concentrations, in circular or elliptical orbits in different tidal environments. Models with and without initial mass

Global patterns of evolutionary distinct and globally endangered amphibians and mammals.

Science.gov (United States)

Safi, Kamran; Armour-Marshall, Katrina; Baillie, Jonathan E M; Isaac, Nick J B

2013-01-01

Conservation of phylogenetic diversity allows maximising evolutionary information preserved within fauna and flora. The "EDGE of Existence" programme is the first institutional conservation initiative that prioritises species based on phylogenetic information. Species are ranked in two ways: one according to their evolutionary distinctiveness (ED) and second, by including IUCN extinction status, their evolutionary distinctiveness and global endangerment (EDGE). Here, we describe the global patterns in the spatial distribution of priority ED and EDGE species, in order to identify conservation areas for mammalian and amphibian communities. In addition, we investigate whether environmental conditions can predict the observed spatial pattern in ED and EDGE globally. Priority zones with high concentrations of ED and EDGE scores were defined using two different methods. The overlap between mammal and amphibian zones was very small, reflecting the different phylo-biogeographic histories. Mammal ED zones were predominantly found on the African continent and the neotropical forests, whereas in amphibians, ED zones were concentrated in North America. Mammal EDGE zones were mainly in South-East Asia, southern Africa and Madagascar; for amphibians they were in central and south America. The spatial pattern of ED and EDGE was poorly described by a suite of environmental variables. Mapping the spatial distribution of ED and EDGE provides an important step towards identifying priority areas for the conservation of mammalian and amphibian phylogenetic diversity in the EDGE of existence programme.

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

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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.

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

A cluster analysis of patterns of objectively measured physical activity in Hong Kong.

Science.gov (United States)

Lee, Paul H; Yu, Ying-Ying; McDowell, Ian; Leung, Gabriel M; Lam, T H

2013-08-01

The health benefits of exercise are clear. In targeting interventions it would be valuable to know whether characteristic patterns of physical activity (PA) are associated with particular population subgroups. The present study used cluster analysis to identify characteristic hourly PA patterns measured by accelerometer. Cross-sectional design. Objectively measured PA in Hong Kong adults. Four-day accelerometer data were collected during 2009 to 2011 for 1714 participants in Hong Kong (mean age 44?2 years, 45?9% male). Two clusters were identified, one more active than the other. The ‘active cluster’ (n 480) was characterized by a routine PA pattern on weekdays and a more active and varied pattern on weekends; the other, the ‘less active cluster’ (n 1234), by a consistently low PA pattern on both weekdays and weekends with little variation from day to day. Demographic, lifestyle, PA level and health characteristics of the two clusters were compared. They differed in age, sex, smoking, income and level of PA required at work. The odds of having any chronic health conditions was lower for the active group (adjusted OR50?62, 95% CI 0?46, 0?84) but the two groups did not differ in terms of specific chronic health conditions or obesity. Implications are drawn for targeting exercise promotion programmes at the population level.

Comparison of cluster and principal component analysis techniques to derive dietary patterns in Irish adults.

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Hearty, Aine P; Gibney, Michael J

2009-02-01

The aims of the present study were to examine and compare dietary patterns in adults using cluster and factor analyses and to examine the format of the dietary variables on the pattern solutions (i.e. expressed as grams/day (g/d) of each food group or as the percentage contribution to total energy intake). Food intake data were derived from the North/South Ireland Food Consumption Survey 1997-9, which was a randomised cross-sectional study of 7 d recorded food and nutrient intakes of a representative sample of 1379 Irish adults aged 18-64 years. Cluster analysis was performed using the k-means algorithm and principal component analysis (PCA) was used to extract dietary factors. Food data were reduced to thirty-three food groups. For cluster analysis, the most suitable format of the food-group variable was found to be the percentage contribution to energy intake, which produced six clusters: 'Traditional Irish'; 'Continental'; 'Unhealthy foods'; 'Light-meal foods & low-fat milk'; 'Healthy foods'; 'Wholemeal bread & desserts'. For PCA, food groups in the format of g/d were found to be the most suitable format, and this revealed four dietary patterns: 'Unhealthy foods & high alcohol'; 'Traditional Irish'; 'Healthy foods'; 'Sweet convenience foods & low alcohol'. In summary, cluster and PCA identified similar dietary patterns when presented with the same dataset. However, the two dietary pattern methods required a different format of the food-group variable, and the most appropriate format of the input variable should be considered in future studies.

A SOM clustering pattern sequence-based next symbol prediction method for day-ahead direct electricity load and price forecasting

International Nuclear Information System (INIS)

Jin, Cheng Hao; Pok, Gouchol; Lee, Yongmi; Park, Hyun-Woo; Kim, Kwang Deuk; Yun, Unil; Ryu, Keun Ho

2015-01-01

Highlights: • A novel pattern sequence-based direct time series forecasting method was proposed. • Due to the use of SOM’s topology preserving property, only SOM can be applied. • SCPSNSP only deals with the cluster patterns not each specific time series value. • SCPSNSP performs better than recently developed forecasting algorithms. - Abstract: In this paper, we propose a new day-ahead direct time series forecasting method for competitive electricity markets based on clustering and next symbol prediction. In the clustering step, pattern sequence and their topology relations are obtained from self organizing map time series clustering. In the next symbol prediction step, with each cluster label in the pattern sequence represented as a pair of its topologically identical coordinates, artificial neural network is used to predict the topological coordinates of next day by training the relationship between previous daily pattern sequence and its next day pattern. According to the obtained topology relations, the nearest nonzero hits pattern is assigned to next day so that the whole time series values can be directly forecasted from the assigned cluster pattern. The proposed method was evaluated on Spanish, Australian and New York electricity markets and compared with PSF and some of the most recently published forecasting methods. Experimental results show that the proposed method outperforms the best forecasting methods at least 3.64%

Global patterns of evolutionary distinct and globally endangered amphibians and mammals.

Directory of Open Access Journals (Sweden)

Kamran Safi

Full Text Available BACKGROUND: Conservation of phylogenetic diversity allows maximising evolutionary information preserved within fauna and flora. The "EDGE of Existence" programme is the first institutional conservation initiative that prioritises species based on phylogenetic information. Species are ranked in two ways: one according to their evolutionary distinctiveness (ED and second, by including IUCN extinction status, their evolutionary distinctiveness and global endangerment (EDGE. Here, we describe the global patterns in the spatial distribution of priority ED and EDGE species, in order to identify conservation areas for mammalian and amphibian communities. In addition, we investigate whether environmental conditions can predict the observed spatial pattern in ED and EDGE globally. METHODS AND PRINCIPAL FINDINGS: Priority zones with high concentrations of ED and EDGE scores were defined using two different methods. The overlap between mammal and amphibian zones was very small, reflecting the different phylo-biogeographic histories. Mammal ED zones were predominantly found on the African continent and the neotropical forests, whereas in amphibians, ED zones were concentrated in North America. Mammal EDGE zones were mainly in South-East Asia, southern Africa and Madagascar; for amphibians they were in central and south America. The spatial pattern of ED and EDGE was poorly described by a suite of environmental variables. CONCLUSIONS: Mapping the spatial distribution of ED and EDGE provides an important step towards identifying priority areas for the conservation of mammalian and amphibian phylogenetic diversity in the EDGE of existence programme.

Extracting Patterns from Educational Traces via Clustering and Associated Quality Metrics

NARCIS (Netherlands)

Mihaescu, Marian; Tanasie, Alexandru; Dascalu, Mihai; Trausan-Matu, Stefan

2016-01-01

Clustering algorithms, pattern mining techniques and associated quality metrics emerged as reliable methods for modeling learners’ performance, comprehension and interaction in given educational scenarios. The specificity of available data such as missing values, extreme values or outliers,

Different as night and day: Patterns of isolated seizures, clusters, and status epilepticus.

Science.gov (United States)

Goldenholz, Daniel M; Rakesh, Kshitiz; Kapur, Kush; Gaínza-Lein, Marina; Hodgeman, Ryan; Moss, Robert; Theodore, William H; Loddenkemper, Tobias

2018-05-01

Using approximations based on presumed U.S. time zones, we characterized day and nighttime seizure patterns in a patient-reported database, Seizure Tracker. A total of 632 995 seizures (9698 patients) were classified into 4 categories: isolated seizure event (ISE), cluster without status epilepticus (CWOS), cluster including status epilepticus (CIS), and status epilepticus (SE). We used a multinomial mixed-effects logistic regression model to calculate odds ratios (ORs) to determine night/day ratios for the difference between seizure patterns: ISE versus SE, ISE versus CWOS, ISE versus CIS, and CWOS versus CIS. Ranges of OR values were reported across cluster definitions. In adults, ISE was more likely at night compared to CWOS (OR = 1.49, 95% adjusted confidence interval [CI] = 1.36-1.63) and to CIS (OR = 1.61, 95% adjusted CI = 1.34-1.88). The ORs for ISE versus SE and CWOS versus SE were not significantly different regardless of cluster definition. In children, ISE was less likely at night compared to SE (OR = 0.85, 95% adjusted CI = 0.79-0.91). ISE was more likely at night compared to CWOS (OR = 1.35, 95% adjusted CI = 1.26-1.44) and CIS (OR = 1.65, 95% adjusted CI = 1.44-1.86). CWOS was more likely during the night compared to CIS (OR = 1.22, 95% adjusted CI = 1.05-1.39). With the exception of SE in children, our data suggest that more severe patterns favor daytime. This suggests distinct day/night preferences for different seizure patterns in children and adults. Wiley Periodicals, Inc. © 2018 International League Against Epilepsy.

Hierarchical cluster analysis of progression patterns in open-angle glaucoma patients with medical treatment.

Science.gov (United States)

Bae, Hyoung Won; Rho, Seungsoo; Lee, Hye Sun; Lee, Naeun; Hong, Samin; Seong, Gong Je; Sung, Kyung Rim; Kim, Chan Yun

2014-04-29

To classify medically treated open-angle glaucoma (OAG) by the pattern of progression using hierarchical cluster analysis, and to determine OAG progression characteristics by comparing clusters. Ninety-five eyes of 95 OAG patients who received medical treatment, and who had undergone visual field (VF) testing at least once per year for 5 or more years. OAG was classified into subgroups using hierarchical cluster analysis based on the following five variables: baseline mean deviation (MD), baseline visual field index (VFI), MD slope, VFI slope, and Glaucoma Progression Analysis (GPA) printout. After that, other parameters were compared between clusters. Two clusters were made after a hierarchical cluster analysis. Cluster 1 showed -4.06 ± 2.43 dB baseline MD, 92.58% ± 6.27% baseline VFI, -0.28 ± 0.38 dB per year MD slope, -0.52% ± 0.81% per year VFI slope, and all "no progression" cases in GPA printout, whereas cluster 2 showed -8.68 ± 3.81 baseline MD, 77.54 ± 12.98 baseline VFI, -0.72 ± 0.55 MD slope, -2.22 ± 1.89 VFI slope, and seven "possible" and four "likely" progression cases in GPA printout. There were no significant differences in age, sex, mean IOP, central corneal thickness, and axial length between clusters. However, cluster 2 included more high-tension glaucoma patients and used a greater number of antiglaucoma eye drops significantly compared with cluster 1. Hierarchical cluster analysis of progression patterns divided OAG into slow and fast progression groups, evidenced by assessing the parameters of glaucomatous progression in VF testing. In the fast progression group, the prevalence of high-tension glaucoma was greater and the number of antiglaucoma medications administered was increased versus the slow progression group. Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.

Structural profiles of human miRNA families from pairwise clustering

DEFF Research Database (Denmark)

Kaczkowski, Bogumil; Þórarinsson, Elfar; Reiche, Kristin

2009-01-01

secondary structure already predicted, little is known about the patterns of structural conservation among pre-miRNAs. We address this issue by clustering the human pre-miRNA sequences based on pairwise, sequence and secondary structure alignment using FOLDALIGN, followed by global multiple alignment...... of obtained clusters by WAR. As a result, the common secondary structure was successfully determined for four FOLDALIGN clusters: the RF00027 structural family of the Rfam database and three clusters with previously undescribed consensus structures. Availability: http://genome.ku.dk/resources/mirclust...

Satellite communications - Intelsat and global patterns

Science.gov (United States)

Astrain, S.

1983-10-01

The global pattern of mankind's population growth is examined, taking into account the exponential increase in population which began only in the 17th century. As world population has grown, trade has increased, and transportation and communications have become vitally important. A revolution in global communications was initiated when Intelsat launched the first international communications satellite, 'Early Bird', in April 1965. Since April 1965, a tremendous development in global communications by means of satellites has taken place. The Intelsat VI satellite will have a capacity of 36,000 telephone circuits plus 2 TV channels, while the capacity of Early Bird was only 240 telephone circuits. Today, Intelsat is truly an international organization which includes 108 member countries. Attention is given to the particular importance of the Intelsat services to the developing countries, the exploration of new technologies and system concepts, and the extension of services to those portions of the global village which have remained electronically isolated.

Optimizing human activity patterns using global sensitivity analysis.

Science.gov (United States)

Fairchild, Geoffrey; Hickmann, Kyle S; Mniszewski, Susan M; Del Valle, Sara Y; Hyman, James M

2014-12-01

Implementing realistic activity patterns for a population is crucial for modeling, for example, disease spread, supply and demand, and disaster response. Using the dynamic activity simulation engine, DASim, we generate schedules for a population that capture regular (e.g., working, eating, and sleeping) and irregular activities (e.g., shopping or going to the doctor). We use the sample entropy (SampEn) statistic to quantify a schedule's regularity for a population. We show how to tune an activity's regularity by adjusting SampEn, thereby making it possible to realistically design activities when creating a schedule. The tuning process sets up a computationally intractable high-dimensional optimization problem. To reduce the computational demand, we use Bayesian Gaussian process regression to compute global sensitivity indices and identify the parameters that have the greatest effect on the variance of SampEn. We use the harmony search (HS) global optimization algorithm to locate global optima. Our results show that HS combined with global sensitivity analysis can efficiently tune the SampEn statistic with few search iterations. We demonstrate how global sensitivity analysis can guide statistical emulation and global optimization algorithms to efficiently tune activities and generate realistic activity patterns. Though our tuning methods are applied to dynamic activity schedule generation, they are general and represent a significant step in the direction of automated tuning and optimization of high-dimensional computer simulations.

Modeling and clustering water demand patterns from real-world smart meter data

Directory of Open Access Journals (Sweden)

N. Cheifetz

2017-08-01

Full Text Available Nowadays, drinking water utilities need an acute comprehension of the water demand on their distribution network, in order to efficiently operate the optimization of resources, manage billing and propose new customer services. With the emergence of smart grids, based on automated meter reading (AMR, a better understanding of the consumption modes is now accessible for smart cities with more granularities. In this context, this paper evaluates a novel methodology for identifying relevant usage profiles from the water consumption data produced by smart meters. The methodology is fully data-driven using the consumption time series which are seen as functions or curves observed with an hourly time step. First, a Fourier-based additive time series decomposition model is introduced to extract seasonal patterns from time series. These patterns are intended to represent the customer habits in terms of water consumption. Two functional clustering approaches are then used to classify the extracted seasonal patterns: the functional version of K-means, and the Fourier REgression Mixture (FReMix model. The K-means approach produces a hard segmentation and K representative prototypes. On the other hand, the FReMix is a generative model and also produces K profiles as well as a soft segmentation based on the posterior probabilities. The proposed approach is applied to a smart grid deployed on the largest water distribution network (WDN in France. The two clustering strategies are evaluated and compared. Finally, a realistic interpretation of the consumption habits is given for each cluster. The extensive experiments and the qualitative interpretation of the resulting clusters allow one to highlight the effectiveness of the proposed methodology.

Modeling and clustering water demand patterns from real-world smart meter data

Science.gov (United States)

Cheifetz, Nicolas; Noumir, Zineb; Samé, Allou; Sandraz, Anne-Claire; Féliers, Cédric; Heim, Véronique

2017-08-01

Nowadays, drinking water utilities need an acute comprehension of the water demand on their distribution network, in order to efficiently operate the optimization of resources, manage billing and propose new customer services. With the emergence of smart grids, based on automated meter reading (AMR), a better understanding of the consumption modes is now accessible for smart cities with more granularities. In this context, this paper evaluates a novel methodology for identifying relevant usage profiles from the water consumption data produced by smart meters. The methodology is fully data-driven using the consumption time series which are seen as functions or curves observed with an hourly time step. First, a Fourier-based additive time series decomposition model is introduced to extract seasonal patterns from time series. These patterns are intended to represent the customer habits in terms of water consumption. Two functional clustering approaches are then used to classify the extracted seasonal patterns: the functional version of K-means, and the Fourier REgression Mixture (FReMix) model. The K-means approach produces a hard segmentation and K representative prototypes. On the other hand, the FReMix is a generative model and also produces K profiles as well as a soft segmentation based on the posterior probabilities. The proposed approach is applied to a smart grid deployed on the largest water distribution network (WDN) in France. The two clustering strategies are evaluated and compared. Finally, a realistic interpretation of the consumption habits is given for each cluster. The extensive experiments and the qualitative interpretation of the resulting clusters allow one to highlight the effectiveness of the proposed methodology.

GLOBAL CLASSIFICATION OF DERMATITIS DISEASE WITH K-MEANS CLUSTERING IMAGE SEGMENTATION METHODS

OpenAIRE

Prafulla N. Aerkewar1 & Dr. G. H. Agrawal2

2018-01-01

The objective of this paper to presents a global technique for classification of different dermatitis disease lesions using the process of k-Means clustering image segmentation method. The word global is used such that the all dermatitis disease having skin lesion on body are classified in to four category using k-means image segmentation and nntool of Matlab. Through the image segmentation technique and nntool can be analyze and study the segmentation properties of skin lesions occurs in...

Determination of atomic cluster structure with cluster fusion algorithm

DEFF Research Database (Denmark)

Obolensky, Oleg I.; Solov'yov, Ilia; Solov'yov, Andrey V.

2005-01-01

We report an efficient scheme of global optimization, called cluster fusion algorithm, which has proved its reliability and high efficiency in determination of the structure of various atomic clusters.......We report an efficient scheme of global optimization, called cluster fusion algorithm, which has proved its reliability and high efficiency in determination of the structure of various atomic clusters....

Global Clustering Quality Coefficient Assessing the Efficiency of PCA Class Assignment

Directory of Open Access Journals (Sweden)

Mirela Praisler

2014-01-01

Full Text Available An essential factor influencing the efficiency of the predictive models built with principal component analysis (PCA is the quality of the data clustering revealed by the score plots. The sensitivity and selectivity of the class assignment are strongly influenced by the relative position of the clusters and by their dispersion. We are proposing a set of indicators inspired from analytical geometry that may be used for an objective quantitative assessment of the data clustering quality as well as a global clustering quality coefficient (GCQC that is a measure of the overall predictive power of the PCA models. The use of these indicators for evaluating the efficiency of the PCA class assignment is illustrated by a comparative study performed for the identification of the preprocessing function that is generating the most efficient PCA system screening for amphetamines based on their GC-FTIR spectra. The GCQC ranking of the tested feature weights is explained based on estimated density distributions and validated by using quadratic discriminant analysis (QDA.

Cyclist–motorist crash patterns in Denmark: A latent class clustering approach

DEFF Research Database (Denmark)

Kaplan, Sigal; Prato, Carlo Giacomo

2013-01-01

to prioritize safety issues and to devise efficient preventive measures. Method: The current study focused on cyclist–motorist crashes that occurred in Denmark during the period between 2007 and 2011. To uncover crash patterns, the current analysis applied latent class clustering, an unsupervised probabilistic...

Fuzzy C-Means Clustering Model Data Mining For Recognizing Stock Data Sampling Pattern

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Sylvia Jane Annatje Sumarauw

2007-06-01

Full Text Available Abstract Capital market has been beneficial to companies and investor. For investors, the capital market provides two economical advantages, namely deviden and capital gain, and a non-economical one that is a voting .} hare in Shareholders General Meeting. But, it can also penalize the share owners. In order to prevent them from the risk, the investors should predict the prospect of their companies. As a consequence of having an abstract commodity, the share quality will be determined by the validity of their company profile information. Any information of stock value fluctuation from Jakarta Stock Exchange can be a useful consideration and a good measurement for data analysis. In the context of preventing the shareholders from the risk, this research focuses on stock data sample category or stock data sample pattern by using Fuzzy c-Me, MS Clustering Model which providing any useful information jar the investors. lite research analyses stock data such as Individual Index, Volume and Amount on Property and Real Estate Emitter Group at Jakarta Stock Exchange from January 1 till December 31 of 204. 'he mining process follows Cross Industry Standard Process model for Data Mining (CRISP,. DM in the form of circle with these steps: Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation and Deployment. At this modelling process, the Fuzzy c-Means Clustering Model will be applied. Data Mining Fuzzy c-Means Clustering Model can analyze stock data in a big database with many complex variables especially for finding the data sample pattern, and then building Fuzzy Inference System for stimulating inputs to be outputs that based on Fuzzy Logic by recognising the pattern. Keywords: Data Mining, AUz..:y c-Means Clustering Model, Pattern Recognition

Diabetes Changes Symptoms Cluster Patterns in Persons Living With HIV.

Science.gov (United States)

Zuniga, Julie Ann; Bose, Eliezer; Park, Jungmin; Lapiz-Bluhm, M Danet; García, Alexandra A

Approximately 10-15% of persons living with HIV (PLWH) have a comorbid diagnosis of diabetes mellitus (DM). Both of these long-term chronic conditions are associated with high rates of symptom burden. The purpose of our study was to describe symptom patterns for PLWH with DM (PLWH+DM) using a large secondary dataset. The prevalence, burden, and bothersomeness of symptoms reported by patients in routine clinic visits during 2015 were assessed using the 20-item HIV Symptom Index. Principal component analysis was used to identify symptom clusters. Three main clusters were identified: (a) neurological/psychological, (b) gastrointestinal/flu-like, and (c) physical changes. The most prevalent symptoms were fatigue, poor sleep, aches, neuropathy, and sadness. When compared to a previous symptom study with PLWH, symptoms clustered differently in our sample of patients with dual diagnoses of HIV and diabetes. Clinicians should appropriately assess symptoms for their patients' comorbid conditions. Copyright © 2017 Association of Nurses in AIDS Care. Published by Elsevier Inc. All rights reserved.

A parsimonious characterization of change in global age-specific and total fertility rates

Science.gov (United States)

2018-01-01

This study aims to understand trends in global fertility from 1950-2010 though the analysis of age-specific fertility rates. This approach incorporates both the overall level, as when the total fertility rate is modeled, and different patterns of age-specific fertility to examine the relationship between changes in age-specific fertility and fertility decline. Singular value decomposition is used to capture the variation in age-specific fertility curves while reducing the number of dimensions, allowing curves to be described nearly fully with three parameters. Regional patterns and trends over time are evident in parameter values, suggesting this method provides a useful tool for considering fertility decline globally. The second and third parameters were analyzed using model-based clustering to examine patterns of age-specific fertility over time and place; four clusters were obtained. A country’s demographic transition can be traced through time by membership in the different clusters, and regional patterns in the trajectories through time and with fertility decline are identified. PMID:29377899

Ecosystem health pattern analysis of urban clusters based on emergy synthesis: Results and implication for management

International Nuclear Information System (INIS)

Su, Meirong; Fath, Brian D.; Yang, Zhifeng; Chen, Bin; Liu, Gengyuan

2013-01-01

The evaluation of ecosystem health in urban clusters will help establish effective management that promotes sustainable regional development. To standardize the application of emergy synthesis and set pair analysis (EM–SPA) in ecosystem health assessment, a procedure for using EM–SPA models was established in this paper by combining the ability of emergy synthesis to reflect health status from a biophysical perspective with the ability of set pair analysis to describe extensive relationships among different variables. Based on the EM–SPA model, the relative health levels of selected urban clusters and their related ecosystem health patterns were characterized. The health states of three typical Chinese urban clusters – Jing-Jin-Tang, Yangtze River Delta, and Pearl River Delta – were investigated using the model. The results showed that the health status of the Pearl River Delta was relatively good; the health for the Yangtze River Delta was poor. As for the specific health characteristics, the Pearl River Delta and Yangtze River Delta urban clusters were relatively strong in Vigor, Resilience, and Urban ecosystem service function maintenance, while the Jing-Jin-Tang was relatively strong in organizational structure and environmental impact. Guidelines for managing these different urban clusters were put forward based on the analysis of the results of this study. - Highlights: • The use of integrated emergy synthesis and set pair analysis model was standardized. • The integrated model was applied on the scale of an urban cluster. • Health patterns of different urban clusters were compared. • Policy suggestions were provided based on the health pattern analysis

Competition, transmission and pattern evolution: A network analysis of global oil trade

International Nuclear Information System (INIS)

Zhang, Hai-Ying; Ji, Qiang; Fan, Ying

2014-01-01

This paper studies the competition among oil importers using complex network theory, combined with several alternative measures of competition intensity, to analyze the evolution of the pattern and transmission of oil-trading competition. The results indicate that oil trade has formed a global competition pattern and that the role played by the Asian-Pacific region in the evolution of this competition pattern is becoming increasingly prominent. In addition, global competition intensity has continued to rise, and non-OECD countries have become the main driving force for this increase in global competition intensity. The large oil importers are the most significant parts of the global oil-trading competition pattern. They are not only the major participants in the competition for oil resources but also play important roles in the transmission of oil-trading competition. China and the United States especially display the feature of globalization, whose impacts of transmission reach across the whole oil-trading competition network. Finally, a “5C” (changeability, contestability, cooperation, commitment and circumstances) policy framework is put forward to maintain the stability of oil trade and improve the energy security of oil importers in various aspects. - Highlights: • An oil-trading competition network is constructed using complex network theory. • Oil trade has formed a global competition pattern and its intensity has kept rising. • The status of the Asian-Pacific region in the competition pattern becomes prominent. • Large oil importers play important roles in transmitting the trading competition. • A “5C” policy framework is put forward to cope with the intensive competition

Cadenas de valor globales y desarrollo de cluster locales: Una mirada a pequeñas empresas colombianas de la industria de animación en tercera dimensión Global Value Chains and Local Cluster Development: A Perspective on Domestic Small Enterprises in the 3D-Animation Industry in Colombia

Directory of Open Access Journals (Sweden)

Sascha Fuerst

2010-06-01

Full Text Available Este artículo utiliza el concepto de “cadena de valor global” para hacer un análisis de cómo se desarrolla en Colombia el cluster de animaciones 3D. Se argumenta que la participación en cadenas de valor globales trae un impacto positivo al crecimiento y la innovación del cluster, e igualmente a sus empresas. El artículo utiliza la representación de diamante presentada por Porter  para mostrar las características que influyen positivamente en el desarrollo de este cluster en específico y para identificar recomendaciones a nivel de políticas necesarias que pueden mejorar la inserción del cluster de animaciones 3D en cadenas de valor globales This article draws on the framework of the “global value chain” to describe local cluster development in the 3D -animation industry in Colombia. It is argued that the participation in global value chains can have a positive impact on cluster growth and innovation, and the individual firm as well. Porter’s diamond is used to illustrate the characteristics that drive dynamic cluster development in this case and to point out the necessary policy recommendations for further enhancing the linkage of the 3D-animation cluster into global value chains.

Unsupervised Video Shot Detection Using Clustering Ensemble with a Color Global Scale-Invariant Feature Transform Descriptor

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Yuchou Chang

2008-02-01

Full Text Available Scale-invariant feature transform (SIFT transforms a grayscale image into scale-invariant coordinates of local features that are invariant to image scale, rotation, and changing viewpoints. Because of its scale-invariant properties, SIFT has been successfully used for object recognition and content-based image retrieval. The biggest drawback of SIFT is that it uses only grayscale information and misses important visual information regarding color. In this paper, we present the development of a novel color feature extraction algorithm that addresses this problem, and we also propose a new clustering strategy using clustering ensembles for video shot detection. Based on Fibonacci lattice-quantization, we develop a novel color global scale-invariant feature transform (CGSIFT for better description of color contents in video frames for video shot detection. CGSIFT first quantizes a color image, representing it with a small number of color indices, and then uses SIFT to extract features from the quantized color index image. We also develop a new space description method using small image regions to represent global color features as the second step of CGSIFT. Clustering ensembles focusing on knowledge reuse are then applied to obtain better clustering results than using single clustering methods for video shot detection. Evaluation of the proposed feature extraction algorithm and the new clustering strategy using clustering ensembles reveals very promising results for video shot detection.

Unsupervised Video Shot Detection Using Clustering Ensemble with a Color Global Scale-Invariant Feature Transform Descriptor

Directory of Open Access Journals (Sweden)

Hong Yi

2008-01-01

Full Text Available Abstract Scale-invariant feature transform (SIFT transforms a grayscale image into scale-invariant coordinates of local features that are invariant to image scale, rotation, and changing viewpoints. Because of its scale-invariant properties, SIFT has been successfully used for object recognition and content-based image retrieval. The biggest drawback of SIFT is that it uses only grayscale information and misses important visual information regarding color. In this paper, we present the development of a novel color feature extraction algorithm that addresses this problem, and we also propose a new clustering strategy using clustering ensembles for video shot detection. Based on Fibonacci lattice-quantization, we develop a novel color global scale-invariant feature transform (CGSIFT for better description of color contents in video frames for video shot detection. CGSIFT first quantizes a color image, representing it with a small number of color indices, and then uses SIFT to extract features from the quantized color index image. We also develop a new space description method using small image regions to represent global color features as the second step of CGSIFT. Clustering ensembles focusing on knowledge reuse are then applied to obtain better clustering results than using single clustering methods for video shot detection. Evaluation of the proposed feature extraction algorithm and the new clustering strategy using clustering ensembles reveals very promising results for video shot detection.

User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm.

Science.gov (United States)

Bourobou, Serge Thomas Mickala; Yoo, Younghwan

2015-05-21

This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen's temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home.

User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm

Directory of Open Access Journals (Sweden)

Serge Thomas Mickala Bourobou

2015-05-01

Full Text Available This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen’s temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home.

Noise-induced synchronization, desynchronization, and clustering in globally coupled nonidentical oscillators

KAUST Repository

Lai, Yi Ming

2013-07-09

We study ensembles of globally coupled, nonidentical phase oscillators subject to correlated noise, and we identify several important factors that cause noise and coupling to synchronize or desynchronize a system. By introducing noise in various ways, we find an estimate for the onset of synchrony of a system in terms of the coupling strength, noise strength, and width of the frequency distribution of its natural oscillations. We also demonstrate that noise alone can be sufficient to synchronize nonidentical oscillators. However, this synchrony depends on the first Fourier mode of a phase-sensitivity function, through which we introduce common noise into the system. We show that higher Fourier modes can cause desynchronization due to clustering effects, and that this can reinforce clustering caused by different forms of coupling. Finally, we discuss the effects of noise on an ensemble in which antiferromagnetic coupling causes oscillators to form two clusters in the absence of noise. © 2013 American Physical Society.

Patterns of hybrid loss of imprinting reveal tissue- and cluster-specific regulation.

Directory of Open Access Journals (Sweden)

Christopher D Wiley

Full Text Available Crosses between natural populations of two species of deer mice, Peromyscus maniculatus (BW, and P. polionotus (PO, produce parent-of-origin effects on growth and development. BW females mated to PO males (bwxpo produce growth-retarded but otherwise healthy offspring. In contrast, PO females mated to BW males (POxBW produce overgrown and severely defective offspring. The hybrid phenotypes are pronounced in the placenta and include POxBW conceptuses which lack embryonic structures. Evidence to date links variation in control of genomic imprinting with the hybrid defects, particularly in the POxBW offspring. Establishment of genomic imprinting is typically mediated by gametic DNA methylation at sites known as gDMRs. However, imprinted gene clusters vary in their regulation by gDMR sequences.Here we further assess imprinted gene expression and DNA methylation at different cluster types in order to discern patterns. These data reveal POxBW misexpression at the Kcnq1ot1 and Peg3 clusters, both of which lose ICR methylation in placental tissues. In contrast, some embryonic transcripts (Peg10, Kcnq1ot1 reactivated the silenced allele with little or no loss of DNA methylation. Hybrid brains also display different patterns of imprinting perturbations. Several cluster pairs thought to use analogous regulatory mechanisms are differentially affected in the hybrids.These data reinforce the hypothesis that placental and somatic gene regulation differs significantly, as does that between imprinted gene clusters and between species. That such epigenetic regulatory variation exists in recently diverged species suggests a role in reproductive isolation, and that this variation is likely to be adaptive.

Dietary Patterns Derived by Cluster Analysis are Associated with Cognitive Function among Korean Older Adults.

Science.gov (United States)

Kim, Jihye; Yu, Areum; Choi, Bo Youl; Nam, Jung Hyun; Kim, Mi Kyung; Oh, Dong Hoon; Yang, Yoon Jung

2015-05-29

The objective of this study was to investigate major dietary patterns among older Korean adults through cluster analysis and to determine an association between dietary patterns and cognitive function. This is a cross-sectional study. The data from the Korean Multi-Rural Communities Cohort Study was used. Participants included 765 participants aged 60 years and over. A quantitative food frequency questionnaire with 106 items was used to investigate dietary intake. The Korean version of the MMSE-KC (Mini-Mental Status Examination-Korean version) was used to assess cognitive function. Two major dietary patterns were identified using K-means cluster analysis. The "MFDF" dietary pattern indicated high consumption of Multigrain rice, Fish, Dairy products, Fruits and fruit juices, while the "WNC" dietary pattern referred to higher intakes of White rice, Noodles, and Coffee. Means of the total MMSE-KC and orientation score of the participants in the MFDF dietary pattern were higher than those of the WNC dietary pattern. Compared with the WNC dietary pattern, the MFDF dietary pattern showed a lower risk of cognitive impairment after adjusting for covariates (OR 0.64, 95% CI 0.44-0.94). The MFDF dietary pattern, with high consumption of multigrain rice, fish, dairy products, and fruits may be related to better cognition among Korean older adults.

Diametrical clustering for identifying anti-correlated gene clusters.

Science.gov (United States)

Dhillon, Inderjit S; Marcotte, Edward M; Roshan, Usman

2003-09-01

Clustering genes based upon their expression patterns allows us to predict gene function. Most existing clustering algorithms cluster genes together when their expression patterns show high positive correlation. However, it has been observed that genes whose expression patterns are strongly anti-correlated can also be functionally similar. Biologically, this is not unintuitive-genes responding to the same stimuli, regardless of the nature of the response, are more likely to operate in the same pathways. We present a new diametrical clustering algorithm that explicitly identifies anti-correlated clusters of genes. Our algorithm proceeds by iteratively (i). re-partitioning the genes and (ii). computing the dominant singular vector of each gene cluster; each singular vector serving as the prototype of a 'diametric' cluster. We empirically show the effectiveness of the algorithm in identifying diametrical or anti-correlated clusters. Testing the algorithm on yeast cell cycle data, fibroblast gene expression data, and DNA microarray data from yeast mutants reveals that opposed cellular pathways can be discovered with this method. We present systems whose mRNA expression patterns, and likely their functions, oppose the yeast ribosome and proteosome, along with evidence for the inverse transcriptional regulation of a number of cellular systems.

Complementary feeding: a Global Network cluster randomized controlled trial

Directory of Open Access Journals (Sweden)

Pasha Omrana

2011-01-01

Full Text Available Abstract Background Inadequate and inappropriate complementary feeding are major factors contributing to excess morbidity and mortality in young children in low resource settings. Animal source foods in particular are cited as essential to achieve micronutrient requirements. The efficacy of the recommendation for regular meat consumption, however, has not been systematically evaluated. Methods/Design A cluster randomized efficacy trial was designed to test the hypothesis that 12 months of daily intake of beef added as a complementary food would result in greater linear growth velocity than a micronutrient fortified equi-caloric rice-soy cereal supplement. The study is being conducted in 4 sites of the Global Network for Women's and Children's Health Research located in Guatemala, Pakistan, Democratic Republic of the Congo (DRC and Zambia in communities with toddler stunting rates of at least 20%. Five clusters per country were randomized to each of the food arms, with 30 infants in each cluster. The daily meat or cereal supplement was delivered to the home by community coordinators, starting when the infants were 6 months of age and continuing through 18 months. All participating mothers received nutrition education messages to enhance complementary feeding practices delivered by study coordinators and through posters at the local health center. Outcome measures, obtained at 6, 9, 12, and 18 months by a separate assessment team, included anthropometry; dietary variety and diversity scores; biomarkers of iron, zinc and Vitamin B12 status (18 months; neurocognitive development (12 and 18 months; and incidence of infectious morbidity throughout the trial. The trial was supervised by a trial steering committee, and an independent data monitoring committee provided oversight for the safety and conduct of the trial. Discussion Findings from this trial will test the efficacy of daily intake of meat commencing at age 6 months and, if beneficial, will

Pattern Classification of Tropical Cyclone Tracks over the Western North Pacific using a Fuzzy Clustering Method

Science.gov (United States)

Kim, H.; Ho, C.; Kim, J.

2008-12-01

This study presents the pattern classification of tropical cyclone (TC) tracks over the western North Pacific (WNP) basin during the typhoon season (June through October) for 1965-2006 (total 42 years) using a fuzzy clustering method. After the fuzzy c-mean clustering algorithm to the TC trajectory interpolated into 20 segments of equivalent length, we divided the whole tracks into 7 patterns. The optimal number of the fuzzy cluster is determined by several validity measures. The classified TC track patterns represent quite different features in the recurving latitudes, genesis locations, and geographical pathways: TCs mainly forming in east-northern part of the WNP and striking Korean and Japan (C1); mainly forming in west-southern part of the WNP, traveling long pathway, and partly striking Japan (C2); mainly striking Taiwan and East China (C3); traveling near the east coast of Japan (C4); traveling the distant ocean east of Japan (C5); moving toward South China and Vietnam straightly (C6); and forming in the South China Sea (C7). Atmospheric environments related to each cluster show physically consistent with each TC track patterns. The straight track pattern is closely linked to a developed anticyclonic circulation to the north of the TC. It implies that this ridge acts as a steering flow forcing TCs to move to the northwest with a more west-oriented track. By contrast, recurving patterns occur commonly under the influence of the strong anomalous westerlies over the TC pathway but there definitely exist characteristic anomalous circulations over the mid- latitudes by pattern. Some clusters are closely related to the well-known large-scale phenomena. The C1 and C2 are highly related to the ENSO phase: The TCs in the C1 (C2) is more active during La Niña (El Niño). The TC activity in the C3 is associated with the WNP summer monsoon. The TCs in the C4 is more (less) vigorous during the easterly (westerly) phase of the stratospheric quasi-biennial oscillation

Three-dimensional visualization of cultural clusters in the 1878 yellow fever epidemic of New Orleans.

Science.gov (United States)

Curtis, Andrew J

2008-08-22

An epidemic may exhibit different spatial patterns with a change in geographic scale, with each scale having different conduits and impediments to disease spread. Mapping disease at each of these scales often reveals different cluster patterns. This paper will consider this change of geographic scale in an analysis of yellow fever deaths for New Orleans in 1878. Global clustering for the whole city, will be followed by a focus on the French Quarter, then clusters of that area, and finally street-level patterns of a single cluster. The three-dimensional visualization capabilities of a GIS will be used as part of a cluster creation process that incorporates physical buildings in calculating mortality-to-mortality distance. Including nativity of the deceased will also capture cultural connection. Twenty-two yellow fever clusters were identified for the French Quarter. These generally mirror the results of other global cluster and density surfaces created for the entire epidemic in New Orleans. However, the addition of building-distance, and disease specific time frame between deaths reveal that disease spread contains a cultural component. Same nativity mortality clusters emerge in a similar time frame irrespective of proximity. Italian nativity mortalities were far more densely grouped than any of the other cohorts. A final examination of mortalities for one of the nativity clusters reveals that further sub-division is present, and that this pattern would only be revealed at this scale (street level) of investigation. Disease spread in an epidemic is complex resulting from a combination of geographic distance, geographic distance with specific connection to the built environment, disease-specific time frame between deaths, impediments such as herd immunity, and social or cultural connection. This research has shown that the importance of cultural connection may be more important than simple proximity, which in turn might mean traditional quarantine measures should be

Global myeloma research clusters, output, and citations: a bibliometric mapping and clustering analysis.

Directory of Open Access Journals (Sweden)

Jens Peter Andersen

Full Text Available International collaborative research is a mechanism for improving the development of disease-specific therapies and for improving health at the population level. However, limited data are available to assess the trends in research output related to orphan diseases.We used bibliometric mapping and clustering methods to illustrate the level of fragmentation in myeloma research and the development of collaborative efforts. Publication data from Thomson Reuters Web of Science were retrieved for 2005-2009 and followed until 2013. We created a database of multiple myeloma publications, and we analysed impact and co-authorship density to identify scientific collaborations, developments, and international key players over time. The global annual publication volume for studies on multiple myeloma increased from 1,144 in 2005 to 1,628 in 2009, which represents a 43% increase. This increase is high compared to the 24% and 14% increases observed for lymphoma and leukaemia. The major proportion (>90% of publications was from the US and EU over the study period. The output and impact in terms of citations, identified several successful groups with a large number of intra-cluster collaborations in the US and EU. The US-based myeloma clusters clearly stand out as the most productive and highly cited, and the European Myeloma Network members exhibited a doubling of collaborative publications from 2005 to 2009, still increasing up to 2013.Multiple myeloma research output has increased substantially in the past decade. The fragmented European myeloma research activities based on national or regional groups are progressing, but they require a broad range of targeted research investments to improve multiple myeloma health care.

Divisibility patterns of natural numbers on a complex network.

Science.gov (United States)

Shekatkar, Snehal M; Bhagwat, Chandrasheel; Ambika, G

2015-09-16

Investigation of divisibility properties of natural numbers is one of the most important themes in the theory of numbers. Various tools have been developed over the centuries to discover and study the various patterns in the sequence of natural numbers in the context of divisibility. In the present paper, we study the divisibility of natural numbers using the framework of a growing complex network. In particular, using tools from the field of statistical inference, we show that the network is scale-free but has a non-stationary degree distribution. Along with this, we report a new kind of similarity pattern for the local clustering, which we call "stretching similarity", in this network. We also show that the various characteristics like average degree, global clustering coefficient and assortativity coefficient of the network vary smoothly with the size of the network. Using analytical arguments we estimate the asymptotic behavior of global clustering and average degree which is validated using numerical analysis.

Dietary Patterns Derived by Cluster Analysis are Associated with Cognitive Function among Korean Older Adults

Directory of Open Access Journals (Sweden)

Jihye Kim

2015-05-01

Full Text Available The objective of this study was to investigate major dietary patterns among older Korean adults through cluster analysis and to determine an association between dietary patterns and cognitive function. This is a cross-sectional study. The data from the Korean Multi-Rural Communities Cohort Study was used. Participants included 765 participants aged 60 years and over. A quantitative food frequency questionnaire with 106 items was used to investigate dietary intake. The Korean version of the MMSE-KC (Mini-Mental Status Examination–Korean version was used to assess cognitive function. Two major dietary patterns were identified using K-means cluster analysis. The “MFDF” dietary pattern indicated high consumption of Multigrain rice, Fish, Dairy products, Fruits and fruit juices, while the “WNC” dietary pattern referred to higher intakes of White rice, Noodles, and Coffee. Means of the total MMSE-KC and orientation score of the participants in the MFDF dietary pattern were higher than those of the WNC dietary pattern. Compared with the WNC dietary pattern, the MFDF dietary pattern showed a lower risk of cognitive impairment after adjusting for covariates (OR 0.64, 95% CI 0.44–0.94. The MFDF dietary pattern, with high consumption of multigrain rice, fish, dairy products, and fruits may be related to better cognition among Korean older adults.

Hierarchical clustering of HPV genotype patterns in the ASCUS-LSIL triage study

Science.gov (United States)

Wentzensen, Nicolas; Wilson, Lauren E.; Wheeler, Cosette M.; Carreon, Joseph D.; Gravitt, Patti E.; Schiffman, Mark; Castle, Philip E.

2010-01-01

Anogenital cancers are associated with about 13 carcinogenic HPV types in a broader group that cause cervical intraepithelial neoplasia (CIN). Multiple concurrent cervical HPV infections are common which complicate the attribution of HPV types to different grades of CIN. Here we report the analysis of HPV genotype patterns in the ASCUS-LSIL triage study using unsupervised hierarchical clustering. Women who underwent colposcopy at baseline (n = 2780) were grouped into 20 disease categories based on histology and cytology. Disease groups and HPV genotypes were clustered using complete linkage. Risk of 2-year cumulative CIN3+, viral load, colposcopic impression, and age were compared between disease groups and major clusters. Hierarchical clustering yielded four major disease clusters: Cluster 1 included all CIN3 histology with abnormal cytology; Cluster 2 included CIN3 histology with normal cytology and combinations with either CIN2 or high-grade squamous intraepithelial lesion (HSIL) cytology; Cluster 3 included older women with normal or low grade histology/cytology and low viral load; Cluster 4 included younger women with low grade histology/cytology, multiple infections, and the highest viral load. Three major groups of HPV genotypes were identified: Group 1 included only HPV16; Group 2 included nine carcinogenic types plus non-carcinogenic HPV53 and HPV66; and Group 3 included non-carcinogenic types plus carcinogenic HPV33 and HPV45. Clustering results suggested that colposcopy missed a prevalent precancer in many women with no biopsy/normal histology and HSIL. This result was confirmed by an elevated 2-year risk of CIN3+ in these groups. Our novel approach to study multiple genotype infections in cervical disease using unsupervised hierarchical clustering can address complex genotype distributions on a population level. PMID:20959485

Global patterns in mangrove soil carbon stocks and losses

KAUST Repository

Atwood, Trisha B.

