Spatio-temporal Hotelling observer for signal detection from image sequences.

Science.gov (United States)

Caucci, Luca; Barrett, Harrison H; Rodriguez, Jeffrey J

2009-06-22

Detection of signals in noisy images is necessary in many applications, including astronomy and medical imaging. The optimal linear observer for performing a detection task, called the Hotelling observer in the medical literature, can be regarded as a generalization of the familiar prewhitening matched filter. Performance on the detection task is limited by randomness in the image data, which stems from randomness in the object, randomness in the imaging system, and randomness in the detector outputs due to photon and readout noise, and the Hotelling observer accounts for all of these effects in an optimal way. If multiple temporal frames of images are acquired, the resulting data set is a spatio-temporal random process, and the Hotelling observer becomes a spatio-temporal linear operator. This paper discusses the theory of the spatio-temporal Hotelling observer and estimation of the required spatio-temporal covariance matrices. It also presents a parallel implementation of the observer on a cluster of Sony PLAYSTATION 3 gaming consoles. As an example, we consider the use of the spatio-temporal Hotelling observer for exoplanet detection.

The World Spatiotemporal Analytics and Mapping Project (WSTAMP): Discovering, Exploring, and Mapping Spatiotemporal Patterns Across Heterogenous Space-Time Data

Science.gov (United States)

Morton, A.; Stewart, R.; Held, E.; Piburn, J.; Allen, M. R.; McManamay, R.; Sanyal, J.; Sorokine, A.; Bhaduri, B. L.

2017-12-01

Spatiotemporal (ST) analytics applied to major spatio-temporal data sources from major vendors such as USGS, NOAA, World Bank and World Health Organization have tremendous value in shedding light on the evolution of physical, cultural, and geopolitical landscapes on a local and global level. Especially powerful is the integration of these physical and cultural datasets across multiple and disparate formats, facilitating new interdisciplinary analytics and insights. Realizing this potential first requires an ST data model that addresses challenges in properly merging data from multiple authors, with evolving ontological perspectives, semantical differences, changing attributes, and content that is textual, numeric, categorical, and hierarchical. Equally challenging is the development of analytical and visualization approaches that provide a serious exploration of this integrated data while remaining accessible to practitioners with varied backgrounds. The WSTAMP project at the Oak Ridge National Laboratory has yielded two major results in addressing these challenges: 1) development of the WSTAMP database, a significant advance in ST data modeling that integrates 16000+ attributes covering 200+ countries for over 50 years from over 30 major sources and 2) a novel online ST exploratory and analysis tool providing an array of modern statistical and visualization techniques for analyzing these data temporally, spatially, and spatiotemporally under a standard analytic workflow. We report on these advances, provide an illustrative case study, and inform how others may freely access the tool.

AN ADAPTIVE ORGANIZATION METHOD OF GEOVIDEO DATA FOR SPATIO-TEMPORAL ASSOCIATION ANALYSIS

Directory of Open Access Journals (Sweden)

C. Wu

2015-07-01

Full Text Available Public security incidents have been increasingly challenging to address with their new features, including large-scale mobility, multi-stage dynamic evolution, spatio-temporal concurrency and uncertainty in the complex urban environment, which require spatio-temporal association analysis among multiple regional video data for global cognition. However, the existing video data organizational methods that view video as a property of the spatial object or position in space dissever the spatio-temporal relationship of scattered video shots captured from multiple video channels, limit the query functions on interactive retrieval between a camera and its video clips and hinder the comprehensive management of event-related scattered video shots. GeoVideo, which maps video frames onto a geographic space, is a new approach to represent the geographic world, promote security monitoring in a spatial perspective and provide a highly feasible solution to this problem. This paper analyzes the large-scale personnel mobility in public safety events and proposes a multi-level, event-related organization method with massive GeoVideo data by spatio-temporal trajectory. This paper designs a unified object identify(ID structure to implicitly store the spatio-temporal relationship of scattered video clips and support the distributed storage management of massive cases. Finally, the validity and feasibility of this method are demonstrated through suspect tracking experiments.

Comparing infants' use of featural and spatiotemporal information when individuating objects in an event monitoring design

DEFF Research Database (Denmark)

Krøjgaard, Peter

. The results obtained using this design reveal that infants are more successful using spatiotemporal object information than when using featural information. However, recent studies using the less cognitively demanding event monitoring design have revealed that even younger infants are capable of object...... in the present series of experiments in which infants' use of spatiotemporal and featural information is compared directly using the less demanding event monitoring design. The results are discussed in relation to existing empirical evidence......., to what extent infants rely on spatiotemporal or featural object information when individuating objects is currently under debate. Hitherto, infants' use of spatiotemporal and featural object information has only been compared directly using the rather cognitively demanding event mapping design...

Spatiotemporal Wave Patterns: Information Dynamics

Energy Technology Data Exchange (ETDEWEB)

Mikhail Rabinovich; Lev Tsimring

2006-01-20

Pattern formation has traditionally been studied in non-equilibrium physics from the viewpoint of describing the basic structures and their interactions. While this is still an important area of research, the emphasis in the last few years has shifted towards analysis of specific properties of patterns in various complex media. For example, diverse and unexpected phenomena occur in neuro-like media that are characterized by highly non-trivial local dynamics. We carried out an active research program on analysis of spatio-temporal patterns in various physical systems (convection, oscillating fluid layer, soap film), as well as in neuro-like media, with an emphasis on informational aspects of the dynamics. Nonlinear nonequilibrium media and their discrete analogs have a unique ability to represent, memorize, and process the information contained in spatio-temporal patterns. Recent neurophysiological experiments demonstrated a certain universality of spatio-temporal representation of information by neural ensembles. Information processing is also revealed in the spatio-temporal dynamics of cellular patterns in nonequilibrium media. It is extremely important for many applications to study the informational aspects of these dynamics, including the origins and mechanisms of information generation, propagation and storage. Some of our results are: the discovery of self-organization of periodically oscillatory patterns in chaotic heterogeneous media; the analysis of the propagation of the information along a chaotic media as function of the entropy of the signal; the analysis of wave propagation in discrete non-equilibrium media with autocatalytic properties, which simulates the calcium dynamics in cellular membranes. Based on biological experiments we suggest the mechanism by which the spatial sensory information is transferred into the spatio-temporal code in the neural media. We also found a new mechanism of self-pinning in cellular structures and the related phenomenon

Spatio-temporal regulation of circular RNA expression during porcine embryonic brain development

DEFF Research Database (Denmark)

Venø, Morten T; Hansen, Thomas B; Venø, Susanne T

2015-01-01

BACKGROUND: Recently, thousands of circular RNAs (circRNAs) have been discovered in various tissues and cell types from human, mouse, fruit fly and nematodes. However, expression of circRNAs across mammalian brain development has never been examined. RESULTS: Here we profile the expression of circ......RNA in five brain tissues at up to six time-points during fetal porcine development, constituting the first report of circRNA in the brain development of a large animal. An unbiased analysis reveals a highly complex regulation pattern of thousands of circular RNAs, with a distinct spatio-temporal expression...... are functionally conserved between mouse and human. Furthermore, we observe that "hot-spot" genes produce multiple circRNA isoforms, which are often differentially expressed across porcine brain development. A global comparison of porcine circRNAs reveals that introns flanking circularized exons are longer than...

Spatio-Temporal Rule Mining

DEFF Research Database (Denmark)

Gidofalvi, Gyozo; Pedersen, Torben Bach

2005-01-01

Recent advances in communication and information technology, such as the increasing accuracy of GPS technology and the miniaturization of wireless communication devices pave the road for Location-Based Services (LBS). To achieve high quality for such services, spatio-temporal data mining techniques...... are needed. In this paper, we describe experiences with spatio-temporal rule mining in a Danish data mining company. First, a number of real world spatio-temporal data sets are described, leading to a taxonomy of spatio-temporal data. Second, the paper describes a general methodology that transforms...... the spatio-temporal rule mining task to the traditional market basket analysis task and applies it to the described data sets, enabling traditional association rule mining methods to discover spatio-temporal rules for LBS. Finally, unique issues in spatio-temporal rule mining are identified and discussed....

Transition to turbulence via spatiotemporal intermittency in stimulated Raman backscattering

International Nuclear Information System (INIS)

Skoric, M.M.; Jovanovic, M.S.; Rajkovic, M.R.

1996-01-01

The spatiotemporal evolution of stimulated Raman backscattering in a bounded, uniform, weakly dissipative plasma is studied. The nonlinear model of a three-wave interaction involves a quadratic coupling of slowly varying complex amplitudes of the laser pump, the backscattered and the electron plasma wave. The corresponding set of coupled partial differential equations with nonlinear phase detuning that is taken into account is solved numerically in space time with fixed nonzero source boundary conditions. The study of the above open, convective, weakly confined system reveals a quasiperiodic transition to spatiotemporal chaos via spatiotemporal intermittency. In the analysis of transitions a dual scheme borrowed from fields of nonlinear dynamics and statistical physics is applied. An introduction of a nonlinear three-wave interaction to a growing family of paradigmatic equations which exhibit a route to turbulence via spatiotemporal intermittency is outlined in this work. copyright 1996 The American Physical Society

Analysis of Relations between Spatiotemporal Movement Regulation and Performance of Discrete Actions Reveals Functionality in Skilled Climbing.

Science.gov (United States)

Orth, Dominic; Kerr, Graham; Davids, Keith; Seifert, Ludovic

2017-01-01

In this review of research on climbing expertise, we focus on different measures of climbing performance, including spatiotemporal measures related to fluency and activity states (i.e., discrete actions), adopted by climbers for achieving overall performance goals of getting to the end of a route efficiently and safely. Currently, a broad range of variables have been reported, however, many of these fail to capture how climbers adapt to a route whilst climbing. We argue that spatiotemporal measures should be considered concurrently with evaluation of activity states (such as reaching or exploring) in order gain a more comprehensive picture of how climbers successfully adapt to a route. Spatial and temporal movement measures taken at the hip are a traditional means of assessing efficiency of climbing behaviors. More recently, performatory and exploratory actions of the limbs have been used in combination with spatiotemporal indicators, highlighting the influence of limb states on climbing efficiency and skill transfer. However, only a few studies have attempted to combine spatiotemporal and activity state measures taken during route climbing. This review brings together existing approaches for observing climbing skill at performance outcome (i.e., spatiotemporal assessments) and process (i.e., limb activity states) levels of analysis. Skill level is associated with a spatially efficient route progression and lower levels of immobility. However, more difficult hold architecture designs require significantly greater mobility and more complex movement patterning to maintain performance. Different forms of functional, or goal-supportive, movement variability, including active recovery and hold exploration, have been implicated as important adaptations to physiological and environmental dynamics that emerge during the act of climbing. Indeed, recently it has also been shown that, when climbing on new routes, efficient exploration can improve the transfer of skill. This

A hybrid spatiotemporal drought forecasting model for operational use

Science.gov (United States)

Vasiliades, L.; Loukas, A.

2010-09-01

Drought forecasting plays an important role in the planning and management of natural resources and water resource systems in a river basin. Early and timelines forecasting of a drought event can help to take proactive measures and set out drought mitigation strategies to alleviate the impacts of drought. Spatiotemporal data mining is the extraction of unknown and implicit knowledge, structures, spatiotemporal relationships, or patterns not explicitly stored in spatiotemporal databases. As one of data mining techniques, forecasting is widely used to predict the unknown future based upon the patterns hidden in the current and past data. This study develops a hybrid spatiotemporal scheme for integrated spatial and temporal forecasting. Temporal forecasting is achieved using feed-forward neural networks and the temporal forecasts are extended to the spatial dimension using a spatial recurrent neural network model. The methodology is demonstrated for an operational meteorological drought index the Standardized Precipitation Index (SPI) calculated at multiple timescales. 48 precipitation stations and 18 independent precipitation stations, located at Pinios river basin in Thessaly region, Greece, were used for the development and spatiotemporal validation of the hybrid spatiotemporal scheme. Several quantitative temporal and spatial statistical indices were considered for the performance evaluation of the models. Furthermore, qualitative statistical criteria based on contingency tables between observed and forecasted drought episodes were calculated. The results show that the lead time of forecasting for operational use depends on the SPI timescale. The hybrid spatiotemporal drought forecasting model could be operationally used for forecasting up to three months ahead for SPI short timescales (e.g. 3-6 months) up to six months ahead for large SPI timescales (e.g. 24 months). The above findings could be useful in developing a drought preparedness plan in the region.

Spatiotemporal resonances in mixing of open viscous fluids

DEFF Research Database (Denmark)

Okkels, Fridolin; Tabeling, Patrick

2004-01-01

In this Letter, we reveal a new dynamical phenomenon, called "spatiotemporal resonance," which is expected to take place in a broad range of viscous, periodically forced, open systems. The observation originates from a numerical and theoretical analysis of a micromixer, and is supported...

Spatiotemporal Data Mining: A Computational Perspective

Directory of Open Access Journals (Sweden)

Shashi Shekhar

2015-10-01

Full Text Available Explosive growth in geospatial and temporal data as well as the emergence of new technologies emphasize the need for automated discovery of spatiotemporal knowledge. Spatiotemporal data mining studies the process of discovering interesting and previously unknown, but potentially useful patterns from large spatiotemporal databases. It has broad application domains including ecology and environmental management, public safety, transportation, earth science, epidemiology, and climatology. The complexity of spatiotemporal data and intrinsic relationships limits the usefulness of conventional data science techniques for extracting spatiotemporal patterns. In this survey, we review recent computational techniques and tools in spatiotemporal data mining, focusing on several major pattern families: spatiotemporal outlier, spatiotemporal coupling and tele-coupling, spatiotemporal prediction, spatiotemporal partitioning and summarization, spatiotemporal hotspots, and change detection. Compared with other surveys in the literature, this paper emphasizes the statistical foundations of spatiotemporal data mining and provides comprehensive coverage of computational approaches for various pattern families. ISPRS Int. J. Geo-Inf. 2015, 4 2307 We also list popular software tools for spatiotemporal data analysis. The survey concludes with a look at future research needs.

Spatio-temporal dynamics of ocean conditions and forage taxa reveal regional structuring of seabird–prey relationships.

Science.gov (United States)

Santora, Jarrod A; Schroeder, Isaac D; Field, John C; Wells, Brian K; Sydeman, William J

Studies of predator–prey demographic responses and the physical drivers of such relationships are rare, yet essential for predicting future changes in the structure and dynamics of marine ecosystems. Here, we hypothesize that predator–prey relationships vary spatially in association with underlying physical ocean conditions, leading to observable changes in demographic rates, such as reproduction. To test this hypothesis, we quantified spatio-temporal variability in hydrographic conditions, krill, and forage fish to model predator (seabird) demographic responses over 18 years (1990–2007). We used principal component analysis and spatial correlation maps to assess coherence among ocean conditions, krill, and forage fish, and generalized additive models to quantify interannual variability in seabird breeding success relative to prey abundance. The first principal component of four hydrographic measurements yielded an index that partitioned “warm/weak upwelling” and “cool/strong upwelling” years. Partitioning of krill and forage fish time series among shelf and oceanic regions yielded spatially explicit indicators of prey availability. Krill abundance within the oceanic region was remarkably consistent between years, whereas krill over the shelf showed marked interannual fluctuations in relation to ocean conditions. Anchovy abundance varied on the shelf, and was greater in years of strong stratification, weak upwelling and warmer temperatures. Spatio-temporal variability of juvenile forage fish co-varied strongly with each other and with krill, but was weakly correlated with hydrographic conditions. Demographic responses between seabirds and prey availability revealed spatially variable associations indicative of the dynamic nature of “predator–habitat” relationships. Quantification of spatially explicit demographic responses, and their variability through time, demonstrate the possibility of delineating specific critical areas where the

Analysis of Relations between Spatiotemporal Movement Regulation and Performance of Discrete Actions Reveals Functionality in Skilled Climbing

Directory of Open Access Journals (Sweden)

Dominic Orth

2017-10-01

Full Text Available In this review of research on climbing expertise, we focus on different measures of climbing performance, including spatiotemporal measures related to fluency and activity states (i.e., discrete actions, adopted by climbers for achieving overall performance goals of getting to the end of a route efficiently and safely. Currently, a broad range of variables have been reported, however, many of these fail to capture how climbers adapt to a route whilst climbing. We argue that spatiotemporal measures should be considered concurrently with evaluation of activity states (such as reaching or exploring in order gain a more comprehensive picture of how climbers successfully adapt to a route. Spatial and temporal movement measures taken at the hip are a traditional means of assessing efficiency of climbing behaviors. More recently, performatory and exploratory actions of the limbs have been used in combination with spatiotemporal indicators, highlighting the influence of limb states on climbing efficiency and skill transfer. However, only a few studies have attempted to combine spatiotemporal and activity state measures taken during route climbing. This review brings together existing approaches for observing climbing skill at performance outcome (i.e., spatiotemporal assessments and process (i.e., limb activity states levels of analysis. Skill level is associated with a spatially efficient route progression and lower levels of immobility. However, more difficult hold architecture designs require significantly greater mobility and more complex movement patterning to maintain performance. Different forms of functional, or goal-supportive, movement variability, including active recovery and hold exploration, have been implicated as important adaptations to physiological and environmental dynamics that emerge during the act of climbing. Indeed, recently it has also been shown that, when climbing on new routes, efficient exploration can improve the transfer

Spatiotemporal drought forecasting using nonlinear models

Science.gov (United States)

Vasiliades, Lampros; Loukas, Athanasios

2010-05-01

Spatiotemporal data mining is the extraction of unknown and implicit knowledge, structures, spatiotemporal relationships, or patterns not explicitly stored in spatiotemporal databases. As one of data mining techniques, forecasting is widely used to predict the unknown future based upon the patterns hidden in the current and past data. In order to achieve spatiotemporal forecasting, some mature analysis tools, e.g., time series and spatial statistics are extended to the spatial dimension and the temporal dimension, respectively. Drought forecasting plays an important role in the planning and management of natural resources and water resource systems in a river basin. Early and timelines forecasting of a drought event can help to take proactive measures and set out drought mitigation strategies to alleviate the impacts of drought. Despite the widespread application of nonlinear mathematical models, comparative studies on spatiotemporal drought forecasting using different models are still a huge task for modellers. This study uses a promising approach, the Gamma Test (GT), to select the input variables and the training data length, so that the trial and error workload could be greatly reduced. The GT enables to quickly evaluate and estimate the best mean squared error that can be achieved by a smooth model on any unseen data for a given selection of inputs, prior to model construction. The GT is applied to forecast droughts using monthly Standardized Precipitation Index (SPI) timeseries at multiple timescales in several precipitation stations at Pinios river basin in Thessaly region, Greece. Several nonlinear models have been developed efficiently, with the aid of the GT, for 1-month up to 12-month ahead forecasting. Several temporal and spatial statistical indices were considered for the performance evaluation of the models. The predicted results show reasonably good agreement with the actual data for short lead times, whereas the forecasting accuracy decreases with

The influence of spatiotemporal structure of noisy stimuli in decision making.

Science.gov (United States)

Insabato, Andrea; Dempere-Marco, Laura; Pannunzi, Mario; Deco, Gustavo; Romo, Ranulfo

2014-04-01

Decision making is a process of utmost importance in our daily lives, the study of which has been receiving notable attention for decades. Nevertheless, the neural mechanisms underlying decision making are still not fully understood. Computational modeling has revealed itself as a valuable asset to address some of the fundamental questions. Biophysically plausible models, in particular, are useful in bridging the different levels of description that experimental studies provide, from the neural spiking activity recorded at the cellular level to the performance reported at the behavioral level. In this article, we have reviewed some of the recent progress made in the understanding of the neural mechanisms that underlie decision making. We have performed a critical evaluation of the available results and address, from a computational perspective, aspects of both experimentation and modeling that so far have eluded comprehension. To guide the discussion, we have selected a central theme which revolves around the following question: how does the spatiotemporal structure of sensory stimuli affect the perceptual decision-making process? This question is a timely one as several issues that still remain unresolved stem from this central theme. These include: (i) the role of spatiotemporal input fluctuations in perceptual decision making, (ii) how to extend the current results and models derived from two-alternative choice studies to scenarios with multiple competing evidences, and (iii) to establish whether different types of spatiotemporal input fluctuations affect decision-making outcomes in distinctive ways. And although we have restricted our discussion mostly to visual decisions, our main conclusions are arguably generalizable; hence, their possible extension to other sensory modalities is one of the points in our discussion.

Secondary Instabilities and Spatiotemporal Chaos in Parametric Surface Waves

International Nuclear Information System (INIS)

Zhang, W.; Vinals, J.

1995-01-01

A 2D model is introduced to study the onset of parametric surface waves, their secondary instabilities, and the transition to spatiotemporal chaos. We obtain the stability boundary of a periodic standing wave above onset against Eckhaus, zigzag, and transverse amplitude modulations (TAM), as a function of the control parameter var-epsilon and the wavelength of the pattern. The Eckhaus and TAM boundaries cross at a finite value of var-epsilon, thus explaining the finite threshold for the TAM observed experimentally. At larger values of var-epsilon, a numerical solution reveals a transition to spatiotemporal chaotic states mediated by the TAM instability

Single Turnover at Molecular Polymerization Catalysts Reveals Spatiotemporally Resolved Reactions.

Science.gov (United States)

Easter, Quinn T; Blum, Suzanne A

2017-10-23

Multiple active individual molecular ruthenium catalysts have been pinpointed within growing polynorbornene, thereby revealing information on the reaction dynamics and location that is unavailable through traditional ensemble experiments. This is the first single-turnover imaging of a molecular catalyst by fluorescence microscopy and allows detection of individual monomer reactions at an industrially important molecular ruthenium ring-opening metathesis polymerization (ROMP) catalyst under synthetically relevant conditions (e.g. unmodified industrial catalyst, ambient pressure, condensed phase, ca. 0.03 m monomer). These results further establish the key fundamentals of this imaging technique for characterizing the reactivity and location of active molecular catalysts even when they are the minor components. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

An evaluation of space time cube representation of spatiotemporal patterns.

Science.gov (United States)

Kristensson, Per Ola; Dahlbäck, Nils; Anundi, Daniel; Björnstad, Marius; Gillberg, Hanna; Haraldsson, Jonas; Mårtensson, Ingrid; Nordvall, Mathias; Ståhl, Josefine

2009-01-01

Space time cube representation is an information visualization technique where spatiotemporal data points are mapped into a cube. Information visualization researchers have previously argued that space time cube representation is beneficial in revealing complex spatiotemporal patterns in a data set to users. The argument is based on the fact that both time and spatial information are displayed simultaneously to users, an effect difficult to achieve in other representations. However, to our knowledge the actual usefulness of space time cube representation in conveying complex spatiotemporal patterns to users has not been empirically validated. To fill this gap, we report on a between-subjects experiment comparing novice users' error rates and response times when answering a set of questions using either space time cube or a baseline 2D representation. For some simple questions, the error rates were lower when using the baseline representation. For complex questions where the participants needed an overall understanding of the spatiotemporal structure of the data set, the space time cube representation resulted in on average twice as fast response times with no difference in error rates compared to the baseline. These results provide an empirical foundation for the hypothesis that space time cube representation benefits users analyzing complex spatiotemporal patterns.

Spatiotemporal representation of cardiac vectorcardiogram (VCG signals

Directory of Open Access Journals (Sweden)

Yang Hui

2012-03-01

Full Text Available Abstract Background Vectorcardiogram (VCG signals monitor both spatial and temporal cardiac electrical activities along three orthogonal planes of the body. However, the absence of spatiotemporal resolution in conventional VCG representations is a major impediment for medical interpretation and clinical usage of VCG. This is especially so because time-domain features of 12-lead ECG, instead of both spatial and temporal characteristics of VCG, are widely used for the automatic assessment of cardiac pathological patterns. Materials and methods We present a novel representation approach that captures critical spatiotemporal heart dynamics by displaying the real time motion of VCG cardiac vectors in a 3D space. Such a dynamic display can also be realized with only one lead ECG signal (e.g., ambulatory ECG through an alternative lag-reconstructed ECG representation from nonlinear dynamics principles. Furthermore, the trajectories are color coded with additional dynamical properties of space-time VCG signals, e.g., the curvature, speed, octant and phase angles to enhance the information visibility. Results In this investigation, spatiotemporal VCG signal representation is used to characterize various spatiotemporal pathological patterns for healthy control (HC, myocardial infarction (MI, atrial fibrillation (AF and bundle branch block (BBB. The proposed color coding scheme revealed that the spatial locations of the peak of T waves are in the Octant 6 for the majority (i.e., 74 out of 80 of healthy recordings in the PhysioNet PTB database. In contrast, the peak of T waves from 31.79% (117/368 of MI subjects are found to remain in Octant 6 and the rest (68.21% spread over all other octants. The spatiotemporal VCG signal representation is shown to capture the same important heart characteristics as the 12-lead ECG plots and more. Conclusions Spatiotemporal VCG signal representation is shown to facilitate the characterization of space-time cardiac

The spatiotemporal pattern of Src activation at lipid rafts revealed by diffusion-corrected FRET imaging.

Directory of Open Access Journals (Sweden)

Shaoying Lu

2008-07-01

Full Text Available Genetically encoded biosensors based on fluorescence resonance energy transfer (FRET have been widely applied to visualize the molecular activity in live cells with high spatiotemporal resolution. However, the rapid diffusion of biosensor proteins hinders a precise reconstruction of the actual molecular activation map. Based on fluorescence recovery after photobleaching (FRAP experiments, we have developed a finite element (FE method to analyze, simulate, and subtract the diffusion effect of mobile biosensors. This method has been applied to analyze the mobility of Src FRET biosensors engineered to reside at different subcompartments in live cells. The results indicate that the Src biosensor located in the cytoplasm moves 4-8 folds faster (0.93+/-0.06 microm(2/sec than those anchored on different compartments in plasma membrane (at lipid raft: 0.11+/-0.01 microm(2/sec and outside: 0.18+/-0.02 microm(2/sec. The mobility of biosensor at lipid rafts is slower than that outside of lipid rafts and is dominated by two-dimensional diffusion. When this diffusion effect was subtracted from the FRET ratio images, high Src activity at lipid rafts was observed at clustered regions proximal to the cell periphery, which remained relatively stationary upon epidermal growth factor (EGF stimulation. This result suggests that EGF induced a Src activation at lipid rafts with well-coordinated spatiotemporal patterns. Our FE-based method also provides an integrated platform of image analysis for studying molecular mobility and reconstructing the spatiotemporal activation maps of signaling molecules in live cells.

Mortality in Danish Swine herds: Spatio-temporal clusters and risk factors

DEFF Research Database (Denmark)

Lopes Antunes, Ana Carolina; Ersbøll, Annette Kjær; Bihrmann, Kristine

2017-01-01

-temporal analysis included data description for spatial, temporal, and spatio-temporal cluster analysis for three age groups: weaners (up to 30 kg), sows and finishers. Logistic regression models were used to assess the potential factors associated with finisher and weaner herds being included within multiple...

Spatiotemporal Data Organization and Application Research

Science.gov (United States)

Tan, C.; Yan, S.

2017-09-01

Organization and management of spatiotemporal data is a key support technology for intelligence in all fields of the smart city. The construction of a smart city cannot be realized without spatiotemporal data. Oriented to support intelligent applications this paper proposes an organizational model for spatiotemporal data, and details the construction of a spatiotemporal big data calculation, analysis, and service framework for highly efficient management and intelligent application of spatiotemporal data for the entire data life cycle.

High Resolution Spatiotemporal Climate Reconstruction and Variability in East Asia during Little Ice Age

Science.gov (United States)

Lin, K. H. E.; Wang, P. K.; Lee, S. Y.; Liao, Y. C.; Fan, I. C.; Liao, H. M.

2017-12-01

The Little ice Age (LIA) is one of the most prominent epochs in paleoclimate reconstruction of the Common Era. While the signals of LIA were generally discovered across hemispheres, wide arrays of regional variability were found, and the reconstructed anomalies were sometimes inconsistent across studies by using various proxy data or historical records. This inconsistency is mainly attributed to limited data coverage at fine resolution that can assist high-resolution climate reconstruction in the continuous spatiotemporal trends. Qing dynasty (1644-1911 CE) of China existed in the coldest period of LIA. Owing to a long-standing tradition that acquired local officials to record odds and social or meteorological events, thousands of local chronicles were left. Zhang eds. (2004) took two decades to compile all these meteorological records in a compendium, for which we then digitized and coded all records into our REACHS database system for reconstructing climate. There were in total 1,435 points (sites) in our database for over 80,000 events in the period of time. After implementing two-rounds coding check for data quality control (accuracy rate 87.2%), multiple indexes were retrieved for reconstructing annually and seasonally resolved temperature and precipitation series for North, Central, and South China. The reconstruction methods include frequency count and grading, with usage of multiple regression models to test sensitivity and to calculate correlations among several reconstructed series. Validation was also conducted through comparison with instrumental data and with other reconstructed series in previous studies. Major research results reveal interannual (3-5 years), decadal (8-12 years), and interdecadal (≈30 years) variabilities with strong regional expressions across East China. Cooling effect was not homogenously distributed in space and time. Flood and drought conditions frequently repeated but the spatiotemporal pattern was variant, indicating likely

Emergent dynamics of spatio-temporal chaos in a heterogeneous excitable medium.

Science.gov (United States)

Bittihn, Philip; Berg, Sebastian; Parlitz, Ulrich; Luther, Stefan

2017-09-01

Self-organized activation patterns in excitable media such as spiral waves and spatio-temporal chaos underlie dangerous cardiac arrhythmias. While the interaction of single spiral waves with different types of heterogeneity has been studied extensively, the effect of heterogeneity on fully developed spatio-temporal chaos remains poorly understood. We investigate how the complexity and stability properties of spatio-temporal chaos in the Bär-Eiswirth model of excitable media depend on the heterogeneity of the underlying medium. We employ different measures characterizing the chaoticity of the system and find that the spatial arrangement of multiple discrete lower excitability regions has a strong impact on the complexity of the dynamics. Varying the number, shape, and spatial arrangement of the heterogeneities, we observe strong emergent effects ranging from increases in chaoticity to the complete cessation of chaos, contrasting the expectation from the homogeneous behavior. The implications of our findings for the development and treatment of arrhythmias in the heterogeneous cardiac muscle are discussed.

Neurog1 Genetic Inducible Fate Mapping (GIFM) Reveals the Existence of Complex Spatiotemporal Cyto-Architectures in the Developing Cerebellum.

Science.gov (United States)

Obana, Edwin A; Lundell, Travis G; Yi, Kevin J; Radomski, Kryslaine L; Zhou, Qiong; Doughty, Martin L

2015-06-01

Neurog1 is a pro-neural basic helix-loop-helix (bHLH) transcription factor expressed in progenitor cells located in the ventricular zone and subsequently the presumptive white matter tracts of the developing mouse cerebellum. We used genetic inducible fate mapping (GIFM) with a transgenic Neurog1-CreER allele to characterize the contributions of Neurog1 lineages to cerebellar circuit formation in mice. GIFM reveals Neurog1-expressing progenitors are fate-mapped to become Purkinje cells and all GABAergic interneuron cell types of the cerebellar cortex but not glia. The spatiotemporal sequence of GIFM is unique to each neuronal cell type. GIFM on embryonic days (E) 10.5 to E12.5 labels Purkinje cells with different medial-lateral settling patterns depending on the day of tamoxifen delivery. GIFM on E11.5 to P7 labels interneurons and the timing of tamoxifen administration correlates with the final inside-to-outside resting position of GABAergic interneurons in the cerebellar cortex. Proliferative status and long-term BrdU retention of GIFM lineages reveals Purkinje cells express Neurog1 around the time they become post-mitotic. In contrast, GIFM labels mitotic and post-mitotic interneurons. Neurog1-CreER GIFM reveals a correlation between the timing of Neurog1 expression and the spatial organization of GABAergic neurons in the cerebellar cortex with possible implications for cerebellar circuit assembly.

A spatiotemporal mixed model to assess the influence of environmental and socioeconomic factors on the incidence of hand, foot and mouth disease

Directory of Open Access Journals (Sweden)

Lianfa Li

2018-02-01

Full Text Available Abstract Background As a common infectious disease, hand, foot and mouth disease (HFMD is affected by multiple environmental and socioeconomic factors, and its pathogenesis is complex. Furthermore, the transmission of HFMD is characterized by strong spatial clustering and autocorrelation, and the classical statistical approach may be biased without consideration of spatial autocorrelation. In this paper, we propose to embed spatial characteristics into a spatiotemporal additive model to improve HFMD incidence assessment. Methods Using incidence data (6439 samples from 137 monitoring district for Shandong Province, China, along with meteorological, environmental and socioeconomic spatial and spatiotemporal covariate data, we proposed a spatiotemporal mixed model to estimate HFMD incidence. Geo-additive regression was used to model the non-linear effects of the covariates on the incidence risk of HFMD in univariate and multivariate models. Furthermore, the spatial effect was constructed to capture spatial autocorrelation at the sub-regional scale, and clusters (hotspots of high risk were generated using spatiotemporal scanning statistics as a predictor. Linear and non-linear effects were compared to illustrate the usefulness of non-linear associations. Patterns of spatial effects and clusters were explored to illustrate the variation of the HFMD incidence across geographical sub-regions. To validate our approach, 10-fold cross-validation was conducted. Results The results showed that there were significant non-linear associations of the temporal index, spatiotemporal meteorological factors and spatial environmental and socioeconomic factors with HFMD incidence. Furthermore, there were strong spatial autocorrelation and clusters for the HFMD incidence. Spatiotemporal meteorological parameters, the normalized difference vegetation index (NDVI, the temporal index, spatiotemporal clustering and spatial effects played important roles as predictors in

Zebrafish con/disp1 reveals multiple spatiotemporal requirements for Hedgehog-signaling in craniofacial development

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Schwend Tyler

2009-11-01

Full Text Available Abstract Background The vertebrate head skeleton is derived largely from cranial neural crest cells (CNCC. Genetic studies in zebrafish and mice have established that the Hedgehog (Hh-signaling pathway plays a critical role in craniofacial development, partly due to the pathway's role in CNCC development. Disruption of the Hh-signaling pathway in humans can lead to the spectral disorder of Holoprosencephaly (HPE, which is often characterized by a variety of craniofacial defects including midline facial clefting and cyclopia 12. Previous work has uncovered a role for Hh-signaling in zebrafish dorsal neurocranium patterning and chondrogenesis, however Hh-signaling mutants have not been described with respect to the ventral pharyngeal arch (PA skeleton. Lipid-modified Hh-ligands require the transmembrane-spanning receptor Dispatched 1 (Disp1 for proper secretion from Hh-synthesizing cells to the extracellular field where they act on target cells. Here we study chameleon mutants, lacking a functional disp1(con/disp1. Results con/disp1 mutants display reduced and dysmorphic mandibular and hyoid arch cartilages and lack all ceratobranchial cartilage elements. CNCC specification and migration into the PA primorida occurs normally in con/disp1 mutants, however disp1 is necessary for post-migratory CNCC patterning and differentiation. We show that disp1 is required for post-migratory CNCC to become properly patterned within the first arch, while the gene is dispensable for CNCC condensation and patterning in more posterior arches. Upon residing in well-formed pharyngeal epithelium, neural crest condensations in the posterior PA fail to maintain expression of two transcription factors essential for chondrogenesis, sox9a and dlx2a, yet continue to robustly express other neural crest markers. Histology reveals that posterior arch residing-CNCC differentiate into fibrous-connective tissue, rather than becoming chondrocytes. Treatments with Cyclopamine, to

Spatio-temporal analyses of impacts of multiple climatic hazards in a savannah ecosystem of Ghana

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Gerald A.B. Yiran, PhD

2016-01-01

Full Text Available Ghana’s savannah ecosystem has been subjected to a number of climatic hazards of varying severity. This paper presents a spatial, time-series analysis of the impacts of multiple hazards on the ecosystem and human livelihoods over the period 1983–2012, using the Upper East Region of Ghana as a case study. Our aim is to understand the nature of hazards (their frequency, magnitude and duration and how they cumulatively affect humans. Primary data were collected using questionnaires, focus group discussions, in-depth interviews and personal observations. Secondary data were collected from documents and reports. Calculations of the standard precipitation index (SPI and crop failure index used rainfall data from 4 weather stations (Manga, Binduri, Vea and Navrongo and crop yield data of 5 major crops (maize, sorghum, millet, rice and groundnuts respectively. Temperature and windstorms were analysed from the observed weather data. We found that temperatures were consistently high and increasing. From the SPI, drought frequency varied spatially from 9 at Binduri to 13 occurrences at Vea; dry spells occurred at least twice every year and floods occurred about 6 times on average, with slight spatial variations, during 1988–2012, a period with consistent data from all stations. Impacts from each hazard varied spatio-temporally. Within the study period, more 70% of years recorded severe crop losses with greater impacts when droughts and floods occur in the same year, especially in low lying areas. The effects of crop losses were higher in districts with no/little irrigation (Talensi, Nabdam, Garu-Tempane, Kassena-Nankana East. Frequency and severity of diseases and sicknesses such as cerebrospinal meningitis, heat rashes, headaches and malaria related to both dry and wet conditions have increased steadily over time. Other impacts recorded with spatio-temporal variations included destruction to housing, displacement, injury and death of people. These

Spatio-temporal flow maps for visualizing movement and contact patterns

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Bing Ni

2017-03-01

Full Text Available The advanced telecom technologies and massive volumes of intelligent mobile phone users have yielded a huge amount of real-time data of people’s all-in-one telecommunication records, which we call telco big data. With telco data and the domain knowledge of an urban city, we are now able to analyze the movement and contact patterns of humans in an unprecedented scale. Flow map is widely used to display the movements of humans from one single source to multiple destinations by representing locations as nodes and movements as edges. However, it fails the task of visualizing both movement and contact data. In addition, analysts often need to compare and examine the patterns side by side, and do various quantitative analysis. In this work, we propose a novel spatio-temporal flow map layout to visualize when and where people from different locations move into the same places and make contact. We also propose integrating the spatiotemporal flow maps into existing spatiotemporal visualization techniques to form a suite of techniques for visualizing the movement and contact patterns. We report a potential application the proposed techniques can be applied to. The results show that our design and techniques properly unveil hidden information, while analysis can be achieved efficiently. Keywords: Spatio-temporal data, Flow map, Urban mobility

Spatiotemporal mapping of ground water pollution in a Greek lignite basin, using geostatistics

Energy Technology Data Exchange (ETDEWEB)

Modis, K. [National Technical Univ. of Athens, Athens (Greece)

2010-07-01

An issue of significant interest in the mining industry in Greece is the occurrence of chemical pollutants in ground water. Ammonium, nitrites and nitrates concentrations have been monitored through an extensive sampling network in the Ptolemais lignite opencast mining area in Greece. Due to intensive mining efforts in the area, the surface topology is continuously altered, affecting the life span of the water boreholes and resulting in messy spatiotemporal distribution of data. This paper discussed the spatiotemporal mapping of ground water pollution in the Ptolemais lignite basin, using geostatistics. More specifically, the spatiotemporal distribution of ground water contamination was examined by the application of the bayesian maximum entropy theory which allows merging spatial and temporal estimations in a single model. The paper provided a description of the site and discussed the materials and methods, including samples and statistics; variography; and spatiotemporal mapping. It was concluded that in the case of the Ptolemais mining area, results revealed an underlying average yearly variation pattern of pollutant concentrations. Inspection of the produced spatiotemporal maps demonstrated a continuous increase in the risk of ammonium contamination, while risk for the other two pollutants appeared in hot spots. 18 refs., 1 tab., 7 figs.

Spatiotemporal mapping of ground water pollution in a Greek lignite basin, using geostatistics

International Nuclear Information System (INIS)

Modis, K.

2010-01-01

An issue of significant interest in the mining industry in Greece is the occurrence of chemical pollutants in ground water. Ammonium, nitrites and nitrates concentrations have been monitored through an extensive sampling network in the Ptolemais lignite opencast mining area in Greece. Due to intensive mining efforts in the area, the surface topology is continuously altered, affecting the life span of the water boreholes and resulting in messy spatiotemporal distribution of data. This paper discussed the spatiotemporal mapping of ground water pollution in the Ptolemais lignite basin, using geostatistics. More specifically, the spatiotemporal distribution of ground water contamination was examined by the application of the bayesian maximum entropy theory which allows merging spatial and temporal estimations in a single model. The paper provided a description of the site and discussed the materials and methods, including samples and statistics; variography; and spatiotemporal mapping. It was concluded that in the case of the Ptolemais mining area, results revealed an underlying average yearly variation pattern of pollutant concentrations. Inspection of the produced spatiotemporal maps demonstrated a continuous increase in the risk of ammonium contamination, while risk for the other two pollutants appeared in hot spots. 18 refs., 1 tab., 7 figs.

Characteristics of juvenile survivors reveal spatio-temporal differences in early life stage survival of Baltic cod

DEFF Research Database (Denmark)

Huwer, Bastian; Hinrichsen, H.H.; Böttcher, U.

2014-01-01

with previous modeling studies on the survival chances of early-stage larvae and with general spatio-temporal patterns of larval prey availability suggests that differences in survival are related to food availability during the early larval stage. Results are discussed in relation to the recruitment process...

Spatiotemporal Modeling of Community Risk

Science.gov (United States)

2016-03-01

Ertugay, and Sebnem Duzgun, “Exploratory and Inferential Methods for Spatio-Temporal Analysis of Residential Fire Clustering in Urban Areas,” Fire ...response in communities.”26 In “Exploratory and Inferential Methods for Spatio-temporal Analysis of Residential Fire Clustering in Urban Areas,” Ceyhan...of fire resources spread across the community. Spatiotemporal modeling shows that actualized risk is dynamic and relatively patterned. Though

Structured spatio-temporal shot-noise Cox point process models, with a view to modelling forest fires

DEFF Research Database (Denmark)

Møller, Jesper; Diaz-Avalos, Carlos

Spatio-temporal Cox point process models with a multiplicative structure for the driving random intensity, incorporating covariate information into temporal and spatial components, and with a residual term modelled by a shot-noise process, are considered. Such models are flexible and tractable fo...... dataset consisting of 2796 days and 5834 spatial locations of fires. The model is compared with a spatio-temporal log-Gaussian Cox point process model, and likelihood-based methods are discussed to some extent....

Structured Spatio-temporal shot-noise Cox point process models, with a view to modelling forest fires

DEFF Research Database (Denmark)

Møller, Jesper; Diaz-Avalos, Carlos

2010-01-01

Spatio-temporal Cox point process models with a multiplicative structure for the driving random intensity, incorporating covariate information into temporal and spatial components, and with a residual term modelled by a shot-noise process, are considered. Such models are flexible and tractable fo...... data set consisting of 2796 days and 5834 spatial locations of fires. The model is compared with a spatio-temporal log-Gaussian Cox point process model, and likelihood-based methods are discussed to some extent....

Transient spatiotemporal chaos in the Morris-Lecar neuronal ring network

Energy Technology Data Exchange (ETDEWEB)

Keplinger, Keegan, E-mail: keegankeplinger@gmail.com; Wackerbauer, Renate, E-mail: rawackerbauer@alaska.edu [Department of Physics, University of Alaska, Fairbanks, Alaska 99775-5920 (United States)

2014-03-15

Transient behavior is thought to play an integral role in brain functionality. Numerical simulations of the firing activity of diffusively coupled, excitable Morris-Lecar neurons reveal transient spatiotemporal chaos in the parameter regime below the saddle-node on invariant circle bifurcation point. The neighborhood of the chaotic saddle is reached through perturbations of the rest state, in which few initially active neurons at an effective spatial distance can initiate spatiotemporal chaos. The system escapes from the neighborhood of the chaotic saddle to either the rest state or to a state of pulse propagation. The lifetime of the chaotic transients is manipulated in a statistical sense through a singular application of a synchronous perturbation to a group of neurons.

Transient spatiotemporal chaos in the Morris-Lecar neuronal ring network.

Science.gov (United States)

Keplinger, Keegan; Wackerbauer, Renate

2014-03-01

Transient behavior is thought to play an integral role in brain functionality. Numerical simulations of the firing activity of diffusively coupled, excitable Morris-Lecar neurons reveal transient spatiotemporal chaos in the parameter regime below the saddle-node on invariant circle bifurcation point. The neighborhood of the chaotic saddle is reached through perturbations of the rest state, in which few initially active neurons at an effective spatial distance can initiate spatiotemporal chaos. The system escapes from the neighborhood of the chaotic saddle to either the rest state or to a state of pulse propagation. The lifetime of the chaotic transients is manipulated in a statistical sense through a singular application of a synchronous perturbation to a group of neurons.

Spatiotemporal chaos from bursting dynamics

International Nuclear Information System (INIS)

Berenstein, Igal; De Decker, Yannick

2015-01-01

In this paper, we study the emergence of spatiotemporal chaos from mixed-mode oscillations, by using an extended Oregonator model. We show that bursting dynamics consisting of fast/slow mixed mode oscillations along a single attractor can lead to spatiotemporal chaotic dynamics, although the spatially homogeneous solution is itself non-chaotic. This behavior is observed far from the Hopf bifurcation and takes the form of a spatiotemporal intermittency where the system locally alternates between the fast and the slow phases of the mixed mode oscillations. We expect this form of spatiotemporal chaos to be generic for models in which one or several slow variables are coupled to activator-inhibitor type of oscillators

Elimination of spiral waves and spatiotemporal chaos by the pulse with a specific spatiotemporal configuration

International Nuclear Information System (INIS)

Yuan Guoyong; Yang Shiping; Wang Guangrui; Chen Shigang

2008-01-01

Spiral waves and spatiotemporal chaos are sometimes harmful and should be controlled. In this paper spiral waves and spatiotemporal chaos are successfully eliminated by the pulse with a very specific spatiotemporal configuration. The excited position D of spiral waves or spatiotemporal chaos is first recorded at an arbitrary time (t 0 ). When the system at the domain D enters a recovering state, the external pulse is injected into the domain. If the intensity and the working time of the pulse are appropriate, spiral waves and spatiotemporal chaos can finally be eliminated because counter-directional waves can be generated by the pulse. There are two advantages in the method. One is that the tip can be quickly eliminated together with the body of spiral wave, and the other is that the injected pulse may be weak and the duration can be very short so that the original system is nearly not affected, which is important for practical applications

Tensor-based spatiotemporal saliency detection

Science.gov (United States)

Dou, Hao; Li, Bin; Deng, Qianqian; Zhang, LiRui; Pan, Zhihong; Tian, Jinwen

2018-03-01

This paper proposes an effective tensor-based spatiotemporal saliency computation model for saliency detection in videos. First, we construct the tensor representation of video frames. Then, the spatiotemporal saliency can be directly computed by the tensor distance between different tensors, which can preserve the complete temporal and spatial structure information of object in the spatiotemporal domain. Experimental results demonstrate that our method can achieve encouraging performance in comparison with the state-of-the-art methods.

Bayesian spatio-temporal analysis and geospatial risk factors of human monocytic ehrlichiosis.

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Ram K Raghavan

Full Text Available Variations in spatio-temporal patterns of Human Monocytic Ehrlichiosis (HME infection in the state of Kansas, USA were examined and the relationship between HME relative risk and various environmental, climatic and socio-economic variables were evaluated. HME data used in the study was reported to the Kansas Department of Health and Environment between years 2005-2012, and geospatial variables representing the physical environment [National Land cover/Land use, NASA Moderate Resolution Imaging Spectroradiometer (MODIS], climate [NASA MODIS, Prediction of Worldwide Renewable Energy (POWER], and socio-economic conditions (US Census Bureau were derived from publicly available sources. Following univariate screening of candidate variables using logistic regressions, two Bayesian hierarchical models were fit; a partial spatio-temporal model with random effects and a spatio-temporal interaction term, and a second model that included additional covariate terms. The best fitting model revealed that spatio-temporal autocorrelation in Kansas increased steadily from 2005-2012, and identified poverty status, relative humidity, and an interactive factor, 'diurnal temperature range x mixed forest area' as significant county-level risk factors for HME. The identification of significant spatio-temporal pattern and new risk factors are important in the context of HME prevention, for future research in the areas of ecology and evolution of HME, and as well as climate change impacts on tick-borne diseases.

Spatiotemporal and plantar pressure patterns of 1000 healthy individuals aged 3-101 years.

Science.gov (United States)

McKay, Marnee J; Baldwin, Jennifer N; Ferreira, Paulo; Simic, Milena; Vanicek, Natalie; Wojciechowski, Elizabeth; Mudge, Anita; Burns, Joshua

2017-10-01

The purpose of this study was to establish normative reference values for spatiotemporal and plantar pressure parameters, and to investigate the influence of demographic, anthropometric and physical characteristics. In 1000 healthy males and females aged 3-101 years, spatiotemporal and plantar pressure data were collected barefoot with the Zeno™ walkway and Emed ® platform. Correlograms were developed to visualise the relationships between widely reported spatiotemporal and pressure variables with demographic (age, gender), anthropometric (height, mass, waist circumference) and physical characteristics (ankle strength, ankle range of motion, vibration perception) in children aged 3-9 years, adolescents aged 10-19 years, adults aged 20-59 years and older adults aged over 60 years. A comprehensive catalogue of 31 spatiotemporal and pressure variables were generated from 1000 healthy individuals. The key findings were that gait velocity was stable during adolescence and adulthood, while children and older adults walked at a comparable slower speed. Peak pressures increased during childhood to older adulthood. Children demonstrated highest peak pressures beneath the rearfoot whilst adolescents, adults and older adults demonstrated highest pressures at the forefoot. Main factors influencing spatiotemporal and pressure parameters were: increased age, height, body mass and waist circumference, as well as ankle dorsiflexion and plantarflexion strength. This study has established whole of life normative reference values of widely used spatiotemporal and plantar pressure parameters, and revealed changes to be expected across the lifespan. Copyright © 2017 Elsevier B.V. All rights reserved.

Nonlinear Spatio-Temporal Dynamics and Chaos in Semiconductors

Science.gov (United States)

Schöll, Eckehard

2005-08-01

Nonlinear transport phenomena are an increasingly important aspect of modern semiconductor research. This volume deals with complex nonlinear dynamics, pattern formation, and chaotic behavior in such systems. It bridges the gap between two well-established fields: the theory of dynamic systems and nonlinear charge transport in semiconductors. This unified approach helps reveal important electronic transport instabilities. The initial chapters lay a general framework for the theoretical description of nonlinear self-organized spatio-temporal patterns, such as current filaments, field domains, fronts, and analysis of their stability. Later chapters consider important model systems in detail: impact ionization induced impurity breakdown, Hall instabilities, superlattices, and low-dimensional structures. State-of-the-art results include chaos control, spatio-temporal chaos, multistability, pattern selection, activator-inhibitor kinetics, and global coupling, linking fundamental issues to electronic device applications. This book will be of great value to semiconductor physicists and nonlinear scientists alike.

Monitoring and identification of spatiotemporal landscape changes in multiple remote sensing images by using a stratified conditional Latin hypercube sampling approach and geostatistical simulation.

Science.gov (United States)

Lin, Yu-Pin; Chu, Hone-Jay; Huang, Yu-Long; Tang, Chia-Hsi; Rouhani, Shahrokh

2011-06-01

This study develops a stratified conditional Latin hypercube sampling (scLHS) approach for multiple, remotely sensed, normalized difference vegetation index (NDVI) images. The objective is to sample, monitor, and delineate spatiotemporal landscape changes, including spatial heterogeneity and variability, in a given area. The scLHS approach, which is based on the variance quadtree technique (VQT) and the conditional Latin hypercube sampling (cLHS) method, selects samples in order to delineate landscape changes from multiple NDVI images. The images are then mapped for calibration and validation by using sequential Gaussian simulation (SGS) with the scLHS selected samples. Spatial statistical results indicate that in terms of their statistical distribution, spatial distribution, and spatial variation, the statistics and variograms of the scLHS samples resemble those of multiple NDVI images more closely than those of cLHS and VQT samples. Moreover, the accuracy of simulated NDVI images based on SGS with scLHS samples is significantly better than that of simulated NDVI images based on SGS with cLHS samples and VQT samples, respectively. However, the proposed approach efficiently monitors the spatial characteristics of landscape changes, including the statistics, spatial variability, and heterogeneity of NDVI images. In addition, SGS with the scLHS samples effectively reproduces spatial patterns and landscape changes in multiple NDVI images.

Spatiotemporal fusion of multiple-satellite aerosol optical depth (AOD) products using Bayesian maximum entropy method

Science.gov (United States)

Tang, Qingxin; Bo, Yanchen; Zhu, Yuxin

2016-04-01

Merging multisensor aerosol optical depth (AOD) products is an effective way to produce more spatiotemporally complete and accurate AOD products. A spatiotemporal statistical data fusion framework based on a Bayesian maximum entropy (BME) method was developed for merging satellite AOD products in East Asia. The advantages of the presented merging framework are that it not only utilizes the spatiotemporal autocorrelations but also explicitly incorporates the uncertainties of the AOD products being merged. The satellite AOD products used for merging are the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 5.1 Level-2 AOD products (MOD04_L2) and the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Deep Blue Level 2 AOD products (SWDB_L2). The results show that the average completeness of the merged AOD data is 95.2%,which is significantly superior to the completeness of MOD04_L2 (22.9%) and SWDB_L2 (20.2%). By comparing the merged AOD to the Aerosol Robotic Network AOD records, the results show that the correlation coefficient (0.75), root-mean-square error (0.29), and mean bias (0.068) of the merged AOD are close to those (the correlation coefficient (0.82), root-mean-square error (0.19), and mean bias (0.059)) of the MODIS AOD. In the regions where both MODIS and SeaWiFS have valid observations, the accuracy of the merged AOD is higher than those of MODIS and SeaWiFS AODs. Even in regions where both MODIS and SeaWiFS AODs are missing, the accuracy of the merged AOD is also close to the accuracy of the regions where both MODIS and SeaWiFS have valid observations.

The influence of weather and lemmings on spatiotemporal variation in the abundance of multiple avian guilds in the arctic.

Directory of Open Access Journals (Sweden)

Barry G Robinson

Full Text Available Climate change is occurring more rapidly in the Arctic than other places in the world, which is likely to alter the distribution and abundance of migratory birds breeding there. A warming climate can provide benefits to birds by decreasing spring snow cover, but increases in the frequency of summer rainstorms, another product of climate change, may reduce foraging opportunities for insectivorous birds. Cyclic lemming populations in the Arctic also influence bird abundance because Arctic foxes begin consuming bird eggs when lemmings decline. The complex interaction between summer temperature, precipitation, and the lemming cycle hinder our ability to predict how Arctic-breeding birds will respond to climate change. The main objective of this study was to investigate the relationship between annual variation in weather, spring snow cover, lemming abundance and spatiotemporal variation in the abundance of multiple avian guilds in a tundra ecosystem in central Nunavut, Canada: songbirds, shorebirds, gulls, loons, and geese. We spatially stratified our study area based on vegetation productivity, terrain ruggedness, and freshwater abundance, and conducted distance sampling to estimate strata-specific densities of each guild during the summers of 2010-2012. We also monitored temperature, rainfall, spring snow cover, and lemming abundance each year. Spatial variation in bird abundance matched what was expected based on previous ecological knowledge, but weather and lemming abundance also significantly influenced the abundance of some guilds. In particular, songbirds were less abundant during the cool, wet summer with moderate snow cover, and shorebirds and gulls declined with lemming abundance. The abundance of geese did not vary over time, possibly because benefits created by moderate spring snow cover were offset by increased fox predation when lemmings were scarce. Our study provides an example of a simple way to monitor the correlation between

Multivariate spatiotemporal visualizations for mobile devices in Flyover Country

Science.gov (United States)

Loeffler, S.; Thorn, R.; Myrbo, A.; Roth, R.; Goring, S. J.; Williams, J.

2017-12-01

Visualizing and interacting with complex multivariate and spatiotemporal datasets on mobile devices is challenging due to their smaller screens, reduced processing power, and limited data connectivity. Pollen data require visualizing pollen assemblages spatially, temporally, and across multiple taxa to understand plant community dynamics through time. Drawing from cartography, information visualization, and paleoecology, we have created new mobile-first visualization techniques that represent multiple taxa across many sites and enable user interaction. Using pollen datasets from the Neotoma Paleoecology Database as a case study, the visualization techniques allow ecological patterns and trends to be quickly understood on a mobile device compared to traditional pollen diagrams and maps. This flexible visualization system can be used for datasets beyond pollen, with the only requirements being point-based localities and multiple variables changing through time or depth.

Uncertainty assessment of PM2.5 contamination mapping using spatiotemporal sequential indicator simulations and multi-temporal monitoring data

Science.gov (United States)

Yang, Yong; Christakos, George; Huang, Wei; Lin, Chengda; Fu, Peihong; Mei, Yang

2016-04-01

Because of the rapid economic growth in China, many regions are subjected to severe particulate matter pollution. Thus, improving the methods of determining the spatiotemporal distribution and uncertainty of air pollution can provide considerable benefits when developing risk assessments and environmental policies. The uncertainty assessment methods currently in use include the sequential indicator simulation (SIS) and indicator kriging techniques. However, these methods cannot be employed to assess multi-temporal data. In this work, a spatiotemporal sequential indicator simulation (STSIS) based on a non-separable spatiotemporal semivariogram model was used to assimilate multi-temporal data in the mapping and uncertainty assessment of PM2.5 distributions in a contaminated atmosphere. PM2.5 concentrations recorded throughout 2014 in Shandong Province, China were used as the experimental dataset. Based on the number of STSIS procedures, we assessed various types of mapping uncertainties, including single-location uncertainties over one day and multiple days and multi-location uncertainties over one day and multiple days. A comparison of the STSIS technique with the SIS technique indicate that a better performance was obtained with the STSIS method.

Rainfall spatiotemporal variability relation to wetlands hydroperiods

Science.gov (United States)

Serrano-Hidalgo, Carmen; Guardiola-Albert, Carolina; Fernandez-Naranjo, Nuria

2017-04-01

Doñana natural space (Southwestern Spain) is one of the largest protected wetlands in Europe. The wide marshes present in this natural space have such ecological value that this wetland has been declared a Ramsar reserve in 1982. Apart from the extensive marsh, there are also small lagoons and seasonally flooded areas which are likewise essential to maintain a wide variety of valuable habitats. Hydroperiod, the length of time each point remains flooded along an annual cycle, is a critical ecological parameter that shapes aquatic plants and animals distribution and determines available habitat for many of the living organisms in the marshes. Recently, there have been published two different works estimating the hydroperiod of Doñana lagoons with Landsat Time Series images (Cifuentes et al., 2015; Díaz-Delgado et al., 2016). In both works the flooding cycle hydroperiod in Doñana marshes reveals a flooding regime mainly driven by rainfall, evapotranspiration, topography and local hydrological management actions. The correlation found between rainfall and hydroperiod is studied differently in both works. While in one the rainfall is taken from one raingauge (Cifuentes et al., 2015), the one performed by Díaz-Delgado (2016) uses annual rainfall maps interpolated with the inverse of the distance method. The rainfall spatiotemporal variability in this area can be highly significant; however the amount of this importance has not been quantified at the moment. In the present work the geostatistical tool known as spatiotemporal variogram is used to study the rainfall spatiotemporal variability. The spacetime package implemented in R (Pebesma, 2012) facilities its computation from a high rainfall data base of more than 100 raingauges from 1950 to 2016. With the aid of these variograms the rainfall spatiotemporal variability is quantified. The principal aim of the present work is the study of the relation between the rainfall spatiotemporal variability and the

Mapping the spatiotemporal evolution of solute transport in articular cartilage explants reveals how cartilage recovers fluid within the contact area during sliding.

Science.gov (United States)

Graham, Brian T; Moore, Axel C; Burris, David L; Price, Christopher

2018-04-11

The interstitial fluid within articular cartilage shields the matrix from mechanical stresses, reduces friction and wear, enables biochemical processes, and transports solutes into and out of the avascular extracellular matrix. The balanced competition between fluid exudation and recovery under load is thus critical to the mechanical and biological functions of the tissue. We recently discovered that sliding alone can induce rapid solute transport into buried cartilage contact areas via a phenomenon termed tribological rehydration. In this study, we use in situ confocal microscopy measurements to track the spatiotemporal propagation of a small neutral solute into the buried contact area to clarify the fluid mechanics underlying the tribological rehydration phenomenon. Sliding experiments were interrupted by periodic static loading to enable scanning of the entire contact area. Spatiotemporal patterns of solute transport combined with tribological data suggested pressure driven flow through the extracellular matrix from the contact periphery rather than into the surface via a fluid film. Interestingly, these testing interruptions also revealed dynamic, repeatable and history-independent fluid loss and recovery processes consistent with those observed in vivo. Unlike the migrating contact area, which preserves hydration by moving faster than interstitial fluid can flow, our results demonstrate that the stationary contact area can maintain and actively recover hydration through a dynamic competition between load-induced exudation and sliding-induced recovery. The results demonstrate that sliding contributes to the recovery of fluid and solutes by cartilage within the contact area while clarifying the means by which it occurs. Copyright © 2018 Elsevier Ltd. All rights reserved.

The role of climate and socioeconomic factors on the spatiotemporal variability of cholera in Nigeria

Science.gov (United States)

Abdussalam, Auwal; Thornes, John; Leckebusch, Gregor

2015-04-01

Nigeria has a number of climate-sensitive infectious diseases; one of the most important of these diseases that remains a threat to public health is cholera. This study investigates the influences of both meteorological and socioeconomic factors on the spatiotemporal variability of cholera in Nigeria. A stepwise multiple regression models are used to estimate the influence of the year-to-year variations of cholera cases and deaths for individual states in the country and as well for three groups of states that are classified based on annual rainfall amount. Specifically, seasonal mean maximum and minimum temperatures and annual rainfall totals were analysed with annual aggregate count of cholera cases and deaths, taking into account of the socioeconomic factors that are potentially enhancing vulnerability such as: absolute poverty, adult literacy, access to pipe borne water and population density. Result reveals that the most important explanatory meteorological and socioeconomic variables in explaining the spatiotemporal variability of the disease are rainfall totals, seasonal mean maximum temperature, absolute poverty, and accessibility to pipe borne water. The influences of socioeconomic factors appeared to be more pronounced in the northern part of the country, and vice-versa in the case of meteorological factors. Also, cross validated models output suggests a strong possibility of disease prediction, which will help authorities to put effective control measures in place which depend on prevention, and or efficient response.

Spatio-Temporal Variations and Source Apportionment of Water Pollution in Danjiangkou Reservoir Basin, Central China

Directory of Open Access Journals (Sweden)

Pan Chen

2015-05-01

Full Text Available Understanding the spatio-temporal variation and the potential source of water pollution could greatly improve our knowledge of human impacts on the environment. In this work, data of 11 water quality indices were collected during 2012–2014 at 10 monitoring sites in the mainstream and major tributaries of the Danjiangkou Reservoir Basin, Central China. The fuzzy comprehensive assessment (FCA, the cluster analysis (CA and the discriminant analysis (DA were used to assess the water pollution status and analyze its spatio-temporal variation. Ten sites were classified by the high pollution (HP region and the low pollution (LP region, while 12 months were divided into the wet season and the dry season. It was found that the HP region was mainly in the small tributaries with small drainage areas and low average annual discharges, and it was also found that most of these rivers went through urban areas with industrial and domestic sewages input into the water body. Principal component analysis/factor analysis (PCA/FA was applied to reveal potential pollution sources, whereas absolute principal component score-multiple linear regression (APCS-MLR was used to identify their contributions to each water quality variable. The study area was found as being generally affected by industrial and domestic sewage. Furthermore, the HP region was polluted by chemical industries, and the LP region was influenced by agricultural and livestock sewage.

Spatiotemporal optical solitons

International Nuclear Information System (INIS)

Malomed, Boris A; Mihalache, Dumitru; Wise, Frank; Torner, Lluis

2005-01-01

In the course of the past several years, a new level of understanding has been achieved about conditions for the existence, stability, and generation of spatiotemporal optical solitons, which are nondiffracting and nondispersing wavepackets propagating in nonlinear optical media. Experimentally, effectively two-dimensional (2D) spatiotemporal solitons that overcome diffraction in one transverse spatial dimension have been created in quadratic nonlinear media. With regard to the theory, fundamentally new features of light pulses that self-trap in one or two transverse spatial dimensions and do not spread out in time, when propagating in various optical media, were thoroughly investigated in models with various nonlinearities. Stable vorticity-carrying spatiotemporal solitons have been predicted too, in media with competing nonlinearities (quadratic-cubic or cubic-quintic). This article offers an up-to-date survey of experimental and theoretical results in this field. Both achievements and outstanding difficulties are reviewed, and open problems are highlighted. Also briefly described are recent predictions for stable 2D and 3D solitons in Bose-Einstein condensates supported by full or low-dimensional optical lattices. (review article)

Spatio-Temporal Data Exchange Standards

DEFF Research Database (Denmark)

Jensen, Christian Søndergaard; Schmidt, Albrecht

2003-01-01

We believe that research that concerns aspects of spatio-temporal data management may benefit from taking into account the various standards for spatio-temporal data formats. For example, this may contribute to rendering prototype software “open” and more readily useful. This paper thus identifies...... and briefly surveys standardization in relation to primarily the exchange and integration of spatio-temporal data. An overview of several data exchange languages is offered, along with reviews their potential for facilitating the collection of test data and the leveraging of prototypes. The standards, most...... of which are XML-based, lend themselves to the integration of prototypes into middleware architectures, e.g., as Web services....

Second-order analysis of structured inhomogeneous spatio-temporal point processes

DEFF Research Database (Denmark)

Møller, Jesper; Ghorbani, Mohammad

Statistical methodology for spatio-temporal point processes is in its infancy. We consider second-order analysis based on pair correlation functions and K-functions for first general inhomogeneous spatio-temporal point processes and second inhomogeneous spatio-temporal Cox processes. Assuming...... spatio-temporal separability of the intensity function, we clarify different meanings of second-order spatio-temporal separability. One is second-order spatio-temporal independence and relates e.g. to log-Gaussian Cox processes with an additive covariance structure of the underlying spatio......-temporal Gaussian process. Another concerns shot-noise Cox processes with a separable spatio-temporal covariance density. We propose diagnostic procedures for checking hypotheses of second-order spatio-temporal separability, which we apply on simulated and real data (the UK 2001 epidemic foot and mouth disease data)....