2017-06-26

Mangrove soils represent a large sink for otherwise rapidly recycled carbon (C). However, widespread deforestation threatens the preservation of this important C stock. It is therefore imperative that global patterns in mangrove soil C stocks and their susceptibility to remineralization are understood. Here, we present patterns in mangrove soil C stocks across hemispheres, latitudes, countries and mangrove community compositions, and estimate potential annual CO2 emissions for countries where mangroves occur. Global potential CO2 emissions from soils as a result of mangrove loss were estimated to be ~7.0 Tg CO2e yr−1. Countries with the highest potential CO2 emissions from soils are Indonesia (3,410 Gg CO2e yr−1) and Malaysia (1,288 Gg CO2e yr−1). The patterns described serve as a baseline by which countries can assess their mangrove soil C stocks and potential emissions from mangrove deforestation.

Local Clusters in a Globalized World

DEFF Research Database (Denmark)

Reinau, Kristian Hegner

Currently there is growing focus on how cluster internal and cluster external relations affect the creation of knowledge in companies placed in clusters. However, the current theories on this topic are too simple and the interplay between internal and external relations is relatively unknown. Thi...

Global Dispersal Pattern of HIV Type 1 Subtype CRF01_AE

OpenAIRE

Poljak, Mario; Angelis, Konstantinos; Albert, Jan; Mamais, Ioannis; Magiorkinis, Gkikas; Hatzakis, Angelos; Hamouda, Osamah; Stuck, Daniel; Vercauteren, Jurgen; Wensing, Annemarie; Alexiev, Ivailo

2016-01-01

Background. Human immunodeficiency virus type 1 (HIV-1) subtype CRF01_AE originated in Africa and then passed to Thailand, where it established a major epidemic. Despite the global presence of CRF01_AE, little is known about its subsequent dispersal pattern. Methods. We assembled a global data set of 2736 CRF01_AE sequences by pooling sequences from public databases and patient-cohort studies. We estimated viral dispersal patterns, using statistical phylogeographic analysis run over bootstrap...

The MUSIC of galaxy clusters - II. X-ray global properties and scaling relations

Science.gov (United States)

Biffi, V.; Sembolini, F.; De Petris, M.; Valdarnini, R.; Yepes, G.; Gottlöber, S.

2014-03-01

We present the X-ray properties and scaling relations of a large sample of clusters extracted from the Marenostrum MUltidark SImulations of galaxy Clusters (MUSIC) data set. We focus on a sub-sample of 179 clusters at redshift z ˜ 0.11, with 3.2 × 1014 h-1 M⊙ mass. We employed the X-ray photon simulator PHOX to obtain synthetic Chandra observations and derive observable-like global properties of the intracluster medium (ICM), as X-ray temperature (TX) and luminosity (LX). TX is found to slightly underestimate the true mass-weighted temperature, although tracing fairly well the cluster total mass. We also study the effects of TX on scaling relations with cluster intrinsic properties: total (M500 and gas Mg,500 mass; integrated Compton parameter (YSZ) of the Sunyaev-Zel'dovich (SZ) thermal effect; YX = Mg,500 TX. We confirm that YX is a very good mass proxy, with a scatter on M500-YX and YSZ-YX lower than 5 per cent. The study of scaling relations among X-ray, intrinsic and SZ properties indicates that simulated MUSIC clusters reasonably resemble the self-similar prediction, especially for correlations involving TX. The observational approach also allows for a more direct comparison with real clusters, from which we find deviations mainly due to the physical description of the ICM, affecting TX and, particularly, LX.

Coulomb Fission in Multiply-Charged Ammonia Clusters: Accurate Measurements of the Rayleigh Instability Limit from Fragmentation Patterns.

Science.gov (United States)

Harris, Christopher; Stace, Anthony J

2018-03-15

A series of experiments have been undertaken on the fragmentation of multiply charged ammonia clusters, (NH 3 ) n z+ , where z ≤ 8 and n ≤ 850, to establish Rayleigh instability limits, whereby clusters at certain critical sizes become unstable due to Coulomb repulsion between the resident charges. Experimental results on size-selected clusters are found to be in excellent agreement with theoretical predictions of Rayleigh instability limits at all values of the charge. Electrostatic theory has been used to help identify fragmentation patterns on the assumption that the clusters separate into two dielectric spheres, and the predicted Coulomb repulsion energies used to establish pathways and the sizes of cluster fragments. The results show that fragmentation is very asymmetric in terms of both the numbers of molecules involved and the amount of charge each fragment accommodates. For clusters carrying a charge ≤+4, the results show that fragmentation proceeds via the loss of small, singly charged clusters. When clusters carry a charge of +5 or more, the experimental observations suggest a marked switch in behavior. Although the laboratory measurements equate to fragmentation via the loss of a large dication cluster, electrostatic theory supports an interpretation that involves the sequential loss of two smaller, singly charged clusters possibly accompanied by the extensive evaporation of neutral molecules. It is suggested that this change in fragmentation pattern is driven by the channelling of Coulomb repulsion energy into intermolecular modes within these larger clusters. Overall, the results appear to support the ion evaporation model that is frequently used to interpret electrospray experiments.

Regional health care planning: a methodology to cluster facilities using community utilization patterns.

Science.gov (United States)

Delamater, Paul L; Shortridge, Ashton M; Messina, Joseph P

2013-08-22

Community-based health care planning and regulation necessitates grouping facilities and areal units into regions of similar health care use. Limited research has explored the methodologies used in creating these regions. We offer a new methodology that clusters facilities based on similarities in patient utilization patterns and geographic location. Our case study focused on Hospital Groups in Michigan, the allocation units used for predicting future inpatient hospital bed demand in the state's Bed Need Methodology. The scientific, practical, and political concerns that were considered throughout the formulation and development of the methodology are detailed. The clustering methodology employs a 2-step K-means + Ward's clustering algorithm to group hospitals. The final number of clusters is selected using a heuristic that integrates both a statistical-based measure of cluster fit and characteristics of the resulting Hospital Groups. Using recent hospital utilization data, the clustering methodology identified 33 Hospital Groups in Michigan. Despite being developed within the politically charged climate of Certificate of Need regulation, we have provided an objective, replicable, and sustainable methodology to create Hospital Groups. Because the methodology is built upon theoretically sound principles of clustering analysis and health care service utilization, it is highly transferable across applications and suitable for grouping facilities or areal units.

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

HANPP Collection: Global Patterns in Net Primary Productivity (NPP)

Data.gov (United States)

National Aeronautics and Space Administration — The Global Patterns in Net Primary Productivity (NPP) portion of the Human Appropriation of Net Primary Productivity (HANPP) Collection maps the net amount of solar...

A Global Model for Circumgalactic and Cluster-core Precipitation

Science.gov (United States)

Voit, G. Mark; Meece, Greg; Li, Yuan; O'Shea, Brian W.; Bryan, Greg L.; Donahue, Megan

2017-08-01

We provide an analytic framework for interpreting observations of multiphase circumgalactic gas that is heavily informed by recent numerical simulations of thermal instability and precipitation in cool-core galaxy clusters. We start by considering the local conditions required for the formation of multiphase gas via two different modes: (1) uplift of ambient gas by galactic outflows, and (2) condensation in a stratified stationary medium in which thermal balance is explicitly maintained. Analytic exploration of these two modes provides insights into the relationships between the local ratio of the cooling and freefall timescales (I.e., {t}{cool}/{t}{ff}), the large-scale gradient of specific entropy, and the development of precipitation and multiphase media in circumgalactic gas. We then use these analytic findings to interpret recent simulations of circumgalactic gas in which global thermal balance is maintained. We show that long-lasting configurations of gas with 5≲ \\min ({t}{cool}/{t}{ff})≲ 20 and radial entropy profiles similar to observations of cool cores in galaxy clusters are a natural outcome of precipitation-regulated feedback. We conclude with some observational predictions that follow from these models. This work focuses primarily on precipitation and AGN feedback in galaxy-cluster cores, because that is where the observations of multiphase gas around galaxies are most complete. However, many of the physical principles that govern condensation in those environments apply to circumgalactic gas around galaxies of all masses.

HANPP Collection: Global Patterns in Net Primary Productivity (NPP)

Data.gov (United States)

National Aeronautics and Space Administration — The Global Patterns in Net Primary Productivity (NPP) portion of the HANPP Collection maps the net amount of solar energy converted to plant organic matter through...

Electroless deposition of metal nanoparticle clusters: Effect of pattern distance

KAUST Repository

Gentile, Francesco

2014-04-03

Electroless plating is a deposition technique in which metal ions are reduced as atoms on specific patterned sites of a silicon surface to form metal nanoparticles (NPs) aggregates with the desired characteristics. Those NPs, in turn, can be used as constituents of surface enhanced Raman spectroscopy substrates, which are devices where the electromagnetic field and effects thereof are giantly amplified. Here, the electroless formation of nanostructures was studied as a function of the geometry of the substrate. High resolution, electron beam lithography techniques were used to obtain nonperiodic arrays of circular patterns, in which the spacing of patterns was varied over a significant range. In depositing silver atoms in those circuits, the authors found that the characteristics of the aggregates vary with the pattern distance. When the patterns are in close proximity, the interference of different groups of adjacent aggregates cannot be disregarded and the overall growth is reduced. Differently from this, when the patterns are sufficiently distant, the formation of metal clusters of NPs is independent on the spacing of the patterns. For the particular subset of parameters used here, this critical correlation distance is about three times the pattern diameter. These findings were explained within the framework of a diffusion limited aggregation model, which is a simulation method that can decipher the formation of nanoaggregates at an atomic level. In the discussion, the authors showed how this concept can be used to fabricate ordered arrays of silver nanospheres, where the size of those spheres may be regulated on varying the pattern distance, for applications in biosensing and single molecule detection.

Electroless deposition of metal nanoparticle clusters: Effect of pattern distance

KAUST Repository

Gentile, Francesco; Laura Coluccio, Maria; Candeloro, Patrizio; Barberio, Marianna; Perozziello, Gerardo; Francardi, Marco; Di Fabrizio, Enzo M.

2014-01-01

Electroless plating is a deposition technique in which metal ions are reduced as atoms on specific patterned sites of a silicon surface to form metal nanoparticles (NPs) aggregates with the desired characteristics. Those NPs, in turn, can be used as constituents of surface enhanced Raman spectroscopy substrates, which are devices where the electromagnetic field and effects thereof are giantly amplified. Here, the electroless formation of nanostructures was studied as a function of the geometry of the substrate. High resolution, electron beam lithography techniques were used to obtain nonperiodic arrays of circular patterns, in which the spacing of patterns was varied over a significant range. In depositing silver atoms in those circuits, the authors found that the characteristics of the aggregates vary with the pattern distance. When the patterns are in close proximity, the interference of different groups of adjacent aggregates cannot be disregarded and the overall growth is reduced. Differently from this, when the patterns are sufficiently distant, the formation of metal clusters of NPs is independent on the spacing of the patterns. For the particular subset of parameters used here, this critical correlation distance is about three times the pattern diameter. These findings were explained within the framework of a diffusion limited aggregation model, which is a simulation method that can decipher the formation of nanoaggregates at an atomic level. In the discussion, the authors showed how this concept can be used to fabricate ordered arrays of silver nanospheres, where the size of those spheres may be regulated on varying the pattern distance, for applications in biosensing and single molecule detection.

Modelling Global Pattern Formations for Collaborative Learning Environments

DEFF Research Database (Denmark)

Grappiolo, Corrado; Cheong, Yun-Gyung; Khaled, Rilla

2012-01-01

We present our research towards the design of a computational framework capable of modelling the formation and evolution of global patterns (i.e. group structures) in a population of social individuals. The framework is intended to be used in collaborative environments, e.g. social serious games...

An Evolving Triadic World: A Theoretical Framework for Global Communication Research

Directory of Open Access Journals (Sweden)

Shelton A. Gunaratne

2015-08-01

Full Text Available A macro theory that recognizes the world’s three competing center-clusters and their respective hinterlands o?ers a realistic framework for global communication research. This study has used recent data on world trade, computers, Internet hosts, and high-tech exports to map the triadization of the world in the Information Age. The original dependency theory and world-system theory perspectives emphasized the hierarchical linking of national societies to the capitalist world-economy in a center-periphery structure. The proposed global-triadization formulation looks at the center-periphery structure in terms of a capitalist world-economy dominated by three competing center economic clusters, each of which has a dependent hinterland comprising peripheral economic clusters. These clusters may not necessarily be geographically contiguous. Strong-weak relationships may exist within each center-cluster, as well as within each periphery-cluster, with one center-cluster occupying a hegemonic role. The rudimentary Information-Society Power Index, constructed for this study, can guide the researcher to test an abundance of hypotheses on the pattern of global communication and information ?ow with particular attention to source, message, channel, and receiver.

Global patterns of city size distributions and their fundamental drivers.

Directory of Open Access Journals (Sweden)

Ethan H Decker

2007-09-01

Full Text Available Urban areas and their voracious appetites are increasingly dominating the flows of energy and materials around the globe. Understanding the size distribution and dynamics of urban areas is vital if we are to manage their growth and mitigate their negative impacts on global ecosystems. For over 50 years, city size distributions have been assumed to universally follow a power function, and many theories have been put forth to explain what has become known as Zipf's law (the instance where the exponent of the power function equals unity. Most previous studies, however, only include the largest cities that comprise the tail of the distribution. Here we show that national, regional and continental city size distributions, whether based on census data or inferred from cluster areas of remotely-sensed nighttime lights, are in fact lognormally distributed through the majority of cities and only approach power functions for the largest cities in the distribution tails. To explore generating processes, we use a simple model incorporating only two basic human dynamics, migration and reproduction, that nonetheless generates distributions very similar to those found empirically. Our results suggest that macroscopic patterns of human settlements may be far more constrained by fundamental ecological principles than more fine-scale socioeconomic factors.

Global patterns of phytoplankton dynamics in coastal ecosystems

Science.gov (United States)

Paerl, H.; Yin, Kedong; Cloern, J.

2011-01-01

Scientific Committee on Ocean Research Working Group 137 Meeting; Hangzhou, China, 17-21 October 2010; Phytoplankton biomass and community structure have undergone dramatic changes in coastal ecosystems over the past several decades in response to climate variability and human disturbance. These changes have short- and long-term impacts on global carbon and nutrient cycling, food web structure and productivity, and coastal ecosystem services. There is a need to identify the underlying processes and measure the rates at which they alter coastal ecosystems on a global scale. Hence, the Scientific Committee on Ocean Research (SCOR) formed Working Group 137 (WG 137), "Global Patterns of Phytoplankton Dynamics in Coastal Ecosystems: A Comparative Analysis of Time Series Observations" (http://wg137.net/). This group evolved from a 2007 AGU-sponsored Chapman Conference entitled "Long Time-Series Observations in Coastal Ecosystems: Comparative Analyses of Phytoplankton Dynamics on Regional to Global Scales.".

What drives the formation of global oil trade patterns?

International Nuclear Information System (INIS)

Zhang, Hai-Ying; Ji, Qiang; Fan, Ying

2015-01-01

In this paper, the spatial characteristics of current global oil trade patterns are investigated by proposing a new indicator Moran-F. Meanwhile, the factors that influence the formation of oil trade patterns are identified by constructing four different kinds of spatial econometric models. The findings indicate that most oil exporters have an obvious export focus in North America and a relatively balanced export in Europe and the Asia-Pacific region. Besides supply and demand factors, technological progress and energy efficiency have also significantly influenced the oil trade. Moreover, there is a spillover effect of trade flow among different regions, but its impact is weak. In addition, oil importers in the same region have the potential to cooperate due to their similar import sources. Finally, promotion of oil importers' R&D investments can effectively reduce the demand for global oil trade. - Highlights: • A new spatial association Moran-F indicator that applies to trade flows is proposed. • Driving factors affecting the formation of oil trade patterns are identified. • Oil-exporting countries implement various export strategies in different regions. • Supply, demand and technological factors contribute to the oil trade patterns. • Spillover effect of each factor affecting oil trade flows does exist but is limited

A hydroclimatic model of global fire patterns

Science.gov (United States)

Boer, Matthias

2015-04-01

Satellite-based earth observation is providing an increasingly accurate picture of global fire patterns. The highest fire activity is observed in seasonally dry (sub-)tropical environments of South America, Africa and Australia, but fires occur with varying frequency, intensity and seasonality in almost all biomes on Earth. The particular combination of these fire characteristics, or fire regime, is known to emerge from the combined influences of climate, vegetation, terrain and land use, but has so far proven difficult to reproduce by global models. Uncertainty about the biophysical drivers and constraints that underlie current global fire patterns is propagated in model predictions of how ecosystems, fire regimes and biogeochemical cycles may respond to projected future climates. Here, I present a hydroclimatic model of global fire patterns that predicts the mean annual burned area fraction (F) of 0.25° x 0.25° grid cells as a function of the climatic water balance. Following Bradstock's four-switch model, long-term fire activity levels were assumed to be controlled by fuel productivity rates and the likelihood that the extant fuel is dry enough to burn. The frequency of ignitions and favourable fire weather were assumed to be non-limiting at long time scales. Fundamentally, fuel productivity and fuel dryness are a function of the local water and energy budgets available for the production and desiccation of plant biomass. The climatic water balance summarizes the simultaneous availability of biologically usable energy and water at a site, and may therefore be expected to explain a significant proportion of global variation in F. To capture the effect of the climatic water balance on fire activity I focused on the upper quantiles of F, i.e. the maximum level of fire activity for a given climatic water balance. Analysing GFED4 data for annual burned area together with gridded climate data, I found that nearly 80% of the global variation in the 0.99 quantile of F

Three-dimensional visualization of cultural clusters in the 1878 yellow fever epidemic of New Orleans

Directory of Open Access Journals (Sweden)

Curtis Andrew J

2008-08-01

Full Text Available Abstract Background An epidemic may exhibit different spatial patterns with a change in geographic scale, with each scale having different conduits and impediments to disease spread. Mapping disease at each of these scales often reveals different cluster patterns. This paper will consider this change of geographic scale in an analysis of yellow fever deaths for New Orleans in 1878. Global clustering for the whole city, will be followed by a focus on the French Quarter, then clusters of that area, and finally street-level patterns of a single cluster. The three-dimensional visualization capabilities of a GIS will be used as part of a cluster creation process that incorporates physical buildings in calculating mortality-to-mortality distance. Including nativity of the deceased will also capture cultural connection. Results Twenty-two yellow fever clusters were identified for the French Quarter. These generally mirror the results of other global cluster and density surfaces created for the entire epidemic in New Orleans. However, the addition of building-distance, and disease specific time frame between deaths reveal that disease spread contains a cultural component. Same nativity mortality clusters emerge in a similar time frame irrespective of proximity. Italian nativity mortalities were far more densely grouped than any of the other cohorts. A final examination of mortalities for one of the nativity clusters reveals that further sub-division is present, and that this pattern would only be revealed at this scale (street level of investigation. Conclusion Disease spread in an epidemic is complex resulting from a combination of geographic distance, geographic distance with specific connection to the built environment, disease-specific time frame between deaths, impediments such as herd immunity, and social or cultural connection. This research has shown that the importance of cultural connection may be more important than simple proximity, which in

Analysis of Roanoke Region Weather Patterns Under Global Teleconnections

OpenAIRE

LaRocque, Eric John

2006-01-01

This work attempts to relate global teleconnections, through physical phenomena such as the El Nino-Southern Oscillation (ENSO), Artic Oscillation (AO), North Atlantic Oscillation (NAO), and the Pacific North American (PNA) pattern to synoptic-scale weather patterns and precipitation in the Roanoke, Virginia region. The first chapter describes the behavior of the El Nino-Southern Oscillation (ENSO) by implementing non-homogeneous and homogeneous Markov Chain models on a monthly time series o...

Using Clustering Techniques To Detect Usage Patterns in a Web-based Information System.

Science.gov (United States)

Chen, Hui-Min; Cooper, Michael D.

2001-01-01

This study developed an analytical approach to detecting groups with homogenous usage patterns in a Web-based information system. Principal component analysis was used for data reduction, cluster analysis for categorizing usage into groups. The methodology was demonstrated and tested using two independent samples of user sessions from the…

Application of a clustering-remote sensing method in analyzing security patterns

Science.gov (United States)

López-Caloca, Alejandra; Martínez-Viveros, Elvia; Chapela-Castañares, José Ignacio

2009-04-01

In Mexican academic and government circles, research on criminal spatial behavior has been neglected. Only recently has there been an interest in criminal data geo-reference. However, more sophisticated spatial analyses models are needed to disclose spatial patterns of crime and pinpoint their changes overtime. The main use of these models lies in supporting policy making and strategic intelligence. In this paper we present a model for finding patterns associated with crime. It is based on a fuzzy logic algorithm which finds the best fit within cluster numbers and shapes of groupings. We describe the methodology for building the model and its validation. The model was applied to annual data for types of felonies from 2005 to 2006 in the Mexican city of Hermosillo. The results are visualized as a standard deviational ellipse computed for the points identified to be a "cluster". These areas indicate a high to low demand for public security, and they were cross-related to urban structure analyzed by SPOT images and statistical data such as population, poverty levels, urbanization, and available services. The fusion of the model results with other geospatial data allows detecting obstacles and opportunities for crime commission in specific high risk zones and guide police activities and criminal investigations.

Coupled local facilitation and global hydrologic inhibition drive landscape geometry in a patterned peatland

Science.gov (United States)

Acharya, S.; Kaplan, D. A.; Casey, S.; Cohen, M. J.; Jawitz, J. W.

2015-05-01

Self-organized landscape patterning can arise in response to multiple processes. Discriminating among alternative patterning mechanisms, particularly where experimental manipulations are untenable, requires process-based models. Previous modeling studies have attributed patterning in the Everglades (Florida, USA) to sediment redistribution and anisotropic soil hydraulic properties. In this work, we tested an alternate theory, the self-organizing-canal (SOC) hypothesis, by developing a cellular automata model that simulates pattern evolution via local positive feedbacks (i.e., facilitation) coupled with a global negative feedback based on hydrology. The model is forced by global hydroperiod that drives stochastic transitions between two patch types: ridge (higher elevation) and slough (lower elevation). We evaluated model performance using multiple criteria based on six statistical and geostatistical properties observed in reference portions of the Everglades landscape: patch density, patch anisotropy, semivariogram ranges, power-law scaling of ridge areas, perimeter area fractal dimension, and characteristic pattern wavelength. Model results showed strong statistical agreement with reference landscapes, but only when anisotropically acting local facilitation was coupled with hydrologic global feedback, for which several plausible mechanisms exist. Critically, the model correctly generated fractal landscapes that had no characteristic pattern wavelength, supporting the invocation of global rather than scale-specific negative feedbacks.

Influence of patterned topographic features on the formation of cardiac cell clusters and their rhythmic activities

International Nuclear Information System (INIS)

Wang, L; Liu, L; Magome, N; Agladze, K; Chen, Y

2013-01-01

In conventional primary cultures, cardiac cells prepared from a newborn rat undergo spontaneous formation of cell clusters after several days. These cell clusters may be non-homogeneously distributed on a flat surface and show irregular beating which can be recorded by calcium ion imaging. In order to improve the cell cluster homogeneity and the beating regularity, patterned topographic features were used to guide the cellular growth and the cell layer formation. On the substrate with an array of broadly spaced cross features made of photoresist, cells grew on the places that were not occupied by the crosses and thus formed a cell layer with interconnected cell clusters. Accordingly, spatially coordinated regular beating could be recorded over the whole patterned area. In contrast, when cultured on the substrate with broadly spaced but inter-connected cross features, the cardiac cell layer showed beatings which were neither coordinated in space nor regular in time. Finally, when cultured on the substrate with narrowly spaced features, the cell beating became spatially coordinated but still remained irregular. Our results suggest a way to improve the rhythmic property of cultured cardiac cell layers which might be useful for further investigations. (paper)

Global patterns of fragmentation and connectivity of mammalian carnivore habitat

OpenAIRE

Crooks, Kevin R.; Burdett, Christopher L.; Theobald, David M.; Rondinini, Carlo; Boitani, Luigi

2011-01-01

Although mammalian carnivores are vulnerable to habitat fragmentation and require landscape connectivity, their global patterns of fragmentation and connectivity have not been examined. We use recently developed high-resolution habitat suitability models to conduct comparative analyses and to identify global hotspots of fragmentation and connectivity for the world's terrestrial carnivores. Species with less fragmentation (i.e. more interior high-quality habitat) had larger geographical ranges...

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

Birth Location, Migration and Clustering of Important Composers: Historical Patterns

DEFF Research Database (Denmark)

Borowiecki, Karol; O’Hagan, John

2010-01-01

and 1899. This information is compiled from the large, Grove Music Online (2009) encyclopedia. There is also some discussion of the biases evident in choosing “significant” composers. The data show a marked level ofmigration of important composers going back many centuries suggesting that the phenomenon......This article examines the 522 most important composers in the last 800 years, as identified by Charles Murray (2003), in terms of their birth location and migration. It also examines detailed patterns of migration and tendencies to cluster in certain cities for those composers born between 1750...

Structural transition of (InSb)n clusters at n = 6-10

Science.gov (United States)

Lu, Qi Liang; Luo, Qi Quan; Huang, Shou Guo; Li, Yi De

2016-10-01

An optimization strategy combining global semi-empirical quantum mechanical search with all-electron density functional theory was adopted to determine the lowest energy structure of (InSb)n clusters with n = 6-10. A new structural growth pattern of the clusters was observed. The lowest energy structures of (InSb)6 and (InSb)8 were different from that of previously reported results. Competition existed between core-shell and cage-like structures of (InSb)8. The structural transition of (InSb)n clusters occurred at size n = 8-9. For (InSb)9 and (InSb)10 clusters, core-shell structure were more energetically favorable than the cage. The corresponding electronic properties were investigated.

Symmetry, Hopf bifurcation, and the emergence of cluster solutions in time delayed neural networks.

Science.gov (United States)

Wang, Zhen; Campbell, Sue Ann

2017-11-01

We consider the networks of N identical oscillators with time delayed, global circulant coupling, modeled by a system of delay differential equations with Z N symmetry. We first study the existence of Hopf bifurcations induced by the coupling time delay and then use symmetric Hopf bifurcation theory to determine how these bifurcations lead to different patterns of symmetric cluster oscillations. We apply our results to a case study: a network of FitzHugh-Nagumo neurons with diffusive coupling. For this model, we derive the asymptotic stability, global asymptotic stability, absolute instability, and stability switches of the equilibrium point in the plane of coupling time delay (τ) and excitability parameter (a). We investigate the patterns of cluster oscillations induced by the time delay and determine the direction and stability of the bifurcating periodic orbits by employing the multiple timescales method and normal form theory. We find that in the region where stability switching occurs, the dynamics of the system can be switched from the equilibrium point to any symmetric cluster oscillation, and back to equilibrium point as the time delay is increased.

Symmetry, Hopf bifurcation, and the emergence of cluster solutions in time delayed neural networks

Science.gov (United States)

Wang, Zhen; Campbell, Sue Ann

2017-11-01

We consider the networks of N identical oscillators with time delayed, global circulant coupling, modeled by a system of delay differential equations with ZN symmetry. We first study the existence of Hopf bifurcations induced by the coupling time delay and then use symmetric Hopf bifurcation theory to determine how these bifurcations lead to different patterns of symmetric cluster oscillations. We apply our results to a case study: a network of FitzHugh-Nagumo neurons with diffusive coupling. For this model, we derive the asymptotic stability, global asymptotic stability, absolute instability, and stability switches of the equilibrium point in the plane of coupling time delay (τ) and excitability parameter (a). We investigate the patterns of cluster oscillations induced by the time delay and determine the direction and stability of the bifurcating periodic orbits by employing the multiple timescales method and normal form theory. We find that in the region where stability switching occurs, the dynamics of the system can be switched from the equilibrium point to any symmetric cluster oscillation, and back to equilibrium point as the time delay is increased.

Different Patterns of the Urban Heat Island Intensity from Cluster Analysis

Science.gov (United States)

Silva, F. B.; Longo, K.

2014-12-01

This study analyzes the different variability patterns of the Urban Heat Island intensity (UHII) in the Metropolitan Area of Rio de Janeiro (MARJ), one of the largest urban agglomerations in Brazil. The UHII is defined as the difference in the surface air temperature between the urban/suburban and rural/vegetated areas. To choose one or more stations that represent those areas we used the technique of cluster analysis on the air temperature observations from 14 surface weather stations in the MARJ. The cluster analysis aims to classify objects based on their characteristics, gathering similar groups. The results show homogeneity patterns between air temperature observations, with 6 homogeneous groups being defined. Among those groups, one might be a natural choice for the representative urban area (Central station); one corresponds to suburban area (Afonsos station); and another group referred as rural area is compound of three stations (Ecologia, Santa Cruz and Xerém) that are located in vegetated regions. The arithmetic mean of temperature from the three rural stations is taken to represent the rural station temperature. The UHII is determined from these homogeneous groups. The first UHII is estimated from urban and rural temperature areas (Case 1), whilst the second UHII is obtained from suburban and rural temperature areas (Case 2). In Case 1, the maximum UHII occurs in two periods, one in the early morning and the other at night, while the minimum UHII occurs in the afternoon. In Case 2, the maximum UHII is observed during afternoon/night and the minimum during dawn/early morning. This study demonstrates that the stations choice reflects different UHII patterns, evidencing that distinct behaviors of this phenomenon can be identified.

Spectral embedded clustering: a framework for in-sample and out-of-sample spectral clustering.

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Nie, Feiping; Zeng, Zinan; Tsang, Ivor W; Xu, Dong; Zhang, Changshui

2011-11-01

Spectral clustering (SC) methods have been successfully applied to many real-world applications. The success of these SC methods is largely based on the manifold assumption, namely, that two nearby data points in the high-density region of a low-dimensional data manifold have the same cluster label. However, such an assumption might not always hold on high-dimensional data. When the data do not exhibit a clear low-dimensional manifold structure (e.g., high-dimensional and sparse data), the clustering performance of SC will be degraded and become even worse than K -means clustering. In this paper, motivated by the observation that the true cluster assignment matrix for high-dimensional data can be always embedded in a linear space spanned by the data, we propose the spectral embedded clustering (SEC) framework, in which a linearity regularization is explicitly added into the objective function of SC methods. More importantly, the proposed SEC framework can naturally deal with out-of-sample data. We also present a new Laplacian matrix constructed from a local regression of each pattern and incorporate it into our SEC framework to capture both local and global discriminative information for clustering. Comprehensive experiments on eight real-world high-dimensional datasets demonstrate the effectiveness and advantages of our SEC framework over existing SC methods and K-means-based clustering methods. Our SEC framework significantly outperforms SC using the Nyström algorithm on unseen data.

The Globalization of Food Systems: A Conceptual Framework and Empirical Patterns

OpenAIRE

Senauer, Benjamin; Venturini, Luciano

2005-01-01

This paper discusses a number of stylized facts and empirical patterns regarding agri-food trade flows as well as foreign direct investments in food processing and retailing. This evidence supports the hypothesis of an increasingly global food system. We identify the main factors at work such as push/supply side, pull/demand-side, and enabling/external factors. We show how the shift from national to global retailing is a recent phenomenon whose relevance for the globalization of upstream sect...

Exploratory Cluster Analysis to Identify Patterns of Chronic Kidney Disease in the 500 Cities Project.

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Liu, Shelley H; Li, Yan; Liu, Bian

2018-05-17

Chronic kidney disease is a leading cause of death in the United States. We used cluster analysis to explore patterns of chronic kidney disease in 500 of the largest US cities. After adjusting for socio-demographic characteristics, we found that unhealthy behaviors, prevention measures, and health outcomes related to chronic kidney disease differ between cities in Utah and those in the rest of the United States. Cluster analysis can be useful for identifying geographic regions that may have important policy implications for preventing chronic kidney disease.

Discovery of Teleconnections Using Data Mining Technologies in Global Climate Datasets

Directory of Open Access Journals (Sweden)

Fan Lin

2007-10-01

Full Text Available In this paper, we apply data mining technologies to a 100-year global land precipitation dataset and a 100-year Sea Surface Temperature (SST dataset. Some interesting teleconnections are discovered, including well-known patterns and unknown patterns (to the best of our knowledge, such as teleconnections between the abnormally low temperature events of the North Atlantic and floods in Northern Bolivia, abnormally low temperatures of the Venezuelan Coast and floods in Northern Algeria and Tunisia, etc. In particular, we use a high dimensional clustering method and a method that mines episode association rules in event sequences. The former is used to cluster the original time series datasets into higher spatial granularity, and the later is used to discover teleconnection patterns among events sequences that are generated by the clustering method. In order to verify our method, we also do experiments on the SOI index and a 100-year global land precipitation dataset and find many well-known teleconnections, such as teleconnections between SOI lower events and drought events of Eastern Australia, South Africa, and North Brazil; SOI lower events and flood events of the middle-lower reaches of Yangtze River; etc. We also do explorative experiments to help domain scientists discover new knowledge.

The Complexities of Implementing Cluster Supply Chain - Case Study of JCH

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Xue, Xiao; Zhang, Jibiao; Wang, Yang

As a new type of management pattern, "cluster supply chain" (CSC) can help SMEs to face the global challenges through all kinds of collaboration. However, a major challenge in implementing CSC is the gap between theory and practice in the field. In an effort to provide a better understanding of this emerging phenomenon, this paper presents the implementation process of CSC in the context of JingCheng Mechanical & Electrical Holding co., ltd.(JCH) as a case study. The cast study of JCH suggests that the key problems in the practice of cluster supply chain: How do small firms use cluster supply chain? Only after we clarify the problem, the actual construction and operation of cluster supply chain does show successful results as it should be.

Neural Global Pattern Similarity Underlies True and False Memories.

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Ye, Zhifang; Zhu, Bi; Zhuang, Liping; Lu, Zhonglin; Chen, Chuansheng; Xue, Gui

2016-06-22

The neural processes giving rise to human memory strength signals remain poorly understood. Inspired by formal computational models that posit a central role of global matching in memory strength, we tested a novel hypothesis that the strengths of both true and false memories arise from the global similarity of an item's neural activation pattern during retrieval to that of all the studied items during encoding (i.e., the encoding-retrieval neural global pattern similarity [ER-nGPS]). We revealed multiple ER-nGPS signals that carried distinct information and contributed differentially to true and false memories: Whereas the ER-nGPS in the parietal regions reflected semantic similarity and was scaled with the recognition strengths of both true and false memories, ER-nGPS in the visual cortex contributed solely to true memory. Moreover, ER-nGPS differences between the parietal and visual cortices were correlated with frontal monitoring processes. By combining computational and neuroimaging approaches, our results advance a mechanistic understanding of memory strength in recognition. What neural processes give rise to memory strength signals, and lead to our conscious feelings of familiarity? Using fMRI, we found that the memory strength of a given item depends not only on how it was encoded during learning, but also on the similarity of its neural representation with other studied items. The global neural matching signal, mainly in the parietal lobule, could account for the memory strengths of both studied and unstudied items. Interestingly, a different global matching signal, originated from the visual cortex, could distinguish true from false memories. The findings reveal multiple neural mechanisms underlying the memory strengths of events registered in the brain. Copyright © 2016 the authors 0270-6474/16/366792-11$15.00/0.

Food, Populations and Health — global Patterns and Challenges

DEFF Research Database (Denmark)

2016-01-01

The present volume is based on presentations at a symposium at the Royal Danish Academy of Sciences and Letters in September 2014 with the title Food, Population and Health – global Patterns and Challenges. Food has played a fundamental role in the history of all societies over the World. Availab...

Towards global patterns in the diversity and community structure of ectomycorrhizal fungi

DEFF Research Database (Denmark)

Tedersoo, Leho; Bahram, Mohammad; Toots, Märt

2012-01-01

Global species richness patterns of soil micro-organisms remain poorly understood compared to macro-organisms. We use a global analysis to disentangle the global determinants of diversity and community composition for ectomycorrhizal (EcM) fungi—microbial symbionts that play key roles in plant...... nutrition in most temperate and many tropical forest ecosystems. Host plant family has the strongest effect on the phylogenetic community composition of fungi, whereas temperature and precipitation mostly affect EcM fungal richness that peaks in the temperate and boreal forest biomes, contrasting...... with latitudinal patterns of macro-organisms. Tropical ecosystems experience rapid turnover of organic material and have weak soil stratification, suggesting that poor habitat conditions may contribute to the relatively low richness of EcM fungi, and perhaps other soil biota, in most tropical ecosystems. For EcM...

Average correlation clustering algorithm (ACCA) for grouping of co-regulated genes with similar pattern of variation in their expression values.

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Bhattacharya, Anindya; De, Rajat K

2010-08-01

Distance based clustering algorithms can group genes that show similar expression values under multiple experimental conditions. They are unable to identify a group of genes that have similar pattern of variation in their expression values. Previously we developed an algorithm called divisive correlation clustering algorithm (DCCA) to tackle this situation, which is based on the concept of correlation clustering. But this algorithm may also fail for certain cases. In order to overcome these situations, we propose a new clustering algorithm, called average correlation clustering algorithm (ACCA), which is able to produce better clustering solution than that produced by some others. ACCA is able to find groups of genes having more common transcription factors and similar pattern of variation in their expression values. Moreover, ACCA is more efficient than DCCA with respect to the time of execution. Like DCCA, we use the concept of correlation clustering concept introduced by Bansal et al. ACCA uses the correlation matrix in such a way that all genes in a cluster have the highest average correlation values with the genes in that cluster. We have applied ACCA and some well-known conventional methods including DCCA to two artificial and nine gene expression datasets, and compared the performance of the algorithms. The clustering results of ACCA are found to be more significantly relevant to the biological annotations than those of the other methods. Analysis of the results show the superiority of ACCA over some others in determining a group of genes having more common transcription factors and with similar pattern of variation in their expression profiles. Availability of the software: The software has been developed using C and Visual Basic languages, and can be executed on the Microsoft Windows platforms. The software may be downloaded as a zip file from http://www.isical.ac.in/~rajat. Then it needs to be installed. Two word files (included in the zip file) need to

Dietary patterns derived from principal component- and k-means cluster analysis: long-term association with coronary heart disease and stroke.

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Stricker, M D; Onland-Moret, N C; Boer, J M A; van der Schouw, Y T; Verschuren, W M M; May, A M; Peeters, P H M; Beulens, J W J

2013-03-01

Studies comparing dietary patterns derived from different a posteriori methods in view of predicting disease risk are scarce. We aimed to explore differences between dietary patterns derived from principal component- (PCA) and k-means cluster analysis (KCA) in relation to their food group composition and ability to predict CHD and stroke risk. The study was conducted in the EPIC-NL cohort that consists of 40,011 men and women. Baseline dietary intake was measured using a validated food-frequency questionnaire. Food items were consolidated into 31 food groups. Occurrence of CHD and stroke was assessed through linkage with registries. After 13 years of follow-up, 1,843 CHD and 588 stroke cases were documented. Both PCA and KCA extracted a prudent pattern (high intakes of fish, high-fiber products, raw vegetables, wine) and a western pattern (high consumption of French fries, fast food, low-fiber products, other alcoholic drinks, soft drinks with sugar) with small variation between components and clusters. The prudent component was associated with a reduced risk of CHD (HR for extreme quartiles: 0.87; 95%-CI: 0.75-1.00) and stroke (0.68; 0.53-0.88). The western component was not related to any outcome. The prudent cluster was related with a lower risk of CHD (0.91; 0.82-1.00) and stroke (0.79; 0.67-0.94) compared to the western cluster. PCA and KCA found similar underlying patterns with comparable associations with CHD and stroke risk. A prudent pattern reduced the risk of CHD and stroke. Copyright © 2012 Elsevier B.V. All rights reserved.

Comparing 3 dietary pattern methods--cluster analysis, factor analysis, and index analysis--With colorectal cancer risk: The NIH-AARP Diet and Health Study.

Science.gov (United States)

Reedy, Jill; Wirfält, Elisabet; Flood, Andrew; Mitrou, Panagiota N; Krebs-Smith, Susan M; Kipnis, Victor; Midthune, Douglas; Leitzmann, Michael; Hollenbeck, Albert; Schatzkin, Arthur; Subar, Amy F

2010-02-15

The authors compared dietary pattern methods-cluster analysis, factor analysis, and index analysis-with colorectal cancer risk in the National Institutes of Health (NIH)-AARP Diet and Health Study (n = 492,306). Data from a 124-item food frequency questionnaire (1995-1996) were used to identify 4 clusters for men (3 clusters for women), 3 factors, and 4 indexes. Comparisons were made with adjusted relative risks and 95% confidence intervals, distributions of individuals in clusters by quintile of factor and index scores, and health behavior characteristics. During 5 years of follow-up through 2000, 3,110 colorectal cancer cases were ascertained. In men, the vegetables and fruits cluster, the fruits and vegetables factor, the fat-reduced/diet foods factor, and all indexes were associated with reduced risk; the meat and potatoes factor was associated with increased risk. In women, reduced risk was found with the Healthy Eating Index-2005 and increased risk with the meat and potatoes factor. For men, beneficial health characteristics were seen with all fruit/vegetable patterns, diet foods patterns, and indexes, while poorer health characteristics were found with meat patterns. For women, findings were similar except that poorer health characteristics were seen with diet foods patterns. Similarities were found across methods, suggesting basic qualities of healthy diets. Nonetheless, findings vary because each method answers a different question.

Global patterns of drought recovery

Energy Technology Data Exchange (ETDEWEB)

Schwalm, Christopher R.; Anderegg, William R. L.; Michalak, Anna M.; Fisher, Joshua B.; Biondi, Franco; Koch, George; Litvak, Marcy; Ogle, Kiona; Shaw, John D.; Wolf, Adam; Huntzinger, Deborah N.; Schaefer, Kevin; Cook, Robert; Wei, Yaxing; Fang, Yuanyuan; Hayes, Daniel; Huang, Maoyi; Jain, Atul; Tian, Hanqin

2017-08-09

Drought is a recurring multi-factor phenomenon with major impacts on natural and human systems1-3. Drought is especially important for land carbon sink variability, influencing climate regulation of the terrestrial biosphere4. While 20th Century trends in drought regime are ambiguous, “more extreme extremes” as well as more frequent and severe droughts3,7 are expected in the 21st Century. Recovery time, the length of time an ecosystem requires to revert to its pre-drought functional state, is a critical metric of drought impact. Yet the spatiotemporal patterning and controls of drought recovery are largely unknown. Here we use three distinct global datasets of gross primary productivity to show that across diverse terrestrial ecosystems drought recovery times are driven by biological productivity and biodiversity, with drought length and severity of secondary importance. Recovery time, especially for extreme droughts, and the areal extent of ecosystems in recovery from drought generally increase over the 20th Century, supporting an increase globally in drought impact8. Our results indicate that if future Anthropocene droughts become more widespread as expected, that droughts will become more frequent relative to recovery time. This increases the risk of entering a new regime where vegetation never recovers to its original state and widespread degradation of the land carbon sink ensues.

Clusters of week-specific maternal gestational weight gain pattern and their association with birthweight: an observational cohort study.

Science.gov (United States)

Liang, Huan; Yin, Chuanmin; Dong, Xinran; Acharya, Ganesh; Li, Xiaotian

2017-10-01

Gestational weight gain varies widely among different populations, and an inappropriate gestational weight gain is associated with adverse pregnancy outcomes. We aimed to investigate week-specific serial changes in gestational weight gain in an urban Chinese population to derive clusters of gestational weight gain patterns and explore the impact of gestational weight gain patterns on birthweight. This was an observational cohort study of 6130 women delivered at a university hospital in Shanghai, China. Pre-pregnancy bodyweight, height, week-specific and total gestational weight gain, pregnancy outcome and birthweight were extracted using electronic medical records. The association between gestational weight gain and gestational age was tested using linear regression, and week-specific reference percentiles for gestational weight gain were calculated. Hierarchical clustering was used to derive gestational weight gain clusters. Mean birthweight among the clusters was compared using Dunnet's test. We found a significant linear association between gestational weight gain and gestational age (r = 0.56; p gain pattern were identified. The birthweight significantly correlated with gestational weight gain (r = 0.28; p gain throughout the pregnancy, the mean birthweight among the clusters that had abnormal gestational weight gain (inadequate or excessive) in the third trimester was significantly different (p gain (between 5 and 95 percentile) in the third-trimester had similar mean birthweight. Women with abnormal gestational weight gain before the third-trimester still had a fair chance of delivering a normal birthweight baby if their gestational weight gain was normal in the third-trimester, suggesting that interventions started even late in pregnancy may have a positive effect on fetal growth. © 2017 Nordic Federation of Societies of Obstetrics and Gynecology.

Application of a parallel genetic algorithm to the global optimization of medium-sized Au-Pd sub-nanometre clusters

Science.gov (United States)

Hussein, Heider A.; Demiroglu, Ilker; Johnston, Roy L.

2018-02-01

To contribute to the discussion of the high activity and reactivity of Au-Pd system, we have adopted the BPGA-DFT approach to study the structural and energetic properties of medium-sized Au-Pd sub-nanometre clusters with 11-18 atoms. We have examined the structural behaviour and stability as a function of cluster size and composition. The study suggests 2D-3D crossover points for pure Au clusters at 14 and 16 atoms, whereas pure Pd clusters are all found to be 3D. For Au-Pd nanoalloys, the role of cluster size and the influence of doping were found to be extensive and non-monotonic in altering cluster structures. Various stability criteria (e.g. binding energies, second differences in energy, and mixing energies) are used to evaluate the energetics, structures, and tendency of segregation in sub-nanometre Au-Pd clusters. HOMO-LUMO gaps were calculated to give additional information on cluster stability and a systematic homotop search was used to evaluate the energies of the generated global minima of mono-substituted clusters and the preferred doping sites, as well as confirming the validity of the BPGA-DFT approach.

Regional mantle upwelling on Venus: The Beta-Atla-Themis anomaly and correlation with global tectonic patterns

Science.gov (United States)

Crumpler, L. S.; Head, J. W.; Aubele, Jayne C.

1993-01-01

The morphology and global distribution of volcanic centers and their association with other geological characteristics offers significant insight into the global patterns of geology, tectonic style, thermal state, and interior dynamics of Venus. Magellan data permit the detailed geological interpretation necessary to address questions about interior dynamics of Venus particularly as they reflect relatively physical, chemical, and thermal conditions of the interior. This paper focuses on the distribution of anomalous concentrations of volcanic centers on Venus and regional patterns of tectonic deformation as it may relate to the identification of global internal anomalies, including mantle dynamic, petrological, or thermal patterns.

Coordinated ground-based, low altitude satellite and Cluster observations on global and local scales during a transient post-noon sector excursion of the magnetospheric cusp

Directory of Open Access Journals (Sweden)

H. J. Opgenoorth

Full Text Available On 14 January 2001, the four Cluster spacecraft passed through the northern magnetospheric mantle in close conjunction to the EISCAT Svalbard Radar (ESR and approached the post-noon dayside magnetopause over Green-land between 13:00 and 14:00 UT. During that interval, a sudden reorganisation of the high-latitude dayside convection pattern occurred after 13:20 UT, most likely caused by a direction change of the Solar wind magnetic field. The result was an eastward and poleward directed flow-channel, as monitored by the SuperDARN radar network and also by arrays of ground-based magnetometers in Canada, Greenland and Scandinavia. After an initial eastward and later poleward expansion of the flow-channel between 13:20 and 13:40 UT, the four Cluster spacecraft, and the field line footprints covered by the eastward looking scan cycle of the Söndre Strömfjord incoherent scatter radar were engulfed by cusp-like precipitation with transient magnetic and electric field signatures. In addition, the EISCAT Svalbard Radar detected strong transient effects of the convection reorganisation, a poleward moving precipitation, and a fast ion flow-channel in association with the auroral structures that suddenly formed to the west and north of the radar. From a detailed analysis of the coordinated Cluster and ground-based data, it was found that this extraordinary transient convection pattern, indeed, had moved the cusp precipitation from its former pre-noon position into the late post-noon sector, allowing for the first and quite unexpected encounter of the cusp by the Cluster spacecraft. Our findings illustrate the large amplitude of cusp dynamics even in response to moderate solar wind forcing. The global ground-based data proves to be an invaluable tool to monitor the dynamics and width of the affected magnetospheric regions.

Key words. Magnetospheric cusp, ionosphere, reconnection, convection flow-channel, Cluster, ground-based observations

Assessing the drivers shaping global patterns of urban vegetation landscape structure.

Science.gov (United States)

Dobbs, C; Nitschke, C; Kendal, D

2017-08-15

Vegetation is one of the main resources involve in ecosystem functioning and providing ecosystem services in urban areas. Little is known on the landscape structure patterns of vegetation existing in urban areas at the global scale and the drivers of these patterns. We studied the landscape structure of one hundred cities around the globe, and their relation to demography (population), socioeconomic factors (GDP, Gini Index), climate factors (temperature and rain) and topographic characteristics (altitude, variation in altitude). The data revealed that the best descriptors of landscape structure were amount, fragmentation and spatial distribution of vegetation. Populated cities tend to have less, more fragmented, less connected vegetation with a centre of the city with low vegetation cover. Results also provided insights on the influence of socioeconomics at a global scale, as landscape structure was more fragmented in areas that are economically unequal and coming from emergent economies. This study shows the effects of the social system and climate on urban landscape patterns that gives useful insights for the distribution in the provision of ecosystem services in urban areas and therefore the maintenance of human well-being. This information can support local and global policy and planning which is committing our cities to provide accessible and inclusive green space for all urban inhabitants. Copyright © 2017 Elsevier B.V. All rights reserved.

The global Minmax k-means algorithm.

Science.gov (United States)

Wang, Xiaoyan; Bai, Yanping

2016-01-01

The global k -means algorithm is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure from suitable initial positions, and employs k -means to minimize the sum of the intra-cluster variances. However the global k -means algorithm sometimes results singleton clusters and the initial positions sometimes are bad, after a bad initialization, poor local optimal can be easily obtained by k -means algorithm. In this paper, we modified the global k -means algorithm to eliminate the singleton clusters at first, and then we apply MinMax k -means clustering error method to global k -means algorithm to overcome the effect of bad initialization, proposed the global Minmax k -means algorithm. The proposed clustering method is tested on some popular data sets and compared to the k -means algorithm, the global k -means algorithm and the MinMax k -means algorithm. The experiment results show our proposed algorithm outperforms other algorithms mentioned in the paper.

Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer Monsoon

Directory of Open Access Journals (Sweden)

Moumita Saha

2016-01-01

Full Text Available Forecasting the Indian summer monsoon is a challenging task due to its complex and nonlinear behavior. A large number of global climatic variables with varying interaction patterns over years influence monsoon. Various statistical and neural prediction models have been proposed for forecasting monsoon, but many of them fail to capture variability over years. The skill of predictor variables of monsoon also evolves over time. In this article, we propose a joint-clustering of monsoon years and predictors for understanding and predicting the monsoon. This is achieved by subspace clustering algorithm. It groups the years based on prevailing global climatic condition using statistical clustering technique and subsequently for each such group it identifies significant climatic predictor variables which assist in better prediction. Prediction model is designed to frame individual cluster using random forest of regression tree. Prediction of aggregate and regional monsoon is attempted. Mean absolute error of 5.2% is obtained for forecasting aggregate Indian summer monsoon. Errors in predicting the regional monsoons are also comparable in comparison to the high variation of regional precipitation. Proposed joint-clustering based ensemble model is observed to be superior to existing monsoon prediction models and it also surpasses general nonclustering based prediction models.

Attribution of Large-Scale Climate Patterns to Seasonal Peak-Flow and Prospects for Prediction Globally

Science.gov (United States)

Lee, Donghoon; Ward, Philip; Block, Paul

2018-02-01

Flood-related fatalities and impacts on society surpass those from all other natural disasters globally. While the inclusion of large-scale climate drivers in streamflow (or high-flow) prediction has been widely studied, an explicit link to global-scale long-lead prediction is lacking, which can lead to an improved understanding of potential flood propensity. Here we attribute seasonal peak-flow to large-scale climate patterns, including the El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), and Atlantic Multidecadal Oscillation (AMO), using streamflow station observations and simulations from PCR-GLOBWB, a global-scale hydrologic model. Statistically significantly correlated climate patterns and streamflow autocorrelation are subsequently applied as predictors to build a global-scale season-ahead prediction model, with prediction performance evaluated by the mean squared error skill score (MSESS) and the categorical Gerrity skill score (GSS). Globally, fair-to-good prediction skill (20% ≤ MSESS and 0.2 ≤ GSS) is evident for a number of locations (28% of stations and 29% of land area), most notably in data-poor regions (e.g., West and Central Africa). The persistence of such relevant climate patterns can improve understanding of the propensity for floods at the seasonal scale. The prediction approach developed here lays the groundwork for further improving local-scale seasonal peak-flow prediction by identifying relevant global-scale climate patterns. This is especially attractive for regions with limited observations and or little capacity to develop flood early warning systems.

Global Patterns of the Isotopic Composition of Soil and Plant Nitrogen

Science.gov (United States)

Amundson, R.; Yoo, K.

2014-12-01

From a societal perspective, soil N follows only soil C in the importance of soil to 21st century environmental issues. Amundson et al (2003) developed a mass balance model for soil N and the ratio of 15N/14N, and provided the first global projections of the spatial patterns of soil and plant δ15N values. It was hypothesized that state factors, particularly climate, should drive broad patterns of soil and plant δ15N values in a manner analogous to the known patterns of total soil N (e.g. Post et al., 1984). At that time, the N isotope data available to explore the effect of individual factors was modest. In the past decade, numerous papers from a broad spectrum of locations have created a rich database that can be used to further refine the initial projections made more than a decade ago. In this paper, hundreds of published measurements will be used to more deeply examine the climatic impacts on soil and plant δ15N values. Additionally, we will focus on the local controls of topography on ecosystem N cycling, which can create local isotopic variation that is similar in magnitude to the global effects of climate. The adoption of process-based models from the hillslope geomorphology community appears to be a powerful tool for explaining some existing data from toposequences, designing new studies of topographic controls on biogeochemistry, and particularly for parameterization in global models. Amundson, R., A.T. Austin, E.A.G. Schuur, K. Yoo, V. Matzek, C. Kendall, A. Uebersax, D. Brenner, and W.T. Baisden. 2003. Global Biogeochemical Cycles 17(1):1031.

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.

Observations and calculations of two-dimensional angular optical scattering (TAOS) patterns of a single levitated cluster of two and four microspheres

International Nuclear Information System (INIS)

Krieger, U.K.; Meier, P.

2011-01-01

We use single bi-sphere particles levitated in an electrodynamic balance to record two-dimensional angular scattering patterns at different angles of the coordinate system of the aggregate relative to the incident laser beam. Due to Brownian motion the particle covers the whole set of possible angles with time and allows to select patterns with high symmetry for analysis. These are qualitatively compared to numerical calculations. A small cluster of four spheres shows complex scattering patterns, comparison with computations suggest a low compactness for these clusters. An experimental procedure is proposed for studying restructuring effects occurring in mixed particles upon evaporation. - Research highlights: → Single levitated bi-sphere particle. → Two-dimensional angular scattering pattern. → Comparison experiment with computations.

Global classification of human facial healthy skin using PLS discriminant analysis and clustering analysis.

Science.gov (United States)

Guinot, C; Latreille, J; Tenenhaus, M; Malvy, D J

2001-04-01

Today's classifications of healthy skin are predominantly based on a very limited number of skin characteristics, such as skin oiliness or susceptibility to sun exposure. The aim of the present analysis was to set up a global classification of healthy facial skin, using mathematical models. This classification is based on clinical, biophysical skin characteristics and self-reported information related to the skin, as well as the results of a theoretical skin classification assessed separately for the frontal and the malar zones of the face. In order to maximize the predictive power of the models with a minimum of variables, the Partial Least Square (PLS) discriminant analysis method was used. The resulting PLS components were subjected to clustering analyses to identify the plausible number of clusters and to group the individuals according to their proximities. Using this approach, four PLS components could be constructed and six clusters were found relevant. So, from the 36 hypothetical combinations of the theoretical skin types classification, we tended to a strengthened six classes proposal. Our data suggest that the association of the PLS discriminant analysis and the clustering methods leads to a valid and simple way to classify healthy human skin and represents a potentially useful tool for cosmetic and dermatological research.

Global patterns in mangrove soil carbon stocks and losses

KAUST Repository

Atwood, Trisha B.; Connolly, Rod M.; Almahasheer, Hanan; Carnell, Paul E.; Duarte, Carlos M.; Ewers Lewis, Carolyn J.; Irigoien, Xabier; Kelleway, Jeffrey J.; Lavery, Paul S.; Macreadie, Peter I.; Serrano, Oscar; Sanders, Christian J.; Santos, Isaac; Steven, Andrew D. L.; Lovelock, Catherine E.

2017-01-01

. Global potential CO2 emissions from soils as a result of mangrove loss were estimated to be ~7.0 Tg CO2e yr−1. Countries with the highest potential CO2 emissions from soils are Indonesia (3,410 Gg CO2e yr−1) and Malaysia (1,288 Gg CO2e yr−1). The patterns

Application of hierarchical clustering method to classify of space-time rainfall patterns

Science.gov (United States)

Yu, Hwa-Lung; Chang, Tu-Je

2010-05-01

Understanding the local precipitation patterns is essential to the water resources management and flooding mitigation. The precipitation patterns can vary in space and time depending upon the factors from different spatial scales such as local topological changes and macroscopic atmospheric circulation. The spatiotemporal variation of precipitation in Taiwan is significant due to its complex terrain and its location at west pacific and subtropical area, where is the boundary between the pacific ocean and Asia continent with the complex interactions among the climatic processes. This study characterizes local-scale precipitation patterns by classifying the historical space-time precipitation records. We applied the hierarchical ascending clustering method to analyze the precipitation records from 1960 to 2008 at the six rainfall stations located in Lan-yang catchment at the northeast of the island. Our results identify the four primary space-time precipitation types which may result from distinct driving forces from the changes of atmospheric variables and topology at different space-time scales. This study also presents an important application of the statistical downscaling to combine large-scale upper-air circulation with local space-time precipitation patterns.

Identification of spatiotemporal nutrient patterns in a coastal bay via an integrated k-means clustering and gravity model.

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Chang, Ni-Bin; Wimberly, Brent; Xuan, Zhemin

2012-03-01

This study presents an integrated k-means clustering and gravity model (IKCGM) for investigating the spatiotemporal patterns of nutrient and associated dissolved oxygen levels in Tampa Bay, Florida. By using a k-means clustering analysis to first partition the nutrient data into a user-specified number of subsets, it is possible to discover the spatiotemporal patterns of nutrient distribution in the bay and capture the inherent linkages of hydrodynamic and biogeochemical features. Such patterns may then be combined with a gravity model to link the nutrient source contribution from each coastal watershed to the generated clusters in the bay to aid in the source proportion analysis for environmental management. The clustering analysis was carried out based on 1 year (2008) water quality data composed of 55 sample stations throughout Tampa Bay collected by the Environmental Protection Commission of Hillsborough County. In addition, hydrological and river water quality data of the same year were acquired from the United States Geological Survey's National Water Information System to support the gravity modeling analysis. The results show that the k-means model with 8 clusters is the optimal choice, in which cluster 2 at Lower Tampa Bay had the minimum values of total nitrogen (TN) concentrations, chlorophyll a (Chl-a) concentrations, and ocean color values in every season as well as the minimum concentration of total phosphorus (TP) in three consecutive seasons in 2008. The datasets indicate that Lower Tampa Bay is an area with limited nutrient input throughout the year. Cluster 5, located in Middle Tampa Bay, displayed elevated TN concentrations, ocean color values, and Chl-a concentrations, suggesting that high values of colored dissolved organic matter are linked with some nutrient sources. The data presented by the gravity modeling analysis indicate that the Alafia River Basin is the major contributor of nutrients in terms of both TP and TN values in all seasons

Biomarker clusters are differentially associated with longitudinal cognitive decline in late midlife

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Racine, Annie M.; Koscik, Rebecca L.; Berman, Sara E.; Nicholas, Christopher R.; Clark, Lindsay R.; Okonkwo, Ozioma C.; Rowley, Howard A.; Asthana, Sanjay; Bendlin, Barbara B.; Blennow, Kaj; Zetterberg, Henrik; Gleason, Carey E.; Carlsson, Cynthia M.

2016-01-01

The ability to detect preclinical Alzheimer’s disease is of great importance, as this stage of the Alzheimer’s continuum is believed to provide a key window for intervention and prevention. As Alzheimer’s disease is characterized by multiple pathological changes, a biomarker panel reflecting co-occurring pathology will likely be most useful for early detection. Towards this end, 175 late middle-aged participants (mean age 55.9 ± 5.7 years at first cognitive assessment, 70% female) were recruited from two longitudinally followed cohorts to undergo magnetic resonance imaging and lumbar puncture. Cluster analysis was used to group individuals based on biomarkers of amyloid pathology (cerebrospinal fluid amyloid-β42/amyloid-β40 assay levels), magnetic resonance imaging-derived measures of neurodegeneration/atrophy (cerebrospinal fluid-to-brain volume ratio, and hippocampal volume), neurofibrillary tangles (cerebrospinal fluid phosphorylated tau181 assay levels), and a brain-based marker of vascular risk (total white matter hyperintensity lesion volume). Four biomarker clusters emerged consistent with preclinical features of (i) Alzheimer’s disease; (ii) mixed Alzheimer’s disease and vascular aetiology; (iii) suspected non-Alzheimer’s disease aetiology; and (iv) healthy ageing. Cognitive decline was then analysed between clusters using longitudinal assessments of episodic memory, semantic memory, executive function, and global cognitive function with linear mixed effects modelling. Cluster 1 exhibited a higher intercept and greater rates of decline on tests of episodic memory. Cluster 2 had a lower intercept on a test of semantic memory and both Cluster 2 and Cluster 3 had steeper rates of decline on a test of global cognition. Additional analyses on Cluster 3, which had the smallest hippocampal volume, suggest that its biomarker profile is more likely due to hippocampal vulnerability and not to detectable specific volume loss exceeding the rate of normal

Cluster fusion algorithm: application to Lennard-Jones clusters

DEFF Research Database (Denmark)

Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter

2006-01-01

paths up to the cluster size of 150 atoms. We demonstrate that in this way all known global minima structures of the Lennard-Jones clusters can be found. Our method provides an efficient tool for the calculation and analysis of atomic cluster structure. With its use we justify the magic number sequence......We present a new general theoretical framework for modelling the cluster structure and apply it to description of the Lennard-Jones clusters. Starting from the initial tetrahedral cluster configuration, adding new atoms to the system and absorbing its energy at each step, we find cluster growing...... for the clusters of noble gas atoms and compare it with experimental observations. We report the striking correspondence of the peaks in the dependence of the second derivative of the binding energy per atom on cluster size calculated for the chain of the Lennard-Jones clusters based on the icosahedral symmetry...

Cluster fusion algorithm: application to Lennard-Jones clusters

DEFF Research Database (Denmark)

Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter

2008-01-01

paths up to the cluster size of 150 atoms. We demonstrate that in this way all known global minima structures of the Lennard-Jones clusters can be found. Our method provides an efficient tool for the calculation and analysis of atomic cluster structure. With its use we justify the magic number sequence......We present a new general theoretical framework for modelling the cluster structure and apply it to description of the Lennard-Jones clusters. Starting from the initial tetrahedral cluster configuration, adding new atoms to the system and absorbing its energy at each step, we find cluster growing...... for the clusters of noble gas atoms and compare it with experimental observations. We report the striking correspondence of the peaks in the dependence of the second derivative of the binding energy per atom on cluster size calculated for the chain of the Lennard-Jones clusters based on the icosahedral symmetry...

An investigation on thermal patterns in Iran based on spatial autocorrelation

Science.gov (United States)

Fallah Ghalhari, Gholamabbas; Dadashi Roudbari, Abbasali

2018-02-01

The present study aimed at investigating temporal-spatial patterns and monthly patterns of temperature in Iran using new spatial statistical methods such as cluster and outlier analysis, and hotspot analysis. To do so, climatic parameters, monthly average temperature of 122 synoptic stations, were assessed. Statistical analysis showed that January with 120.75% had the most fluctuation among the studied months. Global Moran's Index revealed that yearly changes of temperature in Iran followed a strong spatially clustered pattern. Findings showed that the biggest thermal cluster pattern in Iran, 0.975388, occurred in May. Cluster and outlier analyses showed that thermal homogeneity in Iran decreases in cold months, while it increases in warm months. This is due to the radiation angle and synoptic systems which strongly influence thermal order in Iran. The elevations, however, have the most notable part proved by Geographically weighted regression model. Iran's thermal analysis through hotspot showed that hot thermal patterns (very hot, hot, and semi-hot) were dominant in the South, covering an area of 33.5% (about 552,145.3 km2). Regions such as mountain foot and low lands lack any significant spatial autocorrelation, 25.2% covering about 415,345.1 km2. The last is the cold thermal area (very cold, cold, and semi-cold) with about 25.2% covering about 552,145.3 km2 of the whole area of Iran.

The Determination of Children's Knowledge of Global Lunar Patterns from Online Essays Using Text Mining Analysis

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Cheon, Jongpil; Lee, Sangno; Smith, Walter; Song, Jaeki; Kim, Yongjin

2013-01-01

The purpose of this study was to use text mining analysis of early adolescents' online essays to determine their knowledge of global lunar patterns. Australian and American students in grades five to seven wrote about global lunar patterns they had discovered by sharing observations with each other via the Internet. These essays were analyzed for…

Determination of Arctic sea ice variability modes on interannual timescales via nonhierarchical clustering

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Fučkar, Neven-Stjepan; Guemas, Virginie; Massonnet, François; Doblas-Reyes, Francisco

2015-04-01

Over the modern observational era, the northern hemisphere sea ice concentration, age and thickness have experienced a sharp long-term decline superimposed with strong internal variability. Hence, there is a crucial need to identify robust patterns of Arctic sea ice variability on interannual timescales and disentangle them from the long-term trend in noisy datasets. The principal component analysis (PCA) is a versatile and broadly used method for the study of climate variability. However, the PCA has several limiting aspects because it assumes that all modes of variability have symmetry between positive and negative phases, and suppresses nonlinearities by using a linear covariance matrix. Clustering methods offer an alternative set of dimension reduction tools that are more robust and capable of taking into account possible nonlinear characteristics of a climate field. Cluster analysis aggregates data into groups or clusters based on their distance, to simultaneously minimize the distance between data points in a given cluster and maximize the distance between the centers of the clusters. We extract modes of Arctic interannual sea-ice variability with nonhierarchical K-means cluster analysis and investigate the mechanisms leading to these modes. Our focus is on the sea ice thickness (SIT) as the base variable for clustering because SIT holds most of the climate memory for variability and predictability on interannual timescales. We primarily use global reconstructions of sea ice fields with a state-of-the-art ocean-sea-ice model, but we also verify the robustness of determined clusters in other Arctic sea ice datasets. Applied cluster analysis over the 1958-2013 period shows that the optimal number of detrended SIT clusters is K=3. Determined SIT cluster patterns and their time series of occurrence are rather similar between different seasons and months. Two opposite thermodynamic modes are characterized with prevailing negative or positive SIT anomalies over the

Rapid and coordinated processing of global motion images by local clusters of retinal ganglion cells.

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Matsumoto, Akihiro; Tachibana, Masao

2017-01-01

Even when the body is stationary, the whole retinal image is always in motion by fixational eye movements and saccades that move the eye between fixation points. Accumulating evidence indicates that the brain is equipped with specific mechanisms for compensating for the global motion induced by these eye movements. However, it is not yet fully understood how the retina processes global motion images during eye movements. Here we show that global motion images evoke novel coordinated firing in retinal ganglion cells (GCs). We simultaneously recorded the firing of GCs in the goldfish isolated retina using a multi-electrode array, and classified each GC based on the temporal profile of its receptive field (RF). A moving target that accompanied the global motion (simulating a saccade following a period of fixational eye movements) modulated the RF properties and evoked synchronized and correlated firing among local clusters of the specific GCs. Our findings provide a novel concept for retinal information processing during eye movements.

Using cluster analysis to organize and explore regional GPS velocities

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Simpson, Robert W.; Thatcher, Wayne; Savage, James C.

2012-01-01

Cluster analysis offers a simple visual exploratory tool for the initial investigation of regional Global Positioning System (GPS) velocity observations, which are providing increasingly precise mappings of actively deforming continental lithosphere. The deformation fields from dense regional GPS networks can often be concisely described in terms of relatively coherent blocks bounded by active faults, although the choice of blocks, their number and size, can be subjective and is often guided by the distribution of known faults. To illustrate our method, we apply cluster analysis to GPS velocities from the San Francisco Bay Region, California, to search for spatially coherent patterns of deformation, including evidence of block-like behavior. The clustering process identifies four robust groupings of velocities that we identify with four crustal blocks. Although the analysis uses no prior geologic information other than the GPS velocities, the cluster/block boundaries track three major faults, both locked and creeping.

Children's Brain Responses to Optic Flow Vary by Pattern Type and Motion Speed.

Directory of Open Access Journals (Sweden)

Rick O Gilmore

Full Text Available Structured patterns of global visual motion called optic flow provide crucial information about an observer's speed and direction of self-motion and about the geometry of the environment. Brain and behavioral responses to optic flow undergo considerable postnatal maturation, but relatively little brain imaging evidence describes the time course of development in motion processing systems in early to middle childhood, a time when psychophysical data suggest that there are changes in sensitivity. To fill this gap, electroencephalographic (EEG responses were recorded in 4- to 8-year-old children who viewed three time-varying optic flow patterns (translation, rotation, and radial expansion/contraction at three different speeds (2, 4, and 8 deg/s. Modulations of global motion coherence evoked coherent EEG responses at the first harmonic that differed by flow pattern and responses at the third harmonic and dot update rate that varied by speed. Pattern-related responses clustered over right lateral channels while speed-related responses clustered over midline channels. Both children and adults show widespread responses to modulations of motion coherence at the second harmonic that are not selective for pattern or speed. The results suggest that the developing brain segregates the processing of optic flow pattern from speed and that an adult-like pattern of neural responses to optic flow has begun to emerge by early to middle childhood.

Relevant Subspace Clustering

DEFF Research Database (Denmark)

Müller, Emmanuel; Assent, Ira; Günnemann, Stephan

2009-01-01

Subspace clustering aims at detecting clusters in any subspace projection of a high dimensional space. As the number of possible subspace projections is exponential in the number of dimensions, the result is often tremendously large. Recent approaches fail to reduce results to relevant subspace...... clusters. Their results are typically highly redundant, i.e. many clusters are detected multiple times in several projections. In this work, we propose a novel model for relevant subspace clustering (RESCU). We present a global optimization which detects the most interesting non-redundant subspace clusters...... achieves top clustering quality while competing approaches show greatly varying performance....

Mars Global Digital Dune Database (MGD3): Global dune distribution and wind pattern observations

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Hayward, Rosalyn K.; Fenton, Lori; Titus, Timothy N.

2014-01-01

The Mars Global Digital Dune Database (MGD3) is complete and now extends from 90°N to 90°S latitude. The recently released south pole (SP) portion (MC-30) of MGD3 adds ∼60,000 km2 of medium to large-size dark dune fields and ∼15,000 km2 of sand deposits and smaller dune fields to the previously released equatorial (EQ, ∼70,000 km2), and north pole (NP, ∼845,000 km2) portions of the database, bringing the global total to ∼975,000 km2. Nearly all NP dunes are part of large sand seas, while the majority of EQ and SP dune fields are individual dune fields located in craters. Despite the differences between Mars and Earth, their dune and dune field morphologies are strikingly similar. Bullseye dune fields, named for their concentric ring pattern, are the exception, possibly owing their distinctive appearance to winds that are unique to the crater environment. Ground-based wind directions are derived from slipface (SF) orientation and dune centroid azimuth (DCA), a measure of the relative location of a dune field inside a crater. SF and DCA often preserve evidence of different wind directions, suggesting the importance of local, topographically influenced winds. In general however, ground-based wind directions are broadly consistent with expected global patterns, such as polar easterlies. Intriguingly, between 40°S and 80°S latitude both SF and DCA preserve their strongest, though different, dominant wind direction, with transport toward the west and east for SF-derived winds and toward the north and west for DCA-derived winds.

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

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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.

A Novel Divisive Hierarchical Clustering Algorithm for Geospatial Analysis

Directory of Open Access Journals (Sweden)

Shaoning Li

2017-01-01

Full Text Available In the fields of geographic information systems (GIS and remote sensing (RS, the clustering algorithm has been widely used for image segmentation, pattern recognition, and cartographic generalization. Although clustering analysis plays a key role in geospatial modelling, traditional clustering methods are limited due to computational complexity, noise resistant ability and robustness. Furthermore, traditional methods are more focused on the adjacent spatial context, which makes it hard for the clustering methods to be applied to multi-density discrete objects. In this paper, a new method, cell-dividing hierarchical clustering (CDHC, is proposed based on convex hull retraction. The main steps are as follows. First, a convex hull structure is constructed to describe the global spatial context of geospatial objects. Then, the retracting structure of each borderline is established in sequence by setting the initial parameter. The objects are split into two clusters (i.e., “sub-clusters” if the retracting structure intersects with the borderlines. Finally, clusters are repeatedly split and the initial parameter is updated until the terminate condition is satisfied. The experimental results show that CDHC separates the multi-density objects from noise sufficiently and also reduces complexity compared to the traditional agglomerative hierarchical clustering algorithm.

Seasonality of cholera from 1974 to 2005: a review of global patterns

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Feldacker Caryl

2008-06-01

Full Text Available Abstract Background The seasonality of cholera is described in various study areas throughout the world. However, no study examines how temporal cycles of the disease vary around the world or reviews its hypothesized causes. This paper reviews the literature on the seasonality of cholera and describes its temporal cycles by compiling and analyzing 32 years of global cholera data. This paper also provides a detailed literature review on regional patterns and environmental and climatic drivers of cholera patterns. Data, Methods, and Results Cholera data are compiled from 1974 to 2005 from the World Health Organization Weekly Epidemiological Reports, a database that includes all reported cholera cases in 140 countries. The data are analyzed to measure whether season, latitude, and their interaction are significantly associated with the country-level number of outbreaks in each of the 12 preceding months using separate negative binomial regression models for northern, southern, and combined hemispheres. Likelihood ratios tests are used to determine the model of best fit. The results suggest that cholera outbreaks demonstrate seasonal patterns in higher absolute latitudes, but closer to the equator, cholera outbreaks do not follow a clear seasonal pattern. Conclusion The findings suggest that environmental and climatic factors partially control the temporal variability of cholera. These results also indirectly contribute to the growing debate about the effects of climate change and global warming. As climate change threatens to increase global temperature, resulting rises in sea levels and temperatures may influence the temporal fluctuations of cholera, potentially increasing the frequency and duration of cholera outbreaks.

Stable Chimeras and Independently Synchronizable Clusters

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Cho, Young Sul; Nishikawa, Takashi; Motter, Adilson E.

2017-08-01

Cluster synchronization is a phenomenon in which a network self-organizes into a pattern of synchronized sets. It has been shown that diverse patterns of stable cluster synchronization can be captured by symmetries of the network. Here, we establish a theoretical basis to divide an arbitrary pattern of symmetry clusters into independently synchronizable cluster sets, in which the synchronization stability of the individual clusters in each set is decoupled from that in all the other sets. Using this framework, we suggest a new approach to find permanently stable chimera states by capturing two or more symmetry clusters—at least one stable and one unstable—that compose the entire fully symmetric network.

Modifiable lifestyle behavior patterns, sedentary time and physical activity contexts: a cluster analysis among middle school boys and girls in the SALTA study.

Science.gov (United States)

Marques, Elisa A; Pizarro, Andreia N; Figueiredo, Pedro; Mota, Jorge; Santos, Maria P

2013-06-01

To analyze how modifiable health-related variables are clustered and associated with children's participation in play, active travel and structured exercise and sport among boys and girls. Data were collected from 9 middle-schools in Porto (Portugal) area. A total of 636 children in the 6th grade (340 girls and 296 boys) with a mean age of 11.64 years old participated in the study. Cluster analyses were used to identify patterns of lifestyle and healthy/unhealthy behaviors. Multinomial logistic regression analysis was used to estimate associations between cluster allocation, sedentary time and participation in three different physical activity (PA) contexts: play, active travel, and structured exercise/sport. Four distinct clusters were identified based on four lifestyle risk factors. The most disadvantaged cluster was characterized by high body mass index, low high-density lipoprotein cholesterol and cardiorespiratory fitness and a moderate level of moderate to vigorous PA. Everyday outdoor play (OR=1.85, 95%CI 0.318-0.915) and structured exercise/sport (OR=1.85, 95%CI 0.291-0.990) were associated with healthier lifestyle patterns. There were no significant associations between health patterns and sedentary time or travel mode. Outdoor play and sport/exercise participation seem more important than active travel from school in influencing children's healthy cluster profiles. Copyright © 2013 Elsevier Inc. All rights reserved.

Are child eating patterns being transformed globally?

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Adair, Linda S; Popkin, Barry M

2005-07-01

To examine the extent to which child dietary patterns and trends are changing globally. Diets of children 2 to 19 years of age were studied with nationally representative data from Russia and the United States, nationwide data from China, and regional data from metropolitan Cebu, Philippines. Twenty-four-hour dietary recalls were examined at several points in time to examine trends in calories consumed away from home, snacking behavior, and soft drink and modern fast food consumption. Urban-rural trends were compared. U.S. and Cebu youth consume more than one-third of their daily calories and a higher proportion of snack calories from foods prepared away from home. In contrast, away from home food consumption is minimal in Chinese and Russian children. U.S. and Cebu youth consume about one-fifth of their total daily energy from snacks, but snacks provide a much lower proportion of energy in Russia ( approximately 16%) and China (where snacks provide only approximately 1% of energy). Fast food plays a much more dominant role in the American diet ( approximately 20% of energy vs. 2% to 7% in the other countries), but as yet does not contribute substantially to children's diets in the other countries. Urban-rural differences were found to be important, but narrowing over time, for China and Cebu, whereas they are widening for Russia. This research suggests that globalization of the fast food and other modern food sectors is beginning to affect child eating patterns in several countries undergoing nutrition transition. However, the contribution of fast food and soft drinks to the diet of children remains relatively small in China, Russia, and Cebu, Philippines, relative to the United States.

HANPP Collection: Global Patterns in Human Appropriation of Net Primary Productivity (HANPP)

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National Aeronautics and Space Administration — The Global Patterns in Human Appropriation of Net Primary Productivity (HANPP) portion of the HANPP Collection represents a digital map of human appropriation of net...

Effective Hamiltonian and low-lying eigenenergy clustering patterns of four-sublattice antiferromagnets

DEFF Research Database (Denmark)

Zhang, N.G.; Henley, C.L.; Rischel, C.

2002-01-01

We study the low-lying eigenenergy clustering patterns of quantum antiferromagnets with p sublattices (in particular p = 4). We treat each sublattice as a large spin, and using second-order degenerate perturbation theory, we derive the effective (biquadratic) Hamiltonian coupling the p large spins....... In order to compare with exact diagonalizations, the Hamiltonian is explicitly written for a finite-size lattice, and it contains information on energies of excited states as well as the ground state. The result is applied to the face-centered-cubic Type-I antiferromagnet of spin 1/2, including second...

Global control of colored moiré pattern in layered optical structures

Science.gov (United States)

Li, Kunyang; Zhou, Yangui; Pan, Di; Ma, Xueyan; Ma, Hongqin; Liang, Haowen; Zhou, Jianying

2018-05-01

Accurate description of visual effect of colored moiré pattern caused by layered optical structures consisting of gratings and Fresnel lens is proposed in this work. The colored moiré arising from the periodic and quasi-periodic structures is numerically simulated and experimentally verified. It is found that the visibility of moiré pattern generated by refractive optical elements is related to not only the spatial structures of gratings but also the viewing angles. To effectively control the moiré visibility, two constituting gratings are slightly separated. Such scheme is proved to be effective to globally eliminate moiré pattern for displays containing refractive optical films with quasi-periodic structures.

The function of Shp2 tyrosine phosphatase in the dispersal of acetylcholine receptor clusters

Directory of Open Access Journals (Sweden)

Madhavan Raghavan

2008-07-01

Full Text Available Abstract Background A crucial event in the development of the vertebrate neuromuscular junction (NMJ is the postsynaptic enrichment of muscle acetylcholine (ACh receptors (AChRs. This process involves two distinct steps: the local clustering of AChRs at synapses, which depends on the activation of the muscle-specific receptor tyrosine kinase MuSK by neural agrin, and the global dispersal of aneural or "pre-patterned" AChR aggregates, which is triggered by ACh or by synaptogenic stimuli. We and others have previously shown that tyrosine phosphatases, such as the SH2 domain-containing phosphatase Shp2, regulate AChR cluster formation in muscle cells, and that tyrosine phosphatases also mediate the dispersal of pre-patterned AChR clusters by synaptogenic stimuli, although the specific phosphatases involved in this latter step remain unknown. Results Using an assay system that allows AChR cluster assembly and disassembly to be studied separately and quantitatively, we describe a previously unrecognized role of the tyrosine phosphatase Shp2 in AChR cluster disassembly. Shp2 was robustly expressed in embryonic Xenopus muscle in vivo and in cultured myotomal muscle cells, and treatment of the muscle cultures with an inhibitor of Shp2 (NSC-87877 blocked the dispersal of pre-patterned AChR clusters by synaptogenic stimuli. In contrast, over-expression in muscle cells of either wild-type or constitutively active Shp2 accelerated cluster dispersal. Significantly, forced expression in muscle of the Shp2-activator SIRPα1 (signal regulatory protein α1 also enhanced the disassembly of AChR clusters, whereas the expression of a truncated SIRPα1 mutant that suppresses Shp2 signaling inhibited cluster disassembly. Conclusion Our results suggest that Shp2 activation by synaptogenic stimuli, through signaling intermediates such as SIRPα1, promotes the dispersal of pre-patterned AChR clusters to facilitate the selective accumulation of AChRs at developing NMJs.

Objectively Measured Baseline Physical Activity Patterns in Women in the mPED Trial: Cluster Analysis.

Science.gov (United States)

Fukuoka, Yoshimi; Zhou, Mo; Vittinghoff, Eric; Haskell, William; Goldberg, Ken; Aswani, Anil

2018-02-01

Determining patterns of physical activity throughout the day could assist in developing more personalized interventions or physical activity guidelines in general and, in particular, for women who are less likely to be physically active than men. The aims of this report are to identify clusters of women based on accelerometer-measured baseline raw metabolic equivalent of task (MET) values and a normalized version of the METs ≥3 data, and to compare sociodemographic and cardiometabolic risks among these identified clusters. A total of 215 women who were enrolled in the Mobile Phone Based Physical Activity Education (mPED) trial and wore an accelerometer for at least 8 hours per day for the 7 days prior to the randomization visit were analyzed. The k-means clustering method and the Lloyd algorithm were used on the data. We used the elbow method to choose the number of clusters, looking at the percentage of variance explained as a function of the number of clusters. The results of the k-means cluster analyses of raw METs revealed three different clusters. The unengaged group (n=102) had the highest depressive symptoms score compared with the afternoon engaged (n=65) and morning engaged (n=48) groups (overall Pcluster groups using a large national dataset. ClinicalTrials.gov NCT01280812; https://clinicaltrials.gov/ct2/show/NCT01280812 (Archived by WebCite at http://www.webcitation.org/6vVyLzwft). ©Yoshimi Fukuoka, Mo Zhou, Eric Vittinghoff, William Haskell, Ken Goldberg, Anil Aswani. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 01.02.2018.

Explaining ecological clusters of maternal depression in South Western Sydney

Science.gov (United States)

2014-01-01

Background The aim of the qualitative study reported here was to: 1) explain the observed clustering of postnatal depressive symptoms in South Western Sydney; and 2) identify group-level mechanisms that would add to our understanding of the social determinants of maternal depression. Methods Critical realism provided the methodological underpinning for the study. The setting was four local government areas in South Western Sydney, Australia. Child and Family practitioners and mothers in naturally occurring mothers groups were interviewed. Results Using an open coding approach to maximise emergence of patterns and relationships we have identified seven theoretical concepts that might explain the observed spatial clustering of maternal depression. The theoretical concepts identified were: Community-level social networks; Social Capital and Social Cohesion; "Depressed community"; Access to services at the group level; Ethnic segregation and diversity; Supportive social policy; and Big business. Conclusions We postulate that these regional structural, economic, social and cultural mechanisms partially explain the pattern of maternal depression observed in families and communities within South Western Sydney. We further observe that powerful global economic and political forces are having an impact on the local situation. The challenge for policy and practice is to support mothers and their families within this adverse regional and global-economic context. PMID:24460690

Explaining ecological clusters of maternal depression in South Western Sydney.

Science.gov (United States)

Eastwood ED, John; Kemp, Lynn; Jalaludin, Bin

2014-01-24

The aim of the qualitative study reported here was to: 1) explain the observed clustering of postnatal depressive symptoms in South Western Sydney; and 2) identify group-level mechanisms that would add to our understanding of the social determinants of maternal depression. Critical realism provided the methodological underpinning for the study. The setting was four local government areas in South Western Sydney, Australia. Child and Family practitioners and mothers in naturally occurring mothers groups were interviewed. Using an open coding approach to maximise emergence of patterns and relationships we have identified seven theoretical concepts that might explain the observed spatial clustering of maternal depression. The theoretical concepts identified were: Community-level social networks; Social Capital and Social Cohesion; "Depressed community"; Access to services at the group level; Ethnic segregation and diversity; Supportive social policy; and Big business. We postulate that these regional structural, economic, social and cultural mechanisms partially explain the pattern of maternal depression observed in families and communities within South Western Sydney. We further observe that powerful global economic and political forces are having an impact on the local situation. The challenge for policy and practice is to support mothers and their families within this adverse regional and global-economic context.

Elucidation of the Pattern of the Onset of Male Lower Urinary Tract Symptoms Using Cluster Analysis: Efficacy of Tamsulosin in Each Symptom Group.

Science.gov (United States)

Aikawa, Ken; Kataoka, Masao; Ogawa, Soichiro; Akaihata, Hidenori; Sato, Yuichi; Yabe, Michihiro; Hata, Junya; Koguchi, Tomoyuki; Kojima, Yoshiyuki; Shiragasawa, Chihaya; Kobayashi, Toshimitsu; Yamaguchi, Osamu

2015-08-01

To present a new grouping of male patients with lower urinary tract symptoms (LUTS) based on symptom patterns and clarify whether the therapeutic effect of α1-blocker differs among the groups. We performed secondary analysis of anonymous data from 4815 patients enrolled in a postmarketing surveillance study of tamsulosin in Japan. Data on 7 International Prostate Symptom Score (IPSS) items at the initial visit were used in the cluster analysis. IPSS and quality of life (QOL) scores before and after tamsulosin treatment for 12 weeks were assessed in each cluster. Partial correlation coefficients were also obtained for IPSS and QOL scores based on changes before and after treatment. Five symptom groups were identified by cluster analysis of IPSS. On their symptom profile, each cluster was labeled as minimal type (cluster 1), multiple severe type (cluster 2), weak stream type (cluster 3), storage type (cluster 4), and voiding type (cluster 5). Prevalence and the mean symptom score were significantly improved in almost all symptoms in all clusters by tamsulosin treatment. Nocturia and weak stream had the strongest effect on QOL in clusters 1, 2, and 4 and clusters 3 and 5, respectively. The study clarified that 5 characteristic symptom patterns exist by cluster analysis of IPSS in male patients with LUTS. Tamsulosin improved various symptoms and QOL in each symptom group. The study reports many male patients with LUTS being satisfied with monotherapy using tamsulosin and suggests the usefulness of α1-blockers as a drug of first choice. Copyright © 2015 Elsevier Inc. All rights reserved.

Global patterns of renewable energy innovation, 1990–2009

OpenAIRE

Bayer, Patrick; Dolan, Lindsay; Urpelainen, Johannes

2013-01-01

Cost-effective approaches to mitigating climate change depend on advances in clean energy technologies, such as solar and wind power. Given increased technology innovation in developing countries, led by China, we focus our attention on global patterns of renewable energy innovation. Utilizing highly valuable international patents as our indicator of innovation, we examine the economic and political determinants of energy innovation in 74 countries across the world, 1990–2009. We find that hi...

Allocating Scarce Resources Strategically - An Evaluation and Discussion of the Global Fund's Pattern of Disbursements

Science.gov (United States)

McCoy, David; Kinyua, Kelvin

2012-01-01

Background The Global Fund is under pressure to improve its rationing of financial support. This study describes the GF's pattern of disbursements in relation to total health expenditure (THE), government health expenditure (GHE), income status and the burden of HIV/AIDS, TB and malaria. It also examines the potential for recipient countries to increase domestic public financing for health. Methods This is a cross-sectional study of 104 countries that received Global Fund disbursements in 2009. It analyses data on Global Fund disbursements; health financing indicators; government revenue and expenditure; and burden of disease. Findings Global Fund disbursements made up 0.37% of THE across all 104 countries; but with considerable country variation ranging from 0.002% to 53.4%. Global Fund disbursements to government amounted to 0.47% of GHE across the 104 countries, but again with considerable variation (in three countries more than half of GHE was based on Global Fund support). Although the Global Fund provides progressively more funding for lower income countries on average, there is much variation at the country such that here was no correlation between per capita GF disbursements and per capita THE, nor between per capita GF disbursement to government and per capita GHE. There was only a slight positive correlation between per capita GF disbursement and burden of disease. Several countries with a high degree of 'financial dependency' upon the Fund have the potential to increase levels of domestic financing for health. Discussion The Global Fund can improve its targeting of resources so that it better matches the pattern of global need. To do this it needs to: a) reduce the extent to which funds are allocated on a demand-driven basis; and b) align its funding model to broader health systems financing and patterns of health expenditure beyond the three diseases. PMID:22590496

Allocating scarce resources strategically--an evaluation and discussion of the Global Fund's pattern of disbursements.