Amplitude death and spatiotemporal bifurcations in nonlocally delay-coupled oscillators

International Nuclear Information System (INIS)

Guo, Yuxiao; Niu, Ben

2015-01-01

Amplitude death and spatiotemporal oscillations are remarkable patterns in coupled systems. We consider a ring of n identical oscillators with distance-dependent couplings and time delay. The amplitude death region is the intersection of three stable regions. Employing the method of multiple scales and normal form theory, the stability and criticality of spatiotemporal oscillations are determined. Around the amplitude death boundary there exist one branch of synchronized oscillations, n − 3 branches of co-existing phase-locked oscillations, n branches of mirror-reflecting oscillations, n branches of standing-wave oscillations, one branch of quasiperiodic oscillations and two branches of co-existing synchronized oscillations. It is proved that amplitude death is robust to small inhomogeneity of couplings, and the stability of synchronized or phase-locked oscillations inherits that of the individual decoupled oscillator. For the arbitrary form of coupling functions, some general results are also obtained for the thermodynamic limit. Finally, two examples are given to support the main results. (paper)

Spatio-temporal scaling effects on longshore sediment transport pattern along the nearshore zone

Science.gov (United States)

Khorram, Saeed; Ergil, Mustafa

2018-03-01

A measure of uncertainties, entropy has been employed in such different applications as coastal engineering probability inferences. Entropy sediment transport integration theories present novel visions in coastal analyses/modeling the application and development of which are still far-reaching. Effort has been made in the present paper to propose a method that needs an entropy-power index for spatio-temporal patterns analyses. Results have shown that the index is suitable for marine/hydrological ecosystem components analyses based on a beach area case study. The method makes use of six Makran Coastal monthly data (1970-2015) and studies variables such as spatio-temporal patterns, LSTR (long-shore sediment transport rate), wind speed, and wave height all of which are time-dependent and play considerable roles in terrestrial coastal investigations; the mentioned variables show meaningful spatio-temporal variability most of the time, but explanation of their combined performance is not easy. Accordingly, the use of an entropy-power index can show considerable signals that facilitate the evaluation of water resources and will provide an insight regarding hydrological parameters' interactions at scales as large as beach areas. Results have revealed that an STDDPI (entropy based spatio-temporal disorder dynamics power index) can simulate wave, long-shore sediment transport rate, and wind when granulometry, concentration, and flow conditions vary.

PARALLEL SPATIOTEMPORAL SPECTRAL CLUSTERING WITH MASSIVE TRAJECTORY DATA

Directory of Open Access Journals (Sweden)

Y. Z. Gu

2017-09-01

Full Text Available Massive trajectory data contains wealth useful information and knowledge. Spectral clustering, which has been shown to be effective in finding clusters, becomes an important clustering approaches in the trajectory data mining. However, the traditional spectral clustering lacks the temporal expansion on the algorithm and limited in its applicability to large-scale problems due to its high computational complexity. This paper presents a parallel spatiotemporal spectral clustering based on multiple acceleration solutions to make the algorithm more effective and efficient, the performance is proved due to the experiment carried out on the massive taxi trajectory dataset in Wuhan city, China.

Aspects of second-order analysis of structured inhomogeneous spatio-temporal processes

DEFF Research Database (Denmark)

Møller, Jesper; Ghorbani, Mohammad

2012-01-01

Statistical methodology for spatio-temporal point processes is in its infancy. We consider second-order analysis based on pair correlation functions and K-functions for general inhomogeneous spatio-temporal point processes and for inhomogeneous spatio-temporal Cox processes. Assuming spatio......-temporal separability of the intensity function, we clarify different meanings of second-order spatio-temporal separability. One is second-order spatio-temporal independence and relates to log-Gaussian Cox processes with an additive covariance structure of the underlying spatio-temporal Gaussian process. Another...... concerns shot-noise Cox processes with a separable spatio-temporal covariance density. We propose diagnostic procedures for checking hypotheses of second-order spatio-temporal separability, which we apply on simulated and real data....

High-resolution spatiotemporal strain mapping reveals non-uniform deformation in micropatterned elastomers

Science.gov (United States)

Aksoy, B.; Rehman, A.; Bayraktar, H.; Alaca, B. E.

2017-04-01

Micropatterns are generated on a vast selection of polymeric substrates for various applications ranging from stretchable electronics to cellular mechanobiological systems. When these patterned substrates are exposed to external loading, strain field is primarily affected by the presence of microfabricated structures and similarly by fabrication-related defects. The capturing of such nonhomogeneous strain fields is of utmost importance in cases where study of the mechanical behavior with a high spatial resolution is necessary. Image-based non-contact strain measurement techniques are favorable and have recently been extended to scanning tunneling microscope and scanning electron microscope images for the characterization of mechanical properties of metallic materials, e.g. steel and aluminum, at the microscale. A similar real-time analysis of strain heterogeneity in elastomers is yet to be achieved during the entire loading sequence. The available measurement methods for polymeric materials mostly depend on cross-head displacement or precalibrated strain values. Thus, they suffer either from the lack of any real-time analysis, spatiotemporal distribution or high resolution in addition to a combination of these factors. In this work, these challenges are addressed by integrating a tensile stretcher with an inverted optical microscope and developing a subpixel particle tracking algorithm. As a proof of concept, the patterns with a critical dimension of 200 µm are generated on polydimethylsiloxane substrates and strain distribution in the vicinity of the patterns is captured with a high spatiotemporal resolution. In the field of strain measurement, there is always a tradeoff between minimum measurable strain value and spatial resolution. Current noncontact techniques on elastomers can deliver a strain resolution of 0.001% over a minimum length of 5 cm. More importantly, inhomogeneities within this quite large region cannot be captured. The proposed technique can

High-resolution spatiotemporal strain mapping reveals non-uniform deformation in micropatterned elastomers

International Nuclear Information System (INIS)

Aksoy, B; Alaca, B E; Rehman, A; Bayraktar, H

2017-01-01

Micropatterns are generated on a vast selection of polymeric substrates for various applications ranging from stretchable electronics to cellular mechanobiological systems. When these patterned substrates are exposed to external loading, strain field is primarily affected by the presence of microfabricated structures and similarly by fabrication-related defects. The capturing of such nonhomogeneous strain fields is of utmost importance in cases where study of the mechanical behavior with a high spatial resolution is necessary. Image-based non-contact strain measurement techniques are favorable and have recently been extended to scanning tunneling microscope and scanning electron microscope images for the characterization of mechanical properties of metallic materials, e.g. steel and aluminum, at the microscale. A similar real-time analysis of strain heterogeneity in elastomers is yet to be achieved during the entire loading sequence. The available measurement methods for polymeric materials mostly depend on cross-head displacement or precalibrated strain values. Thus, they suffer either from the lack of any real-time analysis, spatiotemporal distribution or high resolution in addition to a combination of these factors. In this work, these challenges are addressed by integrating a tensile stretcher with an inverted optical microscope and developing a subpixel particle tracking algorithm. As a proof of concept, the patterns with a critical dimension of 200 µ m are generated on polydimethylsiloxane substrates and strain distribution in the vicinity of the patterns is captured with a high spatiotemporal resolution. In the field of strain measurement, there is always a tradeoff between minimum measurable strain value and spatial resolution. Current noncontact techniques on elastomers can deliver a strain resolution of 0.001% over a minimum length of 5 cm. More importantly, inhomogeneities within this quite large region cannot be captured. The proposed technique can

SPATIOTEMPORAL CONTRAST SENSITIVITY OF EARLY VISION

NARCIS (Netherlands)

Hateren, J.H. van

Based on the spatial and temporal statistics of natural images, a theory is developed that specifies spatiotemporal filters that maximize the flow of information through noisy channels of limited dynamic range. Sensitivities resulting from these spatiotemporal filters are very similar to the human

Whole brain white matter changes revealed by multiple diffusion metrics in multiple sclerosis: A TBSS study

International Nuclear Information System (INIS)

Liu, Yaou; Duan, Yunyun; He, Yong; Yu, Chunshui; Wang, Jun; Huang, Jing; Ye, Jing; Parizel, Paul M.; Li, Kuncheng; Shu, Ni

2012-01-01

Objective: To investigate whole brain white matter changes in multiple sclerosis (MS) by multiple diffusion indices, we examined patients with diffusion tensor imaging and utilized tract-based spatial statistics (TBSS) method to analyze the data. Methods: Forty-one relapsing-remitting multiple sclerosis (RRMS) patients and 41 age- and gender-matched normal controls were included in this study. Diffusion weighted images were acquired by employing a single-shot echo planar imaging sequence on a 1.5 T MR scanner. Voxel-wise analyses of multiple diffusion metrics, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) were performed with TBSS. Results: The MS patients had significantly decreased FA (9.11%), increased MD (8.26%), AD (3.48%) and RD (13.17%) in their white matter skeletons compared with the controls. Through TBSS analyses, we found abnormal diffusion changes in widespread white matter regions in MS patients. Specifically, decreased FA, increased MD and increased RD were involved in whole-brain white matter, while several regions exhibited increased AD. Furthermore, white matter regions with significant correlations between the diffusion metrics and the clinical variables (the EDSS scores, disease durations and white matter lesion loads) in MS patients were identified. Conclusion: Widespread white matter abnormalities were observed in MS patients revealed by multiple diffusion metrics. The diffusion changes and correlations with clinical variables were mainly attributed to increased RD, implying the predominant role of RD in reflecting the subtle pathological changes in MS

Whole brain white matter changes revealed by multiple diffusion metrics in multiple sclerosis: A TBSS study

Energy Technology Data Exchange (ETDEWEB)

Liu, Yaou, E-mail: asiaeurope80@gmail.com [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Duan, Yunyun, E-mail: xiaoyun81.love@163.com [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); He, Yong, E-mail: yong.h.he@gmail.com [State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875 (China); Yu, Chunshui, E-mail: csyuster@gmail.com [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Wang, Jun, E-mail: jun_wang@bnu.edu.cn [State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875 (China); Huang, Jing, E-mail: sainthj@126.com [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Ye, Jing, E-mail: jingye.2007@yahoo.com.cn [Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Parizel, Paul M., E-mail: paul.parizel@ua.ac.be [Department of Radiology, Antwerp University Hospital and University of Antwerp, Wilrijkstraat 10, 2650 Edegem, 8 Belgium (Belgium); Li, Kuncheng, E-mail: kunchengli55@gmail.com [Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing 100053 (China); Shu, Ni, E-mail: nshu55@gmail.com [State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875 (China)

2012-10-15

Objective: To investigate whole brain white matter changes in multiple sclerosis (MS) by multiple diffusion indices, we examined patients with diffusion tensor imaging and utilized tract-based spatial statistics (TBSS) method to analyze the data. Methods: Forty-one relapsing-remitting multiple sclerosis (RRMS) patients and 41 age- and gender-matched normal controls were included in this study. Diffusion weighted images were acquired by employing a single-shot echo planar imaging sequence on a 1.5 T MR scanner. Voxel-wise analyses of multiple diffusion metrics, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) were performed with TBSS. Results: The MS patients had significantly decreased FA (9.11%), increased MD (8.26%), AD (3.48%) and RD (13.17%) in their white matter skeletons compared with the controls. Through TBSS analyses, we found abnormal diffusion changes in widespread white matter regions in MS patients. Specifically, decreased FA, increased MD and increased RD were involved in whole-brain white matter, while several regions exhibited increased AD. Furthermore, white matter regions with significant correlations between the diffusion metrics and the clinical variables (the EDSS scores, disease durations and white matter lesion loads) in MS patients were identified. Conclusion: Widespread white matter abnormalities were observed in MS patients revealed by multiple diffusion metrics. The diffusion changes and correlations with clinical variables were mainly attributed to increased RD, implying the predominant role of RD in reflecting the subtle pathological changes in MS.

Analysis of Spatiotemporal Statistical Properties of Rainfall in the Phoenix Metropolitan Area

Science.gov (United States)

Mascaro, G.

2016-12-01

The analysis of the rainfall statistical properties at multiple spatiotemporal scales is a necessary preliminary step to support modeling of urban hydrology, including flood prediction and simulation of impacts of land use changes. In this contribution, the rainfall statistical properties are analyzed in the Phoenix Metropolitan area and its surroundings ( 29600 km2) in Arizona using observations from 310 gauges of the Flood Control District of the Maricopa County network. Different techniques are applied to investigate the rainfall properties at temporal scales from 1 min to years and to quantify the associated spatial variability. Results reveal the following. The rainfall regime is characterized by high interannual variability, which is partially explained by teleconnections with El Niño Southern Oscillation, and marked seasonality, with two maxima in the monsoon season from July to September and in winter from November to March. Elevation has a significant control on seasonal rainfall accumulation, strength of thermal convective activity during the monsoon, and peak occurrence of the rainfall diurnal cycle present in summer. The spatial correlation of wintertime rainfall is high even at short aggregation times (cells).

Spatiotemporal Signal Analysis via the Phase Velocity Transform

International Nuclear Information System (INIS)

Mattor, Nathan

2000-01-01

The phase velocity transform (PVT) is an integral transform that divides a function of space and time into components that propagate at uniform phase velocities without distortion. This paper examines the PVT as a method to analyze spatiotemporal fluctuation data. The transform is extended to systems with discretely sampled data on a periodic domain, and applied to data from eight azimuthally distributed probes on the Sustained Spheromak Physics Experiment (SSPX). This reveals features not shown by Fourier analysis, particularly regarding nonsinusoidal mode structure. (c) 2000 The American Physical Society

A Flexible Spatio-Temporal Model for Air Pollution with Spatial and Spatio-Temporal Covariates

OpenAIRE

Lindström, Johan; Szpiro, Adam A; Sampson, Paul D; Oron, Assaf P; Richards, Mark; Larson, Tim V; Sheppard, Lianne

2013-01-01

The development of models that provide accurate spatio-temporal predictions of ambient air pollution at small spatial scales is of great importance for the assessment of potential health effects of air pollution. Here we present a spatio-temporal framework that predicts ambient air pollution by combining data from several different monitoring networks and deterministic air pollution model(s) with geographic information system (GIS) covariates. The model presented in this paper has been implem...

Neurogenomic signatures of spatiotemporal memories in time-trained forager honey bees

Science.gov (United States)

Naeger, Nicholas L.; Van Nest, Byron N.; Johnson, Jennifer N.; Boyd, Sam D.; Southey, Bruce R.; Rodriguez-Zas, Sandra L.; Moore, Darrell; Robinson, Gene E.

2011-01-01

Honey bees can form distinct spatiotemporal memories that allow them to return repeatedly to different food sources at different times of day. Although it is becoming increasingly clear that different behavioral states are associated with different profiles of brain gene expression, it is not known whether this relationship extends to states that are as dynamic and specific as those associated with foraging-related spatiotemporal memories. We tested this hypothesis by training different groups of foragers from the same colony to collect sucrose solution from one of two artificial feeders; each feeder was in a different location and had sucrose available at a different time, either in the morning or afternoon. Bees from both training groups were collected at both the morning and afternoon training times to result in one set of bees that was undergoing stereotypical food anticipatory behavior and another that was inactive for each time of day. Between the two groups with the different spatiotemporal memories, microarray analysis revealed that 1329 genes were differentially expressed in the brains of honey bees. Many of these genes also varied with time of day, time of training or state of food anticipation. Some of these genes are known to be involved in a variety of biological processes, including metabolism and behavior. These results indicate that distinct spatiotemporal foraging memories in honey bees are associated with distinct neurogenomic signatures, and the decomposition of these signatures into sets of genes that are also influenced by time or activity state hints at the modular composition of this complex neurogenomic phenotype. PMID:21346126

EXTRACTING SPATIOTEMPORAL OBJECTS FROM RASTER DATA TO REPRESENT PHYSICAL FEATURES AND ANALYZE RELATED PROCESSES

Directory of Open Access Journals (Sweden)

J. A. Zollweg

2017-10-01

Full Text Available Numerous ground-based, airborne, and orbiting platforms provide remotely-sensed data of remarkable spatial resolution at short time intervals. However, this spatiotemporal data is most valuable if it can be processed into information, thereby creating meaning. We live in a world of objects: cars, buildings, farms, etc. On a stormy day, we don’t see millions of cubes of atmosphere; we see a thunderstorm ‘object’. Temporally, we don’t see the properties of those individual cubes changing, we see the thunderstorm as a whole evolving and moving. There is a need to represent the bulky, raw spatiotemporal data from remote sensors as a small number of relevant spatiotemporal objects, thereby matching the human brain’s perception of the world. This presentation reveals an efficient algorithm and system to extract the objects/features from raster-formatted remotely-sensed data. The system makes use of the Python object-oriented programming language, SciPy/NumPy for matrix manipulation and scientific computation, and export/import to the GeoJSON standard geographic object data format. The example presented will show how thunderstorms can be identified and characterized in a spatiotemporal continuum using a Python program to process raster data from NOAA’s High-Resolution Rapid Refresh v2 (HRRRv2 data stream.

Extracting Spatiotemporal Objects from Raster Data to Represent Physical Features and Analyze Related Processes

Science.gov (United States)

Zollweg, J. A.

2017-10-01

Numerous ground-based, airborne, and orbiting platforms provide remotely-sensed data of remarkable spatial resolution at short time intervals. However, this spatiotemporal data is most valuable if it can be processed into information, thereby creating meaning. We live in a world of objects: cars, buildings, farms, etc. On a stormy day, we don't see millions of cubes of atmosphere; we see a thunderstorm `object'. Temporally, we don't see the properties of those individual cubes changing, we see the thunderstorm as a whole evolving and moving. There is a need to represent the bulky, raw spatiotemporal data from remote sensors as a small number of relevant spatiotemporal objects, thereby matching the human brain's perception of the world. This presentation reveals an efficient algorithm and system to extract the objects/features from raster-formatted remotely-sensed data. The system makes use of the Python object-oriented programming language, SciPy/NumPy for matrix manipulation and scientific computation, and export/import to the GeoJSON standard geographic object data format. The example presented will show how thunderstorms can be identified and characterized in a spatiotemporal continuum using a Python program to process raster data from NOAA's High-Resolution Rapid Refresh v2 (HRRRv2) data stream.

Phenology Data Products to Support Assessment and Forecasting of Phenology on Multiple Spatiotemporal Scales

Science.gov (United States)

Gerst, K.; Enquist, C.; Rosemartin, A.; Denny, E. G.; Marsh, L.; Moore, D. J.; Weltzin, J. F.

2014-12-01

The USA National Phenology Network (USA-NPN; www.usanpn.org) serves science and society by promoting a broad understanding of plant and animal phenology and the relationships among phenological patterns and environmental change. The National Phenology Database maintained by USA-NPN now has over 3.7 million records for plants and animals for the period 1954-2014, with the majority of these observations collected since 2008 as part of a broad, national contributory science strategy. These data have been used in a number of science, conservation and resource management applications, including national assessments of historical and potential future trends in phenology, regional assessments of spatio-temporal variation in organismal activity, and local monitoring for invasive species detection. Customizable data downloads are freely available, and data are accompanied by FGDC-compliant metadata, data-use and data-attribution policies, vetted and documented methodologies and protocols, and version control. While users are free to develop custom algorithms for data cleaning, winnowing and summarization prior to analysis, the National Coordinating Office of USA-NPN is developing a suite of standard data products to facilitate use and application by a diverse set of data users. This presentation provides a progress report on data product development, including: (1) Quality controlled raw phenophase status data; (2) Derived phenometrics (e.g. onset, duration) at multiple scales; (3) Data visualization tools; (4) Tools to support assessment of species interactions and overlap; (5) Species responsiveness to environmental drivers; (6) Spatially gridded phenoclimatological products; and (7) Algorithms for modeling and forecasting future phenological responses. The prioritization of these data products is a direct response to stakeholder needs related to informing management and policy decisions. We anticipate that these products will contribute to broad understanding of plant

Spatio-Temporal Patterns of Barmah Forest Virus Disease in Queensland, Australia

Science.gov (United States)

Naish, Suchithra; Hu, Wenbiao; Mengersen, Kerrie; Tong, Shilu

2011-01-01

Background Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis. Methods/Principal Findings We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ2 = 7587, df = 7327,pQueensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland. PMID:22022430

Exploring Spatiotemporal Patterns of Long-Distance Taxi Rides in Shanghai

Directory of Open Access Journals (Sweden)

Hangbin Wu

2017-11-01

Full Text Available Floating Car Data (FCD has been analyzed for various purposes in past years. However, limited research about the behaviors of taking long-distance taxi rides has been made available. In this paper, we used data from over 12,000 taxis during a six-month period in Shanghai to analyze the spatiotemporal patterns of long-distance taxi trips. We investigated these spatiotemporal patterns by comparing them with metro usage in Shanghai, in order to determine the extent and how the suburban trains divert the passenger flow from taxis. The results identified 12 pick-up and six drop-off hotspots in Shanghai. Overall, the pick-up locations were relatively more concentrated than the drop-off locations. Temporal patterns were also revealed. Passengers on long-distance taxi rides were observed to avoid the rush hours on the street as their first priority and tried to avoid the inconvenience of interchanges on the metro lines as their second priority.

Spatio-Temporal Data Construction

Directory of Open Access Journals (Sweden)

Hai Ha Le

2013-08-01

Full Text Available On the route to a spatio-temporal geoscience information system, an appropriate data model for geo-objects in space and time has been developed. In this model, geo-objects are represented as sequences of geometries and properties with continuous evolution in each time interval. Because geomodeling software systems usually model objects at specific time instances, we want to interpolate the geometry and properties from two models of an object with only geometrical constraints (no physical or mechanical constraints. This process is called spatio-temporal data construction or morphological interpolation of intermediate geometries. This paper is strictly related to shape morphing, shape deformation, cross-parameterization and compatible remeshing and is only concerned with geological surfaces. In this study, two main sub-solutions construct compatible meshes and find trajectories in which vertices of the mesh evolve. This research aims to find an algorithm to construct spatio-temporal data with some constraints from the geosciences, such as cutting surfaces by faulting or fracturing phenomena and evolving boundaries attached to other surfaces. Another goal of this research is the implementation of the algorithm in a software product, namely a gOcad plug-in. The four main procedures of the algorithm are cutting the surfaces, setting up constraints, partitioning and calculating the parameterizations and trajectories. The software has been tested to construct data for a salt dome and other surfaces in regard to the geological processes of faulting, deposition and erosion. The result of this research is an algorithm and software for the construction of spatio-temporal data.

Collaborative simulation method with spatiotemporal synchronization process control

Science.gov (United States)

Zou, Yisheng; Ding, Guofu; Zhang, Weihua; Zhang, Jian; Qin, Shengfeng; Tan, John Kian

2016-10-01

When designing a complex mechatronics system, such as high speed trains, it is relatively difficult to effectively simulate the entire system's dynamic behaviors because it involves multi-disciplinary subsystems. Currently,a most practical approach for multi-disciplinary simulation is interface based coupling simulation method, but it faces a twofold challenge: spatial and time unsynchronizations among multi-directional coupling simulation of subsystems. A new collaborative simulation method with spatiotemporal synchronization process control is proposed for coupling simulating a given complex mechatronics system across multiple subsystems on different platforms. The method consists of 1) a coupler-based coupling mechanisms to define the interfacing and interaction mechanisms among subsystems, and 2) a simulation process control algorithm to realize the coupling simulation in a spatiotemporal synchronized manner. The test results from a case study show that the proposed method 1) can certainly be used to simulate the sub-systems interactions under different simulation conditions in an engineering system, and 2) effectively supports multi-directional coupling simulation among multi-disciplinary subsystems. This method has been successfully applied in China high speed train design and development processes, demonstrating that it can be applied in a wide range of engineering systems design and simulation with improved efficiency and effectiveness.

New type of chimera and mutual synchronization of spatiotemporal structures in two coupled ensembles of nonlocally interacting chaotic maps

Science.gov (United States)

Bukh, Andrei; Rybalova, Elena; Semenova, Nadezhda; Strelkova, Galina; Anishchenko, Vadim

2017-11-01

We study numerically the dynamics of a network made of two coupled one-dimensional ensembles of discrete-time systems. The first ensemble is represented by a ring of nonlocally coupled Henon maps and the second one by a ring of nonlocally coupled Lozi maps. We find that the network of coupled ensembles can realize all the spatio-temporal structures which are observed both in the Henon map ensemble and in the Lozi map ensemble while uncoupled. Moreover, we reveal a new type of spatiotemporal structure, a solitary state chimera, in the considered network. We also establish and describe the effect of mutual synchronization of various complex spatiotemporal patterns in the system of two coupled ensembles of Henon and Lozi maps.

Spatio-temporal hotspots of satellite-tracked arctic foxes reveal a large detection range in a mammalian predator.

Science.gov (United States)

Lai, Sandra; Bêty, Joël; Berteaux, Dominique

2015-01-01

The scale at which animals perceive their environment is a strong fitness determinant, yet few empirical estimates of animal detection ranges exist, especially in mammalian predators. Using daily Argos satellite tracking of 26 adult arctic foxes (Vulpes lagopus) during a single winter in the High Canadian Arctic, we investigated the detection range of arctic foxes by detecting hotspots of fox activity on the sea ice. While maintaining territories in the tundra, these solitary foragers occasionally used the sea ice where they sometimes formed spatio-temporal hotspots, likely scavenging on marine mammal carcasses. We detected 35 movements by 13 individuals forming five hotspots. Foxes often traveled more than 10 km, and up to 40 km, to reach hotspots, which lasted one-two weeks and could gather up to 12 individuals. The likelihood of a fox joining a hotspot was neither influenced by its distance from the hotspot nor by the distance of its home range to the coast. Observed traveling distances may indicate a high detection range in arctic foxes, and our results suggest their ability to detect food sources on the sea ice from their terrestrial home range. While revealing a wide knowledge gap regarding resource detection abilities in mammalian predators, our study provides estimates of detection range useful for interpreting and modeling animal movements. It also allows a better understanding of foraging behavior and navigation capacity in terrestrial predators.

The Voronoi spatio-temporal data structure

Science.gov (United States)

Mioc, Darka

2002-04-01

Current GIS models cannot integrate the temporal dimension of spatial data easily. Indeed, current GISs do not support incremental (local) addition and deletion of spatial objects, and they can not support the temporal evolution of spatial data. Spatio-temporal facilities would be very useful in many GIS applications: harvesting and forest planning, cadastre, urban and regional planning, and emergency planning. The spatio-temporal model that can overcome these problems is based on a topological model---the Voronoi data structure. Voronoi diagrams are irregular tessellations of space, that adapt to spatial objects and therefore they are a synthesis of raster and vector spatial data models. The main advantage of the Voronoi data structure is its local and sequential map updates, which allows us to automatically record each event and performed map updates within the system. These map updates are executed through map construction commands that are composed of atomic actions (geometric algorithms for addition, deletion, and motion of spatial objects) on the dynamic Voronoi data structure. The formalization of map commands led to the development of a spatial language comprising a set of atomic operations or constructs on spatial primitives (points and lines), powerful enough to define the complex operations. This resulted in a new formal model for spatio-temporal change representation, where each update is uniquely characterized by the numbers of newly created and inactivated Voronoi regions. This is used for the extension of the model towards the hierarchical Voronoi data structure. In this model, spatio-temporal changes induced by map updates are preserved in a hierarchical data structure that combines events and corresponding changes in topology. This hierarchical Voronoi data structure has an implicit time ordering of events visible through changes in topology, and it is equivalent to an event structure that can support temporal data without precise temporal

World Spatiotemporal Analytics and Mapping Project (wstamp): Discovering, Exploring, and Mapping Spatiotemporal Patterns across the World's Largest Open Soruce Data Sets

Science.gov (United States)

Stewart, R.; Piburn, J.; Sorokine, A.; Myers, A.; Moehl, J.; White, D.

2015-07-01

The application of spatiotemporal (ST) analytics to integrated data from major sources such as the World Bank, United Nations, and dozens of others holds tremendous potential for shedding new light on the evolution of cultural, health, economic, and geopolitical landscapes on a global level. Realizing this potential first requires an ST data model that addresses challenges in properly merging data from multiple authors, with evolving ontological perspectives, semantical differences, and changing attributes, as well as content that is textual, numeric, categorical, and hierarchical. Equally challenging is the development of analytical and visualization approaches that provide a serious exploration of this integrated data while remaining accessible to practitioners with varied backgrounds. The WSTAMP project at Oak Ridge National Laboratory has yielded two major results in addressing these challenges: 1) development of the WSTAMP database, a significant advance in ST data modeling that integrates 10,000+ attributes covering over 200 nation states spanning over 50 years from over 30 major sources and 2) a novel online ST exploratory and analysis tool providing an array of modern statistical and visualization techniques for analyzing these data temporally, spatially, and spatiotemporally under a standard analytic workflow. We discuss the status of this work and report on major findings.

Dynamic spatiotemporal analysis of indigenous dengue fever at street-level in Guangzhou city, China

Science.gov (United States)

Xia, Yao; Zhang, Yingtao; Huang, Xiaodong; Huang, Jiawei; Nie, Enqiong; Jing, Qinlong; Wang, Guoling; Yang, Zhicong; Hu, Wenbiao

2018-01-01

Background This study aimed to investigate the spatiotemporal clustering and socio-environmental factors associated with dengue fever (DF) incidence rates at street level in Guangzhou city, China. Methods Spatiotemporal scan technique was applied to identify the high risk region of DF. Multiple regression model was used to identify the socio-environmental factors associated with DF infection. A Poisson regression model was employed to examine the spatiotemporal patterns in the spread of DF. Results Spatial clusters of DF were primarily concentrated at the southwest part of Guangzhou city. Age group (65+ years) (Odd Ratio (OR) = 1.49, 95% Confidence Interval (CI) = 1.13 to 2.03), floating population (OR = 1.09, 95% CI = 1.05 to 1.15), low-education (OR = 1.08, 95% CI = 1.01 to 1.16) and non-agriculture (OR = 1.07, 95% CI = 1.03 to 1.11) were associated with DF transmission. Poisson regression results indicated that changes in DF incidence rates were significantly associated with longitude (β = -5.08, P<0.01) and latitude (β = -1.99, P<0.01). Conclusions The study demonstrated that social-environmental factors may play an important role in DF transmission in Guangzhou. As geographic range of notified DF has significantly expanded over recent years, an early warning systems based on spatiotemporal model with socio-environmental is urgently needed to improve the effectiveness and efficiency of dengue control and prevention. PMID:29561835

Moving mountains with mobiles: Spatiotemporal perspectives on mHealth in Nepal

Directory of Open Access Journals (Sweden)

Arul Chib

2012-03-01

Full Text Available Community healthcare workers (CHW are an important component of rural healthcare service delivery to remote rural communities in developing countries. The field of mHealth proposes that mobile technologies will have a beneficial impact on rural healthcare development. Current analyses advance the proposition that the utilization of mobile technologies leads to the shifting of space and time (Ling & Campbell, 2009. The current research examined the potential for a sustainable mHealth system for CHW in Achham, Nepal. The community aspect of mobile usage was overlaid with a spatio-temporal lens to examine the information and communication needs and practices of stakeholders within the healthcare infrastructure. Fieldwork was conducted in conjunction with Nyaya Health, at the Bayalpata Hospital, in Accham, Nepal. Qualitative research methods, focus group discussions, and in-depth interviews included 57 respondents. The findings revealed that limited relevance and information-sharing, limited access due to individual ownership and low income, and ineffective training programs were key barriers to the delivery of rural healthcare services. The spatio-temporal perspective, particularly community communicative practices, revealed technological mHealth design solutions to alleviate the problems identified. The potential shifts in power relationships by using mobile technologies and hybrid fixed wireless technologies provide opportunities for further theoretical investigation.

a Web-Based Interactive Platform for Co-Clustering Spatio-Temporal Data

Science.gov (United States)

Wu, X.; Poorthuis, A.; Zurita-Milla, R.; Kraak, M.-J.

2017-09-01

Since current studies on clustering analysis mainly focus on exploring spatial or temporal patterns separately, a co-clustering algorithm is utilized in this study to enable the concurrent analysis of spatio-temporal patterns. To allow users to adopt and adapt the algorithm for their own analysis, it is integrated within the server side of an interactive web-based platform. The client side of the platform, running within any modern browser, is a graphical user interface (GUI) with multiple linked visualizations that facilitates the understanding, exploration and interpretation of the raw dataset and co-clustering results. Users can also upload their own datasets and adjust clustering parameters within the platform. To illustrate the use of this platform, an annual temperature dataset from 28 weather stations over 20 years in the Netherlands is used. After the dataset is loaded, it is visualized in a set of linked visualizations: a geographical map, a timeline and a heatmap. This aids the user in understanding the nature of their dataset and the appropriate selection of co-clustering parameters. Once the dataset is processed by the co-clustering algorithm, the results are visualized in the small multiples, a heatmap and a timeline to provide various views for better understanding and also further interpretation. Since the visualization and analysis are integrated in a seamless platform, the user can explore different sets of co-clustering parameters and instantly view the results in order to do iterative, exploratory data analysis. As such, this interactive web-based platform allows users to analyze spatio-temporal data using the co-clustering method and also helps the understanding of the results using multiple linked visualizations.

Spatiotemporal exploratory models for broad-scale survey data.

Science.gov (United States)

Fink, Daniel; Hochachka, Wesley M; Zuckerberg, Benjamin; Winkler, David W; Shaby, Ben; Munson, M Arthur; Hooker, Giles; Riedewald, Mirek; Sheldon, Daniel; Kelling, Steve

2010-12-01

The distributions of animal populations change and evolve through time. Migratory species exploit different habitats at different times of the year. Biotic and abiotic features that determine where a species lives vary due to natural and anthropogenic factors. This spatiotemporal variation needs to be accounted for in any modeling of species' distributions. In this paper we introduce a semiparametric model that provides a flexible framework for analyzing dynamic patterns of species occurrence and abundance from broad-scale survey data. The spatiotemporal exploratory model (STEM) adds essential spatiotemporal structure to existing techniques for developing species distribution models through a simple parametric structure without requiring a detailed understanding of the underlying dynamic processes. STEMs use a multi-scale strategy to differentiate between local and global-scale spatiotemporal structure. A user-specified species distribution model accounts for spatial and temporal patterning at the local level. These local patterns are then allowed to "scale up" via ensemble averaging to larger scales. This makes STEMs especially well suited for exploring distributional dynamics arising from a variety of processes. Using data from eBird, an online citizen science bird-monitoring project, we demonstrate that monthly changes in distribution of a migratory species, the Tree Swallow (Tachycineta bicolor), can be more accurately described with a STEM than a conventional bagged decision tree model in which spatiotemporal structure has not been imposed. We also demonstrate that there is no loss of model predictive power when a STEM is used to describe a spatiotemporal distribution with very little spatiotemporal variation; the distribution of a nonmigratory species, the Northern Cardinal (Cardinalis cardinalis).

Cryptanalysis on an image block encryption algorithm based on spatiotemporal chaos

International Nuclear Information System (INIS)

Wang Xing-Yuan; He Guo-Xiang

2012-01-01

An image block encryption scheme based on spatiotemporal chaos has been proposed recently. In this paper, we analyse the security weakness of the proposal. The main problem of the original scheme is that the generated keystream remains unchanged for encrypting every image. Based on the flaws, we demonstrate a chosen plaintext attack for revealing the equivalent keys with only 6 pairs of plaintext/ciphertext used. Finally, experimental results show the validity of our attack. (general)

Evaluation of white spot syndrome virus variable DNA loci as molecular markers of virus spread at intermediate spatiotemporal scales

NARCIS (Netherlands)

Bui Thi Minh Dieu,; Marks, H.; Zwart, M.P.; Vlak, J.M.

2010-01-01

Variable genomic loci have been employed in a number of molecular epidemiology studies of white spot syndrome virus (WSSV), but it is unknown which loci are suitable molecular markers for determining WSSV spread on different spatiotemporal scales. Although previous work suggests that multiple

VAUD: A Visual Analysis Approach for Exploring Spatio-Temporal Urban Data.

Science.gov (United States)

Chen, Wei; Huang, Zhaosong; Wu, Feiran; Zhu, Minfeng; Guan, Huihua; Maciejewski, Ross

2017-10-02

Urban data is massive, heterogeneous, and spatio-temporal, posing a substantial challenge for visualization and analysis. In this paper, we design and implement a novel visual analytics approach, Visual Analyzer for Urban Data (VAUD), that supports the visualization, querying, and exploration of urban data. Our approach allows for cross-domain correlation from multiple data sources by leveraging spatial-temporal and social inter-connectedness features. Through our approach, the analyst is able to select, filter, aggregate across multiple data sources and extract information that would be hidden to a single data subset. To illustrate the effectiveness of our approach, we provide case studies on a real urban dataset that contains the cyber-, physical-, and socialinformation of 14 million citizens over 22 days.

Spatio-temporal networks: reachability, centrality and robustness.

Science.gov (United States)

Williams, Matthew J; Musolesi, Mirco

2016-06-01

Recent advances in spatial and temporal networks have enabled researchers to more-accurately describe many real-world systems such as urban transport networks. In this paper, we study the response of real-world spatio-temporal networks to random error and systematic attack, taking a unified view of their spatial and temporal performance. We propose a model of spatio-temporal paths in time-varying spatially embedded networks which captures the property that, as in many real-world systems, interaction between nodes is non-instantaneous and governed by the space in which they are embedded. Through numerical experiments on three real-world urban transport systems, we study the effect of node failure on a network's topological, temporal and spatial structure. We also demonstrate the broader applicability of this framework to three other classes of network. To identify weaknesses specific to the behaviour of a spatio-temporal system, we introduce centrality measures that evaluate the importance of a node as a structural bridge and its role in supporting spatio-temporally efficient flows through the network. This exposes the complex nature of fragility in a spatio-temporal system, showing that there is a variety of failure modes when a network is subject to systematic attacks.

Selecting a separable parametric spatiotemporal covariance structure for longitudinal imaging data.

Science.gov (United States)

George, Brandon; Aban, Inmaculada

2015-01-15

Longitudinal imaging studies allow great insight into how the structure and function of a subject's internal anatomy changes over time. Unfortunately, the analysis of longitudinal imaging data is complicated by inherent spatial and temporal correlation: the temporal from the repeated measures and the spatial from the outcomes of interest being observed at multiple points in a patient's body. We propose the use of a linear model with a separable parametric spatiotemporal error structure for the analysis of repeated imaging data. The model makes use of spatial (exponential, spherical, and Matérn) and temporal (compound symmetric, autoregressive-1, Toeplitz, and unstructured) parametric correlation functions. A simulation study, inspired by a longitudinal cardiac imaging study on mitral regurgitation patients, compared different information criteria for selecting a particular separable parametric spatiotemporal correlation structure as well as the effects on types I and II error rates for inference on fixed effects when the specified model is incorrect. Information criteria were found to be highly accurate at choosing between separable parametric spatiotemporal correlation structures. Misspecification of the covariance structure was found to have the ability to inflate the type I error or have an overly conservative test size, which corresponded to decreased power. An example with clinical data is given illustrating how the covariance structure procedure can be performed in practice, as well as how covariance structure choice can change inferences about fixed effects. Copyright © 2014 John Wiley & Sons, Ltd.

Analyzing Spatiotemporal Anomalies through Interactive Visualization

Directory of Open Access Journals (Sweden)

Tao Zhang

2014-06-01

Full Text Available As we move into the big data era, data grows not just in size, but also in complexity, containing a rich set of attributes, including location and time information, such as data from mobile devices (e.g., smart phones, natural disasters (e.g., earthquake and hurricane, epidemic spread, etc. We are motivated by the rising challenge and build a visualization tool for exploring generic spatiotemporal data, i.e., records containing time location information and numeric attribute values. Since the values often evolve over time and across geographic regions, we are particularly interested in detecting and analyzing the anomalous changes over time/space. Our analytic tool is based on geographic information system and is combined with spatiotemporal data mining algorithms, as well as various data visualization techniques, such as anomaly grids and anomaly bars superimposed on the map. We study how effective the tool may guide users to find potential anomalies through demonstrating and evaluating over publicly available spatiotemporal datasets. The tool for spatiotemporal anomaly analysis and visualization is useful in many domains, such as security investigation and monitoring, situation awareness, etc.

Unsupervised Learning of Spatiotemporal Features by Video Completion

OpenAIRE

Nallabolu, Adithya Reddy

2017-01-01

In this work, we present an unsupervised representation learning approach for learning rich spatiotemporal features from videos without the supervision from semantic labels. We propose to learn the spatiotemporal features by training a 3D convolutional neural network (CNN) using video completion as a surrogate task. Using a large collection of unlabeled videos, we train the CNN to predict the missing pixels of a spatiotemporal hole given the remaining parts of the video through minimizing per...

Comparison of Spatiotemporal Fusion Models: A Review

Directory of Open Access Journals (Sweden)

Bin Chen

2015-02-01

Full Text Available Simultaneously capturing spatial and temporal dynamics is always a challenge for the remote sensing community. Spatiotemporal fusion has gained wide interest in various applications for its superiority in integrating both fine spatial resolution and frequent temporal coverage. Though many advances have been made in spatiotemporal fusion model development and applications in the past decade, a unified comparison among existing fusion models is still limited. In this research, we classify the models into three categories: transformation-based, reconstruction-based, and learning-based models. The objective of this study is to (i compare four fusion models (STARFM, ESTARFM, ISTAFM, and SPSTFM under a one Landsat-MODIS (L-M pair prediction mode and two L-M pair prediction mode using time-series datasets from the Coleambally irrigation area and Poyang Lake wetland; (ii quantitatively assess prediction accuracy considering spatiotemporal comparability, landscape heterogeneity, and model parameter selection; and (iii discuss the advantages and disadvantages of the three categories of spatiotemporal fusion models.

WORLD SPATIOTEMPORAL ANALYTICS AND MAPPING PROJECT (WSTAMP: DISCOVERING, EXPLORING, AND MAPPING SPATIOTEMPORAL PATTERNS ACROSS THE WORLD’S LARGEST OPEN SORUCE DATA SETS

Directory of Open Access Journals (Sweden)

R. Stewart

2015-07-01

Full Text Available The application of spatiotemporal (ST analytics to integrated data from major sources such as the World Bank, United Nations, and dozens of others holds tremendous potential for shedding new light on the evolution of cultural, health, economic, and geopolitical landscapes on a global level. Realizing this potential first requires an ST data model that addresses challenges in properly merging data from multiple authors, with evolving ontological perspectives, semantical differences, and changing attributes, as well as content that is textual, numeric, categorical, and hierarchical. Equally challenging is the development of analytical and visualization approaches that provide a serious exploration of this integrated data while remaining accessible to practitioners with varied backgrounds. The WSTAMP project at Oak Ridge National Laboratory has yielded two major results in addressing these challenges: 1 development of the WSTAMP database, a significant advance in ST data modeling that integrates 10,000+ attributes covering over 200 nation states spanning over 50 years from over 30 major sources and 2 a novel online ST exploratory and analysis tool providing an array of modern statistical and visualization techniques for analyzing these data temporally, spatially, and spatiotemporally under a standard analytic workflow. We discuss the status of this work and report on major findings.

Simultaneous spatio-temporal focusing for tissue manipulation

Directory of Open Access Journals (Sweden)

Squier J.

2013-11-01

Full Text Available Simultaneous spatiotemporal focusing (SSTF is applied to lens tissue and compared directly with standard femtosecond micromachining of the tissue at the same numerical aperture. Third harmonic generation imaging is used for spatio-temporal characterization of the processing conditions obtained with both a standard and SSTF focus.

[Exome sequencing revealed Allan-Herndon-Dudley syndrome underlying multiple disabilities].

Science.gov (United States)

Arvio, Maria; Philips, Anju K; Ahvenainen, Minna; Somer, Mirja; Kalscheuer, Vera; Järvelä, Irma

2014-01-01

Normal function of the thyroid gland is the cornerstone of a child's mental development and physical growth. We describe a Finnish family, in which the diagnosis of three brothers became clear after investigations that lasted for more than 30 years. Two of the sons have already died. DNA analysis of the third one, a 16-year-old boy, revealed in exome sequencing of the complete X chromosome a mutation in the SLC16A2 gene, i.e. MCT8, coding for a thyroid hormone transport protein. Allan-Herndon-Dudley syndrome was thus shown to be the cause of multiple disabilities.

iRaster: a novel information visualization tool to explore spatiotemporal patterns in multiple spike trains.

Science.gov (United States)

Somerville, J; Stuart, L; Sernagor, E; Borisyuk, R

2010-12-15

Over the last few years, simultaneous recordings of multiple spike trains have become widely used by neuroscientists. Therefore, it is important to develop new tools for analysing multiple spike trains in order to gain new insight into the function of neural systems. This paper describes how techniques from the field of visual analytics can be used to reveal specific patterns of neural activity. An interactive raster plot called iRaster has been developed. This software incorporates a selection of statistical procedures for visualization and flexible manipulations with multiple spike trains. For example, there are several procedures for the re-ordering of spike trains which can be used to unmask activity propagation, spiking synchronization, and many other important features of multiple spike train activity. Additionally, iRaster includes a rate representation of neural activity, a combined representation of rate and spikes, spike train removal and time interval removal. Furthermore, it provides multiple coordinated views, time and spike train zooming windows, a fisheye lens distortion, and dissemination facilities. iRaster is a user friendly, interactive, flexible tool which supports a broad range of visual representations. This tool has been successfully used to analyse both synthetic and experimentally recorded datasets. In this paper, the main features of iRaster are described and its performance and effectiveness are demonstrated using various types of data including experimental multi-electrode array recordings from the ganglion cell layer in mouse retina. iRaster is part of an ongoing research project called VISA (Visualization of Inter-Spike Associations) at the Visualization Lab in the University of Plymouth. The overall aim of the VISA project is to provide neuroscientists with the ability to freely explore and analyse their data. The software is freely available from the Visualization Lab website (see www.plymouth.ac.uk/infovis). Copyright © 2010

Revealing Pathway Dynamics in Heart Diseases by Analyzing Multiple Differential Networks.

Directory of Open Access Journals (Sweden)

Xiaoke Ma

2015-06-01

Full Text Available Development of heart diseases is driven by dynamic changes in both the activity and connectivity of gene pathways. Understanding these dynamic events is critical for understanding pathogenic mechanisms and development of effective treatment. Currently, there is a lack of computational methods that enable analysis of multiple gene networks, each of which exhibits differential activity compared to the network of the baseline/healthy condition. We describe the iMDM algorithm to identify both unique and shared gene modules across multiple differential co-expression networks, termed M-DMs (multiple differential modules. We applied iMDM to a time-course RNA-Seq dataset generated using a murine heart failure model generated on two genotypes. We showed that iMDM achieves higher accuracy in inferring gene modules compared to using single or multiple co-expression networks. We found that condition-specific M-DMs exhibit differential activities, mediate different biological processes, and are enriched for genes with known cardiovascular phenotypes. By analyzing M-DMs that are present in multiple conditions, we revealed dynamic changes in pathway activity and connectivity across heart failure conditions. We further showed that module dynamics were correlated with the dynamics of disease phenotypes during the development of heart failure. Thus, pathway dynamics is a powerful measure for understanding pathogenesis. iMDM provides a principled way to dissect the dynamics of gene pathways and its relationship to the dynamics of disease phenotype. With the exponential growth of omics data, our method can aid in generating systems-level insights into disease progression.

Reshaping-induced spatiotemporal chaos in driven, damped sine-Gordon systems

International Nuclear Information System (INIS)

Chacon, R.

2007-01-01

Spatiotemporal chaos arising from the competition between sine-Gordon-breather and kink-antikink-pair solitons by reshaping an ac force is demonstrated. After introducing soliton collective coordinates, Melnikov's method is applied to the resulting effective equation of motion to estimate the parameter-space regions of the ac force where homoclinic bifurcations are induced. The analysis reveals that the chaos-order threshold exhibits sensitivity to small changes in the force shape. Computer simulations of the sine-Gordon system show good agreement with these theoretical predictions

Reshaping-induced spatiotemporal chaos in driven, damped sine-Gordon systems

Energy Technology Data Exchange (ETDEWEB)

Chacon, R. [Departamento de Electronica e Ingenieria Electromecanica, Escuela de Ingenierias Industriales, Universidad de Extremadura, E-06071 Badajoz (Spain)]. E-mail: rchacon@unex.es

2007-03-15

Spatiotemporal chaos arising from the competition between sine-Gordon-breather and kink-antikink-pair solitons by reshaping an ac force is demonstrated. After introducing soliton collective coordinates, Melnikov's method is applied to the resulting effective equation of motion to estimate the parameter-space regions of the ac force where homoclinic bifurcations are induced. The analysis reveals that the chaos-order threshold exhibits sensitivity to small changes in the force shape. Computer simulations of the sine-Gordon system show good agreement with these theoretical predictions.

Agent-based modeling of autophagy reveals emergent regulatory behavior of spatio-temporal autophagy dynamics.

Science.gov (United States)

Börlin, Christoph S; Lang, Verena; Hamacher-Brady, Anne; Brady, Nathan R

2014-09-10

Autophagy is a vesicle-mediated pathway for lysosomal degradation, essential under basal and stressed conditions. Various cellular components, including specific proteins, protein aggregates, organelles and intracellular pathogens, are targets for autophagic degradation. Thereby, autophagy controls numerous vital physiological and pathophysiological functions, including cell signaling, differentiation, turnover of cellular components and pathogen defense. Moreover, autophagy enables the cell to recycle cellular components to metabolic substrates, thereby permitting prolonged survival under low nutrient conditions. Due to the multi-faceted roles for autophagy in maintaining cellular and organismal homeostasis and responding to diverse stresses, malfunction of autophagy contributes to both chronic and acute pathologies. We applied a systems biology approach to improve the understanding of this complex cellular process of autophagy. All autophagy pathway vesicle activities, i.e. creation, movement, fusion and degradation, are highly dynamic, temporally and spatially, and under various forms of regulation. We therefore developed an agent-based model (ABM) to represent individual components of the autophagy pathway, subcellular vesicle dynamics and metabolic feedback with the cellular environment, thereby providing a framework to investigate spatio-temporal aspects of autophagy regulation and dynamic behavior. The rules defining our ABM were derived from literature and from high-resolution images of autophagy markers under basal and activated conditions. Key model parameters were fit with an iterative method using a genetic algorithm and a predefined fitness function. From this approach, we found that accurate prediction of spatio-temporal behavior required increasing model complexity by implementing functional integration of autophagy with the cellular nutrient state. The resulting model is able to reproduce short-term autophagic flux measurements (up to 3�

Visualization and assessment of spatio-temporal covariance properties

KAUST Repository

Huang, Huang

2017-11-23

Spatio-temporal covariances are important for describing the spatio-temporal variability of underlying random fields in geostatistical data. For second-order stationary random fields, there exist subclasses of covariance functions that assume a simpler spatio-temporal dependence structure with separability and full symmetry. However, it is challenging to visualize and assess separability and full symmetry from spatio-temporal observations. In this work, we propose a functional data analysis approach that constructs test functions using the cross-covariances from time series observed at each pair of spatial locations. These test functions of temporal lags summarize the properties of separability or symmetry for the given spatial pairs. We use functional boxplots to visualize the functional median and the variability of the test functions, where the extent of departure from zero at all temporal lags indicates the degree of non-separability or asymmetry. We also develop a rank-based nonparametric testing procedure for assessing the significance of the non-separability or asymmetry. Essentially, the proposed methods only require the analysis of temporal covariance functions. Thus, a major advantage over existing approaches is that there is no need to estimate any covariance matrix for selected spatio-temporal lags. The performances of the proposed methods are examined by simulations with various commonly used spatio-temporal covariance models. To illustrate our methods in practical applications, we apply it to real datasets, including weather station data and climate model outputs.

Dynamic decomposition of spatiotemporal neural signals.

Directory of Open Access Journals (Sweden)

Luca Ambrogioni

2017-05-01

Full Text Available Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framework that uses a linearized model of these dynamic states in order to decompose the measured neural signal into a series of components that capture both rhythmic and non-rhythmic neural activity. The method is based on stochastic differential equations and Gaussian process regression. Through computer simulations and analysis of magnetoencephalographic data, we demonstrate the efficacy of the method in identifying meaningful modulations of oscillatory signals corrupted by structured temporal and spatiotemporal noise. These results suggest that the method is particularly suitable for the analysis and interpretation of complex temporal and spatiotemporal neural signals.

Complete mitochondrial genome sequences of Korean native horse from Jeju Island: uncovering the spatio-temporal dynamics.

Science.gov (United States)

Yoon, Sook Hee; Kim, Jaemin; Shin, Donghyun; Cho, Seoae; Kwak, Woori; Lee, Hak-Kyo; Park, Kyoung-Do; Kim, Heebal

2017-04-01

The Korean native horse (Jeju horse) is one of the most important animals in Korean historical, cultural, and economical viewpoints. In the early 1980s, the Jeju horse was close to extinction. The aim of this study is to explore the phylogenomics of Korean native horse focusing on spatio-temporal dynamics. We determined complete mitochondrial genome sequences for the first Korean native (n = 6) and additional Mongolian (n = 2) horses. Those sequences were analyzed together with 143 published ones using Bayesian coalescent approach as well as three different phylogenetic analysis methods, Bayesian inference, maximum likelihood, and neighbor-joining methods. The phylogenomic trees revealed that the Korean native horses had multiple origins and clustered together with some horses from four European and one Middle Eastern breeds. Our phylogenomic analyses also supported that there was no apparent association between breed or geographic location and the evolution of global horses. Time of the most recent common ancestor of the Korean native horse was approximately 13,200-63,200 years, which was much younger than 0.696 My of modern horses. Additionally, our results showed that all global horse lineages including Korean native horse existed prior to their domestication events occurred in about 6000-10,000 years ago. This is the first study on phylogenomics of the Korean native horse focusing on spatio-temporal dynamics. Our findings increase our understanding of the domestication history of the Korean native horses, and could provide useful information for horse conservation projects as well as for horse genomics, emergence, and the geographical distribution.

Controlling spatiotemporal chaos in one- and two-dimensional coupled logistic map lattices

International Nuclear Information System (INIS)

Astakhov, V.V.; Anishchenko, V.S.; Strelkova, G.I.; Shabunin, A.V.

1996-01-01

A method of control of spatiotemporal chaos in lattices of coupled maps is proposed in this work. Forms of spatiotemporal perturbations of a system parameter are analytically determined for one- and two-dimensional logistic map lattices with different kinds of coupling to stabilize chosen spatiotemporal states previously unstable. The results are illustrated by numerical simulation. Controlled transition from the regime of spatiotemporal chaos to the previously chosen regular spatiotemporal patterns is demonstrated. copyright 1996 American Institute of Physics

Spatiotemporal psychopathology I: No rest for the brain's resting state activity in depression? Spatiotemporal psychopathology of depressive symptoms.

Science.gov (United States)

Northoff, Georg

2016-01-15

Despite intense neurobiological investigation in psychiatric disorders like major depressive disorder (MDD), the basic disturbance that underlies the psychopathological symptoms of MDD remains, nevertheless, unclear. Neuroimaging has focused mainly on the brain's extrinsic activity, specifically task-evoked or stimulus-induced activity, as related to the various sensorimotor, affective, cognitive, and social functions. Recently, the focus has shifted to the brain's intrinsic activity, otherwise known as its resting state activity. While various abnormalities have been observed during this activity, their meaning and significance for depression, along with its various psychopathological symptoms, are yet to be defined. Based on findings in healthy brain resting state activity and its particular spatial and temporal structure - defined in a functional and physiological sense rather than anatomical and structural - I claim that the various depressive symptoms are spatiotemporal disturbances of the resting state activity and its spatiotemporal structure. This is supported by recent findings that link ruminations and increased self-focus in depression to abnormal spatial organization of resting state activity. Analogously, affective and cognitive symptoms like anhedonia, suicidal ideation, and thought disorder can be traced to an increased focus on the past, increased past-focus as basic temporal disturbance o the resting state. Based on these findings, I conclude that the various depressive symptoms must be conceived as spatiotemporal disturbances of the brain's resting state's activity and its spatiotemporal structure. Importantly, this entails a new form of psychopathology, "Spatiotemporal Psychopathology" that directly links the brain and psyche, therefore having major diagnostic and therapeutic implications for clinical practice. Copyright © 2015 Elsevier B.V. All rights reserved.

Spatiotemporal maps reveal regional differences in the effects on gut motility for Lactobacillus reuteri and rhamnosus strains.

Science.gov (United States)

Wu, R Y; Pasyk, M; Wang, B; Forsythe, P; Bienenstock, J; Mao, Y-K; Sharma, P; Stanisz, A M; Kunze, W A

2013-03-01

Commensal bacteria such as probiotics that are neuroactive acutely affect the amplitudes of intestinal migrating motor complexes (MMCs). What is lacking for an improved understanding of these motility effects are region specific measurements of velocity and frequency. We have combined intraluminal pressure recordings with spatiotemporal diameter maps to analyze more completely effects of different strains of beneficial bacteria on motility. Intraluminal peak pressure (PPr) was measured and video recordings made of mouse ex vivo jejunum and colon segments before and after intraluminal applications of Lactobacillus rhamnosus (JB-1) or Lactobacillus reuteri (DSM 17938). Migrating motor complex frequency and velocity were calculated. JB-1 decreased jejunal frequencies by 56% and 34% in colon. Jejunal velocities increased 171%, but decreased 31% in colon. Jejunal PPr decreased by 55% and in colon by 21%. DSM 17938 increased jejunal frequencies 63% and in colon 75%; jejunal velocity decreased 57%, but increased in colon 146%; jejunal PPr was reduced 26% and 12% in colon. TRAM-34 decreased frequency by 71% and increased velocity 200% for jejunum, but increased frequency 46% and velocity 50% for colon; PPr was decreased 59% for jejunum and 39% for colon. The results show that probiotics and other beneficial bacteria have strain and region-specific actions on gut motility that can be successfully discriminated using spatiotemporal mapping of diameter changes. Effects are not necessarily the same in colon and jejunum. Further research is needed on the detailed effects of the strains on enteric neuron currents for each gut region. © 2013 Blackwell Publishing Ltd.

Spatiotemporal distribution and national measurement of the global carbonate carbon sink.

Science.gov (United States)

Li, Huiwen; Wang, Shijie; Bai, Xiaoyong; Luo, Weijun; Tang, Hong; Cao, Yue; Wu, Luhua; Chen, Fei; Li, Qin; Zeng, Cheng; Wang, Mingming

2018-06-21

The magnitudes, spatial distributions and contributions to global carbon budget of the global carbonate carbon sink (CCS) still remain uncertain, allowing the problem of national measurement of CCS remain unresolved which will directly influence the fairness of global carbon markets and emission trading. Here, based on high spatiotemporal resolution ecological, meteorological raster data and chemical field monitoring data, combining highly reliable machine learning algorithm with the thermodynamic dissolution equilibrium model, we estimated the new CCS of 0.89 ± 0.23 petagrams of carbon per year (Pg C yr -1 ), amounting to 74.50% of global net forest sink and accounting for 28.75% of terrestrial sinks or 46.81% of the missing sink. Our measurement for 142 nations of CCS showed that Russia, Canada, China and the USA contribute over half of the global CCS. We also presented the first global fluxes maps of the CCS with spatial resolution of 0.05°, exhibiting two peaks in equatorial regions (10°S to 10°N) and low latitudes (10°N to 35°N) in Northern Hemisphere. By contrast, there are no peaks in Southern Hemisphere. The greatest average carbon sink flux (CCSF), i.e., 2.12 tC ha -1  yr -1 , for 2000 to 2014 was contributed by tropical rainforest climate near the equator, and the smallest average CCSF was presented in tropical arid zones, showing a magnitude of 0.26 tC ha -1  yr -1 . This research estimated the magnitudes, spatial distributions, variations and contributions to the global carbon budget of the CCS in a higher spatiotemporal representativeness and expandability way, which, via multiple mechanisms, introduced an important sink in the terrestrial carbon sink system and the global missing sink and that can help us further reveal and support our understanding of global rock weathering carbon sequestration, terrestrial carbon sink system and global carbon cycle dynamics which make our understanding of global change more comprehensive

A hybrid spatio-temporal data indexing method for trajectory databases.

Science.gov (United States)

Ke, Shengnan; Gong, Jun; Li, Songnian; Zhu, Qing; Liu, Xintao; Zhang, Yeting

2014-07-21

In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type.

A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases

Directory of Open Access Journals (Sweden)

Shengnan Ke

2014-07-01

Full Text Available In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type.

A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases

Science.gov (United States)

Ke, Shengnan; Gong, Jun; Li, Songnian; Zhu, Qing; Liu, Xintao; Zhang, Yeting

2014-01-01

In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type. PMID:25051028

A spatio-temporal model for estimating the long-term effects of air pollution on respiratory hospital admissions in Greater London.

Science.gov (United States)

Rushworth, Alastair; Lee, Duncan; Mitchell, Richard

2014-07-01

It has long been known that air pollution is harmful to human health, as many epidemiological studies have been conducted into its effects. Collectively, these studies have investigated both the acute and chronic effects of pollution, with the latter typically based on individual level cohort designs that can be expensive to implement. As a result of the increasing availability of small-area statistics, ecological spatio-temporal study designs are also being used, with which a key statistical problem is allowing for residual spatio-temporal autocorrelation that remains after the covariate effects have been removed. We present a new model for estimating the effects of air pollution on human health, which allows for residual spatio-temporal autocorrelation, and a study into the long-term effects of air pollution on human health in Greater London, England. The individual and joint effects of different pollutants are explored, via the use of single pollutant models and multiple pollutant indices. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Atypical Functional Brain Activation during a Multiple Object Tracking Task in Girls with Turner Syndrome: Neurocorrelates of Reduced Spatiotemporal Resolution

Science.gov (United States)

Beaton, Elliott A.; Stoddard, Joel; Lai, Song; Lackey, John; Shi, Jianrong; Ross, Judith L.; Simon, Tony J.

2010-01-01

Turner syndrome is associated with spatial and numerical cognitive impairments. We hypothesized that these nonverbal cognitive impairments result from limits in spatial and temporal processing, particularly as it affects attention. To examine spatiotemporal attention in girls with Turner syndrome versus typically developing controls, we used a…

A feasibility study: Selection of a personalized radiotherapy fractionation schedule using spatiotemporal optimization

International Nuclear Information System (INIS)

Kim, Minsun; Stewart, Robert D.; Phillips, Mark H.

2015-01-01

Purpose: To investigate the impact of using spatiotemporal optimization, i.e., intensity-modulated spatial optimization followed by fractionation schedule optimization, to select the patient-specific fractionation schedule that maximizes the tumor biologically equivalent dose (BED) under dose constraints for multiple organs-at-risk (OARs). Methods: Spatiotemporal optimization was applied to a variety of lung tumors in a phantom geometry using a range of tumor sizes and locations. The optimal fractionation schedule for a patient using the linear-quadratic cell survival model depends on the tumor and OAR sensitivity to fraction size (α/β), the effective tumor doubling time (T d ), and the size and location of tumor target relative to one or more OARs (dose distribution). The authors used a spatiotemporal optimization method to identify the optimal number of fractions N that maximizes the 3D tumor BED distribution for 16 lung phantom cases. The selection of the optimal fractionation schedule used equivalent (30-fraction) OAR constraints for the heart (D mean ≤ 45 Gy), lungs (D mean ≤ 20 Gy), cord (D max ≤ 45 Gy), esophagus (D max ≤ 63 Gy), and unspecified tissues (D 05 ≤ 60 Gy). To assess plan quality, the authors compared the minimum, mean, maximum, and D 95 of tumor BED, as well as the equivalent uniform dose (EUD) for optimized plans to conventional intensity-modulated radiation therapy plans prescribing 60 Gy in 30 fractions. A sensitivity analysis was performed to assess the effects of T d (3–100 days), tumor lag-time (T k = 0–10 days), and the size of tumors on optimal fractionation schedule. Results: Using an α/β ratio of 10 Gy, the average values of tumor max, min, mean BED, and D 95 were up to 19%, 21%, 20%, and 19% larger than those from conventional prescription, depending on T d and T k used. Tumor EUD was up to 17% larger than the conventional prescription. For fast proliferating tumors with T d less than 10 days, there was no

Analysis of Spatiotemporal Characteristics of Pandemic SARS Spread in Mainland China.

Science.gov (United States)

Cao, Chunxiang; Chen, Wei; Zheng, Sheng; Zhao, Jian; Wang, Jinfeng; Cao, Wuchun

2016-01-01

Severe acute respiratory syndrome (SARS) is one of the most severe emerging infectious diseases of the 21st century so far. SARS caused a pandemic that spread throughout mainland China for 7 months, infecting 5318 persons in 194 administrative regions. Using detailed mainland China epidemiological data, we study spatiotemporal aspects of this person-to-person contagious disease and simulate its spatiotemporal transmission dynamics via the Bayesian Maximum Entropy (BME) method. The BME reveals that SARS outbreaks show autocorrelation within certain spatial and temporal distances. We use BME to fit a theoretical covariance model that has a sine hole spatial component and exponential temporal component and obtain the weights of geographical and temporal autocorrelation factors. Using the covariance model, SARS dynamics were estimated and simulated under the most probable conditions. Our study suggests that SARS transmission varies in its epidemiological characteristics and SARS outbreak distributions exhibit palpable clusters on both spatial and temporal scales. In addition, the BME modelling demonstrates that SARS transmission features are affected by spatial heterogeneity, so we analyze potential causes. This may benefit epidemiological control of pandemic infectious diseases.

An Arabidopsis introgression zone studied at high spatio-temporal resolution: interglacial and multiple genetic contact exemplified using whole nuclear and plastid genomes.