Directory of Open Access Journals (Sweden)

David McCoy

Full Text Available BACKGROUND: The Global Fund is under pressure to improve its rationing of financial support. This study describes the GF's pattern of disbursements in relation to total health expenditure (THE, government health expenditure (GHE, income status and the burden of HIV/AIDS, TB and malaria. It also examines the potential for recipient countries to increase domestic public financing for health. METHODS: This is a cross-sectional study of 104 countries that received Global Fund disbursements in 2009. It analyses data on Global Fund disbursements; health financing indicators; government revenue and expenditure; and burden of disease. FINDINGS: Global Fund disbursements made up 0.37% of THE across all 104 countries; but with considerable country variation ranging from 0.002% to 53.4%. Global Fund disbursements to government amounted to 0.47% of GHE across the 104 countries, but again with considerable variation (in three countries more than half of GHE was based on Global Fund support. Although the Global Fund provides progressively more funding for lower income countries on average, there is much variation at the country such that here was no correlation between per capita GF disbursements and per capita THE, nor between per capita GF disbursement to government and per capita GHE. There was only a slight positive correlation between per capita GF disbursement and burden of disease. Several countries with a high degree of 'financial dependency' upon the Fund have the potential to increase levels of domestic financing for health. DISCUSSION: The Global Fund can improve its targeting of resources so that it better matches the pattern of global need. To do this it needs to: a reduce the extent to which funds are allocated on a demand-driven basis; and b align its funding model to broader health systems financing and patterns of health expenditure beyond the three diseases.

Intracluster age gradients in numerous young stellar clusters

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Getman, K. V.; Feigelson, E. D.; Kuhn, M. A.; Bate, M. R.; Broos, P. S.; Garmire, G. P.

2018-05-01

The pace and pattern of star formation leading to rich young stellar clusters is quite uncertain. In this context, we analyse the spatial distribution of ages within 19 young (median t ≲ 3 Myr on the Siess et al. time-scale), morphologically simple, isolated, and relatively rich stellar clusters. Our analysis is based on young stellar object (YSO) samples from the Massive Young Star-Forming Complex Study in Infrared and X-ray and Star Formation in Nearby Clouds surveys, and a new estimator of pre-main sequence (PMS) stellar ages, AgeJX, derived from X-ray and near-infrared photometric data. Median cluster ages are computed within four annular subregions of the clusters. We confirm and extend the earlier result of Getman et al. (2014): 80 per cent of the clusters show age trends where stars in cluster cores are younger than in outer regions. Our cluster stacking analyses establish the existence of an age gradient to high statistical significance in several ways. Time-scales vary with the choice of PMS evolutionary model; the inferred median age gradient across the studied clusters ranges from 0.75 to 1.5 Myr pc-1. The empirical finding reported in the present study - late or continuing formation of stars in the cores of star clusters with older stars dispersed in the outer regions - has a strong foundation with other observational studies and with the astrophysical models like the global hierarchical collapse model of Vázquez-Semadeni et al.

Posttraumatic stress disorder among refugees: Measurement invariance of Harvard Trauma Questionnaire scores across global regions and response patterns.

Science.gov (United States)

Rasmussen, Andrew; Verkuilen, Jay; Ho, Emily; Fan, Yuyu

2015-12-01

Despite the central role of posttraumatic stress disorder (PTSD) in international humanitarian aid work, there has been little examination of the measurement invariance of PTSD measures across culturally defined refugee subgroups. This leaves mental health workers in disaster settings with little to support inferences made using the results of standard clinical assessment tools, such as the severity of symptoms and prevalence rates. We examined measurement invariance in scores from the most widely used PTSD measure in refugee populations, the Harvard Trauma Questionnaire (HTQ; Mollica et al., 1992), in a multinational and multilingual sample of asylum seekers from 81 countries of origin in 11 global regions. Clustering HTQ responses to justify grouping regional groups by response patterns resulted in 3 groups for testing measurement invariance: West Africans, Himalayans, and all others. Comparing log-likelihood ratios showed that while configural invariance seemed to hold, metric and scalar invariance did not. These findings call into question the common practice of using standard cut-off scores on PTSD measures across culturally dissimilar refugee populations. In addition, high correlation between factors suggests that the construct validity of scores from North American and European measures of PTSD may not hold globally. (c) 2015 APA, all rights reserved).

Quantum annealing for combinatorial clustering

Science.gov (United States)

Kumar, Vaibhaw; Bass, Gideon; Tomlin, Casey; Dulny, Joseph

2018-02-01

Clustering is a powerful machine learning technique that groups "similar" data points based on their characteristics. Many clustering algorithms work by approximating the minimization of an objective function, namely the sum of within-the-cluster distances between points. The straightforward approach involves examining all the possible assignments of points to each of the clusters. This approach guarantees the solution will be a global minimum; however, the number of possible assignments scales quickly with the number of data points and becomes computationally intractable even for very small datasets. In order to circumvent this issue, cost function minima are found using popular local search-based heuristic approaches such as k-means and hierarchical clustering. Due to their greedy nature, such techniques do not guarantee that a global minimum will be found and can lead to sub-optimal clustering assignments. Other classes of global search-based techniques, such as simulated annealing, tabu search, and genetic algorithms, may offer better quality results but can be too time-consuming to implement. In this work, we describe how quantum annealing can be used to carry out clustering. We map the clustering objective to a quadratic binary optimization problem and discuss two clustering algorithms which are then implemented on commercially available quantum annealing hardware, as well as on a purely classical solver "qbsolv." The first algorithm assigns N data points to K clusters, and the second one can be used to perform binary clustering in a hierarchical manner. We present our results in the form of benchmarks against well-known k-means clustering and discuss the advantages and disadvantages of the proposed techniques.

Large-scale distribution patterns of mangrove nematodes: A global meta-analysis.

Science.gov (United States)

Brustolin, Marco C; Nagelkerken, Ivan; Fonseca, Gustavo

2018-05-01

Mangroves harbor diverse invertebrate communities, suggesting that macroecological distribution patterns of habitat-forming foundation species drive the associated faunal distribution. Whether these are driven by mangrove biogeography is still ambiguous. For small-bodied taxa, local factors and landscape metrics might be as important as macroecology. We performed a meta-analysis to address the following questions: (1) can richness of mangrove trees explain macroecological patterns of nematode richness? and (2) do local landscape attributes have equal or higher importance than biogeography in structuring nematode richness? Mangrove areas of Caribbean-Southwest Atlantic, Western Indian, Central Indo-Pacific, and Southwest Pacific biogeographic regions. We used random-effects meta-analyses based on natural logarithm of the response ratio (lnRR) to assess the importance of macroecology (i.e., biogeographic regions, latitude, longitude), local factors (i.e., aboveground mangrove biomass and tree richness), and landscape metrics (forest area and shape) in structuring nematode richness from 34 mangroves sites around the world. Latitude, mangrove forest area, and forest shape index explained 19% of the heterogeneity across studies. Richness was higher at low latitudes, closer to the equator. At local scales, richness increased slightly with landscape complexity and decreased with forest shape index. Our results contrast with biogeographic diversity patterns of mangrove-associated taxa. Global-scale nematode diversity may have evolved independently of mangrove tree richness, and diversity of small-bodied metazoans is probably more closely driven by latitude and associated climates, rather than local, landscape, or global biogeographic patterns.

La importancia de los clusters para la competitividad de las PYME en una economía global

Directory of Open Access Journals (Sweden)

Josep Capó-Vicedo

2007-05-01

Full Text Available En este trabajo se analiza la importancia de los clusters para las Pequeñas y Medianas Empresas (PYME, en su camino hacia economías cada vez más globalizadas y basadas en el conocimiento. Para ello se estudia la influencia que tiene la globalización sobre las economías locales, así como la importancia de la concentración geográfica de las empresas para aumentar su competitividad. Entre las conclusiones obtenidas destaca el hecho de que en estas concentraciones se pueden establecer verdaderas comunidades de conocimiento, en las que éste se genere e intercambie, al mismo tiempo que se potencia la innovación, con lo que se consigue aumentar su ventaja competitiva. Finalmente, se estudia brevemente el caso del Cluster Textil Valenciano (España, validando lo visto en la parte teórica del artículoIn this work the importance of clusters for the particular case of SMEs is analyzed, in its road toward knowledge-based and globalize economies. In short the influence that globalization has about the local economies is studied, as well as the importance of the geographical concentration of the companies in order to increase their competitiveness. Among the obtained conclusions it highlights the fact that in these concentrations true communities of knowledge can settle down, where new knowledge is generated and exchanged, at the same time that innovation is empowered, and their competitive advantage is increased. Finally, the particular case of the Valencia Textile Cluster (Spain is studied, in order to validate the theoretical part of the article

Three-pattern decomposition of global atmospheric circulation: part I—decomposition model and theorems

Science.gov (United States)

Hu, Shujuan; Chou, Jifan; Cheng, Jianbo

2018-04-01

In order to study the interactions between the atmospheric circulations at the middle-high and low latitudes from the global perspective, the authors proposed the mathematical definition of three-pattern circulations, i.e., horizontal, meridional and zonal circulations with which the actual atmospheric circulation is expanded. This novel decomposition method is proved to accurately describe the actual atmospheric circulation dynamics. The authors used the NCEP/NCAR reanalysis data to calculate the climate characteristics of those three-pattern circulations, and found that the decomposition model agreed with the observed results. Further dynamical analysis indicates that the decomposition model is more accurate to capture the major features of global three dimensional atmospheric motions, compared to the traditional definitions of Rossby wave, Hadley circulation and Walker circulation. The decomposition model for the first time realized the decomposition of global atmospheric circulation using three orthogonal circulations within the horizontal, meridional and zonal planes, offering new opportunities to study the large-scale interactions between the middle-high latitudes and low latitudes circulations.

Global and Regional Patterns in Riverine Fish Species Richness: A Review

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Thierry Oberdorff

2011-01-01

Full Text Available We integrate the respective role of global and regional factors driving riverine fish species richness patterns, to develop a synthetic model of potential mechanisms and processes generating these patterns. This framework allows species richness to be broken down into different components specific to each spatial extent and to establish links between these components and the processes involved. This framework should help to answer the questions that are currently being asked by society, including the effects of species invasions, habitat loss, or fragmentation and climate change on freshwater biodiversity.

Exploring the individual patterns of spiritual well-being in people newly diagnosed with advanced cancer: a cluster analysis.

Science.gov (United States)

Bai, Mei; Dixon, Jane; Williams, Anna-Leila; Jeon, Sangchoon; Lazenby, Mark; McCorkle, Ruth

2016-11-01

Research shows that spiritual well-being correlates positively with quality of life (QOL) for people with cancer, whereas contradictory findings are frequently reported with respect to the differentiated associations between dimensions of spiritual well-being, namely peace, meaning and faith, and QOL. This study aimed to examine individual patterns of spiritual well-being among patients newly diagnosed with advanced cancer. Cluster analysis was based on the twelve items of the 12-item Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being Scale at Time 1. A combination of hierarchical and k-means (non-hierarchical) clustering methods was employed to jointly determine the number of clusters. Self-rated health, depressive symptoms, peace, meaning and faith, and overall QOL were compared at Time 1 and Time 2. Hierarchical and k-means clustering methods both suggested four clusters. Comparison of the four clusters supported statistically significant and clinically meaningful differences in QOL outcomes among clusters while revealing contrasting relations of faith with QOL. Cluster 1, Cluster 3, and Cluster 4 represented high, medium, and low levels of overall QOL, respectively, with correspondingly high, medium, and low levels of peace, meaning, and faith. Cluster 2 was distinguished from other clusters by its medium levels of overall QOL, peace, and meaning and low level of faith. This study provides empirical support for individual difference in response to a newly diagnosed cancer and brings into focus conceptual and methodological challenges associated with the measure of spiritual well-being, which may partly contribute to the attenuated relation between faith and QOL.

Clustering of energy balance-related behaviors in 5-year-old children: Lifestyle patterns and their longitudinal association with weight status development in early childhood

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Gubbels Jessica S

2012-06-01

Full Text Available Abstract Background This study identified lifestyle patterns by examining the clustering of eating routines (e.g. eating together as a family, having the television on during meals, duration of meals and various activity-related behaviors (i.e. physical activity (PA and sedentary screen-based behavior in 5-year-old children, as well as the longitudinal association of these patterns with weight status (BMI and overweight development up to age 8. Methods Data originated from the KOALA Birth Cohort Study (N = 2074 at age 5. Principal component analysis (PCA was used to identify lifestyle patterns. Backward regression analyses were used to examine the association of lifestyle patterns with parent and child background characteristics, as well as the longitudinal associations between the patterns and weight status development. Results Four lifestyle patterns emerged from the PCA: a ‘Television–Snacking’ pattern, a ‘Sports–Computer’ pattern, a ‘Traditional Family’ pattern, and a “Fast’ Food’ pattern. Child gender and parental educational level, working hours and body mass index were significantly associated with the scores for the patterns. The Television–Snacking pattern was positively associated with BMI (standardized regression coefficient β = 0.05; p p = 0.06. In addition, the Sports–Computer pattern was significantly positively associated with an increased risk of becoming overweight at age 7 (OR = 1.28, p  Conclusions The current study showed the added value of including eating routines in cross-behavioral clustering analyses. The findings indicate that future interventions to prevent childhood overweight should address eating routines and activity/inactivity simultaneously, using the synergy between clustered behaviors (e.g. between television viewing and snacking.

Structure based alignment and clustering of proteins (STRALCP)

Science.gov (United States)

Zemla, Adam T.; Zhou, Carol E.; Smith, Jason R.; Lam, Marisa W.

2013-06-18

Disclosed are computational methods of clustering a set of protein structures based on local and pair-wise global similarity values. Pair-wise local and global similarity values are generated based on pair-wise structural alignments for each protein in the set of protein structures. Initially, the protein structures are clustered based on pair-wise local similarity values. The protein structures are then clustered based on pair-wise global similarity values. For each given cluster both a representative structure and spans of conserved residues are identified. The representative protein structure is used to assign newly-solved protein structures to a group. The spans are used to characterize conservation and assign a "structural footprint" to the cluster.

CHEMICAL ABUNDANCE PATTERNS IN THE INNER GALAXY: THE SCUTUM RED SUPERGIANT CLUSTERS

International Nuclear Information System (INIS)

Davies, Ben; Origlia, Livia; Kudritzki, Rolf-Peter; Figer, Don F.; Rich, R. Michael; Najarro, Francisco; Negueruela, Ignacio; Clark, J. Simon

2009-01-01

The location of the Scutum Red Supergiant (RSG) clusters at the end of the Galactic Bar makes them an excellent probe of the Galaxy's secular evolution, while the clusters themselves are ideal testbeds in which to study the predictions of stellar evolutionary theory. To this end, we present a study of the RSG's surface abundances using a combination of high-resolution Keck/NIRSPEC H-band spectroscopy and spectral synthesis analysis. We provide abundance measurements for elements C, O, Si, Mg, Ti, and Fe. We find that the surface abundances of the stars studied are consistent with CNO burning and deep, rotationally enhanced mixing. The average α/Fe ratios of the clusters are solar, consistent with a thin-disk population. However, we find significantly subsolar Fe/H ratios for each cluster, a result which strongly contradicts a simple extrapolation of the Galactic metallicity gradient to lower Galactocentric distances. We suggest that a simple one-dimensional parameterization of the Galaxy's abundance patterns is insufficient at low Galactocentric distances, as large azimuthal variations may be present. Indeed, we show that the abundances of O, Si, and Mg are consistent with independent measurements of objects in similar locations in the Galaxy. In combining our results with other data in the literature, we present evidence for large-scale (∼ kpc) azimuthal variations in abundances at Galactocentric distances of 3-5 kpc. While we cannot rule out that this observed behavior is due to systematic offsets between different measurement techniques, we do find evidence for similar behavior in a study of the barred spiral galaxy NGC 4736 which uses homogeneous methodology. We suggest that these azimuthal abundance variations could result from the intense but patchy star formation driven by the potential of the central bar.

Local control of globally competing patterns in coupled Swift-Hohenberg equations

Science.gov (United States)

Becker, Maximilian; Frenzel, Thomas; Niedermayer, Thomas; Reichelt, Sina; Mielke, Alexander; Bär, Markus

2018-04-01

We present analytical and numerical investigations of two anti-symmetrically coupled 1D Swift-Hohenberg equations (SHEs) with cubic nonlinearities. The SHE provides a generic formulation for pattern formation at a characteristic length scale. A linear stability analysis of the homogeneous state reveals a wave instability in addition to the usual Turing instability of uncoupled SHEs. We performed weakly nonlinear analysis in the vicinity of the codimension-two point of the Turing-wave instability, resulting in a set of coupled amplitude equations for the Turing pattern as well as left- and right-traveling waves. In particular, these complex Ginzburg-Landau-type equations predict two major things: there exists a parameter regime where multiple different patterns are stable with respect to each other and that the amplitudes of different patterns interact by local mutual suppression. In consequence, different patterns can coexist in distinct spatial regions, separated by localized interfaces. We identified specific mechanisms for controlling the position of these interfaces, which distinguish what kinds of patterns the interface connects and thus allow for global pattern selection. Extensive simulations of the original SHEs confirm our results.

Identification of linearised RMS-voltage dip patterns based on clustering in renewable plants

Energy Technology Data Exchange (ETDEWEB)

García-Sánchez, Tania; Gómez-Lázaro, Emilio; Muljadi, Edward; Kessler, Mathieu; Muñoz-Benavente, Irene; Molina-García, Angel

2018-03-27

Generation units connected to the grid are currently required to meet low-voltage ride-through (LVRT) requirements. In most developed countries, these requirements also apply to renewable sources, mainly wind power plants and photovoltaic installations connected to the grid. This study proposes an alternative characterisation solution to classify and visualise a large number of collected events in light of current limits and requirements. The authors' approach is based on linearised root-mean-square-(RMS)-voltage trajectories, taking into account LRVT requirements, and a clustering process to identify the most likely pattern trajectories. The proposed solution gives extensive information on an event's severity by providing a simple but complete visualisation of the linearised RMS-voltage patterns. In addition, these patterns are compared to current LVRT requirements to determine similarities or discrepancies. A large number of collected events can then be automatically classified and visualised for comparative purposes. Real disturbances collected from renewable sources in Spain are used to assess the proposed solution. Extensive results and discussions are also included in this study.

Global patterns in post-dispersal seed removal by invertebrates and vertebrates.

Science.gov (United States)

Peco, Begoña; Laffan, Shawn W; Moles, Angela T

2014-01-01

It is commonly accepted that species interactions such as granivory are more intense in the tropics. However, this has rarely been tested. A global dataset of post-dispersal seed removal by invertebrates and vertebrates for 79 native plant species from semi-natural and natural terrestrial habitats ranging from 55° N to 45° S, was compiled from the global literature to test the hypothesis that post-dispersal seed removal by invertebrates and vertebrates is more intense at lower latitudes. We also quantified the relationship between post-dispersal seed removal by vertebrates and by invertebrates to global climatic features including temperature, actual evapotranspiration (AET) and rainfall seasonality. Linear mixed effect models were applied to describe the relationships between seed removal and latitude, hemisphere and climatic variables controlling for the effect of seed mass. Post-dispersal seed removal by invertebrates was negatively related to latitude. In contrast, post-dispersal seed removal by vertebrates was positively but weakly related to latitude. Mean annual temperature and actual evapotranspiration were positively related to post-dispersal seed removal by invertebrates, but not to post-dispersal seed removal by vertebrates, which was only marginally negatively related to rainfall seasonality. The inclusion of seed mass improved the fit of all models, but the term for seed mass was not significant in any model. Although a good climatic model for predicting post-dispersal seed predation by vertebrates at the global level was not found, our results suggest different and opposite latitudinal patterns of post-dispersal seed removal by invertebrates vs vertebrates. This is the first time that a negative relationship between post-dispersal seed removal by invertebrates and latitude, and a positive relationship with temperature and AET have been documented at a global-scale. These results have important implications for understanding global patterns in plant

Investigating Solution Convergence in a Global Ocean Model Using a 2048-Processor Cluster of Distributed Shared Memory Machines

Directory of Open Access Journals (Sweden)

Chris Hill

2007-01-01

Full Text Available Up to 1920 processors of a cluster of distributed shared memory machines at the NASA Ames Research Center are being used to simulate ocean circulation globally at horizontal resolutions of 1/4, 1/8, and 1/16-degree with the Massachusetts Institute of Technology General Circulation Model, a finite volume code that can scale to large numbers of processors. The study aims to understand physical processes responsible for skill improvements as resolution is increased and to gain insight into what resolution is sufficient for particular purposes. This paper focuses on the computational aspects of reaching the technical objective of efficiently performing these global eddy-resolving ocean simulations. At 1/16-degree resolution the model grid contains 1.2 billion cells. At this resolution it is possible to simulate approximately one month of ocean dynamics in about 17 hours of wallclock time with a model timestep of two minutes on a cluster of four 512-way NUMA Altix systems. The Altix systems' large main memory and I/O subsystems allow computation and disk storage of rich sets of diagnostics during each integration, supporting the scientific objective to develop a better understanding of global ocean circulation model solution convergence as model resolution is increased.

Reproducibility of Cognitive Profiles in Psychosis Using Cluster Analysis.

Science.gov (United States)

Lewandowski, Kathryn E; Baker, Justin T; McCarthy, Julie M; Norris, Lesley A; Öngür, Dost

2018-04-01

Cognitive dysfunction is a core symptom dimension that cuts across the psychoses. Recent findings support classification of patients along the cognitive dimension using cluster analysis; however, data-derived groupings may be highly determined by sampling characteristics and the measures used to derive the clusters, and so their interpretability must be established. We examined cognitive clusters in a cross-diagnostic sample of patients with psychosis and associations with clinical and functional outcomes. We then compared our findings to a previous report of cognitive clusters in a separate sample using a different cognitive battery. Participants with affective or non-affective psychosis (n=120) and healthy controls (n=31) were administered the MATRICS Consensus Cognitive Battery, and clinical and community functioning assessments. Cluster analyses were performed on cognitive variables, and clusters were compared on demographic, cognitive, and clinical measures. Results were compared to findings from our previous report. A four-cluster solution provided a good fit to the data; profiles included a neuropsychologically normal cluster, a globally impaired cluster, and two clusters of mixed profiles. Cognitive burden was associated with symptom severity and poorer community functioning. The patterns of cognitive performance by cluster were highly consistent with our previous findings. We found evidence of four cognitive subgroups of patients with psychosis, with cognitive profiles that map closely to those produced in our previous work. Clusters were associated with clinical and community variables and a measure of premorbid functioning, suggesting that they reflect meaningful groupings: replicable, and related to clinical presentation and functional outcomes. (JINS, 2018, 24, 382-390).

Reload pattern optimization by application of multiple cyclic interchange algorithms

Energy Technology Data Exchange (ETDEWEB)

Geemert, R. van; Quist, A.J.; Hoogenboom, J.E. [Technische Univ. Delft (Netherlands)

1996-09-01

Reload pattern optimization procedures are proposed which are based on the multiple cyclic interchange approach, according to which the search for the reload pattern associated with the highest objective function value can be thought of as divided in multiple stages. The transition from the initial to the final stage is characterized by an increase in the degree of locality of the search procedure. The general idea is that, during the first stages, the `elite` cluster containing the group of best patterns must be located, after which the solution space is sampled in a more and more local sense to find the local optimum in this cluster. The transition(s) from global search behaviour to local search behaviour can be either prompt, by defining strictly separate search regimes, or gradual by introducing stochastic tests for the number of fuel bundles involved in a cyclic interchange. Equilibrium cycle optimization results are reported for a test PWR reactor core of modest size. (author)

Reload pattern optimization by application of multiple cyclic interchange algorithms

International Nuclear Information System (INIS)

Geemert, R. van; Quist, A.J.; Hoogenboom, J.E.

1996-01-01

Reload pattern optimization procedures are proposed which are based on the multiple cyclic interchange approach, according to which the search for the reload pattern associated with the highest objective function value can be thought of as divided in multiple stages. The transition from the initial to the final stage is characterized by an increase in the degree of locality of the search procedure. The general idea is that, during the first stages, the 'elite' cluster containing the group of best patterns must be located, after which the solution space is sampled in a more and more local sense to find the local optimum in this cluster. The transition(s) from global search behaviour to local search behaviour can be either prompt, by defining strictly separate search regimes, or gradual by introducing stochastic tests for the number of fuel bundles involved in a cyclic interchange. Equilibrium cycle optimization results are reported for a test PWR reactor core of modest size. (author)

Clustering methods for the optimization of atomic cluster structure

Science.gov (United States)

Bagattini, Francesco; Schoen, Fabio; Tigli, Luca

2018-04-01

In this paper, we propose a revised global optimization method and apply it to large scale cluster conformation problems. In the 1990s, the so-called clustering methods were considered among the most efficient general purpose global optimization techniques; however, their usage has quickly declined in recent years, mainly due to the inherent difficulties of clustering approaches in large dimensional spaces. Inspired from the machine learning literature, we redesigned clustering methods in order to deal with molecular structures in a reduced feature space. Our aim is to show that by suitably choosing a good set of geometrical features coupled with a very efficient descent method, an effective optimization tool is obtained which is capable of finding, with a very high success rate, all known putative optima for medium size clusters without any prior information, both for Lennard-Jones and Morse potentials. The main result is that, beyond being a reliable approach, the proposed method, based on the idea of starting a computationally expensive deep local search only when it seems worth doing so, is capable of saving a huge amount of searches with respect to an analogous algorithm which does not employ a clustering phase. In this paper, we are not claiming the superiority of the proposed method compared to specific, refined, state-of-the-art procedures, but rather indicating a quite straightforward way to save local searches by means of a clustering scheme working in a reduced variable space, which might prove useful when included in many modern methods.

The Influence of Low-carbon Economy on Global Trade Pattern

Science.gov (United States)

Xiao-jing, Guo

Since global warming has seriously endangered the living environment of human being and their health and safety, the development of low-carbon economy has become an irreversible global trend. Under the background of economic globalization, low-carbon economy will surely exert a significant impact on global trade pattern. Countries are paying more and more attention to the green trade. The emission permits trade of carbon between the developed countries and the developing countries has become more mature than ever. The carbon tariff caused by the distribution of the "big cake" will make the low-cost advantage in developing countries cease to exist, which will, in turn, affect the foreign trade, economic development, employment and people's living in developing countries. Therefore, under the background of this trend, we should perfect the relevant laws and regulations on trade and environment as soon as possible, optimize trade structure, promote greatly the development of service trade, transform thoroughly the mode of development in foreign trade, take advantage of the international carbon trading market by increasing the added value of export products resulted from technological innovation to achieve mutual benefit and win-win results and promote common development.

Partitional clustering algorithms

CERN Document Server

2015-01-01

This book summarizes the state-of-the-art in partitional clustering. Clustering, the unsupervised classification of patterns into groups, is one of the most important tasks in exploratory data analysis. Primary goals of clustering include gaining insight into, classifying, and compressing data. Clustering has a long and rich history that spans a variety of scientific disciplines including anthropology, biology, medicine, psychology, statistics, mathematics, engineering, and computer science. As a result, numerous clustering algorithms have been proposed since the early 1950s. Among these algorithms, partitional (nonhierarchical) ones have found many applications, especially in engineering and computer science. This book provides coverage of consensus clustering, constrained clustering, large scale and/or high dimensional clustering, cluster validity, cluster visualization, and applications of clustering. Examines clustering as it applies to large and/or high-dimensional data sets commonly encountered in reali...

Patterns of comorbidity in community-dwelling older people hospitalised for fall-related injury: A cluster analysis

Directory of Open Access Journals (Sweden)

Finch Caroline F

2011-08-01

Full Text Available Abstract Background Community-dwelling older people aged 65+ years sustain falls frequently; these can result in physical injuries necessitating medical attention including emergency department care and hospitalisation. Certain health conditions and impairments have been shown to contribute independently to the risk of falling or experiencing a fall injury, suggesting that individuals with these conditions or impairments should be the focus of falls prevention. Since older people commonly have multiple conditions/impairments, knowledge about which conditions/impairments coexist in at-risk individuals would be valuable in the implementation of a targeted prevention approach. The objective of this study was therefore to examine the prevalence and patterns of comorbidity in this population group. Methods We analysed hospitalisation data from Victoria, Australia's second most populous state, to estimate the prevalence of comorbidity in patients hospitalised at least once between 2005-6 and 2007-8 for treatment of acute fall-related injuries. In patients with two or more comorbid conditions (multicomorbidity we used an agglomerative hierarchical clustering method to cluster comorbidity variables and identify constellations of conditions. Results More than one in four patients had at least one comorbid condition and among patients with comorbidity one in three had multicomorbidity (range 2-7. The prevalence of comorbidity varied by gender, age group, ethnicity and injury type; it was also associated with a significant increase in the average cumulative length of stay per patient. The cluster analysis identified five distinct, biologically plausible clusters of comorbidity: cardiopulmonary/metabolic, neurological, sensory, stroke and cancer. The cardiopulmonary/metabolic cluster was the largest cluster among the clusters identified. Conclusions The consequences of comorbidity clustering in terms of falls and/or injury outcomes of hospitalised patients

Fragmentation of atomic clusters: A theoretical study

International Nuclear Information System (INIS)

Lopez, M.J.; Jellinek, J.

1994-01-01

Collisionless fragmentation of nonrotating model n-atom metal clusters (n=12, 13, and 14) is studied using isoergic molecular-dynamics simulations. Minimum-energy paths for fragmentation are mapped out as functions of the distance between the centers of mass of the fragments. These paths provide information on the fragmentation energies for the different fragmentation channels. Fragmentation patterns (distributions of the fragmentation channel probabilities) and global and channel-specific fragmentation rate constants are computed and analyzed as functions of the internal energy and of the size of the clusters. The trends derived from the dynamics are compared with those obtained using the RRK and TST statistical approaches. The dynamics of the fragmentation process is analyzed in terms of characteristic quantities such as the distance between the centers of mass of the fragments, their relative translational energy, and their interaction energy, all considered as functions of time

Clustering eating habits: frequent consumption of different dietary patterns among the Italian general population in the association with obesity, physical activity, sociocultural characteristics and psychological factors.

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Denoth, Francesca; Scalese, Marco; Siciliano, Valeria; Di Renzo, Laura; De Lorenzo, Antonino; Molinaro, Sabrina

2016-06-01

(a) To identify clusters of eating patterns among the Italian population aged 15-64 years, focusing on typical Mediterranean diet (Med-diet) items consumption; (b) to examine the distribution of eating habits, as identified clusters, among age classes and genders; (c) evaluate the impact of: belonging to a specific eating cluster, level of physical activity (PA), sociocultural and psychological factors, as elements determining weight abnormalities. Data for this cross-sectional study were collected using self-reporting questionnaires administered to a sample of 33,127 subjects participating in the Italian population survey on alcohol and other drugs (IPSAD(®)2011). The cluster analysis was performed on a subsample (n = 5278 subjects) which provided information on eating habits, and adapted to identify categories of eating patterns. Stepwise multinomial regression analysis was performed to evaluate the associations between weight categories and eating clusters, adjusted for the following background variables: PA levels, sociocultural and psychological factors. Three clusters were identified: "Mediterranean-like", "Western-like" and "low fruit/vegetables". Frequent consumption of Med-diet patterns was more common among females and elderly. The relationship between overweight/obesity and male gender, educational level, PA, depression and eating disorders (p obesity. The low consumption of Med-diet patterns among youth, and the frequent association of sociocultural, psychological issues and inappropriate lifestyle with overweight/obesity, highlight the need for an interdisciplinary approach including market policies, to promote a wider awareness of the Mediterranean eating habit benefits in combination with an appropriate lifestyle.

XML documents cluster research based on frequent subpatterns

Science.gov (United States)

Ding, Tienan; Li, Wei; Li, Xiongfei

2015-12-01

XML data is widely used in the information exchange field of Internet, and XML document data clustering is the hot research topic. In the XML document clustering process, measure differences between two XML documents is time costly, and impact the efficiency of XML document clustering. This paper proposed an XML documents clustering method based on frequent patterns of XML document dataset, first proposed a coding tree structure for encoding the XML document, and translate frequent pattern mining from XML documents into frequent pattern mining from string. Further, using the cosine similarity calculation method and cohesive hierarchical clustering method for XML document dataset by frequent patterns. Because of frequent patterns are subsets of the original XML document data, so the time consumption of XML document similarity measure is reduced. The experiment runs on synthetic dataset and the real datasets, the experimental result shows that our method is efficient.

A Historical Approach to Clustering in Emerging Economies

DEFF Research Database (Denmark)

Giacomin, Valeria

of external factors. Indeed, researchers have explained clusters as self-contained entities and reduced their success to local exceptionality. In contrast, emerging literature has shown that clusters are integrated in broader structures beyond their location and are rather building blocks of today’s global...... economy. The working paper goes on to present two historical cases from the global south to explain how clusters work as major tools for international business. Particularly in the developing world, multinationals have used clusters as platforms for channeling foreign investment, knowledge, and imported...... inputs. The study concludes by stressing the importance of using historical evidence and data to look at clusters as agglomerations of actors and companies operating not just at the local level but across broader global networks. In doing so the historical perspective provides explanations lacking...

Analysis of the Structures and Properties of (GaSb)n (n = 4-9) Clusters through Density Functional Theory.

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Lu, Qi Liang; Luo, Qi Quan; Huang, Shou Guo; Li, Yi De; Wan, Jian Guo

2016-07-07

An optimization strategy combining global semiempirical quantum mechanical search with all-electron density functional theory was adopted to determine the lowest energy structure of (GaSb)n clusters up to n = 9. The growth pattern of the clusters differed from those of previously reported group III-V binary clusters. A cagelike configuration was found for cluster sizes n ≤ 7. The structure of (GaSb)6 deviated from that of other III-V clusters. Competition existed between core-shell and hollow cage structures of (GaSb)7. Novel noncagelike structures were energetically preferred over the cages for the (GaSb)8 and (GaSb)9 clusters. Electronic properties, such as vertical ionization potential, adiabatic electron affinities, HOMO-LUMO gaps, and average on-site charges on Ga or Sb atoms, as well as binding energies, were computed.

Global patterns of NDVI-indicated vegetation extremes and their sensitivity to climate extremes

International Nuclear Information System (INIS)

Liu Guo; Liu Hongyan; Yin Yi

2013-01-01

Extremes in climate have significant impacts on ecosystems and are expected to increase under future climate change. Extremes in vegetation could capture such impacts and indicate the vulnerability of ecosystems, but currently have not received a global long-term assessment. In this study, a robust method has been developed to detect significant extremes (low values) in biweekly time series of global normalized difference vegetation index (NDVI) from 1982 to 2006 and thus to acquire a global pattern of vegetation extreme frequency. This pattern coincides with vegetation vulnerability patterns suggested by earlier studies using different methods over different time spans, indicating a consistent mechanism of regulation. Vegetation extremes were found to aggregate in Amazonia and in the semi-arid and semi-humid regions in low and middle latitudes, while they seldom occurred in high latitudes. Among the environmental variables studied, extreme low precipitation has the highest slope against extreme vegetation. For the eight biomes analyzed, these slopes are highest in temperate broadleaf forest and temperate grassland, suggesting a higher sensitivity in these environments. The results presented here contradict the hypothesis that vegetation in water-limited semi-arid and semi-humid regions might be adapted to drought and suggest that vegetation in these regions (especially temperate broadleaf forest and temperate grassland) is highly prone to vegetation extreme events under more severe precipitation extremes. It is also suggested here that more attention be paid to precipitation-induced vegetation changes than to temperature-induced events. (letter)

Information processing architecture of functionally defined clusters in the macaque cortex.

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Shen, Kelly; Bezgin, Gleb; Hutchison, R Matthew; Gati, Joseph S; Menon, Ravi S; Everling, Stefan; McIntosh, Anthony R

2012-11-28

Computational and empirical neuroimaging studies have suggested that the anatomical connections between brain regions primarily constrain their functional interactions. Given that the large-scale organization of functional networks is determined by the temporal relationships between brain regions, the structural limitations may extend to the global characteristics of functional networks. Here, we explored the extent to which the functional network community structure is determined by the underlying anatomical architecture. We directly compared macaque (Macaca fascicularis) functional connectivity (FC) assessed using spontaneous blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI) to directed anatomical connectivity derived from macaque axonal tract tracing studies. Consistent with previous reports, FC increased with increasing strength of anatomical connection, and FC was also present between regions that had no direct anatomical connection. We observed moderate similarity between the FC of each region and its anatomical connectivity. Notably, anatomical connectivity patterns, as described by structural motifs, were different within and across functional modules: partitioning of the functional network was supported by dense bidirectional anatomical connections within clusters and unidirectional connections between clusters. Together, our data directly demonstrate that the FC patterns observed in resting-state BOLD-fMRI are dictated by the underlying neuroanatomical architecture. Importantly, we show how this architecture contributes to the global organizational principles of both functional specialization and integration.

Assessment of Multivariate Neural Time Series by Phase Synchrony Clustering in a Time-Frequency-Topography Representation

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M. A. Porta-Garcia

2018-01-01

Full Text Available Most EEG phase synchrony measures are of bivariate nature. Those that are multivariate focus on producing global indices of the synchronization state of the system. Thus, better descriptions of spatial and temporal local interactions are still in demand. A framework for characterization of phase synchrony relationships between multivariate neural time series is presented, applied either in a single epoch or over an intertrial assessment, relying on a proposed clustering algorithm, termed Multivariate Time Series Clustering by Phase Synchrony, which generates fuzzy clusters for each multivalued time sample and thereupon obtains hard clusters according to a circular variance threshold; such cluster modes are then depicted in Time-Frequency-Topography representations of synchrony state beyond mere global indices. EEG signals from P300 Speller sessions of four subjects were analyzed, obtaining useful insights of synchrony patterns related to the ERP and even revealing steady-state artifacts at 7.6 Hz. Further, contrast maps of Levenshtein Distance highlight synchrony differences between ERP and no-ERP epochs, mainly at delta and theta bands. The framework, which is not limited to one synchrony measure, allows observing dynamics of phase changes and interactions among channels and can be applied to analyze other cognitive states rather than ERP versus no ERP.

Computing and Learning Year-Round Daily Patterns of Hourly Wind Speed and Direction and Their Global Associations with Meteorological Factors

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Hsing-Ti Wu

2015-08-01

Full Text Available Daily wind patterns and their relational associations with other metocean (oceanographic and meteorological variables were algorithmically computed and extracted from a year-long wind and weather dataset, which was collected hourly from an ocean buoy located in the Penghu archipelago of Taiwan. The computational algorithm is called data cloud geometry (DCG. This DCG algorithm is a clustering-based nonparametric learning approach that was constructed and developed implicitly based on various entropy concepts. Regarding the bivariate aspect of wind speed and wind direction, the resulting multiscale clustering hierarchy revealed well-known wind characteristics of year-round pattern cycles pertaining to the particular geographic location of the buoy. A wind pattern due to a set of extreme weather days was also identified. Moreover, in terms of the relational aspect of wind and other weather variables, causal patterns were revealed through applying the DCG algorithm alternatively on the row and column axes of a data matrix by iteratively adapting distance measures to computed DCG tree structures. This adaptation technically constructed and integrated a multiscale, two-sample testing into the distance measure. These computed wind patterns and pattern-based causal relationships are useful for both general sailing and competition planning.

Cluster analysis

CERN Document Server

Everitt, Brian S; Leese, Morven; Stahl, Daniel

2011-01-01

Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics.This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data.Real life examples are used throughout to demons

Computational genetic neuroanatomy of the developing mouse brain: dimensionality reduction, visualization, and clustering

Science.gov (United States)

2013-01-01

Background The structured organization of cells in the brain plays a key role in its functional efficiency. This delicate organization is the consequence of unique molecular identity of each cell gradually established by precise spatiotemporal gene expression control during development. Currently, studies on the molecular-structural association are beginning to reveal how the spatiotemporal gene expression patterns are related to cellular differentiation and structural development. Results In this article, we aim at a global, data-driven study of the relationship between gene expressions and neuroanatomy in the developing mouse brain. To enable visual explorations of the high-dimensional data, we map the in situ hybridization gene expression data to a two-dimensional space by preserving both the global and the local structures. Our results show that the developing brain anatomy is largely preserved in the reduced gene expression space. To provide a quantitative analysis, we cluster the reduced data into groups and measure the consistency with neuroanatomy at multiple levels. Our results show that the clusters in the low-dimensional space are more consistent with neuroanatomy than those in the original space. Conclusions Gene expression patterns and developing brain anatomy are closely related. Dimensionality reduction and visual exploration facilitate the study of this relationship. PMID:23845024

Formation of an Approach to the Clustered Management of Foreign Economic Activity of Enterprises in the Conditions of Global Competition

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Sushchenko Olena A.

2015-09-01

Full Text Available The article is aimed at formation of an approach to the clustered management of foreign economic activity of enterprises in the conditions of global competition. Expedience of use of the cluster approach in the field of management of foreign economic activity of enterprises has been substantiated. A basic framework has been developed and a cluster model for management of foreign economic activity of enterprises providing a description of such management as a complex mechanism with the specified parameters has been created. The basic elements of the cluster model of management of foreign economic activity of enterprise have been allocated. Purposes for selecting elemental clusters in the process of management of foreign economic activity of enterprise have been defined. The partial functions of management that display the functional purpose of the cluster model of management of foreign economic activity of enterprises, as well as the composition of its elements, have been allocated. A generalized hierarchical view of the cluster model of management of foreign economic activity of enterprises has been proposed. A scheme of the operational administration of functioning of the cluster model of management of foreign economic activity of enterprises, based on the core principles and basics of situational simulation, has been presented. Effectiveness of the presented management model is determined by the increasing share of enterprises in the external markets in the context of the relevant clusters, an expansion of the types of foreign economic activity of enterprises, implementation of innovations

The enigmatic SAR202 cluster up close: shedding light on a globally distributed dark ocean lineage involved in sulfur cycling.

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Mehrshad, Maliheh; Rodriguez-Valera, Francisco; Amoozegar, Mohammad Ali; López-García, Purificación; Ghai, Rohit

2018-03-01

The dark ocean microbiota represents the unknown majority in the global ocean waters. The SAR202 cluster belonging to the phylum Chloroflexi was the first microbial lineage discovered to specifically inhabit the aphotic realm, where they are abundant and globally distributed. The absence of SAR202 cultured representatives is a significant bottleneck towards understanding their metabolic capacities and role in the marine environment. In this work, we use a combination of metagenome-assembled genomes from deep-sea datasets and publicly available single-cell genomes to construct a genomic perspective of SAR202 phylogeny, metabolism and biogeography. Our results suggest that SAR202 cluster members are medium sized, free-living cells with a heterotrophic lifestyle, broadly divided into two distinct clades. We present the first evidence of vertical stratification of these microbes along the meso- and bathypelagic ocean layers. Remarkably, two distinct species of SAR202 cluster are highly abundant in nearly all deep bathypelagic metagenomic datasets available so far. SAR202 members metabolize multiple organosulfur compounds, many appear to be sulfite-oxidizers and are predicted to play a major role in sulfur turnover in the dark water column. This concomitantly suggests an unsuspected availability of these nutrient sources to allow for the high abundance of these microbes in the deep sea.

Global patterns of foliar nitrogen isotopes and their relationships with climate, mycorrhizal fungi, foliar nutrient concentrations, and nitrogen availability

Science.gov (United States)

Joseph M. Craine; Andrew J. Elmore; Marcos P. M. Aidar; Mercedes Bustamante; Todd E. Dawson; Erik A. Hobbie; Ansgar Kahmen; Michelle C. Mack; Kendra K. McLauchlan; Anders Michelsen; Gabriela Nardoto; Linda H. Pardo; Josep Penuelas; Peter B. Reich; Edward A.G. Schuur; William D. Stock; Pamela H. Templer; Ross A. Virginia; Jeffrey M. Welker; Ian J. Wright

2009-01-01

Ratios of nitrogen (N) isotopes in leaves could elucidate underlying patterns of N cycling across ecological gradients. To better understand global-scale patterns of N cycling, we compiled data on foliar N isotope ratios, foliar N concentrations, mycorrhizal type and climate for over 11 000 plants worldwide. Global-scale comparisons of other components of the N cycle...

A two-step patterning process increases the robustness of periodic patterning in the fly eye.