Science.gov (United States)

Hohmann, Nora; Koch, Marcus A

2017-10-23

Gene flow between species, across ploidal levels, and even between evolutionary lineages is a common phenomenon in the genus Arabidopsis. However, apart from two genetically fully stabilized allotetraploid species that have been investigated in detail, the extent and temporal dynamics of hybridization are not well understood. An introgression zone, with tetraploid A. arenosa introgressing into A. lyrata subsp. petraea in the Eastern Austrian Forealps and subsequent expansion towards pannonical lowlands, was described previously based on morphological observations as well as molecular data using microsatellite and plastid DNA markers. Here we investigate the spatio-temporal context of this suture zone, making use of the potential of next-generation sequencing and whole-genome data. By utilizing a combination of nuclear and plastid genomic data, the extent, direction and temporal dynamics of gene flow are elucidated in detail and Late Pleistocene evolutionary processes are resolved. Analysis of nuclear genomic data significantly recognizes the clinal structure of the introgression zone, but also reveals that hybridization and introgression is more common and substantial than previously thought. Also tetraploid A. lyrata and A. arenosa subsp. borbasii from outside the previously defined suture zone show genomic signals of past introgression. A. lyrata is shown to serve usually as the maternal parent in these hybridizations, but one exception is identified from plastome-based phylogenetic reconstruction. Using plastid phylogenomics with secondary time calibration, the origin of A. lyrata and A. arenosa lineages is pre-dating the last three glaciation complexes (approx. 550,000 years ago). Hybridization and introgression followed during the last two glacial-interglacial periods (since approx. 300,000 years ago) with later secondary contact at the northern and southern border of the introgression zone during the Holocene. Footprints of adaptive introgression in the

Spatiotemporal Change Detection Using Landsat Imagery: the Case Study of Karacabey Flooded Forest, Bursa, Turkey

Science.gov (United States)

Akay, A. E.; Gencal, B.; Taş, İ.

2017-11-01

This short paper aims to detect spatiotemporal detection of land use/land cover change within Karacabey Flooded Forest region. Change detection analysis applied to Landsat 5 TM images representing July 2000 and a Landsat 8 OLI representing June 2017. Various image processing tools were implemented using ERDAS 9.2, ArcGIS 10.4.1, and ENVI programs to conduct spatiotemporal change detection over these two images such as band selection, corrections, subset, classification, recoding, accuracy assessment, and change detection analysis. Image classification revealed that there are five significant land use/land cover types, including forest, flooded forest, swamp, water, and other lands (i.e. agriculture, sand, roads, settlement, and open areas). The results indicated that there was increase in flooded forest, water, and other lands, while the cover of forest and swamp decreased.

Integrating GIS and ABM to Explore Spatiotemporal Dynamics

Science.gov (United States)

Sun, M.; Jiang, Y.; Yang, C.

2013-12-01

Agent-based modeling as a methodology for the bottom-up exploration with the account of adaptive behavior and heterogeneity of system components can help discover the development and pattern of the complex social and environmental system. However, ABM is a computationally intensive process especially when the number of system components becomes large and the agent-agent/agent-environmental interaction is modeled very complex. Most of traditional ABM frameworks developed based on CPU do not have a satisfying computing capacity. To address the problem and as the emergence of advanced techniques, GPU computing with CUDA can provide powerful parallel structure to enable the complex simulation of spatiotemporal dynamics. In this study, we first develop a GPU-based ABM system. Secondly, in order to visualize the dynamics generated from the movement of agent and the change of agent/environmental attributes during the simulation, we integrate GIS into the ABM system. Advanced geovisualization technologies can be utilized for representing the spatiotemporal change events, such as proper 2D/3D maps with state-of-the-art symbols, space-time cube and multiple layers each of which presents pattern in one time-stamp, etc. Thirdly, visual analytics which include interactive tools (e.g. grouping, filtering, linking, etc.) is included in our ABM-GIS system to help users conduct real-time data exploration during the progress of simulation. Analysis like flow analysis and spatial cluster analysis can be integrated according to the geographical problem we want to explore.

Mining Spatiotemporal Patterns of the Elder's Daily Movement

Science.gov (United States)

Chen, C. R.; Chen, C. F.; Liu, M. E.; Tsai, S. J.; Son, N. T.; Kinh, L. V.

2016-06-01

With rapid developments in wearable device technology, a vast amount of spatiotemporal data, such as people's movement and physical activities, are generated. Information derived from the data reveals important knowledge that can contribute a long-term care and psychological assessment of the elders' living condition especially in long-term care institutions. This study aims to develop a method to investigate the spatial-temporal movement patterns of the elders with their outdoor trajectory information. To achieve the goal, GPS based location data of the elderly subjects from long-term care institutions are collected and analysed with geographic information system (GIS). A GIS statistical model is developed to mine the elderly subjects' spatiotemporal patterns with the location data and represent their daily movement pattern at particular time. The proposed method first finds the meaningful trajectory and extracts the frequent patterns from the time-stamp location data. Then, a density-based clustering method is used to identify the major moving range and the gather/stay hotspot in both spatial and temporal dimensions. The preliminary results indicate that the major moving area of the elderly people encompasses their dorm and has a short moving distance who often stay in the same site. Subjects' outdoor appearance are corresponded to their life routine. The results can be useful for understanding elders' social network construction, risky area identification and medical care monitoring.

Full-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets

KAUST Repository

Zhang, Bohai

2014-01-01

Various continuously-indexed spatio-temporal process models have been constructed to characterize spatio-temporal dependence structures, but the computational complexity for model fitting and predictions grows in a cubic order with the size of dataset and application of such models is not feasible for large datasets. This article extends the full-scale approximation (FSA) approach by Sang and Huang (2012) to the spatio-temporal context to reduce computational complexity. A reversible jump Markov chain Monte Carlo (RJMCMC) algorithm is proposed to select knots automatically from a discrete set of spatio-temporal points. Our approach is applicable to nonseparable and nonstationary spatio-temporal covariance models. We illustrate the effectiveness of our method through simulation experiments and application to an ozone measurement dataset.

Bridge damage detection using spatiotemporal patterns extracted from dense sensor network

International Nuclear Information System (INIS)

Liu, Chao; Sarkar, Soumik; Gong, Yongqiang; Laflamme, Simon; Phares, Brent

2017-01-01

The alarmingly degrading state of transportation infrastructures combined with their key societal and economic importance calls for automatic condition assessment methods to facilitate smart management of maintenance and repairs. With the advent of ubiquitous sensing and communication capabilities, scalable data-driven approaches is of great interest, as it can utilize large volume of streaming data without requiring detailed physical models that can be inaccurate and computationally expensive to run. Properly designed, a data-driven methodology could enable fast and automatic evaluation of infrastructures, discovery of causal dependencies among various sub-system dynamic responses, and decision making with uncertainties and lack of labeled data. In this work, a spatiotemporal pattern network (STPN) strategy built on symbolic dynamic filtering (SDF) is proposed to explore spatiotemporal behaviors in a bridge network. Data from strain gauges installed on two bridges are generated using finite element simulation for three types of sensor networks from a density perspective (dense, nominal, sparse). Causal relationships among spatially distributed strain data streams are extracted and analyzed for vehicle identification and detection, and for localization of structural degradation in bridges. Multiple case studies show significant capabilities of the proposed approach in: (i) capturing spatiotemporal features to discover causality between bridges (geographically close), (ii) robustness to noise in data for feature extraction, (iii) detecting and localizing damage via comparison of bridge responses to similar vehicle loads, and (iv) implementing real-time health monitoring and decision making work flow for bridge networks. Also, the results demonstrate increased sensitivity in detecting damages and higher reliability in quantifying the damage level with increase in sensor network density. (paper)

Spatio-temporal dependencies between hospital beds, physicians and health expenditure using visual variables and data classification in statistical table

Science.gov (United States)

Medyńska-Gulij, Beata; Cybulski, Paweł

2016-06-01

This paper analyses the use of table visual variables of statistical data of hospital beds as an important tool for revealing spatio-temporal dependencies. It is argued that some of conclusions from the data about public health and public expenditure on health have a spatio-temporal reference. Different from previous studies, this article adopts combination of cartographic pragmatics and spatial visualization with previous conclusions made in public health literature. While the significant conclusions about health care and economic factors has been highlighted in research papers, this article is the first to apply visual analysis to statistical table together with maps which is called previsualisation.

Spatio-temporal dependencies between hospital beds, physicians and health expenditure using visual variables and data classification in statistical table

Directory of Open Access Journals (Sweden)

Medyńska-Gulij Beata

2016-06-01

Full Text Available This paper analyses the use of table visual variables of statistical data of hospital beds as an important tool for revealing spatio-temporal dependencies. It is argued that some of conclusions from the data about public health and public expenditure on health have a spatio-temporal reference. Different from previous studies, this article adopts combination of cartographic pragmatics and spatial visualization with previous conclusions made in public health literature. While the significant conclusions about health care and economic factors has been highlighted in research papers, this article is the first to apply visual analysis to statistical table together with maps which is called previsualisation.

Spatio-temporal scaling of channels in braided streams.

Science.gov (United States)

A.G. Hunt; G.E. Grant; V.K. Gupta

2006-01-01

The spatio-temporal scaling relationship for individual channels in braided streams is shown to be identical to the spatio-temporal scaling associated with constant Froude number, e.g., Fr = l. A means to derive this relationship is developed from a new theory of sediment transport. The mechanism by which the Fr = l condition apparently governs the scaling seems to...

Visual representation of spatiotemporal structure

Science.gov (United States)

Schill, Kerstin; Zetzsche, Christoph; Brauer, Wilfried; Eisenkolb, A.; Musto, A.

1998-07-01

The processing and representation of motion information is addressed from an integrated perspective comprising low- level signal processing properties as well as higher-level cognitive aspects. For the low-level processing of motion information we argue that a fundamental requirement is the existence of a spatio-temporal memory. Its key feature, the provision of an orthogonal relation between external time and its internal representation, is achieved by a mapping of temporal structure into a locally distributed activity distribution accessible in parallel by higher-level processing stages. This leads to a reinterpretation of the classical concept of `iconic memory' and resolves inconsistencies on ultra-short-time processing and visual masking. The spatial-temporal memory is further investigated by experiments on the perception of spatio-temporal patterns. Results on the direction discrimination of motion paths provide evidence that information about direction and location are not processed and represented independent of each other. This suggests a unified representation on an early level, in the sense that motion information is internally available in form of a spatio-temporal compound. For the higher-level representation we have developed a formal framework for the qualitative description of courses of motion that may occur with moving objects.

Spatiotemporal profiles of receptive fields of neurons in the lateral posterior nucleus of the cat LP-pulvinar complex.

Science.gov (United States)

Piché, Marilyse; Thomas, Sébastien; Casanova, Christian

2015-10-01

The pulvinar is the largest extrageniculate thalamic visual nucleus in mammals. It establishes reciprocal connections with virtually all visual cortexes and likely plays a role in transthalamic cortico-cortical communication. In cats, the lateral posterior nucleus (LP) of the LP-pulvinar complex can be subdivided in two subregions, the lateral (LPl) and medial (LPm) parts, which receive a predominant input from the striate cortex and the superior colliculus, respectively. Here, we revisit the receptive field structure of LPl and LPm cells in anesthetized cats by determining their first-order spatiotemporal profiles through reverse correlation analysis following sparse noise stimulation. Our data reveal the existence of previously unidentified receptive field profiles in the LP nucleus both in space and time domains. While some cells responded to only one stimulus polarity, the majority of neurons had receptive fields comprised of bright and dark responsive subfields. For these neurons, dark subfields' size was larger than that of bright subfields. A variety of receptive field spatial organization types were identified, ranging from totally overlapped to segregated bright and dark subfields. In the time domain, a large spectrum of activity overlap was found, from cells with temporally coinciding subfield activity to neurons with distinct, time-dissociated subfield peak activity windows. We also found LP neurons with space-time inseparable receptive fields and neurons with multiple activity periods. Finally, a substantial degree of homology was found between LPl and LPm first-order receptive field spatiotemporal profiles, suggesting a high integration of cortical and subcortical inputs within the LP-pulvinar complex. Copyright © 2015 the American Physiological Society.

A Spatiotemporal Multi-View-Based Learning Method for Short-Term Traffic Forecasting

Directory of Open Access Journals (Sweden)

Shifen Cheng

2018-06-01

Full Text Available Short-term traffic forecasting plays an important part in intelligent transportation systems. Spatiotemporal k-nearest neighbor models (ST-KNNs have been widely adopted for short-term traffic forecasting in which spatiotemporal matrices are constructed to describe traffic conditions. The performance of the models is closely related to the spatial dependencies, the temporal dependencies, and the interaction of spatiotemporal dependencies. However, these models use distance functions and correlation coefficients to identify spatial neighbors and measure the temporal interaction by only considering the temporal closeness of traffic, which result in existing ST-KNNs that cannot fully reflect the essential features of road traffic. This study proposes an improved spatiotemporal k-nearest neighbor model for short-term traffic forecasting by utilizing a multi-view learning algorithm named MVL-STKNN that fully considers the spatiotemporal dependencies of traffic data. First, the spatial neighbors for each road segment are automatically determined using cross-correlation under different temporal dependencies. Three spatiotemporal views are built on the constructed spatiotemporal closeness, periodic, and trend matrices to represent spatially heterogeneous traffic states. Second, a spatiotemporal weighting matrix is introduced into the ST-KNN model to recognize similar traffic patterns in the three spatiotemporal views. Finally, the results of traffic pattern recognition under these three spatiotemporal views are aggregated by using a neural network algorithm to describe the interaction of spatiotemporal dependencies. Extensive experiments were conducted using real vehicular-speed datasets collected on city roads and expressways. In comparison with baseline methods, the results show that the MVL-STKNN model greatly improves short-term traffic forecasting by lowering the mean absolute percentage error between 28.24% and 46.86% for the city road dataset and

A feasibility study: Selection of a personalized radiotherapy fractionation schedule using spatiotemporal optimization

Energy Technology Data Exchange (ETDEWEB)

Kim, Minsun, E-mail: mk688@uw.edu; Stewart, Robert D. [Department of Radiation Oncology, University of Washington, Seattle, Washington 98195-6043 (United States); Phillips, Mark H. [Departments of Radiation Oncology and Neurological Surgery, University of Washington, Seattle, Washington 98195-6043 (United States)

2015-11-15

Purpose: To investigate the impact of using spatiotemporal optimization, i.e., intensity-modulated spatial optimization followed by fractionation schedule optimization, to select the patient-specific fractionation schedule that maximizes the tumor biologically equivalent dose (BED) under dose constraints for multiple organs-at-risk (OARs). Methods: Spatiotemporal optimization was applied to a variety of lung tumors in a phantom geometry using a range of tumor sizes and locations. The optimal fractionation schedule for a patient using the linear-quadratic cell survival model depends on the tumor and OAR sensitivity to fraction size (α/β), the effective tumor doubling time (T{sub d}), and the size and location of tumor target relative to one or more OARs (dose distribution). The authors used a spatiotemporal optimization method to identify the optimal number of fractions N that maximizes the 3D tumor BED distribution for 16 lung phantom cases. The selection of the optimal fractionation schedule used equivalent (30-fraction) OAR constraints for the heart (D{sub mean} ≤ 45 Gy), lungs (D{sub mean} ≤ 20 Gy), cord (D{sub max} ≤ 45 Gy), esophagus (D{sub max} ≤ 63 Gy), and unspecified tissues (D{sub 05} ≤ 60 Gy). To assess plan quality, the authors compared the minimum, mean, maximum, and D{sub 95} of tumor BED, as well as the equivalent uniform dose (EUD) for optimized plans to conventional intensity-modulated radiation therapy plans prescribing 60 Gy in 30 fractions. A sensitivity analysis was performed to assess the effects of T{sub d} (3–100 days), tumor lag-time (T{sub k} = 0–10 days), and the size of tumors on optimal fractionation schedule. Results: Using an α/β ratio of 10 Gy, the average values of tumor max, min, mean BED, and D{sub 95} were up to 19%, 21%, 20%, and 19% larger than those from conventional prescription, depending on T{sub d} and T{sub k} used. Tumor EUD was up to 17% larger than the conventional prescription. For fast proliferating

A Mixed Land Cover Spatio-temporal Data Model Based on Object-oriented and Snapshot

Directory of Open Access Journals (Sweden)

LI Yinchao

2016-07-01

Full Text Available Spatio-temporal data model (STDM is one of the hot topics in the domains of spatio-temporal database and data analysis. There is a common view that a universal STDM is always of high complexity due to the various situation of spatio-temporal data. In this article, a mixed STDM is proposed based on object-oriented and snapshot models for modelling and analyzing landcover change (LCC. This model uses the object-oriented STDM to describe the spatio-temporal processes of land cover patches and organize their spatial and attributive properties. In the meantime, it uses the snapshot STDM to present the spatio-temporal distribution of LCC on the whole via snapshot images. The two types of models are spatially and temporally combined into a mixed version. In addition to presenting the spatio-temporal events themselves, this model could express the transformation events between different classes of spatio-temporal objects. It can be used to create database for historical data of LCC, do spatio-temporal statistics, simulation and data mining with the data. In this article, the LCC data in Heilongjiang province is used for case study to validate spatio-temporal data management and analysis abilities of mixed STDM, including creating database, spatio-temporal query, global evolution analysis and patches spatio-temporal process expression.

Monitoring urban greenness dynamics using multiple endmember spectral mixture analysis.

Directory of Open Access Journals (Sweden)

Muye Gan

Full Text Available Urban greenness is increasingly recognized as an essential constituent of the urban environment and can provide a range of services and enhance residents' quality of life. Understanding the pattern of urban greenness and exploring its spatiotemporal dynamics would contribute valuable information for urban planning. In this paper, we investigated the pattern of urban greenness in Hangzhou, China, over the past two decades using time series Landsat-5 TM data obtained in 1990, 2002, and 2010. Multiple endmember spectral mixture analysis was used to derive vegetation cover fractions at the subpixel level. An RGB-vegetation fraction model, change intensity analysis and the concentric technique were integrated to reveal the detailed, spatial characteristics and the overall pattern of change in the vegetation cover fraction. Our results demonstrated the ability of multiple endmember spectral mixture analysis to accurately model the vegetation cover fraction in pixels despite the complex spectral confusion of different land cover types. The integration of multiple techniques revealed various changing patterns in urban greenness in this region. The overall vegetation cover has exhibited a drastic decrease over the past two decades, while no significant change occurred in the scenic spots that were studied. Meanwhile, a remarkable recovery of greenness was observed in the existing urban area. The increasing coverage of small green patches has played a vital role in the recovery of urban greenness. These changing patterns were more obvious during the period from 2002 to 2010 than from 1990 to 2002, and they revealed the combined effects of rapid urbanization and greening policies. This work demonstrates the usefulness of time series of vegetation cover fractions for conducting accurate and in-depth studies of the long-term trajectories of urban greenness to obtain meaningful information for sustainable urban development.

Optimization of Spatiotemporal Apertures in Channel Sounding

DEFF Research Database (Denmark)

Pedersen, Troels; Pedersen, Claus; Yin, Xuefeng

2008-01-01

a spatiotemporal model which can describe parallel as well as switched sounding systems. The proposed model is applicable for arbitrary layouts of the spatial arrays. To simplify the derivations we investigate the special case of linear spatial arrays. However, the results obtained for linear arrays can......In this paper we investigate the impact of the spatio-temporal aperture of a channel sounding system equipped with antenna arrays at the transmitter and receiver on the accuracy of joint estimation of Doppler frequency and bi-direction. The contribution of this work is three-fold. Firstly, we state...... be generalized to arbitrary arrays. Secondly, we give the necessary and sufficient conditions for a spatio-temporal array to yield the minimum Cramér-Rao lower bound in the single-path case and Bayesian Cramér-Rao Lower Bound in the multipath case. The obtained conditions amount to an orthogonality condition...

Spatiotemporal Diffusive Evolution and Fractal Structure of Ground Motion

Science.gov (United States)

Suwada, Tsuyoshi

2018-02-01

The spatiotemporal diffusive evolution and fractal structure of ground motion have been investigated at the in-ground tunnel of the KEK B-Factory (KEKB) injector linear accelerator (linac). The slow dynamic fluctuating displacements of the tunnel floor are measured in real time with a new remote-controllable sensing system based on a laser-based alignment system. Based on spatiotemporal analyses with linear-regression models, which were applied in both the time and frequency domains to time-series data recorded over a period of approximately 8 months, both coherent and stochastic components in the displacements of the tunnel floor were clearly observed along the entire length of the linac. In particular, it was clearly observed that the stochastic components exhibited characteristic spatiotemporal diffusive evolution with the fractal structure and fractional dimension. This report describes in detail the experimental techniques and analyses of the spatiotemporal diffusive evolution of ground motion observed at the in-ground tunnel of the injector linac using a real-time remote-controllable sensing system.

Indeterminacy and Spatiotemporal Data

DEFF Research Database (Denmark)

Pfoser, D.; Tryfona, N.; Jensen, Christian Søndergaard

2005-01-01

For some spatiotemporal applications, it can be assumed that the modeled world is precise and bounded, and that our record of it is precise. While these simplifying assumptions are sufficient in applications like a land information system, they are unnecessarily crude for many other applications...

Multiple skeletal muscle metastases revealing a cardiac intimal sarcoma

Energy Technology Data Exchange (ETDEWEB)

Crombe, Amandine [Institut Bergonie, Department of Radiology, Bordeaux (France); Lintingre, Pierre-Francois; Dallaudiere, Benjamin [Clinique du Sport de Bordeaux-Merignac, Department of Musculoskeletal Radiology, Merignac (France); Le Loarer, Francois [Institut Bergonie, Department of Pathology, Bordeaux (France); Lachatre, Denis [Dupuytren University Hospital, Department of Radiology, Limoges (France)

2018-01-15

We report the case of a 59-year-old female with progressive bilateral painful swelling of the thighs. MRI revealed multiple intramuscular necrotic masses with similar morphologic patterns. Whole-body CT and 18-FDG PET-CT scans demonstrated additional hypermetabolic muscular masses and a lobulated lesion within the left atrial cavity. As biopsy of a muscular mass was compatible with a poorly differentiated sarcoma with MDM2 oncogene amplification, two diagnoses were discussed: a dedifferentiated liposarcoma with muscle and heart metastases or a primary cardiac sarcoma, mainly a cardiac intimal sarcoma, with muscular metastases, which was finally confirmed by array-comparative genomic hybridization (aCGH) in a sarcoma reference center. This case emphasizes the potential for intimal sarcoma to disseminate in skeletal muscle prior to any other organ and the need for a genomic approach in addition to classical radiopathologic analyses to distinguish primary from secondary locations facing simultaneous tumors of the heart and skeletal muscles with MDM2 amplification. (orig.)

Spatio-temporal coherent control of atomic systems: weak to strong field transition and breaking of symmetry in 2D maps

Energy Technology Data Exchange (ETDEWEB)

Suchowski, H; Natan, A; Bruner, B D; Silberberg, Y [Physics of Complex Systems, Weizmann Institute of Science, Rehovot (Israel)], E-mail: haim.suchowski@weizmann.ac.il

2008-04-14

Coherent control of resonant and non-resonant two-photon absorption processes was examined using a spatio-temporal pulse-shaping technique. By utilizing a combination of temporal focusing and femtosecond pulse-shaping techniques, we spatially control multiphoton absorption processes in a completely deterministic manner. Distinctive symmetry properties emerge through two-dimensional mapping of spatio-temporal data. These symmetries break down in the transition to strong fields, revealing details of strong-field effects such as power broadenings and dynamic Stark shifts. We also present demonstrations of chirp-dependent population transfer in atomic rubidium, as well as the spatial separation of resonant and non-resonant excitation pathways in atomic caesium.

Spatio-temporal coherent control of atomic systems: weak to strong field transition and breaking of symmetry in 2D maps

International Nuclear Information System (INIS)

Suchowski, H; Natan, A; Bruner, B D; Silberberg, Y

2008-01-01

Coherent control of resonant and non-resonant two-photon absorption processes was examined using a spatio-temporal pulse-shaping technique. By utilizing a combination of temporal focusing and femtosecond pulse-shaping techniques, we spatially control multiphoton absorption processes in a completely deterministic manner. Distinctive symmetry properties emerge through two-dimensional mapping of spatio-temporal data. These symmetries break down in the transition to strong fields, revealing details of strong-field effects such as power broadenings and dynamic Stark shifts. We also present demonstrations of chirp-dependent population transfer in atomic rubidium, as well as the spatial separation of resonant and non-resonant excitation pathways in atomic caesium

Analysis of Spatiotemporal Characteristics of Pandemic SARS Spread in Mainland China

Directory of Open Access Journals (Sweden)

Chunxiang Cao

2016-01-01

Full Text Available Severe acute respiratory syndrome (SARS is one of the most severe emerging infectious diseases of the 21st century so far. SARS caused a pandemic that spread throughout mainland China for 7 months, infecting 5318 persons in 194 administrative regions. Using detailed mainland China epidemiological data, we study spatiotemporal aspects of this person-to-person contagious disease and simulate its spatiotemporal transmission dynamics via the Bayesian Maximum Entropy (BME method. The BME reveals that SARS outbreaks show autocorrelation within certain spatial and temporal distances. We use BME to fit a theoretical covariance model that has a sine hole spatial component and exponential temporal component and obtain the weights of geographical and temporal autocorrelation factors. Using the covariance model, SARS dynamics were estimated and simulated under the most probable conditions. Our study suggests that SARS transmission varies in its epidemiological characteristics and SARS outbreak distributions exhibit palpable clusters on both spatial and temporal scales. In addition, the BME modelling demonstrates that SARS transmission features are affected by spatial heterogeneity, so we analyze potential causes. This may benefit epidemiological control of pandemic infectious diseases.

Multi-perspective analysis and spatiotemporal mapping of air pollution monitoring data.

Science.gov (United States)

Kolovos, Alexander; Skupin, André; Jerrett, Michael; Christakos, George

2010-09-01

Space-time data analysis and assimilation techniques in atmospheric sciences typically consider input from monitoring measurements. The input is often processed in a manner that acknowledges characteristics of the measurements (e.g., underlying patterns, fluctuation features) under conditions of uncertainty; it also leads to the derivation of secondary information that serves study-oriented goals, and provides input to space-time prediction techniques. We present a novel approach that blends a rigorous space-time prediction model (Bayesian maximum entropy, BME) with a cognitively informed visualization of high-dimensional data (spatialization). The combined BME and spatialization approach (BME-S) is used to study monthly averaged NO2 and mean annual SO4 measurements in California over the 15-year period 1988-2002. Using the original scattered measurements of these two pollutants BME generates spatiotemporal predictions on a regular grid across the state. Subsequently, the prediction network undergoes the spatialization transformation into a lower-dimensional geometric representation, aimed at revealing patterns and relationships that exist within the input data. The proposed BME-S provides a powerful spatiotemporal framework to study a variety of air pollution data sources.

Placing invasive species management in a spatiotemporal context.

Science.gov (United States)

Baker, Christopher M; Bode, Michael

2016-04-01

Invasive species are a worldwide issue, both ecologically and economically. A large body of work focuses on various aspects of invasive species control, including how to allocate control efforts to eradicate an invasive population as cost effectively as possible: There are a diverse range of invasive species management problems, and past mathematical analyses generally focus on isolated examples, making it hard to identify and understand parallels between the different contexts. In this study, we use a single spatiotemporal model to tackle the problem of allocating control effort for invasive species when suppressing an island invasive species, and for long-term spatial suppression projects. Using feral cat suppression as an illustrative example, we identify the optimal resource allocation for island and mainland suppression projects. Our results demonstrate how using a single model to solve different problems reveals similar characteristics of the solutions in different scenarios. As well as illustrating the insights offered by linking problems through a spatiotemporal model, we also derive novel and practically applicable results for our case studies. For temporal suppression projects on islands, we find that lengthy projects are more cost effective and that rapid control projects are only economically cost effective when population growth rates are high or diminishing returns on control effort are low. When suppressing invasive species around conservation assets (e.g., national parks or exclusion fences), we find that the size of buffer zones should depend on the ratio of the species growth and spread rate.

Effective and efficient analysis of spatio-temporal data

Science.gov (United States)

Zhang, Zhongnan

Spatio-temporal data mining, i.e., mining knowledge from large amount of spatio-temporal data, is a highly demanding field because huge amounts of spatio-temporal data have been collected in various applications, ranging from remote sensing, to geographical information systems (GIS), computer cartography, environmental assessment and planning, etc. The collection data far exceeded human's ability to analyze which make it crucial to develop analysis tools. Recent studies on data mining have extended to the scope of data mining from relational and transactional datasets to spatial and temporal datasets. Among the various forms of spatio-temporal data, remote sensing images play an important role, due to the growing wide-spreading of outer space satellites. In this dissertation, we proposed two approaches to analyze the remote sensing data. The first one is about applying association rules mining onto images processing. Each image was divided into a number of image blocks. We built a spatial relationship for these blocks during the dividing process. This made a large number of images into a spatio-temporal dataset since each image was shot in time-series. The second one implemented co-occurrence patterns discovery from these images. The generated patterns represent subsets of spatial features that are located together in space and time. A weather analysis is composed of individual analysis of several meteorological variables. These variables include temperature, pressure, dew point, wind, clouds, visibility and so on. Local-scale models provide detailed analysis and forecasts of meteorological phenomena ranging from a few kilometers to about 100 kilometers in size. When some of above meteorological variables have some special change tendency, some kind of severe weather will happen in most cases. Using the discovery of association rules, we found that some special meteorological variables' changing has tight relation with some severe weather situation that will happen

Dynamical topology and statistical properties of spatiotemporal chaos.

Science.gov (United States)

Zhuang, Quntao; Gao, Xun; Ouyang, Qi; Wang, Hongli

2012-12-01

For spatiotemporal chaos described by partial differential equations, there are generally locations where the dynamical variable achieves its local extremum or where the time partial derivative of the variable vanishes instantaneously. To a large extent, the location and movement of these topologically special points determine the qualitative structure of the disordered states. We analyze numerically statistical properties of the topologically special points in one-dimensional spatiotemporal chaos. The probability distribution functions for the number of point, the lifespan, and the distance covered during their lifetime are obtained from numerical simulations. Mathematically, we establish a probabilistic model to describe the dynamics of these topologically special points. In spite of the different definitions in different spatiotemporal chaos, the dynamics of these special points can be described in a uniform approach.

Spatiotemporal Psychopathology II: How does a psychopathology of the brain's resting state look like? Spatiotemporal approach and the history of psychopathology.

Science.gov (United States)

Northoff, Georg

2016-01-15

Psychopathology as the investigation and classification of experience, behavior and symptoms in psychiatric patients is an old discipline that ranges back to the end of the 19th century. Since then different approaches to psychopathology have been suggested. Recent investigations showing abnormalities in the brain on different levels raise the question how the gap between brain and psyche, between neural abnormalities and alteration in experience and behavior can be bridged. Historical approaches like descriptive (Jaspers) and structural (Minkoswki) psychopathology as well as the more current phenomenological psychopathology (Paarnas, Fuchs, Sass, Stanghellini) remain on the side of the psyche giving detailed description of the phenomenal level of experience while leaving open the link to the brain. In contrast, the recently introduced Research Domain Classification (RDoC) aims at explicitly linking brain and psyche by starting from so-called 'neuro-behavioral constructs'. How does Spatiotemporal Psychopathology, as demonstrated in the first paper on depression, stand in relation to these approaches? In a nutshell, Spatiotemporal Psychopathology aims to bridge the gap between brain and psyche. Specifically, as demonstrated in depression in the first paper, the focus is on the spatiotemporal features of the brain's intrinsic activity and how they are transformed into corresponding spatiotemporal features in experience on the phenomenal level and behavioral changes, which can well account for the symptoms in these patients. This second paper focuses on some of the theoretical background assumptions in Spatiotemporal Psychopathology by directly comparing it to descriptive, structural, and phenomenological psychopathology as well as to RDoC. Copyright © 2015 Elsevier B.V. All rights reserved.

Building spatio-temporal database model based on ontological approach using relational database environment

International Nuclear Information System (INIS)

Mahmood, N.; Burney, S.M.A.

2017-01-01

Everything in this world is encapsulated by space and time fence. Our daily life activities are utterly linked and related with other objects in vicinity. Therefore, a strong relationship exist with our current location, time (including past, present and future) and event through with we are moving as an object also affect our activities in life. Ontology development and its integration with database are vital for the true understanding of the complex systems involving both spatial and temporal dimensions. In this paper we propose a conceptual framework for building spatio-temporal database model based on ontological approach. We have used relational data model for modelling spatio-temporal data content and present our methodology with spatio-temporal ontological accepts and its transformation into spatio-temporal database model. We illustrate the implementation of our conceptual model through a case study related to cultivated land parcel used for agriculture to exhibit the spatio-temporal behaviour of agricultural land and related entities. Moreover, it provides a generic approach for designing spatiotemporal databases based on ontology. The proposed model is capable to understand the ontological and somehow epistemological commitments and to build spatio-temporal ontology and transform it into a spatio-temporal data model. Finally, we highlight the existing and future research challenges. (author)

A full time-domain approach to spatio-temporal dynamics of semiconductor lasers. II. Spatio-temporal dynamics

Science.gov (United States)

Böhringer, Klaus; Hess, Ortwin

The spatio-temporal dynamics of novel semiconductor lasers is discussed on the basis of a space- and momentum-dependent full time-domain approach. To this means the space-, time-, and momentum-dependent Full-Time Domain Maxwell Semiconductor Bloch equations, derived and discussed in our preceding paper I [K. Böhringer, O. Hess, A full time-domain approach to spatio-temporal dynamics of semiconductor lasers. I. Theoretical formulation], are solved by direct numerical integration. Focussing on the device physics of novel semiconductor lasers that profit, in particular, from recent advances in nanoscience and nanotechnology, we discuss the examples of photonic band edge surface emitting lasers (PBE-SEL) and semiconductor disc lasers (SDLs). It is demonstrated that photonic crystal effects can be obtained for finite crystal structures, and leading to a significant improvement in laser performance such as reduced lasing thresholds. In SDLs, a modern device concept designed to increase the power output of surface-emitters in combination with near-diffraction-limited beam quality, we explore the complex interplay between the intracavity optical fields and the quantum well gain material in SDL structures. Our simulations reveal the dynamical balance between carrier generation due to pumping into high energy states, momentum relaxation of carriers, and stimulated recombination from states near the band edge. Our full time-domain approach is shown to also be an excellent framework for the modelling of the interaction of high-intensity femtosecond and picosecond pulses with semiconductor nanostructures. It is demonstrated that group velocity dispersion, dynamical gain saturation and fast self-phase modulation (SPM) are the main causes for the induced changes and asymmetries in the amplified pulse shape and spectrum of an ultrashort high-intensity pulse. We attest that the time constants of the intraband scattering processes are critical to gain recovery. Moreover, we present

On the genesis of spatio-temporal self-organized structures in plasma devices

International Nuclear Information System (INIS)

Talasman, S.J.; Sanduloviciu, M.

1995-01-01

The genesis of luminous sharply defined nearly spherical space charges structures formed in an Argon plasma column was experimental investigated. The results reveal spatio-temporal characteristics proper to systems resulting after a self-organization process. Their phenomenology involves electrical charges separation produced by symmetry breaking and spatial separation of the excitation and ionization cross sections functions in a region where electrons are accelerated and, as a result, the appearance of electrostatic forces that, acting as long range correlations, assures, together with dissipative effects, its stability. (Author) 8 Figs., 31 Refs

Whole-brain activity maps reveal stereotyped, distributed networks for visuomotor behavior.

Science.gov (United States)

Portugues, Ruben; Feierstein, Claudia E; Engert, Florian; Orger, Michael B

2014-03-19

Most behaviors, even simple innate reflexes, are mediated by circuits of neurons spanning areas throughout the brain. However, in most cases, the distribution and dynamics of firing patterns of these neurons during behavior are not known. We imaged activity, with cellular resolution, throughout the whole brains of zebrafish performing the optokinetic response. We found a sparse, broadly distributed network that has an elaborate but ordered pattern, with a bilaterally symmetrical organization. Activity patterns fell into distinct clusters reflecting sensory and motor processing. By correlating neuronal responses with an array of sensory and motor variables, we find that the network can be clearly divided into distinct functional modules. Comparing aligned data from multiple fish, we find that the spatiotemporal activity dynamics and functional organization are highly stereotyped across individuals. These experiments systematically reveal the functional architecture of neural circuits underlying a sensorimotor behavior in a vertebrate brain. Copyright © 2014 Elsevier Inc. All rights reserved.

Spatio-temporal Eigenvector Filtering: Application on Bioenergy Crop Impacts

Science.gov (United States)

Wang, M.; Kamarianakis, Y.; Georgescu, M.

2017-12-01

A suite of 10-year ensemble-based simulations was conducted to investigate the hydroclimatic impacts due to large-scale deployment of perennial bioenergy crops across the continental United States. Given the large size of the simulated dataset (about 60Tb), traditional hierarchical spatio-temporal statistical modelling cannot be implemented for the evaluation of physics parameterizations and biofuel impacts. In this work, we propose a filtering algorithm that takes into account the spatio-temporal autocorrelation structure of the data while avoiding spatial confounding. This method is used to quantify the robustness of simulated hydroclimatic impacts associated with bioenergy crops to alternative physics parameterizations and observational datasets. Results are evaluated against those obtained from three alternative Bayesian spatio-temporal specifications.

Spatiotemporal radiotherapy planning using a global optimization approach

Science.gov (United States)

Adibi, Ali; Salari, Ehsan

2018-02-01

This paper aims at quantifying the extent of potential therapeutic gain, measured using biologically effective dose (BED), that can be achieved by altering the radiation dose distribution over treatment sessions in fractionated radiotherapy. To that end, a spatiotemporally integrated planning approach is developed, where the spatial and temporal dose modulations are optimized simultaneously. The concept of equivalent uniform BED (EUBED) is used to quantify and compare the clinical quality of spatiotemporally heterogeneous dose distributions in target and critical structures. This gives rise to a large-scale non-convex treatment-plan optimization problem, which is solved using global optimization techniques. The proposed spatiotemporal planning approach is tested on two stylized cancer cases resembling two different tumor sites and sensitivity analysis is performed for radio-biological and EUBED parameters. Numerical results validate that spatiotemporal plans are capable of delivering a larger BED to the target volume without increasing the BED in critical structures compared to conventional time-invariant plans. In particular, this additional gain is attributed to the irradiation of different regions of the target volume at different treatment sessions. Additionally, the trade-off between the potential therapeutic gain and the number of distinct dose distributions is quantified, which suggests a diminishing marginal gain as the number of dose distributions increases.

Spatio-temporal databases complex motion pattern queries

CERN Document Server

Vieira, Marcos R

2013-01-01

This brief presents several new query processing techniques, called complex motion pattern queries, specifically designed for very large spatio-temporal databases of moving objects. The brief begins with the definition of flexible pattern queries, which are powerful because of the integration of variables and motion patterns. This is followed by a summary of the expressive power of patterns and flexibility of pattern queries. The brief then present the Spatio-Temporal Pattern System (STPS) and density-based pattern queries. STPS databases contain millions of records with information about mobi

Live cell CRISPR-imaging in plants reveals dynamic telomere movements

KAUST Repository

Dreissig, Steven

2017-05-16

Elucidating the spatio-temporal organization of the genome inside the nucleus is imperative to understand the regulation of genes and non-coding sequences during development and environmental changes. Emerging techniques of chromatin imaging promise to bridge the long-standing gap between sequencing studies which reveal genomic information and imaging studies that provide spatial and temporal information of defined genomic regions. Here, we demonstrate such an imaging technique based on two orthologues of the bacterial CRISPR-Cas9 system. By fusing eGFP/mRuby2 to the catalytically inactive version of Streptococcus pyogenes and Staphylococcus aureus Cas9, we show robust visualization of telomere repeats in live leaf cells of Nicotiana benthamiana. By tracking the dynamics of telomeres visualized by CRISPR-dCas9, we reveal dynamic telomere movements of up to 2 μm within 30 minutes during interphase. Furthermore, we show that CRISPR-dCas9 can be combined with fluorescence-labelled proteins to visualize DNA-protein interactions in vivo. By simultaneously using two dCas9 orthologues, we pave the way for imaging of multiple genomic loci in live plants cells. CRISPR-imaging bears the potential to significantly improve our understanding of the dynamics of chromosomes in live plant cells.

Comparison of Spatiotemporal Mapping Techniques for Enormous Etl and Exploitation Patterns

Science.gov (United States)

Deiotte, R.; La Valley, R.

2017-10-01

The need to extract, transform, and exploit enormous volumes of spatiotemporal data has exploded with the rise of social media, advanced military sensors, wearables, automotive tracking, etc. However, current methods of spatiotemporal encoding and exploitation simultaneously limit the use of that information and increase computing complexity. Current spatiotemporal encoding methods from Niemeyer and Usher rely on a Z-order space filling curve, a relative of Peano's 1890 space filling curve, for spatial hashing and interleaving temporal hashes to generate a spatiotemporal encoding. However, there exist other space-filling curves, and that provide different manifold coverings that could promote better hashing techniques for spatial data and have the potential to map spatiotemporal data without interleaving. The concatenation of Niemeyer's and Usher's techniques provide a highly efficient space-time index. However, other methods have advantages and disadvantages regarding computational cost, efficiency, and utility. This paper explores the several methods using a range of sizes of data sets from 1K to 10M observations and provides a comparison of the methods.

COMPARISON OF SPATIOTEMPORAL MAPPING TECHNIQUES FOR ENORMOUS ETL AND EXPLOITATION PATTERNS

Directory of Open Access Journals (Sweden)

R. Deiotte

2017-10-01

Full Text Available The need to extract, transform, and exploit enormous volumes of spatiotemporal data has exploded with the rise of social media, advanced military sensors, wearables, automotive tracking, etc. However, current methods of spatiotemporal encoding and exploitation simultaneously limit the use of that information and increase computing complexity. Current spatiotemporal encoding methods from Niemeyer and Usher rely on a Z-order space filling curve, a relative of Peano’s 1890 space filling curve, for spatial hashing and interleaving temporal hashes to generate a spatiotemporal encoding. However, there exist other space-filling curves, and that provide different manifold coverings that could promote better hashing techniques for spatial data and have the potential to map spatiotemporal data without interleaving. The concatenation of Niemeyer’s and Usher’s techniques provide a highly efficient space-time index. However, other methods have advantages and disadvantages regarding computational cost, efficiency, and utility. This paper explores the several methods using a range of sizes of data sets from 1K to 10M observations and provides a comparison of the methods.

Research on spatio-temporal database techniques for spatial information service

Science.gov (United States)

Zhao, Rong; Wang, Liang; Li, Yuxiang; Fan, Rongshuang; Liu, Ping; Li, Qingyuan

2007-06-01

Geographic data should be described by spatial, temporal and attribute components, but the spatio-temporal queries are difficult to be answered within current GIS. This paper describes research into the development and application of spatio-temporal data management system based upon GeoWindows GIS software platform which was developed by Chinese Academy of Surveying and Mapping (CASM). Faced the current and practical requirements of spatial information application, and based on existing GIS platform, one kind of spatio-temporal data model which integrates vector and grid data together was established firstly. Secondly, we solved out the key technique of building temporal data topology, successfully developed a suit of spatio-temporal database management system adopting object-oriented methods. The system provides the temporal data collection, data storage, data management and data display and query functions. Finally, as a case study, we explored the application of spatio-temporal data management system with the administrative region data of multi-history periods of China as the basic data. With all the efforts above, the GIS capacity of management and manipulation in aspect of time and attribute of GIS has been enhanced, and technical reference has been provided for the further development of temporal geographic information system (TGIS).

A Flexible High-Performance Photoimaging Device Based on Bioinspired Hierarchical Multiple-Patterned Plasmonic Nanostructures.

Science.gov (United States)

Lee, Yoon Ho; Lee, Tae Kyung; Kim, Hongki; Song, Inho; Lee, Jiwon; Kang, Saewon; Ko, Hyunhyub; Kwak, Sang Kyu; Oh, Joon Hak

2018-03-01

In insect eyes, ommatidia with hierarchical structured cornea play a critical role in amplifying and transferring visual signals to the brain through optic nerves, enabling the perception of various visual signals. Here, inspired by the structure and functions of insect ommatidia, a flexible photoimaging device is reported that can simultaneously detect and record incoming photonic signals by vertically stacking an organic photodiode and resistive memory device. A single-layered, hierarchical multiple-patterned back reflector that can exhibit various plasmonic effects is incorporated into the organic photodiode. The multiple-patterned flexible organic photodiodes exhibit greatly enhanced photoresponsivity due to the increased light absorption in comparison with the flat systems. Moreover, the flexible photoimaging device shows a well-resolved spatiotemporal mapping of optical signals with excellent operational and mechanical stabilities at low driving voltages below half of the flat systems. Theoretical calculation and scanning near-field optical microscopy analyses clearly reveal that multiple-patterned electrodes have much stronger surface plasmon coupling than flat and single-patterned systems. The developed methodology provides a versatile and effective route for realizing high-performance optoelectronic and photonic systems. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Spatiotemporal Data Mining, Analysis, and Visualization of Human Activity Data

Science.gov (United States)

Li, Xun

2012-01-01

This dissertation addresses the research challenge of developing efficient new methods for discovering useful patterns and knowledge in large volumes of electronically collected spatiotemporal activity data. I propose to analyze three types of such spatiotemporal activity data in a methodological framework that integrates spatial analysis, data…

Markovian Limit of a Spatio-Temporal Correlated Open Systems

Science.gov (United States)

Monnai, T.

Large fluctuation of Brownian particles is affected by the finiteness of the correlation length of the background noise field. Indeed a Fokker—Planck equation is derived in a Markovian limit of a spatio-temporal short correlated noise. Corresponding kinetic quantities are renormalized due to the spatio-temporal memory. We also investigate the case of open system by connecting a thermostat to the system.

Multiple remote sensing data sources to assess spatio-temporal patterns of fire incidence over Campos Amazônicos Savanna Vegetation Enclave (Brazilian Amazon).

Science.gov (United States)

Alves, Daniel Borini; Pérez-Cabello, Fernando

2017-12-01

Fire activity plays an important role in the past, present and future of Earth system behavior. Monitoring and assessing spatial and temporal fire dynamics have a fundamental relevance in the understanding of ecological processes and the human impacts on different landscapes and multiple spatial scales. This work analyzes the spatio-temporal distribution of burned areas in one of the biggest savanna vegetation enclaves in the southern Brazilian Amazon, from 2000 to 2016, deriving information from multiple remote sensing data sources (Landsat and MODIS surface reflectance, TRMM pluviometry and Vegetation Continuous Field tree cover layers). A fire scars database with 30 m spatial resolution was generated using a Landsat time series. MODIS daily surface reflectance was used for accurate dating of the fire scars. TRMM pluviometry data were analyzed to dynamically establish time limits of the yearly dry season and burning periods. Burned area extent, frequency and recurrence were quantified comparing the results annually/seasonally. Additionally, Vegetation Continuous Field tree cover layers were used to analyze fire incidence over different types of tree cover domains. In the last seventeen years, 1.03millionha were burned within the study area, distributed across 1432 fire occurrences, highlighting 2005, 2010 and 2014 as the most affected years. Middle dry season fires represent 86.21% of the total burned areas and 32.05% of fire occurrences, affecting larger amount of higher density tree surfaces than other burning periods. The results provide new insights into the analysis of burned areas of the neotropical savannas, spatially and statistically reinforcing important aspects linked to the seasonality patterns of fire incidence in this landscape. Copyright © 2017 Elsevier B.V. All rights reserved.

Mercury Toolset for Spatiotemporal Metadata

Science.gov (United States)

Devarakonda, Ranjeet; Palanisamy, Giri; Green, James; Wilson, Bruce; Rhyne, B. Timothy; Lindsley, Chris

2010-06-01

Mercury (http://mercury.ornl.gov) is a set of tools for federated harvesting, searching, and retrieving metadata, particularly spatiotemporal metadata. Version 3.0 of the Mercury toolset provides orders of magnitude improvements in search speed, support for additional metadata formats, integration with Google Maps for spatial queries, facetted type search, support for RSS (Really Simple Syndication) delivery of search results, and enhanced customization to meet the needs of the multiple projects that use Mercury. It provides a single portal to very quickly search for data and information contained in disparate data management systems, each of which may use different metadata formats. Mercury harvests metadata and key data from contributing project servers distributed around the world and builds a centralized index. The search interfaces then allow the users to perform a variety of fielded, spatial, and temporal searches across these metadata sources. This centralized repository of metadata with distributed data sources provides extremely fast search results to the user, while allowing data providers to advertise the availability of their data and maintain complete control and ownership of that data. Mercury periodically (typically daily)harvests metadata sources through a collection of interfaces and re-indexes these metadata to provide extremely rapid search capabilities, even over collections with tens of millions of metadata records. A number of both graphical and application interfaces have been constructed within Mercury, to enable both human users and other computer programs to perform queries. Mercury was also designed to support multiple different projects, so that the particular fields that can be queried and used with search filters are easy to configure for each different project.

Mercury Toolset for Spatiotemporal Metadata

Science.gov (United States)

Wilson, Bruce E.; Palanisamy, Giri; Devarakonda, Ranjeet; Rhyne, B. Timothy; Lindsley, Chris; Green, James

2010-01-01

Mercury (http://mercury.ornl.gov) is a set of tools for federated harvesting, searching, and retrieving metadata, particularly spatiotemporal metadata. Version 3.0 of the Mercury toolset provides orders of magnitude improvements in search speed, support for additional metadata formats, integration with Google Maps for spatial queries, facetted type search, support for RSS (Really Simple Syndication) delivery of search results, and enhanced customization to meet the needs of the multiple projects that use Mercury. It provides a single portal to very quickly search for data and information contained in disparate data management systems, each of which may use different metadata formats. Mercury harvests metadata and key data from contributing project servers distributed around the world and builds a centralized index. The search interfaces then allow the users to perform a variety of fielded, spatial, and temporal searches across these metadata sources. This centralized repository of metadata with distributed data sources provides extremely fast search results to the user, while allowing data providers to advertise the availability of their data and maintain complete control and ownership of that data. Mercury periodically (typically daily) harvests metadata sources through a collection of interfaces and re-indexes these metadata to provide extremely rapid search capabilities, even over collections with tens of millions of metadata records. A number of both graphical and application interfaces have been constructed within Mercury, to enable both human users and other computer programs to perform queries. Mercury was also designed to support multiple different projects, so that the particular fields that can be queried and used with search filters are easy to configure for each different project.

Spatiotemporal dynamics of a digital phase-locked loop based coupled map lattice system

Energy Technology Data Exchange (ETDEWEB)

Banerjee, Tanmoy, E-mail: tbanerjee@phys.buruniv.ac.in; Paul, Bishwajit; Sarkar, B. C. [Department of Physics, University of Burdwan, Burdwan, West Bengal 713 104 (India)

2014-03-15

We explore the spatiotemporal dynamics of a coupled map lattice (CML) system, which is realized with a one dimensional array of locally coupled digital phase-locked loops (DPLLs). DPLL is a nonlinear feedback-controlled system widely used as an important building block of electronic communication systems. We derive the phase-error equation of the spatially extended system of coupled DPLLs, which resembles a form of the equation of a CML system. We carry out stability analysis for the synchronized homogeneous solutions using the circulant matrix formalism. It is shown through extensive numerical simulations that with the variation of nonlinearity parameter and coupling strength the system shows transitions among several generic features of spatiotemporal dynamics, viz., synchronized fixed point solution, frozen random pattern, pattern selection, spatiotemporal intermittency, and fully developed spatiotemporal chaos. We quantify the spatiotemporal dynamics using quantitative measures like average quadratic deviation and spatial correlation function. We emphasize that instead of using an idealized model of CML, which is usually employed to observe the spatiotemporal behaviors, we consider a real world physical system and establish the existence of spatiotemporal chaos and other patterns in this system. We also discuss the importance of the present study in engineering application like removal of clock-skew in parallel processors.

Exploring Multi-Scale Spatiotemporal Twitter User Mobility Patterns with a Visual-Analytics Approach

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Junjun Yin

2016-10-01

Full Text Available Understanding human mobility patterns is of great importance for urban planning, traffic management, and even marketing campaign. However, the capability of capturing detailed human movements with fine-grained spatial and temporal granularity is still limited. In this study, we extracted high-resolution mobility data from a collection of over 1.3 billion geo-located Twitter messages. Regarding the concerns of infringement on individual privacy, such as the mobile phone call records with restricted access, the dataset is collected from publicly accessible Twitter data streams. In this paper, we employed a visual-analytics approach to studying multi-scale spatiotemporal Twitter user mobility patterns in the contiguous United States during the year 2014. Our approach included a scalable visual-analytics framework to deliver efficiency and scalability in filtering large volume of geo-located tweets, modeling and extracting Twitter user movements, generating space-time user trajectories, and summarizing multi-scale spatiotemporal user mobility patterns. We performed a set of statistical analysis to understand Twitter user mobility patterns across multi-level spatial scales and temporal ranges. In particular, Twitter user mobility patterns measured by the displacements and radius of gyrations of individuals revealed multi-scale or multi-modal Twitter user mobility patterns. By further studying such mobility patterns in different temporal ranges, we identified both consistency and seasonal fluctuations regarding the distance decay effects in the corresponding mobility patterns. At the same time, our approach provides a geo-visualization unit with an interactive 3D virtual globe web mapping interface for exploratory geo-visual analytics of the multi-level spatiotemporal Twitter user movements.

Malaria infection has spatial, temporal, and spatiotemporal heterogeneity in unstable malaria transmission areas in northwest Ethiopia.

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Kassahun Alemu

Full Text Available BACKGROUND: Malaria elimination requires successful nationwide control efforts. Detecting the spatiotemporal distribution and mapping high-risk areas are useful to effectively target pockets of malaria endemic regions for interventions. OBJECTIVE: The aim of the study was to identify patterns of malaria distribution by space and time in unstable malaria transmission areas in northwest Ethiopia. METHODS: Data were retrieved from the monthly reports stored in the district malaria offices for the period between 2003 and 2012. Eighteen districts in the highland and fringe malaria areas were included and geo-coded for the purpose of this study. The spatial data were created in ArcGIS10 for each district. The Poisson model was used by applying Kulldorff methods using the SaTScan™ software to analyze the purely temporal, spatial and space-time clusters of malaria at a district levels. RESULTS: The study revealed that malaria case distribution has spatial, temporal, and spatiotemporal heterogeneity in unstable transmission areas. Most likely spatial malaria clusters were detected at Dera, Fogera, Farta, Libokemkem and Misrak Este districts (LLR =197764.1, p<0.001. Significant spatiotemporal malaria clusters were detected at Dera, Fogera, Farta, Libokemkem and Misrak Este districts (LLR=197764.1, p<0.001 between 2003/1/1 and 2012/12/31. A temporal scan statistics identified two high risk periods from 2009/1/1 to 2010/12/31 (LLR=72490.5, p<0.001 and from 2003/1/1 to 2005/12/31 (LLR=26988.7, p<0.001. CONCLUSION: In unstable malaria transmission areas, detecting and considering the spatiotemporal heterogeneity would be useful to strengthen malaria control efforts and ultimately achieve elimination.

Experimental devices for the spatio-temporal characterization of femtosecond high-power laser chains

International Nuclear Information System (INIS)

Gallet, Valentin

2014-01-01

performed the very first characterization of several TW laser chains. The measurements have revealed the existence of residual space-time couplings leading to a significant decrease of the peak intensity at focus. These results show that it is essential to spatio-temporally characterize high power femtosecond laser chains to obtain the maximum intensity at focus. (author) [fr

Evaluating the disparity of female breast cancer mortality among racial groups - a spatiotemporal analysis

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Jacobson Holly

2004-02-01

Full Text Available Abstract Background The literature suggests that the distribution of female breast cancer mortality demonstrates spatial concentration. There remains a lack of studies on how the mortality burden may impact racial groups across space and over time. The present study evaluated the geographic variations in breast cancer mortality in Texas females according to three predominant racial groups (non-Hispanic White, Black, and Hispanic females over a twelve-year period. It sought to clarify whether the spatiotemporal trend might place an uneven burden on particular racial groups, and whether the excess trend has persisted into the current decade. Methods The Spatial Scan Statistic was employed to examine the geographic excess of breast cancer mortality by race in Texas counties between 1990 and 2001. The statistic was conducted with a scan window of a maximum of 90% of the study period and a spatial cluster size of 50% of the population at risk. The next scan was conducted with a purely spatial option to verify whether the excess mortality persisted further. Spatial queries were performed to locate the regions of excess mortality affecting multiple racial groups. Results The first scan identified 4 regions with breast cancer mortality excess in both non-Hispanic White and Hispanic female populations. The most likely excess mortality with a relative risk of 1.12 (p = 0.001 occurred between 1990 and 1996 for non-Hispanic Whites, including 42 Texas counties along Gulf Coast and Central Texas. For Hispanics, West Texas with a relative risk of 1.18 was the most probable region of excess mortality (p = 0.001. Results of the second scan were identical to the first. This suggested that the excess mortality might not persist to the present decade. Spatial queries found that 3 counties in Southeast and 9 counties in Central Texas had excess mortality involving multiple racial groups. Conclusion Spatiotemporal variations in breast cancer mortality affected racial

Spatio-temporal data analytics for wind energy integration

CERN Document Server

Yang, Lei; Zhang, Junshan

2014-01-01

This SpringerBrief presents spatio-temporal data analytics for wind energy integration using stochastic modeling and optimization methods. It explores techniques for efficiently integrating renewable energy generation into bulk power grids. The operational challenges of wind, and its variability are carefully examined. A spatio-temporal analysis approach enables the authors to develop Markov-chain-based short-term forecasts of wind farm power generation. To deal with the wind ramp dynamics, a support vector machine enhanced Markov model is introduced. The stochastic optimization of economic di

Spatiotemporal patterns and predictability of cyberattacks.

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Yu-Zhong Chen

Full Text Available A relatively unexplored issue in cybersecurity science and engineering is whether there exist intrinsic patterns of cyberattacks. Conventional wisdom favors absence of such patterns due to the overwhelming complexity of the modern cyberspace. Surprisingly, through a detailed analysis of an extensive data set that records the time-dependent frequencies of attacks over a relatively wide range of consecutive IP addresses, we successfully uncover intrinsic spatiotemporal patterns underlying cyberattacks, where the term "spatio" refers to the IP address space. In particular, we focus on analyzing macroscopic properties of the attack traffic flows and identify two main patterns with distinct spatiotemporal characteristics: deterministic and stochastic. Strikingly, there are very few sets of major attackers committing almost all the attacks, since their attack "fingerprints" and target selection scheme can be unequivocally identified according to the very limited number of unique spatiotemporal characteristics, each of which only exists on a consecutive IP region and differs significantly from the others. We utilize a number of quantitative measures, including the flux-fluctuation law, the Markov state transition probability matrix, and predictability measures, to characterize the attack patterns in a comprehensive manner. A general finding is that the attack patterns possess high degrees of predictability, potentially paving the way to anticipating and, consequently, mitigating or even preventing large-scale cyberattacks using macroscopic approaches.

Spatio-Temporal Metabolite Profiling of the Barley Germination Process by MALDI MS Imaging.

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Karin Gorzolka

Full Text Available MALDI mass spectrometry imaging was performed to localize metabolites during the first seven days of the barley germination. Up to 100 mass signals were detected of which 85 signals were identified as 48 different metabolites with highly tissue-specific localizations. Oligosaccharides were observed in the endosperm and in parts of the developed embryo. Lipids in the endosperm co-localized in dependency on their fatty acid compositions with changes in the distributions of diacyl phosphatidylcholines during germination. 26 potentially antifungal hordatines were detected in the embryo with tissue-specific localizations of their glycosylated, hydroxylated, and O-methylated derivates. In order to reveal spatio-temporal patterns in local metabolite compositions, multiple MSI data sets from a time series were analyzed in one batch. This requires a new preprocessing strategy to achieve comparability between data sets as well as a new strategy for unsupervised clustering. The resulting spatial segmentation for each time point sample is visualized in an interactive cluster map and enables simultaneous interactive exploration of all time points. Using this new analysis approach and visualization tool germination-dependent developments of metabolite patterns with single MS position accuracy were discovered. This is the first study that presents metabolite profiling of a cereals' germination process over time by MALDI MSI with the identification of a large number of peaks of agronomically and industrially important compounds such as oligosaccharides, lipids and antifungal agents. Their detailed localization as well as the MS cluster analyses for on-tissue metabolite profile mapping revealed important information for the understanding of the germination process, which is of high scientific interest.

Nutrients and toxin producing phytoplankton control algal blooms - a spatio-temporal study in a noisy environment.

Science.gov (United States)

Sarkar, Ram Rup; Malchow, Horst

2005-12-01

A phytoplankton-zooplankton prey-predator model has been investigated for temporal, spatial and spatio-temporal dissipative pattern formation in a deterministic and noisy environment, respectively. The overall carrying capacity for the phytoplankton population depends on the nutrient level. The role of nutrient concentrations and toxin producing phytoplankton for controlling the algal blooms has been discussed. The local analysis yields a number of stationary and/or oscillatory regimes and their combinations. Correspondingly interesting is the spatio-temporal behaviour, modelled by stochastic reaction-diffusion equations. The present study also reveals the fact that the rate of toxin production by toxin producing phytoplankton (TPP) plays an important role for controlling oscillations in the plankton system. We also observe that different mortality functions of zooplankton due to TPP have significant influence in controlling oscillations, coexistence, survival or extinction of the zoo-plankton population. External noise can enhance the survival and spread of zooplankton that would go extinct in the deterministic system due to a high rate of toxin production.

Research of Cadastral Data Modelling and Database Updating Based on Spatio-temporal Process

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ZHANG Feng

2016-02-01

Full Text Available The core of modern cadastre management is to renew the cadastre database and keep its currentness,topology consistency and integrity.This paper analyzed the changes and their linkage of various cadastral objects in the update process.Combined object-oriented modeling technique with spatio-temporal objects' evolution express,the paper proposed a cadastral data updating model based on the spatio-temporal process according to people's thought.Change rules based on the spatio-temporal topological relations of evolution cadastral spatio-temporal objects are drafted and further more cascade updating and history back trace of cadastral features,land use and buildings are realized.This model implemented in cadastral management system-ReGIS.Achieved cascade changes are triggered by the direct driving force or perceived external events.The system records spatio-temporal objects' evolution process to facilitate the reconstruction of history,change tracking,analysis and forecasting future changes.

An implicit spatiotemporal shape model for human activity localization and recognition

NARCIS (Netherlands)

Oikonomopoulos, A.; Patras, I.; Pantic, Maja

2009-01-01

In this paper we address the problem of localisation and recognition of human activities in unsegmented image sequences. The main contribution of the proposed method is the use of an implicit representation of the spatiotemporal shape of the activity which relies on the spatiotemporal localization

What Is Spatio-Temporal Data Warehousing?

Science.gov (United States)

Vaisman, Alejandro; Zimányi, Esteban

In the last years, extending OLAP (On-Line Analytical Processing) systems with spatial and temporal features has attracted the attention of the GIS (Geographic Information Systems) and database communities. However, there is no a commonly agreed definition of what is a spatio-temporal data warehouse and what functionality such a data warehouse should support. Further, the solutions proposed in the literature vary considerably in the kind of data that can be represented as well as the kind of queries that can be expressed. In this paper we present a conceptual framework for defining spatio-temporal data warehouses using an extensible data type system. We also define a taxonomy of different classes of queries of increasing expressive power, and show how to express such queries using an extension of the tuple relational calculus with aggregated functions.

Spatio-temporal modeling of nonlinear distributed parameter systems

CERN Document Server

Li, Han-Xiong

2011-01-01

The purpose of this volume is to provide a brief review of the previous work on model reduction and identifi cation of distributed parameter systems (DPS), and develop new spatio-temporal models and their relevant identifi cation approaches. In this book, a systematic overview and classifi cation on the modeling of DPS is presented fi rst, which includes model reduction, parameter estimation and system identifi cation. Next, a class of block-oriented nonlinear systems in traditional lumped parameter systems (LPS) is extended to DPS, which results in the spatio-temporal Wiener and Hammerstein s

Spatiotemporal dynamics of the spin transition in [Fe (HB(tz)3) 2] single crystals

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Ridier, Karl; Rat, Sylvain; Shepherd, Helena J.; Salmon, Lionel; Nicolazzi, William; Molnár, Gábor; Bousseksou, Azzedine

2017-10-01

The spatiotemporal dynamics of the spin transition have been thoroughly investigated in single crystals of the mononuclear spin-crossover (SCO) complex [Fe (HB (tz )3)2] (tz = 1 ,2 ,4-triazol-1-yl) by optical microscopy. This compound exhibits an abrupt spin transition centered at 334 K with a narrow thermal hysteresis loop of ˜1 K (first-order transition). Most single crystals of this compound reveal exceptional resilience upon repeated switching (several hundred cycles), which allowed repeatable and quantitative measurements of the spatiotemporal dynamics of the nucleation and growth processes to be carried out. These experiments revealed remarkable properties of the thermally induced spin transition: high stability of the thermal hysteresis loop, unprecedented large velocities of the macroscopic low-spin/high-spin phase boundaries up to 500 µm/s, and no visible dependency on the temperature scan rate. We have also studied the dynamics of the low-spin → high-spin transition induced by a local photothermal excitation generated by a spatially localized (Ø = 2 μ m ) continuous laser beam. Interesting phenomena have been evidenced both in quasistatic and dynamic conditions (e.g., threshold effects and long incubation periods, thermal activation of the phase boundary propagation, stabilization of the crystal in a stationary biphasic state, and thermal cutoff frequency). These measurements demonstrated the importance of thermal effects in the transition dynamics, and they enabled an accurate determination of the thermal properties of the SCO compound in the framework of a simple theoretical model.

Mycobacterium malmesburyense sp. nov., a non-tuberculous species of the genus Mycobacterium revealed by multiple gene sequence characterization

CSIR Research Space (South Africa)

Gcebe, N

2017-04-01

Full Text Available Journal of Systematic and Evolutionary Microbiology: DOI 10.1099/ijsem.0.001678 Mycobacterium malmesburyense sp. nov., a non-tuberculous species of the genus Mycobacterium revealed by multiple gene sequence characterization Gcebe N Rutten V Gey...

A spatio-temporal analysis of suicide in El Salvador.

Science.gov (United States)

Carcach, Carlos

2017-04-20

In 2012, international statistics showed El Salvador's suicide rate as 40th in the world and the highest in Latin America. Over the last 15 years, national statistics show the suicide death rate declining as opposed to an increasing rate of homicide. Though completed suicide is an important social and health issue, little is known about its prevalence, incidence, etiology and spatio-temporal behavior. The primary objective of this study was to examine completed suicide and homicide using the stream analogy to lethal violence within a spatio-temporal framework. A Bayesian model was applied to examine the spatio-temporal evolution of the tendency of completed suicide over homicide in El Salvador. Data on numbers of suicides and homicides at the municipal level were obtained from the Instituto de Medicina Legal (IML) and population counts, from the Dirección General de Estadística y Censos (DIGESTYC), for the period of 2002 to 2012. Data on migration were derived from the 2007 Population Census, and inequality data were obtained from a study by Damianović, Valenzuela and Vera. The data reveal a stable standardized rate of total lethal violence (completed suicide plus homicide) across municipalities over time; a decline in suicide; and a standardized suicide rate decreasing with income inequality but increasing with social isolation. Municipalities clustered in terms of both total lethal violence and suicide standardized rates. Spatial effects for suicide were stronger among municipalities located in the north-east and center-south sides of the country. New clusters of municipalities with large suicide standardized rates were detected in the north-west, south-west and center-south regions, all of which are part of time-stable clusters of homicide. Prevention efforts to reduce income inequality and mitigate the negative effects of weak relational systems should focus upon municipalities forming time-persistent clusters with a large rate of death by suicide. In

A spatio-temporal analysis of suicide in El Salvador

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Carlos Carcach

2017-04-01

Full Text Available Abstract Background In 2012, international statistics showed El Salvador’s suicide rate as 40th in the world and the highest in Latin America. Over the last 15 years, national statistics show the suicide death rate declining as opposed to an increasing rate of homicide. Though completed suicide is an important social and health issue, little is known about its prevalence, incidence, etiology and spatio-temporal behavior. The primary objective of this study was to examine completed suicide and homicide using the stream analogy to lethal violence within a spatio-temporal framework. Methods A Bayesian model was applied to examine the spatio-temporal evolution of the tendency of completed suicide over homicide in El Salvador. Data on numbers of suicides and homicides at the municipal level were obtained from the Instituto de Medicina Legal (IML and population counts, from the Dirección General de Estadística y Censos (DIGESTYC, for the period of 2002 to 2012. Data on migration were derived from the 2007 Population Census, and inequality data were obtained from a study by Damianović, Valenzuela and Vera. Results The data reveal a stable standardized rate of total lethal violence (completed suicide plus homicide across municipalities over time; a decline in suicide; and a standardized suicide rate decreasing with income inequality but increasing with social isolation. Municipalities clustered in terms of both total lethal violence and suicide standardized rates. Conclusions Spatial effects for suicide were stronger among municipalities located in the north-east and center-south sides of the country. New clusters of municipalities with large suicide standardized rates were detected in the north-west, south-west and center-south regions, all of which are part of time-stable clusters of homicide. Prevention efforts to reduce income inequality and mitigate the negative effects of weak relational systems should focus upon municipalities forming time

Bayesian spatio-temporal modeling of particulate matter concentrations in Peninsular Malaysia

Science.gov (United States)

Manga, Edna; Awang, Norhashidah

2016-06-01

This article presents an application of a Bayesian spatio-temporal Gaussian process (GP) model on particulate matter concentrations from Peninsular Malaysia. We analyze daily PM10 concentration levels from 35 monitoring sites in June and July 2011. The spatiotemporal model set in a Bayesian hierarchical framework allows for inclusion of informative covariates, meteorological variables and spatiotemporal interactions. Posterior density estimates of the model parameters are obtained by Markov chain Monte Carlo methods. Preliminary data analysis indicate information on PM10 levels at sites classified as industrial locations could explain part of the space time variations. We include the site-type indicator in our modeling efforts. Results of the parameter estimates for the fitted GP model show significant spatio-temporal structure and positive effect of the location-type explanatory variable. We also compute some validation criteria for the out of sample sites that show the adequacy of the model for predicting PM10 at unmonitored sites.

McMaster Mesonet soil moisture dataset: description and spatio-temporal variability analysis

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K. C. Kornelsen

2013-04-01

Full Text Available This paper introduces and describes the hourly, high-resolution soil moisture dataset continuously recorded by the McMaster Mesonet located in the Hamilton-Halton Watershed in Southern Ontario, Canada. The McMaster Mesonet consists of a network of time domain reflectometer (TDR probes collecting hourly soil moisture data at six depths between 10 cm and 100 cm at nine locations per site, spread across four sites in the 1250 km2 watershed. The sites for the soil moisture arrays are designed to further improve understanding of soil moisture dynamics in a seasonal climate and to capture soil moisture transitions in areas that have different topography, soil and land cover. The McMaster Mesonet soil moisture constitutes a unique database in Canada because of its high spatio-temporal resolution. In order to provide some insight into the dominant processes at the McMaster Mesonet sites, a spatio-temporal and temporal stability analysis were conducted to identify spatio-temporal patterns in the data and to suggest some physical interpretation of soil moisture variability. It was found that the seasonal climate of the Great Lakes Basin causes a transition in soil moisture patterns at seasonal timescales. During winter and early spring months, and at the meadow sites, soil moisture distribution is governed by topographic redistribution, whereas following efflorescence in the spring and summer, soil moisture spatial distribution at the forested site was also controlled by vegetation canopy. Analysis of short-term temporal stability revealed that the relative difference between sites was maintained unless there was significant rainfall (> 20 mm or wet conditions a priori. Following a disturbance in the spatial soil moisture distribution due to wetting, the relative soil moisture pattern re-emerged in 18 to 24 h. Access to the McMaster Mesonet data can be provided by visiting www.hydrology.mcmaster.ca/mesonet.

The Spatiotemporal pattern and driving forces of the paddy in the Northeastern China

Science.gov (United States)

Du, G.; Li, Q.; Chun, X.

2017-12-01

The cropland is the production place that protects the regional food security, and the paddy is the main part of the cropland. Since the 21st century, the China's socio-economy has been grown, the structure of the cropland has significantly changed. The Northeast region has gradually become one of the major commodity grain production bases. Meanwhile, the paddy also has gradually increased year by year. Therefore, it is necessary that analyze the tempo-spatial characteristics and the influencing factors of the northeast in China, and the results provide the basis that reveals the change of cropland structure and its causes.In this study, we use the spatial models of GIS and mathematical statistics methods to analyze the tempo-spatial characteristics and the influencing facts of the paddy in the Northeastern China with the spatial data from 2000 to 2015. In order to fully characterize the spatiotemporal characteristics of the paddy, we choose single land use type dynamic degree and land use extension index to quantitatively describe the change degree and the speed of the regional paddy, and the characteristics are visualized with "3S" means. Meanwhile, the relative change rate and the center of gravity model are chosen to explore the region differences and the distribution of the distribution center of paddy field change in Northeast China. In addition, in order to further reveal the cause of the paddy change, we use the OLS, SAM or SEM models to analyze the main influencing factors of spatiotemporal variation of the paddy field.

Fast multidimensional ensemble empirical mode decomposition for the analysis of big spatio-temporal datasets.

Science.gov (United States)

Wu, Zhaohua; Feng, Jiaxin; Qiao, Fangli; Tan, Zhe-Min

2016-04-13

In this big data era, it is more urgent than ever to solve two major issues: (i) fast data transmission methods that can facilitate access to data from non-local sources and (ii) fast and efficient data analysis methods that can reveal the key information from the available data for particular purposes. Although approaches in different fields to address these two questions may differ significantly, the common part must involve data compression techniques and a fast algorithm. This paper introduces the recently developed adaptive and spatio-temporally local analysis method, namely the fast multidimensional ensemble empirical mode decomposition (MEEMD), for the analysis of a large spatio-temporal dataset. The original MEEMD uses ensemble empirical mode decomposition to decompose time series at each spatial grid and then pieces together the temporal-spatial evolution of climate variability and change on naturally separated timescales, which is computationally expensive. By taking advantage of the high efficiency of the expression using principal component analysis/empirical orthogonal function analysis for spatio-temporally coherent data, we design a lossy compression method for climate data to facilitate its non-local transmission. We also explain the basic principles behind the fast MEEMD through decomposing principal components instead of original grid-wise time series to speed up computation of MEEMD. Using a typical climate dataset as an example, we demonstrate that our newly designed methods can (i) compress data with a compression rate of one to two orders; and (ii) speed-up the MEEMD algorithm by one to two orders. © 2016 The Authors.

Harbour porpoise distribution can vary at small spatiotemporal scales in energetic habitats

Science.gov (United States)

Benjamins, Steven; van Geel, Nienke; Hastie, Gordon; Elliott, Jim; Wilson, Ben

2017-07-01

Marine habitat heterogeneity underpins species distribution and can be generated through interactions between physical and biological drivers at multiple spatiotemporal scales. Passive acoustic monitoring (PAM) is used worldwide to study potential impacts of marine industrial activities on cetaceans, but understanding of animals' site use at small spatiotemporal scales (marine renewable energy development (MRED) sites was investigated by deploying dense arrays of C-POD passive acoustic detectors at a wave energy test site (the European Marine Energy Centre [Billia Croo, Orkney]) and by a minor tidal-stream site (Scarba [Inner Hebrides]). Respective arrays consisted of 7 and 11 moorings containing two C-PODs each and were deployed for up to 55 days. Minimum inter-mooring distances varied between 300-600 m. All C-POD data were analysed at a temporal resolution of whole minutes, with each minute classified as 1 or 0 on the basis of presence/absence of porpoise click trains (Porpoise-Positive Minutes/PPMs). Porpoise detection rates were analysed using Generalised Additive Models (GAMs) with Generalised Estimation Equations (GEEs). Although there were many porpoise detections (wave test site: N=3,432; tidal-stream site: N=17,366), daily detection rates varied significantly within both arrays. Within the wave site array (<1 km diameter), average daily detection rates varied from 4.3 to 14.8 PPMs/day. Within the tidal-stream array (<2 km diameter), average daily detection rates varied from 10.3 to 49.7 PPMs/day. GAM-GEE model results for individual moorings within both arrays indicated linkages between porpoise presence and small-scale heterogeneity among different environmental covariates (e.g., tidal phase, time of day). Porpoise detection rates varied considerably but with coherent patterns between moorings only several hundred metres apart and within hours. These patterns presumably have ecological relevance. These results indicate that, in energetically active and

BioenergyKDF: Enabling Spatiotemporal Data Synthesis and Research Collaboration

Energy Technology Data Exchange (ETDEWEB)

Myers, Aaron T [ORNL; Movva, Sunil [ORNL; Karthik, Rajasekar [ORNL; Bhaduri, Budhendra L [ORNL; White, Devin A [ORNL; Thomas, Neil [ORNL; Chase, Adrian S Z [ORNL

2014-01-01

The Bioenergy Knowledge Discovery Framework (BioenergyKDF) is a scalable, web-based collaborative environment for scientists working on bioenergy related research in which the connections between data, literature, and models can be explored and more clearly understood. The fully-operational and deployed system, built on multiple open source libraries and architectures, stores contributions from the community of practice and makes them easy to find, but that is just its base functionality. The BioenergyKDF provides a national spatiotemporal decision support capability that enables data sharing, analysis, modeling, and visualization as well as fosters the development and management of the U.S. bioenergy infrastructure, which is an essential component of the national energy infrastructure. The BioenergyKDF is built on a flexible, customizable platform that can be extended to support the requirements of any user community especially those that work with spatiotemporal data. While there are several community data-sharing software platforms available, some developed and distributed by national governments, none of them have the full suite of capabilities available in BioenergyKDF. For example, this component-based platform and database independent architecture allows it to be quickly deployed to existing infrastructure and to connect to existing data repositories (spatial or otherwise). As new data, analysis, and features are added; the BioenergyKDF will help lead research and support decisions concerning bioenergy into the future, but will also enable the development and growth of additional communities of practice both inside and outside of the Department of Energy. These communities will be able to leverage the substantial investment the agency has made in the KDF platform to quickly stand up systems that are customized to their data and research needs.

Wave fronts and spatiotemporal chaos in an array of coupled Lorenz oscillators

International Nuclear Information System (INIS)

Pazo, Diego; Montejo, Noelia; Perez-Munuzuri, Vicente

2001-01-01

The effects of coupling strength and single-cell dynamics (SCD) on spatiotemporal pattern formation are studied in an array of Lorenz oscillators. Different spatiotemporal structures (stationary patterns, propagating wave fronts, short wavelength bifurcation) arise for bistable SCD, and two well differentiated types of spatiotemporal chaos for chaotic SCD (in correspondence with the transition from stationary patterns to propagating fronts). Wave-front propagation in the bistable regime is studied in terms of global bifurcation theory, while a short wavelength pattern region emerges through a pitchfork bifurcation

A flexible spatiotemporal method for fusing satellite images with different resolutions

Science.gov (United States)

Xiaolin Zhu; Eileen H. Helmer; Feng Gao; Desheng Liu; Jin Chen; Michael A. Lefsky

2016-01-01

Studies of land surface dynamics in heterogeneous landscapes often require remote sensing datawith high acquisition frequency and high spatial resolution. However, no single sensor meets this requirement. This study presents a new spatiotemporal data fusion method, the Flexible Spatiotemporal DAta Fusion (FSDAF) method, to generate synthesized frequent high spatial...

Multiple surveys employing a new sample-processing protocol reveal the genetic diversity of placozoans in Japan.

Science.gov (United States)

Miyazawa, Hideyuki; Nakano, Hiroaki

2018-03-01

Placozoans, flat free-living marine invertebrates, possess an extremely simple bauplan lacking neurons and muscle cells and represent one of the earliest-branching metazoan phyla. They are widely distributed from temperate to tropical oceans. Based on mitochondrial 16S rRNA sequences, 19 haplotypes forming seven distinct clades have been reported in placozoans to date. In Japan, placozoans have been found at nine locations, but 16S genotyping has been performed at only two of these locations. Here, we propose a new processing protocol, "ethanol-treated substrate sampling," for collecting placozoans from natural environments. We also report the collection of placozoans from three new locations, the islands of Shikine-jima, Chichi-jima, and Haha-jima, and we present the distribution of the 16S haplotypes of placozoans in Japan. Multiple surveys conducted at multiple locations yielded five haplotypes that were not reported previously, revealing high genetic diversity in Japan, especially at Shimoda and Shikine-jima Island. The observed geographic distribution patterns were different among haplotypes; some were widely distributed, while others were sampled only from a single location. However, samplings conducted on different dates at the same sites yielded different haplotypes, suggesting that placozoans of a given haplotype do not inhabit the same site constantly throughout the year. Continued sampling efforts conducted during all seasons at multiple locations worldwide and the development of molecular markers within the haplotypes are needed to reveal the geographic distribution pattern and dispersal history of placozoans in greater detail.

ELASTIC CLOUD COMPUTING ARCHITECTURE AND SYSTEM FOR HETEROGENEOUS SPATIOTEMPORAL COMPUTING

Directory of Open Access Journals (Sweden)

X. Shi

2017-10-01

Full Text Available Spatiotemporal computation implements a variety of different algorithms. When big data are involved, desktop computer or standalone application may not be able to complete the computation task due to limited memory and computing power. Now that a variety of hardware accelerators and computing platforms are available to improve the performance of geocomputation, different algorithms may have different behavior on different computing infrastructure and platforms. Some are perfect for implementation on a cluster of graphics processing units (GPUs, while GPUs may not be useful on certain kind of spatiotemporal computation. This is the same situation in utilizing a cluster of Intel's many-integrated-core (MIC or Xeon Phi, as well as Hadoop or Spark platforms, to handle big spatiotemporal data. Furthermore, considering the energy efficiency requirement in general computation, Field Programmable Gate Array (FPGA may be a better solution for better energy efficiency when the performance of computation could be similar or better than GPUs and MICs. It is expected that an elastic cloud computing architecture and system that integrates all of GPUs, MICs, and FPGAs could be developed and deployed to support spatiotemporal computing over heterogeneous data types and computational problems.

Elastic Cloud Computing Architecture and System for Heterogeneous Spatiotemporal Computing

Science.gov (United States)

Shi, X.

2017-10-01

Spatiotemporal computation implements a variety of different algorithms. When big data are involved, desktop computer or standalone application may not be able to complete the computation task due to limited memory and computing power. Now that a variety of hardware accelerators and computing platforms are available to improve the performance of geocomputation, different algorithms may have different behavior on different computing infrastructure and platforms. Some are perfect for implementation on a cluster of graphics processing units (GPUs), while GPUs may not be useful on certain kind of spatiotemporal computation. This is the same situation in utilizing a cluster of Intel's many-integrated-core (MIC) or Xeon Phi, as well as Hadoop or Spark platforms, to handle big spatiotemporal data. Furthermore, considering the energy efficiency requirement in general computation, Field Programmable Gate Array (FPGA) may be a better solution for better energy efficiency when the performance of computation could be similar or better than GPUs and MICs. It is expected that an elastic cloud computing architecture and system that integrates all of GPUs, MICs, and FPGAs could be developed and deployed to support spatiotemporal computing over heterogeneous data types and computational problems.

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

Directory of Open Access Journals (Sweden)

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

Artificial neural network does better spatiotemporal compressive sampling

Science.gov (United States)

Lee, Soo-Young; Hsu, Charles; Szu, Harold

2012-06-01

Spatiotemporal sparseness is generated naturally by human visual system based on artificial neural network modeling of associative memory. Sparseness means nothing more and nothing less than the compressive sensing achieves merely the information concentration. To concentrate the information, one uses the spatial correlation or spatial FFT or DWT or the best of all adaptive wavelet transform (cf. NUS, Shen Shawei). However, higher dimensional spatiotemporal information concentration, the mathematics can not do as flexible as a living human sensory system. The reason is obviously for survival reasons. The rest of the story is given in the paper.

Multiple roles of integrin-linked kinase in epidermal development, maturation and pigmentation revealed by molecular profiling.

Directory of Open Access Journals (Sweden)

David Judah

Full Text Available Integrin-linked kinase (ILK is an important scaffold protein that mediates a variety of cellular responses to integrin stimulation by extracellular matrix proteins. Mice with epidermis-restricted inactivation of the Ilk gene exhibit pleiotropic phenotypic defects, including impaired hair follicle morphogenesis, reduced epidermal adhesion to the basement membrane, compromised epidermal integrity, as well as wasting and failure to thrive leading to perinatal death. To better understand the underlying molecular mechanisms that cause such a broad range of alterations, we investigated the impact of Ilk gene inactivation on the epidermis transcriptome. Microarray analysis showed over 700 differentially regulated mRNAs encoding proteins involved in multiple aspects of epidermal function, including keratinocyte differentiation and barrier formation, inflammation, regeneration after injury, and fundamental epidermal developmental pathways. These studies also revealed potential effects on genes not previously implicated in ILK functions, including those important for melanocyte and melanoblast development and function, regulation of cytoskeletal dynamics, and homeobox genes. This study shows that ILK is a critical regulator of multiple aspects of epidermal function and homeostasis, and reveals the previously unreported involvement of ILK not only in epidermal differentiation and barrier formation, but also in melanocyte genesis and function.

Multiple roles of integrin-linked kinase in epidermal development, maturation and pigmentation revealed by molecular profiling.

Science.gov (United States)

Judah, David; Rudkouskaya, Alena; Wilson, Ryan; Carter, David E; Dagnino, Lina

2012-01-01

Integrin-linked kinase (ILK) is an important scaffold protein that mediates a variety of cellular responses to integrin stimulation by extracellular matrix proteins. Mice with epidermis-restricted inactivation of the Ilk gene exhibit pleiotropic phenotypic defects, including impaired hair follicle morphogenesis, reduced epidermal adhesion to the basement membrane, compromised epidermal integrity, as well as wasting and failure to thrive leading to perinatal death. To better understand the underlying molecular mechanisms that cause such a broad range of alterations, we investigated the impact of Ilk gene inactivation on the epidermis transcriptome. Microarray analysis showed over 700 differentially regulated mRNAs encoding proteins involved in multiple aspects of epidermal function, including keratinocyte differentiation and barrier formation, inflammation, regeneration after injury, and fundamental epidermal developmental pathways. These studies also revealed potential effects on genes not previously implicated in ILK functions, including those important for melanocyte and melanoblast development and function, regulation of cytoskeletal dynamics, and homeobox genes. This study shows that ILK is a critical regulator of multiple aspects of epidermal function and homeostasis, and reveals the previously unreported involvement of ILK not only in epidermal differentiation and barrier formation, but also in melanocyte genesis and function.

Spiking neural network for recognizing spatiotemporal sequences of spikes

International Nuclear Information System (INIS)

Jin, Dezhe Z.

2004-01-01

Sensory neurons in many brain areas spike with precise timing to stimuli with temporal structures, and encode temporally complex stimuli into spatiotemporal spikes. How the downstream neurons read out such neural code is an important unsolved problem. In this paper, we describe a decoding scheme using a spiking recurrent neural network. The network consists of excitatory neurons that form a synfire chain, and two globally inhibitory interneurons of different types that provide delayed feedforward and fast feedback inhibition, respectively. The network signals recognition of a specific spatiotemporal sequence when the last excitatory neuron down the synfire chain spikes, which happens if and only if that sequence was present in the input spike stream. The recognition scheme is invariant to variations in the intervals between input spikes within some range. The computation of the network can be mapped into that of a finite state machine. Our network provides a simple way to decode spatiotemporal spikes with diverse types of neurons

Spatiotemporal mapping of sex differences during attentional processing.

Science.gov (United States)

Neuhaus, Andres H; Opgen-Rhein, Carolin; Urbanek, Carsten; Gross, Melanie; Hahn, Eric; Ta, Thi Minh Tam; Koehler, Simone; Dettling, Michael

2009-09-01

Functional neuroimaging studies have increasingly aimed at approximating neural substrates of human cognitive sex differences elicited by visuospatial challenge. It has been suggested that females and males use different behaviorally relevant neurocognitive strategies. In females, greater right prefrontal cortex activation has been found in several studies. The spatiotemporal dynamics of neural events associated with these sex differences is still unclear. We studied 22 female and 22 male participants matched for age, education, and nicotine with 29-channel-electroencephalogram recorded under a visual selective attention paradigm, the Attention Network Test. Visual event-related potentials (ERP) were topographically analyzed and neuroelectric sources were estimated. In absence of behavioral differences, ERP analysis revealed a novel frontal-occipital second peak of visual N100 that was significantly increased in females relative to males. Further, in females exclusively, a corresponding central ERP component at around 220 ms was found; here, a strong correlation between stimulus salience and sex difference of the central ERP component amplitude was observed. Subsequent source analysis revealed increased cortical current densities in right rostral prefrontal (BA 10) and occipital cortex (BA 19) in female subjects. This is the first study to report on a tripartite association between sex differences in ERPs, visual stimulus salience, and right prefrontal cortex activation during attentional processing. 2009 Wiley-Liss, Inc.

Molecular epidemiology and spatiotemporal analysis of hospital-acquired Acinetobacter baumannii infection in a tertiary care hospital in southern Thailand.

Science.gov (United States)

Chusri, S; Chongsuvivatwong, V; Rivera, J I; Silpapojakul, K; Singkhamanan, K; McNeil, E; Doi, Y

2017-01-01

Acinetobacter baumannii is a major hospital-acquired pathogen in Thailand that has a negative effect on patient survival. The nature of its transmission is poorly understood. To investigate the genotypic and spatiotemporal pattern of A. baumannii infection at a hospital in Thailand. The medical records of patients infected with A. baumannii at an 800-bed tertiary care hospital in southern Thailand between January 2010 and December 2011 were reviewed retrospectively. A. baumannii was identified at the genomospecies level. Carbapenemase genes were identified among carbapenem-resistant isolates associated with A. baumannii infection. A spatiotemporal analysis was performed by admission ward, time of infection and pulsed-field gel electrophoresis (PFGE) groups of A. baumannii. Nine PFGE groups were identified among the 197 A. baumannii infections. All A. baumannii isolates were assigned to International Clonal Lineage II. bla OXA-23 was the most prevalent carbapenemase gene. Outbreaks were observed mainly in respiratory and intensive care units. The association between PFGE group and hospital unit was significant. Spatiotemporal analysis identified 20 clusters of single PFGE group infections. Approximately half of the clusters involved multiple hospital units simultaneously. A. baumannii transmitted both within and between hospital wards. Better understanding and control of the transmission of A. baumannii are needed. Copyright © 2016 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.

A Hidden Markov Model Representing the Spatial and Temporal Correlation of Multiple Wind Farms

DEFF Research Database (Denmark)

Fang, Jiakun; Su, Chi; Hu, Weihao

2015-01-01

To accommodate the increasing wind energy with stochastic nature becomes a major issue on power system reliability. This paper proposes a methodology to characterize the spatiotemporal correlation of multiple wind farms. First, a hierarchical clustering method based on self-organizing maps is ado....... The proposed statistical modeling framework is compatible with the sequential power system reliability analysis. A case study on optimal sizing and location of fast-response regulation sources is presented.......To accommodate the increasing wind energy with stochastic nature becomes a major issue on power system reliability. This paper proposes a methodology to characterize the spatiotemporal correlation of multiple wind farms. First, a hierarchical clustering method based on self-organizing maps...... is adopted to categorize the similar output patterns of several wind farms into joint states. Then the hidden Markov model (HMM) is then designed to describe the temporal correlations among these joint states. Unlike the conventional Markov chain model, the accumulated wind power is taken into consideration...

Application of SCM with Bayesian B-Spline to Spatio-Temporal Analysis of Hypertension in China.

Science.gov (United States)

Ye, Zirong; Xu, Li; Zhou, Zi; Wu, Yafei; Fang, Ya

2018-01-02

Most previous research on the disparities of hypertension risk has neither simultaneously explored the spatio-temporal disparities nor considered the spatial information contained in the samples, thus the estimated results may be unreliable. Our study was based on the China Health and Nutrition Survey (CHNS), including residents over 12 years old in seven provinces from 1991 to 2011. Bayesian B-spline was used in the extended shared component model (SCM) for fitting temporal-related variation to explore spatio-temporal distribution in the odds ratio (OR) of hypertension, reveal gender variation, and explore latent risk factors. Our results revealed that the prevalence of hypertension increased from 14.09% in 1991 to 32.37% in 2011, with men experiencing a more obvious change than women. From a spatial perspective, a standardized prevalence ratio (SPR) remaining at a high level was found in Henan and Shandong for both men and women. Meanwhile, before 1997, the temporal distribution of hypertension risk for both men and women remained low. After that, notably since 2004, the OR of hypertension in each province increased to a relatively high level, especially in Northern China. Notably, the OR of hypertension in Shandong and Jiangsu, which was over 1.2, continuously stood out after 2004 for males, while that in Shandong and Guangxi was relatively high for females. The findings suggested that obvious spatial-temporal patterns for hypertension exist in the regions under research and this pattern was quite different between men and women.

The Multiple Modernities of Europe

DEFF Research Database (Denmark)

Thomassen, Bjørn

What Europe? Eric Voegelin on the Mediterranean and the Atlantic modernities. The concept ‘multiple modernities’ has during the last decade established itself in social and political theory, not least due to contributions made by Shmul Eisenstadt. The debate on multiple moderntities has served...... to question certain eurocentric assumptions about modernity and has also reignited the question of European particularity in a world historical perspective. This paper will discuss how ‘Europe’ itself can be considered a result of (at least) two different modernities, as proposed by the political theorist......, Eric Voegelin. Eric Voegelin talked of two spatio-temporal specific modernities, the Mediterranean and the Atlantic modernities. In short, for Voegelin the Atlantic modernity with its breakthroughs in the 17th and 18th centuries was a specific figuration that should not be mistaken for ‘modernity...

Neural avalanches at the critical point between replay and non-replay of spatiotemporal patterns.

Directory of Open Access Journals (Sweden)

Silvia Scarpetta

Full Text Available We model spontaneous cortical activity with a network of coupled spiking units, in which multiple spatio-temporal patterns are stored as dynamical attractors. We introduce an order parameter, which measures the overlap (similarity between the activity of the network and the stored patterns. We find that, depending on the excitability of the network, different working regimes are possible. For high excitability, the dynamical attractors are stable, and a collective activity that replays one of the stored patterns emerges spontaneously, while for low excitability, no replay is induced. Between these two regimes, there is a critical region in which the dynamical attractors are unstable, and intermittent short replays are induced by noise. At the critical spiking threshold, the order parameter goes from zero to one, and its fluctuations are maximized, as expected for a phase transition (and as observed in recent experimental results in the brain. Notably, in this critical region, the avalanche size and duration distributions follow power laws. Critical exponents are consistent with a scaling relationship observed recently in neural avalanches measurements. In conclusion, our simple model suggests that avalanche power laws in cortical spontaneous activity may be the effect of a network at the critical point between the replay and non-replay of spatio-temporal patterns.

Estimating the state of large spatio-temporally chaotic systems

International Nuclear Information System (INIS)

Ott, E.; Hunt, B.R.; Szunyogh, I.; Zimin, A.V.; Kostelich, E.J.; Corazza, M.; Kalnay, E.; Patil, D.J.; Yorke, J.A.

2004-01-01

We consider the estimation of the state of a large spatio-temporally chaotic system from noisy observations and knowledge of a system model. Standard state estimation techniques using the Kalman filter approach are not computationally feasible for systems with very many effective degrees of freedom. We present and test a new technique (called a Local Ensemble Kalman Filter), generally applicable to large spatio-temporally chaotic systems for which correlations between system variables evaluated at different points become small at large separation between the points

Statistical methods for spatio-temporal systems

CERN Document Server

Finkenstadt, Barbel

2006-01-01

Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will promote advances in research and a greater understanding between the mechanistic and the statistical modeling communities.Contributed by leading researchers in the field, each self-contained chapter starts with an introduction of the topic and progresses to recent research results. Presenting specific examples of epidemic data of bovine tuberculosis, gastroenteric disease, and the U.K. foot-and-mouth outbreak, the first chapter uses stochastic models, such as point process models, to provide the probabilistic backbone that facilitates statistical inference from data. The next chapter discusses the critical issue of modeling random growth objects in diverse biological systems, such as bacteria colonies, tumors, and plant populations. The subsequent chapter examines data transformation tools using examples from ecology and air quality data, followed by a chapter on space-time co...

Methodology for Clustering High-Resolution Spatiotemporal Solar Resource Data

Energy Technology Data Exchange (ETDEWEB)

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

2015-09-01

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

Task-relevant information is prioritized in spatiotemporal contextual cueing.

Science.gov (United States)

Higuchi, Yoko; Ueda, Yoshiyuki; Ogawa, Hirokazu; Saiki, Jun

2016-11-01

Implicit learning of visual contexts facilitates search performance-a phenomenon known as contextual cueing; however, little is known about contextual cueing under situations in which multidimensional regularities exist simultaneously. In everyday vision, different information, such as object identity and location, appears simultaneously and interacts with each other. We tested the hypothesis that, in contextual cueing, when multiple regularities are present, the regularities that are most relevant to our behavioral goals would be prioritized. Previous studies of contextual cueing have commonly used the visual search paradigm. However, this paradigm is not suitable for directing participants' attention to a particular regularity. Therefore, we developed a new paradigm, the "spatiotemporal contextual cueing paradigm," and manipulated task-relevant and task-irrelevant regularities. In four experiments, we demonstrated that task-relevant regularities were more responsible for search facilitation than task-irrelevant regularities. This finding suggests our visual behavior is focused on regularities that are relevant to our current goal.

Assessment of spatio-temporal gait parameters from trunk accelerations during human walking

NARCIS (Netherlands)

Zijlstra, W; Hof, AL

2003-01-01

This paper studies the feasibility of an analysis of spatio-temporal gait parameters based upon accelerometry. To this purpose, acceleration patterns of the trunk and their relationships with spatio-temporal gait parameters were analysed in healthy subjects. Based on model predictions of the body's

Micro-precise spatiotemporal delivery system embedded in 3D printing for complex tissue regeneration.

Science.gov (United States)

Tarafder, Solaiman; Koch, Alia; Jun, Yena; Chou, Conrad; Awadallah, Mary R; Lee, Chang H

2016-04-25

Three dimensional (3D) printing has emerged as an efficient tool for tissue engineering and regenerative medicine, given its advantages for constructing custom-designed scaffolds with tunable microstructure/physical properties. Here we developed a micro-precise spatiotemporal delivery system embedded in 3D printed scaffolds. PLGA microspheres (μS) were encapsulated with growth factors (GFs) and then embedded inside PCL microfibers that constitute custom-designed 3D scaffolds. Given the substantial difference in the melting points between PLGA and PCL and their low heat conductivity, μS were able to maintain its original structure while protecting GF's bioactivities. Micro-precise spatial control of multiple GFs was achieved by interchanging dispensing cartridges during a single printing process. Spatially controlled delivery of GFs, with a prolonged release, guided formation of multi-tissue interfaces from bone marrow derived mesenchymal stem/progenitor cells (MSCs). To investigate efficacy of the micro-precise delivery system embedded in 3D printed scaffold, temporomandibular joint (TMJ) disc scaffolds were fabricated with micro-precise spatiotemporal delivery of CTGF and TGFβ3, mimicking native-like multiphase fibrocartilage. In vitro, TMJ disc scaffolds spatially embedded with CTGF/TGFβ3-μS resulted in formation of multiphase fibrocartilaginous tissues from MSCs. In vivo, TMJ disc perforation was performed in rabbits, followed by implantation of CTGF/TGFβ3-μS-embedded scaffolds. After 4 wks, CTGF/TGFβ3-μS embedded scaffolds significantly improved healing of the perforated TMJ disc as compared to the degenerated TMJ disc in the control group with scaffold embedded with empty μS. In addition, CTGF/TGFβ3-μS embedded scaffolds significantly prevented arthritic changes on TMJ condyles. In conclusion, our micro-precise spatiotemporal delivery system embedded in 3D printing may serve as an efficient tool to regenerate complex and inhomogeneous tissues.

Audio-Visual Speaker Diarization Based on Spatiotemporal Bayesian Fusion.

Science.gov (United States)

Gebru, Israel D; Ba, Sileye; Li, Xiaofei; Horaud, Radu

2018-05-01

Speaker diarization consists of assigning speech signals to people engaged in a dialogue. An audio-visual spatiotemporal diarization model is proposed. The model is well suited for challenging scenarios that consist of several participants engaged in multi-party interaction while they move around and turn their heads towards the other participants rather than facing the cameras and the microphones. Multiple-person visual tracking is combined with multiple speech-source localization in order to tackle the speech-to-person association problem. The latter is solved within a novel audio-visual fusion method on the following grounds: binaural spectral features are first extracted from a microphone pair, then a supervised audio-visual alignment technique maps these features onto an image, and finally a semi-supervised clustering method assigns binaural spectral features to visible persons. The main advantage of this method over previous work is that it processes in a principled way speech signals uttered simultaneously by multiple persons. The diarization itself is cast into a latent-variable temporal graphical model that infers speaker identities and speech turns, based on the output of an audio-visual association process, executed at each time slice, and on the dynamics of the diarization variable itself. The proposed formulation yields an efficient exact inference procedure. A novel dataset, that contains audio-visual training data as well as a number of scenarios involving several participants engaged in formal and informal dialogue, is introduced. The proposed method is thoroughly tested and benchmarked with respect to several state-of-the art diarization algorithms.

Monoclonal antibodies reveal multiple forms of expression of human microsomal epoxide hydrolase

Energy Technology Data Exchange (ETDEWEB)

Duan, Hongying; Takagi, Akira [Department of Microbiology, Faculty of Medicine, Saitama Medical University, Moroyama-cho, Iruma-gun, Saitama 350-0495 (Japan); Kayano, Hidekazu [Department of Pathology, Faculty of Medicine, Saitama Medical University, Moroyama-cho, Iruma-gun, Saitama 350-0495 (Japan); Koyama, Isamu [Department of Digestive and General Surgery, Saitama International Medical Center, Faculty of Medicine, Saitama Medical University, 1397-1 Yamane, Hidaka, Saitama 350-1298 (Japan); Morisseau, Christophe; Hammock, Bruce D. [Department of Entomology and Cancer Center, University of California, Davis, One Shields Avenue, Davis, CA 95616-8584 (United States); Akatsuka, Toshitaka, E-mail: akatsuka@saitama-med.ac.jp [Department of Microbiology, Faculty of Medicine, Saitama Medical University, Moroyama-cho, Iruma-gun, Saitama 350-0495 (Japan)

2012-04-01

In a previous study, we developed five kinds of monoclonal antibodies against different portions of human mEH: three, anti-N-terminal; one, anti-C-terminal; one, anti-conformational epitope. Using them, we stained the intact and the permeabilized human cells of various kinds and performed flow cytometric analysis. Primary hepatocytes and peripheral blood mononuclear cells (PBMC) showed remarkable differences. On the surface, hepatocytes exhibited 4 out of 5 epitopes whereas PBMC did not show any of the epitopes. mEH was detected inside both cell types, but the most prominent expression was observed for the conformational epitope in the hepatocytes and the two N-terminal epitopes in PBMC. These differences were also observed between hepatocyte-derived cell lines and mononuclear cell-derived cell lines. In addition, among each group, there were several differences which may be related to the cultivation, the degree of differentiation, or the original cell subsets. We also noted that two glioblastoma cell lines reveal marked expression of the conformational epitope on the surface which seemed to correlate with the brain tumor-associated antigen reported elsewhere. Several cell lines also underwent selective permeabilization before flow cytometric analysis, and we noticed that the topological orientation of mEH on the ER membrane in those cells was in accordance with the previous report. However, the orientation on the cell surface was inconsistent with the report and had a great variation between the cells. These findings show the multiple mode of expression of mEH which may be possibly related to the multiple roles that mEH plays in different cells. -- Highlights: ► We examine expression of five mEH epitopes in human cells. ► Remarkable differences exist between hepatocytes and PBMC. ► mEH expression in cell lines differs depending on several factors. ► Some glioblastoma cell lines reveal marked surface expression of mEH. ► Topology of mEH on the cell

The spatio-temporal Development of Copenhagen's bicycle infrastructure 1912-2013

DEFF Research Database (Denmark)

Carstensen, Trine Agervig; Olafsson, Anton Stahl; Bech, Nynne Marie

2015-01-01

Cycling plays an important role in low-carbon transitions. Around the globe, cities are constructing bicycle infrastructure. The city of Copenhagen has a bicycle-friendly infrastructure celebrated for its fine-meshed network. This study documents the spatio-temporal development of Copenhagen......’s bicycle infrastructure and explores how the development corresponds to other processes of urban transformation. The study builds on historical maps of bicycle infrastructure that are digitised into geographical information, which allows for a comprehensive analysis of the formation of the network....... In search for identifying drivers, the study analyses the city’s spatial growth pattern, migration pattern, development of road network and changes in the transport culture. Analyses reveal that the bicycle infrastructure expanded at a relatively constant pace during distinct periods of urban transformation...

Spatio-Temporal Saliency Perception via Hypercomplex Frequency Spectral Contrast

Directory of Open Access Journals (Sweden)

Zhiqiang Tian

2013-03-01

Full Text Available Salient object perception is the process of sensing the salient information from the spatio-temporal visual scenes, which is a rapid pre-attention mechanism for the target location in a visual smart sensor. In recent decades, many successful models of visual saliency perception have been proposed to simulate the pre-attention behavior. Since most of the methods usually need some ad hoc parameters or high-cost preprocessing, they are difficult to rapidly detect salient object or be implemented by computing parallelism in a smart sensor. In this paper, we propose a novel spatio-temporal saliency perception method based on spatio-temporal hypercomplex spectral contrast (HSC. Firstly, the proposed HSC algorithm represent the features in the HSV (hue, saturation and value color space and features of motion by a hypercomplex number. Secondly, the spatio-temporal salient objects are efficiently detected by hypercomplex Fourier spectral contrast in parallel. Finally, our saliency perception model also incorporates with the non-uniform sampling, which is a common phenomenon of human vision that directs visual attention to the logarithmic center of the image/video in natural scenes. The experimental results on the public saliency perception datasets demonstrate the effectiveness of the proposed approach compared to eleven state-of-the-art approaches. In addition, we extend the proposed model to moving object extraction in dynamic scenes, and the proposed algorithm is superior to the traditional algorithms.

Oscillations, complex spatiotemporal behavior, and information transport in networks of excitatory and inhibitory neurons

International Nuclear Information System (INIS)

Destexhe, A.

1994-01-01

Various types of spatiotemporal behavior are described for two-dimensional networks of excitatory and inhibitory neurons with time delayed interactions. It is described how the network behaves as several structural parameters are varied, such as the number of neurons, the connectivity, and the values of synaptic weights. A transition from spatially uniform oscillations to spatiotemporal chaos via intermittentlike behavior is observed. The properties of spatiotemporally chaotic solutions are investigated by evaluating the largest positive Lyapunov exponent and the loss of correlation with distance. Finally, properties of information transport are evaluated during uniform oscillations and spatiotemporal chaos. It is shown that the diffusion coefficient increases significantly in the spatiotemporal phase similar to the increase of transport coefficients at the onset of fluid turbulence. It is proposed that such a property should be seen in other media, such as chemical turbulence or networks of oscillators. The possibility of measuring information transport from appropriate experiments is also discussed

Spatio-Temporal Data Mining for Location-Based Services

DEFF Research Database (Denmark)

Gidofalvi, Gyozo

. The objectives of the presented thesis are three-fold. First, to extend popular data mining methods to the spatio-temporal domain. Second, to demonstrate the usefulness of the extended methods and the derived knowledge in promising LBS examples. Finally, to eliminate privacy concerns in connection with spatio......-temporal data mining by devising systems for privacy-preserving location data collection and mining.......Location-Based Services (LBS) are continuously gaining popularity. Innovative LBSes integrate knowledge about the users into the service. Such knowledge can be derived by analyzing the location data of users. Such data contain two unique dimensions, space and time, which need to be analyzed...

Spatio-temporal modeling for residential burglary

NARCIS (Netherlands)

Mahfoud, M.; Bhulai, Sandjai; van der Mei, R.D.; Bhulai, Sandjai; Kardaras, Dimitris

2017-01-01

Spatio-temporal modeling is widely recognized as a promising means for predicting crime patterns. Despite their enormous potential, the available methods are still in their infancy. A lot of research focuses on crime hotspot detection and geographic crime clusters, while a systematic approach to

INCREMENTAL PRINCIPAL COMPONENT ANALYSIS BASED OUTLIER DETECTION METHODS FOR SPATIOTEMPORAL DATA STREAMS

Directory of Open Access Journals (Sweden)

A. Bhushan

2015-07-01

Full Text Available In this paper, we address outliers in spatiotemporal data streams obtained from sensors placed across geographically distributed locations. Outliers may appear in such sensor data due to various reasons such as instrumental error and environmental change. Real-time detection of these outliers is essential to prevent propagation of errors in subsequent analyses and results. Incremental Principal Component Analysis (IPCA is one possible approach for detecting outliers in such type of spatiotemporal data streams. IPCA has been widely used in many real-time applications such as credit card fraud detection, pattern recognition, and image analysis. However, the suitability of applying IPCA for outlier detection in spatiotemporal data streams is unknown and needs to be investigated. To fill this research gap, this paper contributes by presenting two new IPCA-based outlier detection methods and performing a comparative analysis with the existing IPCA-based outlier detection methods to assess their suitability for spatiotemporal sensor data streams.

A FRAMEWORK FOR ONLINE SPATIO-TEMPORAL DATA VISUALIZATION BASED ON HTML5

Directory of Open Access Journals (Sweden)

B. Mao

2012-07-01

Full Text Available Web is entering a new phase – HTML5. New features of HTML5 should be studied for online spatio-temporal data visualization. In the proposed framework, spatio-temporal data is stored in the data server and is sent to user browsers with WebSocket. Public geo-data such as Internet digital map is integrated into the browsers. Then animation is implemented through the canvas object defined by the HTML5 specification. To simulate the spatio-temporal data source, we collected the daily location of 15 users with GPS tracker. The current positions of the users are collected every minute and are recorded in a file. Based on this file, we generate a real time spatio-temporal data source which sends out current user location every second.By enlarging the real time scales by 60 times, we can observe the movement clearly. The data transmitted with WebSocket is the coordinates of users' current positions, which will can be demonstrated in client browsers.

Tensor analysis methods for activity characterization in spatiotemporal data

Energy Technology Data Exchange (ETDEWEB)

Haass, Michael Joseph; Van Benthem, Mark Hilary; Ochoa, Edward M

2014-03-01

Tensor (multiway array) factorization and decomposition offers unique advantages for activity characterization in spatio-temporal datasets because these methods are compatible with sparse matrices and maintain multiway structure that is otherwise lost in collapsing for regular matrix factorization. This report describes our research as part of the PANTHER LDRD Grand Challenge to develop a foundational basis of mathematical techniques and visualizations that enable unsophisticated users (e.g. users who are not steeped in the mathematical details of matrix algebra and mulitway computations) to discover hidden patterns in large spatiotemporal data sets.

Routes to spatiotemporal chaos in Kerr optical frequency combs.

Science.gov (United States)

Coillet, Aurélien; Chembo, Yanne K

2014-03-01

We investigate the various routes to spatiotemporal chaos in Kerr optical frequency combs, obtained through pumping an ultra-high Q-factor whispering-gallery mode resonator with a continuous-wave laser. The Lugiato-Lefever model is used to build bifurcation diagrams with regards to the parameters that are externally controllable, namely, the frequency and the power of the pumping laser. We show that the spatiotemporal chaos emerging from Turing patterns and solitons display distinctive dynamical features. Experimental spectra of chaotic Kerr combs are also presented for both cases, in excellent agreement with theoretical spectra.

Size-dependent diffusion promotes the emergence of spatiotemporal patterns

DEFF Research Database (Denmark)

Zhang, Lai; Thygesen, Uffe Høgsbro; Banerjee, Malay

2014-01-01

intraspecific physiological variations at the individual level. Here we explore the impacts of size variation within species resulting from individual ontogeny, on the emergence of spatiotemporal patterns in a fully size-structured population model. We found that size dependency of animal's diffusivity greatly......, we found that the single-generation cycle is more likely to drive spatiotemporal patterns compared to predator-prey cycles, meaning that the mechanism of Hopf bifurcation might be more common than hitherto appreciated since the former cycle is more widespread than the latter in case of interacting...

How about a Bayesian M/EEG imaging method correcting for incomplete spatio-temporal priors

DEFF Research Database (Denmark)

Stahlhut, Carsten; Attias, Hagai T.; Sekihara, Kensuke

2013-01-01

previous spatio-temporal inverse M/EEG models, the proposed model benefits of consisting of two source terms, namely, a spatio-temporal pattern term limiting the source configuration to a spatio-temporal subspace and a source correcting term to pick up source activity not covered by the spatio......-temporal prior belief. We have tested the model on both artificial data and real EEG data in order to demonstrate the efficacy of the model. The model was tested at different SNRs (-10.0,-5.2, -3.0, -1.0, 0, 0.8, 3.0 dB) using white noise. At all SNRs the sAquavit performs best in AUC measure, e.g. at SNR=0d...

Evaluation of spatial and spatiotemporal estimation methods in simulation of precipitation variability patterns

Science.gov (United States)

Bayat, Bardia; Zahraie, Banafsheh; Taghavi, Farahnaz; Nasseri, Mohsen

2013-08-01

Identification of spatial and spatiotemporal precipitation variations plays an important role in different hydrological applications such as missing data estimation. In this paper, the results of Bayesian maximum entropy (BME) and ordinary kriging (OK) are compared for modeling spatial and spatiotemporal variations of annual precipitation with and without incorporating elevation variations. The study area of this research is Namak Lake watershed located in the central part of Iran with an area of approximately 90,000 km2. The BME and OK methods have been used to model the spatial and spatiotemporal variations of precipitation in this watershed, and their performances have been evaluated using cross-validation statistics. The results of the case study have shown the superiority of BME over OK in both spatial and spatiotemporal modes. The results have shown that BME estimates are less biased and more accurate than OK. The improvements in the BME estimates are mostly related to incorporating hard and soft data in the estimation process, which resulted in more detailed and reliable results. Estimation error variance for BME results is less than OK estimations in the study area in both spatial and spatiotemporal modes.

Compressing spatio-temporal trajectories

DEFF Research Database (Denmark)

Gudmundsson, Joachim; Katajainen, Jyrki; Merrick, Damian

2009-01-01

such that the most common spatio-temporal queries can still be answered approximately after the compression has taken place. In the process, we develop an implementation of the Douglas–Peucker path-simplification algorithm which works efficiently even in the case where the polygonal path given as input is allowed...... to self-intersect. For a polygonal path of size n, the processing time is O(nlogkn) for k=2 or k=3 depending on the type of simplification....

Evaluation of high resolution spatio-temporal precipitation extremes from a stochastic weather generator

DEFF Research Database (Denmark)

Sørup, Hjalte Jomo Danielsen; Christensen, O. B.; Arnbjerg-Nielsen, Karsten

2017-01-01

Spatio-temporal rainfall is modelled for the North-Eastern part of Zealand (Denmark) using the Spatio-Temporal Neyman-Scott Rectangular Pulses model as implemented in the RainSim software. Hourly precipitation series for fitting the model are obtained from a dense network of tipping bucket rain...... gauges in the model area. The spatiotemporal performance of the model with respect to precipitation extremes is evaluated in the points of a 2x2 km regular grid covering the full model area. The model satisfactorily reproduces the extreme behaviour of the observed precipitation with respect to event...... intensity levels and unconditional spatial correlation when evaluated using an event based ranking approach at point scale and an advanced spatiotemporal coupling of extreme events. Prospectively the model can be used as a tool to evaluate the impact of climate change without relying on precipitation output...

Energy prediction using spatiotemporal pattern networks

Energy Technology Data Exchange (ETDEWEB)

Jiang, Zhanhong; Liu, Chao; Akintayo, Adedotun; Henze, Gregor P.; Sarkar, Soumik

2017-11-01

This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamical filtering, the STPN framework is used to capture not only the individual system characteristics but also the pair-wise causal dependencies among different sub-systems. To quantify causal dependencies, a mutual information based metric is presented and an energy prediction approach is subsequently proposed based on the STPN framework. To validate the proposed scheme, two case studies are presented, one involving wind turbine power prediction (supply side energy) using the Western Wind Integration data set generated by the National Renewable Energy Laboratory (NREL) for identifying spatiotemporal characteristics, and the other, residential electric energy disaggregation (demand side energy) using the Building America 2010 data set from NREL for exploring temporal features. In the energy disaggregation context, convex programming techniques beyond the STPN framework are developed and applied to achieve improved disaggregation performance.

Spatiotemporal Analysis of the 2014 Ebola Epidemic in West Africa.

Directory of Open Access Journals (Sweden)

Jantien A Backer

2016-12-01

Full Text Available In 2014-2016, Guinea, Sierra Leone and Liberia in West Africa experienced the largest and longest Ebola epidemic since the discovery of the virus in 1976. During the epidemic, incidence data were collected and published at increasing resolution. To monitor the epidemic as it spread within and between districts, we develop an analysis method that exploits the full spatiotemporal resolution of the data by combining a local model for time-varying effective reproduction numbers with a gravity-type model for spatial dispersion of the infection. We test this method in simulations and apply it to the weekly incidences of confirmed and probable cases per district up to June 2015, as reported by the World Health Organization. Our results indicate that, of the newly infected cases, only a small percentage, between 4% and 10%, migrates to another district, and a minority of these migrants, between 0% and 23%, leave their country. The epidemics in the three countries are found to be similar in estimated effective reproduction numbers, and in the probability of importing infection into a district. The countries might have played different roles in cross-border transmissions, although a sensitivity analysis suggests that this could also be related to underreporting. The spatiotemporal analysis method can exploit available longitudinal incidence data at different geographical locations to monitor local epidemics, determine the extent of spatial spread, reveal the contribution of local and imported cases, and identify sources of introductions in uninfected areas. With good quality data on incidence, this data-driven method can help to effectively control emerging infections.

SPAN: spike pattern association neuron for learning spatio-temporal sequences

OpenAIRE

Mohemmed, A; Schliebs, S; Matsuda, S; Kasabov, N

2012-01-01

Spiking Neural Networks (SNN) were shown to be suitable tools for the processing of spatio-temporal information. However, due to their inherent complexity, the formulation of efficient supervised learning algorithms for SNN is difficult and remains an important problem in the research area. This article presents SPAN — a spiking neuron that is able to learn associations of arbitrary spike trains in a supervised fashion allowing the processing of spatio-temporal information encoded in the prec...

Spatio-temporal interpolation of precipitation during monsoon periods in Pakistan

Science.gov (United States)

Hussain, Ijaz; Spöck, Gunter; Pilz, Jürgen; Yu, Hwa-Lung

2010-08-01

Spatio-temporal estimation of precipitation over a region is essential to the modeling of hydrologic processes for water resources management. The changes of magnitude and space-time heterogeneity of rainfall observations make space-time estimation of precipitation a challenging task. In this paper we propose a Box-Cox transformed hierarchical Bayesian multivariate spatio-temporal interpolation method for the skewed response variable. The proposed method is applied to estimate space-time monthly precipitation in the monsoon periods during 1974-2000, and 27-year monthly average precipitation data are obtained from 51 stations in Pakistan. The results of transformed hierarchical Bayesian multivariate spatio-temporal interpolation are compared to those of non-transformed hierarchical Bayesian interpolation by using cross-validation. The software developed by [11] is used for Bayesian non-stationary multivariate space-time interpolation. It is observed that the transformed hierarchical Bayesian method provides more accuracy than the non-transformed hierarchical Bayesian method.

Annotating spatio-temporal datasets for meaningful analysis in the Web

Science.gov (United States)

Stasch, Christoph; Pebesma, Edzer; Scheider, Simon

2014-05-01

More and more environmental datasets that vary in space and time are available in the Web. This comes along with an advantage of using the data for other purposes than originally foreseen, but also with the danger that users may apply inappropriate analysis procedures due to lack of important assumptions made during the data collection process. In order to guide towards a meaningful (statistical) analysis of spatio-temporal datasets available in the Web, we have developed a Higher-Order-Logic formalism that captures some relevant assumptions in our previous work [1]. It allows to proof on meaningful spatial prediction and aggregation in a semi-automated fashion. In this poster presentation, we will present a concept for annotating spatio-temporal datasets available in the Web with concepts defined in our formalism. Therefore, we have defined a subset of the formalism as a Web Ontology Language (OWL) pattern. It allows capturing the distinction between the different spatio-temporal variable types, i.e. point patterns, fields, lattices and trajectories, that in turn determine whether a particular dataset can be interpolated or aggregated in a meaningful way using a certain procedure. The actual annotations that link spatio-temporal datasets with the concepts in the ontology pattern are provided as Linked Data. In order to allow data producers to add the annotations to their datasets, we have implemented a Web portal that uses a triple store at the backend to store the annotations and to make them available in the Linked Data cloud. Furthermore, we have implemented functions in the statistical environment R to retrieve the RDF annotations and, based on these annotations, to support a stronger typing of spatio-temporal datatypes guiding towards a meaningful analysis in R. [1] Stasch, C., Scheider, S., Pebesma, E., Kuhn, W. (2014): "Meaningful spatial prediction and aggregation", Environmental Modelling & Software, 51, 149-165.

GISpark: A Geospatial Distributed Computing Platform for Spatiotemporal Big Data

Science.gov (United States)

Wang, S.; Zhong, E.; Wang, E.; Zhong, Y.; Cai, W.; Li, S.; Gao, S.

2016-12-01

Geospatial data are growing exponentially because of the proliferation of cost effective and ubiquitous positioning technologies such as global remote-sensing satellites and location-based devices. Analyzing large amounts of geospatial data can provide great value for both industrial and scientific applications. Data- and compute- intensive characteristics inherent in geospatial big data increasingly pose great challenges to technologies of data storing, computing and analyzing. Such challenges require a scalable and efficient architecture that can store, query, analyze, and visualize large-scale spatiotemporal data. Therefore, we developed GISpark - a geospatial distributed computing platform for processing large-scale vector, raster and stream data. GISpark is constructed based on the latest virtualized computing infrastructures and distributed computing architecture. OpenStack and Docker are used to build multi-user hosting cloud computing infrastructure for GISpark. The virtual storage systems such as HDFS, Ceph, MongoDB are combined and adopted for spatiotemporal data storage management. Spark-based algorithm framework is developed for efficient parallel computing. Within this framework, SuperMap GIScript and various open-source GIS libraries can be integrated into GISpark. GISpark can also integrated with scientific computing environment (e.g., Anaconda), interactive computing web applications (e.g., Jupyter notebook), and machine learning tools (e.g., TensorFlow/Orange). The associated geospatial facilities of GISpark in conjunction with the scientific computing environment, exploratory spatial data analysis tools, temporal data management and analysis systems make up a powerful geospatial computing tool. GISpark not only provides spatiotemporal big data processing capacity in the geospatial field, but also provides spatiotemporal computational model and advanced geospatial visualization tools that deals with other domains related with spatial property. We

Visual memory performance for color depends on spatiotemporal context.

Science.gov (United States)

Olivers, Christian N L; Schreij, Daniel

2014-10-01

Performance on visual short-term memory for features has been known to depend on stimulus complexity, spatial layout, and feature context. However, with few exceptions, memory capacity has been measured for abruptly appearing, single-instance displays. In everyday life, objects often have a spatiotemporal history as they or the observer move around. In three experiments, we investigated the effect of spatiotemporal history on explicit memory for color. Observers saw a memory display emerge from behind a wall, after which it disappeared again. The test display then emerged from either the same side as the memory display or the opposite side. In the first two experiments, memory improved for intermediate set sizes when the test display emerged in the same way as the memory display. A third experiment then showed that the benefit was tied to the original motion trajectory and not to the display object per se. The results indicate that memory for color is embedded in a richer episodic context that includes the spatiotemporal history of the display.

Selecting salient frames for spatiotemporal video modeling and segmentation.

Science.gov (United States)

Song, Xiaomu; Fan, Guoliang

2007-12-01

We propose a new statistical generative model for spatiotemporal video segmentation. The objective is to partition a video sequence into homogeneous segments that can be used as "building blocks" for semantic video segmentation. The baseline framework is a Gaussian mixture model (GMM)-based video modeling approach that involves a six-dimensional spatiotemporal feature space. Specifically, we introduce the concept of frame saliency to quantify the relevancy of a video frame to the GMM-based spatiotemporal video modeling. This helps us use a small set of salient frames to facilitate the model training by reducing data redundancy and irrelevance. A modified expectation maximization algorithm is developed for simultaneous GMM training and frame saliency estimation, and the frames with the highest saliency values are extracted to refine the GMM estimation for video segmentation. Moreover, it is interesting to find that frame saliency can imply some object behaviors. This makes the proposed method also applicable to other frame-related video analysis tasks, such as key-frame extraction, video skimming, etc. Experiments on real videos demonstrate the effectiveness and efficiency of the proposed method.

Plant diversity increases spatio?temporal niche complementarity in plant?pollinator interactions

OpenAIRE

Venjakob, Christine; Klein, Alexandra?Maria; Ebeling, Anne; Tscharntke, Teja; Scherber, Christoph

2016-01-01

Ongoing biodiversity decline impairs ecosystem processes, including pollination. Flower visitation, an important indicator of pollination services, is influenced by plant species richness. However, the spatio-temporal responses of different pollinator groups to plant species richness have not yet been analyzed experimentally. Here, we used an experimental plant species richness gradient to analyze plant-pollinator interactions with an unprecedented spatio-temporal resolution. We observed four...

Reliable Collaborative Filtering on Spatio-Temporal Privacy Data

Directory of Open Access Journals (Sweden)

Zhen Liu

2017-01-01

Full Text Available Lots of multilayer information, such as the spatio-temporal privacy check-in data, is accumulated in the location-based social network (LBSN. When using the collaborative filtering algorithm for LBSN location recommendation, one of the core issues is how to improve recommendation performance by combining the traditional algorithm with the multilayer information. The existing approaches of collaborative filtering use only the sparse user-item rating matrix. It entails high computational complexity and inaccurate results. A novel collaborative filtering-based location recommendation algorithm called LGP-CF, which takes spatio-temporal privacy information into account, is proposed in this paper. By mining the users check-in behavior pattern, the dataset is segmented semantically to reduce the data size that needs to be computed. Then the clustering algorithm is used to obtain and narrow the set of similar users. User-location bipartite graph is modeled using the filtered similar user set. Then LGP-CF can quickly locate the location and trajectory of users through message propagation and aggregation over the graph. Through calculating users similarity by spatio-temporal privacy data on the graph, we can finally calculate the rating of recommendable locations. Experiments results on the physical clusters indicate that compared with the existing algorithms, the proposed LGP-CF algorithm can make recommendations more accurately.

Stochastic resonance based on modulation instability in spatiotemporal chaos.

Science.gov (United States)

Han, Jing; Liu, Hongjun; Huang, Nan; Wang, Zhaolu

2017-04-03

A novel dynamic of stochastic resonance in spatiotemporal chaos is presented, which is based on modulation instability of perturbed partially coherent wave. The noise immunity of chaos can be reinforced through this effect and used to restore the coherent signal information buried in chaotic perturbation. A theoretical model with fluctuations term is derived from the complex Ginzburg-Landau equation via Wigner transform. It shows that through weakening the nonlinear threshold and triggering energy redistribution, the coherent component dominates the instability damped by incoherent component. The spatiotemporal output showing the properties of stochastic resonance may provide a potential application of signal encryption and restoration.

Spatial and spatio-temporal bayesian models with R - INLA

CERN Document Server

Blangiardo, Marta

2015-01-01

Dedication iiiPreface ix1 Introduction 11.1 Why spatial and spatio-temporal statistics? 11.2 Why do we use Bayesian methods for modelling spatial and spatio-temporal structures? 21.3 Why INLA? 31.4 Datasets 32 Introduction to 212.1 The language 212.2 objects 222.3 Data and session management 342.4 Packages 352.5 Programming in 362.6 Basic statistical analysis with 393 Introduction to Bayesian Methods 533.1 Bayesian Philosophy 533.2 Basic Probability Elements 573.3 Bayes Theorem 623.4 Prior and Posterior Distributions 643.5 Working with the Posterior Distribution 663.6 Choosing the Prior Distr

Spatiotemporal variability in carbon exchange fluxes across the Sahel

DEFF Research Database (Denmark)

Tagesson, Håkan Torbern; Fensholt, Rasmus; Cappelaere, Bernard

2016-01-01

for semi-arid ecosystems. We have synthesized data on the land-atmosphere exchange of CO2 measured with the eddy covariance technique from the six existing sites across the Sahel, one of the largest semi-arid regions in the world. The overall aim of the study is to analyse and quantify the spatiotemporal...... variability in these fluxes and to analyse to which degree spatiotemporal variation can be explained by hydrological, climatic, edaphic and vegetation variables. All ecosystems were C sinks (average ± total error -162 ± 48 g C m-2 y-1), but were smaller when strongly impacted by anthropogenic influences...

Mobile technologies and the spatiotemporal configurations of institutional practice

DEFF Research Database (Denmark)

Shklovski, Irina; Troshynski, Emily; Dourish, Paul

2015-01-01

are specifically concerned with what happens to institutional roles, power relationships, and decision-making processes when a particular type of information—that of spatiotemporal location of people—is made into a technologically tradable object through the use of location-based systems. We examine...... in which broad adoption of location-based and mobile technologies has the capacity to radically reconfigure the spatiotemporal arrangement of institutional processes. The presence of digital location traces creates new forms of institutional accountability, facilitates a shift in the understood relation...... between location and action, and necessitates new models of interpretation and sense making in practice....

Statistical Approaches for Spatiotemporal Prediction of Low Flows

Science.gov (United States)

Fangmann, A.; Haberlandt, U.

2017-12-01

An adequate assessment of regional climate change impacts on streamflow requires the integration of various sources of information and modeling approaches. This study proposes simple statistical tools for inclusion into model ensembles, which are fast and straightforward in their application, yet able to yield accurate streamflow predictions in time and space. Target variables for all approaches are annual low flow indices derived from a data set of 51 records of average daily discharge for northwestern Germany. The models require input of climatic data in the form of meteorological drought indices, derived from observed daily climatic variables, averaged over the streamflow gauges' catchments areas. Four different modeling approaches are analyzed. Basis for all pose multiple linear regression models that estimate low flows as a function of a set of meteorological indices and/or physiographic and climatic catchment descriptors. For the first method, individual regression models are fitted at each station, predicting annual low flow values from a set of annual meteorological indices, which are subsequently regionalized using a set of catchment characteristics. The second method combines temporal and spatial prediction within a single panel data regression model, allowing estimation of annual low flow values from input of both annual meteorological indices and catchment descriptors. The third and fourth methods represent non-stationary low flow frequency analyses and require fitting of regional distribution functions. Method three is subject to a spatiotemporal prediction of an index value, method four to estimation of L-moments that adapt the regional frequency distribution to the at-site conditions. The results show that method two outperforms successive prediction in time and space. Method three also shows a high performance in the near future period, but since it relies on a stationary distribution, its application for prediction of far future changes may be

Spatiotemporal chaos and two-dimensional dissipative rogue waves in Lugiato-Lefever model

Science.gov (United States)

Panajotov, Krassimir; Clerc, Marcel G.; Tlidi, Mustapha

2017-06-01

Driven nonlinear optical cavities can exhibit complex spatiotemporal dynamics. We consider the paradigmatic Lugiato-Lefever model describing driven nonlinear optical resonator. This model is one of the most-studied nonlinear equations in optics. It describes a large spectrum of nonlinear phenomena from bistability, to periodic patterns, localized structures, self-pulsating localized structures and to a complex spatiotemporal behavior. The model is considered also as prototype model to describe several optical nonlinear devices such as Kerr media, liquid crystals, left handed materials, nonlinear fiber cavity, and frequency comb generation. We focus our analysis on a spatiotemporal chaotic dynamics in one-dimension. We identify a route to spatiotemporal chaos through an extended quasiperiodicity. We have estimated the Kaplan-Yorke dimension that provides a measure of the strange attractor complexity. Likewise, we show that the Lugiato-Leferver equation supports rogues waves in two-dimensional settings. We characterize rogue-wave formation by computing the probability distribution of the pulse height. Contribution to the Topical Issue "Theory and Applications of the Lugiato-Lefever Equation", edited by Yanne K. Chembo, Damia Gomila, Mustapha Tlidi, Curtis R. Menyuk.