Science.gov (United States)

Gavish, Avishai; Barkai, Naama

2016-06-01

Complex periodic patterns can self-organize through dynamic interactions between diffusible activators and inhibitors. In the biological context, self-organized patterning is challenged by spatial heterogeneities ('noise') inherent to biological systems. How spatial variability impacts the periodic patterning mechanism and how it can be buffered to ensure precise patterning is not well understood. We examine the effect of spatial heterogeneity on the periodic patterning of the fruit fly eye, an organ composed of ∼800 miniature eye units (ommatidia) whose periodic arrangement along a hexagonal lattice self-organizes during early stages of fly development. The patterning follows a two-step process, with an initial formation of evenly spaced clusters of ∼10 cells followed by a subsequent refinement of each cluster into a single selected cell. Using a probabilistic approach, we calculate the rate of patterning errors resulting from spatial heterogeneities in cell size, position and biosynthetic capacity. Notably, error rates were largely independent of the desired cluster size but followed the distributions of signaling speeds. Pre-formation of large clusters therefore greatly increases the reproducibility of the overall periodic arrangement, suggesting that the two-stage patterning process functions to guard the pattern against errors caused by spatial heterogeneities. Our results emphasize the constraints imposed on self-organized patterning mechanisms by the need to buffer stochastic effects. Author summary Complex periodic patterns are common in nature and are observed in physical, chemical and biological systems. Understanding how these patterns are generated in a precise manner is a key challenge. Biological patterns are especially intriguing, as they are generated in a noisy environment; cell position and cell size, for example, are subject to stochastic variations, as are the strengths of the chemical signals mediating cell-to-cell communication. The need

Transboundary Clusters in the Coastal Zones of the European Part of Russia: Inventory, Typology, Factors, and Prospects

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Druzhinin A. G.

2017-12-01

Full Text Available This article presents an inventory and a typology of the existing and emerging economic clusters in the coastal zone of the European part of Russia. The authors hold that transboundary clustering takes priority in the Baltic coastal region — nine of the 56 clusters identified are located in the Kaliningrad region and another eight in Saint Petersburg and the Leningrad region. The authors describe major catalysts and immanent inhibitors in coastal zones. The former include a high density of coastal economies, proximity to international markets, and better logistics and communications. The inhibitors comprise geopolitical risks and institutional barriers. It is shown that the potential and prospects of transboundary clustering are affected by both global integration and disintegration patterns, coastal infrastructure, geopolitical and geoeconomic ‘neighbourhood’, cultural excellence, and business and investment environment.

Parity among interpretation methods of MLEE patterns and disparity among clustering methods in epidemiological typing of Candida albicans.

Science.gov (United States)

Boriollo, Marcelo Fabiano Gomes; Rosa, Edvaldo Antonio Ribeiro; Gonçalves, Reginaldo Bruno; Höfling, José Francisco

2006-03-01

The typing of C. albicans by MLEE (multilocus enzyme electrophoresis) is dependent on the interpretation of enzyme electrophoretic patterns, and the study of the epidemiological relationships of these yeasts can be conducted by cluster analysis. Therefore, the aims of the present study were to first determine the discriminatory power of genetic interpretation (deduction of the allelic composition of diploid organisms) and numerical interpretation (mere determination of the presence and absence of bands) of MLEE patterns, and then to determine the concordance (Pearson product-moment correlation coefficient) and similarity (Jaccard similarity coefficient) of the groups of strains generated by three cluster analysis models, and the discriminatory power of such models as well [model A: genetic interpretation, genetic distance matrix of Nei (d(ij)) and UPGMA dendrogram; model B: genetic interpretation, Dice similarity matrix (S(D1)) and UPGMA dendrogram; model C: numerical interpretation, Dice similarity matrix (S(D2)) and UPGMA dendrogram]. MLEE was found to be a powerful and reliable tool for the typing of C. albicans due to its high discriminatory power (>0.9). Discriminatory power indicated that numerical interpretation is a method capable of discriminating a greater number of strains (47 versus 43 subtypes), but also pointed to model B as a method capable of providing a greater number of groups, suggesting its use for the typing of C. albicans by MLEE and cluster analysis. Very good agreement was only observed between the elements of the matrices S(D1) and S(D2), but a large majority of the groups generated in the three UPGMA dendrograms showed similarity S(J) between 4.8% and 75%, suggesting disparities in the conclusions obtained by the cluster assays.

Hierarchical and Complex System Entropy Clustering Analysis Based Validation for Traditional Chinese Medicine Syndrome Patterns of Chronic Atrophic Gastritis.

Science.gov (United States)

Zhang, Yin; Liu, Yue; Li, Yannan; Zhao, Xia; Zhuo, Lin; Zhou, Ajian; Zhang, Li; Su, Zeqi; Chen, Cen; Du, Shiyu; Liu, Daming; Ding, Xia

2018-03-22

Chronic atrophic gastritis (CAG) is the precancerous stage of gastric carcinoma. Traditional Chinese Medicine (TCM) has been widely used in treating CAG. This study aimed to reveal core pathogenesis of CAG by validating the TCM syndrome patterns and provide evidence for optimization of treatment strategies. This is a cross-sectional study conducted in 4 hospitals in China. Hierarchical clustering analysis (HCA) and complex system entropy clustering analysis (CSECA) were performed, respectively, to achieve syndrome pattern validation. Based on HCA, 15 common factors were assigned to 6 syndrome patterns: liver depression and spleen deficiency and blood stasis in the stomach collateral, internal harassment of phlegm-heat and blood stasis in the stomach collateral, phlegm-turbidity internal obstruction, spleen yang deficiency, internal harassment of phlegm-heat and spleen deficiency, and spleen qi deficiency. By CSECA, 22 common factors were assigned to 7 syndrome patterns: qi deficiency, qi stagnation, blood stasis, phlegm turbidity, heat, yang deficiency, and yin deficiency. Combination of qi deficiency, qi stagnation, blood stasis, phlegm turbidity, heat, yang deficiency, and yin deficiency may play a crucial role in CAG pathogenesis. In accord with this, treatment strategies by TCM herbal prescriptions should be targeted to regulating qi, activating blood, resolving turbidity, clearing heat, removing toxin, nourishing yin, and warming yang. Further explorations are needed to verify and expand the current conclusions.

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

Analyses of Crime Patterns in NIBRS Data Based on a Novel Graph Theory Clustering Method: Virginia as a Case Study

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Peixin Zhao

2014-01-01

Full Text Available This paper suggests a novel clustering method for analyzing the National Incident-Based Reporting System (NIBRS data, which include the determination of correlation of different crime types, the development of a likelihood index for crimes to occur in a jurisdiction, and the clustering of jurisdictions based on crime type. The method was tested by using the 2005 assault data from 121 jurisdictions in Virginia as a test case. The analyses of these data show that some different crime types are correlated and some different crime parameters are correlated with different crime types. The analyses also show that certain jurisdictions within Virginia share certain crime patterns. This information assists with constructing a pattern for a specific crime type and can be used to determine whether a jurisdiction may be more likely to see this type of crime occur in their area.

A parallel solution to the cutting stock problem for a cluster of workstations

Energy Technology Data Exchange (ETDEWEB)

Nicklas, L.D.; Atkins, R.W.; Setia, S.V.; Wang, P.Y. [George Mason Univ., Fairfax, VA (United States)

1996-12-31

This paper describes the design and implementation of a solution to the constrained 2-D cutting stock problem on a cluster of workstations. The constrained 2-D cutting stock problem is an irregular problem with a dynamically modified global data set and irregular amounts and patterns of communication. A replicated data structure is used for the parallel solution since the ratio of reads to writes is known to be large. Mutual exclusion and consistency are maintained using a token-based lazy consistency mechanism, and a randomized protocol for dynamically balancing the distributed work queue is employed. Speedups are reported for three benchmark problems executed on a cluster of workstations interconnected by a 10 Mbps Ethernet.

Combinatorial pattern discovery approach for the folding trajectory analysis of a beta-hairpin.

Directory of Open Access Journals (Sweden)

Laxmi Parida

2005-06-01

Full Text Available The study of protein folding mechanisms continues to be one of the most challenging problems in computational biology. Currently, the protein folding mechanism is often characterized by calculating the free energy landscape versus various reaction coordinates, such as the fraction of native contacts, the radius of gyration, RMSD from the native structure, and so on. In this paper, we present a combinatorial pattern discovery approach toward understanding the global state changes during the folding process. This is a first step toward an unsupervised (and perhaps eventually automated approach toward identification of global states. The approach is based on computing biclusters (or patterned clusters-each cluster is a combination of various reaction coordinates, and its signature pattern facilitates the computation of the Z-score for the cluster. For this discovery process, we present an algorithm of time complexity c in RO((N + nm log n, where N is the size of the output patterns and (n x m is the size of the input with n time frames and m reaction coordinates. To date, this is the best time complexity for this problem. We next apply this to a beta-hairpin folding trajectory and demonstrate that this approach extracts crucial information about protein folding intermediate states and mechanism. We make three observations about the approach: (1 The method recovers states previously obtained by visually analyzing free energy surfaces. (2 It also succeeds in extracting meaningful patterns and structures that had been overlooked in previous works, which provides a better understanding of the folding mechanism of the beta-hairpin. These new patterns also interconnect various states in existing free energy surfaces versus different reaction coordinates. (3 The approach does not require calculating the free energy values, yet it offers an analysis comparable to, and sometimes better than, the methods that use free energy landscapes, thus validating the

Combinatorial Pattern Discovery Approach for the Folding Trajectory Analysis of a beta-Hairpin.

Directory of Open Access Journals (Sweden)

2005-06-01

Full Text Available The study of protein folding mechanisms continues to be one of the most challenging problems in computational biology. Currently, the protein folding mechanism is often characterized by calculating the free energy landscape versus various reaction coordinates, such as the fraction of native contacts, the radius of gyration, RMSD from the native structure, and so on. In this paper, we present a combinatorial pattern discovery approach toward understanding the global state changes during the folding process. This is a first step toward an unsupervised (and perhaps eventually automated approach toward identification of global states. The approach is based on computing biclusters (or patterned clusters-each cluster is a combination of various reaction coordinates, and its signature pattern facilitates the computation of the Z-score for the cluster. For this discovery process, we present an algorithm of time complexity cinRO((N + nm log n, where N is the size of the output patterns and (n x m is the size of the input with n time frames and m reaction coordinates. To date, this is the best time complexity for this problem. We next apply this to a beta-hairpin folding trajectory and demonstrate that this approach extracts crucial information about protein folding intermediate states and mechanism. We make three observations about the approach: (1 The method recovers states previously obtained by visually analyzing free energy surfaces. (2 It also succeeds in extracting meaningful patterns and structures that had been overlooked in previous works, which provides a better understanding of the folding mechanism of the beta-hairpin. These new patterns also interconnect various states in existing free energy surfaces versus different reaction coordinates. (3 The approach does not require calculating the free energy values, yet it offers an analysis comparable to, and sometimes better than, the methods that use free energy landscapes, thus validating the

Clustering approaches to identifying gene expression patterns from DNA microarray data.

Science.gov (United States)

Do, Jin Hwan; Choi, Dong-Kug

2008-04-30

The analysis of microarray data is essential for large amounts of gene expression data. In this review we focus on clustering techniques. The biological rationale for this approach is the fact that many co-expressed genes are co-regulated, and identifying co-expressed genes could aid in functional annotation of novel genes, de novo identification of transcription factor binding sites and elucidation of complex biological pathways. Co-expressed genes are usually identified in microarray experiments by clustering techniques. There are many such methods, and the results obtained even for the same datasets may vary considerably depending on the algorithms and metrics for dissimilarity measures used, as well as on user-selectable parameters such as desired number of clusters and initial values. Therefore, biologists who want to interpret microarray data should be aware of the weakness and strengths of the clustering methods used. In this review, we survey the basic principles of clustering of DNA microarray data from crisp clustering algorithms such as hierarchical clustering, K-means and self-organizing maps, to complex clustering algorithms like fuzzy clustering.

Adaptive Scaling of Cluster Boundaries for Large-Scale Social Media Data Clustering.

Science.gov (United States)

Meng, Lei; Tan, Ah-Hwee; Wunsch, Donald C

2016-12-01

The large scale and complex nature of social media data raises the need to scale clustering techniques to big data and make them capable of automatically identifying data clusters with few empirical settings. In this paper, we present our investigation and three algorithms based on the fuzzy adaptive resonance theory (Fuzzy ART) that have linear computational complexity, use a single parameter, i.e., the vigilance parameter to identify data clusters, and are robust to modest parameter settings. The contribution of this paper lies in two aspects. First, we theoretically demonstrate how complement coding, commonly known as a normalization method, changes the clustering mechanism of Fuzzy ART, and discover the vigilance region (VR) that essentially determines how a cluster in the Fuzzy ART system recognizes similar patterns in the feature space. The VR gives an intrinsic interpretation of the clustering mechanism and limitations of Fuzzy ART. Second, we introduce the idea of allowing different clusters in the Fuzzy ART system to have different vigilance levels in order to meet the diverse nature of the pattern distribution of social media data. To this end, we propose three vigilance adaptation methods, namely, the activation maximization (AM) rule, the confliction minimization (CM) rule, and the hybrid integration (HI) rule. With an initial vigilance value, the resulting clustering algorithms, namely, the AM-ART, CM-ART, and HI-ART, can automatically adapt the vigilance values of all clusters during the learning epochs in order to produce better cluster boundaries. Experiments on four social media data sets show that AM-ART, CM-ART, and HI-ART are more robust than Fuzzy ART to the initial vigilance value, and they usually achieve better or comparable performance and much faster speed than the state-of-the-art clustering algorithms that also do not require a predefined number of clusters.

Structure and physical properties of silicon clusters and of vacancy clusters in bulk silicon

International Nuclear Information System (INIS)

Sieck, A.

2000-01-01

In this thesis the growth-pattern of free silicon clusters and vacancy clusters in bulk silicon is investigated. The aim is to describe and to better understand the cluster to bulk transition. Silicon structures in between clusters and solids feature new interesting physical properties. The structure and physical properties of silicon clusters can be revealed by a combination of theory and experiment, only. Low-energy clusters are determined with different optimization techniques and a density-functional based tight-binding method. Additionally, infrared and Raman spectra, and polarizabilities calculated within self-consistent field density-functional theory are provided for the smaller clusters. For clusters with 25 to 35 atoms an analysis of the shape of the clusters and the related mobilities in a buffer gas is given. Finally, the clusters observed in low-temperature experiments are identified via the best match between calculated properties and experimental data. Silicon clusters with 10 to 15 atoms have a tricapped trigonal prism as a common subunit. Clusters with up to about 25 atoms follow a prolate growth-path. In the range from 24 to 30 atoms the geometry of the clusters undergoes a transition towards compact spherical structures. Low-energy clusters with up to 240 atoms feature a bonding pattern strikingly different from the tetrahedral bonding in the solid. It follows that structures with dimensions of several Angstroem have electrical and optical properties different from the solid. The calculated stabilities and positron-lifetimes of vacancy clusters in bulk silicon indicate the positron-lifetimes of about 435 ps detected in irradiated silicon to be related to clusters of 9 or 10 vacancies. The vacancies in these clusters form neighboring hexa-rings and, therefore, minimize the number of dangling bonds. (orig.)

GLOBALIZATION, CONSUMPTION PATTERNS AND POLITICAL STABILITY

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Grzegorz Malinowski

2017-06-01

Full Text Available Rapid changes in technology and economy that we observe nowadays are accompanied by rapid changes in traditional values and attitudes. In the contemporary world, a permanent proximity of internet, computer, television or smartphone makes us all citizens of the globalised, virtual world rather than a physical, geographical, real one. But even if people consider themselves to be citizens of the “global village”, a political architecture of the real world has remained based on the nation-state. One of the main characteristics of a nation-state is its territory defined by its borders. Recognition and respect of nation-state borders is considered to be a principle of national sovereignty, national interest and territorial independence, which shape international relations. Historically, rulers always usurped the right to control what happens on their territory, but there were some areas that had escaped their supervision. The first one is the sphere of science and more broadly – ideas. Whether it was religion, superstition or steam engine, ideas were unstoppable even for isolated countries. Second area is a realm of trade. Rulers were usually rather kind for merchants, therefore borders were always wide open for business people. It is worth mentioning that both ideas and trade are significant driving forces in the history of world. Their influence is sometimes stronger and sometimes weaker but it is always meaningful. Yet the very hypothesis of this article states that in contemporary, globalised world a third important factor has arrived. It was always present but until the economy hasn’t become globalized, its impact wasn’t noticeable. This third factor can be described as universalisation of western consumption patterns and it plays an important role particularly in developing countries.

Data clustering algorithms and applications

CERN Document Server

Aggarwal, Charu C

2013-01-01

Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains.The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as fea

Global minimum-energy structure and spectroscopic properties of I2(*-) x n H2O clusters: a Monte Carlo simulated annealing study.

Science.gov (United States)

Pathak, Arup Kumar; Mukherjee, Tulsi; Maity, Dilip Kumar

2010-01-18

The vibrational (IR and Raman) and photoelectron spectral properties of hydrated iodine-dimer radical-anion clusters, I(2)(*-) x n H(2)O (n=1-10), are presented. Several initial guess structures are considered for each size of cluster to locate the global minimum-energy structure by applying a Monte Carlo simulated annealing procedure including spin-orbit interaction. In the Raman spectrum, hydration reduces the intensity of the I-I stretching band but enhances the intensity of the O-H stretching band of water. Raman spectra of more highly hydrated clusters appear to be simpler than the corresponding IR spectra. Vibrational bands due to simultaneous stretching vibrations of O-H bonds in a cyclic water network are observed for I(2)(*-) x n H(2)O clusters with n > or = 3. The vertical detachment energy (VDE) profile shows stepwise saturation that indicates closing of the geometrical shell in the hydrated clusters on addition of every four water molecules. The calculated VDE of finite-size small hydrated clusters is extrapolated to evaluate the bulk VDE value of I(2)(*-) in aqueous solution as 7.6 eV at the CCSD(T) level of theory. Structure and spectroscopic properties of these hydrated clusters are compared with those of hydrated clusters of Cl(2)(*-) and Br(2)(*-).

Modified genetic algorithms to model cluster structures in medium-size silicon clusters

International Nuclear Information System (INIS)

Bazterra, Victor E.; Ona, Ofelia; Caputo, Maria C.; Ferraro, Marta B.; Fuentealba, Patricio; Facelli, Julio C.

2004-01-01

This paper presents the results obtained using a genetic algorithm (GA) to search for stable structures of medium size silicon clusters. In this work the GA uses a semiempirical energy function to find the best cluster structures, which are further optimized using density-functional theory. For small clusters our results agree well with previously reported structures, but for larger ones different structures appear. This is the case of Si 36 where we report a different structure, with significant lower energy than those previously found using limited search approaches on common structural motifs. This demonstrates the need for global optimization schemes when searching for stable structures of medium-size silicon clusters

INTERNATIONAL BEHAVIOUR AND PERFORMANCE BASED ROMANIAN ENTREPRENEURIAL AND TRADITIONAL FIRM CLUSTERS

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FEDER Emoke - Szidonia

2015-07-01

Full Text Available The micro, small and medium-sized firms (SMEs present a key interest at European level due to their potential positive influence on regional, national and firm level competitiveness. At a certain moment in time, internationalisation became an expected and even unavoidable strategy in firms’ future development, growth and evolution. From theoretical perspective, an integrative complementarily approach is adopted concerning the dominant paradigm of stage models from incremental internationalisation theory and the emergent paradigm of international entrepreneurship theory. Several researcher calls for empirical testing of different theoretical frameworks and international firms. Therefore, the first aim of the quantitative study is to empirically prove, the existence of various internationalisation behaviour configuration based clusters, like sporadic and traditional international firms, born-again global and born global firms, within the framework of Romanian SMEs. Secondly, within the research framework the study propose to assess different distinguishing internationalisation behavioural characteristics and patterns for the delimited clusters, in terms of foreign market scope, internationalisation pace and rhythm, initial and current entry modes, international product portfolio and commitment. Thirdly, internationalisation cluster membership and patterns differential influence and contribution is analysed on firm level international business performance, as internationalisation degree, financial and marketing measures. The framework was tested on a transversal sample consisting of 140 Romanian internationalised SMEs. Findings are especially useful for entrepreneurs and SME managers presenting various decisional possibilities and options on internationalisation behaviours and performance. These emphasize the importance of internationalisation scope, pace, object and opportunity seeking, along with positive influence on performance, indifferent

Clusters of Multidrug-Resistant Mycobacterium tuberculosis Cases, Europe

Science.gov (United States)

Kremer, Kristin; Heersma, Herre; Van Soolingen, Dick

2009-01-01

Molecular surveillance of multidrug-resistant tuberculosis (MDR TB) was implemented in Europe as case reporting in 2005. For all new MDR TB cases detected from January 2003 through June 2007, countries reported case-based epidemiologic data and DNA fingerprint patterns of MDR TB strains when available. International clusters were detected and analyzed. From 2003 through mid-2007 in Europe, 2,494 cases of MDR TB were reported from 24 European countries. Epidemiologic and molecular data were linked for 593 (39%) cases, and 672 insertion sequence 6110 DNA fingerprint patterns were reported from 19 countries. Of these patterns, 288 (43%) belonged to 18 European clusters; 7 clusters (242/288 cases, 84%) were characterized by strains of the Beijing genotype family, including the largest cluster (175/288 cases, 61%). Both clustering and the Beijing genotype were associated with strains originating in eastern European countries. Molecular cluster detection contributes to identification of transmission profile, risk factors, and control measures. PMID:19624920

Contextualizing the Cluster

DEFF Research Database (Denmark)

Giacomin, Valeria

This dissertation examines the case of the palm oil cluster in Malaysia and Indonesia, today one of the largest agricultural clusters in the world. My analysis focuses on the evolution of the cluster from the 1880s to the 1970s in order to understand how it helped these two countries to integrate...... into the global economy in both colonial and post-colonial times. The study is based on empirical material drawn from five UK archives and background research using secondary sources, interviews, and archive visits to Malaysia and Singapore. The dissertation comprises three articles, each discussing a major under...

Feasibility Study of Parallel Finite Element Analysis on Cluster-of-Clusters

Science.gov (United States)

Muraoka, Masae; Okuda, Hiroshi

With the rapid growth of WAN infrastructure and development of Grid middleware, it's become a realistic and attractive methodology to connect cluster machines on wide-area network for the execution of computation-demanding applications. Many existing parallel finite element (FE) applications have been, however, designed and developed with a single computing resource in mind, since such applications require frequent synchronization and communication among processes. There have been few FE applications that can exploit the distributed environment so far. In this study, we explore the feasibility of FE applications on the cluster-of-clusters. First, we classify FE applications into two types, tightly coupled applications (TCA) and loosely coupled applications (LCA) based on their communication pattern. A prototype of each application is implemented on the cluster-of-clusters. We perform numerical experiments executing TCA and LCA on both the cluster-of-clusters and a single cluster. Thorough these experiments, by comparing the performances and communication cost in each case, we evaluate the feasibility of FEA on the cluster-of-clusters.

Patterns of Spatial Variation of Assemblages Associated with Intertidal Rocky Shores: A Global Perspective

Science.gov (United States)

Cruz-Motta, Juan José; Miloslavich, Patricia; Palomo, Gabriela; Iken, Katrin; Konar, Brenda; Pohle, Gerhard; Trott, Tom; Benedetti-Cecchi, Lisandro; Herrera, César; Hernández, Alejandra; Sardi, Adriana; Bueno, Andrea; Castillo, Julio; Klein, Eduardo; Guerra-Castro, Edlin; Gobin, Judith; Gómez, Diana Isabel; Riosmena-Rodríguez, Rafael; Mead, Angela; Bigatti, Gregorio; Knowlton, Ann; Shirayama, Yoshihisa

2010-01-01

Assemblages associated with intertidal rocky shores were examined for large scale distribution patterns with specific emphasis on identifying latitudinal trends of species richness and taxonomic distinctiveness. Seventy-two sites distributed around the globe were evaluated following the standardized sampling protocol of the Census of Marine Life NaGISA project (www.nagisa.coml.org). There were no clear patterns of standardized estimators of species richness along latitudinal gradients or among Large Marine Ecosystems (LMEs); however, a strong latitudinal gradient in taxonomic composition (i.e., proportion of different taxonomic groups in a given sample) was observed. Environmental variables related to natural influences were strongly related to the distribution patterns of the assemblages on the LME scale, particularly photoperiod, sea surface temperature (SST) and rainfall. In contrast, no environmental variables directly associated with human influences (with the exception of the inorganic pollution index) were related to assemblage patterns among LMEs. Correlations of the natural assemblages with either latitudinal gradients or environmental variables were equally strong suggesting that neither neutral models nor models based solely on environmental variables sufficiently explain spatial variation of these assemblages at a global scale. Despite the data shortcomings in this study (e.g., unbalanced sample distribution), we show the importance of generating biological global databases for the use in large-scale diversity comparisons of rocky intertidal assemblages to stimulate continued sampling and analyses. PMID:21179546

Patterns of spatial variation of assemblages associated with intertidal rocky shores: a global perspective.

Directory of Open Access Journals (Sweden)

Juan José Cruz-Motta

2010-12-01

Full Text Available Assemblages associated with intertidal rocky shores were examined for large scale distribution patterns with specific emphasis on identifying latitudinal trends of species richness and taxonomic distinctiveness. Seventy-two sites distributed around the globe were evaluated following the standardized sampling protocol of the Census of Marine Life NaGISA project (www.nagisa.coml.org. There were no clear patterns of standardized estimators of species richness along latitudinal gradients or among Large Marine Ecosystems (LMEs; however, a strong latitudinal gradient in taxonomic composition (i.e., proportion of different taxonomic groups in a given sample was observed. Environmental variables related to natural influences were strongly related to the distribution patterns of the assemblages on the LME scale, particularly photoperiod, sea surface temperature (SST and rainfall. In contrast, no environmental variables directly associated with human influences (with the exception of the inorganic pollution index were related to assemblage patterns among LMEs. Correlations of the natural assemblages with either latitudinal gradients or environmental variables were equally strong suggesting that neither neutral models nor models based solely on environmental variables sufficiently explain spatial variation of these assemblages at a global scale. Despite the data shortcomings in this study (e.g., unbalanced sample distribution, we show the importance of generating biological global databases for the use in large-scale diversity comparisons of rocky intertidal assemblages to stimulate continued sampling and analyses.

Global patterns of fragmentation and connectivity of mammalian carnivore habitat.

Science.gov (United States)

Crooks, Kevin R; Burdett, Christopher L; Theobald, David M; Rondinini, Carlo; Boitani, Luigi

2011-09-27

Although mammalian carnivores are vulnerable to habitat fragmentation and require landscape connectivity, their global patterns of fragmentation and connectivity have not been examined. We use recently developed high-resolution habitat suitability models to conduct comparative analyses and to identify global hotspots of fragmentation and connectivity for the world's terrestrial carnivores. Species with less fragmentation (i.e. more interior high-quality habitat) had larger geographical ranges, a greater proportion of habitat within their range, greater habitat connectivity and a lower risk of extinction. Species with higher connectivity (i.e. less habitat isolation) also had a greater proportion of high-quality habitat, but had smaller, not larger, ranges, probably reflecting shorter distances between habitat patches for species with restricted distributions; such species were also more threatened, as would be expected given the negative relationship between range size and extinction risk. Fragmentation and connectivity did not differ among Carnivora families, and body mass was associated with connectivity but not fragmentation. On average, only 54.3 per cent of a species' geographical range comprised high-quality habitat, and more troubling, only 5.2 per cent of the range comprised such habitat within protected areas. Identification of global hotspots of fragmentation and connectivity will help guide strategic priorities for carnivore conservation.

A Network of Networks Perspective on Global Trade.

Science.gov (United States)

Maluck, Julian; Donner, Reik V

2015-01-01

Mutually intertwined supply chains in contemporary economy result in a complex network of trade relationships with a highly non-trivial topology that varies with time. In order to understand the complex interrelationships among different countries and economic sectors, as well as their dynamics, a holistic view on the underlying structural properties of this network is necessary. This study employs multi-regional input-output data to decompose 186 national economies into 26 industry sectors and utilizes the approach of interdependent networks to analyze the substructure of the resulting international trade network for the years 1990-2011. The partition of the network into national economies is observed to be compatible with the notion of communities in the sense of complex network theory. By studying internal versus cross-subgraph contributions to established complex network metrics, new insights into the architecture of global trade are obtained, which allow to identify key elements of global economy. Specifically, financial services and business activities dominate domestic trade whereas electrical and machinery industries dominate foreign trade. In order to further specify each national sector's role individually, (cross-)clustering coefficients and cross-betweenness are obtained for different pairs of subgraphs. The corresponding analysis reveals that specific industrial sectors tend to favor distinct directionality patterns and that the cross-clustering coefficient for geographically close country pairs is remarkably high, indicating that spatial factors are still of paramount importance for the organization of trade patterns in modern economy. Regarding the evolution of the trade network's substructure, globalization is well-expressed by trends of several structural characteristics (e.g., link density and node strength) in the interacting network framework. Extreme events, such as the financial crisis 2008/2009, are manifested as anomalies superimposed to

A Network of Networks Perspective on Global Trade

Science.gov (United States)

Maluck, Julian; Donner, Reik V.

2015-01-01

Mutually intertwined supply chains in contemporary economy result in a complex network of trade relationships with a highly non-trivial topology that varies with time. In order to understand the complex interrelationships among different countries and economic sectors, as well as their dynamics, a holistic view on the underlying structural properties of this network is necessary. This study employs multi-regional input-output data to decompose 186 national economies into 26 industry sectors and utilizes the approach of interdependent networks to analyze the substructure of the resulting international trade network for the years 1990–2011. The partition of the network into national economies is observed to be compatible with the notion of communities in the sense of complex network theory. By studying internal versus cross-subgraph contributions to established complex network metrics, new insights into the architecture of global trade are obtained, which allow to identify key elements of global economy. Specifically, financial services and business activities dominate domestic trade whereas electrical and machinery industries dominate foreign trade. In order to further specify each national sector’s role individually, (cross-)clustering coefficients and cross-betweenness are obtained for different pairs of subgraphs. The corresponding analysis reveals that specific industrial sectors tend to favor distinct directionality patterns and that the cross-clustering coefficient for geographically close country pairs is remarkably high, indicating that spatial factors are still of paramount importance for the organization of trade patterns in modern economy. Regarding the evolution of the trade network’s substructure, globalization is well-expressed by trends of several structural characteristics (e.g., link density and node strength) in the interacting network framework. Extreme events, such as the financial crisis 2008/2009, are manifested as anomalies superimposed

Defining global neuroendocrine gene expression patterns associated with reproductive seasonality in fish.

Directory of Open Access Journals (Sweden)

Dapeng Zhang

Full Text Available BACKGROUND: Many vertebrates, including the goldfish, exhibit seasonal reproductive rhythms, which are a result of interactions between external environmental stimuli and internal endocrine systems in the hypothalamo-pituitary-gonadal axis. While it is long believed that differential expression of neuroendocrine genes contributes to establishing seasonal reproductive rhythms, no systems-level investigation has yet been conducted. METHODOLOGY/PRINCIPAL FINDINGS: In the present study, by analyzing multiple female goldfish brain microarray datasets, we have characterized global gene expression patterns for a seasonal cycle. A core set of genes (873 genes in the hypothalamus were identified to be differentially expressed between May, August and December, which correspond to physiologically distinct stages that are sexually mature (prespawning, sexual regression, and early gonadal redevelopment, respectively. Expression changes of these genes are also shared by another brain region, the telencephalon, as revealed by multivariate analysis. More importantly, by examining one dataset obtained from fish in October who were kept under long-daylength photoperiod (16 h typical of the springtime breeding season (May, we observed that the expression of identified genes appears regulated by photoperiod, a major factor controlling vertebrate reproductive cyclicity. Gene ontology analysis revealed that hormone genes and genes functionally involved in G-protein coupled receptor signaling pathway and transmission of nerve impulses are significantly enriched in an expression pattern, whose transition is located between prespawning and sexually regressed stages. The existence of seasonal expression patterns was verified for several genes including isotocin, ependymin II, GABA(A gamma2 receptor, calmodulin, and aromatase b by independent samplings of goldfish brains from six seasonal time points and real-time PCR assays. CONCLUSIONS/SIGNIFICANCE: Using both

15th Cluster workshop

CERN Document Server

Laakso, Harri; Escoubet, C. Philippe; The Cluster Active Archive : Studying the Earth’s Space Plasma Environment

2010-01-01

Since the year 2000 the ESA Cluster mission has been investigating the small-scale structures and processes of the Earth's plasma environment, such as those involved in the interaction between the solar wind and the magnetospheric plasma, in global magnetotail dynamics, in cross-tail currents, and in the formation and dynamics of the neutral line and of plasmoids. This book contains presentations made at the 15th Cluster workshop held in March 2008. It also presents several articles about the Cluster Active Archive and its datasets, a few overview papers on the Cluster mission, and articles reporting on scientific findings on the solar wind, the magnetosheath, the magnetopause and the magnetotail.

Patterns of Dysmorphic Features in Schizophrenia

Science.gov (United States)

Scutt, L.E.; Chow, E.W.C.; Weksberg, R.; Honer, W.G.; Bassett, Anne S.

2011-01-01

Congenital dysmorphic features are prevalent in schizophrenia and may reflect underlying neurodevelopmental abnormalities. A cluster analysis approach delineating patterns of dysmorphic features has been used in genetics to classify individuals into more etiologically homogeneous subgroups. In the present study, this approach was applied to schizophrenia, using a sample with a suspected genetic syndrome as a testable model. Subjects (n = 159) with schizophrenia or schizoaffective disorder were ascertained from chronic patient populations (random, n=123) or referred with possible 22q11 deletion syndrome (referred, n = 36). All subjects were evaluated for presence or absence of 70 reliably assessed dysmorphic features, which were used in a three-step cluster analysis. The analysis produced four major clusters with different patterns of dysmorphic features. Significant between-cluster differences were found for rates of 37 dysmorphic features (P dysmorphic features (P = 0.0001), and validating features not used in the cluster analysis: mild mental retardation (P = 0.001) and congenital heart defects (P = 0.002). Two clusters (1 and 4) appeared to represent more developmental subgroups of schizophrenia with elevated rates of dysmorphic features and validating features. Cluster 1 (n = 27) comprised mostly referred subjects. Cluster 4 (n= 18) had a different pattern of dysmorphic features; one subject had a mosaic Turner syndrome variant. Two other clusters had lower rates and patterns of features consistent with those found in previous studies of schizophrenia. Delineating patterns of dysmorphic features may help identify subgroups that could represent neurodevelopmental forms of schizophrenia with more homogeneous origins. PMID:11803519

Symmetries of cluster configurations

International Nuclear Information System (INIS)

Kramer, P.

1975-01-01

A deeper understanding of clustering phenomena in nuclei must encompass at least two interrelated aspects of the subject: (A) Given a system of A nucleons with two-body interactions, what are the relevant and persistent modes of clustering involved. What is the nature of the correlated nucleon groups which form the clusters, and what is their mutual interaction. (B) Given the cluster modes and their interaction, what systematic patterns of nuclear structure and reactions emerge from it. Are there, for example, families of states which share the same ''cluster parents''. Which cluster modes are compatible or exclude each other. What quantum numbers could characterize cluster configurations. There is no doubt that we can learn a good deal from the experimentalists who have discovered many of the features relevant to aspect (B). Symmetries specific to cluster configurations which can throw some light on both aspects of clustering are discussed

An improved Pearson's correlation proximity-based hierarchical clustering for mining biological association between genes.

Science.gov (United States)

Booma, P M; Prabhakaran, S; Dhanalakshmi, R

2014-01-01

Microarray gene expression datasets has concerned great awareness among molecular biologist, statisticians, and computer scientists. Data mining that extracts the hidden and usual information from datasets fails to identify the most significant biological associations between genes. A search made with heuristic for standard biological process measures only the gene expression level, threshold, and response time. Heuristic search identifies and mines the best biological solution, but the association process was not efficiently addressed. To monitor higher rate of expression levels between genes, a hierarchical clustering model was proposed, where the biological association between genes is measured simultaneously using proximity measure of improved Pearson's correlation (PCPHC). Additionally, the Seed Augment algorithm adopts average linkage methods on rows and columns in order to expand a seed PCPHC model into a maximal global PCPHC (GL-PCPHC) model and to identify association between the clusters. Moreover, a GL-PCPHC applies pattern growing method to mine the PCPHC patterns. Compared to existing gene expression analysis, the PCPHC model achieves better performance. Experimental evaluations are conducted for GL-PCPHC model with standard benchmark gene expression datasets extracted from UCI repository and GenBank database in terms of execution time, size of pattern, significance level, biological association efficiency, and pattern quality.

Cardiovascular Disease Risk Factor Patterns and Their Implications for Intervention Strategies in Vietnam

Directory of Open Access Journals (Sweden)

Quang Ngoc Nguyen

2012-01-01

Methods. A cross-sectional survey in 2009 (2,130 adults was done to collect data on behavioural CVDRF, anthropometry and blood pressure, lipidaemia profiles, and oral glucose tolerance tests. Four metabolic CVDRFs (hypertension, dyslipidaemia, diabetes, and obesity and five behavioural CVDRFs (smoking, excessive alcohol intake, unhealthy diet, physical inactivity, and stress were analysed to identify their prevalence, cluster patterns, and social predictors. Framingham scores were applied to estimate the global 10-year CVD risks and potential benefits of CVD prevention strategies. Results. The age-standardised prevalence of having at least 2/4 metabolic, 2/5 behavioural, or 4/9 major CVDRF was 28%, 27%, 13% in women and 32%, 62%, 34% in men. Within-individual clustering of metabolic factors was more common among older women and in urban areas. High overall CVD risk (≥20% over 10 years identified 20% of men and 5% of women—especially at higher ages—who had coexisting CVDRF. Conclusion. Multiple CVDRFs were common in Vietnamese adults with different clustering patterns across sex/age groups. Tackling any single risk factor would not be efficient.

Dietary habits and physical activity patterns among Slovenian elderly: cross-sectional survey with cluster analysis

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Joca Zurc

2015-03-01

Full Text Available Introduction: Physical activity and a healthy diet are significant predictors of healthy ageing—they help the elderly maintain their physical and mental health, and prevent chronic diseases. Methods: The data for the empirical quantitative survey were collected on the sample of 218 elderly community-dwelling participants (aged 65 years or more, using a structured questionnaire for self-reporting. Data analyses were proceed with the bivariate statistics, and multivariate hierarchical cluster analysis. Results: Most respondents reported good dietary habits (83.1% and a satisfactory physical activity level (60.5%. On average, the elderly eat 3-4 meals per day (59.8% and engage in physical activity at least three times a week (58.6%, with interventions lasting 15 minutes or more (84.4 % and non-organized activity prevailing (96.2%. Ward’s method yielded three clusters with homogenous dietary and physical activity patterns: ‘Health Conscious’ (30.8%, ‘At Risk’ (42.7% and ‘Special Requirements’ (26.5%. Significant differences were identified between clusters and educational level (p = 0.001. Discussion and conclusions: In the future, special attention should be placed on the elderly group with a lower educational level and special dietary and physical activity requirements. Additional studies on representative samples are required for a comprehensive investigation into the lifestyle behaviours of elderly individuals.

Global and nonglobal parameters of horizontal-branch morphology of globular clusters

International Nuclear Information System (INIS)

Milone, A. P.; Marino, A. F.; Dotter, A.; Norris, J. E.; Jerjen, H.; Asplund, M.

2014-01-01

The horizontal-branch (HB) morphology of globular clusters (GCs) is mainly determined by metallicity. However, the fact that GCs with almost the same metallicity exhibit different HB morphologies demonstrates that at least one more parameter is needed to explain the HB morphology. It has been suggested that one of these should be a global parameter that varies from GC to GC and the other a nonglobal parameter that varies within the GC. In this study we provide empirical evidence corroborating this idea. We used the photometric catalogs obtained with the Advanced Camera for Surveys of the Hubble Space Telescope and analyze the color-magnitude diagrams of 74 GCs. The HB morphology of our sample of GCs has been investigated on the basis of the two new parameters L1 and L2 that measure the distance between the red giant branch and the coolest part of the HB and the color extension of the HB, respectively. We find that L1 correlates with both metallicity and age, whereas L2 most strongly correlates with the mass of the hosting GC. The range of helium abundance among the stars in a GC, characterized by ΔY and associated with the presence of multiple stellar populations, has been estimated in a few GCs to date. In these GCs we find a close relationship among ΔY, GC mass, and L2. We conclude that age and metallicity are the main global parameters, while the range of helium abundance within a GC is the main nonglobal parameter defining the HB morphology of Galactic GCs.

GIS-supported analysis of global patterns of anthropogenic forest degradation. A sectoral application of the syndrome concept

International Nuclear Information System (INIS)

Cassel-Gintz, M.

2001-06-01

The geographical analysis of a Syndrome is performed in several steps integrating GIS with concepts of fuzzy logic and qualitative reasoning. In the first step a syndrome specific network of interactions is formulated by analysing case studies, theories and expert assessments. Based on this systemic representation the natural and socio-economic conditions under which the syndrome specific mechanisms can be active are identified. This evaluation is called the disposition of a region towards a specific Syndrome. The resulting indicator can be used as an early warning indicator for the possible germination of a non-sustainable development. Based on the constituting elements of the Syndrome, a complex indicator for the intensity of the active Syndrome is derived in the next step of the analysis. This indicator assesses the critical states in the dynamical evolution of the non-sustainable patterns of civilisation nature interaction. Complete Syndrome analyses are performed for the main Syndromes of deforestation. The resulting spatial distribution of the combined dispositions and intensities of the different Syndromes present a unique global assessment describing the current damage and future regional threats to forests by their underlying global cause-effect patterns of civilisation-nature interaction. Specially the assessment of the threat by coupling of momentarily active and potentially active cause-effect patterns provides a previously not achieved systematic insight into the complex interaction of different patterns of global deforestation and forest degradation. (orig.)

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.

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.

Emerging pattern of global change in the upper atmosphere and ionosphere

Directory of Open Access Journals (Sweden)

J. Laštovička

2008-05-01

Full Text Available In the upper atmosphere, greenhouse gases produce a cooling effect, instead of a warming effect. Increases in greenhouse gas concentrations are expected to induce substantial changes in the mesosphere, thermosphere, and ionosphere, including a thermal contraction of these layers. In this article we construct for the first time a pattern of the observed long-term global change in the upper atmosphere, based on trend studies of various parameters. The picture we obtain is qualitative, and contains several gaps and a few discrepancies, but the overall pattern of observed long-term changes throughout the upper atmosphere is consistent with model predictions of the effect of greenhouse gas increases. Together with the large body of lower atmospheric trend research, our synthesis indicates that anthropogenic emissions of greenhouse gases are affecting the atmosphere at nearly all altitudes between ground and space.

Characteristics of autumn-winter extreme precipitation on the Norwegian west coast identified by cluster analysis

Energy Technology Data Exchange (ETDEWEB)

Heikkilae, U. [Bjerknes Centre for Climate Research, Uni Bjerknes Centre, Bergen (Norway); Australian Nuclear Science and Technology Organisation (ANSTO), Lucas Heights, NSW (Australia); Sorteberg, A. [University of Bergen, Geophysical Institute, Bergen (Norway); University of Bergen, Bjerknes Centre for Climate Research, Bergen (Norway)

2012-08-15

Extremely high autumn and winter precipitation events on the European west coast are often driven by low-pressure systems in the North Atlantic. Climate projections suggest the number and intensity of these events is likely to increase far more than the mean precipitation. In this study we investigate the autumn-winter extreme precipitation on the Norwegian west coast and the connection between its spatial distribution and sea level pressure (SLP) patterns using the k-means cluster analysis. We use three relatively high resolved downscalings of one global coupled model: the Arpege global atmospheric model (stretched grid with 35-km horizontal resolution over Norway) and the WRF-downscaled Arpege model (30 and 10-km) for the 30-year periods of 1961-1990 and 2021-2050. The cluster analysis finds three main SLP patterns responsible for extreme precipitation in different parts of the country. The SLP patterns found are similar to the NAO positive pattern known to strengthen the westerly flow towards European coast. We then apply the method to investigate future change in extreme precipitation. We find an increase in the number of days with extreme precipitation of 15, 39 and 35% in the two simulations (Arpege 35-km and WRF 30 and 10-km, respectively). We do not find evidence of a significant change in the frequency of weather patterns between the present and the future periods. Rather, it is the probability of a given weather pattern to cause extreme precipitation which is increased in the future, probably due to higher temperatures and an increased moisture content of the air. The WRF model predicts the increase in this probability caused by the most important SLP patterns to be >50%. The Arpege model does not predict such a significant change because the general increase in extreme precipitation predicted is smaller, probably due to its coarser resolution over ocean which leads to smoother representation of the low pressure systems. (orig.)

Identification of Clusters in Travel Patterns using Smart Card Data

DEFF Research Database (Denmark)

Reinau, Kristian Hegner; Harder, Henrik

2016-01-01

This article is the first contribution, which explores the potential of smart card data in relation to optimization of public transport in a peripheral monocentric region. A modularity analysis is made using smart card data collected in Northern Jutland in Marts and July 2014, and the analysis...... reveals first, that smart card data can be used to identify clusters within flows of public transport passengers in a peripheral region. Second, that the presence of such clusters suggest that public transport should be planned to give the users the possibility of travelling with good connectivity...... and easiness within these clusters. Third, the presence of such clusters in peripheral areas of the monocentric region suggest, that the overall current focus in public transport planning on transport possibilities to and from the central city in monocentric regions is questionable....

Orbit Clustering Based on Transfer Cost

Science.gov (United States)

Gustafson, Eric D.; Arrieta-Camacho, Juan J.; Petropoulos, Anastassios E.

2013-01-01

We propose using cluster analysis to perform quick screening for combinatorial global optimization problems. The key missing component currently preventing cluster analysis from use in this context is the lack of a useable metric function that defines the cost to transfer between two orbits. We study several proposed metrics and clustering algorithms, including k-means and the expectation maximization algorithm. We also show that proven heuristic methods such as the Q-law can be modified to work with cluster analysis.