Distillation and Visualization of Spatiotemporal Structures in Turbulent Flow Fields

International Nuclear Information System (INIS)

Hege, Hans-Christian; Hotz, Ingrid; Kasten, Jens

2011-01-01

Although turbulence suggests randomness and disorder, organized motions that cause spatiotemporal 'coherent structures' are of particular interest. Revealing such structures in numerically given turbulent or semi-turbulent flows is of interest both for practically working engineers and theoretically oriented physicists. However, as long as there is no common agreement about the mathematical definition of coherent structures, extracting such structures is a vaguely defined task. Instead of searching for a general definition, the data visualization community takes a pragmatic approach and provides various tool chains implemented in flexible software frameworks that allow the user to extract distinct flow field structures. Thus physicists or engineers can select those flow structures which might advance their insight best. We present different approaches to distill important features from turbulent flows and discuss the necessary steps to be taken on the example of Lagrangian coherent structures.

Approximate spatio-temporal top-k publish/subscribe

KAUST Repository

Chen, Lisi

2018-04-26

Location-based publish/subscribe plays a significant role in mobile information disseminations. In this light, we propose and study a novel problem of processing location-based top-k subscriptions over spatio-temporal data streams. We define a new type of approximate location-based top-k subscription, Approximate Temporal Spatial-Keyword Top-k (ATSK) Subscription, that continuously feeds users with relevant spatio-temporal messages by considering textual similarity, spatial proximity, and information freshness. Different from existing location-based top-k subscriptions, Approximate Temporal Spatial-Keyword Top-k (ATSK) Subscription can automatically adjust the triggering condition by taking the triggering score of other subscriptions into account. The group filtering efficacy can be substantially improved by sacrificing the publishing result quality with a bounded guarantee. We conduct extensive experiments on two real datasets to demonstrate the performance of the developed solutions.

Approximate spatio-temporal top-k publish/subscribe

KAUST Repository

Chen, Lisi; Shang, Shuo

2018-01-01

Location-based publish/subscribe plays a significant role in mobile information disseminations. In this light, we propose and study a novel problem of processing location-based top-k subscriptions over spatio-temporal data streams. We define a new type of approximate location-based top-k subscription, Approximate Temporal Spatial-Keyword Top-k (ATSK) Subscription, that continuously feeds users with relevant spatio-temporal messages by considering textual similarity, spatial proximity, and information freshness. Different from existing location-based top-k subscriptions, Approximate Temporal Spatial-Keyword Top-k (ATSK) Subscription can automatically adjust the triggering condition by taking the triggering score of other subscriptions into account. The group filtering efficacy can be substantially improved by sacrificing the publishing result quality with a bounded guarantee. We conduct extensive experiments on two real datasets to demonstrate the performance of the developed solutions.

Spatio-temporal modeling of 210Pb transportation in lake environments

International Nuclear Information System (INIS)

Kuelahci, Fatih; Sen, Zekai

2009-01-01

Radioactive particle movement analysis in any environment gives valuable information about the effects of the concerned environment on the particle and the transportation phenomenon. In this study, the spatio-temporal point cumulative semivariogram (STPCSV) approach is proposed for the analysis of the spatio-temporal changes in the radioactive particle movement within a surface water body. This methodology is applied to the 210 Pb radioactive isotope measurements at 44 stations, which are determined beforehand in order to characterize the Keban Dam water environment on the Euphrates River in the southeastern part of Turkey. It considers the contributions coming from all the stations and provides information about the spatio-temporal behavior of 210 Pb in the water environment. After having identified the radii of influences at each station it is possible to draw maps for further interpretations. In order to see holistically the spatial changes of the radioisotope after 1st, 3rd and 5th hours, the radius of influence maps are prepared and interpreted accordingly.

Spatio-temporal resolved diagnostics of the single filament barrier discharge in air

International Nuclear Information System (INIS)

Wagner, H.E.; Brandenburg, R.; Michel, P.; Kozlov, K.V.

2001-01-01

First experimental results on the spatio-temporal development of single filaments of DBDs in dry air at atmospheric pressure are presented. The measurements allow a detailed visualisation and interpretation of the streamer development. In combination with the kinetic model they are used to get information on the spatiotemporal development of the reduced field-strength E/n, too

Dynamical Properties of Transient Spatio-Temporal Patterns in Bacterial Colony of Proteus mirabilis

Science.gov (United States)

Watanabe, Kazuhiko; Wakita, Jun-ichi; Itoh, Hiroto; Shimada, Hirotoshi; Kurosu, Sayuri; Ikeda, Takemasa; Yamazaki, Yoshihiro; Matsuyama, Tohey; Matsushita, Mitsugu

2002-02-01

Spatio-temporal patterns emerged inside a colony of bacterial species Proteus mirabilis on the surface of nutrient-rich semisolid agar medium have been investigated. We observed various patterns composed of the following basic types: propagating stripe, propagating stripe with fixed dislocation, expanding and shrinking target, and rotating spiral. The remarkable point is that the pattern changes immediately when we alter the position for observation, but it returns to the original if we restore the observing position within a few minutes. We further investigated mesoscopic and microscopic properties of the spatio-temporal patterns. It turned out that whenever the spatio-temporal patterns are observed in a colony, the areas are composed of two superimposed monolayers of elongated bacterial cells. In each area they are aligned almost parallel with each other like a two-dimensional nematic liquid crystal, and move collectively and independently of another layer. It has been found that the observed spatio-temporal patterns are explained as the moiré effect.

Quantifying Gait Impairment Using an Instrumented Treadmill in People with Multiple Sclerosis

Science.gov (United States)

Kalron, Alon; Dvir, Zeevi; Frid, Lior; Achiron, Anat

2013-01-01

Background and Objective. Treadmill gait analysis has been proposed as an attractive alternative for overground walking measuring systems. The purpose of this study was twofold: first to determine spatiotemporal parameters of treadmill gait in patients with multiple sclerosis (MS) and second to examine whether these parameters are associated with specific functional impairments in this cohort. Method. Eighty-seven relapsing-remitting patients diagnosed with MS, 50 women and 37 men, aged 40.9 ± 11.9 with an expanded disability status scale (EDSS) score of 2.7 ± 1.6, participated in this study. Twenty-five apparently healthy subjects, 14 women and 11 men, aged 38.5 ± 9.4, served as controls. Spatiotemporal gait parameters were obtained using the Zebris FDM-T Treadmill (Zebris Medical GmbH, Germany). People with MS demonstrated significantly shorter steps, extended stride time, wider base of support, longer step time, reduced single support phase, and a prolonged double support phase compared to the healthy controls. The EDSS score was significantly correlated with all spatiotemporal gait parameters. Conclusion. The instrumented treadmill may be an effective tool in assessing ambulation capabilities of people with MS. PMID:23878746

Spatio-Temporal Series Remote Sensing Image Prediction Based on Multi-Dictionary Bayesian Fusion

Directory of Open Access Journals (Sweden)

Chu He

2017-11-01

Full Text Available Contradictions in spatial resolution and temporal coverage emerge from earth observation remote sensing images due to limitations in technology and cost. Therefore, how to combine remote sensing images with low spatial yet high temporal resolution as well as those with high spatial yet low temporal resolution to construct images with both high spatial resolution and high temporal coverage has become an important problem called spatio-temporal fusion problem in both research and practice. A Multi-Dictionary Bayesian Spatio-Temporal Reflectance Fusion Model (MDBFM has been proposed in this paper. First, multiple dictionaries from regions of different classes are trained. Second, a Bayesian framework is constructed to solve the dictionary selection problem. A pixel-dictionary likehood function and a dictionary-dictionary prior function are constructed under the Bayesian framework. Third, remote sensing images before and after the middle moment are combined to predict images at the middle moment. Diverse shapes and textures information is learned from different landscapes in multi-dictionary learning to help dictionaries capture the distinctions between regions. The Bayesian framework makes full use of the priori information while the input image is classified. The experiments with one simulated dataset and two satellite datasets validate that the MDBFM is highly effective in both subjective and objective evaluation indexes. The results of MDBFM show more precise details and have a higher similarity with real images when dealing with both type changes and phenology changes.

Spatio-temporal epidemiology of the cholera outbreak in Papua New Guinea, 2009-2011.

Science.gov (United States)

Horwood, Paul F; Karl, Stephan; Mueller, Ivo; Jonduo, Marinjho H; Pavlin, Boris I; Dagina, Rosheila; Ropa, Berry; Bieb, Sibauk; Rosewell, Alexander; Umezaki, Masahiro; Siba, Peter M; Greenhill, Andrew R

2014-08-20

Cholera continues to be a devastating disease in many developing countries where inadequate safe water supply and poor sanitation facilitate spread. From July 2009 until late 2011 Papua New Guinea experienced the first outbreak of cholera recorded in the country, resulting in >15,500 cases and >500 deaths. Using the national cholera database, we analysed the spatio-temporal distribution and clustering of the Papua New Guinea cholera outbreak. The Kulldorff space-time permutation scan statistic, contained in the software package SatScan v9.2 was used to describe the first 8 weeks of the outbreak in Morobe Province before cholera cases spread throughout other regions of the country. Data were aggregated at the provincial level to describe the spread of the disease to other affected provinces. Spatio-temporal and cluster analyses revealed that the outbreak was characterized by three distinct phases punctuated by explosive propagation of cases when the outbreak spread to a new region. The lack of road networks across most of Papua New Guinea is likely to have had a major influence on the slow spread of the disease during this outbreak. Identification of high risk areas and the likely mode of spread can guide government health authorities to formulate public health strategies to mitigate the spread of the disease through education campaigns, vaccination, increased surveillance in targeted areas and interventions to improve water, sanitation and hygiene.

Visual search of cyclic spatio-temporal events

Science.gov (United States)

Gautier, Jacques; Davoine, Paule-Annick; Cunty, Claire

2018-05-01

The analysis of spatio-temporal events, and especially of relationships between their different dimensions (space-time-thematic attributes), can be done with geovisualization interfaces. But few geovisualization tools integrate the cyclic dimension of spatio-temporal event series (natural events or social events). Time Coil and Time Wave diagrams represent both the linear time and the cyclic time. By introducing a cyclic temporal scale, these diagrams may highlight the cyclic characteristics of spatio-temporal events. However, the settable cyclic temporal scales are limited to usual durations like days or months. Because of that, these diagrams cannot be used to visualize cyclic events, which reappear with an unusual period, and don't allow to make a visual search of cyclic events. Also, they don't give the possibility to identify the relationships between the cyclic behavior of the events and their spatial features, and more especially to identify localised cyclic events. The lack of possibilities to represent the cyclic time, outside of the temporal diagram of multi-view geovisualization interfaces, limits the analysis of relationships between the cyclic reappearance of events and their other dimensions. In this paper, we propose a method and a geovisualization tool, based on the extension of Time Coil and Time Wave, to provide a visual search of cyclic events, by allowing to set any possible duration to the diagram's cyclic temporal scale. We also propose a symbology approach to push the representation of the cyclic time into the map, in order to improve the analysis of relationships between space and the cyclic behavior of events.

The use of spatio-temporal correlation to forecast critical transitions

Science.gov (United States)

Karssenberg, Derek; Bierkens, Marc F. P.

2010-05-01

Complex dynamical systems may have critical thresholds at which the system shifts abruptly from one state to another. Such critical transitions have been observed in systems ranging from the human body system to financial markets and the Earth system. Forecasting the timing of critical transitions before they are reached is of paramount importance because critical transitions are associated with a large shift in dynamical regime of the system under consideration. However, it is hard to forecast critical transitions, because the state of the system shows relatively little change before the threshold is reached. Recently, it was shown that increased spatio-temporal autocorrelation and variance can serve as alternative early warning signal for critical transitions. However, thus far these second order statistics have not been used for forecasting in a data assimilation framework. Here we show that the use of spatio-temporal autocorrelation and variance in the state of the system reduces the uncertainty in the predicted timing of critical transitions compared to classical approaches that use the value of the system state only. This is shown by assimilating observed spatio-temporal autocorrelation and variance into a dynamical system model using a Particle Filter. We adapt a well-studied distributed model of a logistically growing resource with a fixed grazing rate. The model describes the transition from an underexploited system with high resource biomass to overexploitation as grazing pressure crosses the critical threshold, which is a fold bifurcation. To represent limited prior information, we use a large variance in the prior probability distributions of model parameters and the system driver (grazing rate). First, we show that the rate of increase in spatio-temporal autocorrelation and variance prior to reaching the critical threshold is relatively consistent across the uncertainty range of the driver and parameter values used. This indicates that an increase in

Spatiotemporal complexity in coupled map lattices

International Nuclear Information System (INIS)

Kaneko, Kunihiko

1986-01-01

Some spatiotemporal patterns of couple map lattices are presented. The chaotic kink-like motions are shown for the phase motion of the coupled circle lattices. An extension of the couple map lattice approach to Hamiltonian dynamics is briefly reported. An attempt to characterize the high-dimensional attractor by the extension of the correlation dimension is discussed. (author)

Adjusted functional boxplots for spatio-temporal data visualization and outlier detection

KAUST Repository

Sun, Ying

2011-10-24

This article proposes a simulation-based method to adjust functional boxplots for correlations when visualizing functional and spatio-temporal data, as well as detecting outliers. We start by investigating the relationship between the spatio-temporal dependence and the 1.5 times the 50% central region empirical outlier detection rule. Then, we propose to simulate observations without outliers on the basis of a robust estimator of the covariance function of the data. We select the constant factor in the functional boxplot to control the probability of correctly detecting no outliers. Finally, we apply the selected factor to the functional boxplot of the original data. As applications, the factor selection procedure and the adjusted functional boxplots are demonstrated on sea surface temperatures, spatio-temporal precipitation and general circulation model (GCM) data. The outlier detection performance is also compared before and after the factor adjustment. © 2011 John Wiley & Sons, Ltd.

Dynamic characterizers of spatiotemporal intermittency

OpenAIRE

Gupte, Neelima; Jabeen, Zahera

2006-01-01

Systems of coupled sine circle maps show regimes of spatiotemporally intermittent behaviour with associated scaling exponents which belong to the DP class, as well as regimes of spatially intermittent behaviour (with associated regular dynamical behaviour) which do not belong to the DP class. Both types of behaviour are seen along the bifurcation boundaries of the synchronized solutions, and contribute distinct signatures to the dynamical characterizers of the system, viz. the distribution of...

Short-term spatio-temporal wind power forecast in robust look-ahead power system dispatch

KAUST Repository

Xie, Le

2014-01-01

We propose a novel statistical wind power forecast framework, which leverages the spatio-temporal correlation in wind speed and direction data among geographically dispersed wind farms. Critical assessment of the performance of spatio-temporal wind power forecast is performed using realistic wind farm data from West Texas. It is shown that spatio-temporal wind forecast models are numerically efficient approaches to improving forecast quality. By reducing uncertainties in near-term wind power forecasts, the overall cost benefits on system dispatch can be quantified. We integrate the improved forecast with an advanced robust look-ahead dispatch framework. This integrated forecast and economic dispatch framework is tested in a modified IEEE RTS 24-bus system. Numerical simulation suggests that the overall generation cost can be reduced by up to 6% using a robust look-ahead dispatch coupled with spatio-temporal wind forecast as compared with persistent wind forecast models. © 2013 IEEE.

Bayesian spatiotemporal model of fMRI data using transfer functions.

Science.gov (United States)

Quirós, Alicia; Diez, Raquel Montes; Wilson, Simon P

2010-09-01

This research describes a new Bayesian spatiotemporal model to analyse BOLD fMRI studies. In the temporal dimension, we describe the shape of the hemodynamic response function (HRF) with a transfer function model. The spatial continuity and local homogeneity of the evoked responses are modelled by a Gaussian Markov random field prior on the parameter indicating activations. The proposal constitutes an extension of the spatiotemporal model presented in a previous approach [Quirós, A., Montes Diez, R. and Gamerman, D., 2010. Bayesian spatiotemporal model of fMRI data, Neuroimage, 49: 442-456], offering more flexibility in the estimation of the HRF and computational advantages in the resulting MCMC algorithm. Simulations from the model are performed in order to ascertain the performance of the sampling scheme and the ability of the posterior to estimate model parameters, as well as to check the model sensitivity to signal to noise ratio. Results are shown on synthetic data and on a real data set from a block-design fMRI experiment. Copyright (c) 2010 Elsevier Inc. All rights reserved.

Spatio-temporal patterns of gun violence in Syracuse, New York 2009-2015.

Science.gov (United States)

Larsen, David A; Lane, Sandra; Jennings-Bey, Timothy; Haygood-El, Arnett; Brundage, Kim; Rubinstein, Robert A

2017-01-01

Gun violence in the United States of America is a large public health problem that disproportionately affects urban areas. The epidemiology of gun violence reflects various aspects of an infectious disease including spatial and temporal clustering. We examined the spatial and temporal trends of gun violence in Syracuse, New York, a city of 145,000. We used a spatial scan statistic to reveal spatio-temporal clusters of gunshots investigated and corroborated by Syracuse City Police Department for the years 2009-2015. We also examined predictors of areas with increased gun violence using a multi-level zero-inflated Poisson regression with data from the 2010 census. Two space-time clusters of gun violence were revealed in the city. Higher rates of segregation, poverty and the summer months were all associated with increased risk of gun violence. Previous gunshots in the area were associated with a 26.8% increase in the risk of gun violence. Gun violence in Syracuse, NY is both spatially and temporally stable, with some neighborhoods of the city greatly afflicted.

UNDERSTANDING SEVERE WEATHER PROCESSES THROUGH SPATIOTEMPORAL RELATIONAL RANDOM FORESTS

Data.gov (United States)

National Aeronautics and Space Administration — UNDERSTANDING SEVERE WEATHER PROCESSES THROUGH SPATIOTEMPORAL RELATIONAL RANDOM FORESTS AMY MCGOVERN, TIMOTHY SUPINIE, DAVID JOHN GAGNE II, NATHANIEL TROUTMAN,...

Spatiotemporal Characteristics for the Depth from Luminance Contrast

Directory of Open Access Journals (Sweden)

Kazuya Matsubara

2011-05-01

Full Text Available Images with higher luminance contrast tend to be perceived closer in depth. To investigate a spatiotemporal characteristic of this effect, we evaluated subjective depth of a test stimulus with various spatial and temporal frequencies. For the purpose, the depth of a reference stimulus was matched to that of the test stimulus by changing the binocular disparity. The results showed that the test stimulus was perceived closer with higher luminance contrast for all conditions. Contrast efficiency was obtained from the contrast that provided the subjective depth for each spatiotemporal frequency. The shape of the contrast efficiency function was spatially low-pass and temporally band-pass. This characteristic is different from the one measure for a detection task. This suggests that only subset of contrast signals are used for depth from contrast.

Evolution of spatio-temporal drought characteristics: validation, projections and effect of adaptation scenarios

Science.gov (United States)

Vidal, J.-P.; Martin, E.; Kitova, N.; Najac, J.; Soubeyroux, J.-M.

2012-08-01

Drought events develop in both space and time and they are therefore best described through summary joint spatio-temporal characteristics, such as mean duration, mean affected area and total magnitude. This paper addresses the issue of future projections of such characteristics of drought events over France through three main research questions: (1) Are downscaled climate projections able to simulate spatio-temporal characteristics of meteorological and agricultural droughts in France over a present-day period? (2) How such characteristics will evolve over the 21st century? (3) How to use standardized drought indices to represent theoretical adaptation scenarios? These questions are addressed using the Isba land surface model, downscaled climate projections from the ARPEGE General Circulation Model under three emissions scenarios, as well as results from a previously performed 50-yr multilevel and multiscale drought reanalysis over France. Spatio-temporal characteristics of meteorological and agricultural drought events are computed using the Standardized Precipitation Index and the Standardized Soil Wetness Index, respectively, and for time scales of 3 and 12 months. Results first show that the distributions of joint spatio-temporal characteristics of observed events are well simulated by the downscaled hydroclimate projections over a present-day period. All spatio-temporal characteristics of drought events are then found to dramatically increase over the 21st century, with stronger changes for agricultural droughts. Two theoretical adaptation scenarios are eventually built based on hypotheses of adaptation to evolving climate and hydrological normals, either retrospective or prospective. The perceived spatio-temporal characteristics of drought events derived from these theoretical adaptation scenarios show much reduced changes, but they call for more realistic scenarios at both the catchment and national scale in order to accurately assess the combined effect of

Spatiotemporal conceptual platform for querying archaeological information systems

Science.gov (United States)

Partsinevelos, Panagiotis; Sartzetaki, Mary; Sarris, Apostolos

2015-04-01

Spatial and temporal distribution of archaeological sites has been shown to associate with several attributes including marine, water, mineral and food resources, climate conditions, geomorphological features, etc. In this study, archeological settlement attributes are evaluated under various associations in order to provide a specialized query platform in a geographic information system (GIS). Towards this end, a spatial database is designed to include a series of archaeological findings for a secluded geographic area of Crete in Greece. The key categories of the geodatabase include the archaeological type (palace, burial site, village, etc.), temporal information of the habitation/usage period (pre Minoan, Minoan, Byzantine, etc.), and the extracted geographical attributes of the sites (distance to sea, altitude, resources, etc.). Most of the related spatial attributes are extracted with readily available GIS tools. Additionally, a series of conceptual data attributes are estimated, including: Temporal relation of an era to a future one in terms of alteration of the archaeological type, topologic relations of various types and attributes, spatial proximity relations between various types. These complex spatiotemporal relational measures reveal new attributes towards better understanding of site selection for prehistoric and/or historic cultures, yet their potential combinations can become numerous. Therefore, after the quantification of the above mentioned attributes, they are classified as of their importance for archaeological site location modeling. Under this new classification scheme, the user may select a geographic area of interest and extract only the important attributes for a specific archaeological type. These extracted attributes may then be queried against the entire spatial database and provide a location map of possible new archaeological sites. This novel type of querying is robust since the user does not have to type a standard SQL query but

A Bayesian spatio-temporal geostatistical model with an auxiliary lattice for large datasets

KAUST Repository

Xu, Ganggang

2015-01-01

When spatio-temporal datasets are large, the computational burden can lead to failures in the implementation of traditional geostatistical tools. In this paper, we propose a computationally efficient Bayesian hierarchical spatio-temporal model in which the spatial dependence is approximated by a Gaussian Markov random field (GMRF) while the temporal correlation is described using a vector autoregressive model. By introducing an auxiliary lattice on the spatial region of interest, the proposed method is not only able to handle irregularly spaced observations in the spatial domain, but it is also able to bypass the missing data problem in a spatio-temporal process. Because the computational complexity of the proposed Markov chain Monte Carlo algorithm is of the order O(n) with n the total number of observations in space and time, our method can be used to handle very large spatio-temporal datasets with reasonable CPU times. The performance of the proposed model is illustrated using simulation studies and a dataset of precipitation data from the coterminous United States.

a Comparative Analysis of Spatiotemporal Data Fusion Models for Landsat and Modis Data

Science.gov (United States)

Hazaymeh, K.; Almagbile, A.

2018-04-01

In this study, three documented spatiotemporal data fusion models were applied to Landsat-7 and MODIS surface reflectance, and NDVI. The algorithms included the spatial and temporal adaptive reflectance fusion model (STARFM), sparse representation based on a spatiotemporal reflectance fusion model (SPSTFM), and spatiotemporal image-fusion model (STI-FM). The objectives of this study were to (i) compare the performance of these three fusion models using a one Landsat-MODIS spectral reflectance image pairs using time-series datasets from the Coleambally irrigation area in Australia, and (ii) quantitatively evaluate the accuracy of the synthetic images generated from each fusion model using statistical measurements. Results showed that the three fusion models predicted the synthetic Landsat-7 image with adequate agreements. The STI-FM produced more accurate reconstructions of both Landsat-7 spectral bands and NDVI. Furthermore, it produced surface reflectance images having the highest correlation with the actual Landsat-7 images. This study indicated that STI-FM would be more suitable for spatiotemporal data fusion applications such as vegetation monitoring, drought monitoring, and evapotranspiration.

Coexistence of collapse and stable spatiotemporal solitons in multimode fibers

Science.gov (United States)

Shtyrina, Olga V.; Fedoruk, Mikhail P.; Kivshar, Yuri S.; Turitsyn, Sergei K.

2018-01-01

We analyze spatiotemporal solitons in multimode optical fibers and demonstrate the existence of stable solitons, in a sharp contrast to earlier predictions of collapse of multidimensional solitons in three-dimensional media. We discuss the coexistence of blow-up solutions and collapse stabilization by a low-dimensional external potential in graded-index media, and also predict the existence of stable higher-order nonlinear waves such as dipole-mode spatiotemporal solitons. To support the main conclusions of our numerical studies we employ a variational approach and derive analytically the stability criterion for input powers for the collapse stabilization.

Spatiotemporal chaos of self-replicating spots in reaction-diffusion systems.

Science.gov (United States)

Wang, Hongli; Ouyang, Qi

2007-11-23

The statistical properties of self-replicating spots in the reaction-diffusion Gray-Scott model are analyzed. In the chaotic regime of the system, the spots that dominate the spatiotemporal chaos grow and divide in two or decay into the background randomly and continuously. The rates at which the spots are created and decay are observed to be linearly dependent on the number of spots in the system. We derive a probabilistic description of the spot dynamics based on the statistical independence of spots and thus propose a characterization of the spatiotemporal chaos dominated by replicating spots.

Pattern control and suppression of spatiotemporal chaos using geometrical resonance

International Nuclear Information System (INIS)

Gonzalez, J.A.; Bellorin, A.; Reyes, L.I.; Vasquez, C.; Guerrero, L.E.

2004-01-01

We generalize the concept of geometrical resonance to perturbed sine-Gordon, Nonlinear Schroedinger, phi (cursive,open) Greek 4 , and Complex Ginzburg-Landau equations. Using this theory we can control different dynamical patterns. For instance, we can stabilize breathers and oscillatory patterns of large amplitudes successfully avoiding chaos. On the other hand, this method can be used to suppress spatiotemporal chaos and turbulence in systems where these phenomena are already present. This method can be generalized to even more general spatiotemporal systems. A short report of some of our results has been published in [Europhys. Lett. 64 (2003) 743

Precipitation-productivity Relation in Grassland in Northern China: Investigations at Multiple Spatiotemporal Scales

Science.gov (United States)

Hu, Z.

2017-12-01

Climate change is predicted to cause dramatic variability in precipitation regime, not only in terms of change in annual precipitation amount, but also in precipitation seasonal distribution and precipitation event characteristics (high frenquency extrem precipitation, larger but fewer precipitation events), which combined to influence productivity of grassland in arid and semiarid regions. In this study, combining remote sensing products with in-situ measurements of aboveground net primary productivity (ANPP) and gross primary productivity (GPP) data from eddy covariance system in grassland of northern China, we quantified the effects of spatio-temporal vairation in precipitation on productivity from local sites to region scale. We found that, for an individual precipitation event, the duration of GPP-response to the individual precipitation event and the maximum absolute GPP response induced by the individual precipitation event increased linearly with the size of precipitation events. Comparison of the productivity-precipitation relationships between multi-sites determined that the predominant characteristics of precipitation events (PEC) that affected GPP differed remarkably between the water-limited temperate steppe and the temperature-limited alpine meadow. The number of heavy precipitation events (>10 mm d-1) was the most important PEC to impact GPP in the temperate steppe through affecting soil moisture at different soil profiles, while precipitation interval was the factor that affected GPP most in the alpine meadow via its effects on temperature. At the region scale, shape of ANPP-precipitation relationship varies with distinct spatial scales, and besides annual precipitation, precipitation seasonal distribution also has comparable impacts on spatial variation in ANPP. Temporal variability in ANPP was lower at both the dry and wet end, and peaked at a precipitation of 243.1±3.5mm, which is the transition region between typical steppe and desert steppe

Design and implementation of segment oriented spatio-temporal model in urban panoramic maps

Science.gov (United States)

Li, Haiting; Fei, Lifan; Peng, Qingshan; Li, Yanhong

2009-10-01

Object-oriented spatio-temporal model is directed by human cognition that each object has what/where/when attributes. The precise and flexible structure of such models supports multi-semantics of space and time. This paper reviews current research of spatio-temporal models using object-oriented approach and proposed a new spatio-temporal model based on segmentation in order to resolve the updating problem of some special GIS system by taking advantages of object-oriented spatio-temporal model and adopting category theory. Category theory can be used as a unifying framework for specifying complex systems and it provides rules on how objects may be joined. It characterizes the segments of object through mappings between them. The segment-oriented spatio-temporal model designed for urban panoramic maps is described and implemented. We take points and polylines as objects in this model in the management of panoramic map data. For the randomness of routes which transportation vehicle adopts each time, road objects in this model are split into some segments by crossing points. The segments still remains polyline type, but the splitting makes it easier to update the panoramic data when new photos are captured. This model is capable of eliminating redundant data and accelerating data access when panoramas are unchanged. For evaluation purpose, the data types and operations are designed and implemented in PostgreSQL and the results of experiments come out to prove that this model is efficient and expedient in the application of urban panoramic maps.

Spatiotemporal alignment of in utero BOLD-MRI series.

Science.gov (United States)

Turk, Esra Abaci; Luo, Jie; Gagoski, Borjan; Pascau, Javier; Bibbo, Carolina; Robinson, Julian N; Grant, P Ellen; Adalsteinsson, Elfar; Golland, Polina; Malpica, Norberto

2017-08-01

To present a method for spatiotemporal alignment of in-utero magnetic resonance imaging (MRI) time series acquired during maternal hyperoxia for enabling improved quantitative tracking of blood oxygen level-dependent (BOLD) signal changes that characterize oxygen transport through the placenta to fetal organs. The proposed pipeline for spatiotemporal alignment of images acquired with a single-shot gradient echo echo-planar imaging includes 1) signal nonuniformity correction, 2) intravolume motion correction based on nonrigid registration, 3) correction of motion and nonrigid deformations across volumes, and 4) detection of the outlier volumes to be discarded from subsequent analysis. BOLD MRI time series collected from 10 pregnant women during 3T scans were analyzed using this pipeline. To assess pipeline performance, signal fluctuations between consecutive timepoints were examined. In addition, volume overlap and distance between manual region of interest (ROI) delineations in a subset of frames and the delineations obtained through propagation of the ROIs from the reference frame were used to quantify alignment accuracy. A previously demonstrated rigid registration approach was used for comparison. The proposed pipeline improved anatomical alignment of placenta and fetal organs over the state-of-the-art rigid motion correction methods. In particular, unexpected temporal signal fluctuations during the first normoxia period were significantly decreased (P quantitative studies of placental function by improving spatiotemporal alignment across placenta and fetal organs. 1 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:403-412. © 2017 International Society for Magnetic Resonance in Medicine.

The influence of the interactions between anthropogenic activities and multiple ecological factors on land surface temperatures of urban forests

Science.gov (United States)

Ren, Y.

2017-12-01

Context Land surface temperatures (LSTs) spatio-temporal distribution pattern of urban forests are influenced by many ecological factors; the identification of interaction between these factors can improve simulations and predictions of spatial patterns of urban cold islands. This quantitative research requires an integrated method that combines multiple sources data with spatial statistical analysis. Objectives The purpose of this study was to clarify urban forest LST influence interaction between anthropogenic activities and multiple ecological factors using cluster analysis of hot and cold spots and Geogdetector model. We introduced the hypothesis that anthropogenic activity interacts with certain ecological factors, and their combination influences urban forests LST. We also assumed that spatio-temporal distributions of urban forest LST should be similar to those of ecological factors and can be represented quantitatively. Methods We used Jinjiang as a representative city in China as a case study. Population density was employed to represent anthropogenic activity. We built up a multi-source data (forest inventory, digital elevation models (DEM), population, and remote sensing imagery) on a unified urban scale to support urban forest LST influence interaction research. Through a combination of spatial statistical analysis results, multi-source spatial data, and Geogdetector model, the interaction mechanisms of urban forest LST were revealed. Results Although different ecological factors have different influences on forest LST, in two periods with different hot spots and cold spots, the patch area and dominant tree species were the main factors contributing to LST clustering in urban forests. The interaction between anthropogenic activity and multiple ecological factors increased LST in urban forest stands, linearly and nonlinearly. Strong interactions between elevation and dominant species were generally observed and were prevalent in either hot or cold spots

World Spatiotemporal Analytics and Mapping Project (WSTAMP): Discovering, Exploring, and Mapping Spatiotemporal Patterns across the World s Largest Open Source Geographic Data Sets

Energy Technology Data Exchange (ETDEWEB)

Stewart, Robert N [ORNL; Piburn, Jesse O [ORNL; Sorokine, Alexandre [ORNL; Myers, Aaron T [ORNL; White, Devin A [ORNL

2015-01-01

The application of spatiotemporal (ST) analytics to integrated data from major sources such as the World Bank, United Nations, and dozens of others holds tremendous potential for shedding new light on the evolution of cultural, health, economic, and geopolitical landscapes on a global level. Realizing this potential first requires an ST data model that addresses challenges in properly merging data from multiple authors, with evolving ontological perspectives, semantical differences, and changing attributes, as well as content that is textual, numeric, categorical, and hierarchical. Equally challenging is the development of analytical and visualization approaches that provide a serious exploration of this integrated data while remaining accessible to practitioners with varied backgrounds. The WSTAMP project at Oak Ridge National Laboratory has yielded two major results in addressing these challenges: 1) development of the WSTAMP database, a significant advance in ST data modeling that integrates 10,000+ attributes covering over 200 nation states spanning over 50 years from over 30 major sources and 2) a novel online ST exploratory and analysis tool providing an array of modern statistical and visualization techniques for analyzing these data temporally, spatially, and spatiotemporally under a standard analytic workflow. We discuss the status of this work and report on major findings. Acknowledgment Prepared by Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, Tennessee 37831-6285, managed by UT-Battelle, LLC for the U. S. Department of Energy under contract no. DEAC05-00OR22725. Copyright This manuscript has been authored by employees of UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy. Accordingly, the United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or

Spatiotemporal chaos in coupled logistic maps

International Nuclear Information System (INIS)

Varella Guedes, Andre; Amorim Savi, Marcelo

2010-01-01

The objective of this work is to investigate the spatiotemporal dynamics of coupled logistic maps. These maps are prototypes of high-dimensional dynamical systems and have been used to describe the evolution and pattern formation in different systems. Here, the logistic map lattice is coupled by a power law and, therefore, each map is influenced by other maps in its neighborhood. The Kolmogorov-Sinai entropy density is employed to quantify the complexity of system behavior, permitting a general qualitative understanding of different aspects of system dynamics. Three kinds of boundary conditions are treated and the influence of initial conditions is also of concern. Non-homogeneous maps are investigated, showing interesting aspects of spatiotemporal dynamics. The idea is to analyze the spatial interaction between two qualitative different types of behavior from a grid that is split into two parts. Numerical simulations show what types of conditions present a greater tendency to develop chaotic, periodic and synchronized responses. It should be highlighted that non-homogeneous grids have situations where a chaotic pattern can emerge from two periodic responses and also situations where a periodic pattern can emerge from chaos.

Mapping, Learning, Visualization, Classification, and Understanding of fMRI Data in the NeuCube Evolving Spatiotemporal Data Machine of Spiking Neural Networks.

Science.gov (United States)

Kasabov, Nikola K; Doborjeh, Maryam Gholami; Doborjeh, Zohreh Gholami

2017-04-01

This paper introduces a new methodology for dynamic learning, visualization, and classification of functional magnetic resonance imaging (fMRI) as spatiotemporal brain data. The method is based on an evolving spatiotemporal data machine of evolving spiking neural networks (SNNs) exemplified by the NeuCube architecture [1]. The method consists of several steps: mapping spatial coordinates of fMRI data into a 3-D SNN cube (SNNc) that represents a brain template; input data transformation into trains of spikes; deep, unsupervised learning in the 3-D SNNc of spatiotemporal patterns from data; supervised learning in an evolving SNN classifier; parameter optimization; and 3-D visualization and model interpretation. Two benchmark case study problems and data are used to illustrate the proposed methodology-fMRI data collected from subjects when reading affirmative or negative sentences and another one-on reading a sentence or seeing a picture. The learned connections in the SNNc represent dynamic spatiotemporal relationships derived from the fMRI data. They can reveal new information about the brain functions under different conditions. The proposed methodology allows for the first time to analyze dynamic functional and structural connectivity of a learned SNN model from fMRI data. This can be used for a better understanding of brain activities and also for online generation of appropriate neurofeedback to subjects for improved brain functions. For example, in this paper, tracing the 3-D SNN model connectivity enabled us for the first time to capture prominent brain functional pathways evoked in language comprehension. We found stronger spatiotemporal interaction between left dorsolateral prefrontal cortex and left temporal while reading a negated sentence. This observation is obviously distinguishable from the patterns generated by either reading affirmative sentences or seeing pictures. The proposed NeuCube-based methodology offers also a superior classification accuracy

Control of Spiral Waves and Spatiotemporal Chaos by Exciting Travel Wave Trains

International Nuclear Information System (INIS)

Yuan Guoyong; Wang Guangrui; Chen Shigang

2005-01-01

Spiral waves and spatiotemporal chaos usually are harmful and need to be suppressed. In this paper, a method is proposed to control them. Travel wave trains can be generated by periodic excitations near left boundary, spiral waves and spatiotemporal chaos can be eliminated by the trains for some certain excitation periods. Obvious resonant behavior can be observed from the relation between the periods of the trains and excitation ones. The method is against noise.

Using nonlinearity and spatiotemporal property modulation to control effective structural properties: dynamic rods

DEFF Research Database (Denmark)

Thomsen, Jon Juel; Blekhman, Iliya I.

2007-01-01

What are the effective properties of a generally nonlinear material or structure, whose local properties are modulated in both space and time? It has been suggested to use spatiotemporal modulation of structural properties to create materials and structures with adjustable effective properties......, and to call these dynamic materials or spatiotemporal composites. Also, according to theoretical predictions, structural nonlinearity enhances the possibilities of achieving specific effective properties. For example, with an elastic rod having cubical elastic nonlinearities, it seems possible to control......, and exemplified. Then simple approximate analytical expressions are derived for the effective wave speed and natural frequencies for one-dimensional wave propagation in a nonlinear elastic rod, where the spatiotemporal modulation is imposed as a high-frequency standing wave, supposed to be given. Finally the more...

Partial Fourier techniques in single-shot cross-term spatiotemporal encoded MRI.

Science.gov (United States)

Zhang, Zhiyong; Frydman, Lucio

2018-03-01

Cross-term spatiotemporal encoding (xSPEN) is a single-shot approach with exceptional immunity to field heterogeneities, the images of which faithfully deliver 2D spatial distributions without requiring a priori information or using postacquisition corrections. xSPEN, however, suffers from signal-to-noise ratio penalties due to its non-Fourier nature and due to diffusion losses-especially when seeking high resolution. This study explores partial Fourier transform approaches that, acting along either the readout or the spatiotemporally encoded dimensions, reduce these penalties. xSPEN uses an orthogonal (e.g., z) gradient to read, in direct space, the low-bandwidth (e.g., y) dimension. This substantially changes the nature of partial Fourier acquisitions vis-à-vis conventional imaging counterparts. A suitable theoretical analysis is derived to implement these procedures, along either the spatiotemporally or readout axes. Partial Fourier single-shot xSPEN images were recorded on preclinical and human scanners. Owing to their reduction in the experiments' acquisition times, this approach provided substantial sensitivity gains vis-à-vis previous implementations for a given targeted in-plane resolution. The physical origins of these gains are explained. Partial Fourier approaches, particularly when implemented along the low-bandwidth spatiotemporal dimension, provide several-fold sensitivity advantages at minimal costs to the execution and processing of the single-shot experiments. Magn Reson Med 79:1506-1514, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Spatiotemporal modeling of WNV in mosquitoes in Suffolk County

Data.gov (United States)

U.S. Environmental Protection Agency — R code and dataset to produce spatial models. This dataset is associated with the following publication: Meyer, M., S. Campbell, and J. Johnston. Spatiotemporal...

Discovery of spatio-temporal patterns from location-based social networks

Science.gov (United States)

Béjar, J.; Álvarez, S.; García, D.; Gómez, I.; Oliva, L.; Tejeda, A.; Vázquez-Salceda, J.

2016-03-01

Location-based social networks (LBSNs) such as Twitter or Instagram are a good source for user spatio-temporal behaviour. These networks collect data from users in such a way that they can be seen as a set of collective and distributed sensors of a geographical area. A low rate sampling of user's location information can be obtained during large intervals of time that can be used to discover complex patterns, including mobility profiles, points of interest or unusual events. These patterns can be used as the elements of a knowledge base for different applications in different domains such as mobility route planning, touristic recommendation systems or city planning. The aim of this paper is twofold, first to analyse the frequent spatio-temporal patterns that users share when living and visiting a city. This behaviour is studied by means of frequent itemsets algorithms in order to establish some associations among visits that can be interpreted as interesting routes or spatio-temporal connections. Second, to analyse how the spatio-temporal behaviour of a large number of users can be segmented in different profiles. These behavioural profiles are obtained by means of clustering algorithms that show the different patterns of behaviour of visitors and citizens. The data analysed were obtained from the public data feeds of Twitter and Instagram within an area surrounding the cities of Barcelona and Milan for a period of several months. The analysis of these data shows that these kinds of algorithms can be successfully applied to data from any city (or general area) to discover useful patterns that can be interpreted on terms of singular places and areas and their temporal relationships.

Against Laplacian Reduction of Newtonian Mass to Spatiotemporal Quantities

Science.gov (United States)

Martens, Niels C. M.

2018-03-01

Laplace wondered about the minimal choice of initial variables and parameters corresponding to a well-posed initial value problem. Discussions of Laplace's problem in the literature have focused on choosing between spatiotemporal variables relative to absolute space (i.e. substantivalism) or merely relative to other material bodies (i.e. relationalism) and between absolute masses (i.e. absolutism) or merely mass ratios (i.e. comparativism). This paper extends these discussions of Laplace's problem, in the context of Newtonian Gravity, by asking whether mass needs to be included in the initial state at all, or whether a purely spatiotemporal initial state suffices. It is argued that mass indeed needs to be included; removing mass from the initial state drastically reduces the predictive and explanatory power of Newtonian Gravity.

Spatiotemporal Stochastic Resonance:Theory and Experiment

Science.gov (United States)

Peter, Jung

1996-03-01

The amplification of weak periodic signals in bistable or excitable systems via stochastic resonance has been studied intensively over the last years. We are going one step further and ask: Can noise enhance spatiotemporal patterns in excitable media and can this effect be observed in nature? To this end, we are looking at large, two dimensional arrays of coupled excitable elements. Due to the coupling, excitation can propagate through the array in form of nonlinear waves. We observe target waves, rotating spiral waves and other wave forms. If the coupling between the elements is below a critical threshold, any excitational pattern will die out in the absence of noise. Below this threshold, large scale rotating spiral waves - as they are observed above threshold - can be maintained by a proper level of the noise[1]. Furthermore, their geometric features, such as the curvature can be controlled by the homogeneous noise level[2]. If the noise level is too large, break up of spiral waves and collisions with spontaneously nucleated waves yields spiral turbulence. Driving our array with a spatiotemporal pattern, e.g. a rotating spiral wave, we show that for weak coupling the excitational response of the array shows stochastic resonance - an effect we have termed spatiotemporal stochastic resonance. In the last part of the talk I'll make contact with calcium waves, observed in astrocyte cultures and hippocampus slices[3]. A. Cornell-Bell and collaborators[3] have pointed out the role of calcium waves for long-range glial signaling. We demonstrate the similarity of calcium waves with nonlinear waves in noisy excitable media. The noise level in the tissue is characterized by spontaneous activity and can be controlled by applying neuro-transmitter substances[3]. Noise effects in our model are compared with the effect of neuro-transmitters on calcium waves. [1]P. Jung and G. Mayer-Kress, CHAOS 5, 458 (1995). [2]P. Jung and G. Mayer-Kress, Phys. Rev. Lett.62, 2682 (1995). [3

Spatiotemporal behavior and nonlinear dynamics in a phase conjugate resonator

Science.gov (United States)

Liu, Siuying Raymond

1993-01-01

The work described can be divided into two parts. The first part is an investigation of the transient behavior and stability property of a phase conjugate resonator (PCR) below threshold. The second part is an experimental and theoretical study of the PCR's spatiotemporal dynamics above threshold. The time-dependent coupled wave equations for four-wave mixing (FWM) in a photorefractive crystal, with two distinct interaction regions caused by feedback from an ordinary mirror, was used to model the transient dynamics of a PCR below threshold. The conditions for self-oscillation were determined and the solutions were used to define the PCR's transfer function and analyze its stability. Experimental results for the buildup and decay times confirmed qualitatively the predicted behavior. Experiments were carried out above threshold to study the spatiotemporal dynamics of the PCR as a function of Pragg detuning and the resonator's Fresnel number. The existence of optical vortices in the wavefront were identified by optical interferometry. It was possible to describe the transverse dynamics and the spatiotemporal instabilities by modeling the three-dimensional-coupled wave equations in photorefractive FWM using a truncated modal expansion approach.

Spatiotemporal Characterization of Land Subsidence and Uplift (2009–2010 over Wuhan in Central China Revealed by TerraSAR-X InSAR Analysis

Directory of Open Access Journals (Sweden)

Lin Bai

2016-04-01

Full Text Available The effects of ground deformation pose a significant geo-hazard to the environment and infrastructure in Wuhan, the most populous city in Central China, in the eastern Jianghan Plain at the intersection of the Yangtze and Han rivers. Prior to this study, however, rates and patterns of region-wide ground deformation in Wuhan were little known. Here we employ multi-temporal SAR interferometry to detect and characterize spatiotemporal variations of ground deformation in major metropolitan areas in Wuhan. A total of twelve TerraSAR-X images acquired during 2009–2010 are used in the InSAR time series analysis. InSAR-derived results are validated by levelling survey measurements and reveal a distinct subsidence pattern within six zones in major commercial and industrial areas, with a maximum subsidence rate up to −67.3 mm/year. A comparison analysis between subsiding patterns and urban developments as well as geological conditions suggests that land subsidence in Wuhan is mainly attributed to anthropogenic activities, natural compaction of soft soil, and karst dissolution of subsurface carbonate rocks. However, anthropogenic activities related to intensive municipal construction and industrial production have more significant impacts on the measured subsidence than natural factors. Moreover, remarkable signals of secular land uplift are found along both banks of the Yangtze River, especially along the southern bank, with deformation rates ranging mostly from +5 mm/year to +17.5 mm/year. A strong temporal correlation is highlighted between the detected displacement evolutions and the water level records of the Yangtze River, inferring that this previously unknown deformation phenomenon is likely related to seasonal fluctuations in water levels of the Yangtze River.

Replication Strategy for Spatiotemporal Data Based on Distributed Caching System.

Science.gov (United States)

Xiong, Lian; Yang, Liu; Tao, Yang; Xu, Juan; Zhao, Lun

2018-01-14

The replica strategy in distributed cache can effectively reduce user access delay and improve system performance. However, developing a replica strategy suitable for varied application scenarios is still quite challenging, owing to differences in user access behavior and preferences. In this paper, a replication strategy for spatiotemporal data (RSSD) based on a distributed caching system is proposed. By taking advantage of the spatiotemporal locality and correlation of user access, RSSD mines high popularity and associated files from historical user access information, and then generates replicas and selects appropriate cache node for placement. Experimental results show that the RSSD algorithm is simple and efficient, and succeeds in significantly reducing user access delay.

On the potentials of multiple climate variables in assessing the spatio-temporal characteristics of hydrological droughts over the Volta Basin.

Science.gov (United States)

Ndehedehe, Christopher E; Awange, Joseph L; Corner, Robert J; Kuhn, Michael; Okwuashi, Onuwa

2016-07-01

Multiple drought episodes over the Volta basin in recent reports may lead to food insecurity and loss of revenue. However, drought studies over the Volta basin are rather generalised and largely undocumented due to sparse ground observations and unsuitable framework to determine their space-time occurrence. In this study, we examined the utility of standardised indicators (standardised precipitation index (SPI), standardised runoff index (SRI), standardised soil moisture index (SSI), and multivariate standardised drought index (MSDI)) and Gravity Recovery and Climate Experiment (GRACE) derived terrestrial water storage to assess hydrological drought characteristics over the basin. In order to determine the space-time patterns of hydrological drought in the basin, Independent Component Analysis (ICA), a higher order statistical technique was employed. The results show that SPI and SRI exhibit inconsistent behaviour in observed wet years presupposing a non-linear relationship that reflects the slow response of river discharge to precipitation especially after a previous extreme dry period. While the SPI and SSI show a linear relationship with a correlation of 0.63, the correlation between the MSDIs derived from combining precipitation/river discharge and precipitation/soil moisture indicates a significant value of 0.70 and shows an improved skill in hydrological drought monitoring over the Volta basin during the study period. The ICA-derived spatio-temporal hydrological drought patterns show Burkina Faso and the Lake Volta areas as predominantly drought zones. Further, the statistically significant negative correlations of pacific decadal oscillations (0.39 and 0.25) with temporal evolutions of drought in Burkina Faso and Ghana suggest the possible influence of low frequency large scale oscillations in the observed wet and dry regimes over the basin. Finally, our approach in drought assessment over the Volta basin contributes to a broad framework for hydrological

Proteomic profiling in multiple sclerosis clinical courses reveals potential biomarkers of neurodegeneration.

Directory of Open Access Journals (Sweden)

Maria Liguori

Full Text Available The aim of our project was to perform an exploratory analysis of the cerebrospinal fluid (CSF proteomic profiles of Multiple Sclerosis (MS patients, collected in different phases of their clinical course, in order to investigate the existence of peculiar profiles characterizing the different MS phenotypes. The study was carried out on 24 Clinically Isolated Syndrome (CIS, 16 Relapsing Remitting (RR MS, 11 Progressive (Pr MS patients. The CSF samples were analysed using the Matrix Assisted Laser Desorption Ionisation Time Of Flight (MALDI-TOF mass spectrometer in linear mode geometry and in delayed extraction mode (m/z range: 1000-25000 Da. Peak lists were imported for normalization and statistical analysis. CSF data were correlated with demographic, clinical and MRI parameters. The evaluation of MALDI-TOF spectra revealed 348 peak signals with relative intensity ≥ 1% in the study range. The peak intensity of the signals corresponding to Secretogranin II and Protein 7B2 were significantly upregulated in RRMS patients compared to PrMS (p<0.05, whereas the signals of Fibrinogen and Fibrinopeptide A were significantly downregulated in CIS compared to PrMS patients (p<0.04. Additionally, the intensity of the Tymosin β4 peak was the only signal to be significantly discriminated between the CIS and RRMS patients (p = 0.013. Although with caution due to the relatively small size of the study populations, and considering that not all the findings remained significant after adjustment for multiple comparisons, in our opinion this mass spectrometry evaluation confirms that this technique may provide useful and important information to improve our understanding of the complex pathogenesis of MS.

Spatiotemporal Variability of Meltwater Refreezing in Southwest Greenland Ice Sheet Firn

Science.gov (United States)

Rennermalm, A. K.; Hock, R.; Tedesco, M.; Corti, G.; Covi, F.; Miège, C.; Kingslake, J.; Leidman, S. Z.; Munsell, S.

2017-12-01

A substantial fraction of the summer meltwater formed on the surface of the Greenland ice sheet is retained in firn, while the remaining portion runs to the ocean through surface and subsurface channels. Refreezing of meltwater in firn can create impenetrable ice lenses, hence being a crucial process in the redistribution of surface runoff. To quantify the impact of refreezing on runoff and current and future Greenland surface mass balance, a three year National Science Foundation funded project titled "Refreezing in the firn of the Greenland ice sheet: Spatiotemporal variability and implications for ice sheet mass balance" started this past year. Here we present an overview of the project and some initial results from the first field season in May 2017 conducted in proximity of the DYE-2 site in the percolation zone of the Southwest Greenland ice sheet at elevations between 1963 and 2355 m a.s.l.. During this fieldwork two automatic weather stations were deployed, outfitted with surface energy balance sensors and 16 m long thermistor strings, over 300 km of ground penetrating radar data were collected, and five 20-26 m deep firn cores were extracted and analyzed for density and stratigraphy. Winter snow accumulation was measured along the radar tracks. Preliminary work on the firn-core data reveals increasing frequency and thickness of ice lenses at lower ice-sheet elevations, in agreement with other recent work in the area. Data collected within this project will facilitate advances in our understanding of the spatiotemporal variability of firn refreezing and its role in the hydrology and surface mass balance of the Greenland Ice Sheet.

NeuroMap: A spline-based interactive open-source software for spatiotemporal mapping of 2D and 3D MEA data

Directory of Open Access Journals (Sweden)

Oussama eAbdoun

2011-01-01

Full Text Available A major characteristic of neural networks is the complexity of their organization at various spatial scales, from microscopic local circuits to macroscopic brain-scale areas. Understanding how neural information is processed thus entails the ability to study them at multiple scales simultaneously. This is made possible using microelectrodes array (MEA technology. Indeed, high-density MEAs provide large-scale covering (several mm² of whole neural structures combined with microscopic resolution (about 50µm of unit activity. Yet, current options for spatiotemporal representation of MEA-collected data remain limited. Here we present NeuroMap, a new interactive Matlab-based software for spatiotemporal mapping of MEA data. NeuroMap uses thin plate spline interpolation, which provides several assets with respect to conventional mapping methods used currently. First, any MEA design can be considered, including 2D or 3D, regular or irregular, arrangements of electrodes. Second, spline interpolation allows the estimation of activity across the tissue with local extrema not necessarily at recording sites. Finally, this interpolation approach provides a straightforward analytical estimation of the spatial Laplacian for better current sources localization. In this software, coregistration of 2D MEA data on the anatomy of the neural tissue is made possible by fine matching of anatomical data with electrode positions using rigid deformation based correction of anatomical pictures. Overall, NeuroMap provides substantial material for detailed spatiotemporal analysis of MEA data. The package is distributed under GNU General Public License (GPL and available at http://sites.google.com/site/neuromapsoftware.

NeuroMap: A Spline-Based Interactive Open-Source Software for Spatiotemporal Mapping of 2D and 3D MEA Data.

Science.gov (United States)

Abdoun, Oussama; Joucla, Sébastien; Mazzocco, Claire; Yvert, Blaise

2011-01-01

A major characteristic of neural networks is the complexity of their organization at various spatial scales, from microscopic local circuits to macroscopic brain-scale areas. Understanding how neural information is processed thus entails the ability to study them at multiple scales simultaneously. This is made possible using microelectrodes array (MEA) technology. Indeed, high-density MEAs provide large-scale coverage (several square millimeters) of whole neural structures combined with microscopic resolution (about 50 μm) of unit activity. Yet, current options for spatiotemporal representation of MEA-collected data remain limited. Here we present NeuroMap, a new interactive Matlab-based software for spatiotemporal mapping of MEA data. NeuroMap uses thin plate spline interpolation, which provides several assets with respect to conventional mapping methods used currently. First, any MEA design can be considered, including 2D or 3D, regular or irregular, arrangements of electrodes. Second, spline interpolation allows the estimation of activity across the tissue with local extrema not necessarily at recording sites. Finally, this interpolation approach provides a straightforward analytical estimation of the spatial Laplacian for better current sources localization. In this software, coregistration of 2D MEA data on the anatomy of the neural tissue is made possible by fine matching of anatomical data with electrode positions using rigid-deformation-based correction of anatomical pictures. Overall, NeuroMap provides substantial material for detailed spatiotemporal analysis of MEA data. The package is distributed under GNU General Public License and available at http://sites.google.com/site/neuromapsoftware.

Stable isotope analysis of precipitation samples obtained via crowdsourcing reveals the spatiotemporal evolution of Superstorm Sandy.

Directory of Open Access Journals (Sweden)

Stephen P Good

Full Text Available Extra-tropical cyclones, such as 2012 Superstorm Sandy, pose a significant climatic threat to the northeastern United Sates, yet prediction of hydrologic and thermodynamic processes within such systems is complicated by their interaction with mid-latitude water patterns as they move poleward. Fortunately, the evolution of these systems is also recorded in the stable isotope ratios of storm-associated precipitation and water vapor, and isotopic analysis provides constraints on difficult-to-observe cyclone dynamics. During Superstorm Sandy, a unique crowdsourced approach enabled 685 precipitation samples to be obtained for oxygen and hydrogen isotopic analysis, constituting the largest isotopic sampling of a synoptic-scale system to date. Isotopically, these waters span an enormous range of values (> 21‰ for δ(18O, > 160‰ for δ(2H and exhibit strong spatiotemporal structure. Low isotope ratios occurred predominantly in the west and south quadrants of the storm, indicating robust isotopic distillation that tracked the intensity of the storm's warm core. Elevated values of deuterium-excess (> 25‰ were found primarily in the New England region after Sandy made landfall. Isotope mass balance calculations and Lagrangian back-trajectory analysis suggest that these samples reflect the moistening of dry continental air entrained from a mid-latitude trough. These results demonstrate the power of rapid-response isotope monitoring to elucidate the structure and dynamics of water cycling within synoptic-scale systems and improve our understanding of storm evolution, hydroclimatological impacts, and paleo-storm proxies.

Stable isotope analysis of precipitation samples obtained via crowdsourcing reveals the spatiotemporal evolution of Superstorm Sandy.

Science.gov (United States)

Good, Stephen P; Mallia, Derek V; Lin, John C; Bowen, Gabriel J

2014-01-01

Extra-tropical cyclones, such as 2012 Superstorm Sandy, pose a significant climatic threat to the northeastern United Sates, yet prediction of hydrologic and thermodynamic processes within such systems is complicated by their interaction with mid-latitude water patterns as they move poleward. Fortunately, the evolution of these systems is also recorded in the stable isotope ratios of storm-associated precipitation and water vapor, and isotopic analysis provides constraints on difficult-to-observe cyclone dynamics. During Superstorm Sandy, a unique crowdsourced approach enabled 685 precipitation samples to be obtained for oxygen and hydrogen isotopic analysis, constituting the largest isotopic sampling of a synoptic-scale system to date. Isotopically, these waters span an enormous range of values (> 21‰ for δ(18)O, > 160‰ for δ(2)H) and exhibit strong spatiotemporal structure. Low isotope ratios occurred predominantly in the west and south quadrants of the storm, indicating robust isotopic distillation that tracked the intensity of the storm's warm core. Elevated values of deuterium-excess (> 25‰) were found primarily in the New England region after Sandy made landfall. Isotope mass balance calculations and Lagrangian back-trajectory analysis suggest that these samples reflect the moistening of dry continental air entrained from a mid-latitude trough. These results demonstrate the power of rapid-response isotope monitoring to elucidate the structure and dynamics of water cycling within synoptic-scale systems and improve our understanding of storm evolution, hydroclimatological impacts, and paleo-storm proxies.

Pollination Biology and Spatio-Temporal Structuring of Some Major Acacia Species (Leguminosae) of the Arabian Peninsula

International Nuclear Information System (INIS)

Adgaba, N.; Alghamidi, A.; Tadesse, Y.; Getachew, A.; Ansari, M. J.

2016-01-01

Acacias are the dominant woody plant species distributed over the vast tracts of land throughout the Arabian Peninsula. However, information on spatio-temporal structuring and pollination biology of the species is not precisely available. To determine whether any variations exist among the Acacia species in their temporal distribution, their flowering period was determined through monitoring the commencing, peaking and ending of flowering of each species. Moreover, if any variations exist in release of floral rewards among the different co-existing and co-flowering species as mechanisms of partitioning of pollinators, to minimize competition for pollination, the progress of their anthesis over time was recorded by scoring polyads to anthers ratio at different hours of a day. In addition, the amount and dynamics of nectar sugar per inflorescence (N =225/species) was determined following flower nectar sugar washing technique. Types and frequencies of flower visitors and their preferences were determined by recording the visitors 6 times a day. The current study revealed that the Acacia species of the Arabian Peninsula are spatio-temporally structured: some species co-exist yet have different flowering seasons, whereas others co-exist, flowering concurrently yet exhibit a shift in their time of peak flowering and in the time at which the peak pollen is released during the day. This study demonstrates that all Acacia species examined secrete a considerable amount of nectar (2.24+-1.72 -10.02+-4.0mg/inflorescence) which serves as a floral reward for pollinators. Insects of the Order Hymenoptera are the most prevalent visitors to Acacia species in the region. The variations in spatio-temporal structuring of the Acaciaspecies could be due to their adaptation of reducing competition for pollinators and minimizing hetero-specific pollen transfer. (author)

A novel method for one-way hash function construction based on spatiotemporal chaos

Energy Technology Data Exchange (ETDEWEB)

Ren Haijun [College of Software Engineering, Chongqing University, Chongqing 400044 (China); State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044 (China)], E-mail: jhren@cqu.edu.cn; Wang Yong; Xie Qing [Key Laboratory of Electronic Commerce and Logistics of Chongqing, Chongqing University of Posts and Telecommunications, Chongqing 400065 (China); Yang Huaqian [Department of Computer and Modern Education Technology, Chongqing Education of College, Chongqing 400067 (China)

2009-11-30

A novel hash algorithm based on a spatiotemporal chaos is proposed. The original message is first padded with zeros if needed. Then it is divided into a number of blocks each contains 32 bytes. In the hashing process, each block is partitioned into eight 32-bit values and input into the spatiotemporal chaotic system. Then, after iterating the system for four times, the next block is processed by the same way. To enhance the confusion and diffusion effect, the cipher block chaining (CBC) mode is adopted in the algorithm. The hash value is obtained from the final state value of the spatiotemporal chaotic system. Theoretic analyses and numerical simulations both show that the proposed hash algorithm possesses good statistical properties, strong collision resistance and high efficiency, as required by practical keyed hash functions.

A novel method for one-way hash function construction based on spatiotemporal chaos

International Nuclear Information System (INIS)

Ren Haijun; Wang Yong; Xie Qing; Yang Huaqian

2009-01-01

A novel hash algorithm based on a spatiotemporal chaos is proposed. The original message is first padded with zeros if needed. Then it is divided into a number of blocks each contains 32 bytes. In the hashing process, each block is partitioned into eight 32-bit values and input into the spatiotemporal chaotic system. Then, after iterating the system for four times, the next block is processed by the same way. To enhance the confusion and diffusion effect, the cipher block chaining (CBC) mode is adopted in the algorithm. The hash value is obtained from the final state value of the spatiotemporal chaotic system. Theoretic analyses and numerical simulations both show that the proposed hash algorithm possesses good statistical properties, strong collision resistance and high efficiency, as required by practical keyed hash functions.

Synchronization of spatiotemporal chaotic systems and application to secure communication of digital image

International Nuclear Information System (INIS)

Wang Xing-Yuan; Zhang Na; Ren Xiao-Li; Zhang Yong-Lei

2011-01-01

Coupled map lattices (CMLs) are taken as examples to study the synchronization of spatiotemporal chaotic systems. In this paper, we use the nonlinear coupled method to implement the synchronization of two coupled map lattices. Through the appropriate separation of the linear term from the nonlinear term of the spatiotemporal chaotic system, we set the nonlinear term as the coupling function and then we can achieve the synchronization of two coupled map lattices. After that, we implement the secure communication of digital image using this synchronization method. Then, the discrete characteristics of the nonlinear coupling spatiotemporal chaos are applied to the discrete pixel of the digital image. After the synchronization of both the communication parties, the receiver can decrypt the original image. Numerical simulations show the effectiveness and the feasibility of the proposed program. (general)

Spatio-temporal patterns in simple models of marine systems

Science.gov (United States)

Feudel, U.; Baurmann, M.; Gross, T.

2009-04-01

Spatio-temporal patterns in marine systems are a result of the interaction of population dynamics with physical transport processes. These physical transport processes can be either diffusion processes in marine sediments or in the water column. We study the dynamics of one population of bacteria and its nutrient in in a simplified model of a marine sediments, taking into account that the considered bacteria possess an active as well as an inactive state, where activation is processed by signal molecules. Furthermore the nutrients are transported actively by bioirrigation and passively by diffusion. It is shown that under certain conditions Turing patterns can occur which yield heterogeneous spatial patterns of the species. The influence of bioirrigation on Turing patterns leads to the emergence of ''hot spots``, i.e. localized regions of enhanced bacterial activity. All obtained patterns fit quite well to observed patterns in laboratory experiments. Spatio-temporal patterns appear in a predator-prey model, used to describe plankton dynamics. These patterns appear due to the simultaneous emergence of Turing patterns and oscillations in the species abundance in the neighborhood of a Turing-Hopf bifurcation. We observe a large variety of different patterns where i) stationary heterogeneous patterns (e.g. hot and cold spots) compete with spatio-temporal patterns ii) slowly moving patterns are embedded in an oscillatory background iii) moving fronts and spiral waves appear.

Spatiotemporal Interpolation of Rainfall by Combining BME Theory and Satellite Rainfall Estimates

Directory of Open Access Journals (Sweden)

Tingting Shi

2015-09-01

Full Text Available The accurate assessment of spatiotemporal rainfall variability is a crucial and challenging task in many hydrological applications, mainly due to the lack of a sufficient number of rain gauges. The purpose of the present study is to investigate the spatiotemporal variations of annual and monthly rainfall over Fujian province in China by combining the Bayesian maximum entropy (BME method and satellite rainfall estimates. Specifically, based on annual and monthly rainfall data at 20 meteorological stations from 2000 to 2012, (1 the BME method with Tropical Rainfall Measuring Mission (TRMM estimates considered as soft data, (2 ordinary kriging (OK and (3 cokriging (CK were employed to model the spatiotemporal variations of rainfall in Fujian province. Subsequently, the performance of these methods was evaluated using cross-validation statistics. The results demonstrated that BME with TRMM as soft data (BME-TRMM performed better than the other two methods, generating rainfall maps that represented the local rainfall disparities in a more realistic manner. Of the three interpolation (mapping methods, the mean absolute error (MAE and root mean square error (RMSE values of the BME-TRMM method were the smallest. In conclusion, the BME-TRMM method improved spatiotemporal rainfall modeling and mapping by integrating hard data and soft information. Lastly, the study identified new opportunities concerning the application of TRMM rainfall estimates.

Propagation and spatiotemporal coupling characteristics of ultra-short Gaussian vortex pulse

Science.gov (United States)

Nie, Jianye; Liu, Guodong; Zhang, Rongzhu

2018-05-01

Based on Collins diffraction integral formula, the propagation equation of ultra-short Gaussian vortex pulse beam has been derived. Using the equation, the intensity distribution variations of vortex pulse in the propagation process are calculated. Specially, the spatiotemporal coupling characteristics of ultra-short vortex beams are discussed in detail. The results show that some key parameters, such as transverse distance, transmission distance, pulse width and topological charge number will influence the spatiotemporal coupling characteristics significantly. With the increasing of transverse distance, the waveforms of the pulses distort obviously. And when transmission distance is far than 50 mm, the distribution curve of transverse intensity gradually changes into a Gaussian type. In addition, initial pulse width will affect the distribution of light field, however, when initial pulse width is larger than 3 fs, the spatiotemporal coupling effect will be insignificant. Topological charge number does not affect the time delay characteristics, since with the increasing of topological charge number, the waveform of the pulse distorts gradually but the time delay does not occur.

Multiscale recurrence analysis of spatio-temporal data

Science.gov (United States)

Riedl, M.; Marwan, N.; Kurths, J.

2015-12-01

The description and analysis of spatio-temporal dynamics is a crucial task in many scientific disciplines. In this work, we propose a method which uses the mapogram as a similarity measure between spatially distributed data instances at different time points. The resulting similarity values of the pairwise comparison are used to construct a recurrence plot in order to benefit from established tools of recurrence quantification analysis and recurrence network analysis. In contrast to other recurrence tools for this purpose, the mapogram approach allows the specific focus on different spatial scales that can be used in a multi-scale analysis of spatio-temporal dynamics. We illustrate this approach by application on mixed dynamics, such as traveling parallel wave fronts with additive noise, as well as more complicate examples, pseudo-random numbers and coupled map lattices with a semi-logistic mapping rule. Especially the complicate examples show the usefulness of the multi-scale consideration in order to take spatial pattern of different scales and with different rhythms into account. So, this mapogram approach promises new insights in problems of climatology, ecology, or medicine.

Spatio-temporal patterns of coral recruitment at Vamizi Island ...

African Journals Online (AJOL)

Spatio-temporal patterns of coral recruitment at Vamizi Island, Quirimbas Archipelago, Mozambique. ... Spatial and temporal patterns of recruitment of reef corals were assessed for the first time in Mozambique ... AJOL African Journals Online.

Large scale stochastic spatio-temporal modelling with PCRaster

NARCIS (Netherlands)

Karssenberg, D.J.; Drost, N.; Schmitz, O.; Jong, K. de; Bierkens, M.F.P.

2013-01-01

PCRaster is a software framework for building spatio-temporal models of land surface processes (http://www.pcraster.eu). Building blocks of models are spatial operations on raster maps, including a large suite of operations for water and sediment routing. These operations are available to model

Role of Temporal Diversity in Inferring Social Ties Based on Spatio-Temporal Data

OpenAIRE

Desai, Deshana; Nisar, Harsh; Bhardawaj, Rishab

2016-01-01

The last two decades have seen a tremendous surge in research on social networks and their implications. The studies includes inferring social relationships, which in turn have been used for target advertising, recommendations, search customization etc. However, the offline experiences of human, the conversations with people and face-to-face interactions that govern our lives interactions have received lesser attention. We introduce DAIICT Spatio-Temporal Network (DSSN), a spatiotemporal data...

Segment-Tube: Spatio-Temporal Action Localization in Untrimmed Videos with Per-Frame Segmentation

OpenAIRE

Le Wang; Xuhuan Duan; Qilin Zhang; Zhenxing Niu; Gang Hua; Nanning Zheng

2018-01-01

Inspired by the recent spatio-temporal action localization efforts with tubelets (sequences of bounding boxes), we present a new spatio-temporal action localization detector Segment-tube, which consists of sequences of per-frame segmentation masks. The proposed Segment-tube detector can temporally pinpoint the starting/ending frame of each action category in the presence of preceding/subsequent interference actions in untrimmed videos. Simultaneously, the Segment-tube detector produces per-fr...

Application of Deep Learning and Supervised Learning Methods to Recognize Nonlinear Hidden Pattern in Water Stress Levels from Spatiotemporal Datasets across Rural and Urban US Counties

Science.gov (United States)

Eisenhart, T.; Josset, L.; Rising, J. A.; Devineni, N.; Lall, U.

2017-12-01

In the wake of recent water crises, the need to understand and predict the risk of water stress in urban and rural areas has grown. This understanding has the potential to improve decision making in public resource management, policy making, risk management and investment decisions. Assuming an underlying relationship between urban and rural water stress and observable features, we apply Deep Learning and Supervised Learning models to uncover hidden nonlinear patterns from spatiotemporal datasets. Results of interest includes prediction accuracy on extreme categories (i.e. urban areas highly prone to water stress) and not solely the average risk for urban or rural area, which adds complexity to the tuning of model parameters. We first label urban water stressed counties using annual water quality violations and compile a comprehensive spatiotemporal dataset that captures the yearly evolution of climatic, demographic and economic factors of more than 3,000 US counties over the 1980-2010 period. As county-level data reporting is not done on a yearly basis, we test multiple imputation methods to get around the issue of missing data. Using Python libraries, TensorFlow and scikit-learn, we apply and compare the ability of, amongst other methods, Recurrent Neural Networks (testing both LSTM and GRU cells), Convolutional Neural Networks and Support Vector Machines to predict urban water stress. We evaluate the performance of those models over multiple time spans and combine methods to diminish the risk of overfitting and increase prediction power on test sets. This methodology seeks to identify hidden nonlinear patterns to assess the predominant data features that influence urban and rural water stress. Results from this application at the national scale will assess the performance of deep learning models to predict water stress risk areas across all US counties and will highlight a predominant Machine Learning method for modeling water stress risk using spatiotemporal

On the expected value and variance for an estimator of the spatio-temporal product density function

DEFF Research Database (Denmark)

Rodríguez-Corté, Francisco J.; Ghorbani, Mohammad; Mateu, Jorge

Second-order characteristics are used to analyse the spatio-temporal structure of the underlying point process, and thus these methods provide a natural starting point for the analysis of spatio-temporal point process data. We restrict our attention to the spatio-temporal product density function......, and develop a non-parametric edge-corrected kernel estimate of the product density under the second-order intensity-reweighted stationary hypothesis. The expectation and variance of the estimator are obtained, and closed form expressions derived under the Poisson case. A detailed simulation study is presented...... to compare our close expression for the variance with estimated ones for Poisson cases. The simulation experiments show that the theoretical form for the variance gives acceptable values, which can be used in practice. Finally, we apply the resulting estimator to data on the spatio-temporal distribution...

Velocity landscape correlation resolves multiple flowing protein populations from fluorescence image time series.

Science.gov (United States)

Pandžić, Elvis; Abu-Arish, Asmahan; Whan, Renee M; Hanrahan, John W; Wiseman, Paul W

2018-02-16

Molecular, vesicular and organellar flows are of fundamental importance for the delivery of nutrients and essential components used in cellular functions such as motility and division. With recent advances in fluorescence/super-resolution microscopy modalities we can resolve the movements of these objects at higher spatio-temporal resolutions and with better sensitivity. Previously, spatio-temporal image correlation spectroscopy has been applied to map molecular flows by correlation analysis of fluorescence fluctuations in image series. However, an underlying assumption of this approach is that the sampled time windows contain one dominant flowing component. Although this was true for most of the cases analyzed earlier, in some situations two or more different flowing populations can be present in the same spatio-temporal window. We introduce an approach, termed velocity landscape correlation (VLC), which detects and extracts multiple flow components present in a sampled image region via an extension of the correlation analysis of fluorescence intensity fluctuations. First we demonstrate theoretically how this approach works, test the performance of the method with a range of computer simulated image series with varying flow dynamics. Finally we apply VLC to study variable fluxing of STIM1 proteins on microtubules connected to the plasma membrane of Cystic Fibrosis Bronchial Epithelial (CFBE) cells. Copyright © 2018 Elsevier Inc. All rights reserved.

Spatiotemporal coding of inputs for a system of globally coupled phase oscillators

Science.gov (United States)

Wordsworth, John; Ashwin, Peter

2008-12-01

We investigate the spatiotemporal coding of low amplitude inputs to a simple system of globally coupled phase oscillators with coupling function g(ϕ)=-sin(ϕ+α)+rsin(2ϕ+β) that has robust heteroclinic cycles (slow switching between cluster states). The inputs correspond to detuning of the oscillators. It was recently noted that globally coupled phase oscillators can encode their frequencies in the form of spatiotemporal codes of a sequence of cluster states [P. Ashwin, G. Orosz, J. Wordsworth, and S. Townley, SIAM J. Appl. Dyn. Syst. 6, 728 (2007)]. Concentrating on the case of N=5 oscillators we show in detail how the spatiotemporal coding can be used to resolve all of the information that relates the individual inputs to each other, providing that a long enough time series is considered. We investigate robustness to the addition of noise and find a remarkable stability, especially of the temporal coding, to the addition of noise even for noise of a comparable magnitude to the inputs.

Replication Strategy for Spatiotemporal Data Based on Distributed Caching System

Science.gov (United States)

Xiong, Lian; Tao, Yang; Xu, Juan; Zhao, Lun

2018-01-01

The replica strategy in distributed cache can effectively reduce user access delay and improve system performance. However, developing a replica strategy suitable for varied application scenarios is still quite challenging, owing to differences in user access behavior and preferences. In this paper, a replication strategy for spatiotemporal data (RSSD) based on a distributed caching system is proposed. By taking advantage of the spatiotemporal locality and correlation of user access, RSSD mines high popularity and associated files from historical user access information, and then generates replicas and selects appropriate cache node for placement. Experimental results show that the RSSD algorithm is simple and efficient, and succeeds in significantly reducing user access delay. PMID:29342897

Spatio-temporal change detection from multidimensional arrays: Detecting deforestation from MODIS time series

Science.gov (United States)

Lu, Meng; Pebesma, Edzer; Sanchez, Alber; Verbesselt, Jan

2016-07-01

Growing availability of long-term satellite imagery enables change modeling with advanced spatio-temporal statistical methods. Multidimensional arrays naturally match the structure of spatio-temporal satellite data and can provide a clean modeling process for complex spatio-temporal analysis over large datasets. Our study case illustrates the detection of breakpoints in MODIS imagery time series for land cover change in the Brazilian Amazon using the BFAST (Breaks For Additive Season and Trend) change detection framework. BFAST includes an Empirical Fluctuation Process (EFP) to alarm the change and a change point time locating process. We extend the EFP to account for the spatial autocorrelation between spatial neighbors and assess the effects of spatial correlation when applying BFAST on satellite image time series. In addition, we evaluate how sensitive EFP is to the assumption that its time series residuals are temporally uncorrelated, by modeling it as an autoregressive process. We use arrays as a unified data structure for the modeling process, R to execute the analysis, and an array database management system to scale computation. Our results point to BFAST as a robust approach against mild temporal and spatial correlation, to the use of arrays to ease the modeling process of spatio-temporal change, and towards communicable and scalable analysis.

Evaluation of high resolution spatio-temporal precipitation extremes from a stochastic weather generator

DEFF Research Database (Denmark)

Sørup, Hjalte Jomo Danielsen; Christensen, O. B.; Arnbjerg-Nielsen, Karsten

gauges in the model area. The spatio-temporal performance of the model with respect to precipitation extremes is evaluated in the points of a 2x2 km regular grid covering the full model area. The model satisfactorily reproduces the extreme behaviour of the observed precipitation with respect to event...... intensity levels and unconditional spatial correlation when evaluated using an event based ranking approach at point scale and an advanced spatio-temporal coupling of extreme events. Prospectively the model can be used as a tool to evaluate the impact of climate change without relying onprecipitation output......Spatio-temporal rainfall is modelled for the North-Eastern part of Zealand (Denmark) using the Spatio-Temporal Neyman-Scott Rectangular Pulses model as implemented in the RainSim software. Hourly precipitation series for fitting the model are obtained from a dense network of tipping bucket rain...

Using a weather generator to downscale spatio-temporal precipitation at urban scale

DEFF Research Database (Denmark)

Sørup, Hjalte Jomo Danielsen; Christensen, Ole Bøssing; Arnbjerg-Nielsen, Karsten

In recent years, urban flooding has occurred in Denmark due to very local extreme precipitation events with very short lifetime. Several of these floods have been among the most severe ever experienced. The current study demonstrates the applicability of the Spatio-Temporal Neyman-Scott Rectangular...... the observed spatio-temporal differences at very fine scale for all measured parameters. For downscaling, perturbation with a climate change signal, precipitation from four different regional climate model simulations has been analysed. The analysed models are two runs from the ENSEMBLES (RACMO...

Precursor of transition to turbulence: spatiotemporal wave front.

Science.gov (United States)

Bhaumik, S; Sengupta, T K

2014-04-01

To understand transition to turbulence via 3D disturbance growth, we report here results obtained from the solution of Navier-Stokes equation (NSE) to reproduce experimental results obtained by minimizing background disturbances and imposing deterministic excitation inside the shear layer. A similar approach was adopted in Sengupta and Bhaumik [Phys. Rev. Lett. 107, 154501 (2011)], where a route of transition from receptivity to fully developed turbulent stage was explained for 2D flow in terms of the spatio-temporal wave-front (STWF). The STWF was identified as the unit process of 2D turbulence creation for low amplitude wall excitation. Theoretical prediction of STWF for boundary layer was established earlier in Sengupta, Rao, and Venkatasubbaiah [Phys. Rev. Lett. 96, 224504 (2006)] from the Orr-Sommerfeld equation as due to spatiotemporal instability. Here, the same unit process of the STWF during transition is shown to be present for 3D disturbance field from the solution of governing NSE.

Spatiotemporal analysis of land use and land cover change in the Brazilian Amazon.

Science.gov (United States)

Lu, Dengsheng; Li, Guiying; Moran, Emilio; Hetrick, Scott

2013-01-01

This paper provides a comparative analysis of land use and land cover (LULC) changes among three study areas with different biophysical environments in the Brazilian Amazon at multiple scales, from per-pixel, polygon, census sector, to study area. Landsat images acquired in the years of 1990/1991, 1999/2000, and 2008/2010 were used to examine LULC change trajectories with the post-classification comparison approach. A classification system composed of six classes - forest, savanna, other-vegetation (secondary succession and plantations), agro-pasture, impervious surface, and water, was designed for this study. A hierarchical-based classification method was used to classify Landsat images into thematic maps. This research shows different spatiotemporal change patterns, composition and rates among the three study areas and indicates the importance of analyzing LULC change at multiple scales. The LULC change analysis over time for entire study areas provides an overall picture of change trends, but detailed change trajectories and their spatial distributions can be better examined at a per-pixel scale. The LULC change at the polygon scale provides the information of the changes in patch sizes over time, while the LULC change at census sector scale gives new insights on how human-induced activities (e.g., urban expansion, roads, and land use history) affect LULC change patterns and rates. This research indicates the necessity to implement change detection at multiple scales for better understanding the mechanisms of LULC change patterns and rates.

Noise tolerant spatiotemporal chaos computing.

Science.gov (United States)

Kia, Behnam; Kia, Sarvenaz; Lindner, John F; Sinha, Sudeshna; Ditto, William L

2014-12-01

We introduce and design a noise tolerant chaos computing system based on a coupled map lattice (CML) and the noise reduction capabilities inherent in coupled dynamical systems. The resulting spatiotemporal chaos computing system is more robust to noise than a single map chaos computing system. In this CML based approach to computing, under the coupled dynamics, the local noise from different nodes of the lattice diffuses across the lattice, and it attenuates each other's effects, resulting in a system with less noise content and a more robust chaos computing architecture.

How Do Multiple-Star Systems Form? VLA Study Reveals "Smoking Gun"

Science.gov (United States)

2006-12-01

system, all the antennas could provide data for us. In addition, we improved the level of detail by using the Pie Town, NM, antenna of the Very Long Baseline Array, as part of an expanded system," Lim said. The implementation and improvement of the 43 GHz receiving system was a collaborative program among the German Max Planck Institute, the Mexican National Autonomous University, and the U.S. National Radio Astronomy Observatory. Two popular theoretical models for the formation of multiple-star systems are, first, that the two protostars and their surrounding dusty disks fragment from a larger parent disk, and, second, that the protostars form independently and then one captures the other into a mutual orbit. "Our new study shows that the disks of the two main protostars are aligned with each other, and also are aligned with the larger, surrounding disk. In addition, their orbital motion resembles the rotation of the larger disk. This is a 'smoking gun' supporting the fragmentation model," Lim said. However, the new study also revealed a third young star with a dust disk. "The disk of this one is misaligned with those of the other two, so it may be the result of either fragmentation or capture," Takakuwa said. The misalignment of the third disk could have come through gravitational interactions with the other two, larger, protostars, the scientists said. They plan further observations to try to resolve the question. "We have a very firm indication that two of these protostars and their dust disks formed from the same, larger disk-like cloud, then broke out from it in a fragmentation process. That strongly supports one theoretical model for how multiple-star systems are formed. The misalignment of the third protostar and its disk leaves open the possibility that it could have formed elsewhere and been captured, and we'll continue to work on reconstructing the history of this fascinating system," Lim summarized. The National Radio Astronomy Observatory is a facility of

Exploring the spatio-temporal neural basis of face learning

Science.gov (United States)

Yang, Ying; Xu, Yang; Jew, Carol A.; Pyles, John A.; Kass, Robert E.; Tarr, Michael J.

2017-01-01

Humans are experts at face individuation. Although previous work has identified a network of face-sensitive regions and some of the temporal signatures of face processing, as yet, we do not have a clear understanding of how such face-sensitive regions support learning at different time points. To study the joint spatio-temporal neural basis of face learning, we trained subjects to categorize two groups of novel faces and recorded their neural responses using magnetoencephalography (MEG) throughout learning. A regression analysis of neural responses in face-sensitive regions against behavioral learning curves revealed significant correlations with learning in the majority of the face-sensitive regions in the face network, mostly between 150–250 ms, but also after 300 ms. However, the effect was smaller in nonventral regions (within the superior temporal areas and prefrontal cortex) than that in the ventral regions (within the inferior occipital gyri (IOG), midfusiform gyri (mFUS) and anterior temporal lobes). A multivariate discriminant analysis also revealed that IOG and mFUS, which showed strong correlation effects with learning, exhibited significant discriminability between the two face categories at different time points both between 150–250 ms and after 300 ms. In contrast, the nonventral face-sensitive regions, where correlation effects with learning were smaller, did exhibit some significant discriminability, but mainly after 300 ms. In sum, our findings indicate that early and recurring temporal components arising from ventral face-sensitive regions are critically involved in learning new faces. PMID:28570739

The application of a hierarchical Bayesian spatiotemporal model for ...

Indian Academy of Sciences (India)

Process (GP) model by using the Gibbs sampling method. The result for ... good indicator of the HBST method. The statistical ... summary and discussion of future works are given .... spatiotemporal package in R language (R core team. 2013).

Mathematics revealed

CERN Document Server

Berman, Elizabeth

1979-01-01

Mathematics Revealed focuses on the principles, processes, operations, and exercises in mathematics.The book first offers information on whole numbers, fractions, and decimals and percents. Discussions focus on measuring length, percent, decimals, numbers as products, addition and subtraction of fractions, mixed numbers and ratios, division of fractions, addition, subtraction, multiplication, and division. The text then examines positive and negative numbers and powers and computation. Topics include division and averages, multiplication, ratios, and measurements, scientific notation and estim

[Epidemiologic and spatio-temporal characteristics of hepatitis E in China, 2004-2014].

Science.gov (United States)

Liu, Z Q; Zuo, J L; Yan, Q; Fang, Q W; Zhang, T J

2017-10-10

Objective: To describe and analyze the epidemiologic and spatio-temporal characteristics of hepatitis E in China from 2004 to 2014. Methods: Data on the incidence of hepatitis E in 31 provinces (municipality and autonomous region) from 2004 to 2014, were collected. Empirical Mode Decomposition (EMD) was applied to decompose the time-series data to accurately describe the trend of hepatitis E incidence. Mathematic model was used to estimate the annual change of incidence in each age group and the whole province. Software ArcGIS 10.1 and SaTScan 9.01 were used to analyze the spatio-temporal clusters. Results: During 2004-2014, a total of 245 414 hepatitis E cases were reported in China. The overall incidence showed a slight increase ( OR =1.05, 95 %CI : 1.03-1.10). Incidence rates on hepatitis E were discovered different across the provinces, with significant increase appearing in the southern, central and northwestern areas. The highest increase was seen in the elderly, especially in the 65-69 and 70-74 year-olds. Results from the Local spatial autocorrelation analysis showed that the "high-high cluster" was moving from the north to the south and the "low-low cluster" disappeared as time went by. Data from Spatio-temporal scanning showed that there were five spatio-temporal clustering areas across the country. Conclusion: The overall incidence of hepatitis E was on the rise from 2004 to 2014, in China, but with differences seen across the areas and age groups.

Formulating qualitative features using interactive visualization for analysis of multivariate spatiotemporal data

Science.gov (United States)

Porter, M.; Hill, M. C.; Pierce, S. A.; Gil, Y.; Pennington, D. D.

2017-12-01

DiscoverWater is a web-based visualization tool developed to enable the visual representation of data, and thus, aid scientific and societal understanding of hydrologic systems. Open data sources are coalesced to, for example, illustrate the impacts on streamflow of irrigation withdrawals. Scientists and stakeholders are informed through synchronized time-series data plots that correlate multiple spatiotemporal datasets and an interactive time-evolving map that provides a spatial analytical context. Together, these components elucidate trends so that the user can try to envision the relations between groundwater-surface water interactions, the impacts of pumping on these interactions, and the interplay of climate. Aligning data in this manner has the capacity for interdisciplinary knowledge discovery and motivates dialogue about system processes that we seek to enhance through qualitative features informed through quantitative models. DiscoverWater and its connection is demonstrated using two field cases. First, it is used to visualize data sets from the High Plains aquifer, where reservoir- and groundwater-supported irrigation has affected the Arkansas River in western Kansas. Second, data and model results from Barton Springs segment of the Edwards aquifer in Texas reveal the effects of regional pumping on this important urbanizing aquifer system. Identifying what is interesting about the data and the modeled system in the two different case studies is a step towards moving typically static visualization capabilities to an adaptive framework. Additionally, the dashboard interface incorporates both quantitative and qualitative information about distinctive case studies in a machine-readable form, such that a catalog of qualitative models can capture subject matter expertise alongside associated datasets. As the catalog is expanded to include other case studies, the collection has potential to establish a standard framework able to inform intelligent system

Improved kinect-based spatiotemporal and kinematic treadmill gait assessment.

Science.gov (United States)

Eltoukhy, Moataz; Oh, Jeonghoon; Kuenze, Christopher; Signorile, Joseph

2017-01-01

A cost-effective, clinician friendly gait assessment tool that can automatically track patients' anatomical landmarks can provide practitioners with important information that is useful in prescribing rehabilitative and preventive therapies. This study investigated the validity and reliability of the Microsoft Kinect v2 as a potential inexpensive gait analysis tool. Ten healthy subjects walked on a treadmill at 1.3 and 1.6m·s -1 , as spatiotemporal parameters and kinematics were extracted concurrently using the Kinect and three-dimensional motion analysis. Spatiotemporal measures included step length and width, step and stride times, vertical and mediolateral pelvis motion, and foot swing velocity. Kinematic outcomes included hip, knee, and ankle joint angles in the sagittal plane. The absolute agreement and relative consistency between the two systems were assessed using interclass correlations coefficients (ICC2,1), while reproducibility between systems was established using Lin's Concordance Correlation Coefficient (rc). Comparison of ensemble curves and associated 90% confidence intervals (CI90) of the hip, knee, and ankle joint angles were performed to investigate if the Kinect sensor could consistently and accurately assess lower extremity joint motion throughout the gait cycle. Results showed that the Kinect v2 sensor has the potential to be an effective clinical assessment tool for sagittal plane knee and hip joint kinematics, as well as some spatiotemporal temporal variables including pelvis displacement and step characteristics during the gait cycle. Copyright © 2016 Elsevier B.V. All rights reserved.

Enhancement of peak intensity in a filament core with spatiotemporally focused femtosecond laser pulses

Energy Technology Data Exchange (ETDEWEB)

Zeng Bin; Chu Wei; Li Guihua; Zhang Haisu; Ni Jielei [State Key Laboratory of High Field Laser Physics, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800 (China); Graduate School of Chinese Academy of Sciences, Beijing 100080 (China); Gao Hui; Liu Weiwei [Institute of Modern Optics, Nankai University, Tianjin, 300071 (China); Yao Jinping; Cheng Ya; Xu Zhizhan [State Key Laboratory of High Field Laser Physics, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800 (China); Chin, See Leang [Center for Optics, Photonics and Laser (COPL) and Department of Physics, Engineering Physics and Optics, Universite Laval, Quebec City, QC, G1V 0A6 (Canada)

2011-12-15

We demonstrate that the peak intensity in the filament core, which is inherently limited by the intensity clamping effect during femtosecond laser filamentation, can be significantly enhanced using spatiotemporally focused femtosecond laser pulses. In addition, the filament length obtained by spatiotemporally focused femtosecond laser pulses is {approx}25 times shorter than that obtained by a conventional focusing scheme, resulting in improved high spatial resolution.

Spatiotemporal Dynamics of Scrub Typhus Transmission in Mainland China, 2006-2014.

Science.gov (United States)

Wu, Yi-Cheng; Qian, Quan; Soares Magalhaes, Ricardo J; Han, Zhi-Hai; Hu, Wen-Biao; Haque, Ubydul; Weppelmann, Thomas A; Wang, Yong; Liu, Yun-Xi; Li, Xin-Lou; Sun, Hai-Long; Sun, Yan-Song; Clements, Archie C A; Li, Shen-Long; Zhang, Wen-Yi

2016-08-01

Scrub typhus is endemic in the Asia-Pacific region including China, and the number of reported cases has increased dramatically in the past decade. However, the spatial-temporal dynamics and the potential risk factors in transmission of scrub typhus in mainland China have yet to be characterized. This study aims to explore the spatiotemporal dynamics of reported scrub typhus cases in mainland China between January 2006 and December 2014, to detect the location of high risk spatiotemporal clusters of scrub typhus cases, and identify the potential risk factors affecting the re-emergence of the disease. Monthly cases of scrub typhus reported at the county level between 2006 and 2014 were obtained from the Chinese Center for Diseases Control and Prevention. Time-series analyses, spatiotemporal cluster analyses, and spatial scan statistics were used to explore the characteristics of the scrub typhus incidence. To explore the association between scrub typhus incidence and environmental variables panel Poisson regression analysis was conducted. During the time period between 2006 and 2014 a total of 54,558 scrub typhus cases were reported in mainland China, which grew exponentially. The majority of cases were reported each year between July and November, with peak incidence during October every year. The spatiotemporal dynamics of scrub typhus varied over the study period with high-risk clusters identified in southwest, southern, and middle-eastern part of China. Scrub typhus incidence was positively correlated with the percentage of shrub and meteorological variables including temperature and precipitation. The results of this study demonstrate areas in China that could be targeted with public health interventions to mitigate the growing threat of scrub typhus in the country.

Nonparametric evaluation of dynamic disease risk: a spatio-temporal kernel approach.

Directory of Open Access Journals (Sweden)

Zhijie Zhang

Full Text Available Quantifying the distributions of disease risk in space and time jointly is a key element for understanding spatio-temporal phenomena while also having the potential to enhance our understanding of epidemiologic trajectories. However, most studies to date have neglected time dimension and focus instead on the "average" spatial pattern of disease risk, thereby masking time trajectories of disease risk. In this study we propose a new idea titled "spatio-temporal kernel density estimation (stKDE" that employs hybrid kernel (i.e., weight functions to evaluate the spatio-temporal disease risks. This approach not only can make full use of sample data but also "borrows" information in a particular manner from neighboring points both in space and time via appropriate choice of kernel functions. Monte Carlo simulations show that the proposed method performs substantially better than the traditional (i.e., frequency-based kernel density estimation (trKDE which has been used in applied settings while two illustrative examples demonstrate that the proposed approach can yield superior results compared to the popular trKDE approach. In addition, there exist various possibilities for improving and extending this method.

Limiting Data Friction by Reducing Data Download Using Spatiotemporally Aligned Data Organization Through STARE

Science.gov (United States)

Kuo, K. S.; Rilee, M. L.

2017-12-01

Current data processing practice limits the volume and variety of relevant geoscience data that can practically be applied to important problems. File archives in centralized data centers are the principal means by which Earth Science data are accessed. This approach, however, requires laborious search, retrieval, and eventual customization/adaptation for the data to be used. Such fractionation makes it even more difficult to share outcomes, i.e. research artifacts and data products, hampering reusability and repeatability, since end users generally have their own research agenda and preferences as well as scarce resources. Thus, while finding and downloading data files from central data centers are already costly for end users working in their own field, using data products from other disciplines rapidly becomes prohibitive. This curtails scientific productivity, limits avenues of study, and endangers quality and reproducibility. The Spatio-Temporal Adaptive Resolution Encoding ( STARE ) is a unifying scheme that facilitates the indexing, access, and fusion of diverse Earth Science data. STARE implements an innovative encoding of geo-spatiotemporal information, originally developed for aligning datasets with diverse spatiotemporal characteristics in an array database. The spatial component of STARE recursively quadfurcates a root polyhedron, producing a hierarchical scheme for addressing geographic locations and regions. The temporal component of STARE uses conventional date-time units as an indexing hierarchy. The additional encoding of spatial and temporal resolution information in STARE enables comparisons and conditional selections across diverse datasets. Moreover, spatiotemporal set-operations, e.g. union and intersection, are mapped to efficient integer operations with STARE. Applied to existing data models (point, grid, spacecraft swath) and corresponding granules, STARE indexes provide a streamlined description usable as geo-spatiotemporal metadata. When

Preschoolers' use of spatiotemporal history, appearance, and proper name in determining individual identity.

Science.gov (United States)

Gutheil, Grant; Gelman, Susan A; Klein, Eileen; Michos, Katherine; Kelaita, Kara

2008-04-01

Humans construe their environment as composed largely of discrete individuals, which are also members of kinds (e.g., trees, cars, and people). On what basis do young children determine individual identity? How important are featural properties (e.g., physical appearance, name) relative to spatiotemporal history? Two studies examined the relative importance of these factors in preschoolers' and adults' identity judgments. Participants were shown pairs of individuals who looked identical but differed in their spatiotemporal history (e.g., two physically distinct but identical Winnie-the-Pooh dolls), and were asked whether both members in the pair would have access to knowledge that had been supplied to only one of the pairs. The results provide clear support for spatiotemporal history as the primary basis of identity judgments in both preschoolers and adults, and further place issues of identity within the broader cognitive framework of psychological essentialism.

Optimization of spatiotemporally fractionated radiotherapy treatments with bounds on the achievable benefit

Science.gov (United States)

Gaddy, Melissa R.; Yıldız, Sercan; Unkelbach, Jan; Papp, Dávid

2018-01-01

Spatiotemporal fractionation schemes, that is, treatments delivering different dose distributions in different fractions, can potentially lower treatment side effects without compromising tumor control. This can be achieved by hypofractionating parts of the tumor while delivering approximately uniformly fractionated doses to the surrounding tissue. Plan optimization for such treatments is based on biologically effective dose (BED); however, this leads to computationally challenging nonconvex optimization problems. Optimization methods that are in current use yield only locally optimal solutions, and it has hitherto been unclear whether these plans are close to the global optimum. We present an optimization framework to compute rigorous bounds on the maximum achievable normal tissue BED reduction for spatiotemporal plans. The approach is demonstrated on liver tumors, where the primary goal is to reduce mean liver BED without compromising any other treatment objective. The BED-based treatment plan optimization problems are formulated as quadratically constrained quadratic programming (QCQP) problems. First, a conventional, uniformly fractionated reference plan is computed using convex optimization. Then, a second, nonconvex, QCQP model is solved to local optimality to compute a spatiotemporally fractionated plan that minimizes mean liver BED, subject to the constraints that the plan is no worse than the reference plan with respect to all other planning goals. Finally, we derive a convex relaxation of the second model in the form of a semidefinite programming problem, which provides a rigorous lower bound on the lowest achievable mean liver BED. The method is presented on five cases with distinct geometries. The computed spatiotemporal plans achieve 12-35% mean liver BED reduction over the optimal uniformly fractionated plans. This reduction corresponds to 79-97% of the gap between the mean liver BED of the uniform reference plans and our lower bounds on the lowest

Spatio-Temporal Process Simulation of Dam-Break Flood Based on SPH

Science.gov (United States)

Wang, H.; Ye, F.; Ouyang, S.; Li, Z.

2018-04-01

On the basis of introducing the SPH (Smooth Particle Hydrodynamics) simulation method, the key research problems were given solutions in this paper, which ere the spatial scale and temporal scale adapting to the GIS(Geographical Information System) application, the boundary condition equations combined with the underlying surface, and the kernel function and parameters applicable to dam-break flood simulation. In this regards, a calculation method of spatio-temporal process emulation with elaborate particles for dam-break flood was proposed. Moreover the spatio-temporal process was dynamic simulated by using GIS modelling and visualization. The results show that the method gets more information, objectiveness and real situations.

Spatio-temporal joins on symbolic indoor tracking data

DEFF Research Database (Denmark)

Lu, Hua; Yang, Bin; Jensen, Christian S.

2011-01-01

and studies probabilistic, spatio-temporal joins on historical indoor tracking data. Two meaningful types of join are defined. They return object pairs that satisfy spatial join predicates either at a time point or during a time interval. The predicates considered include “same X,” where X is a semantic...

Spatiotemporal Features for Asynchronous Event-based Data

Directory of Open Access Journals (Sweden)

Xavier eLagorce

2015-02-01

Full Text Available Bio-inspired asynchronous event-based vision sensors are currently introducing a paradigm shift in visual information processing. These new sensors rely on a stimulus-driven principle of light acquisition similar to biological retinas. They are event-driven and fully asynchronous, thereby reducing redundancy and encoding exact times of input signal changes, leading to a very precise temporal resolution. Approaches for higher-level computer vision often rely on the realiable detection of features in visual frames, but similar definitions of features for the novel dynamic and event-based visual input representation of silicon retinas have so far been lacking. This article addresses the problem of learning and recognizing features for event-based vision sensors, which capture properties of truly spatiotemporal volumes of sparse visual event information. A novel computational architecture for learning and encoding spatiotemporal features is introduced based on a set of predictive recurrent reservoir networks, competing via winner-take-all selection. Features are learned in an unsupervised manner from real-world input recorded with event-based vision sensors. It is shown that the networks in the architecture learn distinct and task-specific dynamic visual features, and can predict their trajectories over time.

Spatiotemporal Interpolation Methods for Solar Event Trajectories

Science.gov (United States)

Filali Boubrahimi, Soukaina; Aydin, Berkay; Schuh, Michael A.; Kempton, Dustin; Angryk, Rafal A.; Ma, Ruizhe

2018-05-01

This paper introduces four spatiotemporal interpolation methods that enrich complex, evolving region trajectories that are reported from a variety of ground-based and space-based solar observatories every day. Our interpolation module takes an existing solar event trajectory as its input and generates an enriched trajectory with any number of additional time–geometry pairs created by the most appropriate method. To this end, we designed four different interpolation techniques: MBR-Interpolation (Minimum Bounding Rectangle Interpolation), CP-Interpolation (Complex Polygon Interpolation), FI-Interpolation (Filament Polygon Interpolation), and Areal-Interpolation, which are presented here in detail. These techniques leverage k-means clustering, centroid shape signature representation, dynamic time warping, linear interpolation, and shape buffering to generate the additional polygons of an enriched trajectory. Using ground-truth objects, interpolation effectiveness is evaluated through a variety of measures based on several important characteristics that include spatial distance, area overlap, and shape (boundary) similarity. To our knowledge, this is the first research effort of this kind that attempts to address the broad problem of spatiotemporal interpolation of solar event trajectories. We conclude with a brief outline of future research directions and opportunities for related work in this area.

Spatio-temporal analysis of Salmonella surveillance data in Thailand

DEFF Research Database (Denmark)

Coutinho Calado Domingues, Ana Rita; Vieira, Antonio; Hendriksen, Rene S.

2014-01-01

This study evaluates the usefulness of spatio-temporal statistical tools to detect outbreaks using routine surveillance data where limited epidemiological information is available. A dataset from 2002 to 2007 containing information regarding date, origin, source and serotype of 29 586 Salmonella ...

Annual spatiotemporal migration schedules in three larger insectivorous birds

DEFF Research Database (Denmark)

Jacobsen, Lars Bo; Jensen, Niels Odder; Willemoes, Mikkel

2017-01-01

Background: Knowledge of spatiotemporal migration patterns is important for our understanding of migration ecology and ultimately conservation of migratory species. We studied the annual migration schedules of European nightjar, a large nocturnal insectivore and compared it with two other larger ...

Spatiotemporal Thinking in the Geosciences

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Shipley, T. F.; Manduca, C. A.; Ormand, C. J.; Tikoff, B.

2011-12-01

Reasoning about spatial relations is a critical skill for geoscientists. Within the geosciences different disciplines may reason about different sorts of relationships. These relationships may span vastly different spatial and temporal scales (from the spatial alignment in atoms in crystals to the changes in the shape of plates). As part of work in a research center on spatial thinking in STEM education, we have been working to classify the spatial skills required in geology, develop tests for each spatial skill, and develop the cognitive science tools to promote the critical spatial reasoning skills. Research in psychology, neurology and linguistics supports a broad classification of spatial skills along two dimensions: one versus many objects (which roughly translates to object- focused and navigation focused skills) and static versus dynamic spatial relations. The talk will focus on the interaction of space and time in spatial cognition in the geosciences. We are working to develop measures of skill in visualizing spatiotemporal changes. A new test developed to measure visualization of brittle deformations will be presented. This is a skill that has not been clearly recognized in the cognitive science research domain and thus illustrates the value of interdisciplinary work that combines geosciences with cognitive sciences. Teaching spatiotemporal concepts can be challenging. Recent theoretical work suggests analogical reasoning can be a powerful tool to aid student learning to reason about temporal relations using spatial skills. Recent work in our lab has found that progressive alignment of spatial and temporal scales promotes accurate reasoning about temporal relations at geological time scales.

On spatio-temporal Lévy based Cox processes

DEFF Research Database (Denmark)

Prokesova, Michaela; Hellmund, Gunnar; Jensen, Eva Bjørn Vedel

2006-01-01

The paper discusses a new class of models for spatio-temporal Cox point processes. In these models, the driving field is defined by means of an integral of a weight function with respect to a Lévy basis. The relations to other Cox process models studied previously are discussed and formulas for t...

Nonlinear system identification NARMAX methods in the time, frequency, and spatio-temporal domains

CERN Document Server

Billings, Stephen A

2013-01-01

Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains describes a comprehensive framework for the identification and analysis of nonlinear dynamic systems in the time, frequency, and spatio-temporal domains. This book is written with an emphasis on making the algorithms accessible so that they can be applied and used in practice. Includes coverage of: The NARMAX (nonlinear autoregressive moving average with exogenous inputs) modelThe orthogonal least squares algorithm that allows models to be built term by

Assessing global vegetation activity using spatio-temporal Bayesian modelling

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Mulder, Vera L.; van Eck, Christel M.; Friedlingstein, Pierre; Regnier, Pierre A. G.

2016-04-01

This work demonstrates the potential of modelling vegetation activity using a hierarchical Bayesian spatio-temporal model. This approach allows modelling changes in vegetation and climate simultaneous in space and time. Changes of vegetation activity such as phenology are modelled as a dynamic process depending on climate variability in both space and time. Additionally, differences in observed vegetation status can be contributed to other abiotic ecosystem properties, e.g. soil and terrain properties. Although these properties do not change in time, they do change in space and may provide valuable information in addition to the climate dynamics. The spatio-temporal Bayesian models were calibrated at a regional scale because the local trends in space and time can be better captured by the model. The regional subsets were defined according to the SREX segmentation, as defined by the IPCC. Each region is considered being relatively homogeneous in terms of large-scale climate and biomes, still capturing small-scale (grid-cell level) variability. Modelling within these regions is hence expected to be less uncertain due to the absence of these large-scale patterns, compared to a global approach. This overall modelling approach allows the comparison of model behavior for the different regions and may provide insights on the main dynamic processes driving the interaction between vegetation and climate within different regions. The data employed in this study encompasses the global datasets for soil properties (SoilGrids), terrain properties (Global Relief Model based on SRTM DEM and ETOPO), monthly time series of satellite-derived vegetation indices (GIMMS NDVI3g) and climate variables (Princeton Meteorological Forcing Dataset). The findings proved the potential of a spatio-temporal Bayesian modelling approach for assessing vegetation dynamics, at a regional scale. The observed interrelationships of the employed data and the different spatial and temporal trends support

Do spatiotemporal parameters and gait variability differ across the lifespan of healthy adults? A systematic review.

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Herssens, Nolan; Verbecque, Evi; Hallemans, Ann; Vereeck, Luc; Van Rompaey, Vincent; Saeys, Wim

2018-06-12

Aging is often associated with changes in the musculoskeletal system, peripheral and central nervous system. These age-related changes often result in mobility problems influencing gait performance. Compensatory strategies are used as a way to adapt to these physiological changes. The aim of this review is to investigate the differences in spatiotemporal and gait variability measures throughout the healthy adult life. This systematic review was conducted according to the PRISMA guidelines and registered in the PROSPERO database (no. CRD42017057720). Databases MEDLINE (Pubmed), Web of Science (Web of Knowledge), Cochrane Library and ScienceDirect were systematically searched until March 2018. Eighteen of the 3195 original studies met the eligibility criteria and were included in this review. The majority of studies reported spatiotemporal and gait variability measures in adults above the age of 65, followed by the young adult population, information of middle-aged adults is lacking. Spatiotemporal parameters and gait variability measures were extracted from 2112 healthy adults between 18 and 98 years old and, in general, tend to deteriorate with increasing age. Variability measures were only reported in an elderly population and show great variety between studies. The findings of this review suggest that most spatiotemporal parameters significantly differ across different age groups. Elderly populations show a reduction of preferred walking speed, cadence, step and stride length, all related to a more cautious gait, while gait variability measures remain stable over time. A preliminary framework of normative reference data is provided, enabling insights into the influence of aging on spatiotemporal parameters, however spatiotemporal parameters of middle-aged adults should be investigated more thoroughly. Copyright © 2018 Elsevier B.V. All rights reserved.

Spatiotemporal variability of marine renewable energy resources in Norway

NARCIS (Netherlands)

Varlas, George; Christakos, Konstantinos; Cheliotis, Ioannis; Papadopoulos, A.; Steeneveld, G.J.

2017-01-01

Marine Renewable Energy (MRE) resources such as wind and wave energy depend on the complex behaviour of weather and climatic conditions which determine the development of MRE technologies, energy grid, supply and prices. This study investigates the spatiotemporal variability of MRE resources along

The Review of Visual Analysis Methods of Multi-modal Spatio-temporal Big Data

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ZHU Qing

2017-10-01

Full Text Available The visual analysis of spatio-temporal big data is not only the state-of-art research direction of both big data analysis and data visualization, but also the core module of pan-spatial information system. This paper reviews existing visual analysis methods at three levels:descriptive visual analysis, explanatory visual analysis and exploratory visual analysis, focusing on spatio-temporal big data's characteristics of multi-source, multi-granularity, multi-modal and complex association.The technical difficulties and development tendencies of multi-modal feature selection, innovative human-computer interaction analysis and exploratory visual reasoning in the visual analysis of spatio-temporal big data were discussed. Research shows that the study of descriptive visual analysis for data visualizationis is relatively mature.The explanatory visual analysis has become the focus of the big data analysis, which is mainly based on interactive data mining in a visual environment to diagnose implicit reason of problem. And the exploratory visual analysis method needs a major break-through.

Cdc42 and RhoA reveal different spatio-temporal dynamics upon local stimulation with Semaphorin-3A

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Federico eIseppon

2015-08-01

Full Text Available Small RhoGTPases, such as Cdc42 and RhoA, are key players in integrating external cues and intracellular signaling pathways that regulate growth cone (GC motility. Indeed, Cdc42 is involved in actin polymerization and filopodia formation, whereas RhoA induces GC collapse and neurite retraction through actomyosin contraction. In this study we employed Förster Resonance Energy Transfer (FRET microscopy to study the spatio-temporal dynamics of Cdc42 and RhoA in GCs in response to local Semaphorin-3A stimulation obtained with lipid vesicles filled with Semaphorin-3A and positioned near the selected GC using optical tweezers. We found that Cdc42 and RhoA were activated at the leading edge of NG108-15 neuroblastoma cells during spontaneous cycles of protrusion and retraction, respectively. The release of Semaphorin-3A brought to a progressive activation of RhoA within 30 seconds from the stimulus in the central region of the GC that collapsed and retracted. In contrast, the same stimulation evoked waves of Cdc42 activation propagating away from the stimulated region. A more localized stimulation obtained with Sema3A coated beads placed on the GC, led to Cdc42 active waves that propagated in a retrograde manner with a mean period of 70 seconds, and followed by GC retraction. Therefore, Semaphorin-3A activates both Cdc42 and RhoA with a complex and different spatial-temporal dynamics.

A general science-based framework for dynamical spatio-temporal models

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Wikle, C.K.; Hooten, M.B.

2010-01-01

Spatio-temporal statistical models are increasingly being used across a wide variety of scientific disciplines to describe and predict spatially-explicit processes that evolve over time. Correspondingly, in recent years there has been a significant amount of research on new statistical methodology for such models. Although descriptive models that approach the problem from the second-order (covariance) perspective are important, and innovative work is being done in this regard, many real-world processes are dynamic, and it can be more efficient in some cases to characterize the associated spatio-temporal dependence by the use of dynamical models. The chief challenge with the specification of such dynamical models has been related to the curse of dimensionality. Even in fairly simple linear, first-order Markovian, Gaussian error settings, statistical models are often over parameterized. Hierarchical models have proven invaluable in their ability to deal to some extent with this issue by allowing dependency among groups of parameters. In addition, this framework has allowed for the specification of science based parameterizations (and associated prior distributions) in which classes of deterministic dynamical models (e. g., partial differential equations (PDEs), integro-difference equations (IDEs), matrix models, and agent-based models) are used to guide specific parameterizations. Most of the focus for the application of such models in statistics has been in the linear case. The problems mentioned above with linear dynamic models are compounded in the case of nonlinear models. In this sense, the need for coherent and sensible model parameterizations is not only helpful, it is essential. Here, we present an overview of a framework for incorporating scientific information to motivate dynamical spatio-temporal models. First, we illustrate the methodology with the linear case. We then develop a general nonlinear spatio-temporal framework that we call general quadratic

Spatiotemporal modelling and mapping of the bubonic plague epidemic in India

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Christakos George

2006-03-01

Full Text Available Abstract Background This work studies the spatiotemporal evolution of bubonic plague in India during 1896–1906 using stochastic concepts and geographical information science techniques. In the past, most investigations focused on selected cities to conduct different kinds of studies, such as the ecology of rats. No detailed maps existed incorporating the space-time dependence structure and uncertainty sources of the epidemic system and providing a composite space-time picture of the disease propagation characteristics. Results Informative spatiotemporal maps were generated that represented mortality rates and geographical spread of the disease, and epidemic indicator plots were derived that offered meaningful characterizations of the spatiotemporal disease distribution. The bubonic plague in India exhibited strong seasonal and geographical features. During its entire duration, the plague continued to invade new geographical areas, while it followed a re-emergence pattern at many localities; its rate changed significantly during each year and the mortality distribution exhibited space-time heterogeneous patterns; prevalence usually occurred in the autumn and spring, whereas the plague stopped moving towards new locations during the summers. Conclusion Modern stochastic modelling and geographical information science provide powerful means to study the spatiotemporal distribution of the bubonic plague epidemic under conditions of uncertainty and multi-sourced databases; to account for various forms of interdisciplinary knowledge; and to generate informative space-time maps of mortality rates and propagation patterns. To the best of our knowledge, this kind of plague maps and plots become available for the first time, thus providing novel perspectives concerning the distribution and space-time propagation of the deadly epidemic. Furthermore, systematic maps and indicator plots make possible the comparison of the spatial-temporal propagation

Fine Scale Spatiotemporal Clustering of Dengue Virus Transmission in Children and Aedes aegypti in Rural Thai Villages

NARCIS (Netherlands)

Yoon, I.K.; Getis, A.; Aldstadt, J.; Rothman, A.L.; Tannitisupawong, D.; Koenraadt, C.J.M.; Fansiri, T.; Jones, J.W.; Morrison, A.C.; Jarman, R.G.; Nisalak, A.; Mammen Jr., M.P.; Thammapalo, S.; Srikiatkhachorn, A.; Green, S.; Libraty, D.H.; Gibbons, R.V.; Endy, T.; Pimgate, C.; Scott, T.W.

2012-01-01

Background Based on spatiotemporal clustering of human dengue virus (DENV) infections, transmission is thought to occur at fine spatiotemporal scales by horizontal transfer of virus between humans and mosquito vectors. To define the dimensions of local transmission and quantify the factors that

Distinct spatio-temporal profiles of beta-oscillations within visual and sensorimotor areas during action recognition as revealed by MEG.

Science.gov (United States)

Pavlidou, Anastasia; Schnitzler, Alfons; Lange, Joachim

2014-05-01

The neural correlates of action recognition have been widely studied in visual and sensorimotor areas of the human brain. However, the role of neuronal oscillations involved during the process of action recognition remains unclear. Here, we were interested in how the plausibility of an action modulates neuronal oscillations in visual and sensorimotor areas. Subjects viewed point-light displays (PLDs) of biomechanically plausible and implausible versions of the same actions. Using magnetoencephalography (MEG), we examined dynamic changes of oscillatory activity during these action recognition processes. While both actions elicited oscillatory activity in visual and sensorimotor areas in several frequency bands, a significant difference was confined to the beta-band (∼20 Hz). An increase of power for plausible actions was observed in left temporal, parieto-occipital and sensorimotor areas of the brain, in the beta-band in successive order between 1650 and 2650 msec. These distinct spatio-temporal beta-band profiles suggest that the action recognition process is modulated by the degree of biomechanical plausibility of the action, and that spectral power in the beta-band may provide a functional interaction between visual and sensorimotor areas in humans. Copyright © 2014 Elsevier Ltd. All rights reserved.

Impaired heel to toe progression during gait is related to reduced ankle range of motion in people with Multiple Sclerosis.

Science.gov (United States)

Psarakis, Michael; Greene, David; Moresi, Mark; Baker, Michael; Stubbs, Peter; Brodie, Matthew; Lord, Stephen; Hoang, Phu

2017-11-01

Gait impairment in people with Multiple Sclerosis results from neurological impairment, muscle weakness and reduced range of motion. Restrictions in passive ankle range of motion can result in abnormal heel-to-toe progression (weight transfer) and inefficient gait patterns in people with Multiple Sclerosis. The purpose of this study was to determine the associations between gait impairment, heel-to-toe progression and ankle range of motion in people with Multiple Sclerosis. Twelve participants with Multiple Sclerosis and twelve healthy age-matched participants were assessed. Spatiotemporal parameters of gait and individual footprint data were used to investigate group differences. A pressure sensitive walkway was used to divide each footprint into three phases (contact, mid-stance, propulsive) and calculate the heel-to-toe progression during the stance phase of gait. Compared to healthy controls, people with Multiple Sclerosis spent relatively less time in contact phase (7.8% vs 25.1%) and more time in the mid stance phase of gait (57.3% vs 33.7%). Inter-limb differences were observed in people with Multiple Sclerosis between the affected and non-affected sides for contact (7.8% vs 15.3%) and mid stance (57.3% and 47.1%) phases. Differences in heel-to-toe progression remained significant after adjusting for walking speed and were correlated with walking distance and ankle range of motion. Impaired heel-to-toe progression was related to poor ankle range of motion in people with Multiple Sclerosis. Heel-to-toe progression provided a sensitive measure for assessing gait impairments that were not detectable using standard spatiotemporal gait parameters. Copyright © 2017 Elsevier Ltd. All rights reserved.

Spatiotemporal matrix image formation for programmable ultrasound scanners

Science.gov (United States)

Berthon, Beatrice; Morichau-Beauchant, Pierre; Porée, Jonathan; Garofalakis, Anikitos; Tavitian, Bertrand; Tanter, Mickael; Provost, Jean

2018-02-01

As programmable ultrasound scanners become more common in research laboratories, it is increasingly important to develop robust software-based image formation algorithms that can be obtained in a straightforward fashion for different types of probes and sequences with a small risk of error during implementation. In this work, we argue that as the computational power keeps increasing, it is becoming practical to directly implement an approximation to the matrix operator linking reflector point targets to the corresponding radiofrequency signals via thoroughly validated and widely available simulations software. Once such a spatiotemporal forward-problem matrix is constructed, standard and thus highly optimized inversion procedures can be leveraged to achieve very high quality images in real time. Specifically, we show that spatiotemporal matrix image formation produces images of similar or enhanced quality when compared against standard delay-and-sum approaches in phantoms and in vivo, and show that this approach can be used to form images even when using non-conventional probe designs for which adapted image formation algorithms are not readily available.

Research on Process-oriented Spatio-temporal Data Model

Directory of Open Access Journals (Sweden)

XUE Cunjin

2016-02-01

Full Text Available According to the analysis of the present status and existing problems of spatio-temporal data models developed in last 20 years,this paper proposes a process-oriented spatio-temporal data model (POSTDM,aiming at representing,organizing and storing continuity and gradual geographical entities. The dynamic geographical entities are graded and abstracted into process objects series from their intrinsic characteristics,which are process objects,process stage objects,process sequence objects and process state objects. The logical relationships among process entities are further studied and the structure of UML models and storage are also designed. In addition,through the mechanisms of continuity and gradual changes impliedly recorded by process objects,and the modes of their procedure interfaces offered by the customized ObjcetStorageTable,the POSTDM can carry out process representation,storage and dynamic analysis of continuity and gradual geographic entities. Taking a process organization and storage of marine data as an example,a prototype system (consisting of an object-relational database and a functional analysis platform is developed for validating and evaluating the model's practicability.

Spatiotemporal throughfall patterns beneath an urban tree row

Science.gov (United States)

Bogeholz, P.; Van Stan, J. T., II; Hildebrandt, A.; Friesen, J.; Dibble, M.; Norman, Z.

2016-12-01

Much recent research has focused on throughfall patterns in natural forests as they can influence the heterogeneity of surface ecohydrological and biogeochemical processes. However, to the knowledge of the authors, no work has assessed how urban forest structures affect the spatiotemporal variability of throughfall water flux. Urbanization greatly alters not only a significant portion of the land surface, but canopy structure, with the most typical urban forest configuration being landscaped tree rows along streets, swales, parking lot medians, etc. This study examines throughfall spatiotemporal patterns for a landscaped tree row of Pinus elliottii (Engelm., slash pine) on Georgia Southern University's campus (southeastern, USA) using 150 individual observations per storm. Throughfall correlation lengths beneath this tree row were similar to, but appeared to be more stable across storm size than, observations in past studies on natural forests. Individual tree overlap and the planting interval also may more strongly drive throughfall patterns in tree rows. Meteorological influences beyond storm magnitude (intensity, intermittency, wind conditions, and atmospheric moisture demand) are also examined.

Weight Status in Persons with Multiple Sclerosis: Implications for Mobility Outcomes

Science.gov (United States)

Pilutti, Lara A.; Dlugonski, Deirdre; Pula, John H.; Motl, Robert W.

2012-01-01

The accumulation of excess body weight may have important health and disease consequences for persons with multiple sclerosis (MS). This study examined the effect of weight status on mobility using a comprehensive set of mobility outcomes including ambulatory performance (timed 25-foot walk, T25FW; 6-minute walk, 6MW; oxygen cost of walking, Cw; spatiotemporal parameters of gait; self-reported walking impairment, Multiple Sclerosis Walking Scale-12 (MSWS-12); and free-living activity, accelerometry) in 168 ambulatory persons with MS. Mean (SD) BMI was 27.7 (5.1) kg/m2. Of the 168 participants, 31.0% were classified as normal weight (BMI = 18.5–24.9 kg/m2), 36.3% were classified as overweight (BMI = 25.0–29.9 kg/m2), and 32.7% were classified as obese, classes I and II (BMI = 30–39.9 kg/m2). There were no significant differences among BMI groups on T25FW and 6MW, Cw, spatiotemporal gait parameters, MSWS-12, or daily step and movement counts. The prevalence of overweight and obesity in this sample was almost 70%, but there was not a consistent nor significant impact of BMI on outcomes of mobility. The lack of an effect of weight status on mobility emphasizes the need to focus on and identify other factors which may be important targets of ambulatory performance in persons with MS. PMID:23050129

Gaze control during interceptive actions with different spatiotemporal demands.

NARCIS (Netherlands)

Navia, J.A.; Dicks, M.S.; van der Kamp, J; Ruiz, L.

It is widely accepted that the sources of information used to guide interceptive actions depend on conflicting spatiotemporal task demands. However, there is a paucity of evidence that shows how information pick-up during interceptive actions is adapted to such conflicting constraints. The present

Spatio-temporal manipulation of small GTPase activity at subcellular level and on timescale of seconds in living cells.

Science.gov (United States)

DeRose, Robert; Pohlmeyer, Christopher; Umeda, Nobuhiro; Ueno, Tasuku; Nagano, Tetsuo; Kuo, Scot; Inoue, Takanari

2012-03-09

Dynamic regulation of the Rho family of small guanosine triphosphatases (GTPases) with great spatiotemporal precision is essential for various cellular functions and events(1, 2). Their spatiotemporally dynamic nature has been revealed by visualization of their activity and localization in real time(3). In order to gain deeper understanding of their roles in diverse cellular functions at the molecular level, the next step should be perturbation of protein activities at a precise subcellular location and timing. To achieve this goal, we have developed a method for light-induced, spatio-temporally controlled activation of small GTPases by combining two techniques: (1) rapamycin-induced FKBP-FRB heterodimerization and (2) a photo-caging method of rapamycin. With the use of rapamycin-mediated FKBP-FRB heterodimerization, we have developed a method for rapidly inducible activation or inactivation of small GTPases including Rac(4), Cdc42(4), RhoA(4) and Ras(5), in which rapamycin induces translocation of FKBP-fused GTPases, or their activators, to the plasma membrane where FRB is anchored. For coupling with this heterodimerization system, we have also developed a photo-caging system of rapamycin analogs. A photo-caged compound is a small molecule whose activity is suppressed with a photocleavable protecting group known as a caging group. To suppress heterodimerization activity completely, we designed a caged rapamycin that is tethered to a macromolecule such that the resulting large complex cannot cross the plasma membrane, leading to virtually no background activity as a chemical dimerizer inside cells(6). Figure 1 illustrates a scheme of our system. With the combination of these two systems, we locally recruited a Rac activator to the plasma membrane on a timescale of seconds and achieved light-induced Rac activation at the subcellular level(6).

Spatiotemporal patterns, annual baseline and movement-related incidence of Streptococcus agalactiae infection in Danish dairy herds: 2000-2009.

Science.gov (United States)

Mweu, Marshal M; Nielsen, Søren S; Halasa, Tariq; Toft, Nils

2014-02-01

Several decades after the inception of the five-point plan for the control of contagious mastitis pathogens, Streptococcus agalactiae (S. agalactiae) persists as a fundamental threat to the dairy industry in many countries. A better understanding of the relative importance of within- and between-herd sources of new herd infections coupled with the spatiotemporal distribution of the infection, may aid in effective targeting of control efforts. Thus, the objectives of this study were: (1) to describe the spatiotemporal patterns of infection with S. agalactiae in the population of Danish dairy herds from 2000 to 2009 and (2) to estimate the annual herd-level baseline and movement-related incidence risks of S. agalactiae infection over the 10-year period. The analysis involved registry data on bacteriological culture of all bulk tank milk samples collected as part of the mandatory Danish S. agalactiae surveillance scheme as well as live cattle movements into dairy herds during the specified 10-year period. The results indicated that the predicted risk of a herd becoming infected with S. agalactiae varied spatiotemporally; the risk being more homogeneous and higher in the period after 2005. Additionally, the annual baseline risks yielded significant yet distinctive patterns before and after 2005 - the risk of infection being higher in the latter phase. On the contrary, the annual movement-related risks revealed a non-significant pattern over the 10-year period. There was neither evidence for spatial clustering of cases relative to the population of herds at risk nor spatial dependency between herds. Nevertheless, the results signal a need to beef up within-herd biosecurity in order to reduce the risk of new herd infections. Copyright © 2013 Elsevier B.V. All rights reserved.

a New Process-Oriented and Spatiotemporal Data Model for GIS Data

Science.gov (United States)

Shen, Y.

2018-04-01

With the rapid development of wireless sensor and information technology, there is a trend of transition from "digital monitoring" to "intelligence monitoring" advancing process. The traditional model cannot completely match the dynamic data to accurately describe changes of geographical and environmental changes. In this paper, we try to build a process-oriented and real-time spatiotemporal data model to meet the demands. With various types of monitoring devices, detection methods and the utilization of new technologies, the model can simulate the possible waterlog area in a specific year by analyzing the given data. By testing and modifying the spatiotemporal model, we can come to a rational conclusion that our model can forecast the actual situation in certain extent.

Quantification of annual wildfire risk; A spatio-temporal point process approach.

Directory of Open Access Journals (Sweden)

Paula Pereira

2013-10-01

Full Text Available Policy responses for local and global firemanagement depend heavily on the proper understanding of the fire extent as well as its spatio-temporal variation across any given study area. Annual fire risk maps are important tools for such policy responses, supporting strategic decisions such as location-allocation of equipment and human resources. Here, we define risk of fire in the narrow sense as the probability of its occurrence without addressing the loss component. In this paper, we study the spatio-temporal point patterns of wildfires and model them by a log Gaussian Cox processes. Themean of predictive distribution of randomintensity function is used in the narrow sense, as the annual fire risk map for next year.

Spatio-temporal distribution of soil-transmitted helminth infections in Brazil.

Science.gov (United States)

Chammartin, Frédérique; Guimarães, Luiz H; Scholte, Ronaldo Gc; Bavia, Mara E; Utzinger, Jürg; Vounatsou, Penelope

2014-09-18

In Brazil, preventive chemotherapy targeting soil-transmitted helminthiasis is being scaled-up. Hence, spatially explicit estimates of infection risks providing information about the current situation are needed to guide interventions. Available high-resolution national model-based estimates either rely on analyses of data restricted to a given period of time, or on historical data collected over a longer period. While efforts have been made to take into account the spatial structure of the data in the modelling approach, little emphasis has been placed on the temporal dimension. We extracted georeferenced survey data on the prevalence of infection with soil-transmitted helminths (i.e. Ascaris lumbricoides, hookworm and Trichuris trichiura) in Brazil from the Global Neglected Tropical Diseases (GNTD) database. Selection of the most important predictors of infection risk was carried out using a Bayesian geostatistical approach and temporal models that address non-linearity and correlation of the explanatory variables. The spatial process was estimated through a predictive process approximation. Spatio-temporal models were built on the selected predictors with integrated nested Laplace approximation using stochastic partial differential equations. Our models revealed that, over the past 20 years, the risk of soil-transmitted helminth infection has decreased in Brazil, mainly because of the reduction of A. lumbricoides and hookworm infections. From 2010 onwards, we estimate that the infection prevalences with A. lumbricoides, hookworm and T. trichiura are 3.6%, 1.7% and 1.4%, respectively. We also provide a map highlighting municipalities in need of preventive chemotherapy, based on a predicted soil-transmitted helminth infection risk in excess of 20%. The need for treatments in the school-aged population at the municipality level was estimated at 1.8 million doses of anthelminthic tablets per year. The analysis of the spatio-temporal aspect of the risk of infection

Spatiotemporal Super-Resolution Reconstruction Based on Robust Optical Flow and Zernike Moment for Video Sequences

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Meiyu Liang

2013-01-01

Full Text Available In order to improve the spatiotemporal resolution of the video sequences, a novel spatiotemporal super-resolution reconstruction model (STSR based on robust optical flow and Zernike moment is proposed in this paper, which integrates the spatial resolution reconstruction and temporal resolution reconstruction into a unified framework. The model does not rely on accurate estimation of subpixel motion and is robust to noise and rotation. Moreover, it can effectively overcome the problems of hole and block artifacts. First we propose an efficient robust optical flow motion estimation model based on motion details preserving, then we introduce the biweighted fusion strategy to implement the spatiotemporal motion compensation. Next, combining the self-adaptive region correlation judgment strategy, we construct a fast fuzzy registration scheme based on Zernike moment for better STSR with higher efficiency, and then the final video sequences with high spatiotemporal resolution can be obtained by fusion of the complementary and redundant information with nonlocal self-similarity between the adjacent video frames. Experimental results demonstrate that the proposed method outperforms the existing methods in terms of both subjective visual and objective quantitative evaluations.