Fusion process of Lennard-Jones clusters: global minima and magic numbers formation

DEFF Research Database (Denmark)

Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter

2004-01-01

We present a new theoretical framework for modeling the fusion process of Lennard–Jones (LJ) clusters. Starting from the initial tetrahedral cluster configuration, adding new atoms to the system and absorbing its energy at each step, we find cluster growing paths up to the cluster size of 150 atoms...

Method for Determining Appropriate Clustering Criteria of Location-Sensing Data

Directory of Open Access Journals (Sweden)

Youngmin Lee

2016-08-01

Full Text Available Large quantities of location-sensing data are generated from location-based social network services. These data are provided as point properties with location coordinates acquired from a global positioning system or Wi-Fi signal. To show the point data on multi-scale map services, the data should be represented by clusters following a grid-based clustering method, in which an appropriate grid size should be determined. Currently, there are no criteria for determining the proper grid size, and the modifiable areal unit problem has been formulated for the purpose of addressing this issue. The method proposed in this paper is applies a hexagonal grid to geotagged Twitter point data, considering the grid size in terms of both quantity and quality to minimize the limitations associated with the modifiable areal unit problem. Quantitatively, we reduced the original Twitter point data by an appropriate amount using Töpfer’s radical law. Qualitatively, we maintained the original distribution characteristics using Moran’s I. Finally, we determined the appropriate sizes of clusters from zoom levels 9–13 by analyzing the distribution of data on the graphs. Based on the visualized clustering results, we confirm that the original distribution pattern is effectively maintained using the proposed method.

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.

The mass-temperature relation for clusters of galaxies

DEFF Research Database (Denmark)

Hjorth, J.; Oukbir, J.; van Kampen, E.

1998-01-01

A tight mass-temperature relation, M(r)/r proportional to T-x, is expected in most cosmological models if clusters of galaxies are homologous and the intracluster gas is in global equilibrium with the dark matter. We here calibrate this relation using eight clusters with well-defined global tempe...... redshift, the relation represents a new tool for determination of cosmological parameters, notably the cosmological constant Lambda....

Broken colinearity of the amphioxus Hox cluster

Directory of Open Access Journals (Sweden)

Pascual-Anaya Juan

2012-12-01

Full Text Available Abstract Background In most eumetazoans studied so far, Hox genes determine the identity of structures along the main body axis. They are usually linked in genomic clusters and, in the case of the vertebrate embryo, are expressed with spatial and temporal colinearity. Outside vertebrates, temporal colinearity has been reported in the cephalochordate amphioxus (the least derived living relative of the chordate ancestor but only for anterior and central genes, namely Hox1 to Hox4 and Hox6. However, most of the Hox gene expression patterns in amphioxus have not been reported. To gain global insights into the evolution of Hox clusters in chordates, we investigated a more extended expression profile of amphioxus Hox genes. Results Here we report an extended expression profile of the European amphioxus Branchiostoma lanceolatum Hox genes and describe that all Hox genes, except Hox13, are expressed during development. Interestingly, we report the breaking of both spatial and temporal colinearity for at least Hox6 and Hox14, which thus have escaped from the classical Hox code concept. We show a previously unidentified Hox6 expression pattern and a faint expression for posterior Hox genes in structures such as the posterior mesoderm, notochord, and hindgut. Unexpectedly, we found that amphioxus Hox14 had the most divergent expression pattern. This gene is expressed in the anterior cerebral vesicle and pharyngeal endoderm. Amphioxus Hox14 expression represents the first report of Hox gene expression in the most anterior part of the central nervous system. Nevertheless, despite these divergent expression patterns, amphioxus Hox6 and Hox14 seem to be still regulated by retinoic acid. Conclusions Escape from colinearity by Hox genes is not unusual in either vertebrates or amphioxus and we suggest that those genes escaping from it are probably associated with the patterning of lineage-specific morphological traits, requiring the loss of those developmental

Montane ecosystem productivity responds more to global circulation patterns than climatic trends

Science.gov (United States)

Desai, A. R.; Wohlfahrt, G.; Zeeman, M. J.; Katata, G.; Eugster, W.; Montagnani, L.; Gianelle, D.; Mauder, M.; Schmid, H.-P.

2016-02-01

Regional ecosystem productivity is highly sensitive to inter-annual climate variability, both within and outside the primary carbon uptake period. However, Earth system models lack sufficient spatial scales and ecosystem processes to resolve how these processes may change in a warming climate. Here, we show, how for the European Alps, mid-latitude Atlantic ocean winter circulation anomalies drive high-altitude summer forest and grassland productivity, through feedbacks among orographic wind circulation patterns, snowfall, winter and spring temperatures, and vegetation activity. Therefore, to understand future global climate change influence to regional ecosystem productivity, Earth systems models need to focus on improvements towards topographic downscaling of changes in regional atmospheric circulation patterns and to lagged responses in vegetation dynamics to non-growing season climate anomalies.

Montane ecosystem productivity responds more to global circulation patterns than climatic trends

International Nuclear Information System (INIS)

Desai, A R; Wohlfahrt, G; Zeeman, M J; Katata, G; Mauder, M; Schmid, H-P; Eugster, W; Montagnani, L; Gianelle, D

2016-01-01

Regional ecosystem productivity is highly sensitive to inter-annual climate variability, both within and outside the primary carbon uptake period. However, Earth system models lack sufficient spatial scales and ecosystem processes to resolve how these processes may change in a warming climate. Here, we show, how for the European Alps, mid-latitude Atlantic ocean winter circulation anomalies drive high-altitude summer forest and grassland productivity, through feedbacks among orographic wind circulation patterns, snowfall, winter and spring temperatures, and vegetation activity. Therefore, to understand future global climate change influence to regional ecosystem productivity, Earth systems models need to focus on improvements towards topographic downscaling of changes in regional atmospheric circulation patterns and to lagged responses in vegetation dynamics to non-growing season climate anomalies. (letter)

Conjunction of anti-parallel and component reconnection at the dayside MP: Cluster and Double Star coordinated observation on 6 April 2004

Science.gov (United States)

Wang, J.; Pu, Z. Y.; Fu, S. Y.; Wang, X. G.; Xiao, C. J.; Dunlop, M. W.; Wei, Y.; Bogdanova, Y. V.; Zong, Q. G.; Xie, L.

2011-05-01

Previous theoretical and simulation studies have suggested that the anti-parallel and component reconnection can occur simultaneously on the dayside magnetopause. Certain observations have also been reported to support global conjunct pattern of magnetic reconnection. Here, we show direct evidence for the conjunction of anti-parallel and component MR using coordinated observations of Double Star TC-1 and Cluster under the same IMF condition on 6 April, 2004. The global MR X-line configuration constructed is in good agreement with the “S-shape” model.

Robust continuous clustering.

Science.gov (United States)

Shah, Sohil Atul; Koltun, Vladlen

2017-09-12

Clustering is a fundamental procedure in the analysis of scientific data. It is used ubiquitously across the sciences. Despite decades of research, existing clustering algorithms have limited effectiveness in high dimensions and often require tuning parameters for different domains and datasets. We present a clustering algorithm that achieves high accuracy across multiple domains and scales efficiently to high dimensions and large datasets. The presented algorithm optimizes a smooth continuous objective, which is based on robust statistics and allows heavily mixed clusters to be untangled. The continuous nature of the objective also allows clustering to be integrated as a module in end-to-end feature learning pipelines. We demonstrate this by extending the algorithm to perform joint clustering and dimensionality reduction by efficiently optimizing a continuous global objective. The presented approach is evaluated on large datasets of faces, hand-written digits, objects, newswire articles, sensor readings from the Space Shuttle, and protein expression levels. Our method achieves high accuracy across all datasets, outperforming the best prior algorithm by a factor of 3 in average rank.

The Emergence of an Export Cluster

DEFF Research Database (Denmark)

Giacomin, Valeria

2018-01-01

Malaysia and Indonesia account for 90 percent of global exports of palm oil, forming one of the largest agricultural clusters in the world. This article uses archival sources to trace how this cluster emerged from the rubber business in the era of British and Dutch colonialism. Specifically...

Cardiovascular disease risk factor patterns and their implications for intervention strategies in Vietnam.

Science.gov (United States)

Nguyen, Quang Ngoc; Pham, Son Thai; Do, Loi Doan; Nguyen, Viet Lan; Wall, Stig; Weinehall, Lars; Bonita, Ruth; Byass, Peter

2012-01-01

Background. Data on cardiovascular disease risk factors (CVDRFs) in Vietnam are limited. This study explores the prevalence of each CVDRF and how they cluster to evaluate CVDRF burdens and potential prevention strategies. Methods. A cross-sectional survey in 2009 (2,130 adults) was done to collect data on behavioural CVDRF, anthropometry and blood pressure, lipidaemia profiles, and oral glucose tolerance tests. Four metabolic CVDRFs (hypertension, dyslipidaemia, diabetes, and obesity) and five behavioural CVDRFs (smoking, excessive alcohol intake, unhealthy diet, physical inactivity, and stress) were analysed to identify their prevalence, cluster patterns, and social predictors. Framingham scores were applied to estimate the global 10-year CVD risks and potential benefits of CVD prevention strategies. Results. The age-standardised prevalence of having at least 2/4 metabolic, 2/5 behavioural, or 4/9 major CVDRF was 28%, 27%, 13% in women and 32%, 62%, 34% in men. Within-individual clustering of metabolic factors was more common among older women and in urban areas. High overall CVD risk (≥20% over 10 years) identified 20% of men and 5% of women-especially at higher ages-who had coexisting CVDRF. Conclusion. Multiple CVDRFs were common in Vietnamese adults with different clustering patterns across sex/age groups. Tackling any single risk factor would not be efficient.

Global diversity patterns of freshwater fishes - potential victims of their own success

OpenAIRE

Pelayo-Villamil, P.; Guisande, C.; Vari, R. P.; Manjarres-Hernandez, A.; Garcia-Rosello, E.; Gonzalez-Dacosta, J.; Heine, J.; Vilas, L. G.; Patti, B.; Quinci, E. M.; Jimenez, L. F.; Granado-Lorencio, C.; Tedesco, Pablo; Lobo, J. M.

2015-01-01

AimTo examine the pattern and cumulative curve of descriptions of freshwater fishes world-wide, the geographical biases in the available information on that fauna, the relationship between species richness and geographical rarity of such fishes, as well as to assess the relative contributions of different environmental factors on these variables. LocationGlobal. MethodsModestR was used to summarize the geographical distribution of freshwater fish species using information available from data-...

The mass-temperature relation for clusters of galaxies

DEFF Research Database (Denmark)

Hjorth, J.; Oukbir, J.; van Kampen, E.

1998-01-01

A tight mass-temperature relation, M(r)/r proportional to T-x, is expected in most cosmological models if clusters of galaxies are homologous and the intracluster gas is in global equilibrium with the dark matter. We here calibrate this relation using eight clusters with well-defined global...... with wide-held HST imaging could provide a sensitive test of the normalization and intrinsic scatter of the relation, resulting in a powerful and expedient way of measuring masses of clusters of galaxies. In addition, as M(r)/r las derived from lensing) is dependent on the cosmological model at high...

Cluster structures influenced by interaction with a surface.

Science.gov (United States)

Witt, Christopher; Dieterich, Johannes M; Hartke, Bernd

2018-05-30

Clusters on surfaces are vitally important for nanotechnological applications. Clearly, cluster-surface interactions heavily influence the preferred cluster structures, compared to clusters in vacuum. Nevertheless, systematic explorations and an in-depth understanding of these interactions and how they determine the cluster structures are still lacking. Here we present an extension of our well-established non-deterministic global optimization package OGOLEM from isolated clusters to clusters on surfaces. Applying this approach to intentionally simple Lennard-Jones test systems, we produce a first systematic exploration that relates changes in cluster-surface interactions to resulting changes in adsorbed cluster structures.

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...

Patterns of Brucellosis Infection Symptoms in Azerbaijan: A Latent Class Cluster Analysis

Directory of Open Access Journals (Sweden)

Rita Ismayilova

2014-01-01

Full Text Available Brucellosis infection is a multisystem disease, with a broad spectrum of symptoms. We investigated the existence of clusters of infected patients according to their clinical presentation. Using national surveillance data from the Electronic-Integrated Disease Surveillance System, we applied a latent class cluster (LCC analysis on symptoms to determine clusters of brucellosis cases. A total of 454 cases reported between July 2011 and July 2013 were analyzed. LCC identified a two-cluster model and the Vuong-Lo-Mendell-Rubin likelihood ratio supported the cluster model. Brucellosis cases in the second cluster (19% reported higher percentages of poly-lymphadenopathy, hepatomegaly, arthritis, myositis, and neuritis and changes in liver function tests compared to cases of the first cluster. Patients in the second cluster had a severe brucellosis disease course and were associated with longer delay in seeking medical attention. Moreover, most of them were from Beylagan, a region focused on sheep and goat livestock production in south-central Azerbaijan. Patients in cluster 2 accounted for one-quarter of brucellosis cases and had a more severe clinical presentation. Delay in seeking medical care may explain severe illness. Future work needs to determine the factors that influence brucellosis case seeking and identify brucellosis species, particularly among cases from Beylagan.

Identifying patterns of general practitioner service utilisation and their relationship with potentially preventable hospitalisations in people with diabetes: The utility of a cluster analysis approach.

Science.gov (United States)

Ha, Ninh Thi; Harris, Mark; Preen, David; Robinson, Suzanne; Moorin, Rachael

2018-04-01

We aimed to characterise use of general practitioners (GP) simultaneously across multiple attributes in people with diabetes and examine its impact on diabetes related potentially preventable hospitalisations (PPHs). Five-years of panel data from 40,625 adults with diabetes were sourced from Western Australian administrative health records. Cluster analysis (CA) was used to group individuals with similar patterns of GP utilisation characterised by frequency and recency of services. The relationship between GP utilisation cluster and the risk of PPHs was examined using multivariable random-effects negative binomial regression. CA categorised GP utilisation into three clusters: moderate; high and very high usage, having distinct patient characteristics. After adjusting for potential confounders, the rate of PPHs was significantly lower across all GP usage clusters compared with those with no GP usage; IRR = 0.67 (95%CI: 0.62-0.71) among the moderate, IRR = 0.70 (95%CI 0.66-0.73) high and IRR = 0.76 (95%CI 0.72-0.80) very high GP usage clusters. Combination of temporal factors with measures of frequency of use of GP services revealed patterns of primary health care utilisation associated with different underlying patient characteristics. Incorporation of multiple attributes, that go beyond frequency-based approaches may better characterise the complex relationship between use of GP services and diabetes-related hospitalisation. Copyright © 2018 Elsevier B.V. All rights reserved.

Sibling relationship patterns and their associations with child competence and problem behavior.

Science.gov (United States)

Buist, Kirsten L; Vermande, Marjolijn

2014-08-01

The present study is the first to examine patterns in sibling relationship quality and the associations of these patterns with internalizing and externalizing problem behavior, as well as self-perceived competence, in middle childhood. Self-report questionnaires (e.g., Sibling Relationship Questionnaire, Self-Perception Profile for Children, Youth Self Report) were administered among 1,670 Dutch children (Mage = 11.40 years, SD = .83) attending 51 different Dutch schools. Three sibling relationship clusters were found: a conflictual cluster (low on warmth, high on conflict), an affect-intense cluster (above average on warmth and conflict), and a harmonious cluster (high on warmth, low on conflict). Sister pairs were underrepresented in the conflictual cluster and overrepresented in the harmonious cluster. Children with conflictual sibling relationships reported significantly more internalizing and externalizing problems, and lower academic and social competence and global self-worth, than children with harmonious sibling relationships. Children with affect-intense sibling relationships reported less aggression and better social competence than children with conflictual sibling relationships. Our findings indicate that it is fruitful to combine indices of sibling warmth and conflict to examine sibling relationship types. Relationship types differed significantly concerning internalizing and externalizing problems, but also concerning self-perceived competence. These findings extend our knowledge about sibling relationship types and their impact on different aspects of child adjustment. Whereas harmonious sibling relationships are the most beneficial for adjustment, sibling conflict mainly has a negative effect on adjustment in combination with lack of sibling warmth. Implications and future directions are discussed.

Spatial patterns and temporal dynamics of global scale climate-groundwater interactions

Science.gov (United States)

Cuthbert, M. O.; Gleeson, T. P.; Moosdorf, N.; Schneider, A. C.; Hartmann, J.; Befus, K. M.; Lehner, B.

2017-12-01

The interactions between groundwater and climate are important to resolve in both space and time as they influence mass and energy transfers at Earth's land surface. Despite the significance of these processes, little is known about the spatio-temporal distribution of such interactions globally, and many large-scale climate, hydrological and land surface models oversimplify groundwater or exclude it completely. In this study we bring together diverse global geomatic data sets to map spatial patterns in the sensitivity and degree of connectedness between the water table and the land surface, and use the output from a global groundwater model to assess the locations where the lateral import or export of groundwater is significant. We also quantify the groundwater response time, the characteristic time for groundwater systems to respond to a change in boundary conditions, and map its distribution globally to assess the likely dynamics of groundwater's interaction with climate. We find that more than half of the global land surface significantly exports or imports groundwater laterally. Nearly 40% of Earth's landmass has water tables that are strongly coupled to topography with water tables shallow enough to enable a bi-directional exchange of moisture with the climate system. However, only a small proportion (around 12%) of such regions have groundwater response times of 100 years or less and have groundwater fluxes that would significantly respond to rapid environmental changes over this timescale. We last explore fundamental relationships between aridity, groundwater response times and groundwater turnover times. Our results have wide ranging implications for understanding and modelling changes in Earth's water and energy balance and for informing robust future water management and security decisions.

Pupil Clustering in English Secondary Schools: One Pattern or Several?

Science.gov (United States)

Gorard, Stephen; Cheng, Shou Chen

2011-01-01

Previous international work has shown that clustering pupils with similar characteristics in particular schools yields no clear academic benefit, and can be disadvantageous both socially and personally. Understanding how and why this clustering happens, and how it may be reduced, is therefore important for policy. Yet previous work has tended to…

Evaporation-driven clustering of microscale pillars and lamellae

Energy Technology Data Exchange (ETDEWEB)

Kim, Tae-Hong; Kim, Jungchul; Kim, Ho-Young, E-mail: hyk@snu.ac.kr [Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul 08826 (Korea, Republic of)

2016-02-15

As a liquid film covering an array of micro- or nanoscale pillars or lamellae evaporates, its meniscus pulls the elastic patterns together because of capillary effects, leading to clustering of the slender microstructures. While this elastocapillary coalescence may imply various useful applications, it is detrimental to a semiconductor manufacturing process called the spin drying, where a liquid film rinses patterned wafers until drying. To understand the transient mechanism underlying such self-organization during and after liquid evaporation, we visualize the clustering dynamics of polymer micropatterns. Our visualization experiments reveal that the patterns clumped during liquid evaporation can be re-separated when completely dried in some cases. This restoration behavior is explained by considering adhesion energy of the patterns as well as capillary forces, which leads to a regime map to predict whether permanent stiction would occur. This work does not only extend our understanding of micropattern stiction, but also suggests a novel path to control and prevent pattern clustering.

Sleep stages identification in patients with sleep disorder using k-means clustering

Science.gov (United States)

Fadhlullah, M. U.; Resahya, A.; Nugraha, D. F.; Yulita, I. N.

2018-05-01

Data mining is a computational intelligence discipline where a large dataset processed using a certain method to look for patterns within the large dataset. This pattern then used for real time application or to develop some certain knowledge. This is a valuable tool to solve a complex problem, discover new knowledge, data analysis and decision making. To be able to get the pattern that lies inside the large dataset, clustering method is used to get the pattern. Clustering is basically grouping data that looks similar so a certain pattern can be seen in the large data set. Clustering itself has several algorithms to group the data into the corresponding cluster. This research used data from patients who suffer sleep disorders and aims to help people in the medical world to reduce the time required to classify the sleep stages from a patient who suffers from sleep disorders. This study used K-Means algorithm and silhouette evaluation to find out that 3 clusters are the optimal cluster for this dataset which means can be divided to 3 sleep stages.

Cluster analysis by optimal decomposition of induced fuzzy sets

Energy Technology Data Exchange (ETDEWEB)

Backer, E

1978-01-01

Nonsupervised pattern recognition is addressed and the concept of fuzzy sets is explored in order to provide the investigator (data analyst) additional information supplied by the pattern class membership values apart from the classical pattern class assignments. The basic ideas behind the pattern recognition problem, the clustering problem, and the concept of fuzzy sets in cluster analysis are discussed, and a brief review of the literature of the fuzzy cluster analysis is given. Some mathematical aspects of fuzzy set theory are briefly discussed; in particular, a measure of fuzziness is suggested. The optimization-clustering problem is characterized. Then the fundamental idea behind affinity decomposition is considered. Next, further analysis takes place with respect to the partitioning-characterization functions. The iterative optimization procedure is then addressed. The reclassification function is investigated and convergence properties are examined. Finally, several experiments in support of the method suggested are described. Four object data sets serve as appropriate test cases. 120 references, 70 figures, 11 tables. (RWR)

Mapping Diversity of Publication Patterns in the Social Sciences and Humanities: An Approach Making Use of Fuzzy Cluster Analysis

Directory of Open Access Journals (Sweden)

Frederik T. Verleysen

2016-11-01

Full Text Available Purpose: To present a method for systematically mapping diversity of publication patterns at the author level in the social sciences and humanities in terms of publication type, publication language and co-authorship. Design/methodology/approach: In a follow-up to the hard partitioning clustering by Verleysen and Weeren in 2016, we now propose the complementary use of fuzzy cluster analysis, making use of a membership coefficient to study gradual differences between publication styles among authors within a scholarly discipline. The analysis of the probability density function of the membership coefficient allows to assess the distribution of publication styles within and between disciplines. Findings: As an illustration we analyze 1,828 productive authors affiliated in Flanders, Belgium. Whereas a hard partitioning previously identified two broad publication styles, an international one vs. a domestic one, fuzzy analysis now shows gradual differences among authors. Internal diversity also varies across disciplines and can be explained by researchers' specialization and dissemination strategies. Research limitations: The dataset used is limited to one country for the years 2000-2011; a cognitive classification of authors may yield a different result from the affiliation-based classification used here. Practical implications: Our method is applicable to other bibliometric and research evaluation contexts, especially for the social sciences and humanities in non-Anglophone countries. Originality/value: The method proposed is a novel application of cluster analysis to the field of bibliometrics. Applied to publication patterns at the author level in the social sciences and humanities, for the first time it systematically documents intra-disciplinary diversity.

Sexuality generates diversity in the aflatoxin gene cluster: evidence on a global scale.

Directory of Open Access Journals (Sweden)

Geromy G Moore

Full Text Available Aflatoxins are produced by Aspergillus flavus and A. parasiticus in oil-rich seed and grain crops and are a serious problem in agriculture, with aflatoxin B₁ being the most carcinogenic natural compound known. Sexual reproduction in these species occurs between individuals belonging to different vegetative compatibility groups (VCGs. We examined natural genetic variation in 758 isolates of A. flavus, A. parasiticus and A. minisclerotigenes sampled from single peanut fields in the United States (Georgia, Africa (Benin, Argentina (Córdoba, Australia (Queensland and India (Karnataka. Analysis of DNA sequence variation across multiple intergenic regions in the aflatoxin gene clusters of A. flavus, A. parasiticus and A. minisclerotigenes revealed significant linkage disequilibrium (LD organized into distinct blocks that are conserved across different localities, suggesting that genetic recombination is nonrandom and a global occurrence. To assess the contributions of asexual and sexual reproduction to fixation and maintenance of toxin chemotype diversity in populations from each locality/species, we tested the null hypothesis of an equal number of MAT1-1 and MAT1-2 mating-type individuals, which is indicative of a sexually recombining population. All samples were clone-corrected using multi-locus sequence typing which associates closely with VCG. For both A. flavus and A. parasiticus, when the proportions of MAT1-1 and MAT1-2 were significantly different, there was more extensive LD in the aflatoxin cluster and populations were fixed for specific toxin chemotype classes, either the non-aflatoxigenic class in A. flavus or the B₁-dominant and G₁-dominant classes in A. parasiticus. A mating type ratio close to 1∶1 in A. flavus, A. parasiticus and A. minisclerotigenes was associated with higher recombination rates in the aflatoxin cluster and less pronounced chemotype differences in populations. This work shows that the reproductive nature of

Macroecological factors shape local-scale spatial patterns in agriculturalist settlements.

Science.gov (United States)

Tao, Tingting; Abades, Sebastián; Teng, Shuqing; Huang, Zheng Y X; Reino, Luís; Chen, Bin J W; Zhang, Yong; Xu, Chi; Svenning, Jens-Christian

2017-11-15

Macro-scale patterns of human systems ranging from population distribution to linguistic diversity have attracted recent attention, giving rise to the suggestion that macroecological rules shape the assembly of human societies. However, in which aspects the geography of our own species is shaped by macroecological factors remains poorly understood. Here, we provide a first demonstration that macroecological factors shape strong local-scale spatial patterns in human settlement systems, through an analysis of spatial patterns in agriculturalist settlements in eastern mainland China based on high-resolution Google Earth images. We used spatial point pattern analysis to show that settlement spatial patterns are characterized by over-dispersion at fine spatial scales (0.05-1.4 km), consistent with territory segregation, and clumping at coarser spatial scales beyond the over-dispersion signals, indicating territorial clustering. Statistical modelling shows that, at macroscales, potential evapotranspiration and topographic heterogeneity have negative effects on territory size, but positive effects on territorial clustering. These relationships are in line with predictions from territory theory for hunter-gatherers as well as for many animal species. Our results help to disentangle the complex interactions between intrinsic spatial processes in agriculturalist societies and external forcing by macroecological factors. While one may speculate that humans can escape ecological constraints because of unique abilities for environmental modification and globalized resource transportation, our work highlights that universal macroecological principles still shape the geography of current human agricultural societies. © 2017 The Author(s).

Cardiovascular Risk Factors in Cluster Headache.

Science.gov (United States)

Lasaosa, S Santos; Diago, E Bellosta; Calzada, J Navarro; Benito, A Velázquez

2017-06-01

 Patients with cluster headache tend to have a dysregulation of systemic blood pressure such as increased blood pressure variability and decreased nocturnal dipping. This pattern of nocturnal nondipping is associated with end-organ damage and increased risk of cardiovascular disease.  To determine if cluster headache is associated with a higher risk of cardiovascular disease.  Cross-sectional study of 33 cluster headache patients without evidence of cardiovascular disease and 30 age- and gender-matched healthy controls. Ambulatory blood pressure monitoring was performed in all subjects. We evaluate anthropometric, hematologic, and structural parameters (carotid intima-media thickness and ankle-brachial index).  Of the 33 cluster headache patients, 16 (48.5%) were nondippers, a higher percentage than expected. Most of the cluster headache patients (69.7%) also presented a pathological ankle-brachial index. In terms of the carotid intima-media thickness values, 58.3% of the patients were in the 75th percentile, 25% were in the 90th percentile, and 20% were in the 95th percentile. In the control group, only five of the 30 subjects (16.7%) had a nondipper pattern ( P  =   0.004), with 4.54% in the 90th and 95th percentiles ( P  =   0.012 and 0.015).  Compared with healthy controls, patients with cluster headache presented a high incidence (48.5%) of nondipper pattern, pathological ankle-brachial index (69.7%), and intima-media thickness values above the 75th percentile. These findings support the hypothesis that patients with cluster headache present increased risk of cardiovascular disease. © 2016 American Academy of Pain Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

Cluster-based spectrum sensing for cognitive radios with imperfect channel to cluster-head

KAUST Repository

Ben Ghorbel, Mahdi

2012-04-01

Spectrum sensing is considered as the first and main step for cognitive radio systems to achieve an efficient use of spectrum. Cooperation and clustering among cognitive radio users are two techniques that can be employed with spectrum sensing in order to improve the sensing performance by reducing miss-detection and false alarm. In this paper, within the framework of a clustering-based cooperative spectrum sensing scheme, we study the effect of errors in transmitting the local decisions from the secondary users to the cluster heads (or the fusion center), while considering non-identical channel conditions between the secondary users. Closed-form expressions for the global probabilities of detection and false alarm at the cluster head are derived. © 2012 IEEE.

Cluster-based spectrum sensing for cognitive radios with imperfect channel to cluster-head

KAUST Repository

Ben Ghorbel, Mahdi; Nam, Haewoon; Alouini, Mohamed-Slim

2012-01-01

Spectrum sensing is considered as the first and main step for cognitive radio systems to achieve an efficient use of spectrum. Cooperation and clustering among cognitive radio users are two techniques that can be employed with spectrum sensing in order to improve the sensing performance by reducing miss-detection and false alarm. In this paper, within the framework of a clustering-based cooperative spectrum sensing scheme, we study the effect of errors in transmitting the local decisions from the secondary users to the cluster heads (or the fusion center), while considering non-identical channel conditions between the secondary users. Closed-form expressions for the global probabilities of detection and false alarm at the cluster head are derived. © 2012 IEEE.

The Global Precipitation Patterns Associated with Short-Term Extratropical Climate Fluctuations

Science.gov (United States)

Huffman, George J.; Adler, Robert F.; Bolvin, David T.

1999-01-01

Two globally-complete, observation-only precipitation datasets have recently been developed for the Global Precipitation Climatology Project (GPCP). Both depend heavily on a variety of satellite input, as well as gauge data over land. The first, Version 2x79, provides monthly estimates on a 2.5 deg. x 2.5 deg. lat/long grid for the period 1979 through late 1999 (by the time of the conference). The second, the One-Degree Daily (1DD), provides daily estimates on a 1 deg. x l deg. grid for the period 1997 through late 1999 (by the time of the conference). Both are in beta test preparatory to release as official GPCP products. These datasets provide a unique perspective on the hydrological effects of the various atmospheric flow anomalies that have been identified by meteorologists. In this paper we discuss the regional precipitation effects that result from persistent extratropical flow anomalies. We will focus on the Pacific-North America (PNA) and North Atlantic Oscillation (NAO) patterns. Each characteristically becomes established on synoptic time scales, but then persists for periods that can exceed a month. The onset phase of each appears to have systematic mobile features, while the mature phase tend to be more stationary. Accordingly, composites of monthly data for outstanding positive and negative events (separately) contained in the 20-year record reveal the climatological structure of the precipitation during the mature phase. The climatological anomalies of the positive, negative, and (positive-negative) composites show the expected storm-track-related shifts in precipitation, and provide the advantage of putting the known precipitation effects over land in the context of the total pattern over land and ocean. As well, this global perspective points out some unexpected areas of correlation. Day-by-day composites of daily data anchored to the onset date demonstrate the systematic features during the onset. Although the 1DD has a fairly short record, some

Modeling urbanization patterns at a global scale with generative adversarial networks

Science.gov (United States)

Albert, A. T.; Strano, E.; Gonzalez, M.

2017-12-01

Current demographic projections show that, in the next 30 years, global population growth will mostly take place in developing countries. Coupled with a decrease in density, such population growth could potentially double the land occupied by settlements by 2050. The lack of reliable and globally consistent socio-demographic data, coupled with the limited predictive performance underlying traditional urban spatial explicit models, call for developing better predictive methods, calibrated using a globally-consistent dataset. Thus, richer models of the spatial interplay between the urban built-up land, population distribution and energy use are central to the discussion around the expansion and development of cities, and their impact on the environment in the context of a changing climate. In this talk we discuss methods for, and present an analysis of, urban form, defined as the spatial distribution of macroeconomic quantities that characterize a city, using modern machine learning methods and best-available remote-sensing data for the world's largest 25,000 cities. We first show that these cities may be described by a small set of patterns in radial building density, nighttime luminosity, and population density, which highlight, to first order, differences in development and land use across the world. We observe significant, spatially-dependent variance around these typical patterns, which would be difficult to model using traditional statistical methods. We take a first step in addressing this challenge by developing CityGAN, a conditional generative adversarial network model for simulating realistic urban forms. To guide learning and measure the quality of the simulated synthetic cities, we develop a specialized loss function for GAN optimization that incorporates standard spatial statistics used by urban analysis experts. Our framework is a stark departure from both the standard physics-based approaches in the literature (that view urban forms as fractals with a

Atypical Mg-poor Milky Way Field Stars with Globular Cluster Second-generation-like Chemical Patterns

Energy Technology Data Exchange (ETDEWEB)

Fernández-Trincado, J. G.; Geisler, D.; Tang, B.; Villanova, S.; Mennickent, R. E. [Departamento de Astronomía, Universidad de Concepción, Casilla 160-C, Concepción (Chile); Zamora, O.; García-Hernández, D. A.; Dell’Agli, F.; Prieto, Carlos Allende [Instituto de Astrofísica de Canarias, E-38205 La Laguna, Tenerife (Spain); Souto, Diogo; Cunha, Katia [Observatório Nacional, Rua Gal. José Cristino 77, Rio de Janeiro, RJ—20921-400 (Brazil); Schiavon, R. P. [Astrophysics Research Institute, Liverpool John Moores University, 146 Brownlow Hill, Liverpool L3 5RF (United Kingdom); Hasselquist, Sten [New Mexico State University, Las Cruces, NM 88003 (United States); Shetrone, M. [University of Texas at Austin, McDonald Observatory, Fort Davis, TX 79734 (United States); Vieira, K. [Centro de Investigaciones de Astronomía, AP 264, Mérida 5101-A (Venezuela, Bolivarian Republic of); Zasowski, G. [Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218 (United States); Sobeck, J.; Hayes, C. R.; Majewski, S. R. [Department of Astronomy, University of Virginia, Charlottesville, VA 22903 (United States); Placco, V. M., E-mail: jfernandezt@astro-udec.cl, E-mail: jfernandezt87@gmail.com [Department of Physics and JINA Center for the Evolution of the Elements, University of Notre Dame, Notre Dame, IN 46556 (United States); and others

2017-09-01

We report the peculiar chemical abundance patterns of 11 atypical Milky Way (MW) field red giant stars observed by the Apache Point Observatory Galactic Evolution Experiment (APOGEE). These atypical giants exhibit strong Al and N enhancements accompanied by C and Mg depletions, strikingly similar to those observed in the so-called second-generation (SG) stars of globular clusters (GCs). Remarkably, we find low Mg abundances ([Mg/Fe] < 0.0) together with strong Al and N overabundances in the majority (5/7) of the metal-rich ([Fe/H] ≳ −1.0) sample stars, which is at odds with actual observations of SG stars in Galactic GCs of similar metallicities. This chemical pattern is unique and unprecedented among MW stars, posing urgent questions about its origin. These atypical stars could be former SG stars of dissolved GCs formed with intrinsically lower abundances of Mg and enriched Al (subsequently self-polluted by massive AGB stars) or the result of exotic binary systems. We speculate that the stars Mg-deficiency as well as the orbital properties suggest that they could have an extragalactic origin. This discovery should guide future dedicated spectroscopic searches of atypical stellar chemical patterns in our Galaxy, a fundamental step forward to understanding the Galactic formation and evolution.

A global analysis of bird plumage patterns reveals no association between habitat and camouflage

Directory of Open Access Journals (Sweden)

Marius Somveille

2016-11-01

Full Text Available Evidence suggests that animal patterns (motifs function in camouflage. Irregular mottled patterns can facilitate concealment when stationary in cluttered habitats, whereas regular patterns typically prevent capture during movement in open habitats. Bird plumage patterns have predominantly converged on just four types—mottled (irregular, scales, bars and spots (regular—and habitat could be driving convergent evolution in avian patterning. Based on sensory ecology, we therefore predict that irregular patterns would be associated with visually noisy closed habitats and that regular patterns would be associated with open habitats. Regular patterns have also been shown to function in communication for sexually competing males to stand-out and attract females, so we predict that male breeding plumage patterns evolved in both open and closed habitats. Here, taking phylogenetic relatedness into account, we investigate ecological selection for bird plumage patterns across the class Aves. We surveyed plumage patterns in 80% of all avian species worldwide. Of these, 2,756 bird species have regular and irregular plumage patterns as well as habitat information. In this subset, we tested whether adult breeding/non-breeding plumages in each sex, and juvenile plumages, were associated with the habitat types found within the species’ geographical distributions. We found no evidence for an association between habitat and plumage patterns across the world’s birds and little phylogenetic signal. We also found that species with regular and irregular plumage patterns were distributed randomly across the world’s eco-regions without being affected by habitat type. These results indicate that at the global spatial and taxonomic scale, habitat does not predict convergent evolution in bird plumage patterns, contrary to the camouflage hypothesis.

A global analysis of bird plumage patterns reveals no association between habitat and camouflage.

Science.gov (United States)

Somveille, Marius; Marshall, Kate L A; Gluckman, Thanh-Lan

2016-01-01

Evidence suggests that animal patterns (motifs) function in camouflage. Irregular mottled patterns can facilitate concealment when stationary in cluttered habitats, whereas regular patterns typically prevent capture during movement in open habitats. Bird plumage patterns have predominantly converged on just four types-mottled (irregular), scales, bars and spots (regular)-and habitat could be driving convergent evolution in avian patterning. Based on sensory ecology, we therefore predict that irregular patterns would be associated with visually noisy closed habitats and that regular patterns would be associated with open habitats. Regular patterns have also been shown to function in communication for sexually competing males to stand-out and attract females, so we predict that male breeding plumage patterns evolved in both open and closed habitats. Here, taking phylogenetic relatedness into account, we investigate ecological selection for bird plumage patterns across the class Aves. We surveyed plumage patterns in 80% of all avian species worldwide. Of these, 2,756 bird species have regular and irregular plumage patterns as well as habitat information. In this subset, we tested whether adult breeding/non-breeding plumages in each sex, and juvenile plumages, were associated with the habitat types found within the species' geographical distributions. We found no evidence for an association between habitat and plumage patterns across the world's birds and little phylogenetic signal. We also found that species with regular and irregular plumage patterns were distributed randomly across the world's eco-regions without being affected by habitat type. These results indicate that at the global spatial and taxonomic scale, habitat does not predict convergent evolution in bird plumage patterns, contrary to the camouflage hypothesis.

Investigation on the Patterns of Global Vegetation Change Using a Satellite-Sensed Vegetation Index

Directory of Open Access Journals (Sweden)

Ainong Li

2010-06-01

Full Text Available The pattern of vegetation change in response to global change still remains a controversial issue. A Normalized Difference Vegetation Index (NDVI dataset compiled by the Global Inventory Modeling and Mapping Studies (GIMMS was used for analysis. For the period 1982–2006, GIMMS-NDVI analysis indicated that monthly NDVI changes show homogenous trends in middle and high latitude areas in the northern hemisphere and within, or near, the Tropic of Cancer and Capricorn; with obvious spatio-temporal heterogeneity on a global scale over the past two decades. The former areas featured increasing vegetation activity during growth seasons, and the latter areas experienced an even greater amplitude in places where precipitation is adequate. The discussion suggests that one should be cautious of using the NDVI time-series to analyze local vegetation dynamics because of its coarse resolution and uncertainties.

Star clusters in evolving galaxies

Science.gov (United States)

Renaud, Florent

2018-04-01

Their ubiquity and extreme densities make star clusters probes of prime importance of galaxy evolution. Old globular clusters keep imprints of the physical conditions of their assembly in the early Universe, and younger stellar objects, observationally resolved, tell us about the mechanisms at stake in their formation. Yet, we still do not understand the diversity involved: why is star cluster formation limited to 105M⊙ objects in the Milky Way, while some dwarf galaxies like NGC 1705 are able to produce clusters 10 times more massive? Why do dwarfs generally host a higher specific frequency of clusters than larger galaxies? How to connect the present-day, often resolved, stellar systems to the formation of globular clusters at high redshift? And how do these links depend on the galactic and cosmological environments of these clusters? In this review, I present recent advances on star cluster formation and evolution, in galactic and cosmological context. The emphasis is put on the theory, formation scenarios and the effects of the environment on the evolution of the global properties of clusters. A few open questions are identified.

Identification of global oil trade patterns: An empirical research based on complex network theory

International Nuclear Information System (INIS)

Ji, Qiang; Zhang, Hai-Ying; Fan, Ying

2014-01-01

Highlights: • A global oil trade core network is analyzed using complex network theory. • The global oil export core network displays a scale-free behaviour. • The current global oil trade network can be divided into three trading blocs. • The global oil trade network presents a ‘robust and yet fragile’ characteristic. - Abstract: The Global oil trade pattern becomes increasingly complex, which has become one of the most important factors affecting every country’s energy strategy and economic development. In this paper, a global oil trade core network is constructed to analyze the overall features, regional characteristics and stability of the oil trade using complex network theory. The results indicate that the global oil export core network displays a scale-free behaviour, in which the trade position of nodes presents obvious heterogeneity and the ‘hub nodes’ play a ‘bridge’ role in the formation process of the trade network. The current global oil trade network can be divided into three trading blocs, including the ‘South America-West Africa-North America’ trading bloc, the ‘Middle East–Asian–Pacific region’ trading bloc, and ‘the former Soviet Union–North Africa–Europe’ trading bloc. Geopolitics and diplomatic relations are the two main reasons for this regional oil trade structure. Moreover, the global oil trade network presents a ‘robust but yet fragile’ characteristic, and the impacts of trade interruption always tend to spread throughout the whole network even if the occurrence of export disruptions is localised

MARKETING COMMUNICATION TO INDUSTRIAL CLUSTERS OF SLOVAK REPUBLIC

Directory of Open Access Journals (Sweden)

Erika Loučanová

2013-12-01

Full Text Available Currently, the growing attention is paid to the promotion and development of clusters, i.e. concentration of businesses and other cooperating institutions in a sector and region. Despite of the economy globalization and sophisticated global communications technologies, the factor of geographical concentration should be declined, however the experts highlight the importance of direct contact with local and tacit knowledge. The aim of this paper is analyzing of marketing communication tools in different clusters of Slovakia.

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.

Voting-based consensus clustering for combining multiple clusterings of chemical structures

Directory of Open Access Journals (Sweden)

Saeed Faisal

2012-12-01

Full Text Available Abstract Background Although many consensus clustering methods have been successfully used for combining multiple classifiers in many areas such as machine learning, applied statistics, pattern recognition and bioinformatics, few consensus clustering methods have been applied for combining multiple clusterings of chemical structures. It is known that any individual clustering method will not always give the best results for all types of applications. So, in this paper, three voting and graph-based consensus clusterings were used for combining multiple clusterings of chemical structures to enhance the ability of separating biologically active molecules from inactive ones in each cluster. Results The cumulative voting-based aggregation algorithm (CVAA, cluster-based similarity partitioning algorithm (CSPA and hyper-graph partitioning algorithm (HGPA were examined. The F-measure and Quality Partition Index method (QPI were used to evaluate the clusterings and the results were compared to the Ward’s clustering method. The MDL Drug Data Report (MDDR dataset was used for experiments and was represented by two 2D fingerprints, ALOGP and ECFP_4. The performance of voting-based consensus clustering method outperformed the Ward’s method using F-measure and QPI method for both ALOGP and ECFP_4 fingerprints, while the graph-based consensus clustering methods outperformed the Ward’s method only for ALOGP using QPI. The Jaccard and Euclidean distance measures were the methods of choice to generate the ensembles, which give the highest values for both criteria. Conclusions The results of the experiments show that consensus clustering methods can improve the effectiveness of chemical structures clusterings. The cumulative voting-based aggregation algorithm (CVAA was the method of choice among consensus clustering methods.

The Difference Between Clusters and Groups: A Journey from Cluster Cores to Their Outskirts and Beyond

Science.gov (United States)

Bower, Richard G.; Balogh, Michael L.

In this review, we take the reader on a journey. We start by looking at the properties of galaxies in the cores of rich clusters. We have focused on the overall picture: star formation in clusters is strongly suppressed relative to field galaxies at the same redshift. We will argue that the increasing activity and blue populations of clusters with redshift results from a greater level of activity in field galaxies rather than a change in the transformation imposed by the cluster environment. With this in mind, we travel out from the cluster, focusing first on the properties of galaxies in the outskirts of clusters and then on galaxies in isolated groups. At low redshift, we are able to efficiently probe these environments using the Sloan Digital Sky Survey and 2dF redshift surveys. These allow an accurate comparison of galaxy star formation rates in different regions. The current results show a strong suppression of star formation above a critical threshold in local density. The threshold seems similar regardless of the overall mass of the system. At low redshift at least, only galaxies in close, isolated pairs have their star formation rate boosted above the global average. At higher redshift, work on constructing homogeneous catalogs of galaxies in groups and in the infall regions of clusters is still at an early stage. In the final section, we draw these strands together, summarizing what we can deduce about the mechanisms that transform star-forming field galaxies into their quiescent cluster counterparts. We discuss what we can learn about the impact of environment on the global star formation history of the Universe.