Effects of climate change adaptation scenarios on perceived spatio-temporal characteristics of drought events

Science.gov (United States)

Vidal, J.-P.; Martin, E.; Kitova, N.; Najac, J.; Soubeyroux, J.-M.

2012-04-01

Drought events develop in both space and time and they are therefore best described through summary joint spatio-temporal characteristics, like mean duration, mean affected area and total magnitude. This study addresses the issue of future projections of such characteristics of drought events over France through three main research questions: (1) Are downscaled climate projections able to reproduce spatio-temporal characteristics of meteorological and agricultural droughts in France over a present-day period? (2) How such characteristics will evolve over the 21st century under different emissions scenarios? (3) How would perceived drought characteristics evolve under theoretical adaptation scenarios? These questions are addressed using the Isba land surface model, downscaled climate projections from the ARPEGE General Circulation Model under three emissions scenarios, as well as results from a previously performed 50-year multilevel and multiscale drought reanalysis over France (Vidal et al., 2010). Spatio-temporal characteristics of meteorological and agricultural drought events are computed using the Standardized Precipitation Index (SPI) and the Standardized Soil Wetness Index (SSWI), respectively, and for time scales of 3 and 12 months. Results first show that the distributions of joint spatio-temporal characteristics of observed events are well reproduced by the downscaled hydroclimate projections over a present-day period. All spatio-temporal characteristics of drought events are then found to dramatically increase over the 21st century under all considered emissions scenarios, with stronger changes for agricultural droughts. Two theoretical adaptation scenarios are eventually built based on hypotheses of adaptation to evolving climate and hydrological normals. The two scenarios differ by the way the transient adaptation is performed for a given date in the future, with reference to the normals over either the previous 30-year window ("retrospective

Spatio-Temporal Constrained Human Trajectory Generation from the PIR Motion Detector Sensor Network Data: A Geometric Algebra Approach

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Zhaoyuan Yu

2015-12-01

Full Text Available Passive infrared (PIR motion detectors, which can support long-term continuous observation, are widely used for human motion analysis. Extracting all possible trajectories from the PIR sensor networks is important. Because the PIR sensor does not log location and individual information, none of the existing methods can generate all possible human motion trajectories that satisfy various spatio-temporal constraints from the sensor activation log data. In this paper, a geometric algebra (GA-based approach is developed to generate all possible human trajectories from the PIR sensor network data. Firstly, the representation of the geographical network, sensor activation response sequences and the human motion are represented as algebraic elements using GA. The human motion status of each sensor activation are labeled using the GA-based trajectory tracking. Then, a matrix multiplication approach is developed to dynamically generate the human trajectories according to the sensor activation log and the spatio-temporal constraints. The method is tested with the MERL motion database. Experiments show that our method can flexibly extract the major statistical pattern of the human motion. Compared with direct statistical analysis and tracklet graph method, our method can effectively extract all possible trajectories of the human motion, which makes it more accurate. Our method is also likely to provides a new way to filter other passive sensor log data in sensor networks.

Spatio-Temporal Constrained Human Trajectory Generation from the PIR Motion Detector Sensor Network Data: A Geometric Algebra Approach.

Science.gov (United States)

Yu, Zhaoyuan; Yuan, Linwang; Luo, Wen; Feng, Linyao; Lv, Guonian

2015-12-30

Passive infrared (PIR) motion detectors, which can support long-term continuous observation, are widely used for human motion analysis. Extracting all possible trajectories from the PIR sensor networks is important. Because the PIR sensor does not log location and individual information, none of the existing methods can generate all possible human motion trajectories that satisfy various spatio-temporal constraints from the sensor activation log data. In this paper, a geometric algebra (GA)-based approach is developed to generate all possible human trajectories from the PIR sensor network data. Firstly, the representation of the geographical network, sensor activation response sequences and the human motion are represented as algebraic elements using GA. The human motion status of each sensor activation are labeled using the GA-based trajectory tracking. Then, a matrix multiplication approach is developed to dynamically generate the human trajectories according to the sensor activation log and the spatio-temporal constraints. The method is tested with the MERL motion database. Experiments show that our method can flexibly extract the major statistical pattern of the human motion. Compared with direct statistical analysis and tracklet graph method, our method can effectively extract all possible trajectories of the human motion, which makes it more accurate. Our method is also likely to provides a new way to filter other passive sensor log data in sensor networks.

Quantifying Spatiotemporal Dynamics of Solar Radiation over the Northeast China Based on ACO-BPNN Model and Intensity Analysis

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Xiangqian Li

2017-01-01

Full Text Available Reliable information on the spatiotemporal dynamics of solar radiation plays a crucial role in studies relating to global climate change. In this study, a new backpropagation neural network (BPNN model optimized with an Ant Colony Optimization (ACO algorithm was developed to generate the ACO-BPNN model, which had demonstrated superior performance for simulating solar radiation compared to traditional BPNN modelling, for Northeast China. On this basis, we applied an intensity analysis to investigate the spatiotemporal variation of solar radiation from 1982 to 2010 over the study region at three levels: interval, category, and conversion. Research findings revealed that (1 the solar radiation resource in the study region increased from the 1980s to the 2000s and the average annual rate of variation from the 1980s to the 1990s was lower than that from the 1990s to the 2000s and (2 the gains and losses of solar radiation at each level were in different conditions. The poor, normal, and comparatively abundant levels were transferred to higher levels, whereas the abundant level was transferred to lower levels. We believe our findings contribute to implementing ad hoc energy management strategies to optimize the use of solar radiation resources and provide scientific suggestions for policy planning.

Synchronization of spatiotemporal chaotic systems by feedback control

International Nuclear Information System (INIS)

Lai, Y.; Grebogi, C.

1994-01-01

We demonstrate that two identical spatiotemporal chaotic systems can be synchronized by (1) linking one or a few of their dynamical variables, and (2) applying a small feedback control to one of the systems. Numerical examples using the diffusively coupled logistic map lattice are given. The effect of noise and the limitation of the technique are discussed

Identifying food deserts and swamps based on relative healthy food access: a spatio-temporal Bayesian approach.

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Luan, Hui; Law, Jane; Quick, Matthew

2015-12-30

Obesity and other adverse health outcomes are influenced by individual- and neighbourhood-scale risk factors, including the food environment. At the small-area scale, past research has analysed spatial patterns of food environments for one time period, overlooking how food environments change over time. Further, past research has infrequently analysed relative healthy food access (RHFA), a measure that is more representative of food purchasing and consumption behaviours than absolute outlet density. This research applies a Bayesian hierarchical model to analyse the spatio-temporal patterns of RHFA in the Region of Waterloo, Canada, from 2011 to 2014 at the small-area level. RHFA is calculated as the proportion of healthy food outlets (healthy outlets/healthy + unhealthy outlets) within 4-km from each small-area. This model measures spatial autocorrelation of RHFA, temporal trend of RHFA for the study region, and spatio-temporal trends of RHFA for small-areas. For the study region, a significant decreasing trend in RHFA is observed (-0.024), suggesting that food swamps have become more prevalent during the study period. For small-areas, significant decreasing temporal trends in RHFA were observed for all small-areas. Specific small-areas located in south Waterloo, north Kitchener, and southeast Cambridge exhibited the steepest decreasing spatio-temporal trends and are classified as spatio-temporal food swamps. This research demonstrates a Bayesian spatio-temporal modelling approach to analyse RHFA at the small-area scale. Results suggest that food swamps are more prevalent than food deserts in the Region of Waterloo. Analysing spatio-temporal trends of RHFA improves understanding of local food environment, highlighting specific small-areas where policies should be targeted to increase RHFA and reduce risk factors of adverse health outcomes such as obesity.

Pain Recognition using Spatiotemporal Oriented Energy of Facial Muscles

DEFF Research Database (Denmark)

Irani, Ramin; Nasrollahi, Kamal; Moeslund, Thomas B.

2015-01-01

Pain is a critical sign in many medical situations and its automatic detection and recognition using computer vision techniques is of great importance. Utilizes this fact that pain is a spatiotemporal process, the proposed system in this paper employs steerable and separable filters to measures e...

Spatiotemporal Coupling of the Tongue in Amyotrophic Lateral Sclerosis

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Kuruvilla, Mili S.; Green, Jordan R.; Yunusova, Yana; Hanford, Kathy

2012-01-01

Purpose: The primary aim of the investigation was to identify deficits in spatiotemporal coupling between tongue regions in amyotrophic lateral sclerosis (ALS). The relations between disease-related changes in tongue movement patterns and speech intelligibility were also determined. Methods: The authors recorded word productions from 11…

Spatiotemporal evolution of Calophaca (Fabaceae) reveals multiple dispersals in the Central Asian mountains and adjacent regions

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Ming-Li Zhang; Zhi-Bin Wen; Peter W. Fritsch; Stewart C. Sanderson

2015-01-01

The Central Asian flora plays a significant role in Eurasia and the Northern Hemisphere. Calophaca, a member of this flora, includes eight currently recognized species, and is centered in Central Asia, with some taxa extending into adjacent areas. A phylogenetic analysis of the genus utilizing nuclear ribosomal ITS and plastid trnS-trnG and rbcL sequences was carried...

Spatiotemporal Scaling Effect on Rainfall Network Design Using Entropy

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Chiang Wei

2014-08-01

Full Text Available Because of high variation in mountainous areas, rainfall data at different spatiotemporal scales may yield potential uncertainty for network design. However, few studies focus on the scaling effect on both the spatial and the temporal scale. By calculating the maximum joint entropy of hourly typhoon events, monthly, six dry and wet months and annual rainfall between 1992 and 2012 for 1-, 3-, and 5-km grids, the relocated candidate rain gauges in the National Taiwan University Experimental Forest of Central Taiwan are prioritized. The results show: (1 the network exhibits different locations for first prioritized candidate rain gauges for different spatiotemporal scales; (2 the effect of spatial scales is insignificant compared to temporal scales; and (3 a smaller number and a lower percentage of required stations (PRS reach stable joint entropy for a long duration at finer spatial scale. Prioritized candidate rain gauges provide key reference points for adjusting the network to capture more accurate information and minimize redundancy.

Spatiotemporal regulation of ATP and Ca2+ dynamics in vertebrate rod and cone ribbon synapses.

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Johnson, Jerry E; Perkins, Guy A; Giddabasappa, Anand; Chaney, Shawntay; Xiao, Weimin; White, Andrew D; Brown, Joshua M; Waggoner, Jenna; Ellisman, Mark H; Fox, Donald A

2007-06-15

In conventional neurons, Ca2+ enters presynaptic terminals during an action potential and its increased local concentration triggers transient exocytosis. In contrast, vertebrate photoreceptors are nonspiking neurons that maintain sustained depolarization and neurotransmitter release from ribbon synapses in darkness and produce light-dependent graded hyperpolarizing responses. Rods transmit single photon responses with high fidelity, whereas cones are less sensitive and exhibit faster response kinetics. These differences are likely due to variations in presynaptic Ca2+ dynamics. Metabolic coupling and cross-talk between mitochondria, endoplasmic reticulum (ER), plasma membrane Ca2+ ATPase (PMCA), and Na+-Ca2+ exchanger (NCX) coordinately control presynaptic ATP production and Ca2+ dynamics. The goal of our structural and functional studies was to determine the spatiotemporal regulation of ATP and Ca2+ dynamics in rod spherules and cone pedicles. Central retina tissue from C57BL/6 mice was used. Laser scanning confocal microscopy (LSCM) experiments were conducted on fixed-frozen vertical sections. Primary antibodies were selected for their tissue/cellular specificity and ability to recognize single, multiple or all splice variants of selected isoforms. Electron microscopy (EM) and 3-D electron tomography (ET) studies used our standard procedures on thin- and thick-sectioned retinas, respectively. Calibrated fluo-3-Ca2+ imaging experiments of dark- and light-adapted rod and cone terminals in retinal slices were conducted. Confocal microscopy showed that mitochondria, ER, PMCA, and NCX1 exhibited distinct retinal lamination patterns and differential distribution in photoreceptor synapses. Antibodies for three distinct mitochondrial compartments differentially labeled retinal areas with high metabolic demand: rod and cone inner segments, previously undescribed cone juxtanuclear mitochondria and the two plexiform layers. Rod spherule membranes uniformly and intensely

Synchronizing spatiotemporal chaos by introducing a finite flat region in the local map

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J. Y. Chen

2001-01-01

Full Text Available An approach to synchronize spatiotemporal chaos is proposed. It is achieved by introducing a finite flat region in the local map. By using this scheme, a number of orbits in both the drive and the response subsystems are forced to pass through a fixed point in every dimension. With only an arbitrary phase space variable as drive signal, synchronization of spatiotemporal chaos can be achieved rapidly in the response subsystem. This is an advantage when compared with other synchronization methods that require a linear combination of the original phase space variables.

Imaging 2015 Mw 7.8 Gorkha Earthquake and Its Aftershock Sequence Combining Multiple Calibrated Global Seismic Arrays

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LI, B.; Ghosh, A.

2016-12-01

The 2015 Mw 7.8 Gorkha earthquake provides a good opportunity to study the tectonics and earthquake hazards in the Himalayas, one of the most seismically active plate boundaries. Details of the seismicity patterns and associated structures in the Himalayas are poorly understood mainly due to limited instrumentation. Here, we apply a back-projection method to study the mainshock rupture and the following aftershock sequence using four large aperture global seismic arrays. All the arrays show eastward rupture propagation of about 130 km and reveal similar evolution of seismic energy radiation, with strong high-frequency energy burst about 50 km north of Kathmandu. Each single array, however, is typically limited by large azimuthal gap, low resolution, and artifacts due to unmodeled velocity structures. Therefore, we use a self-consistent empirical calibration method to combine four different arrays to image the Gorkha event. It greatly improves the resolution, can better track rupture and reveal details that cannot be resolved by any individual array. In addition, we also use the same arrays at teleseismic distances and apply a back-projection technique to detect and locate the aftershocks immediately following the Gorkha earthquake. We detect about 2.5 times the aftershocks recorded by the Advance National Seismic System comprehensive earthquake catalog during the 19 days following the mainshock. The aftershocks detected by the arrays show an east-west trend in general, with majority of the aftershocks located at the eastern part of the rupture patch and surrounding the rupture zone of the largest Mw 7.3 aftershock. Overall spatiotemporal aftershock pattern agrees well with global catalog, with our catalog showing more details relative to the standard global catalog. The improved aftershock catalog enables us to better study the aftershock dynamics, stress evolution in this region. Moreover, rapid and better imaging of aftershock distribution may aid rapid response

A Multiphysics Framework to Learn and Predict in Presence of Multiple Scales

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Tomin, P.; Lunati, I.

2015-12-01

Modeling complex phenomena in the subsurface remains challenging due to the presence of multiple interacting scales, which can make it impossible to focus on purely macroscopic phenomena (relevant in most applications) and neglect the processes at the micro-scale. We present and discuss a general framework that allows us to deal with the situation in which the lack of scale separation requires the combined use of different descriptions at different scale (for instance, a pore-scale description at the micro-scale and a Darcy-like description at the macro-scale) [1,2]. The method is based on conservation principles and constructs the macro-scale problem by numerical averaging of micro-scale balance equations. By employing spatiotemporal adaptive strategies, this approach can efficiently solve large-scale problems [2,3]. In addition, being based on a numerical volume-averaging paradigm, it offers a tool to illuminate how macroscopic equations emerge from microscopic processes, to better understand the meaning of microscopic quantities, and to investigate the validity of the assumptions routinely used to construct the macro-scale problems. [1] Tomin, P., and I. Lunati, A Hybrid Multiscale Method for Two-Phase Flow in Porous Media, Journal of Computational Physics, 250, 293-307, 2013 [2] Tomin, P., and I. Lunati, Local-global splitting and spatiotemporal-adaptive Multiscale Finite Volume Method, Journal of Computational Physics, 280, 214-231, 2015 [3] Tomin, P., and I. Lunati, Spatiotemporal adaptive multiphysics simulations of drainage-imbibition cycles, Computational Geosciences, 2015 (under review)

DETERMINING SPATIO-TEMPORAL CADASTRAL DATA REQUIREMENT FOR INFRASTRUCTURE OF LADM FOR TURKEY

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M. Alkan

2016-06-01

Full Text Available Nowadays, the nature of land title and cadastral (LTC data in the Turkey is dynamic from a temporal perspective which depends on the LTC operations. Functional requirements with respect to the characteristics are investigated based upon interviews of professionals in public and private sectors. These are; Legal authorities, Land Registry and Cadastre offices, Highway departments, Foundations, Ministries of Budget, Transportation, Justice, Public Works and Settlement, Environment and Forestry, Agriculture and Rural Affairs, Culture and Internal Affairs, State Institute of Statistics (SIS, execution offices, tax offices, real estate offices, private sector, local governments and banks. On the other hand, spatio-temporal LTC data very important component for creating infrastructure of Land Administration Model (LADM. For this reason, spatio-temporal LTC data needs for LADM not only updated but also temporal. The investigations ended up with determine temporal analyses of LTC data, traditional LTC system and tracing temporal analyses in traditional LTC system. In the traditional system, the temporal analyses needed by all these users could not be performed in a rapid and reliable way. The reason for this is that the traditional LTC system is a manual archiving system. The aims and general contents of this paper: (1 define traditional LTC system of Turkey; (2 determining the need for spatio-temporal LTC data and analyses for core domain model for LADM. As a results of temporal and spatio-temporal analysis LTC data needs, new system design is important for the Turkish LADM model. Designing and realizing an efficient and functional Temporal Geographic Information Systems (TGIS is inevitable for the Turkish LADM core infrastructure. Finally this paper outcome is creating infrastructure for design and develop LADM for Turkey.

Determining Spatio-Temporal Cadastral Data Requirement for Infrastructure of Ladm for Turkey

Science.gov (United States)

Alkan, M.; Polat, Z. A.

2016-06-01

Nowadays, the nature of land title and cadastral (LTC) data in the Turkey is dynamic from a temporal perspective which depends on the LTC operations. Functional requirements with respect to the characteristics are investigated based upon interviews of professionals in public and private sectors. These are; Legal authorities, Land Registry and Cadastre offices, Highway departments, Foundations, Ministries of Budget, Transportation, Justice, Public Works and Settlement, Environment and Forestry, Agriculture and Rural Affairs, Culture and Internal Affairs, State Institute of Statistics (SIS), execution offices, tax offices, real estate offices, private sector, local governments and banks. On the other hand, spatio-temporal LTC data very important component for creating infrastructure of Land Administration Model (LADM). For this reason, spatio-temporal LTC data needs for LADM not only updated but also temporal. The investigations ended up with determine temporal analyses of LTC data, traditional LTC system and tracing temporal analyses in traditional LTC system. In the traditional system, the temporal analyses needed by all these users could not be performed in a rapid and reliable way. The reason for this is that the traditional LTC system is a manual archiving system. The aims and general contents of this paper: (1) define traditional LTC system of Turkey; (2) determining the need for spatio-temporal LTC data and analyses for core domain model for LADM. As a results of temporal and spatio-temporal analysis LTC data needs, new system design is important for the Turkish LADM model. Designing and realizing an efficient and functional Temporal Geographic Information Systems (TGIS) is inevitable for the Turkish LADM core infrastructure. Finally this paper outcome is creating infrastructure for design and develop LADM for Turkey.

Spatio-temporal Variability of Albedo and its Impact on Glacier Melt Modelling

Science.gov (United States)

Kinnard, C.; Mendoza, C.; Abermann, J.; Petlicki, M.; MacDonell, S.; Urrutia, R.

2017-12-01

Albedo is an important variable for the surface energy balance of glaciers, yet its representation within distributed glacier mass-balance models is often greatly simplified. Here we study the spatio-temporal evolution of albedo on Glacier Universidad, central Chile (34°S, 70°W), using time-lapse terrestrial photography, and investigate its effect on the shortwave radiation balance and modelled melt rates. A 12 megapixel digital single-lens reflex camera was setup overlooking the glacier and programmed to take three daily images of the glacier during a two-year period (2012-2014). One image was chosen for each day with no cloud shading on the glacier. The RAW images were projected onto a 10m resolution digital elevation model (DEM), using the IMGRAFT software (Messerli and Grinsted, 2015). A six-parameter camera model was calibrated using a single image and a set of 17 ground control points (GCPs), yielding a georeferencing accuracy of accounting for possible camera movement over time. The reflectance values from the projected image were corrected for topographic and atmospheric influences using a parametric solar irradiation model, following a modified algorithm based on Corripio (2004), and then converted to albedo using reference albedo measurements from an on-glacier automatic weather station (AWS). The image-based albedo was found to compare well with independent albedo observations from a second AWS in the glacier accumulation area. Analysis of the albedo maps showed that the albedo is more spatially-variable than the incoming solar radiation, making albedo a more important factor of energy balance spatial variability. The incorporation of albedo maps within an enhanced temperature index melt model revealed that the spatio-temporal variability of albedo is an important factor for the calculation of glacier-wide meltwater fluxes.

Spatiotemporal Land Use Change Analysis Using Open-source GIS and Web Based Application

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Wan Yusryzal Wan Ibrahim

2015-05-01

Full Text Available Spatiotemporal changes are very important information to reveal the characteristics of the urbanization process. Sharing the information is beneficial for public awareness which then improves their participation in adaptive management for spatial planning process. Open-source software and web application are freely available tools that can be the best medium used by any individual or agencies to share this important information. The objective of the paper is to discuss on the spatiotemporal land use change in Iskandar Malaysia by using open-source GIS (Quantum GIS and publish them through web application (Mash-up. Land use in 1994 to 2011 were developed and analyzed to show the landscape change of the region. Subsequently, web application was setup to distribute the findings of the study. The result show there is significant changes of land use in the study area especially on the decline of agricultural and natural land which were converted to urban land uses. Residential and industrial areas largely replaced the agriculture and natural areas particularly along the coastal zone of the region. This information is published through interactive GIS web in order to share it with the public and stakeholders. There are some limitations of web application but still not hindering the advantages of using it. The integration of open-source GIS and web application is very helpful in sharing planning information particularly in the study area that experiences rapid land use and land cover change. Basic information from this study is vital for conducting further study such as projecting future land use change and other related studies in the area.

Mapping child maltreatment risk: a 12-year spatio-temporal analysis of neighborhood influences.

Science.gov (United States)

Gracia, Enrique; López-Quílez, Antonio; Marco, Miriam; Lila, Marisol

2017-10-18

'Place' matters in understanding prevalence variations and inequalities in child maltreatment risk. However, most studies examining ecological variations in child maltreatment risk fail to take into account the implications of the spatial and temporal dimensions of neighborhoods. In this study, we conduct a high-resolution small-area study to analyze the influence of neighborhood characteristics on the spatio-temporal epidemiology of child maltreatment risk. We conducted a 12-year (2004-2015) small-area Bayesian spatio-temporal epidemiological study with all families with child maltreatment protection measures in the city of Valencia, Spain. As neighborhood units, we used 552 census block groups. Cases were geocoded using the family address. Neighborhood-level characteristics analyzed included three indicators of neighborhood disadvantage-neighborhood economic status, neighborhood education level, and levels of policing activity-, immigrant concentration, and residential instability. Bayesian spatio-temporal modelling and disease mapping methods were used to provide area-specific risk estimations. Results from a spatio-temporal autoregressive model showed that neighborhoods with low levels of economic and educational status, with high levels of policing activity, and high immigrant concentration had higher levels of substantiated child maltreatment risk. Disease mapping methods were used to analyze areas of excess risk. Results showed chronic spatial patterns of high child maltreatment risk during the years analyzed, as well as stability over time in areas of low risk. Areas with increased or decreased child maltreatment risk over the years were also observed. A spatio-temporal epidemiological approach to study the geographical patterns, trends over time, and the contextual determinants of child maltreatment risk can provide a useful method to inform policy and action. This method can offer a more accurate description of the problem, and help to inform more

Spatiotemporal Relationships among Audiovisual Stimuli Modulate Auditory Facilitation of Visual Target Discrimination.

Science.gov (United States)

Li, Qi; Yang, Huamin; Sun, Fang; Wu, Jinglong

2015-03-01

Sensory information is multimodal; through audiovisual interaction, task-irrelevant auditory stimuli tend to speed response times and increase visual perception accuracy. However, mechanisms underlying these performance enhancements have remained unclear. We hypothesize that task-irrelevant auditory stimuli might provide reliable temporal and spatial cues for visual target discrimination and behavioral response enhancement. Using signal detection theory, the present study investigated the effects of spatiotemporal relationships on auditory facilitation of visual target discrimination. Three experiments were conducted where an auditory stimulus maintained reliable temporal and/or spatial relationships with visual target stimuli. Results showed that perception sensitivity (d') to visual target stimuli was enhanced only when a task-irrelevant auditory stimulus maintained reliable spatiotemporal relationships with a visual target stimulus. When only reliable spatial or temporal information was contained, perception sensitivity was not enhanced. These results suggest that reliable spatiotemporal relationships between visual and auditory signals are required for audiovisual integration during a visual discrimination task, most likely due to a spread of attention. These results also indicate that auditory facilitation of visual target discrimination follows from late-stage cognitive processes rather than early stage sensory processes. © 2015 SAGE Publications.

Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model

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Liang Wu

2017-11-01

Full Text Available Much of the taxi route-planning literature has focused on driver strategies for finding passengers and determining the hot spot pick-up locations using historical global positioning system (GPS trajectories of taxis based on driver experience, distance from the passenger drop-off location to the next passenger pick-up location and the waiting times at recommended locations for the next passenger. The present work, however, considers the average taxi travel speed mined from historical taxi GPS trajectory data and the allocation of cruising routes to more than one taxi driver in a small-scale region to neighboring pick-up locations. A spatio-temporal trajectory model with load balancing allocations is presented to not only explore pick-up/drop-off information but also provide taxi drivers with cruising routes to the recommended pick-up locations. In simulation experiments, our study shows that taxi drivers using cruising routes recommended by our spatio-temporal trajectory model can significantly reduce the average waiting time and travel less distance to quickly find their next passengers, and the load balancing strategy significantly alleviates road loads. These objective measures can help us better understand spatio-temporal traffic patterns and guide taxi navigation.

Description of spatio-temporal gait parameters in elderly people and their association with history of falls: results of the population-based cross-sectional KORA-Age study.

Science.gov (United States)

Thaler-Kall, Kathrin; Peters, Annette; Thorand, Barbara; Grill, Eva; Autenrieth, Christine S; Horsch, Alexander; Meisinger, Christa

2015-03-25

In this epidemiological study we described the characteristics of spatio-temporal gait parameters among a representative, population-based sample of 890 community-dwelling people aged 65 to 90 years. In addition, we investigated the associations between certain gait parameters and a history of falls in study participants. In descriptive analyses spatio-temporal gait parameters were assessed according to history of falls, frailty, multimorbidity, gender, multiple medication use, disability status, and age group. Logistic regression models were calculated to examine the association between gait velocity and stride length with a history of falls (at least one fall in the last 12 month). Data on gait were collected on an electronic walkway on which participants walked at their usual pace. We found significant differences within gait parameters when stratifying by frailty, multimorbidity, disability and multiple medication use as well as age (cut point 75 years) and sex, with p history of falls, only stride length showed a significant difference (p falls in men aged older than 74 years (OR 1.34 (CI: 1.05-1.70 per 10 cm decrease)), while this was neither the case for women of similar age nor for men or women aged 65 to 74 years. A decreased walking speed was not associated with falls. Age, frailty, multimorbidity, disability, history of falls, sex, and multiple medication use show an association with different gait parameters measured during gait assessment on an electronic walkway in elderly people. Furthermore, stride length is a good indicator to differentiate fallers from non-fallers in older men from the general population.

Spatiotemporal chaos involving wave instability.

Science.gov (United States)

Berenstein, Igal; Carballido-Landeira, Jorge

2017-01-01

In this paper, we investigate pattern formation in a model of a reaction confined in a microemulsion, in a regime where both Turing and wave instability occur. In one-dimensional systems, the pattern corresponds to spatiotemporal intermittency where the behavior of the systems alternates in both time and space between stationary Turing patterns and traveling waves. In two-dimensional systems, the behavior initially may correspond to Turing patterns, which then turn into wave patterns. The resulting pattern also corresponds to a chaotic state, where the system alternates in both space and time between standing wave patterns and traveling waves, and the local dynamics may show vanishing amplitude of the variables.

Spatiotemporally Representative and Cost-Efficient Sampling Design for Validation Activities in Wanglang Experimental Site

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Gaofei Yin

2017-11-01

Full Text Available Spatiotemporally representative Elementary Sampling Units (ESUs are required for capturing the temporal variations in surface spatial heterogeneity through field measurements. Since inaccessibility often coexists with heterogeneity, a cost-efficient sampling design is mandatory. We proposed a sampling strategy to generate spatiotemporally representative and cost-efficient ESUs based on the conditioned Latin hypercube sampling scheme. The proposed strategy was constrained by multi-temporal Normalized Difference Vegetation Index (NDVI imagery, and the ESUs were limited within a sampling feasible region established based on accessibility criteria. A novel criterion based on the Overlapping Area (OA between the NDVI frequency distribution histogram from the sampled ESUs and that from the entire study area was used to assess the sampling efficiency. A case study in Wanglang National Nature Reserve in China showed that the proposed strategy improves the spatiotemporally representativeness of sampling (mean annual OA = 74.7% compared to the single-temporally constrained (OA = 68.7% and the random sampling (OA = 63.1% strategies. The introduction of the feasible region constraint significantly reduces in-situ labour-intensive characterization necessities at expenses of about 9% loss in the spatiotemporal representativeness of the sampling. Our study will support the validation activities in Wanglang experimental site providing a benchmark for locating the nodes of automatic observation systems (e.g., LAINet which need a spatially distributed and temporally fixed sampling design.

A simple spatiotemporal chaotic Lotka-Volterra model

International Nuclear Information System (INIS)

Sprott, J.C.; Wildenberg, J.C.; Azizi, Yousef

2005-01-01

A mathematically simple example of a high-dimensional (many-species) Lotka-Volterra model that exhibits spatiotemporal chaos in one spatial dimension is described. The model consists of a closed ring of identical agents, each competing for fixed finite resources with two of its four nearest neighbors. The model is prototypical of more complicated models in its quasiperiodic route to chaos (including attracting 3-tori), bifurcations, spontaneous symmetry breaking, and spatial pattern formation

Spatiotemporal Distribution of Droughts in the Xijiang River Basin, China and Its Responses to Global Climatic Events

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Jizhong Qiu

2017-04-01

Full Text Available The Xijiang River is a main branch of the Pearl River, the largest river in South China. Droughts in this area have seriously influenced local water resource utilization, and socio-economic development. The spatiotemporal distribution of droughts and its responses to global climatic events are of critical significance for the assessment and early warning of drought disasters. In this paper, the spatiotemporal patterns of droughts characterized by Rotated Empirical Orthogonal Function/Rotated Principal Components (REOF/RPC in the Xijiang River Basin, China were evaluated using the Self-calibrated Palmer Drought Severity Index (Sc-PDSI. The drought responses to El Niño/Southern Oscillation (ENSO, Pacific Decadal Oscillation (PDO, India Ocean Dipole (IOD, and North Atlantic Oscillation (NAO were analysed by Pearson correlation and multiple stepwise regression. The results showed that one year earlier NAO was the dominant factor impacting the droughts in the Xijiang Basin. Its contribution for the RPC2s of the annual, the first and second half years, winter, summer, autumn, and February were −0.556, −0.419, 0.597, −0.447, 0.542, 0.600, and −0.327, respectively. Besides the two adjacent Pacific and India oceans, the droughts seem be influenced by distant Atlantic climatic events. These results offer new reference insights into the early warning of droughts as well as the planning and management of water resources in the study area.

4D cone beam CT via spatiotemporal tensor framelet

International Nuclear Information System (INIS)

Gao, Hao; Li, Ruijiang; Xing, Lei; Lin, Yuting

2012-01-01

Purpose: On-board 4D cone beam CT (4DCBCT) offers respiratory phase-resolved volumetric imaging, and improves the accuracy of target localization in image guided radiation therapy. However, the clinical utility of this technique has been greatly impeded by its degraded image quality, prolonged imaging time, and increased imaging dose. The purpose of this letter is to develop a novel iterative 4DCBCT reconstruction method for improved image quality, increased imaging speed, and reduced imaging dose. Methods: The essence of this work is to introduce the spatiotemporal tensor framelet (STF), a high-dimensional tensor generalization of the 1D framelet for 4DCBCT, to effectively take into account of highly correlated and redundant features of the patient anatomy during respiration, in a multilevel fashion with multibasis sparsifying transform. The STF-based algorithm is implemented on a GPU platform for improved computational efficiency. To evaluate the method, 4DCBCT full-fan scans were acquired within 30 s, with a gantry rotation of 200°; STF is also compared with a state-of-art reconstruction method via spatiotemporal total variation regularization. Results: Both the simulation and experimental results demonstrate that STF-based reconstruction achieved superior image quality. The reconstruction of 20 respiratory phases took less than 10 min on an NVIDIA Tesla C2070 GPU card. The STF codes are available at https://sites.google.com/site/spatiotemporaltensorframelet . Conclusions: By effectively utilizing the spatiotemporal coherence of the patient anatomy among different respiratory phases in a multilevel fashion with multibasis sparsifying transform, the proposed STF method potentially enables fast and low-dose 4DCBCT with improved image quality.

4D cone beam CT via spatiotemporal tensor framelet

Energy Technology Data Exchange (ETDEWEB)

Gao, Hao, E-mail: hao.gao@emory.edu [Departments of Mathematics and Computer Science, and Radiology and Imaging Sciences, Emory University, Atlanta, Georgia 30322 (United States); Li, Ruijiang; Xing, Lei [Department of Radiation Oncology, Stanford University, Stanford, California 94305 (United States); Lin, Yuting [Department of Radiological Sciences, University of California, Irvine, California 92697 (United States)

2012-11-15

Purpose: On-board 4D cone beam CT (4DCBCT) offers respiratory phase-resolved volumetric imaging, and improves the accuracy of target localization in image guided radiation therapy. However, the clinical utility of this technique has been greatly impeded by its degraded image quality, prolonged imaging time, and increased imaging dose. The purpose of this letter is to develop a novel iterative 4DCBCT reconstruction method for improved image quality, increased imaging speed, and reduced imaging dose. Methods: The essence of this work is to introduce the spatiotemporal tensor framelet (STF), a high-dimensional tensor generalization of the 1D framelet for 4DCBCT, to effectively take into account of highly correlated and redundant features of the patient anatomy during respiration, in a multilevel fashion with multibasis sparsifying transform. The STF-based algorithm is implemented on a GPU platform for improved computational efficiency. To evaluate the method, 4DCBCT full-fan scans were acquired within 30 s, with a gantry rotation of 200°; STF is also compared with a state-of-art reconstruction method via spatiotemporal total variation regularization. Results: Both the simulation and experimental results demonstrate that STF-based reconstruction achieved superior image quality. The reconstruction of 20 respiratory phases took less than 10 min on an NVIDIA Tesla C2070 GPU card. The STF codes are available at https://sites.google.com/site/spatiotemporaltensorframelet . Conclusions: By effectively utilizing the spatiotemporal coherence of the patient anatomy among different respiratory phases in a multilevel fashion with multibasis sparsifying transform, the proposed STF method potentially enables fast and low-dose 4DCBCT with improved image quality.

Measurement Error Correction for Predicted Spatiotemporal Air Pollution Exposures.

Science.gov (United States)

Keller, Joshua P; Chang, Howard H; Strickland, Matthew J; Szpiro, Adam A

2017-05-01

Air pollution cohort studies are frequently analyzed in two stages, first modeling exposure then using predicted exposures to estimate health effects in a second regression model. The difference between predicted and unobserved true exposures introduces a form of measurement error in the second stage health model. Recent methods for spatial data correct for measurement error with a bootstrap and by requiring the study design ensure spatial compatibility, that is, monitor and subject locations are drawn from the same spatial distribution. These methods have not previously been applied to spatiotemporal exposure data. We analyzed the association between fine particulate matter (PM2.5) and birth weight in the US state of Georgia using records with estimated date of conception during 2002-2005 (n = 403,881). We predicted trimester-specific PM2.5 exposure using a complex spatiotemporal exposure model. To improve spatial compatibility, we restricted to mothers residing in counties with a PM2.5 monitor (n = 180,440). We accounted for additional measurement error via a nonparametric bootstrap. Third trimester PM2.5 exposure was associated with lower birth weight in the uncorrected (-2.4 g per 1 μg/m difference in exposure; 95% confidence interval [CI]: -3.9, -0.8) and bootstrap-corrected (-2.5 g, 95% CI: -4.2, -0.8) analyses. Results for the unrestricted analysis were attenuated (-0.66 g, 95% CI: -1.7, 0.35). This study presents a novel application of measurement error correction for spatiotemporal air pollution exposures. Our results demonstrate the importance of spatial compatibility between monitor and subject locations and provide evidence of the association between air pollution exposure and birth weight.

One-way hash function construction based on the spatiotemporal chaotic system

International Nuclear Information System (INIS)

Luo Yu-Ling; Du Ming-Hui

2012-01-01

Based on the spatiotemporal chaotic system, a novel algorithm for constructing a one-way hash function is proposed and analysed. The message is divided into fixed length blocks. Each message block is processed by the hash compression function in parallel. The hash compression is constructed based on the spatiotemporal chaos. In each message block, the ASCII code and its position in the whole message block chain constitute the initial conditions and the key of the hash compression function. The final hash value is generated by further compressing the mixed result of all the hash compression values. Theoretic analyses and numerical simulations show that the proposed algorithm presents high sensitivity to the message and key, good statistical properties, and strong collision resistance. (general)

Exploring Spatiotemporal Trends in Commercial Fishing Effort of an Abalone Fishing Zone: A GIS-Based Hotspot Model

Science.gov (United States)

Jalali, M. Ali; Ierodiaconou, Daniel; Gorfine, Harry; Monk, Jacquomo; Rattray, Alex

2015-01-01

Assessing patterns of fisheries activity at a scale related to resource exploitation has received particular attention in recent times. However, acquiring data about the distribution and spatiotemporal allocation of catch and fishing effort in small scale benthic fisheries remains challenging. Here, we used GIS-based spatio-statistical models to investigate the footprint of commercial diving events on blacklip abalone (Haliotis rubra) stocks along the south-west coast of Victoria, Australia from 2008 to 2011. Using abalone catch data matched with GPS location we found catch per unit of fishing effort (CPUE) was not uniformly spatially and temporally distributed across the study area. Spatial autocorrelation and hotspot analysis revealed significant spatiotemporal clusters of CPUE (with distance thresholds of 100’s of meters) among years, indicating the presence of CPUE hotspots focused on specific reefs. Cumulative hotspot maps indicated that certain reef complexes were consistently targeted across years but with varying intensity, however often a relatively small proportion of the full reef extent was targeted. Integrating CPUE with remotely-sensed light detection and ranging (LiDAR) derived bathymetry data using generalized additive mixed model corroborated that fishing pressure primarily coincided with shallow, rugose and complex components of reef structures. This study demonstrates that a geospatial approach is efficient in detecting patterns and trends in commercial fishing effort and its association with seafloor characteristics. PMID:25992800

Exploring Spatiotemporal Trends in Commercial Fishing Effort of an Abalone Fishing Zone: A GIS-Based Hotspot Model.

Directory of Open Access Journals (Sweden)

M Ali Jalali

Full Text Available Assessing patterns of fisheries activity at a scale related to resource exploitation has received particular attention in recent times. However, acquiring data about the distribution and spatiotemporal allocation of catch and fishing effort in small scale benthic fisheries remains challenging. Here, we used GIS-based spatio-statistical models to investigate the footprint of commercial diving events on blacklip abalone (Haliotis rubra stocks along the south-west coast of Victoria, Australia from 2008 to 2011. Using abalone catch data matched with GPS location we found catch per unit of fishing effort (CPUE was not uniformly spatially and temporally distributed across the study area. Spatial autocorrelation and hotspot analysis revealed significant spatiotemporal clusters of CPUE (with distance thresholds of 100's of meters among years, indicating the presence of CPUE hotspots focused on specific reefs. Cumulative hotspot maps indicated that certain reef complexes were consistently targeted across years but with varying intensity, however often a relatively small proportion of the full reef extent was targeted. Integrating CPUE with remotely-sensed light detection and ranging (LiDAR derived bathymetry data using generalized additive mixed model corroborated that fishing pressure primarily coincided with shallow, rugose and complex components of reef structures. This study demonstrates that a geospatial approach is efficient in detecting patterns and trends in commercial fishing effort and its association with seafloor characteristics.

Spatio-temporal intermittency on the sandpile

International Nuclear Information System (INIS)

Erzan, A.; Sinha, S.

1990-08-01

The self-organized critical state exhibited by a sandpile model is shown to correspond to motion on an attractor characterized by an invariant distribution of the height variable. The largest Lyapunov exponent is equal to zero. The model nonetheless displays intermittent chaos, with a multifractal distribution of local expansion coefficients in history space. Laminar spatio-temporal regions are interrupted by chaotic bursts caused by avalanches. We introduce the concept of local histories in configuration space and show that their expansion parameters also exhibit a multifractal distribution in time and space. (author). 22 refs, 5 figs

A novel image block cryptosystem based on a spatiotemporal chaotic system and a chaotic neural network

International Nuclear Information System (INIS)

Wang Xing-Yuan; Bao Xue-Mei

2013-01-01

In this paper, we propose a novel block cryptographic scheme based on a spatiotemporal chaotic system and a chaotic neural network (CNN). The employed CNN comprises a 4-neuron layer called a chaotic neuron layer (CNL), where the spatiotemporal chaotic system participates in generating its weight matrix and other parameters. The spatiotemporal chaotic system used in our scheme is the typical coupled map lattice (CML), which can be easily implemented in parallel by hardware. A 160-bit-long binary sequence is used to generate the initial conditions of the CML. The decryption process is symmetric relative to the encryption process. Theoretical analysis and experimental results prove that the block cryptosystem is secure and practical, and suitable for image encryption. (general)

Spatio-temporal point process filtering methods with an application

Czech Academy of Sciences Publication Activity Database

Frcalová, B.; Beneš, V.; Klement, Daniel

2010-01-01

Roč. 21, 3-4 (2010), s. 240-252 ISSN 1180-4009 R&D Projects: GA AV ČR(CZ) IAA101120604 Institutional research plan: CEZ:AV0Z50110509 Keywords : cox point process * filtering * spatio-temporal modelling * spike Subject RIV: BA - General Mathematics Impact factor: 0.750, year: 2010

Multifractal fluctuations in joint angles during infant spontaneous kicking reveal multiplicativity-driven coordination

International Nuclear Information System (INIS)

Stephen, Damian G.; Hsu, Wen-Hao; Young, Diana; Saltzman, Elliot L.; Holt, Kenneth G.; Newman, Dava J.; Weinberg, Marc; Wood, Robert J.; Nagpal, Radhika; Goldfield, Eugene C.

2012-01-01

Previous research has considered infant spontaneous kicking as a form of exploration. According to this view, spontaneous kicking provides information about motor degrees of freedom and may shape multijoint coordinations for more complex movement patterns such as gait. Recent work has demonstrated that multifractal, multiplicative fluctuations in exploratory movements index energy flows underlying perceptual-motor information. If infant spontaneous kicking is exploratory and occasions an upstream flow of information from the motor periphery, we expected not only that multiplicativity of fluctuations at the hip should promote multiplicativity of fluctuations at more distal joints (i.e., reflecting downstream effects of neural control) but also that multiplicativity at more distal joints should promote multiplicativity at the hip. Multifractal analysis demonstrated that infant spontaneous kicking in four typically developing infants for evidence of multiplicative fluctuations in multiple joint angles along the leg (i.e., hip, knee, and ankle) exhibited multiplicativity. Vector autoregressive modeling demonstrated that only one leg exhibited downstream effects but that both legs exhibited upstream effects. These results confirm the exploratory aspect of infant spontaneous kicking and suggest chaotic dynamics in motor coordination. They also resonate with existing models of chaos-controlled robotics and noise-based interventions for rehabilitating motor coordination in atypically developing patients.

Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform

International Nuclear Information System (INIS)

Tascikaraoglu, Akin; Sanandaji, Borhan M.; Poolla, Kameshwar; Varaiya, Pravin

2016-01-01

Highlights: • We propose a spatio-temporal approach for wind speed forecasting. • The method is based on a combination of Wavelet decomposition and structured-sparse recovery. • Our analyses confirm that low-dimensional structures govern the interactions between stations. • Our method particularly shows improvements for profiles with high ramps. • We examine our approach on real data and illustrate its superiority over a set of benchmark models. - Abstract: Integration of renewable energy resources into the power grid is essential in achieving the envisioned sustainable energy future. Stochasticity and intermittency characteristics of renewable energies, however, present challenges for integrating these resources into the existing grid in a large scale. Reliable renewable energy integration is facilitated by accurate wind forecasts. In this paper, we propose a novel wind speed forecasting method which first utilizes Wavelet Transform (WT) for decomposition of the wind speed data into more stationary components and then uses a spatio-temporal model on each sub-series for incorporating both temporal and spatial information. The proposed spatio-temporal forecasting approach on each sub-series is based on the assumption that there usually exists an intrinsic low-dimensional structure between time series data in a collection of meteorological stations. Our approach is inspired by Compressive Sensing (CS) and structured-sparse recovery algorithms. Based on detailed case studies, we show that the proposed approach based on exploiting the sparsity of correlations between a large set of meteorological stations and decomposing time series for higher-accuracy forecasts considerably improve the short-term forecasts compared to the temporal and spatio-temporal benchmark methods.

Spatio-temporal reasoning and decision support tools

OpenAIRE

Renso, Chiara; Wachowicz, Monica

2014-01-01

Currently, mobility data is revolutionizing the traditional fields of spatio-temporal reasoning and decision making analysis, not only to scale-up to the large and growing data volumes, but also to address complex questions related to change, trends, duration, and evolution. In mobility data, space and time are inextricably linked, since humans, robots and systems that dynamically act, and interact within social networks, are embedded in space, and any change is often the result of actions an...

Spatiotemporal dynamics of cortical representations during and after stimulus presentation

NARCIS (Netherlands)

Nieuwenhuijzen, M.E. van de; Borne, E.W.P. van den; Jensen, O.; Gerven, M.A.J. van

2016-01-01

Visual perception is a spatiotemporally complex process. In this study, we investigated cortical dynamics during and after stimulus presentation. We observed that visual category information related to the difference between faces and objects became apparent in the occipital lobe after 63 ms. Within

ESTEEM: A Novel Framework for Qualitatively Evaluating and Visualizing Spatiotemporal Embeddings in Social Media

Energy Technology Data Exchange (ETDEWEB)

Arendt, Dustin L.; Volkova, Svitlana

2017-07-30

Analyzing and visualizing large amounts of social media communications and contrasting short-term conversation changes over time and geo-locations is extremely important for commercial and government applications. Earlier approaches for large-scale text stream summarization used dynamic topic models and trending words. Instead, we rely on text embeddings – low-dimensional word representations in a continuous vector space where similar words are embedded nearby each other. This paper presents ESTEEM,1 a novel tool for visualizing and evaluating spatiotemporal embeddings learned from streaming social media texts. Our tool allows users to monitor and analyze query words and their closest neighbors with an interactive interface. We used state-of- the-art techniques to learn embeddings and developed a visualization to represent dynamically changing relations between words in social media over time and other dimensions. This is the first interactive visualization of streaming text representations learned from social media texts that also allows users to contrast differences across multiple dimensions of the data.

Learning of spatio-temporal codes in a coupled oscillator system.

Science.gov (United States)

Orosz, Gábor; Ashwin, Peter; Townley, Stuart

2009-07-01

In this paper, we consider a learning strategy that allows one to transmit information between two coupled phase oscillator systems (called teaching and learning systems) via frequency adaptation. The dynamics of these systems can be modeled with reference to a number of partially synchronized cluster states and transitions between them. Forcing the teaching system by steady but spatially nonhomogeneous inputs produces cyclic sequences of transitions between the cluster states, that is, information about inputs is encoded via a "winnerless competition" process into spatio-temporal codes. The large variety of codes can be learned by the learning system that adapts its frequencies to those of the teaching system. We visualize the dynamics using "weighted order parameters (WOPs)" that are analogous to "local field potentials" in neural systems. Since spatio-temporal coding is a mechanism that appears in olfactory systems, the developed learning rules may help to extract information from these neural ensembles.

Using Covariant Lyapunov Vectors to Understand Spatiotemporal Chaos in Fluids

Science.gov (United States)

Paul, Mark; Xu, Mu; Barbish, Johnathon; Mukherjee, Saikat

2017-11-01

The spatiotemporal chaos of fluids present many difficult and fascinating challenges. Recent progress in computing covariant Lyapunov vectors for a variety of model systems has made it possible to probe fundamental ideas from dynamical systems theory including the degree of hyperbolicity, the fractal dimension, the dimension of the inertial manifold, and the decomposition of the dynamics into a finite number of physical modes and spurious modes. We are interested in building upon insights such as these for fluid systems. We first demonstrate the power of covariant Lyapunov vectors using a system of maps on a lattice with a nonlinear coupling. We then compute the covariant Lyapunov vectors for chaotic Rayleigh-Bénard convection for experimentally accessible conditions. We show that chaotic convection is non-hyperbolic and we quantify the spatiotemporal features of the spectrum of covariant Lyapunov vectors. NSF DMS-1622299 and DARPA/DSO Models, Dynamics, and Learning (MoDyL).

Pattern formation and control of spatiotemporal chaos in a reaction diffusion prey–predator system supplying additional food

International Nuclear Information System (INIS)

Ghorai, Santu; Poria, Swarup

2016-01-01

Spatiotemporal dynamics of a predator–prey system in presence of spatial diffusion is investigated in presence of additional food exists for predators. Conditions for stability of Hopf as well as Turing patterns in a spatial domain are determined by making use of the linear stability analysis. Impact of additional food is clear from these conditions. Numerical simulation results are presented in order to validate the analytical findings. Finally numerical simulations are carried out around the steady state under zero flux boundary conditions. With the help of numerical simulations, the different types of spatial patterns (including stationary spatial pattern, oscillatory pattern, and spatiotemporal chaos) are identified in this diffusive predator–prey system in presence of additional food, depending on the quantity, quality of the additional food and the spatial domain and other parameters of the model. The key observation is that spatiotemporal chaos can be controlled supplying suitable additional food to predator. These investigations may be useful to understand complex spatiotemporal dynamics of population dynamical models in presence of additional food.

A Spatiotemporal Indexing Approach for Efficient Processing of Big Array-Based Climate Data with MapReduce

Science.gov (United States)

Li, Zhenlong; Hu, Fei; Schnase, John L.; Duffy, Daniel Q.; Lee, Tsengdar; Bowen, Michael K.; Yang, Chaowei

2016-01-01

Climate observations and model simulations are producing vast amounts of array-based spatiotemporal data. Efficient processing of these data is essential for assessing global challenges such as climate change, natural disasters, and diseases. This is challenging not only because of the large data volume, but also because of the intrinsic high-dimensional nature of geoscience data. To tackle this challenge, we propose a spatiotemporal indexing approach to efficiently manage and process big climate data with MapReduce in a highly scalable environment. Using this approach, big climate data are directly stored in a Hadoop Distributed File System in its original, native file format. A spatiotemporal index is built to bridge the logical array-based data model and the physical data layout, which enables fast data retrieval when performing spatiotemporal queries. Based on the index, a data-partitioning algorithm is applied to enable MapReduce to achieve high data locality, as well as balancing the workload. The proposed indexing approach is evaluated using the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA) climate reanalysis dataset. The experimental results show that the index can significantly accelerate querying and processing (10 speedup compared to the baseline test using the same computing cluster), while keeping the index-to-data ratio small (0.0328). The applicability of the indexing approach is demonstrated by a climate anomaly detection deployed on a NASA Hadoop cluster. This approach is also able to support efficient processing of general array-based spatiotemporal data in various geoscience domains without special configuration on a Hadoop cluster.

Adjusted functional boxplots for spatio-temporal data visualization and outlier detection

KAUST Repository

Sun, Ying; Genton, Marc G.

2011-01-01

This article proposes a simulation-based method to adjust functional boxplots for correlations when visualizing functional and spatio-temporal data, as well as detecting outliers. We start by investigating the relationship between the spatio

A Novel Image Encryption Algorithm Based on DNA Encoding and Spatiotemporal Chaos

Directory of Open Access Journals (Sweden)

Chunyan Song

2015-10-01

Full Text Available DNA computing based image encryption is a new, promising field. In this paper, we propose a novel image encryption scheme based on DNA encoding and spatiotemporal chaos. In particular, after the plain image is primarily diffused with the bitwise Exclusive-OR operation, the DNA mapping rule is introduced to encode the diffused image. In order to enhance the encryption, the spatiotemporal chaotic system is used to confuse the rows and columns of the DNA encoded image. The experiments demonstrate that the proposed encryption algorithm is of high key sensitivity and large key space, and it can resist brute-force attack, entropy attack, differential attack, chosen-plaintext attack, known-plaintext attack and statistical attack.

Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance

Directory of Open Access Journals (Sweden)

Sebastian Meyer

2017-05-01

Full Text Available The availability of geocoded health data and the inherent temporal structure of communicable diseases have led to an increased interest in statistical models and software for spatio-temporal data with epidemic features. The open source R package surveillance can handle various levels of aggregation at which infective events have been recorded: individual-level time-stamped geo-referenced data (case reports in either continuous space or discrete space, as well as counts aggregated by period and region. For each of these data types, the surveillance package implements tools for visualization, likelihoood inference and simulation from recently developed statistical regression frameworks capturing endemic and epidemic dynamics. Altogether, this paper is a guide to the spatio-temporal modeling of epidemic phenomena, exemplified by analyses of public health surveillance data on measles and invasive meningococcal disease.

State estimation of spatio-temporal phenomena

Science.gov (United States)

Yu, Dan

This dissertation addresses the state estimation problem of spatio-temporal phenomena which can be modeled by partial differential equations (PDEs), such as pollutant dispersion in the atmosphere. After discretizing the PDE, the dynamical system has a large number of degrees of freedom (DOF). State estimation using Kalman Filter (KF) is computationally intractable, and hence, a reduced order model (ROM) needs to be constructed first. Moreover, the nonlinear terms, external disturbances or unknown boundary conditions can be modeled as unknown inputs, which leads to an unknown input filtering problem. Furthermore, the performance of KF could be improved by placing sensors at feasible locations. Therefore, the sensor scheduling problem to place multiple mobile sensors is of interest. The first part of the dissertation focuses on model reduction for large scale systems with a large number of inputs/outputs. A commonly used model reduction algorithm, the balanced proper orthogonal decomposition (BPOD) algorithm, is not computationally tractable for large systems with a large number of inputs/outputs. Inspired by the BPOD and randomized algorithms, we propose a randomized proper orthogonal decomposition (RPOD) algorithm and a computationally optimal RPOD (RPOD*) algorithm, which construct an ROM to capture the input-output behaviour of the full order model, while reducing the computational cost of BPOD by orders of magnitude. It is demonstrated that the proposed RPOD* algorithm could construct the ROM in real-time, and the performance of the proposed algorithms on different advection-diffusion equations. Next, we consider the state estimation problem of linear discrete-time systems with unknown inputs which can be treated as a wide-sense stationary process with rational power spectral density, while no other prior information needs to be known. We propose an autoregressive (AR) model based unknown input realization technique which allows us to recover the input

Initial spatio-temporal domain expansion of the Modelfest database

Science.gov (United States)

Carney, Thom; Mozaffari, Sahar; Sun, Sean; Johnson, Ryan; Shirvastava, Sharona; Shen, Priscilla; Ly, Emma

2013-03-01

The first Modelfest group publication appeared in the SPIE Human Vision and Electronic Imaging conference proceedings in 1999. "One of the group's goals is to develop a public database of test images with threshold data from multiple laboratories for designing and testing HVS (Human Vision Models)." After extended discussions the group selected a set of 45 static images thought to best meet that goal and collected psychophysical detection data which is available on the WEB and presented in the 2000 SPIE conference proceedings. Several groups have used these datasets to test spatial modeling ideas. Further discussions led to the preliminary stimulus specification for extending the database into the temporal domain which was published in the 2002 conference proceeding. After a hiatus of 12 years, some of us have collected spatio-temporal thresholds on an expanded stimulus set of 41 video clips; the original specification included 35 clips. The principal change involved adding one additional spatial pattern beyond the three originally specified. The stimuli consisted of 4 spatial patterns, Gaussian Blob, 4 c/d Gabor patch, 11.3 c/d Gabor patch and a 2D white noise patch. Across conditions the patterns were temporally modulated over a range of approximately 0-25 Hz as well as temporal edge and pulse modulation conditions. The display and data collection specifications were as specified by the Modelfest groups in the 2002 conference proceedings. To date seven subjects have participated in this phase of the data collection effort, one of which also participated in the first phase of Modelfest. Three of the spatio-temporal stimuli were identical to conditions in the original static dataset. Small differences in the thresholds were evident and may point to a stimulus limitation. The temporal CSF peaked between 4 and 8 Hz for the 0 c/d (Gaussian blob) and 4 c/d patterns. The 4 c/d and 11.3 c/d Gabor temporal CSF was low pass while the 0 c/d pattern was band pass. This

A Spatio-Temporal Enhanced Metadata Model for Interdisciplinary Instant Point Observations in Smart Cities

Directory of Open Access Journals (Sweden)

Nengcheng Chen

2017-02-01

Full Text Available Due to the incomprehensive and inconsistent description of spatial and temporal information for city data observed by sensors in various fields, it is a great challenge to share the massive, multi-source and heterogeneous interdisciplinary instant point observation data resources. In this paper, a spatio-temporal enhanced metadata model for point observation data sharing was proposed. The proposed Data Meta-Model (DMM focused on the spatio-temporal characteristics and formulated a ten-tuple information description structure to provide a unified and spatio-temporal enhanced description of the point observation data. To verify the feasibility of the point observation data sharing based on DMM, a prototype system was established, and the performance improvement of Sensor Observation Service (SOS for the instant access and insertion of point observation data was realized through the proposed MongoSOS, which is a Not Only SQL (NoSQL SOS based on the MongoDB database and has the capability of distributed storage. For example, the response time of the access and insertion for navigation and positioning data can be realized at the millisecond level. Case studies were conducted, including the gas concentrations monitoring for the gas leak emergency response and the smart city public vehicle monitoring based on BeiDou Navigation Satellite System (BDS used for recording the dynamic observation information. The results demonstrated the versatility and extensibility of the DMM, and the spatio-temporal enhanced sharing for interdisciplinary instant point observations in smart cities.

Spatiotemporal Variability and Change of the South China Spring Precipitation during 1961–2012

Directory of Open Access Journals (Sweden)

Hong-Lan Liu

2015-01-01

Full Text Available We analyze precipitation data from 47 meteorological stations spanning between 1961 and 2012 and NCEP/NCAR reanalysis to understand spatiotemporal variability and change of spring precipitation of South China and their relations to atmospheric circulations. Empirical orthogonal function (EOF analysis and rotated EOF (REOF are used to reveal dominant spatial structures of precipitation anomaly and Mann-Kendall testing method to determine the temporal locations of abrupt changes during the analyzed time span. We find that the first spatial mode of the spring precipitation of the South China has a domain uniform structure; the second is dominated by a spatial dipole; and the third contains six variability centers. 1980s was the decade of the largest amount of precipitation while 1960s the decade of the smallest amount of precipitation. The spring precipitation also appeared to have a decreasing trend since 2000. We also find that spring precipitation of the South China has experienced a few abrupt changes: sudden increment at 1964, sudden decrement at 2002, and sudden increment at 1995. In addition to these abrupt changes, the precipitation could also be characterized by variability of multiple temporal scales, with dominant periodicities of 4 years, 8 years, and 14 years. The South China spring precipitation is also closely tied to the atmospheric circulations: when Aleutian Low strengthens, westerly weakens, and the center of the Western Pacific subtropical high shifts southeastward in the early spring; and the South China precipitation tends to be abundant (positive anomaly. In contrast, when Ural ridge strengthens, the southern branch of the East Asian trough weakens and the Western Pacific subtropical high shifts northwestward in the early spring, the South China precipitation tends to be reduced (negative anomaly.

Spatiotemporal distribution patterns of forest fires in northern Mexico

Science.gov (United States)

Gustavo Pérez-Verdin; M. A. Márquez-Linares; A. Cortes-Ortiz; M. Salmerón-Macias

2013-01-01

Using the 2000-2011 CONAFOR databases, a spatiotemporal analysis of the occurrence of forest fires in Durango, one of the most affected States in Mexico, was conducted. The Moran's index was used to determine a spatial distribution pattern; also, an analysis of seasonal and temporal autocorrelation of the data collected was completed. The geographically weighted...

Mode locking and spatiotemporal chaos in periodically driven Gunn diodes

DEFF Research Database (Denmark)

Mosekilde, Erik; Feldberg, Rasmus; Knudsen, Carsten

1990-01-01

oscillation entrains with the external signal. This produces a devil’s staircase of frequency-locked solutions. At higher microwave amplitudes, period doubling and other forms of mode-converting bifurcations can be seen. In this interval the diode also exhibits spatiotemporal chaos. At still higher microwave...

Spatiotemporal dynamics of the HIV-1 subtype G epidemic in West and Central Africa.

Science.gov (United States)

Delatorre, Edson; Mir, Daiana; Bello, Gonzalo

2014-01-01

The human immunodeficiency virus type 1 (HIV-1) subtype G is the second most prevalent HIV-1 clade in West Africa, accounting for nearly 30% of infections in the region. There is no information about the spatiotemporal dynamics of dissemination of this HIV-1 clade in Africa. To this end, we analyzed a total of 305 HIV-1 subtype G pol sequences isolated from 11 different countries from West and Central Africa over a period of 20 years (1992 to 2011). Evolutionary, phylogeographic and demographic parameters were jointly estimated from sequence data using a Bayesian coalescent-based method. Our analyses indicate that subtype G most probably emerged in Central Africa in 1968 (1956-1976). From Central Africa, the virus was disseminated to West and West Central Africa at multiple times from the middle 1970s onwards. Two subtype G strains probably introduced into Nigeria and Togo between the middle and the late 1970s were disseminated locally and to neighboring countries, leading to the origin of two major western African clades (G WA-I and G WA-II). Subtype G clades circulating in western and central African regions displayed an initial phase of exponential growth followed by a decline in growth rate since the early/middle 1990 s; but the mean epidemic growth rate of G WA-I (0.75 year-1) and G WA-II (0.95 year-1) clades was about two times higher than that estimated for central African lineages (0.47 year-1). Notably, the overall evolutionary and demographic history of G WA-I and G WA-II clades was very similar to that estimated for the CRF06_cpx clade circulating in the same region. These results support the notion that the spatiotemporal dissemination dynamics of major HIV-1 clades circulating in western Africa have probably been shaped by the same ecological factors.

Spatiotemporal optical pulse transformation by a resonant diffraction grating

Energy Technology Data Exchange (ETDEWEB)

Golovastikov, N. V.; Bykov, D. A., E-mail: bykovd@gmail.com; Doskolovich, L. L., E-mail: leonid@smr.ru; Soifer, V. A. [Russian Academy of Sciences, Image Processing Systems Institute (Russian Federation)

2015-11-15

The diffraction of a spatiotemporal optical pulse by a resonant diffraction grating is considered. The pulse diffraction is described in terms of the signal (the spatiotemporal incident pulse envelope) passage through a linear system. An analytic approximation in the form of a rational function of two variables corresponding to the angular and spatial frequencies has been obtained for the transfer function of the system. A hyperbolic partial differential equation describing the general form of the incident pulse envelope transformation upon diffraction by a resonant diffraction grating has been derived from the transfer function. A solution of this equation has been obtained for the case of normal incidence of a pulse with a central frequency lying near the guided-mode resonance of a diffraction structure. The presented results of numerical simulations of pulse diffraction by a resonant grating show profound changes in the pulse envelope shape that closely correspond to the proposed theoretical description. The results of the paper can be applied in creating new devices for optical pulse shape transformation, in optical information processing problems, and analog optical computations.

Time-Resolved and Spatio-Temporal Analysis of Complex Cognitive Processes and their Role in Disorders like Developmental Dyscalculia

Science.gov (United States)

Mórocz, István Akos; Janoos, Firdaus; van Gelderen, Peter; Manor, David; Karni, Avi; Breznitz, Zvia; von Aster, Michael; Kushnir, Tammar; Shalev, Ruth

2012-01-01

The aim of this article is to report on the importance and challenges of a time-resolved and spatio-temporal analysis of fMRI data from complex cognitive processes and associated disorders using a study on developmental dyscalculia (DD). Participants underwent fMRI while judging the incorrectness of multiplication results, and the data were analyzed using a sequence of methods, each of which progressively provided more a detailed picture of the spatio-temporal aspect of this disease. Healthy subjects and subjects with DD performed alike behaviorally though they exhibited parietal disparities using traditional voxel-based group analyses. Further and more detailed differences, however, surfaced with a time-resolved examination of the neural responses during the experiment. While performing inter-group comparisons, a third group of subjects with dyslexia (DL) but with no arithmetic difficulties was included to test the specificity of the analysis and strengthen the statistical base with overall fifty-eight subjects. Surprisingly, the analysis showed a functional dissimilarity during an initial reading phase for the group of dyslexic but otherwise normal subjects, with respect to controls, even though only numerical digits and no alphabetic characters were presented. Thus our results suggest that time-resolved multi-variate analysis of complex experimental paradigms has the ability to yield powerful new clinical insights about abnormal brain function. Similarly, a detailed compilation of aberrations in the functional cascade may have much greater potential to delineate the core processing problems in mental disorders. PMID:22368322

Spatiotemporal Variation of China’s State-Owned Construction Land Supply from 2003 to 2014

Directory of Open Access Journals (Sweden)

Min Jiang

2016-11-01

Full Text Available State-owned construction land is the dominant legal land source for construction in China and its supply influences urban expansion, house prices, and economic development, among other factors. Surprisingly, limited attention has been directly devoted to the spatiotemporal variation in land supply or the driving factors. This paper applied a centroid model and hotspot analysis, and created a newly increased construction land dependence-degree index (NCD to present the spatiotemporal variations of China’s construction land supply magnitude and pattern from 2003 to 2014, using land supply data from 339 cities. A two-way fixed effect model was introduced to reveal the influence of the socio-economic driving factors. The results showed that China’s state-owned construction land supply area (CLSA and newly increased construction land supply area (NCSA both increased during the period from 2003 to 2014, the geographic centroid of CLSA and NCSA moved northwest. NCD showed an overall increasing trend, and hotspots with high NCD migrated from the east region to the west region and shifted from an “east hot and west cold” pattern in 2003 to an “east cold and west hot” pattern in 2014. The gross domestic product (GDP has a U-shape effect on CLSA and NCD. The population, average annual wage of workers, and investment in fixed assets (fiv have positive effects on CLSA, and fiv also has a positive effect on NCD. The increasing ratio of tertiary industry added value to secondary industry added value reduces CLSA and NCD, and the effects of state policies vary from year to year. Different land supply policies should be implemented for cities in different development stages.