Spatio-temporal patterns and climate variables controlling of biomass carbon stock of global grassland ecosystems from 1982 to 2006

Science.gov (United States)

Xia, Jiangzhou; Liu, Shuguang; Liang, Shunlin; Chen, Yang; Xu, Wenfang; Yuan, Wenping

2014-01-01

Grassland ecosystems play an important role in subsistence agriculture and the global carbon cycle. However, the global spatio-temporal patterns and environmental controls of grassland biomass are not well quantified and understood. The goal of this study was to estimate the spatial and temporal patterns of the global grassland biomass and analyze their driving forces using field measurements, Normalized Difference Vegetation Index (NDVI) time series from satellite data, climate reanalysis data, and a satellite-based statistical model. Results showed that the NDVI-based biomass carbon model developed from this study explained 60% of the variance across 38 sites globally. The global carbon stock in grassland aboveground live biomass was 1.05 Pg·C, averaged from 1982 to 2006, and increased at a rate of 2.43 Tg·C·y−1 during this period. Temporal change of the global biomass was significantly and positively correlated with temperature and precipitation. The distribution of biomass carbon density followed the precipitation gradient. The dynamics of regional grassland biomass showed various trends largely determined by regional climate variability, disturbances, and management practices (such as grazing for meat production). The methods and results from this study can be used to monitor the dynamics of grassland aboveground biomass and evaluate grassland susceptibility to climate variability and change, disturbances, and management.

A system for learning statistical motion patterns.

Science.gov (United States)

Hu, Weiming; Xiao, Xuejuan; Fu, Zhouyu; Xie, Dan; Tan, Tieniu; Maybank, Steve

2006-09-01

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy K-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction.

Global patterns of foliar nitrogen isotopes and their relationships with climate, mycorrhizal fungi, foliar nutrient concentrations, and nitrogen availability

DEFF Research Database (Denmark)

Craine, J M; Elmore, A J; Aidar, M P M

2009-01-01

Ratios of nitrogen (N) isotopes in leaves could elucidate underlying patterns of N cycling across ecological gradients. To better understand global-scale patterns of N cycling, we compiled data on foliar N isotope ratios (d15N), foliar N concentrations, mycorrhizal type and climate for over 11 00...

Cluster headache - clinical pattern and a new severity scale in a Swedish cohort.

Science.gov (United States)

Steinberg, Anna; Fourier, Carmen; Ran, Caroline; Waldenlind, Elisabet; Sjöstrand, Christina; Belin, Andrea Carmine

2018-06-01

Background The aim of this study was to investigate clinical features of a cluster headache cohort in Sweden and to construct and test a new scale for grading severity. Methods Subjects were identified by screening medical records for the ICD 10 code G44.0, that is, cluster headache. Five hundred participating research subjects filled in a questionnaire including personal, demographic and medical aspects. We constructed a novel scale for grading cluster headache in this cohort: The Cluster Headache Severity Scale, which included number of attacks per day, attack and period duration. The lowest total score was three and the highest 12, and we used the Cluster Headache Severity Scale to grade subjects suffering from cluster headache. We further implemented the scale by defining a cluster headache maximum severity subgroup with a high Cluster Headache Severity Scale score ≥ 9. Results A majority (66.7%) of the patients reported that attacks appear at certain time intervals. In addition, cluster headache patients who were current tobacco users or had a history of tobacco consumption had a later age of disease onset (31.7 years) compared to non-tobacco users (28.5 years). The Cluster Headache Severity Scale score was higher in the patient group reporting sporadic or no alcohol intake than in the groups reporting an alcohol consumption of three to four standard units per week or more. Maximum severity cluster headache patients were characterised by higher age at disease onset, greater use of prophylactic medication, reduced hours of sleep, and lower alcohol consumption compared to the non-cluster headache maximum severity group. Conclusion There was a wide variation of severity grade among cluster headache patients, with a very marked impact on daily living for the most profoundly affected.

Defining objective clusters for rabies virus sequences using affinity propagation clustering.

Directory of Open Access Journals (Sweden)

Susanne Fischer

2018-01-01

Full Text Available Rabies is caused by lyssaviruses, and is one of the oldest known zoonoses. In recent years, more than 21,000 nucleotide sequences of rabies viruses (RABV, from the prototype species rabies lyssavirus, have been deposited in public databases. Subsequent phylogenetic analyses in combination with metadata suggest geographic distributions of RABV. However, these analyses somewhat experience technical difficulties in defining verifiable criteria for cluster allocations in phylogenetic trees inviting for a more rational approach. Therefore, we applied a relatively new mathematical clustering algorythm named 'affinity propagation clustering' (AP to propose a standardized sub-species classification utilizing full-genome RABV sequences. Because AP has the advantage that it is computationally fast and works for any meaningful measure of similarity between data samples, it has previously been applied successfully in bioinformatics, for analysis of microarray and gene expression data, however, cluster analysis of sequences is still in its infancy. Existing (516 and original (46 full genome RABV sequences were used to demonstrate the application of AP for RABV clustering. On a global scale, AP proposed four clusters, i.e. New World cluster, Arctic/Arctic-like, Cosmopolitan, and Asian as previously assigned by phylogenetic studies. By combining AP with established phylogenetic analyses, it is possible to resolve phylogenetic relationships between verifiably determined clusters and sequences. This workflow will be useful in confirming cluster distributions in a uniform transparent manner, not only for RABV, but also for other comparative sequence analyses.

Ananke: temporal clustering reveals ecological dynamics of microbial communities

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Michael W. Hall

2017-09-01

Full Text Available Taxonomic markers such as the 16S ribosomal RNA gene are widely used in microbial community analysis. A common first step in marker-gene analysis is grouping genes into clusters to reduce data sets to a more manageable size and potentially mitigate the effects of sequencing error. Instead of clustering based on sequence identity, marker-gene data sets collected over time can be clustered based on temporal correlation to reveal ecologically meaningful associations. We present Ananke, a free and open-source algorithm and software package that complements existing sequence-identity-based clustering approaches by clustering marker-gene data based on time-series profiles and provides interactive visualization of clusters, including highlighting of internal OTU inconsistencies. Ananke is able to cluster distinct temporal patterns from simulations of multiple ecological patterns, such as periodic seasonal dynamics and organism appearances/disappearances. We apply our algorithm to two longitudinal marker gene data sets: faecal communities from the human gut of an individual sampled over one year, and communities from a freshwater lake sampled over eleven years. Within the gut, the segregation of the bacterial community around a food-poisoning event was immediately clear. In the freshwater lake, we found that high sequence identity between marker genes does not guarantee similar temporal dynamics, and Ananke time-series clusters revealed patterns obscured by clustering based on sequence identity or taxonomy. Ananke is free and open-source software available at https://github.com/beiko-lab/ananke.

Cluster detection methods applied to the Upper Cape Cod cancer data

Directory of Open Access Journals (Sweden)

Ozonoff David

2005-09-01

Full Text Available Abstract Background A variety of statistical methods have been suggested to assess the degree and/or the location of spatial clustering of disease cases. However, there is relatively little in the literature devoted to comparison and critique of different methods. Most of the available comparative studies rely on simulated data rather than real data sets. Methods We have chosen three methods currently used for examining spatial disease patterns: the M-statistic of Bonetti and Pagano; the Generalized Additive Model (GAM method as applied by Webster; and Kulldorff's spatial scan statistic. We apply these statistics to analyze breast cancer data from the Upper Cape Cancer Incidence Study using three different latency assumptions. Results The three different latency assumptions produced three different spatial patterns of cases and controls. For 20 year latency, all three methods generally concur. However, for 15 year latency and no latency assumptions, the methods produce different results when testing for global clustering. Conclusion The comparative analyses of real data sets by different statistical methods provides insight into directions for further research. We suggest a research program designed around examining real data sets to guide focused investigation of relevant features using simulated data, for the purpose of understanding how to interpret statistical methods applied to epidemiological data with a spatial component.

Observing RAM Pressure Stripping and Morphological Transformation in the Coma Cluster

Science.gov (United States)

Gregg, Michael; West, Michael

2017-07-01

The two largest spirals in the Coma cluster, NGC4911 and NGC4921, are being vigorously ram-pressure stripped by the hot intracluster medium. Our HST ACS and WFC3 images have revealed galactic scale shock fronts, giant "Pillars of Creation", rivulets of dust, and spatially coherent star formation in these grand design spirals. We have now obtained HST WFC3 imaging of five additional large Coma spirals to search for and investigate the effects of ram pressure stripping across the wider cluster environment. The results are equally spectacular as the first two examples. The geometry of the interactions in some cases allows an estimation of the various time scales involved, including gas flows out of the disk leading to creation of the ICM, and the attendant triggered star formation in the galaxy disks. The global star formation patterns yield insights into the spatial and temporal ISM-ICM interactions driving cluster galaxy evolution and ultimately transforming morphologies from spiral to S0. These processes were much more common in the early Universe when the intergalactic and intracluster components were initially created from stripping and destruction of member galaxies.

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.

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.

Spectroscopic constraints on the form of the stellar cluster mass function

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Bastian, N.; Konstantopoulos, I. S.; Trancho, G.; Weisz, D. R.; Larsen, S. S.; Fouesneau, M.; Kaschinski, C. B.; Gieles, M.

2012-05-01

This contribution addresses the question of whether the initial cluster mass function (ICMF) has a fundamental limit (or truncation) at high masses. The shape of the ICMF at high masses can be studied using the most massive young (advantages are that more clusters can be used and that the ICMF leaves a distinct pattern on the global relation between the cluster luminosity and median age within a population. If a truncation is present, a generic prediction (nearly independent of the cluster disruption law adopted) is that the median age of bright clusters should be younger than that of fainter clusters. In the case of an non-truncated ICMF, the median age should be independent of cluster luminosity. Here, we present optical spectroscopy of twelve young stellar clusters in the face-on spiral galaxy NGC 2997. The spectra are used to estimate the age of each cluster, and the brightness of the clusters is taken from the literature. The observations are compared with the model expectations of Larsen (2009, A&A, 494, 539) for various ICMF forms and both mass dependent and mass independent cluster disruption. While there exists some degeneracy between the truncation mass and the amount of mass independent disruption, the observations favour a truncated ICMF. For low or modest amounts of mass independent disruption, a truncation mass of 5-6 × 105 M⊙ is estimated, consistent with previous determinations. Additionally, we investigate possible truncations in the ICMF in the spiral galaxy M 83, the interacting Antennae galaxies, and the collection of spiral and dwarf galaxies present in Larsen (2009, A&A, 494, 539) based on photometric catalogues taken from the literature, and find that all catalogues are consistent with having a truncation in the cluster mass functions. However for the case of the Antennae, we find a truncation mass of a few × 106M⊙ , suggesting a dependence on the environment, as has been previously suggested.

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

Cluster analyses of 20th century growth patterns in high elevation Great Basin bristlecone pine in the Snake Mountain Range, Nevada, USA

Science.gov (United States)

Tran, T. J.; Bruening, J. M.; Bunn, A. G.; Salzer, M. W.; Weiss, S. B.

2015-12-01

Great Basin bristlecone pine (Pinus longaeva) is a useful climate proxy because of the species' long lifespan (up to 5000 years) and the climatic sensitivity of its annually-resolved rings. Past studies have shown that growth of individual trees can be limited by temperature, soil moisture, or a combination of the two depending on biophysical setting at the scale of tens of meters. We extend recent research suggesting that trees vary in their growth response depending on their position on the landscape to analyze how growth patterns vary over time. We used hierarchical cluster analysis to examine the growth of 52 bristlecone pine trees near the treeline of Mount Washington, Nevada, USA. We classified growth of individual trees over the instrumental climate record into one of two possible scenarios: trees belonging to a temperature-sensitive cluster and trees belonging to a precipitation-sensitive cluster. The number of trees in the precipitation-sensitive cluster outnumbered the number of trees in the temperature-sensitive cluster, with trees in colder locations belonging to the temperature-sensitive cluster. When we separated the temporal range into two sections (1895-1949 and 1950-2002) spanning the length of the instrumental climate record, we found that most of the 52 trees remained loyal to their cluster membership (e.g., trees in the temperature-sensitive cluster in 1895-1949 were also in the temperature sensitive cluster in 1950-2002), though not without exception. Of those trees that do not remain consistent in cluster membership, the majority changed from temperature-sensitive to precipitation-sensitive as time progressed. This could signal a switch from temperature limitation to water limitation with warming climate. We speculate that topographic complexity in high mountain environments like Mount Washington might allow for climate refugia where growth response could remain constant over the Holocene.

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.

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.

Atoll-scale patterns in coral reef community structure: Human signatures on Ulithi Atoll, Micronesia.

Science.gov (United States)

Crane, Nicole L; Nelson, Peter; Abelson, Avigdor; Precoda, Kristin; Rulmal, John; Bernardi, Giacomo; Paddack, Michelle

2017-01-01

The dynamic relationship between reefs and the people who utilize them at a subsistence level is poorly understood. This paper characterizes atoll-scale patterns in shallow coral reef habitat and fish community structure, and correlates these with environmental characteristics and anthropogenic factors, critical to conservation efforts for the reefs and the people who depend on them. Hierarchical clustering analyses by site for benthic composition and fish community resulted in the same 3 major clusters: cluster 1-oceanic (close proximity to deep water) and uninhabited (low human impact); cluster 2-oceanic and inhabited (high human impact); and cluster 3-lagoonal (facing the inside of the lagoon) and inhabited (highest human impact). Distance from village, reef exposure to deep water and human population size had the greatest effect in predicting the fish and benthic community structure. Our study demonstrates a strong association between benthic and fish community structure and human use across the Ulithi Atoll (Yap State, Federated States of Micronesia) and confirms a pattern observed by local people that an 'opportunistic' scleractinian coral (Montipora sp.) is associated with more highly impacted reefs. Our findings suggest that small human populations (subsistence fishing) can nevertheless have considerable ecological impacts on reefs due, in part, to changes in fishing practices rather than overfishing per se, as well as larger global trends. Findings from this work can assist in building local capacity to manage reef resources across an atoll-wide scale, and illustrates the importance of anthropogenic impact even in small communities.

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

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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.

The global transmission network of HIV-1.

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Wertheim, Joel O; Leigh Brown, Andrew J; Hepler, N Lance; Mehta, Sanjay R; Richman, Douglas D; Smith, Davey M; Kosakovsky Pond, Sergei L

2014-01-15

Human immunodeficiency virus type 1 (HIV-1) is pandemic, but its contemporary global transmission network has not been characterized. A better understanding of the properties and dynamics of this network is essential for surveillance, prevention, and eventual eradication of HIV. Here, we apply a simple and computationally efficient network-based approach to all publicly available HIV polymerase sequences in the global database, revealing a contemporary picture of the spread of HIV-1 within and between countries. This approach automatically recovered well-characterized transmission clusters and extended other clusters thought to be contained within a single country across international borders. In addition, previously undescribed transmission clusters were discovered. Together, these clusters represent all known modes of HIV transmission. The extent of international linkage revealed by our comprehensive approach demonstrates the need to consider the global diversity of HIV, even when describing local epidemics. Finally, the speed of this method allows for near-real-time surveillance of the pandemic's progression.

Unveiling hidden properties of young star clusters: differential reddening, star-formation spread, and binary fraction

Science.gov (United States)

Bonatto, C.; Lima, E. F.; Bica, E.

2012-04-01

Context. Usually, important parameters of young, low-mass star clusters are very difficult to obtain by means of photometry, especially when differential reddening and/or binaries occur in large amounts. Aims: We present a semi-analytical approach (ASAmin) that, when applied to the Hess diagram of a young star cluster, is able to retrieve the values of mass, age, star-formation spread, distance modulus, foreground and differential reddening, and binary fraction. Methods: The global optimisation method known as adaptive simulated annealing (ASA) is used to minimise the residuals between the observed and simulated Hess diagrams of a star cluster. The simulations are realistic and take the most relevant parameters of young clusters into account. Important features of the simulations are a normal (Gaussian) differential reddening distribution, a time-decreasing star-formation rate, the unresolved binaries, and the smearing effect produced by photometric uncertainties on Hess diagrams. Free parameters are cluster mass, age, distance modulus, star-formation spread, foreground and differential reddening, and binary fraction. Results: Tests with model clusters built with parameters spanning a broad range of values show that ASAmin retrieves the input values with a high precision for cluster mass, distance modulus, and foreground reddening, but they are somewhat lower for the remaining parameters. Given the statistical nature of the simulations, several runs should be performed to obtain significant convergence patterns. Specifically, we find that the retrieved (absolute minimum) parameters converge to mean values with a low dispersion as the Hess residuals decrease. When applied to actual young clusters, the retrieved parameters follow convergence patterns similar to the models. We show how the stochasticity associated with the early phases may affect the results, especially in low-mass clusters. This effect can be minimised by averaging out several twin clusters in the

Global Disparities Since 1800: Trends and Regional Patterns

Directory of Open Access Journals (Sweden)

M. Shahid Alam

2015-08-01

Full Text Available This paper reviews the growing body of evidence on the relative economic standing of different regions of the world in the late eighteenth and early nineteenth centuries. In general, it does not find support for Euro-centric claims regarding Western Europe’s early economic lead. The Eurocentric claims are based primarily on estimates of per capita income, which are plagued by conceptual problems, make demands on historical data that are generally unavailable, and use questionable assumptions to reconstruct early per capita income. A careful examination of these conjectural estimates of per capita income, however, does not support claims that Western Europe had a substantial lead over the rest of the world at the beginning of the nineteenth century. An examination of several alternative indices of living standards in the late eighteenth or early nineteenth centuries—such as real wages, labor productivity in agriculture, and urbanization—also fails to confirm claims of European superiority. In addition, this paper examines the progress of global disparities—including the presence of regional patterns—using estimates of per capita income.

Winter Precipitation Forecast in the European and Mediterranean Regions Using Cluster Analysis

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Totz, Sonja; Tziperman, Eli; Coumou, Dim; Pfeiffer, Karl; Cohen, Judah

2017-12-01

The European climate is changing under global warming, and especially the Mediterranean region has been identified as a hot spot for climate change with climate models projecting a reduction in winter rainfall and a very pronounced increase in summertime heat waves. These trends are already detectable over the historic period. Hence, it is beneficial to forecast seasonal droughts well in advance so that water managers and stakeholders can prepare to mitigate deleterious impacts. We developed a new cluster-based empirical forecast method to predict precipitation anomalies in winter. This algorithm considers not only the strength but also the pattern of the precursors. We compare our algorithm with dynamic forecast models and a canonical correlation analysis-based prediction method demonstrating that our prediction method performs better in terms of time and pattern correlation in the Mediterranean and European regions.

The global pattern of urbanization and economic growth: evidence from the last three decades.

Science.gov (United States)

Chen, Mingxing; Zhang, Hua; Liu, Weidong; Zhang, Wenzhong

2014-01-01

The relationship between urbanization and economic growth has been perplexing. In this paper, we identify the pattern of global change and the correlation of urbanization and economic growth, using cross-sectional, panel estimation and geographic information systems (GIS) methods. The analysis has been carried out on a global geographical scale, while the timescale of the study spans the last 30 years. The data shows that urbanization levels have changed substantially during these three decades. Empirical findings from cross-sectional data and panel data support the general notion of close links between urbanization levels and GDP per capita. However, we also present significant evidence that there is no correlation between urbanization speed and economic growth rate at the global level. Hence, we conclude that a given country cannot obtain the expected economic benefits from accelerated urbanization, especially if it takes the form of government-led urbanization. In addition, only when all facets are taken into consideration can we fully assess the urbanization process.

The global pattern of urbanization and economic growth: evidence from the last three decades.

Directory of Open Access Journals (Sweden)

Mingxing Chen

Full Text Available The relationship between urbanization and economic growth has been perplexing. In this paper, we identify the pattern of global change and the correlation of urbanization and economic growth, using cross-sectional, panel estimation and geographic information systems (GIS methods. The analysis has been carried out on a global geographical scale, while the timescale of the study spans the last 30 years. The data shows that urbanization levels have changed substantially during these three decades. Empirical findings from cross-sectional data and panel data support the general notion of close links between urbanization levels and GDP per capita. However, we also present significant evidence that there is no correlation between urbanization speed and economic growth rate at the global level. Hence, we conclude that a given country cannot obtain the expected economic benefits from accelerated urbanization, especially if it takes the form of government-led urbanization. In addition, only when all facets are taken into consideration can we fully assess the urbanization process.

Accounting for sampling patterns reverses the relative importance of trade and climate for the global sharing of exotic plants

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Sofaer, Helen R.; Jarnevich, Catherine S.

2017-01-01

AimThe distributions of exotic species reflect patterns of human-mediated dispersal, species climatic tolerances and a suite of other biotic and abiotic factors. The relative importance of each of these factors will shape how the spread of exotic species is affected by ongoing economic globalization and climate change. However, patterns of trade may be correlated with variation in scientific sampling effort globally, potentially confounding studies that do not account for sampling patterns.LocationGlobal.Time periodMuseum records, generally from the 1800s up to 2015.Major taxa studiedPlant species exotic to the United States.MethodsWe used data from the Global Biodiversity Information Facility (GBIF) to summarize the number of plant species with exotic occurrences in the United States that also occur in each other country world-wide. We assessed the relative importance of trade and climatic similarity for explaining variation in the number of shared species while evaluating several methods to account for variation in sampling effort among countries.ResultsAccounting for variation in sampling effort reversed the relative importance of trade and climate for explaining numbers of shared species. Trade was strongly correlated with numbers of shared U.S. exotic plants between the United States and other countries before, but not after, accounting for sampling variation among countries. Conversely, accounting for sampling effort strengthened the relationship between climatic similarity and species sharing. Using the number of records as a measure of sampling effort provided a straightforward approach for the analysis of occurrence data, whereas species richness estimators and rarefaction were less effective at removing sampling bias.Main conclusionsOur work provides support for broad-scale climatic limitation on the distributions of exotic species, illustrates the need to account for variation in sampling effort in large biodiversity databases, and highlights the

Cluster growing process and a sequence of magic numbers

DEFF Research Database (Denmark)

Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter

2003-01-01

demonstrate that in this way all known global minimum structures of the Lennard-Jones (LJ) clusters can be found. Our method provides an efficient tool for the calculation and analysis of atomic cluster structure. With its use we justify the magic number sequence for the clusters of noble gas atoms......We present a new theoretical framework for modeling the cluster growing process. Starting from the initial tetrahedral cluster configuration, adding new atoms to the system, and absorbing its energy at each step, we find cluster growing paths up to the cluster sizes of more than 100 atoms. We...

Cancers of the Brain and CNS: Global Patterns and Trends in Incidence.

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Mortazavi, S M J; Mortazavi, S A R; Paknahad, M

2018-03-01

Miranda-Filho et al. in their recently published paper entitled "Cancers of the brain and CNS: global patterns and trends in incidence" provided a global status report of the geographic and temporal variations in the incidence of brain and CNS cancers in different countries across continents worldwide. While the authors confirm the role of genetic risk factors and ionizing radiation exposures, they claimed that no firm conclusion could be drawn about the role of exposure to non-ionizing radiation. The paper authored by Miranda-Filho et al. not only addresses a challenging issue, it can be considered as a good contribution in the field of brain and CNS cancers. However, our correspondence addresses a basic shortcoming of this paper about the role of electromagnetic fields and cancers and provides evidence showing that exposure to radiofrequency electromagnetic fields (RF-EMFs), at least at high levels and long durations, can increases the risk of cancer.

Chaos theory perspective for industry clusters development

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Yu, Haiying; Jiang, Minghui; Li, Chengzhang

2016-03-01

Industry clusters have outperformed in economic development in most developing countries. The contributions of industrial clusters have been recognized as promotion of regional business and the alleviation of economic and social costs. It is no doubt globalization is rendering clusters in accelerating the competitiveness of economic activities. In accordance, many ideas and concepts involve in illustrating evolution tendency, stimulating the clusters development, meanwhile, avoiding industrial clusters recession. The term chaos theory is introduced to explain inherent relationship of features within industry clusters. A preferred life cycle approach is proposed for industrial cluster recessive theory analysis. Lyapunov exponents and Wolf model are presented for chaotic identification and examination. A case study of Tianjin, China has verified the model effectiveness. The investigations indicate that the approaches outperform in explaining chaos properties in industrial clusters, which demonstrates industrial clusters evolution, solves empirical issues and generates corresponding strategies.

Evolution of the stellar mass function in multiple-population globular clusters

Science.gov (United States)

Vesperini, Enrico; Hong, Jongsuk; Webb, Jeremy J.; D'Antona, Franca; D'Ercole, Annibale

2018-05-01

We present the results of a survey of N-body simulations aimed at studying the effects of the long-term dynamical evolution on the stellar mass function (MF) of multiple stellar populations in globular clusters. Our simulations show that if first-(1G) and second-generation (2G) stars have the same initial MF (IMF), the global MFs of the two populations are affected similarly by dynamical evolution and no significant differences between the 1G and 2G MFs arise during the cluster's evolution. If the two populations have different IMFs, dynamical effects do not completely erase memory of the initial differences. Should observations find differences between the global 1G and 2G MFs, these would reveal the fingerprints of differences in their IMFs. Irrespective of whether the 1G and 2G populations have the same global IMF or not, dynamical effects can produce differences between the local (measured at various distances from the cluster centre) 1G and 2G MFs; these differences are a manifestation of the process of mass segregation in populations with different initial structural properties. In dynamically old and spatially mixed clusters, however, differences between the local 1G and 2G MFs can reveal differences between the 1G and 2G global MFs. In general, for clusters with any dynamical age, large differences between the local 1G and 2G MFs are more likely to be associated with differences in the global MF. Our study also reveals a dependence of the spatial mixing rate on the stellar mass, another dynamical consequence of the multiscale nature of multiple-population clusters.

Multifractal Approach to Time Clustering of Earthquakes. Application to Mt. Vesuvio Seismicity

Science.gov (United States)

Codano, C.; Alonzo, M. L.; Vilardo, G.

The clustering structure of the Vesuvian earthquakes occurring is investigated by means of statistical tools: the inter-event time distribution, the running mean and the multifractal analysis. The first cannot clearly distinguish between a Poissonian process and a clustered one due to the difficulties of clearly distinguishing between an exponential distribution and a power law one. The running mean test reveals the clustering of the earthquakes, but looses information about the structure of the distribution at global scales. The multifractal approach can enlighten the clustering at small scales, while the global behaviour remains Poissonian. Subsequently the clustering of the events is interpreted in terms of diffusive processes of the stress in the earth crust.

A Cluster Analysis of Tic Symptoms in Children and Adults with Tourette Syndrome: Clinical Correlates and Treatment Outcome

Science.gov (United States)

McGuire, Joseph F.; Nyirabahizi, Epiphanie; Kircanski, Katharina; Piacentini, John; Peterson, Alan L.; Woods, Douglas W.; Wilhelm, Sabine; Walkup, John T.; Scahill, Lawrence

2013-01-01

Cluster analytic methods have examined the symptom presentation of chronic tic disorders (CTDs), with limited agreement across studies. The present study investigated patterns, clinical correlates, and treatment outcome of tic symptoms. 239 youth and adults with CTDs completed a battery of assessments at baseline to determine diagnoses, tic severity, and clinical characteristics. Participants were randomly assigned to receive either a comprehensive behavioral intervention for tics (CBIT) or psychoeducation and supportive therapy (PST). A cluster analysis was conducted on the baseline Yale Global Tic Severity Scale (YGTSS) symptom checklist to identify the constellations of tic symptoms. Four tic clusters were identified: Impulse Control and Complex Phonic Tics; Complex Motor Tics; Simple Head Motor/Vocal Tics; and Primarily Simple Motor Tics. Frequencies of tic symptoms showed few differences across youth and adults. Tic clusters had small associations with clinical characteristics and showed no associations to the presence of coexisting psychiatric conditions. Cluster membership scores did not predict treatment response to CBIT or tic severity reductions. Tic symptoms distinctly cluster with few difference across youth and adults, or coexisting conditions. This study, which is the first to examine tic clusters in relation to treatment, suggested that tic symptom profiles respond equally well to CBIT. PMID:24144615

Spatial and temporal patterns of stranded intertidal marine debris: is there a picture of global change?

Science.gov (United States)

Browne, Mark Anthony; Chapman, M Gee; Thompson, Richard C; Amaral Zettler, Linda A; Jambeck, Jenna; Mallos, Nicholas J

2015-06-16

Floating and stranded marine debris is widespread. Increasing sea levels and altered rainfall, solar radiation, wind speed, waves, and oceanic currents associated with climatic change are likely to transfer more debris from coastal cities into marine and coastal habitats. Marine debris causes economic and ecological impacts, but understanding the scope of these requires quantitative information on spatial patterns and trends in the amounts and types of debris at a global scale. There are very few large-scale programs to measure debris, but many peer-reviewed and published scientific studies of marine debris describe local patterns. Unfortunately, methods of defining debris, sampling, and interpreting patterns in space or time vary considerably among studies, yet if data could be synthesized across studies, a global picture of the problem may be avaliable. We analyzed 104 published scientific papers on marine debris in order to determine how to evaluate this. Although many studies were well designed to answer specific questions, definitions of what constitutes marine debris, the methods used to measure, and the scale of the scope of the studies means that no general picture can emerge from this wealth of data. These problems are detailed to guide future studies and guidelines provided to enable the collection of more comparable data to better manage this growing problem.

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.

Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor

OpenAIRE

Samir Brahim Belhaouari

2009-01-01

By taking advantage of both k-NN which is highly accurate and K-means cluster which is able to reduce the time of classification, we can introduce Cluster-k-Nearest Neighbor as "variable k"-NN dealing with the centroid or mean point of all subclasses generated by clustering algorithm. In general the algorithm of K-means cluster is not stable, in term of accuracy, for that reason we develop another algorithm for clustering our space which gives a higher accuracy than K-means cluster, less ...

Cluster bomb ocular injuries.

Science.gov (United States)

Mansour, Ahmad M; Hamade, Haya; Ghaddar, Ayman; Mokadem, Ahmad Samih; El Hajj Ali, Mohamad; Awwad, Shady

2012-01-01

To present the visual outcomes and ocular sequelae of victims of cluster bombs. This retrospective, multicenter case series of ocular injury due to cluster bombs was conducted for 3 years after the war in South Lebanon (July 2006). Data were gathered from the reports to the Information Management System for Mine Action. There were 308 victims of clusters bombs; 36 individuals were killed, of which 2 received ocular lacerations and; 272 individuals were injured with 18 receiving ocular injury. These 18 surviving individuals were assessed by the authors. Ocular injury occurred in 6.5% (20/308) of cluster bomb victims. Trauma to multiple organs occurred in 12 of 18 cases (67%) with ocular injury. Ocular findings included corneal or scleral lacerations (16 eyes), corneal foreign bodies (9 eyes), corneal decompensation (2 eyes), ruptured cataract (6 eyes), and intravitreal foreign bodies (10 eyes). The corneas of one patient had extreme attenuation of the endothelium. Ocular injury occurred in 6.5% of cluster bomb victims and 67% of the patients with ocular injury sustained trauma to multiple organs. Visual morbidity in civilians is an additional reason for a global ban on the use of cluster bombs.

Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data

Science.gov (United States)

Hallac, David; Vare, Sagar; Boyd, Stephen; Leskovec, Jure

2018-01-01

Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or clusters. For example, raw sensor data from a fitness-tracking application can be expressed as a timeline of a select few actions (i.e., walking, sitting, running). However, discovering these patterns is challenging because it requires simultaneous segmentation and clustering of the time series. Furthermore, interpreting the resulting clusters is difficult, especially when the data is high-dimensional. Here we propose a new method of model-based clustering, which we call Toeplitz Inverse Covariance-based Clustering (TICC). Each cluster in the TICC method is defined by a correlation network, or Markov random field (MRF), characterizing the interdependencies between different observations in a typical subsequence of that cluster. Based on this graphical representation, TICC simultaneously segments and clusters the time series data. We solve the TICC problem through alternating minimization, using a variation of the expectation maximization (EM) algorithm. We derive closed-form solutions to efficiently solve the two resulting subproblems in a scalable way, through dynamic programming and the alternating direction method of multipliers (ADMM), respectively. We validate our approach by comparing TICC to several state-of-the-art baselines in a series of synthetic experiments, and we then demonstrate on an automobile sensor dataset how TICC can be used to learn interpretable clusters in real-world scenarios. PMID:29770257

Improving local clustering based top-L link prediction methods via asymmetric link clustering information

Science.gov (United States)

Wu, Zhihao; Lin, Youfang; Zhao, Yiji; Yan, Hongyan

2018-02-01

Networks can represent a wide range of complex systems, such as social, biological and technological systems. Link prediction is one of the most important problems in network analysis, and has attracted much research interest recently. Many link prediction methods have been proposed to solve this problem with various techniques. We can note that clustering information plays an important role in solving the link prediction problem. In previous literatures, we find node clustering coefficient appears frequently in many link prediction methods. However, node clustering coefficient is limited to describe the role of a common-neighbor in different local networks, because it cannot distinguish different clustering abilities of a node to different node pairs. In this paper, we shift our focus from nodes to links, and propose the concept of asymmetric link clustering (ALC) coefficient. Further, we improve three node clustering based link prediction methods via the concept of ALC. The experimental results demonstrate that ALC-based methods outperform node clustering based methods, especially achieving remarkable improvements on food web, hamster friendship and Internet networks. Besides, comparing with other methods, the performance of ALC-based methods are very stable in both globalized and personalized top-L link prediction tasks.

Improving clustering with metabolic pathway data.

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Milone, Diego H; Stegmayer, Georgina; López, Mariana; Kamenetzky, Laura; Carrari, Fernando

2014-04-10

It is a common practice in bioinformatics to validate each group returned by a clustering algorithm through manual analysis, according to a-priori biological knowledge. This procedure helps finding functionally related patterns to propose hypotheses for their behavior and the biological processes involved. Therefore, this knowledge is used only as a second step, after data are just clustered according to their expression patterns. Thus, it could be very useful to be able to improve the clustering of biological data by incorporating prior knowledge into the cluster formation itself, in order to enhance the biological value of the clusters. A novel training algorithm for clustering is presented, which evaluates the biological internal connections of the data points while the clusters are being formed. Within this training algorithm, the calculation of distances among data points and neurons centroids includes a new term based on information from well-known metabolic pathways. The standard self-organizing map (SOM) training versus the biologically-inspired SOM (bSOM) training were tested with two real data sets of transcripts and metabolites from Solanum lycopersicum and Arabidopsis thaliana species. Classical data mining validation measures were used to evaluate the clustering solutions obtained by both algorithms. Moreover, a new measure that takes into account the biological connectivity of the clusters was applied. The results of bSOM show important improvements in the convergence and performance for the proposed clustering method in comparison to standard SOM training, in particular, from the application point of view. Analyses of the clusters obtained with bSOM indicate that including biological information during training can certainly increase the biological value of the clusters found with the proposed method. It is worth to highlight that this fact has effectively improved the results, which can simplify their further analysis.The algorithm is available as a

Cluster pattern analysis of energy deposition sites for the brachytherapy sources 103Pd, 125I, 192Ir, 137Cs, and 60Co.

Science.gov (United States)

Villegas, Fernanda; Tilly, Nina; Bäckström, Gloria; Ahnesjö, Anders

2014-09-21

Analysing the pattern of energy depositions may help elucidate differences in the severity of radiation-induced DNA strand breakage for different radiation qualities. It is often claimed that energy deposition (ED) sites from photon radiation form a uniform random pattern, but there is indication of differences in RBE values among different photon sources used in brachytherapy. The aim of this work is to analyse the spatial patterns of EDs from 103Pd, 125I, 192Ir, 137Cs sources commonly used in brachytherapy and a 60Co source as a reference radiation. The results suggest that there is both a non-uniform and a uniform random component to the frequency distribution of distances to the nearest neighbour ED. The closest neighbouring EDs show high spatial correlation for all investigated radiation qualities, whilst the uniform random component dominates for neighbours with longer distances for the three higher mean photon energy sources (192Ir, 137Cs, and 60Co). The two lower energy photon emitters (103Pd and 125I) present a very small uniform random component. The ratio of frequencies of clusters with respect to 60Co differs up to 15% for the lower energy sources and less than 2% for the higher energy sources when the maximum distance between each pair of EDs is 2 nm. At distances relevant to DNA damage, cluster patterns can be differentiated between the lower and higher energy sources. This may be part of the explanation to the reported difference in RBE values with initial DSB yields as an endpoint for these brachytherapy sources.

Space-time clustering characteristics of tuberculosis in China, 2005-2011.

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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.

clusterMaker: a multi-algorithm clustering plugin for Cytoscape

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Morris John H

2011-11-01

Full Text Available Abstract Background In the post-genomic era, the rapid increase in high-throughput data calls for computational tools capable of integrating data of diverse types and facilitating recognition of biologically meaningful patterns within them. For example, protein-protein interaction data sets have been clustered to identify stable complexes, but scientists lack easily accessible tools to facilitate combined analyses of multiple data sets from different types of experiments. Here we present clusterMaker, a Cytoscape plugin that implements several clustering algorithms and provides network, dendrogram, and heat map views of the results. The Cytoscape network is linked to all of the other views, so that a selection in one is immediately reflected in the others. clusterMaker is the first Cytoscape plugin to implement such a wide variety of clustering algorithms and visualizations, including the only implementations of hierarchical clustering, dendrogram plus heat map visualization (tree view, k-means, k-medoid, SCPS, AutoSOME, and native (Java MCL. Results Results are presented in the form of three scenarios of use: analysis of protein expression data using a recently published mouse interactome and a mouse microarray data set of nearly one hundred diverse cell/tissue types; the identification of protein complexes in the yeast Saccharomyces cerevisiae; and the cluster analysis of the vicinal oxygen chelate (VOC enzyme superfamily. For scenario one, we explore functionally enriched mouse interactomes specific to particular cellular phenotypes and apply fuzzy clustering. For scenario two, we explore the prefoldin complex in detail using both physical and genetic interaction clusters. For scenario three, we explore the possible annotation of a protein as a methylmalonyl-CoA epimerase within the VOC superfamily. Cytoscape session files for all three scenarios are provided in the Additional Files section. Conclusions The Cytoscape plugin cluster

Data clustering theory, algorithms, and applications

CERN Document Server

Gan, Guojun; Wu, Jianhong

2007-01-01

Cluster analysis is an unsupervised process that divides a set of objects into homogeneous groups. This book starts with basic information on cluster analysis, including the classification of data and the corresponding similarity measures, followed by the presentation of over 50 clustering algorithms in groups according to some specific baseline methodologies such as hierarchical, center-based, and search-based methods. As a result, readers and users can easily identify an appropriate algorithm for their applications and compare novel ideas with existing results. The book also provides examples of clustering applications to illustrate the advantages and shortcomings of different clustering architectures and algorithms. Application areas include pattern recognition, artificial intelligence, information technology, image processing, biology, psychology, and marketing. Readers also learn how to perform cluster analysis with the C/C++ and MATLAB® programming languages.

Global Neural Pattern Similarity as a Common Basis for Categorization and Recognition Memory

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Xue, Gui; Love, Bradley C.; Preston, Alison R.; Poldrack, Russell A.

2014-01-01

Familiarity, or memory strength, is a central construct in models of cognition. In previous categorization and long-term memory research, correlations have been found between psychological measures of memory strength and activation in the medial temporal lobes (MTLs), which suggests a common neural locus for memory strength. However, activation alone is insufficient for determining whether the same mechanisms underlie neural function across domains. Guided by mathematical models of categorization and long-term memory, we develop a theory and a method to test whether memory strength arises from the global similarity among neural representations. In human subjects, we find significant correlations between global similarity among activation patterns in the MTLs and both subsequent memory confidence in a recognition memory task and model-based measures of memory strength in a category learning task. Our work bridges formal cognitive theories and neuroscientific models by illustrating that the same global similarity computations underlie processing in multiple cognitive domains. Moreover, by establishing a link between neural similarity and psychological memory strength, our findings suggest that there may be an isomorphism between psychological and neural representational spaces that can be exploited to test cognitive theories at both the neural and behavioral levels. PMID:24872552

Relation of major volcanic center concentration on Venus to global tectonic patterns

Science.gov (United States)

Crumpler, L. S.; Head, James W.; Aubele, Jayne C.

1993-01-01

Global analysis of Magellan image data indicates that a major concentration of volcanic centers covering about 40 percent of the surface of Venus occurs between the Beta, Atla, and Themis regions. Associated with this enhanced concentration are geological characteristics commonly interpreted as rifting and mantle upwelling. Interconnected low plains in an annulus around this concentration are characterized by crustal shortening and infrequent volcanic centers that may represent sites of mantle return flow and net downwelling. Together, these observations suggest the existence of relatively simple, large-scale patterns of mantle circulation similar to those associated with concentrations of intraplate volcanism on earth.

Locating irregularly shaped clusters of infection intensity

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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.

Global patterns of organic carbon export and sequestration in the ocean (Arne Richter Award for Outstanding Young Scientists)

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Henson, S.; Sanders, R.; Madsen, E.; Le Moigne, F.; Quartly, G.

2012-04-01

A major term in the global carbon cycle is the ocean's biological carbon pump which is dominated by sinking of small organic particles from the surface ocean to its interior. Here we examine global patterns in particle export efficiency (PEeff), the proportion of primary production that is exported from the surface ocean, and transfer efficiency (Teff), the fraction of exported organic matter that reaches the deep ocean. This is achieved through extrapolating from in situ estimates of particulate organic carbon export to the global scale using satellite-derived data. Global scale estimates derived from satellite data show, in keeping with earlier studies, that PEeff is high at high latitudes and low at low latitudes, but that Teff is low at high latitudes and high at low latitudes. However, in contrast to the relationship observed for deep biomineral fluxes in previous studies, we find that Teff is strongly negatively correlated with opal export flux from the upper ocean, but uncorrelated with calcium carbonate export flux. We hypothesise that the underlying factor governing the spatial patterns observed in Teff is ecosystem function, specifically the degree of recycling occurring in the upper ocean, rather than the availability of calcium carbonate for ballasting. Finally, our estimate of global integrated carbon export is only 50% of previous estimates. The lack of consensus amongst different methodologies on the strength of the biological carbon pump emphasises that our knowledge of a major planetary carbon flux remains incomplete.

Clustering Game Behavior Data

DEFF Research Database (Denmark)

Bauckhage, C.; Drachen, Anders; Sifa, Rafet

2015-01-01

of the causes, the proliferation of behavioral data poses the problem of how to derive insights therefrom. Behavioral data sets can be large, time-dependent and high-dimensional. Clustering offers a way to explore such data and to discover patterns that can reduce the overall complexity of the data. Clustering...... and other techniques for player profiling and play style analysis have, therefore, become popular in the nascent field of game analytics. However, the proper use of clustering techniques requires expertise and an understanding of games is essential to evaluate results. With this paper, we address game data...... scientists and present a review and tutorial focusing on the application of clustering techniques to mine behavioral game data. Several algorithms are reviewed and examples of their application shown. Key topics such as feature normalization are discussed and open problems in the context of game analytics...

Optimizing Instruction Scheduling and Register Allocation for Register-File-Connected Clustered VLIW Architectures

Science.gov (United States)

Tang, Haijing; Wang, Siye; Zhang, Yanjun

2013-01-01

Clustering has become a common trend in very long instruction words (VLIW) architecture to solve the problem of area, energy consumption, and design complexity. Register-file-connected clustered (RFCC) VLIW architecture uses the mechanism of global register file to accomplish the inter-cluster data communications, thus eliminating the performance and energy consumption penalty caused by explicit inter-cluster data move operations in traditional bus-connected clustered (BCC) VLIW architecture. However, the limit number of access ports to the global register file has become an issue which must be well addressed; otherwise the performance and energy consumption would be harmed. In this paper, we presented compiler optimization techniques for an RFCC VLIW architecture called Lily, which is designed for encryption systems. These techniques aim at optimizing performance and energy consumption for Lily architecture, through appropriate manipulation of the code generation process to maintain a better management of the accesses to the global register file. All the techniques have been implemented and evaluated. The result shows that our techniques can significantly reduce the penalty of performance and energy consumption due to access port limitation of global register file. PMID:23970841

Optimizing Instruction Scheduling and Register Allocation for Register-File-Connected Clustered VLIW Architectures

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Haijing Tang

2013-01-01

Full Text Available Clustering has become a common trend in very long instruction words (VLIW architecture to solve the problem of area, energy consumption, and design complexity. Register-file-connected clustered (RFCC VLIW architecture uses the mechanism of global register file to accomplish the inter-cluster data communications, thus eliminating the performance and energy consumption penalty caused by explicit inter-cluster data move operations in traditional bus-connected clustered (BCC VLIW architecture. However, the limit number of access ports to the global register file has become an issue which must be well addressed; otherwise the performance and energy consumption would be harmed. In this paper, we presented compiler optimization techniques for an RFCC VLIW architecture called Lily, which is designed for encryption systems. These techniques aim at optimizing performance and energy consumption for Lily architecture, through appropriate manipulation of the code generation process to maintain a better management of the accesses to the global register file. All the techniques have been implemented and evaluated. The result shows that our techniques can significantly reduce the penalty of performance and energy consumption due to access port limitation of global register file.

Clustering of obesity and dental caries with lifestyle factors among Danish adolescents

DEFF Research Database (Denmark)

Cinar, Ayse Basak; Christensen, Lisa Boge; Hede, Borge

2011-01-01

To assess any clustering between obesity, dental health, and lifestyle factors (dietary patterns, physical activity, smoking, and alcohol consumption) among adolescents.......To assess any clustering between obesity, dental health, and lifestyle factors (dietary patterns, physical activity, smoking, and alcohol consumption) among adolescents....

BioCluster: Tool for Identification and Clustering of Enterobacteriaceae Based on Biochemical Data

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Ahmed Abdullah

2015-06-01

Full Text Available Presumptive identification of different Enterobacteriaceae species is routinely achieved based on biochemical properties. Traditional practice includes manual comparison of each biochemical property of the unknown sample with known reference samples and inference of its identity based on the maximum similarity pattern with the known samples. This process is labor-intensive, time-consuming, error-prone, and subjective. Therefore, automation of sorting and similarity in calculation would be advantageous. Here we present a MATLAB-based graphical user interface (GUI tool named BioCluster. This tool was designed for automated clustering and identification of Enterobacteriaceae based on biochemical test results. In this tool, we used two types of algorithms, i.e., traditional hierarchical clustering (HC and the Improved Hierarchical Clustering (IHC, a modified algorithm that was developed specifically for the clustering and identification of Enterobacteriaceae species. IHC takes into account the variability in result of 1–47 biochemical tests within this Enterobacteriaceae family. This tool also provides different options to optimize the clustering in a user-friendly way. Using computer-generated synthetic data and some real data, we have demonstrated that BioCluster has high accuracy in clustering and identifying enterobacterial species based on biochemical test data. This tool can be freely downloaded at http://microbialgen.du.ac.bd/biocluster/.

Atoll-scale patterns in coral reef community structure: Human signatures on Ulithi Atoll, Micronesia.

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Nicole L Crane

Full Text Available The dynamic relationship between reefs and the people who utilize them at a subsistence level is poorly understood. This paper characterizes atoll-scale patterns in shallow coral reef habitat and fish community structure, and correlates these with environmental characteristics and anthropogenic factors, critical to conservation efforts for the reefs and the people who depend on them. Hierarchical clustering analyses by site for benthic composition and fish community resulted in the same 3 major clusters: cluster 1-oceanic (close proximity to deep water and uninhabited (low human impact; cluster 2-oceanic and inhabited (high human impact; and cluster 3-lagoonal (facing the inside of the lagoon and inhabited (highest human impact. Distance from village, reef exposure to deep water and human population size had the greatest effect in predicting the fish and benthic community structure. Our study demonstrates a strong association between benthic and fish community structure and human use across the Ulithi Atoll (Yap State, Federated States of Micronesia and confirms a pattern observed by local people that an 'opportunistic' scleractinian coral (Montipora sp. is associated with more highly impacted reefs. Our findings suggest that small human populations (subsistence fishing can nevertheless have considerable ecological impacts on reefs due, in part, to changes in fishing practices rather than overfishing per se, as well as larger global trends. Findings from this work can assist in building local capacity to manage reef resources across an atoll-wide scale, and illustrates the importance of anthropogenic impact even in small communities.

Clustering high dimensional data using RIA

Energy Technology Data Exchange (ETDEWEB)

Aziz, Nazrina [School of Quantitative Sciences, College of Arts and Sciences, Universiti Utara Malaysia, 06010 Sintok, Kedah (Malaysia)

2015-05-15

Clustering may simply represent a convenient method for organizing a large data set so that it can easily be understood and information can efficiently be retrieved. However, identifying cluster in high dimensionality data sets is a difficult task because of the curse of dimensionality. Another challenge in clustering is some traditional functions cannot capture the pattern dissimilarity among objects. In this article, we used an alternative dissimilarity measurement called Robust Influence Angle (RIA) in the partitioning method. RIA is developed using eigenstructure of the covariance matrix and robust principal component score. We notice that, it can obtain cluster easily and hence avoid the curse of dimensionality. It is also manage to cluster large data sets with mixed numeric and categorical value.

Human Development Inequality Index and Cancer Pattern: a Global Distributive Study.

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Rezaeian, Shahab; Khazaei, Salman; Khazaei, Somayeh; Mansori, Kamyar; Sanjari Moghaddam, Ali; Ayubi, Erfan

2016-01-01

This study aimed to quantify associations of the human development inequality (HDI) index with incidence, mortality, and mortality to incidence ratios for eight common cancers among different countries. In this ecological study, data about incidence and mortality rates of cancers was obtained from the Global Cancer Project for 169 countries. HDI indices for the same countries was obtained from the United Nations Development Program (UNDP) database. The concentration index was defined as the covariance between cumulative percentage of cancer indicators (incidence, mortality and mortality to incidence ratio) and the cumulative percentage of economic indicators (country economic rank). Results indicated that incidences of cancers of liver, cervix and esophagus were mainly concentrated in countries with a low HDI index while cancers of lung, breast, colorectum, prostate and stomach were concentrated mainly in countries with a high HDI index. The same pattern was observed for mortality from cancer except for prostate cancer that was more concentrated in countries with a low HDI index. Higher MIRs for all cancers were more concentrated in countries with a low HDI index. It was concluded that patterns of cancer occurrence correlate with care disparities at the country level.

Supervised and Unsupervised Classification for Pattern Recognition Purposes

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Catalina COCIANU

2006-01-01

Full Text Available A cluster analysis task has to identify the grouping trends of data, to decide on the sound clusters as well as to validate somehow the resulted structure. The identification of the grouping tendency existing in a data collection assumes the selection of a framework stated in terms of a mathematical model allowing to express the similarity degree between couples of particular objects, quasi-metrics expressing the similarity between an object an a cluster and between clusters, respectively. In supervised classification, we are provided with a collection of preclassified patterns, and the problem is to label a newly encountered pattern. Typically, the given training patterns are used to learn the descriptions of classes which in turn are used to label a new pattern. The final section of the paper presents a new methodology for supervised learning based on PCA. The classes are represented in the measurement/feature space by a continuous repartitions

A possibilistic approach to clustering

Science.gov (United States)

Krishnapuram, Raghu; Keller, James M.

1993-01-01

Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering methods in that total commitment of a vector to a given class is not required at each image pattern recognition iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from the 'Fuzzy C-Means' (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Recently, we cast the clustering problem into the framework of possibility theory using an approach in which the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. We show the ability of this approach to detect linear and quartic curves in the presence of considerable noise.

Physiological characteristics of high yield under cluster planting: photosynthesis and canopy microclimate of cotton

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Ting-ting Xie

2016-01-01

Full Text Available Cotton produces more biomass and economic yield when cluster planting pattern (three plants per hole than in a traditional planting pattern (one plant per hole, even at similar plant densities, indicating that individual plant growth is promoted by cluster planting. The causal factors for this improved growth induced by cluster planting pattern, the light interception, canopy microclimate and photosynthetic rate of cotton were investigated in an arid region of China. The results indicated that the leaf area index and light interception were higher in cluster planting, and significantly different from those in traditional planting during the middle and late growth stages. Cotton canopy humidity at different growth stages was increased but canopy temperatures were reduced by cluster planting. In the later growth stage of cluster planting, the leaf chlorophyll content was higher and the leaf net photosynthetic rate and canopy photosynthetic rate were significantly increased in comparing with traditional planting pattern. We concluded that differences in canopy light interception and photosynthetic rate were the primary factors responsible for increased biomass production and economic yield in cluster planting compared with the traditional planting of cotton.

The clustering of diet, physical activity and sedentary behavior in children and adolescents: a review.

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Leech, Rebecca M; McNaughton, Sarah A; Timperio, Anna

2014-01-22

Diet, physical activity (PA) and sedentary behavior are important, yet modifiable, determinants of obesity. Recent research into the clustering of these behaviors suggests that children and adolescents have multiple obesogenic risk factors. This paper reviews studies using empirical, data-driven methodologies, such as cluster analysis (CA) and latent class analysis (LCA), to identify clustering patterns of diet, PA and sedentary behavior among children or adolescents and their associations with socio-demographic indicators, and overweight and obesity. A literature search of electronic databases was undertaken to identify studies which have used data-driven methodologies to investigate the clustering of diet, PA and sedentary behavior among children and adolescents aged 5-18 years old. Eighteen studies (62% of potential studies) were identified that met the inclusion criteria, of which eight examined the clustering of PA and sedentary behavior and eight examined diet, PA and sedentary behavior. Studies were mostly cross-sectional and conducted in older children and adolescents (≥ 9 years). Findings from the review suggest that obesogenic cluster patterns are complex with a mixed PA/sedentary behavior cluster observed most frequently, but healthy and unhealthy patterning of all three behaviors was also reported. Cluster membership was found to differ according to age, gender and socio-economic status (SES). The tendency for older children/adolescents, particularly females, to comprise clusters defined by low PA was the most robust finding. Findings to support an association between obesogenic cluster patterns and overweight and obesity were inconclusive, with longitudinal research in this area limited. Diet, PA and sedentary behavior cluster together in complex ways that are not well understood. Further research, particularly in younger children, is needed to understand how cluster membership differs according to socio-demographic profile. Longitudinal research is

CATCHprofiles: Clustering and Alignment Tool for ChIP Profiles

DEFF Research Database (Denmark)

G. G. Nielsen, Fiona; Galschiøt Markus, Kasper; Møllegaard Friborg, Rune

2012-01-01

IP-profiling data and detect potentially meaningful patterns, the areas of enrichment must be aligned and clustered, which is an algorithmically and computationally challenging task. We have developed CATCHprofiles, a novel tool for exhaustive pattern detection in ChIP profiling data. CATCHprofiles is built upon...... a computationally efficient implementation for the exhaustive alignment and hierarchical clustering of ChIP profiling data. The tool features a graphical interface for examination and browsing of the clustering results. CATCHprofiles requires no prior knowledge about functional sites, detects known binding patterns...... it an invaluable tool for explorative research based on ChIP profiling data. CATCHprofiles and the CATCH algorithm run on all platforms and is available for free through the CATCH website: http://catch.cmbi.ru.nl/. User support is available by subscribing to the mailing list catch-users@bioinformatics.org....

Emergence of regional clusters

DEFF Research Database (Denmark)

Dahl, Michael S.; Østergaard, Christian Richter; Dalum, Bent

2010-01-01

The literature on regional clusters has increased considerably during the last decade. The emergence and growth patterns are usually explained by such factors as unique local culture, regional capabilities, tacit knowledge or the existence of location-specific externalities (knowledge spillovers...

Hidden Markov model analysis of maternal behavior patterns in inbred and reciprocal hybrid mice.

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Valeria Carola

Full Text Available Individual variation in maternal care in mammals shows a significant heritable component, with the maternal behavior of daughters resembling that of their mothers. In laboratory mice, genetically distinct inbred strains show stable differences in maternal care during the first postnatal week. Moreover, cross fostering and reciprocal breeding studies demonstrate that differences in maternal care between inbred strains persist in the absence of genetic differences, demonstrating a non-genetic or epigenetic contribution to maternal behavior. In this study we applied a mathematical tool, called hidden Markov model (HMM, to analyze the behavior of female mice in the presence of their young. The frequency of several maternal behaviors in mice has been previously described, including nursing/grooming pups and tending to the nest. However, the ordering, clustering, and transitions between these behaviors have not been systematically described and thus a global description of maternal behavior is lacking. Here we used HMM to describe maternal behavior patterns in two genetically distinct mouse strains, C57BL/6 and BALB/c, and their genetically identical reciprocal hybrid female offspring. HMM analysis is a powerful tool to identify patterns of events that cluster in time and to determine transitions between these clusters, or hidden states. For the HMM analysis we defined seven states: arched-backed nursing, blanket nursing, licking/grooming pups, grooming, activity, eating, and sleeping. By quantifying the frequency, duration, composition, and transition probabilities of these states we were able to describe the pattern of maternal behavior in mouse and identify aspects of these patterns that are under genetic and nongenetic inheritance. Differences in these patterns observed in the experimental groups (inbred and hybrid females were detected only after the application of HMM analysis whereas classical statistical methods and analyses were not able to

Global Drainage Patterns to Modern Terrestrial Sedimentary Basins and its Influence on Large River Systems

Science.gov (United States)

Nyberg, B.; Helland-Hansen, W.

2017-12-01

Long-term preservation of alluvial sediments is dependent on the hydrological processes that deposit sediments solely within an area that has available accomodation space and net subsidence know as a sedimentary basin. An understanding of the river processes contributing to terrestrial sedimentary basins is essential to fundamentally constrain and quantify controls on the modern terrestrial sink. Furthermore, the terrestrial source to sink controls place constraints on the entire coastal, shelf and deep marine sediment routing systems. In addition, the geographical importance of modern terrestrial sedimentary basins for agriculture and human settlements has resulted in significant upstream anthropogenic catchment modification for irrigation and energy needs. Yet to our knowledge, a global catchment model depicting the drainage patterns to modern terrestrial sedimentary basins has previously not been established that may be used to address these challenging issues. Here we present a new database of 180,737 global catchments that show the surface drainage patterns to modern terrestrial sedimentary basins. This is achieved by using high resolution river networks derived from digital elevation models in relation to newly acquired maps on global modern sedimentary basins to identify terrestrial sinks. The results show that active tectonic regimes are typically characterized by larger terrestrial sedimentary basins, numerous smaller source catchments and a high source to sink relief ratio. To the contrary passive margins drain catchments to smaller terrestrial sedimentary basins, are composed of fewer source catchments that are relatively larger and a lower source to sink relief ratio. The different geomorphological characteristics of source catchments by tectonic setting influence the spatial and temporal patterns of fluvial architecture within sedimentary basins and the anthropogenic methods of exploiting those rivers. The new digital database resource is aimed to help

KANTS: a stigmergic ant algorithm for cluster analysis and swarm art.

Science.gov (United States)

Fernandes, Carlos M; Mora, Antonio M; Merelo, Juan J; Rosa, Agostinho C

2014-06-01

KANTS is a swarm intelligence clustering algorithm inspired by the behavior of social insects. It uses stigmergy as a strategy for clustering large datasets and, as a result, displays a typical behavior of complex systems: self-organization and global patterns emerging from the local interaction of simple units. This paper introduces a simplified version of KANTS and describes recent experiments with the algorithm in the context of a contemporary artistic and scientific trend called swarm art, a type of generative art in which swarm intelligence systems are used to create artwork or ornamental objects. KANTS is used here for generating color drawings from the input data that represent real-world phenomena, such as electroencephalogram sleep data. However, the main proposal of this paper is an art project based on well-known abstract paintings, from which the chromatic values are extracted and used as input. Colors and shapes are therefore reorganized by KANTS, which generates its own interpretation of the original artworks. The project won the 2012 Evolutionary Art, Design, and Creativity Competition.

A cluster analysis of tic symptoms in children and adults with Tourette syndrome: clinical correlates and treatment outcome.

Science.gov (United States)

McGuire, Joseph F; Nyirabahizi, Epiphanie; Kircanski, Katharina; Piacentini, John; Peterson, Alan L; Woods, Douglas W; Wilhelm, Sabine; Walkup, John T; Scahill, Lawrence

2013-12-30

Cluster analytic methods have examined the symptom presentation of chronic tic disorders (CTDs), with limited agreement across studies. The present study investigated patterns, clinical correlates, and treatment outcome of tic symptoms. 239 youth and adults with CTDs completed a battery of assessments at baseline to determine diagnoses, tic severity, and clinical characteristics. Participants were randomly assigned to receive either a comprehensive behavioral intervention for tics (CBIT) or psychoeducation and supportive therapy (PST). A cluster analysis was conducted on the baseline Yale Global Tic Severity Scale (YGTSS) symptom checklist to identify the constellations of tic symptoms. Four tic clusters were identified: Impulse Control and Complex Phonic Tics; Complex Motor Tics; Simple Head Motor/Vocal Tics; and Primarily Simple Motor Tics. Frequencies of tic symptoms showed few differences across youth and adults. Tic clusters had small associations with clinical characteristics and showed no associations to the presence of coexisting psychiatric conditions. Cluster membership scores did not predict treatment response to CBIT or tic severity reductions. Tic symptoms distinctly cluster with little difference across youth and adults, or coexisting conditions. This study, which is the first to examine tic clusters and response to treatment, suggested that tic symptom profiles respond equally well to CBIT. Clinical trials.gov. identifiers: NCT00218777; NCT00231985. © 2013 Elsevier Ireland Ltd. All rights reserved.

Searching remote homology with spectral clustering with symmetry in neighborhood cluster kernels.

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Ujjwal Maulik

Full Text Available Remote homology detection among proteins utilizing only the unlabelled sequences is a central problem in comparative genomics. The existing cluster kernel methods based on neighborhoods and profiles and the Markov clustering algorithms are currently the most popular methods for protein family recognition. The deviation from random walks with inflation or dependency on hard threshold in similarity measure in those methods requires an enhancement for homology detection among multi-domain proteins. We propose to combine spectral clustering with neighborhood kernels in Markov similarity for enhancing sensitivity in detecting homology independent of "recent" paralogs. The spectral clustering approach with new combined local alignment kernels more effectively exploits the unsupervised protein sequences globally reducing inter-cluster walks. When combined with the corrections based on modified symmetry based proximity norm deemphasizing outliers, the technique proposed in this article outperforms other state-of-the-art cluster kernels among all twelve implemented kernels. The comparison with the state-of-the-art string and mismatch kernels also show the superior performance scores provided by the proposed kernels. Similar performance improvement also is found over an existing large dataset. Therefore the proposed spectral clustering framework over combined local alignment kernels with modified symmetry based correction achieves superior performance for unsupervised remote homolog detection even in multi-domain and promiscuous domain proteins from Genolevures database families with better biological relevance. Source code available upon request.sarkar@labri.fr.

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

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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.

Into and out of the tropics: global diversification patterns in a hyperdiverse clade of ectomycorrhizal fungi.

Science.gov (United States)

Looney, Brian P; Ryberg, Martin; Hampe, Felix; Sánchez-García, Marisol; Matheny, P Brandon

2016-01-01

Ectomycorrhizal (ECM) fungi, symbiotic mutualists of many dominant tree and shrub species, exhibit a biogeographic pattern counter to the established latitudinal diversity gradient of most macroflora and fauna. However, an evolutionary basis for this pattern has not been explicitly tested in a diverse lineage. In this study, we reconstructed a mega-phylogeny of a cosmopolitan and hyperdiverse genus of ECM fungi, Russula, sampling from annotated collections and utilizing publically available sequences deposited in GenBank. Metadata from molecular operational taxonomic unit cluster sets were examined to infer the distribution and plant association of the genus. This allowed us to test for differences in patterns of diversification between tropical and extratropical taxa, as well as how their associations with different plant lineages may be a driver of diversification. Results show that Russula is most species-rich at temperate latitudes and ancestral state reconstruction shows that the genus initially diversified in temperate areas. Migration into and out of the tropics characterizes the early evolution of the genus, and these transitions have been frequent since this time. We propose the 'generalized diversification rate' hypothesis to explain the reversed latitudinal diversity gradient pattern in Russula as we detect a higher net diversification rate in extratropical lineages. Patterns of diversification with plant associates support host switching and host expansion as driving diversification, with a higher diversification rate in lineages associated with Pinaceae and frequent transitions to association with angiosperms. © 2015 John Wiley & Sons Ltd.

A 350 ka record of climate change from Lake El'gygytgyn, Far East Russian Arctic: refining the pattern of climate modes by means of cluster analysis

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U. Frank

2013-07-01

Full Text Available Rock magnetic, biochemical and inorganic records of the sediment cores PG1351 and Lz1024 from Lake El'gygytgyn, Chukotka peninsula, Far East Russian Arctic, were subject to a hierarchical agglomerative cluster analysis in order to refine and extend the pattern of climate modes as defined by Melles et al. (2007. Cluster analysis of the data obtained from both cores yielded similar results, differentiating clearly between the four climate modes warm, peak warm, cold and dry, and cold and moist. In addition, two transitional phases were identified, representing the early stages of a cold phase and slightly colder conditions during a warm phase. The statistical approach can thus be used to resolve gradual changes in the sedimentary units as an indicator of available oxygen in the hypolimnion in greater detail. Based upon cluster analyses on core Lz1024, the published succession of climate modes in core PG1351, covering the last 250 ka, was modified and extended back to 350 ka. Comparison to the marine oxygen isotope (δ18O stack LR04 (Lisiecki and Raymo, 2005 and the summer insolation at 67.5° N, with the extended Lake El'gygytgyn parameter records of magnetic susceptibility (κLF, total organic carbon content (TOC and the chemical index of alteration (CIA; Minyuk et al., 2007, revealed that all stages back to marine isotope stage (MIS 10 and most of the substages are clearly reflected in the pattern derived from the cluster analysis.

Globalization

OpenAIRE

F. Gerard Adams

2008-01-01

The rapid globalization of the world economy is causing fundamental changes in patterns of trade and finance. Some economists have argued that globalization has arrived and that the world is “flat†. While the geographic scope of markets has increased, the author argues that new patterns of trade and finance are a result of the discrepancies between “old†countries and “new†. As the differences are gradually wiped out, particularly if knowledge and technology spread worldwide, the t...

Sm cluster superlattice on graphene/Ir(111)

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Mousadakos, Dimitris; Pivetta, Marina; Brune, Harald; Rusponi, Stefano

2017-12-01

We report on the first example of a self-assembled rare earth cluster superlattice. As a template, we use the moiré pattern formed by graphene on Ir(111); its lattice constant of 2.52 nm defines the interparticle distance. The samarium cluster superlattice forms for substrate temperatures during deposition ranging from 80 to 110 K, and it is stable upon annealing to 140 K. By varying the samarium coverage, the mean cluster size can be increased up to 50 atoms, without affecting the long-range order. The spatial order and the width of the cluster size distribution match the best examples of metal cluster superlattices grown by atomic beam epitaxy on template surfaces.

Patterns of accentuated grey-white differentiation on diffusion-weighted imaging or the apparent diffusion coefficient maps in comatose survivors after global brain injury

International Nuclear Information System (INIS)

Kim, E.; Sohn, C.-H.; Chang, K.-H.; Chang, H.-W.; Lee, D.H.

2011-01-01

Aim: To determine what disease entities show accentuated grey-white differentiation of the cerebral hemisphere on diffusion-weighted images (DWI) or apparent diffusion coefficient (ADC) maps, and whether there is a correlation between the different patterns and the cause of the brain injury. Methods and materials: The DWI and ADC maps of 19 patients with global brain injury were reviewed and evaluated to investigate whether there was a correlation between the different patterns seen on the DWI and ADC maps and the cause of global brain injury. The ADC values were measured for quantitative analysis. Results: There were three different patterns of ADC decrease: a predominant ADC decrease in only the cerebral cortex (n = 8; pattern I); an ADC decrease in both the cerebral cortex and white matter (WM) and a predominant decrease in the WM (n = 9; pattern II); and a predominant ADC decrease in only the WM (n = 3; pattern III). Conclusion: Pattern I is cerebral cortical injury, suggesting cortical laminar necrosis in hypoxic brain injury. Pattern II is cerebral cortical and WM injury, frequently seen in brain death, while pattern 3 is mainly WM injury, especially found in hypoglycaemic brain injury. It is likely that pattern I is decorticate injury and pattern II is decerebrate injury in hypoxic ischaemic encephalopathy.Patterns I and II are found in severe hypoxic brain injury, and pattern II is frequently shown in brain death, whereas pattern III was found in severe hypoglycaemic injury.

The relationship between supplier networks and industrial clusters: an analysis based on the cluster mapping method

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Ichiro IWASAKI

2010-06-01

Full Text Available Michael Porter’s concept of competitive advantages emphasizes the importance of regional cooperation of various actors in order to gain competitiveness on globalized markets. Foreign investors may play an important role in forming such cooperation networks. Their local suppliers tend to concentrate regionally. They can form, together with local institutions of education, research, financial and other services, development agencies, the nucleus of cooperative clusters. This paper deals with the relationship between supplier networks and clusters. Two main issues are discussed in more detail: the interest of multinational companies in entering regional clusters and the spillover effects that may stem from their participation. After the discussion on the theoretical background, the paper introduces a relatively new analytical method: “cluster mapping” - a method that can spot regional hot spots of specific economic activities with cluster building potential. Experience with the method was gathered in the US and in the European Union. After the discussion on the existing empirical evidence, the authors introduce their own cluster mapping results, which they obtained by using a refined version of the original methodology.

A Genetic Algorithm That Exchanges Neighboring Centers for Fuzzy c-Means Clustering

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Chahine, Firas Safwan

2012-01-01

Clustering algorithms are widely used in pattern recognition and data mining applications. Due to their computational efficiency, partitional clustering algorithms are better suited for applications with large datasets than hierarchical clustering algorithms. K-means is among the most popular partitional clustering algorithm, but has a major…

Swarm: robust and fast clustering method for amplicon-based studies

Science.gov (United States)

Rognes, Torbjørn; Quince, Christopher; de Vargas, Colomban; Dunthorn, Micah

2014-01-01

Popular de novo amplicon clustering methods suffer from two fundamental flaws: arbitrary global clustering thresholds, and input-order dependency induced by centroid selection. Swarm was developed to address these issues by first clustering nearly identical amplicons iteratively using a local threshold, and then by using clusters’ internal structure and amplicon abundances to refine its results. This fast, scalable, and input-order independent approach reduces the influence of clustering parameters and produces robust operational taxonomic units. PMID:25276506

Swarm: robust and fast clustering method for amplicon-based studies

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Frédéric Mahé

2014-09-01

Full Text Available Popular de novo amplicon clustering methods suffer from two fundamental flaws: arbitrary global clustering thresholds, and input-order dependency induced by centroid selection. Swarm was developed to address these issues by first clustering nearly identical amplicons iteratively using a local threshold, and then by using clusters’ internal structure and amplicon abundances to refine its results. This fast, scalable, and input-order independent approach reduces the influence of clustering parameters and produces robust operational taxonomic units.

Time series clustering analysis of health-promoting behavior

Science.gov (United States)

Yang, Chi-Ta; Hung, Yu-Shiang; Deng, Guang-Feng

2013-10-01

Health promotion must be emphasized to achieve the World Health Organization goal of health for all. Since the global population is aging rapidly, ComCare elder health-promoting service was developed by the Taiwan Institute for Information Industry in 2011. Based on the Pender health promotion model, ComCare service offers five categories of health-promoting functions to address the everyday needs of seniors: nutrition management, social support, exercise management, health responsibility, stress management. To assess the overall ComCare service and to improve understanding of the health-promoting behavior of elders, this study analyzed health-promoting behavioral data automatically collected by the ComCare monitoring system. In the 30638 session records collected for 249 elders from January, 2012 to March, 2013, behavior patterns were identified by fuzzy c-mean time series clustering algorithm combined with autocorrelation-based representation schemes. The analysis showed that time series data for elder health-promoting behavior can be classified into four different clusters. Each type reveals different health-promoting needs, frequencies, function numbers and behaviors. The data analysis result can assist policymakers, health-care providers, and experts in medicine, public health, nursing and psychology and has been provided to Taiwan National Health Insurance Administration to assess the elder health-promoting behavior.

Multi-Scale Influences of Climate, Spatial Pattern, and Positive Feedback on 20th Century Tree Establishment at Upper Treeline in the Rocky Mountains, USA

Science.gov (United States)

Elliott, G. P.

2009-12-01

The influences of 20th century climate, spatial pattern of tree establishment, and positive feedback were assessed to gain a more holistic understanding of how broad scale abiotic and local scale biotic components interact to govern upper treeline ecotonal dynamics along a latitudinal gradient (ca. 35°N-45°N) in the Rocky Mountains. Study sites (n = 22) were in the Bighorn, Medicine Bow, Front Range, and Sangre de Cristo mountain ranges. Dendroecological techniques were used for a broad scale analysis of climate at treeline. Five-year age-structure classes were compared with identical five-year bins of 20th century climate data using Spearman’s rank correlation and regime shift analysis. Local scale biotic interactions capable of ameliorating broad scale climate inputs through positive feedback were examined by using Ripley’s K to determine the spatial patterns of tree establishment above timberline. Significant correlations (p Medicine Bow and Sangre de Cristo Mountains primarily contain clustered spatial patterns of trees above timberline, which indicates a strong reliance on the amelioration of abiotic conditions through positive feedback with nearby vegetation. Although clustered spatial patterns likely originate in response to harsh abiotic conditions such as drought or constant strong winds, the local scale biotic interactions within a clustered formation of trees appears to override the immediate influence of broad scale climate. This is evidenced both by a lack of significant correlations between tree establishment and climate in these mountain ranges, as well as the considerable lag times between initial climate regime shifts and corresponding shifts in tree age structure. Taken together, this research suggests that the influence of broad scale climate on upper treeline ecotonal dynamics is contingent on the local scale spatial patterns of tree establishment and related influences of positive feedback. These findings have global implications for our

Global pattern of trends in streamflow and water availability in a changing climate.

Science.gov (United States)

Milly, P C D; Dunne, K A; Vecchia, A V

2005-11-17

Water availability on the continents is important for human health, economic activity, ecosystem function and geophysical processes. Because the saturation vapour pressure of water in air is highly sensitive to temperature, perturbations in the global water cycle are expected to accompany climate warming. Regional patterns of warming-induced changes in surface hydroclimate are complex and less certain than those in temperature, however, with both regional increases and decreases expected in precipitation and runoff. Here we show that an ensemble of 12 climate models exhibits qualitative and statistically significant skill in simulating observed regional patterns of twentieth-century multidecadal changes in streamflow. These models project 10-40% increases in runoff in eastern equatorial Africa, the La Plata basin and high-latitude North America and Eurasia, and 10-30% decreases in runoff in southern Africa, southern Europe, the Middle East and mid-latitude western North America by the year 2050. Such changes in sustainable water availability would have considerable regional-scale consequences for economies as well as ecosystems.

Global patterns and predictions of seafloor biomass using random forests.

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Chih-Lin Wei

Full Text Available A comprehensive seafloor biomass and abundance database has been constructed from 24 oceanographic institutions worldwide within the Census of Marine Life (CoML field projects. The machine-learning algorithm, Random Forests, was employed to model and predict seafloor standing stocks from surface primary production, water-column integrated and export particulate organic matter (POM, seafloor relief, and bottom water properties. The predictive models explain 63% to 88% of stock variance among the major size groups. Individual and composite maps of predicted global seafloor biomass and abundance are generated for bacteria, meiofauna, macrofauna, and megafauna (invertebrates and fishes. Patterns of benthic standing stocks were positive functions of surface primary production and delivery of the particulate organic carbon (POC flux to the seafloor. At a regional scale, the census maps illustrate that integrated biomass is highest at the poles, on continental margins associated with coastal upwelling and with broad zones associated with equatorial divergence. Lowest values are consistently encountered on the central abyssal plains of major ocean basins The shift of biomass dominance groups with depth is shown to be affected by the decrease in average body size rather than abundance, presumably due to decrease in quantity and quality of food supply. This biomass census and associated maps are vital components of mechanistic deep-sea food web models and global carbon cycling, and as such provide fundamental information that can be incorporated into evidence-based management.

Global patterns of diversity and selection in human tyrosinase gene.

Science.gov (United States)

Hudjashov, Georgi; Villems, Richard; Kivisild, Toomas

2013-01-01

Global variation in skin pigmentation is one of the most striking examples of environmental adaptation in humans. More than two hundred loci have been identified as candidate genes in model organisms and a few tens of these have been found to be significantly associated with human skin pigmentation in genome-wide association studies. However, the evolutionary history of different pigmentation genes is rather complex: some loci have been subjected to strong positive selection, while others evolved under the relaxation of functional constraints in low UV environment. Here we report the results of a global study of the human tyrosinase gene, which is one of the key enzymes in melanin production, to assess the role of its variation in the evolution of skin pigmentation differences among human populations. We observe a higher rate of non-synonymous polymorphisms in the European sample consistent with the relaxation of selective constraints. A similar pattern was previously observed in the MC1R gene and concurs with UV radiation-driven model of skin color evolution by which mutations leading to lower melanin levels and decreased photoprotection are subject to purifying selection at low latitudes while being tolerated or even favored at higher latitudes because they facilitate UV-dependent vitamin D production. Our coalescent date estimates suggest that the non-synonymous variants, which are frequent in Europe and North Africa, are recent and have emerged after the separation of East and West Eurasian populations.

Clustering by reordering of similarity and Laplacian matrices: Application to galaxy clusters

Science.gov (United States)

Mahmoud, E.; Shoukry, A.; Takey, A.

2018-04-01

Similarity metrics, kernels and similarity-based algorithms have gained much attention due to their increasing applications in information retrieval, data mining, pattern recognition and machine learning. Similarity Graphs are often adopted as the underlying representation of similarity matrices and are at the origin of known clustering algorithms such as spectral clustering. Similarity matrices offer the advantage of working in object-object (two-dimensional) space where visualization of clusters similarities is available instead of object-features (multi-dimensional) space. In this paper, sparse ɛ-similarity graphs are constructed and decomposed into strong components using appropriate methods such as Dulmage-Mendelsohn permutation (DMperm) and/or Reverse Cuthill-McKee (RCM) algorithms. The obtained strong components correspond to groups (clusters) in the input (feature) space. Parameter ɛi is estimated locally, at each data point i from a corresponding narrow range of the number of nearest neighbors. Although more advanced clustering techniques are available, our method has the advantages of simplicity, better complexity and direct visualization of the clusters similarities in a two-dimensional space. Also, no prior information about the number of clusters is needed. We conducted our experiments on two and three dimensional, low and high-sized synthetic datasets as well as on an astronomical real-dataset. The results are verified graphically and analyzed using gap statistics over a range of neighbors to verify the robustness of the algorithm and the stability of the results. Combining the proposed algorithm with gap statistics provides a promising tool for solving clustering problems. An astronomical application is conducted for confirming the existence of 45 galaxy clusters around the X-ray positions of galaxy clusters in the redshift range [0.1..0.8]. We re-estimate the photometric redshifts of the identified galaxy clusters and obtain acceptable values

Computational Experiment Approach to Controlled Evolution of Procurement Pattern in Cluster Supply Chain

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Xiao Xue

2015-01-01

Full Text Available Companies have been aware of the benefits of developing Cluster Supply Chains (CSCs, and they are spending a great deal of time and money attempting to develop the new business pattern. Yet, the traditional techniques for identifying CSCs have strong theoretical antecedents, but seem to have little traction in the field. We believe this is because the standard techniques fail to capture evolution over time, nor provide useful intervention measures to reach goals. To address these problems, we introduce an agent-based modeling approach to evaluate CSCs. Taking collaborative procurement as research object, our approach is composed of three parts: model construction, model instantiation, and computational experiment. We use the approach to explore the service charging policy problem in collaborative procurement. Three kinds of service charging polices are compared in the same experiment environment. Finally, “Fixed Cost” is identified as the optimal policy under the stable market environment. The case study can help us to understand the workflow of applying the approach, and provide valuable decision support applications to industry.

Detection of CO emission in Hydra 1 cluster galaxies

International Nuclear Information System (INIS)

Huchtmeier, W.K.

1990-01-01

A survey of bright Hydra cluster spiral galaxies for the CO(1-0) transition at 115 GHz was performed with the 15m Swedish-ESO submillimeter telescope (SEST). Five out of 15 galaxies observed have been detected in the CO(1-0) line. The largest spiral galaxy in the cluster, NGC 3312, got more CO than any spiral of the Virgo cluster. This Sa-type galaxy is optically largely distorted and disrupted on one side. It is a good candidate for ram pressure stripping while passing through the cluster's central region. A comparison with global CO properties of Virgo cluster spirals shows a relatively good agreement with the detected Hydra cluster galaxies

A Review of Subsequence Time Series Clustering

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Seyedjamal Zolhavarieh

2014-01-01

Full Text Available Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.

A review of subsequence time series clustering.

Science.gov (United States)

Zolhavarieh, Seyedjamal; Aghabozorgi, Saeed; Teh, Ying Wah

2014-01-01

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.

A Review of Subsequence Time Series Clustering

Science.gov (United States)

Teh, Ying Wah

2014-01-01

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies. PMID:25140332

A Trajectory Regression Clustering Technique Combining a Novel Fuzzy C-Means Clustering Algorithm with the Least Squares Method

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Xiangbing Zhou

2018-04-01

Full Text Available Rapidly growing GPS (Global Positioning System trajectories hide much valuable information, such as city road planning, urban travel demand, and population migration. In order to mine the hidden information and to capture better clustering results, a trajectory regression clustering method (an unsupervised trajectory clustering method is proposed to reduce local information loss of the trajectory and to avoid getting stuck in the local optimum. Using this method, we first define our new concept of trajectory clustering and construct a novel partitioning (angle-based partitioning method of line segments; second, the Lagrange-based method and Hausdorff-based K-means++ are integrated in fuzzy C-means (FCM clustering, which are used to maintain the stability and the robustness of the clustering process; finally, least squares regression model is employed to achieve regression clustering of the trajectory. In our experiment, the performance and effectiveness of our method is validated against real-world taxi GPS data. When comparing our clustering algorithm with the partition-based clustering algorithms (K-means, K-median, and FCM, our experimental results demonstrate that the presented method is more effective and generates a more reasonable trajectory.

Forecasting hourly global solar radiation using hybrid k-means and nonlinear autoregressive neural network models

International Nuclear Information System (INIS)

Benmouiza, Khalil; Cheknane, Ali

2013-01-01

Highlights: • An unsupervised clustering algorithm with a neural network model was explored. • The forecasting results of solar radiation time series and the comparison of their performance was simulated. • A new method was proposed combining k-means algorithm and NAR network to provide better prediction results. - Abstract: In this paper, we review our work for forecasting hourly global horizontal solar radiation based on the combination of unsupervised k-means clustering algorithm and artificial neural networks (ANN). k-Means algorithm focused on extracting useful information from the data with the aim of modeling the time series behavior and find patterns of the input space by clustering the data. On the other hand, nonlinear autoregressive (NAR) neural networks are powerful computational models for modeling and forecasting nonlinear time series. Taking the advantage of both methods, a new method was proposed combining k-means algorithm and NAR network to provide better forecasting results

Effect of void cluster on ductile failure evolution

DEFF Research Database (Denmark)

Tvergaard, Viggo

2016-01-01

The behavior of a non-uniform void distribution in a ductile material is investigated by using a cell model analysis to study a material with a periodic pattern of void clusters. The special clusters considered consist of a number of uniformly spaced voids located along a plane perpendicular...

Spatio-Temporal Patterns and Climate Variables Controlling of Biomass Carbon Stock of Global Grassland Ecosystems from 1982 to 2006

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Jiangzhou Xia

2014-02-01

Full Text Available Grassland ecosystems play an important role in subsistence agriculture and the global carbon cycle. However, the global spatio-temporal patterns and environmental controls of grassland biomass are not well quantified and understood. The goal of this study was to estimate the spatial and temporal patterns of the global grassland biomass and analyze their driving forces using field measurements, Normalized Difference Vegetation Index (NDVI time series from satellite data, climate reanalysis data, and a satellite-based statistical model. Results showed that the NDVI-based biomass carbon model developed from this study explained 60% of the variance across 38 sites globally. The global carbon stock in grassland aboveground live biomass was 1.05 Pg·C, averaged from 1982 to 2006, and increased at a rate of 2.43 Tg·C·y−1 during this period. Temporal change of the global biomass was significantly and positively correlated with temperature and precipitation. The distribution of biomass carbon density followed the precipitation gradient. The dynamics of regional grassland biomass showed various trends largely determined by regional climate variability, disturbances, and management practices (such as grazing for meat production. The methods and results from this study can be used to monitor the dynamics of grassland aboveground biomass and evaluate grassland susceptibility to climate variability and change, disturbances, and management.

Explaining ICT Infrastructure and E-Commerce Uses and Benefits in Industrial Clusters-Evidence from a Biotech Cluster

DEFF Research Database (Denmark)

Scupola, Ada; Steinfield, Charles

2006-01-01

in an industrial cluster might utilize and derive benefit from a public, broadband ICT infrastructure, particularly in support of e-commerce applications. A case study of a successful biotech cluster in Denamrk and Sweden-The Medicon Valley-provides a preliminary test of these expectations. Distinctions in uses...... and benefits based upon firm size are considered. A key finding is that small firms that would not otherwise be expected to gain from global e-commerce can rely on the cluster "brand" to enable trade with unknown and distant partners.......The literature on Industrial Clusters has not focused heavely on the role of the ICT infrastructure, nor on the potentail implications of electronic commerce. In this paper, we examine the theoretical bases for bringing these research streams together, and develop expectations for how firms...

Climate Teleconnections and Recent Patterns of Human and Animal Disease Outbreaks

Science.gov (United States)

Anyamba, Assaf; Linthicum, Kenneth J.; Small, Jennifer L.; Collins, Katherine M.; Tucker, Compton J.; Pak, Edwin W.; Britch, Seth C.; Eastman, James Ronald; Pinzon, Jorge E.; Russell, Kevin L.

2011-01-01

Recent clusters of outbreaks of mosquito-borne diseases (Rift Valley fever and chikungunya) in Africa and parts of the Indian Ocean islands illustrate how interannual climate variability influences the changing risk patterns of disease outbreaks. Extremes in rainfall (drought and flood) during the period 2004 - 2009 have privileged different disease vectors. Chikungunya outbreaks occurred during the severe drought from late 2004 to 2006 over coastal East Africa and the western Indian Ocean islands and in the later years India and Southeast Asia. The chikungunya pandemic was caused by a Central/East African genotype that appears to have been precipitated and then enhanced by global-scale and regional climate conditions in these regions. Outbreaks of Rift Valley fever occurred following excessive rainfall period from late 2006 to late 2007 in East Africa and Sudan, and then in 2008 - 2009 in Southern Africa. The shift in the outbreak patterns of Rift Valley fever from East Africa to Southern Africa followed a transition of the El Nino/Southern Oscillation (ENSO) phenomena from the warm El Nino phase (2006-2007) to the cold La Nina phase (2007-2009) and associated patterns of variability in the greater Indian Ocean basin that result in the displacement of the centres of above normal rainfall from Eastern to Southern Africa. Understanding the background patterns of climate variability both at global and regional scale and their impacts on ecological drivers of vector borne-diseases is critical in long-range planning of appropriate response and mitigation measures.

Effects of punctal occlusion on global tear proteins in patients with dry eye.

Science.gov (United States)

Tong, Louis; Zhou, Lei; Beuerman, Roger; Simonyi, Susan; Hollander, David A; Stern, Michael E

2017-10-01

To investigate effects of punctal occlusion on global tear protein levels in patients with dry eye. In this prospective, longitudinal, single-center study, nonabsorbable punctal plugs were inserted bilaterally into the lower punctum of 30 patients with moderate dry eye. Dry eye symptoms, fluorescein corneal staining, Schirmer I test, tear film break-up time, and safety were assessed in the more severely affected eye. Tear proteins at weeks 1 and 3 were quantified by iTRAQ relative to baseline preocclusion levels. Of 29 patients who completed the study, 23 (mean age 49.8 years) had sufficient tear samples for analysis. After 3 weeks, punctal occlusion significantly upregulated tear proteins, including glutathione synthase (mean of 1.6-fold, P = 0.01) and interleukin-1 receptor antagonist (1.7-fold, P = 0.032) and downregulated cholinergic receptor (neuronal) alpha-7 (0.79-fold, P = 0.039) and lymphocyte cytosolic protein-1 (0.66-fold, P = 0.012). Clustering analysis of global tear proteins revealed two clear profile changes; the first group of patients (cluster 1, n = 10) had a reduction in the inflammatory proteins (e.g., S100A8) and rise in lacrimal proteins supporting the ocular surface (e.g., lysozyme), whereas the second group (cluster 2, n = 13) had an increase in inflammatory proteins and a decrease in lacrimal proteins. Logistic regression analysis revealed that cluster 1 patients had significantly (P = 0.006) lower Schirmer scores at baseline (mean [standard deviation]: 4.3 [4.3] mm) than cluster 2 (6.8 [2.6] mm). Punctal plugs produced a beneficial pattern of tear protein change in patients with relatively low Schirmer scores within 3 weeks of punctal occlusion. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

The clustering of diet, physical activity and sedentary behavior in children and adolescents: a review

Science.gov (United States)

2014-01-01

Diet, physical activity (PA) and sedentary behavior are important, yet modifiable, determinants of obesity. Recent research into the clustering of these behaviors suggests that children and adolescents have multiple obesogenic risk factors. This paper reviews studies using empirical, data-driven methodologies, such as cluster analysis (CA) and latent class analysis (LCA), to identify clustering patterns of diet, PA and sedentary behavior among children or adolescents and their associations with socio-demographic indicators, and overweight and obesity. A literature search of electronic databases was undertaken to identify studies which have used data-driven methodologies to investigate the clustering of diet, PA and sedentary behavior among children and adolescents aged 5–18 years old. Eighteen studies (62% of potential studies) were identified that met the inclusion criteria, of which eight examined the clustering of PA and sedentary behavior and eight examined diet, PA and sedentary behavior. Studies were mostly cross-sectional and conducted in older children and adolescents (≥9 years). Findings from the review suggest that obesogenic cluster patterns are complex with a mixed PA/sedentary behavior cluster observed most frequently, but healthy and unhealthy patterning of all three behaviors was also reported. Cluster membership was found to differ according to age, gender and socio-economic status (SES). The tendency for older children/adolescents, particularly females, to comprise clusters defined by low PA was the most robust finding. Findings to support an association between obesogenic cluster patterns and overweight and obesity were inconclusive, with longitudinal research in this area limited. Diet, PA and sedentary behavior cluster together in complex ways that are not well understood. Further research, particularly in younger children, is needed to understand how cluster membership differs according to socio-demographic profile. Longitudinal research