Mining User spatiotemporal Behavior in Geospatial Cyberinfrastructure --using GEOSS Clearinghouse as an example

Science.gov (United States)

XIA, J.; Yang, C.; Liu, K.; Huang, Q.; Li, Z.

2013-12-01

Big Data becomes increasingly important in almost all scientific domains, especially in geoscience where hundreds to millions of sensors are collecting data of the Earth continuously (Whitehouse News 2012). With the explosive growth of data, various Geospatial Cyberinfrastructure (GCI) (Yang et al. 2010) components are developed to manage geospatial resources and provide data access for the public. These GCIs are accessed by different users intensively on a daily basis. However, little research has been done to analyze the spatiotemporal patterns of user behavior, which could be critical to the management of Big Data and the operation of GCIs (Yang et al. 2011). For example, the spatiotemporal distribution of end users helps us better arrange and locate GCI computing facilities. A better indexing and caching mechanism could be developed based on the spatiotemporal pattern of user queries. In this paper, we use GEOSS Clearinghouse as an example to investigate spatiotemporal patterns of user behavior in GCIs. The investigation results show that user behaviors are heterogeneous but with patterns across space and time. Identified patterns include (1) the high access frequency regions; (2) local interests; (3) periodical accesses and rush hours; (4) spiking access. Based on identified patterns, this presentation reports several solutions to better support the operation of the GEOSS Clearinghouse and other GCIs. Keywords: Big Data, EarthCube, CyberGIS, Spatiotemporal Thinking and Computing, Data Mining, User Behavior Reference: Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. 1996. Advances in knowledge discovery and data mining. Whitehouse. 2012. Obama administration unveils 'BIG DATA' initiative: announces $200 million in new R&D investments. Whitehouse. Retrieved from http://www.whitehouse.gov/sites/default/files/microsites/ostp/big_data_press_release_final_2.pdf [Accessed 14 June 2013] Yang, C., Wu, H., Huang, Q., Li, Z., & Li, J. 2011. Using spatial

Recapitulation of physiological spatiotemporal signals promotes in vitro formation of phenotypically stable human articular cartilage

Science.gov (United States)

Wei, Yiyong; Zhou, Bin; Bernhard, Jonathan; Robinson, Samuel; Burapachaisri, Aonnicha; Guo, X. Edward

2017-01-01

Standard isotropic culture fails to recapitulate the spatiotemporal gradients present during native development. Cartilage grown from human mesenchymal stem cells (hMSCs) is poorly organized and unstable in vivo. We report that human cartilage with physiologic organization and in vivo stability can be grown in vitro from self-assembling hMSCs by implementing spatiotemporal regulation during induction. Self-assembling hMSCs formed cartilage discs in Transwell inserts following isotropic chondrogenic induction with transforming growth factor β to set up a dual-compartment culture. Following a switch in the basal compartment to a hypertrophic regimen with thyroxine, the cartilage discs underwent progressive deep-zone hypertrophy and mineralization. Concurrent chondrogenic induction in the apical compartment enabled the maintenance of functional and hyaline cartilage. Cartilage homeostasis, chondrocyte maturation, and terminal differentiation markers were all up-regulated versus isotropic control groups. We assessed the in vivo stability of the cartilage formed under different induction regimens. Cartilage formed under spatiotemporal regulation in vitro resisted endochondral ossification, retained the expression of cartilage markers, and remained organized following s.c. implantation in immunocompromised mice. In contrast, the isotropic control groups underwent endochondral ossification. Cartilage formed from hMSCs remained stable and organized in vivo. Spatiotemporal regulation during induction in vitro recapitulated some aspects of native cartilage development, and potentiated the maturation of self-assembling hMSCs into stable and organized cartilage resembling the native articular cartilage. PMID:28228529

Spatiotemporal multiscaling analysis of impurity transport in plasma turbulence using proper orthogonal decomposition

International Nuclear Information System (INIS)

Futatani, S.; Benkadda, S.; Del-Castillo-Negrete, D.

2009-01-01

The spatiotemporal multiscale dynamics of the turbulent transport of impurities is studied in the context of the collisional drift wave turbulence. Two turbulence regimes are considered: a quasihydrodynamic regime and a quasiadiabatic regime. The impurity is assumed to be a passive scalar advected by the corresponding ExB turbulent flow in the presence of diffusion. Two mixing scenarios are studied: a freely decaying case, and a forced case in which the scalar is forced by an externally imposed gradient. The results of the direct numerical simulations are analyzed using proper orthogonal decomposition (POD) techniques. The multiscale analysis is based on a space-time separable POD of the impurity field. The low rank spatial POD eigenfunctions capture the large scale coherent structures and the high rank eigenfunctions capture the small scale fluctuations. The temporal evolution at each scale is dictated by the corresponding temporal POD eigenfunctions. Contrary to the decaying case in which the POD spectrum decays fast, the spectrum in the forced case is relatively flat. The most striking difference between these two mixing scenarios is in the temporal dynamics of the small scale structures. In the decaying case the POD reveals the presence of 'bursty' dynamics in which successively small (high POD rank) scales are intermittently activated during the mixing process. On the other hand, in the forced simulations the temporal dynamics exhibits stationary fluctuations. Spatial intermittency or 'patchiness' in the mixing process characterizes the distribution of the passive tracer in the decaying quasihydrodynamic regime. In particular, in this case the probability distribution function of the low rank POD spatial reconstruction error is non-Gaussian. The spatiotemporal POD scales exhibit a diffusive-type scaling in the quasiadiabatic regime. However, this scaling seems to be absent in the quasihydrodynamic regime that shows no scaling (in the decaying case) or two

Various sizes of sliding event bursts in the plastic flow of metallic glasses based on a spatiotemporal dynamic model

Energy Technology Data Exchange (ETDEWEB)

Ren, Jingli, E-mail: renjl@zzu.edu.cn, E-mail: g.wang@shu.edu.cn; Chen, Cun [School of Mathematics and Statistics, Zhengzhou University, Zhengzhou 450001 (China); Wang, Gang, E-mail: renjl@zzu.edu.cn, E-mail: g.wang@shu.edu.cn [Laboratory for Microstructures, Shanghai University, Shanghai 200444 (China); Cheung, Wing-Sum [Department of Mathematics, The University of HongKong, HongKong (China); Sun, Baoan; Mattern, Norbert [IFW-dresden, Institute for Complex Materials, P.O. Box 27 01 16, D-01171 Dresden (Germany); Siegmund, Stefan [Department of Mathematics, TU Dresden, D-01062 Dresden (Germany); Eckert, Jürgen [IFW-dresden, Institute for Complex Materials, P.O. Box 27 01 16, D-01171 Dresden (Germany); Institute of Materials Science, TU Dresden, D-01062 Dresden (Germany)

2014-07-21

This paper presents a spatiotemporal dynamic model based on the interaction between multiple shear bands in the plastic flow of metallic glasses during compressive deformation. Various sizes of sliding events burst in the plastic deformation as the generation of different scales of shear branches occurred; microscopic creep events and delocalized sliding events were analyzed based on the established model. This paper discusses the spatially uniform solutions and traveling wave solution. The phase space of the spatially uniform system applied in this study reflected the chaotic state of the system at a lower strain rate. Moreover, numerical simulation showed that the microscopic creep events were manifested at a lower strain rate, whereas the delocalized sliding events were manifested at a higher strain rate.

Spatiotemporal object history affects the selection of task-relevant properties

NARCIS (Netherlands)

Schreij, D.B.B.; Olivers, C.N.L.

2013-01-01

For stable perception, we maintain mental representations of objects across space and time. Whatinformation is linked to such a representation? In this study, we extended our work showing that the spatiotemporal history of an object affects the way the object is attended the next time it is

Spatiotemporal synchronization of drift waves in a magnetron sputtering plasma

Czech Academy of Sciences Publication Activity Database

Martines, E.; Zuin, M.; Cavazzana, R.; Adámek, Jiří; Antoni, V.; Serianni, G.; Spolaore, M.; Vianello, N.

2014-01-01

Roč. 21, č. 10 (2014), s. 102309-102309 ISSN 1070-664X Institutional support: RVO:61389021 Keywords : Drift waves * Magnetron sputtering plasma * Spatiotemporal synchronization Subject RIV: BL - Plasma and Gas Discharge Physics Impact factor: 2.142, year: 2014 http://dx.doi.org/10.1063/1.4898693

Spatiotemporal electromagnetic soliton and spatial ring formation in nonlinear metamaterials

International Nuclear Information System (INIS)

Zhang Jinggui; Wen Shuangchun; Xiang Yuanjiang; Wang Youwen; Luo Hailu

2010-01-01

We present a systematic investigation of ultrashort electromagnetic pulse propagation in metamaterials (MMs) with simultaneous cubic electric and magnetic nonlinearity. We predict that spatiotemporal electromagnetic solitons may exist in the positive-index region of a MM with focusing nonlinearity and anomalous group velocity dispersion (GVD), as well as in the negative-index region of the MM with defocusing nonlinearity and normal GVD. The experimental circumstances for generating and manipulating spatiotemporal electromagnetic solitons can be created by elaborating appropriate MMs. In addition, we find that, in the negative-index region of a MM, a spatial ring may be formed as the electromagnetic pulse propagates for focusing nonlinearity and anomalous GVD; while the phenomenon of temporal splitting of the electromagnetic pulse may appear for the same case except for the defocusing nonlinearity. Finally, we demonstrate that the nonlinear magnetization makes the sign of effective electric nonlinear effect switchable due to the combined action of electric and magnetic nonlinearity, exerting a significant influence on the propagation of electromagnetic pulses.

Predictive spatio-temporal model for spatially sparse global solar radiation data

International Nuclear Information System (INIS)

André, Maïna; Dabo-Niang, Sophie; Soubdhan, Ted; Ould-Baba, Hanany

2016-01-01

This paper introduces a new approach for the forecasting of solar radiation series at a located station for very short time scale. We built a multivariate model in using few stations (3 stations) separated with irregular distances from 26 km to 56 km. The proposed model is a spatio temporal vector autoregressive VAR model specifically designed for the analysis of spatially sparse spatio-temporal data. This model differs from classic linear models in using spatial and temporal parameters where the available predictors are the lagged values at each station. A spatial structure of stations is defined by the sequential introduction of predictors in the model. Moreover, an iterative strategy in the process of our model will select the necessary stations removing the uninteresting predictors and also selecting the optimal p-order. We studied the performance of this model. The metric error, the relative root mean squared error (rRMSE), is presented at different short time scales. Moreover, we compared the results of our model to simple and well known persistence model and those found in literature. - Highlights: • A spatio-temporal VAR forecast model is used for spatially sparse data solar. • Lags and locations are selected by an optimization strategy. • Definition of spatial ordering of predictors influences forecasting results. • The model shows a better performance predictive at 30 min ahead in our context. • Benchmarking study shows a more accurate forecast at 1 h ahead with spatio-temporal VAR.

Spatiotemporal High-Resolution Cloud Mapping with a Ground-Based IR Scanner

NARCIS (Netherlands)

Brede, Benjamin; Thies, Boris; Bendix, Jörg; Feister, Uwe

2017-01-01

The high spatiotemporal variability of clouds requires automated monitoring systems. This study presents a retrieval algorithm that evaluates observations of a hemispherically scanning thermal infrared radiometer, the NubiScope, to produce georeferenced, spatially explicit cloud maps. The algorithm

Benefits of spatiotemporal modeling for short-term wind power forecasting at both individual and aggregated levels

DEFF Research Database (Denmark)

Lenzi, Amanda; Steinsland, Ingelin; Pinson, Pierre

2018-01-01

The share of wind energy in total installed power capacity has grown rapidly in recent years. Producing accurate and reliable forecasts of wind power production, together with a quantification of the uncertainty, is essential to optimally integrate wind energy into power systems. We build...... spatiotemporal models for wind power generation and obtain full probabilistic forecasts from 15 min to 5 h ahead. Detailed analyses of forecast performances on individual wind farms and aggregated wind power are provided. The predictions from our models are evaluated on a data set from wind farms in western...... Denmark using a sliding window approach, for which estimation is performed using only the last available measurements. The case study shows that it is important to have a spatiotemporal model instead of a temporal one to achieve calibrated aggregated forecasts. Furthermore, spatiotemporal models have...

A model for optimizing file access patterns using spatio-temporal parallelism

Energy Technology Data Exchange (ETDEWEB)

Boonthanome, Nouanesengsy [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Patchett, John [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Geveci, Berk [Kitware Inc., Clifton Park, NY (United States); Ahrens, James [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Bauer, Andy [Kitware Inc., Clifton Park, NY (United States); Chaudhary, Aashish [Kitware Inc., Clifton Park, NY (United States); Miller, Ross G. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Shipman, Galen M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Williams, Dean N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2013-01-01

For many years now, I/O read time has been recognized as the primary bottleneck for parallel visualization and analysis of large-scale data. In this paper, we introduce a model that can estimate the read time for a file stored in a parallel filesystem when given the file access pattern. Read times ultimately depend on how the file is stored and the access pattern used to read the file. The file access pattern will be dictated by the type of parallel decomposition used. We employ spatio-temporal parallelism, which combines both spatial and temporal parallelism, to provide greater flexibility to possible file access patterns. Using our model, we were able to configure the spatio-temporal parallelism to design optimized read access patterns that resulted in a speedup factor of approximately 400 over traditional file access patterns.

Spatiotemporal image correlation spectroscopy measurements of flow demonstrated in microfluidic channels

Science.gov (United States)

Rossow, Molly; Mantulin, William W.; Gratton, Enrico

2009-03-01

Accurate blood flow measurements during surgery can improve an operation's chance of success. We developed near-infrared spatio-temporal image spectroscopy (NIR-STICS), which has the potential to make blood flow measurements that are difficult to accomplish with existing methods. Specifically, we propose the technique and we show feasibility on phantom measurements. NIR-STICS has the potential of measuring the fluid velocity in small blood vessels (less than 1 mm in diameter) and of creating a map of blood flow rates over an area of approximately 1 cm2. NIR-STICS employs near-infrared spectroscopy to probe inside blood vessel walls and spatiotemporal image correlation spectroscopy to directly-without the use of a model-extract fluid velocity from the fluctuations within an image. We present computer simulations and experiments on a phantom system that demonstrate the effectiveness of NIR-STICS.

Spatio-temporal variability of ichthyophagous bird assemblage around western Mediterranean open-sea cage fish farms.

Science.gov (United States)

Aguado-Giménez, Felipe; Eguía-Martínez, Sergio; Cerezo-Valverde, Jesús; García-García, Benjamín

2018-06-14

Ichthyophagous birds aggregate at cage fish farms attracted by caged and associated wild fish. Spatio-temporal variability of such birds was studied for a year through seasonal visual counts at eight farms in the western Mediterranean. Correlation with farm and location descriptors was assessed. Considerable spatio-temporal variability in fish-eating bird density and assemblage structure was observed among farms and seasons. Bird density increased from autumn to winter, with the great cormorant being the most abundant species, also accounting largely for differences among farms. Grey heron and little egret were also numerous at certain farms during the coldest seasons. Cattle egret was only observed at one farm. No shags were observed during winter. During spring and summer, bird density decreased markedly and only shags and little egrets were observed at only a few farms. Season and distance from farms to bird breeding/wintering grounds helped to explain some of the spatio-temporal variability. Copyright © 2018 Elsevier Ltd. All rights reserved.

Bayesian Spatiotemporal Analysis of Socio-Ecologic Drivers of Ross River Virus Transmission in Queensland, Australia

Science.gov (United States)

Hu, Wenbiao; Clements, Archie; Williams, Gail; Tong, Shilu; Mengersen, Kerrie

2010-01-01

This study aims to examine the impact of socio-ecologic factors on the transmission of Ross River virus (RRV) infection and to identify areas prone to social and ecologic-driven epidemics in Queensland, Australia. We used a Bayesian spatiotemporal conditional autoregressive model to quantify the relationship between monthly variation of RRV incidence and socio-ecologic factors and to determine spatiotemporal patterns. Our results show that the average increase in monthly RRV incidence was 2.4% (95% credible interval (CrI): 0.1–4.5%) and 2.0% (95% CrI: 1.6–2.3%) for a 1°C increase in monthly average maximum temperature and a 10 mm increase in monthly average rainfall, respectively. A significant spatiotemporal variation and interactive effect between temperature and rainfall on RRV incidence were found. No association between Socio-economic Index for Areas (SEIFA) and RRV was observed. The transmission of RRV in Queensland, Australia appeared to be primarily driven by ecologic variables rather than social factors. PMID:20810846

Spatio-temporal variability of the deposited radioactive materials in forest environments after the Fukushima Daiichi NPP accident

Science.gov (United States)

Kato, H.; Onda, Y.; Komatsu, Y.; Yoda, H.

2012-12-01

Soil, vegetation and other ecological compartments are expected to be highly contaminated by the deposited radionuclides after the Fukushima Daiichi nuclear power plant (NPP) accident triggered by a magnitude 9.1 earthquake and the resulting tsunami on Marchi 11, 2011. Study site have been established in Yamakiya district, Kawamata Town, Fukushima prefecture, located about 35 km from Fukushima power plant, and designated as the evacuated zone. The total deposition of radioactive materials at the study site ranged from 0.02to >10 M Bq/m2 for Cs-137. The mature cedar, young cedar, and broad-leaf stands were selected as experimental site for the monitoring of spatio-temporal variability of the deposited radionuclides after the accidental release of radioactive materials. In order to measure the vertical distribution of radioactivity in forest, a tower with the same height of tree have been established at each experimental site. The measurement of radioactivity by using a portable Ge gamma-ray detector (Detective-DX-100, Ortec) and radionuclide analysis of leaf samples at different height revealed that a large proportion of radionuclides which deposited on forest were trapped by canopies of the cedar forests. In contrast, in the broad-leaf forest highest radioactivity was found at the forest floor. Furthermore, spatio-temporal variability of radioactivity at the forest floor indicated that huge amount of caesium still remains on the canopy of coniferous forest, and subsequently transfers to forest floor in association with throughfall, stemflow, and litter fall.

Full-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets

KAUST Repository

Zhang, Bohai; Sang, Huiyan; Huang, Jianhua Z.

2014-01-01

of dataset and application of such models is not feasible for large datasets. This article extends the full-scale approximation (FSA) approach by Sang and Huang (2012) to the spatio-temporal context to reduce computational complexity. A reversible jump Markov

Precise-spike-driven synaptic plasticity: learning hetero-association of spatiotemporal spike patterns.

Directory of Open Access Journals (Sweden)

Qiang Yu

Full Text Available A new learning rule (Precise-Spike-Driven (PSD Synaptic Plasticity is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe.

Theta variation and spatiotemporal scaling along the septotemporal axis of the hippocampus

Directory of Open Access Journals (Sweden)

Lauren L Long

2015-03-01

Full Text Available Hippocampal theta has been related to locomotor speed, attention, anxiety, sensorimotor integration and memory among other emergent phenomena. One difficulty in understanding the function of theta is that the hippocampus (HPC modulates voluntary behavior at the same time that it processes sensory input. Both functions are correlated with characteristic changes in theta indices. The current review highlights a series of studies examining theta local field potential (LFP signals across the septotemporal or longitudinal axis of the HPC. While the theta signal is coherent throughout the entirety of the HPC, the amplitude, but not the frequency, of theta varies significantly across its three-dimensional expanse. We suggest that the theta signal offers a rich vein of information about how distributed neuronal ensembles support emergent function. Further, we speculate that emergent function across the long axis varies with respect to spatiotemporal scale. Thus, septal hippocampus processes details of the proximal spatiotemporal environment while more temporal aspects process larger spaces and wider time-scales. The degree to which emergent functions are supported by the synchronization of theta across the septotemporal axis is an open question. Our working model is that theta synchrony serves to bind ensembles representing varying resolutions of spatiotemporal information at interdependent septotemporal areas of the HPC. Such synchrony and cooperative interactions along the septotemporal axis likely support memory formation and subsequent consolidation and retrieval.

Precise-spike-driven synaptic plasticity: learning hetero-association of spatiotemporal spike patterns.

Science.gov (United States)

Yu, Qiang; Tang, Huajin; Tan, Kay Chen; Li, Haizhou

2013-01-01

A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe.

Emergence of spatiotemporal chaos arising from far-field breakup of spiral waves in the plankton ecological systems

International Nuclear Information System (INIS)

Quan-Xing, Liu; Gui-Quan, Sun; Zhen, Jin; Bai-Lian, Li

2009-01-01

It has been reported that the minimal spatially extended phytoplankton–zooplankton system exhibits both temporal regular/chaotic behaviour, and spatiotemporal chaos in a patchy environment. As a further investigation by means of computer simulations and theoretical analysis, in this paper we observe that the spiral waves may exist and the spatiotemporal chaos emerge when the parameters are within the mixed Turing–Hopf bifurcation region, which arises from the far-field breakup of the spiral waves over a large range of diffusion coefficients of phytoplankton and zooplankton. Moreover, the spatiotemporal chaos arising from the far-field breakup of spiral waves does not gradually invade the whole space of that region. Our results are confirmed by nonlinear bifurcation of wave trains. We also discuss ecological implications of these spatially structured patterns. (general)

Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts

International Nuclear Information System (INIS)

Fonseca, Jimeno A.; Schlueter, Arno

2015-01-01

Highlights: • A model to describe spatiotemporal building energy demand patterns was developed. • The model integrates existing methods in urban and energy planning domains. • The model is useful to analyze energy efficiency strategies in neighborhoods. • Applicability in educational, urban and energy planning practices was found. - Abstract: We introduce an integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts. The model addresses the need for a comprehensive method to identify present and potential states of building energy consumption in the context of urban transformation. The focus lies on determining the spatiotemporal variability of energy services in both standing and future buildings in the residential, commercial and industrial sectors. This detailed characterization facilitates the assessment of potential energy efficiency measures at the neighborhood and city district scales. In a novel approach we integrated existing methods in urban and energy planning domains such as spatial analysis, dynamic building energy modeling and energy mapping to provide a comprehensive, multi-scale and multi-dimensional model of analysis. The model is part of a geographic information system (GIS), which serves as a platform for the allocation and future dissemination of spatiotemporal data. The model is validated against measured data and a peer model for a city district in Switzerland. In this context, we present practical applications in the analysis of energy efficiency measures in buildings and urban zoning. We furthermore discuss potential applications in educational, urban and energy planning practices

Clinical performance of a free-breathing spatiotemporally accelerated 3-D time-resolved contrast-enhanced pediatric abdominal MR angiography.

Science.gov (United States)

Zhang, Tao; Yousaf, Ufra; Hsiao, Albert; Cheng, Joseph Y; Alley, Marcus T; Lustig, Michael; Pauly, John M; Vasanawala, Shreyas S

2015-10-01

Pediatric contrast-enhanced MR angiography is often limited by respiration, other patient motion and compromised spatiotemporal resolution. To determine the reliability of a free-breathing spatiotemporally accelerated 3-D time-resolved contrast-enhanced MR angiography method for depicting abdominal arterial anatomy in young children. With IRB approval and informed consent, we retrospectively identified 27 consecutive children (16 males and 11 females; mean age: 3.8 years, range: 14 days to 8.4 years) referred for contrast-enhanced MR angiography at our institution, who had undergone free-breathing spatiotemporally accelerated time-resolved contrast-enhanced MR angiography studies. A radio-frequency-spoiled gradient echo sequence with Cartesian variable density k-space sampling and radial view ordering, intrinsic motion navigation and intermittent fat suppression was developed. Images were reconstructed with soft-gated parallel imaging locally low-rank method to achieve both motion correction and high spatiotemporal resolution. Quality of delineation of 13 abdominal arteries in the reconstructed images was assessed independently by two radiologists on a five-point scale. Ninety-five percent confidence intervals of the proportion of diagnostically adequate cases were calculated. Interobserver agreements were also analyzed. Eleven out of 13 arteries achieved acceptable image quality (mean score range: 3.9-5.0) for both readers. Fair to substantial interobserver agreement was reached on nine arteries. Free-breathing spatiotemporally accelerated 3-D time-resolved contrast-enhanced MR angiography frequently yields diagnostic image quality for most abdominal arteries in young children.

Clinical performance of a free-breathing spatiotemporally accelerated 3-D time-resolved contrast-enhanced pediatric abdominal MR angiography

Energy Technology Data Exchange (ETDEWEB)

Zhang, Tao; Cheng, Joseph Y. [Stanford University, Department of Radiology, Stanford, CA (United States); Stanford University, Department of Electrical Engineering, Stanford, CA (United States); Yousaf, Ufra; Alley, Marcus T.; Vasanawala, Shreyas S. [Stanford University, Department of Radiology, Stanford, CA (United States); Hsiao, Albert [University of California, San Diego, Department of Radiology, San Diego, CA (United States); Lustig, Michael [Stanford University, Department of Electrical Engineering, Stanford, CA (United States); University of California, Berkeley, Department of Electrical Engineering and Computer Sciences, Berkeley, CA (United States); Pauly, John M. [Stanford University, Department of Electrical Engineering, Stanford, CA (United States)

2015-10-15

Pediatric contrast-enhanced MR angiography is often limited by respiration, other patient motion and compromised spatiotemporal resolution. To determine the reliability of a free-breathing spatiotemporally accelerated 3-D time-resolved contrast-enhanced MR angiography method for depicting abdominal arterial anatomy in young children. With IRB approval and informed consent, we retrospectively identified 27 consecutive children (16 males and 11 females; mean age: 3.8 years, range: 14 days to 8.4 years) referred for contrast-enhanced MR angiography at our institution, who had undergone free-breathing spatiotemporally accelerated time-resolved contrast-enhanced MR angiography studies. A radio-frequency-spoiled gradient echo sequence with Cartesian variable density k-space sampling and radial view ordering, intrinsic motion navigation and intermittent fat suppression was developed. Images were reconstructed with soft-gated parallel imaging locally low-rank method to achieve both motion correction and high spatiotemporal resolution. Quality of delineation of 13 abdominal arteries in the reconstructed images was assessed independently by two radiologists on a five-point scale. Ninety-five percent confidence intervals of the proportion of diagnostically adequate cases were calculated. Interobserver agreements were also analyzed. Eleven out of 13 arteries achieved acceptable image quality (mean score range: 3.9-5.0) for both readers. Fair to substantial interobserver agreement was reached on nine arteries. Free-breathing spatiotemporally accelerated 3-D time-resolved contrast-enhanced MR angiography frequently yields diagnostic image quality for most abdominal arteries in young children. (orig.)

Clinical performance of a free-breathing spatiotemporally accelerated 3-D time-resolved contrast-enhanced pediatric abdominal MR angiography

International Nuclear Information System (INIS)

Zhang, Tao; Cheng, Joseph Y.; Yousaf, Ufra; Alley, Marcus T.; Vasanawala, Shreyas S.; Hsiao, Albert; Lustig, Michael; Pauly, John M.

2015-01-01

Pediatric contrast-enhanced MR angiography is often limited by respiration, other patient motion and compromised spatiotemporal resolution. To determine the reliability of a free-breathing spatiotemporally accelerated 3-D time-resolved contrast-enhanced MR angiography method for depicting abdominal arterial anatomy in young children. With IRB approval and informed consent, we retrospectively identified 27 consecutive children (16 males and 11 females; mean age: 3.8 years, range: 14 days to 8.4 years) referred for contrast-enhanced MR angiography at our institution, who had undergone free-breathing spatiotemporally accelerated time-resolved contrast-enhanced MR angiography studies. A radio-frequency-spoiled gradient echo sequence with Cartesian variable density k-space sampling and radial view ordering, intrinsic motion navigation and intermittent fat suppression was developed. Images were reconstructed with soft-gated parallel imaging locally low-rank method to achieve both motion correction and high spatiotemporal resolution. Quality of delineation of 13 abdominal arteries in the reconstructed images was assessed independently by two radiologists on a five-point scale. Ninety-five percent confidence intervals of the proportion of diagnostically adequate cases were calculated. Interobserver agreements were also analyzed. Eleven out of 13 arteries achieved acceptable image quality (mean score range: 3.9-5.0) for both readers. Fair to substantial interobserver agreement was reached on nine arteries. Free-breathing spatiotemporally accelerated 3-D time-resolved contrast-enhanced MR angiography frequently yields diagnostic image quality for most abdominal arteries in young children. (orig.)

Physical inactivity displays a mediator role in the association of diabetes and poverty: A spatiotemporal analysis

Directory of Open Access Journals (Sweden)

Lung-Chang Chien

2017-11-01

Full Text Available Physical inactivity is one of the risk factors of diabetes. In addition, physical inactivity is attributed to urbanization-related factors, such as poverty, which is also one of the risk factors of diabetes. We hypothesized that physical inactivity is a mediator in the association between diabetes and poverty, and that spatial heterogeneity exists in these relationships. This study adopted a spatiotemporal modelling approach to conduct this mediator analysis. From 2004-2011, data were collected at the county level in 48 contiguous states (with a total of 3,109 counties from the Behavioral Risk Factor Surveillance System (BRFSS and American Community Survey. Poverty percentage significantly affected physical inactivity prevalence and diabetes prevalence in two separate models. Using a model with both physical inactivity and poverty percentages as independent variables, we verified that physical inactivity prevalence is a significant mediator. In this model, physical inactivity prevalence resulted in a significant positive association with diabetes prevalence, and the influence of poverty percentage on diabetes prevalence was significantly reduced (P=0.0009. An advanced spatiotemporal analysis revealed that 32.65% of counties having a significant positive association between diabetes prevalence and physical inactivity prevalence also had a significant positive association between physical inactivity prevalence and poverty percentage. Those counties were also likely located in the South and Southeast of USA. In summary, the findings of this study demonstrate the mediating effect of physical inactivity between diabetes and poverty. When implementing diabetes prevention in communities with higher poverty, appropriate strategies to reduce the cost burden of physical activity programmes should be considered.

Physical inactivity displays a mediator role in the association of diabetes and poverty: A spatiotemporal analysis.

Science.gov (United States)

Chien, Lung-Chang; Li, Xiao; Staudt, Amanda

2017-11-03

Physical inactivity is one of the risk factors of diabetes. In addition, physical inactivity is attributed to urbanization-related factors, such as poverty, which is also one of the risk factors of diabetes. We hypothesized that physical inactivity is a mediator in the association between diabetes and poverty, and that spatial heterogeneity exists in these relationships. This study adopted a spatiotemporal modelling approach to conduct this mediator analysis. From 2004-2011, data were collected at the county level in 48 contiguous states (with a total of 3,109 counties) from the Behavioral Risk Factor Surveillance System (BRFSS) and American Community Survey. Poverty percentage significantly affected physical inactivity prevalence and diabetes prevalence in two separate models. Using a model with both physical inactivity and poverty percentages as independent variables, we verified that physical inactivity prevalence is a significant mediator. In this model, physical inactivity prevalence resulted in a significant positive association with diabetes prevalence, and the influence of poverty percentage on diabetes prevalence was significantly reduced (P=0.0009). An advanced spatiotemporal analysis revealed that 32.65% of counties having a significant positive association between diabetes prevalence and physical inactivity prevalence also had a significant positive association between physical inactivity prevalence and poverty percentage. Those counties were also likely located in the South and Southeast of USA. In summary, the findings of this study demonstrate the mediating effect of physical inactivity between diabetes and poverty. When implementing diabetes prevention in communities with higher poverty, appropriate strategies to reduce the cost burden of physical activity programmes should be considered.

Joint level-set and spatio-temporal motion detection for cell segmentation.

Science.gov (United States)

Boukari, Fatima; Makrogiannis, Sokratis

2016-08-10

Cell segmentation is a critical step for quantification and monitoring of cell cycle progression, cell migration, and growth control to investigate cellular immune response, embryonic development, tumorigenesis, and drug effects on live cells in time-lapse microscopy images. In this study, we propose a joint spatio-temporal diffusion and region-based level-set optimization approach for moving cell segmentation. Moving regions are initially detected in each set of three consecutive sequence images by numerically solving a system of coupled spatio-temporal partial differential equations. In order to standardize intensities of each frame, we apply a histogram transformation approach to match the pixel intensities of each processed frame with an intensity distribution model learned from all frames of the sequence during the training stage. After the spatio-temporal diffusion stage is completed, we compute the edge map by nonparametric density estimation using Parzen kernels. This process is followed by watershed-based segmentation and moving cell detection. We use this result as an initial level-set function to evolve the cell boundaries, refine the delineation, and optimize the final segmentation result. We applied this method to several datasets of fluorescence microscopy images with varying levels of difficulty with respect to cell density, resolution, contrast, and signal-to-noise ratio. We compared the results with those produced by Chan and Vese segmentation, a temporally linked level-set technique, and nonlinear diffusion-based segmentation. We validated all segmentation techniques against reference masks provided by the international Cell Tracking Challenge consortium. The proposed approach delineated cells with an average Dice similarity coefficient of 89 % over a variety of simulated and real fluorescent image sequences. It yielded average improvements of 11 % in segmentation accuracy compared to both strictly spatial and temporally linked Chan

Spatiotemporal brain dynamics of emotional face processing modulations induced by the serotonin 1A/2A receptor agonist psilocybin.

Science.gov (United States)

Bernasconi, Fosco; Schmidt, André; Pokorny, Thomas; Kometer, Michael; Seifritz, Erich; Vollenweider, Franz X

2014-12-01

Emotional face processing is critically modulated by the serotonergic system. For instance, emotional face processing is impaired by acute psilocybin administration, a serotonin (5-HT) 1A and 2A receptor agonist. However, the spatiotemporal brain mechanisms underlying these modulations are poorly understood. Here, we investigated the spatiotemporal brain dynamics underlying psilocybin-induced modulations during emotional face processing. Electrical neuroimaging analyses were applied to visual evoked potentials in response to emotional faces, following psilocybin and placebo administration. Our results indicate a first time period of strength (i.e., Global Field Power) modulation over the 168-189 ms poststimulus interval, induced by psilocybin. A second time period of strength modulation was identified over the 211-242 ms poststimulus interval. Source estimations over these 2 time periods further revealed decreased activity in response to both neutral and fearful faces within limbic areas, including amygdala and parahippocampal gyrus, and the right temporal cortex over the 168-189 ms interval, and reduced activity in response to happy faces within limbic and right temporo-occipital brain areas over the 211-242 ms interval. Our results indicate a selective and temporally dissociable effect of psilocybin on the neuronal correlates of emotional face processing, consistent with a modulation of the top-down control. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Frequency Modulation and Spatiotemporal Stability of the sCPG in Preterm Infants with RDS

Directory of Open Access Journals (Sweden)

Steven M. Barlow

2012-01-01

Full Text Available The nonnutritive suck (NNS is an observable and accessible motor behavior which is often used to make inference about brain development and pre-feeding skill in preterm and term infants. The purpose of this study was to model NNS burst compression pressure dynamics in the frequency and time domain among two groups of preterm infants, including those with respiratory distress syndrome (RDS, N=15 and 17 healthy controls. Digitized samples of NNS compression pressure waveforms recorded at a 1-week interval were collected 15 minutes prior to a scheduled feed. Regression analysis and ANOVA revealed that healthy preterm infants produced longer NNS bursts and the mean burst initiation cycle frequencies were higher when compared to the RDS group. Moreover, the initial 5 cycles of the NNS burst manifest a frequency modulated (FM segment which is a significant feature of the suck central pattern generator (sCPG, and differentially expressed in healthy and RDS infants. The NNS burst structure revealed significantly lower spatiotemporal index values for control versus RDS preterm infants during FM, and provides additional information on the microstructure of the sCPG which may be used to gauge the developmental status and progression of oromotor control systems among these fragile infants.

Spatiotemporal dynamics of Bose-Einstein condensates in linear- and circular-chain optical lattices

International Nuclear Information System (INIS)

Tsukada, N.

2002-01-01

We investigate the spatiotemporal dynamics of Bose-Einstein condensates in optical lattices that have a linear-or a circular-chain configuration with the tunneling couplings between nearest-neighbor lattice sites. A discrete nonlinear Schroedinger equation has been solved for various initial conditions and for a definite range of repulsive and attractive interatomic interactions. It is shown that the diversity of the spatiotemporal dynamics of the atomic population distribution such as a macroscopic self-trapping, bright and dark solitons, and symmetry breaking is derived from the positive and negative interatomic interactions. For the circular-chain configuration, two types of rotational modes are obtained as we introduce a definite relation for the initial phase conditions

Scalable Top-k Spatio-Temporal Term Querying

DEFF Research Database (Denmark)

Skovsgaard, Anders; Sidlauskas, Darius; Jensen, Christian Søndergaard

2014-01-01

With the rapidly increasing deployment of Internet-connected, location-aware mobile devices, very large and increasing amounts of geo-tagged and timestamped user-generated content, such as microblog posts, are being generated. We present indexing, update, and query processing techniques...... that are capable of providing the top-k terms seen in posts in a user-specified spatio-temporal range. The techniques enable interactive response times in the millisecond range in a realistic setting where the arrival rate of posts exceeds today's average tweet arrival rate by a factor of 4-10. The techniques...

Interpolation of daily rainfall using spatiotemporal models and clustering

KAUST Repository

Militino, A. F.

2014-06-11

Accumulated daily rainfall in non-observed locations on a particular day is frequently required as input to decision-making tools in precision agriculture or for hydrological or meteorological studies. Various solutions and estimation procedures have been proposed in the literature depending on the auxiliary information and the availability of data, but most such solutions are oriented to interpolating spatial data without incorporating temporal dependence. When data are available in space and time, spatiotemporal models usually provide better solutions. Here, we analyse the performance of three spatiotemporal models fitted to the whole sampled set and to clusters within the sampled set. The data consists of daily observations collected from 87 manual rainfall gauges from 1990 to 2010 in Navarre, Spain. The accuracy and precision of the interpolated data are compared with real data from 33 automated rainfall gauges in the same region, but placed in different locations than the manual rainfall gauges. Root mean squared error by months and by year are also provided. To illustrate these models, we also map interpolated daily precipitations and standard errors on a 1km2 grid in the whole region. © 2014 Royal Meteorological Society.

Interpolation of daily rainfall using spatiotemporal models and clustering

KAUST Repository

Militino, A. F.; Ugarte, M. D.; Goicoa, T.; Genton, Marc G.

2014-01-01

Accumulated daily rainfall in non-observed locations on a particular day is frequently required as input to decision-making tools in precision agriculture or for hydrological or meteorological studies. Various solutions and estimation procedures have been proposed in the literature depending on the auxiliary information and the availability of data, but most such solutions are oriented to interpolating spatial data without incorporating temporal dependence. When data are available in space and time, spatiotemporal models usually provide better solutions. Here, we analyse the performance of three spatiotemporal models fitted to the whole sampled set and to clusters within the sampled set. The data consists of daily observations collected from 87 manual rainfall gauges from 1990 to 2010 in Navarre, Spain. The accuracy and precision of the interpolated data are compared with real data from 33 automated rainfall gauges in the same region, but placed in different locations than the manual rainfall gauges. Root mean squared error by months and by year are also provided. To illustrate these models, we also map interpolated daily precipitations and standard errors on a 1km2 grid in the whole region. © 2014 Royal Meteorological Society.

DSTiPE Algorithm for Fuzzy Spatio-Temporal Risk Calculation in Wireless Environments

Energy Technology Data Exchange (ETDEWEB)

Kurt Derr; Milos Manic

2008-09-01

Time and location data play a very significant role in a variety of factory automation scenarios, such as automated vehicles and robots, their navigation, tracking, and monitoring, to services of optimization and security. In addition, pervasive wireless capabilities combined with time and location information are enabling new applications in areas such as transportation systems, health care, elder care, military, emergency response, critical infrastructure, and law enforcement. A person/object in proximity to certain areas for specific durations of time may pose a risk hazard either to themselves, others, or the environment. This paper presents a novel fuzzy based spatio-temporal risk calculation DSTiPE method that an object with wireless communications presents to the environment. The presented Matlab based application for fuzzy spatio-temporal risk cluster extraction is verified on a diagonal vehicle movement example.

SPATIO-TEMPORAL CHARACTERISTICS OF RESIDENT TRIP BASED ON POI AND OD DATA OF FLOAT CAR IN BEIJING

OpenAIRE

N. Mou; N. Mou; J. Li; L. Zhang; W. Liu; Y. Xu

2017-01-01

Due to the influence of the urban inherent regional functional distribution, the daily activities of the residents presented some spatio-temporal patterns (periodic patterns, gathering patterns, etc.). In order to further understand the spatial and temporal characteristics of urban residents, this paper research takes the taxi trajectory data of Beijing as a sample data and studies the spatio-temporal characteristics of the residents' activities on the weekdays. At first, according t...

Quantile-based Bayesian maximum entropy approach for spatiotemporal modeling of ambient air quality levels.

Science.gov (United States)

Yu, Hwa-Lung; Wang, Chih-Hsin

2013-02-05

Understanding the daily changes in ambient air quality concentrations is important to the assessing human exposure and environmental health. However, the fine temporal scales (e.g., hourly) involved in this assessment often lead to high variability in air quality concentrations. This is because of the complex short-term physical and chemical mechanisms among the pollutants. Consequently, high heterogeneity is usually present in not only the averaged pollution levels, but also the intraday variance levels of the daily observations of ambient concentration across space and time. This characteristic decreases the estimation performance of common techniques. This study proposes a novel quantile-based Bayesian maximum entropy (QBME) method to account for the nonstationary and nonhomogeneous characteristics of ambient air pollution dynamics. The QBME method characterizes the spatiotemporal dependence among the ambient air quality levels based on their location-specific quantiles and accounts for spatiotemporal variations using a local weighted smoothing technique. The epistemic framework of the QBME method can allow researchers to further consider the uncertainty of space-time observations. This study presents the spatiotemporal modeling of daily CO and PM10 concentrations across Taiwan from 1998 to 2009 using the QBME method. Results show that the QBME method can effectively improve estimation accuracy in terms of lower mean absolute errors and standard deviations over space and time, especially for pollutants with strong nonhomogeneous variances across space. In addition, the epistemic framework can allow researchers to assimilate the site-specific secondary information where the observations are absent because of the common preferential sampling issues of environmental data. The proposed QBME method provides a practical and powerful framework for the spatiotemporal modeling of ambient pollutants.

Spatio-temporal changes in total annual rainfall and the annual number of rainy days

International Nuclear Information System (INIS)

Limjirakan, Sangchan; Limsakul, Atsamon

2007-01-01

Full text: Full text: Rainfall variability is a critical factor for Thailand's socioeconomic development. Thus, enhancing understanding of rainfall mechanisms and variability is of paramount importance for effective strategies in tackling the severe droughts/floods which are memorable and a recurring problem in Thailand. Through this study, we have examined the variability of total annual rainfall (R,otai) and the annual number of rainy days (Rday) in Thailand during 1951-2003, using an Empirical Orthogonal Function (EOF) analysis. The primary objective is to determine the dominant spatio-temporal patterns, and to illustrate their connection with the El Nino-Southern Oscillation (ENSO). The results reveal that the first two EOF modes, which explain nearly half of the total variance, show a good coherence of spatio-temporal structures. A salient feature of the leading modes of R,otal and Rday in Thailand is that their temporal coefficients exhibit significant relations to the ENSO. On an interannual timescale, the leading modes tended to be greater (lower) than normal during the La Nina (El Nino) phase of the ENSO. Changes in the Walker circulation appear to be the dominant mechanism whereby the ENSO exerts its influence on rainfall variability in Thailand. For an interdecadal timescale, there is evidence of the unusual and persistent deficit in Rtar accompanied by a concomitant reduction of Ranrd over the last three decades. The recent drought-like condition has been closely associated with the shift in the ENSO towards more El Nino events since the late 1970s, and coincided with the high global mean temperature. These natural/anthropogenic-induced climatic changes have important implications for rainfall forecasting, and consequently for water resource and agricultural planning and management in Thailand

Analysis and modelling of spatio-temporal properties of daily rainfall over the Danube basin

Science.gov (United States)

Serinaldi, F.; Kilsby, C. G.

2012-04-01

Central and Eastern Europe are prone to severe floods due to heavy rainfall that cause societal and economic damages, ranging from agriculture to water resources, from the insurance/reinsurance sector to the energy industry. To improve the flood risk analysis, a better characterisation and modelling of the rainfall patterns over this area, which involves the Danube river watershed, is strategically important. In this study, we analyse the spatio-temporal properties of a large data set of daily rainfall time series from 15 countries in the Central Eastern Europe through different lagged and non-lagged indices of associations that quantify both the overall dependence and extreme dependence of pairwise observations. We also show that these measures are linked to each other and can be written in a unique and coherent notation within the copula framework. Moreover, the lagged version of these measures allows exploring some important spatio-temporal properties of the rainfall fields. The exploratory analysis is complemented by the preliminary results of a spatio-temporal rainfall simulation performed via a compound model based upon the Generalized Additive Models for Location, Scale and Shape (GAMLSS) and meta-elliptical multivariate distributions.

The spatiotemporal system dynamics of acquired resistance in an engineered microecology.

Science.gov (United States)

Datla, Udaya Sree; Mather, William H; Chen, Sheng; Shoultz, Isaac W; Täuber, Uwe C; Jones, Caroline N; Butzin, Nicholas C

2017-11-22

Great strides have been made in the understanding of complex networks; however, our understanding of natural microecologies is limited. Modelling of complex natural ecological systems has allowed for new findings, but these models typically ignore the constant evolution of species. Due to the complexity of natural systems, unanticipated interactions may lead to erroneous conclusions concerning the role of specific molecular components. To address this, we use a synthetic system to understand the spatiotemporal dynamics of growth and to study acquired resistance in vivo. Our system differs from earlier synthetic systems in that it focuses on the evolution of a microecology from a killer-prey relationship to coexistence using two different non-motile Escherichia coli strains. Using empirical data, we developed the first ecological model emphasising the concept of the constant evolution of species, where the survival of the prey species is dependent on location (distance from the killer) or the evolution of resistance. Our simple model, when expanded to complex microecological association studies under varied spatial and nutrient backgrounds may help to understand the complex relationships between multiple species in intricate natural ecological networks. This type of microecological study has become increasingly important, especially with the emergence of antibiotic-resistant pathogens.

Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

Energy Technology Data Exchange (ETDEWEB)

Liu, Chao; Akintayo, Adedotun; Jiang, Zhanhong; Henze, Gregor P.; Sarkar, Soumik

2018-02-01

Non-intrusive load monitoring (NILM) of electrical demand for the purpose of identifying load components has thus far mostly been studied using univariate data, e.g., using only whole building electricity consumption time series to identify a certain type of end-use such as lighting load. However, using additional variables in the form of multivariate time series data may provide more information in terms of extracting distinguishable features in the context of energy disaggregation. In this work, a novel probabilistic graphical modeling approach, namely the spatiotemporal pattern network (STPN) is proposed for energy disaggregation using multivariate time-series data. The STPN framework is shown to be capable of handling diverse types of multivariate time-series to improve the energy disaggregation performance. The technique outperforms the state of the art factorial hidden Markov models (FHMM) and combinatorial optimization (CO) techniques in multiple real-life test cases. Furthermore, based on two homes' aggregate electric consumption data, a similarity metric is defined for the energy disaggregation of one home using a trained model based on the other home (i.e., out-of-sample case). The proposed similarity metric allows us to enhance scalability via learning supervised models for a few homes and deploying such models to many other similar but unmodeled homes with significantly high disaggregation accuracy.

Hierarchical Bayesian modeling of spatio-temporal patterns of lung cancer incidence risk in Georgia, USA: 2000-2007

Science.gov (United States)

Yin, Ping; Mu, Lan; Madden, Marguerite; Vena, John E.

2014-10-01

Lung cancer is the second most commonly diagnosed cancer in both men and women in Georgia, USA. However, the spatio-temporal patterns of lung cancer risk in Georgia have not been fully studied. Hierarchical Bayesian models are used here to explore the spatio-temporal patterns of lung cancer incidence risk by race and gender in Georgia for the period of 2000-2007. With the census tract level as the spatial scale and the 2-year period aggregation as the temporal scale, we compare a total of seven Bayesian spatio-temporal models including two under a separate modeling framework and five under a joint modeling framework. One joint model outperforms others based on the deviance information criterion. Results show that the northwest region of Georgia has consistently high lung cancer incidence risk for all population groups during the study period. In addition, there are inverse relationships between the socioeconomic status and the lung cancer incidence risk among all Georgian population groups, and the relationships in males are stronger than those in females. By mapping more reliable variations in lung cancer incidence risk at a relatively fine spatio-temporal scale for different Georgian population groups, our study aims to better support healthcare performance assessment, etiological hypothesis generation, and health policy making.

Cartography in the Age of Spatio-temporal Big Data

Directory of Open Access Journals (Sweden)

WANG Jiayao

2017-10-01

Full Text Available Cartography is an ancient science with almost the same long history as the world's oldest culture.Since ancient times,the movement and change of anything and any phenomena,including human activities,have been carried out in a certain time and space.The development of science and technology and the progress of social civilization have made social management and governance more and more dependent on time and space.The information source,theme,content,carrier,form,production methods and application methods of map are different in different historical periods,so that its all-round value is different. With the arrival of the big data age,the scientific paradigm has now entered the era of "data-intensive" paradigm,so is the cartography,with obvious characteristics of big data science.All big data are caused by movement and change of all things and phenomena in the geographic world,so they have space and time characteristics and thus cannot be separated from the spatial reference and time reference.Therefore,big data is big spatio-temporal data essentially.Since the late 1950s and early 1960s,modern cartography,that is,the cartography in the information age,takes spatio-temporal data as the object,and focuses on the processing and expression of spatio-temporal data,but not in the face of the large scale multi-source heterogeneous and multi-dimensional dynamic data flow(or flow datafrom sky to the sea.The real-time dynamic nature,the theme pertinence,the content complexity,the carrier diversification,the expression form personalization,the production method modernization,the application ubiquity of the map,is incomparable in the past period,which leads to the great changes of the theory,technology and application system of cartography.And all these changes happen to occur in the 60 years since the late 1950s and early 1960s,so this article was written to commemorate the 60th anniversary of the "Acta Geodaetica et Cartographica Sinica".

Spatio-temporal map generalizations with the hierarchical Voronoi data structure

DEFF Research Database (Denmark)

Mioc, Darka; Anton, François; Gold, Christopher M.

implemented in commercial GIS systems. In this research, we used the Voronoi spatial data model for map generalizations. We were able to demonstrate that the map generalization does not affect only spatial objects (points, lines or polygons), but also the events corresponding to the creation and modification...... their spatio-temporal characteristics and their dynamic behaviour....

A Spatiotemporal-Chaos-Based Encryption Having Overall Properties Considerably Better than Advanced Encryption Standard

Science.gov (United States)

Wang, Shi-Hong; Ye, Wei-Ping; Lü, Hua-Ping; Kuang, Jin-Yu; Li, Jing-Hua; Luo, Yun-Lun; Hu, Gang

2003-07-01

Spatiotemporal chaos of a two-dimensional one-way coupled map lattice is used for chaotic cryptography. The chaotic outputs of many space units are used for encryption simultaneously. This system shows satisfactory cryptographic properties of high security, fast encryption (decryption) speed, and robustness against noise disturbances in communication channel. The overall features of this spatiotemporal-chaos-based cryptosystem are better than chaotic cryptosystems known so far, and also than currently used conventional cryptosystems, such as the Advanced Encryption Standard (AES). The project supported by National Natural Science Foundation of China under Grant No. 10175010 and the Special Funds for Major State Basic Research Projects under Grant No. G2000077304

Discrete simulations of spatio-temporal dynamics of small water bodies under varied stream flow discharges

Science.gov (United States)

Daya Sagar, B. S.

2005-01-01

Spatio-temporal patterns of small water bodies (SWBs) under the influence of temporally varied stream flow discharge are simulated in discrete space by employing geomorphologically realistic expansion and contraction transformations. Cascades of expansion-contraction are systematically performed by synchronizing them with stream flow discharge simulated via the logistic map. Templates with definite characteristic information are defined from stream flow discharge pattern as the basis to model the spatio-temporal organization of randomly situated surface water bodies of various sizes and shapes. These spatio-temporal patterns under varied parameters (λs) controlling stream flow discharge patterns are characterized by estimating their fractal dimensions. At various λs, nonlinear control parameters, we show the union of boundaries of water bodies that traverse the water body and non-water body spaces as geomorphic attractors. The computed fractal dimensions of these attractors are 1.58, 1.53, 1.78, 1.76, 1.84, and 1.90, respectively, at λs of 1, 2, 3, 3.46, 3.57, and 3.99. These values are in line with general visual observations.

Discrete simulations of spatio-temporal dynamics of small water bodies under varied stream flow discharges

Directory of Open Access Journals (Sweden)

B. S. Daya Sagar

2005-01-01

Full Text Available Spatio-temporal patterns of small water bodies (SWBs under the influence of temporally varied stream flow discharge are simulated in discrete space by employing geomorphologically realistic expansion and contraction transformations. Cascades of expansion-contraction are systematically performed by synchronizing them with stream flow discharge simulated via the logistic map. Templates with definite characteristic information are defined from stream flow discharge pattern as the basis to model the spatio-temporal organization of randomly situated surface water bodies of various sizes and shapes. These spatio-temporal patterns under varied parameters (λs controlling stream flow discharge patterns are characterized by estimating their fractal dimensions. At various λs, nonlinear control parameters, we show the union of boundaries of water bodies that traverse the water body and non-water body spaces as geomorphic attractors. The computed fractal dimensions of these attractors are 1.58, 1.53, 1.78, 1.76, 1.84, and 1.90, respectively, at λs of 1, 2, 3, 3.46, 3.57, and 3.99. These values are in line with general visual observations.

The effect of a hippotherapy session on spatiotemporal parameters of gait in children with cerebral palsy - pilot study.

Science.gov (United States)

Manikowska, Faustyna; Jóźwiak, Marek; Idzior, Maciej; Chen, Po-Jung Brian; Tarnowski, Dariusz

2013-06-28

Hippotherapy has been shown to produce beneficial effects by improving the most difficult motor functions, such as sitting, running, jumping, coordination, as well as balance and muscle strength in children with motor developmental delays. The aim of this study was to analyze the effect of hippotherapy on spatiotemporal parameters of gait in cerebrally palsied children. 16 ambulatory cerebrally palsied children (GMFCS Level I-III; Female: 10, Male: 6; Age: 5.7-17.5 years old) qualified for hippotherapy were investigated. Basic spatiotemporal parameters of gait, including walking speed, cadence, step length, stride length and the left-right symmetry, were collected using a three-dimensional accelerometer device (DynaPort MiniMod) before and immediately after a hippotherapy session. The Wilcoxon test was used to verify the differences between pre- and post-session results. Changes of walking speed were statistically significant. With the exception of step length, all spatiotemporal parameters improved, i.e. were closer to the respective reference ranges after the session. However, these changes were not statistically significant. One session of hippotherapy may have a significant effect on the spatiotemporal parameters of gait in cerebrally palsied children.

BUILDING A BILLION SPATIO-TEMPORAL OBJECT SEARCH AND VISUALIZATION PLATFORM

Directory of Open Access Journals (Sweden)

D. Kakkar

2017-10-01

Full Text Available With funding from the Sloan Foundation and Harvard Dataverse, the Harvard Center for Geographic Analysis (CGA has developed a prototype spatio-temporal visualization platform called the Billion Object Platform or BOP. The goal of the project is to lower barriers for scholars who wish to access large, streaming, spatio-temporal datasets. The BOP is now loaded with the latest billion geo-tweets, and is fed a real-time stream of about 1 million tweets per day. The geo-tweets are enriched with sentiment and census/admin boundary codes when they enter the system. The system is open source and is currently hosted on Massachusetts Open Cloud (MOC, an OpenStack environment with all components deployed in Docker orchestrated by Kontena. This paper will provide an overview of the BOP architecture, which is built on an open source stack consisting of Apache Lucene, Solr, Kafka, Zookeeper, Swagger, scikit-learn, OpenLayers, and AngularJS. The paper will further discuss the approach used for harvesting, enriching, streaming, storing, indexing, visualizing and querying a billion streaming geo-tweets.

Building a Billion Spatio-Temporal Object Search and Visualization Platform

Science.gov (United States)

Kakkar, D.; Lewis, B.

2017-10-01

With funding from the Sloan Foundation and Harvard Dataverse, the Harvard Center for Geographic Analysis (CGA) has developed a prototype spatio-temporal visualization platform called the Billion Object Platform or BOP. The goal of the project is to lower barriers for scholars who wish to access large, streaming, spatio-temporal datasets. The BOP is now loaded with the latest billion geo-tweets, and is fed a real-time stream of about 1 million tweets per day. The geo-tweets are enriched with sentiment and census/admin boundary codes when they enter the system. The system is open source and is currently hosted on Massachusetts Open Cloud (MOC), an OpenStack environment with all components deployed in Docker orchestrated by Kontena. This paper will provide an overview of the BOP architecture, which is built on an open source stack consisting of Apache Lucene, Solr, Kafka, Zookeeper, Swagger, scikit-learn, OpenLayers, and AngularJS. The paper will further discuss the approach used for harvesting, enriching, streaming, storing, indexing, visualizing and querying a billion streaming geo-tweets.

Spatio-Temporal Parameters\\' Changes in Gait of Male Elderly Subjects

Directory of Open Access Journals (Sweden)

Heydar Sadeghi

2010-03-01

Full Text Available Objectives: The purpose of this study was to compare spatio-temporal gait parameters between elderly and young male subjects. Methods & Materials: 57 able-bodied elderly (72±5.5 years and 57 healthy young (25±8.5 years subjects participated in this study. A four segment model consist of trunk, hip, shank, and foot with 10 reflective markers were used to define lower limbs. Kinematic data collected using four high speed video based cameras at a sampling frequency of 90 Hz.The t-testfor independent samples (α≤0.05 applied for statistical analysis. Results: Significant differences showed longer stance phase (2%, longer push-of time (4%, slower cadence (13%, slower speed (28% and shorter step length (15% for elderly in comparison with young subjects, though no significant differences were seen in double supporttime between two groups. Conclusion: Due to results, spatio-temporal changes are mainly attributed to the age-related decreases in muscular flexibility, joints>ranges of motion and neuromuscular control in elderly people.

Spatiotemporal frequency characteristics of cerebral oscillations during the perception of fundamental frequency contour changes in one-syllable intonation.

Science.gov (United States)

Ueno, Sanae; Okumura, Eiichi; Remijn, Gerard B; Yoshimura, Yuko; Kikuchi, Mitsuru; Shitamichi, Kiyomi; Nagao, Kikuko; Mochiduki, Masayuki; Haruta, Yasuhiro; Hayashi, Norio; Munesue, Toshio; Tsubokawa, Tsunehisa; Oi, Manabu; Nakatani, Hideo; Higashida, Haruhiro; Minabe, Yoshio

2012-05-02

Accurate perception of fundamental frequency (F0) contour changes in the human voice is important for understanding a speaker's intonation, and consequently also his/her attitude. In this study, we investigated the neural processes involved in the perception of F0 contour changes in the Japanese one-syllable interjection "ne" in 21 native-Japanese listeners. A passive oddball paradigm was applied in which "ne" with a high falling F0 contour, used when urging a reaction from the listener, was randomly presented as a rare deviant among a frequent "ne" syllable with a flat F0 contour (i.e., meaningless intonation). We applied an adaptive spatial filtering method to the neuromagnetic time course recorded by whole-head magnetoencephalography (MEG) and estimated the spatiotemporal frequency dynamics of event-related cerebral oscillatory changes in the oddball paradigm. Our results demonstrated a significant elevation of beta band event-related desynchronization (ERD) in the right temporal and frontal areas, in time windows from 100 to 300 and from 300 to 500 ms after the onset of deviant stimuli (high falling F0 contour). This is the first study to reveal detailed spatiotemporal frequency characteristics of cerebral oscillations during the perception of intonational (not lexical) F0 contour changes in the human voice. The results further confirmed that the right hemisphere is associated with perception of intonational F0 contour information in the human voice, especially in early time windows. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Closing the gap between behavior and models in route choice: The role of spatiotemporal constraints and latent traits in choice set formation

DEFF Research Database (Denmark)

Kaplan, Sigal; Prato, Carlo Giacomo

2012-01-01

not account for individual-related spatiotemporal constraints. This paper reduces the gap by proposing a route choice model incorporating spatiotemporal constraints and latent traits. The proposed approach combines stochastic route generation with a latent variable semi-compensatory model representing......A considerable gap exists between the behavioral paradigm of choice set formation in route choice and its representation in route choice modeling. While travelers form their viable choice set by retaining routes that satisfy spatiotemporal constraints, existing route generation techniques do...

Multiple sclerosis

International Nuclear Information System (INIS)

Sadashima, Hiromichi; Kusaka, Hirofumi; Imai, Terukuni; Takahashi, Ryosuke; Matsumoto, Sadayuki; Yamamoto, Toru; Yamasaki, Masahiro; Maya, Kiyomi

1986-01-01

Eleven patients with a definite diagnosis of multiple sclerosis were examined in terms of correlations between the clinical features and the results of cranial computed tomography (CT), and magnetic resonance imaging (MRI). Results: In 5 of the 11 patients, both CT and MRI demonstrated lesions consistent with a finding of multiple sclerosis. In 3 patients, only MRI demonstrated lesions. In the remaining 3 patients, neither CT nor MRI revealed any lesion in the brain. All 5 patients who showed abnormal findings on both CT and MRI had clinical signs either of cerebral or brainstem - cerebellar lesions. On the other hand, two of the 3 patients with normal CT and MRI findings had optic-nerve and spinal-cord signs. Therefore, our results suggested relatively good correlations between the clinical features, CT, and MRI. MRI revealed cerebral lesions in two of the four patients with clinical signs of only optic-nerve and spinal-cord lesions. MRI demonstrated sclerotic lesions in 3 of the 6 patients whose plaques were not detected by CT. In conclusion, MRI proved to be more helpful in the demonstration of lesions attributable to chronic multiple sclerosis. (author)

Spatio-temporal population genetics of the Danish pine marten (Martes martes)

DEFF Research Database (Denmark)

Pertoldi, Cino; Barker, Stuart F.; Madsen, Aksel Bo

2008-01-01

A spatio-temporal study of genetic variation in the Danish pine marten (Martes martes) populations from the Jutland peninsula and from the island of Sealand was performed using 11 microsatellite markers. Samples obtained from 1892 to 2003 were subdivided into historical (prior to 1970) and recent...

Real time eye tracking using Kalman extended spatio-temporal context learning

Science.gov (United States)

Munir, Farzeen; Minhas, Fayyaz ul Amir Asfar; Jalil, Abdul; Jeon, Moongu

2017-06-01

Real time eye tracking has numerous applications in human computer interaction such as a mouse cursor control in a computer system. It is useful for persons with muscular or motion impairments. However, tracking the movement of the eye is complicated by occlusion due to blinking, head movement, screen glare, rapid eye movements, etc. In this work, we present the algorithmic and construction details of a real time eye tracking system. Our proposed system is an extension of Spatio-Temporal context learning through Kalman Filtering. Spatio-Temporal Context Learning offers state of the art accuracy in general object tracking but its performance suffers due to object occlusion. Addition of the Kalman filter allows the proposed method to model the dynamics of the motion of the eye and provide robust eye tracking in cases of occlusion. We demonstrate the effectiveness of this tracking technique by controlling the computer cursor in real time by eye movements.

Spatiotemporal Object History Affects the Selection of Task-Relevant Properties

Science.gov (United States)

Schreij, Daniel; Olivers, Christian N. L.

2013-01-01

For stable perception, we maintain mental representations of objects across space and time. What information is linked to such a representation? In this study, we extended our work showing that the spatiotemporal history of an object affects the way the object is attended the next time it is encountered. Observers conducted a visual search for a…

Displaced calibration of PM10 measurements using spatio-temporal models

Directory of Open Access Journals (Sweden)

Daniela Cocchi

2007-12-01

Full Text Available PM10 monitoring networks are equipped with heterogeneous samplers. Some of these samplers are known to underestimate true levels of concentrations (non-reference samplers. In this paper we propose a hierarchical spatio-temporal Bayesian model for the calibration of measurements recorded using non-reference samplers, by borrowing strength from non co-located reference sampler measurements.

Spatio-temporal dynamics of multimodal EEG-fNIRS signals in the loss and recovery of consciousness under sedation using midazolam and propofol.

Directory of Open Access Journals (Sweden)

Seul-Ki Yeom

Full Text Available On sedation motivated by the clinical needs for safety and reliability, recent studies have attempted to identify brain-specific signatures for tracking patient transition into and out of consciousness, but the differences in neurophysiological effects between 1 the sedative types and 2 the presence/absence of surgical stimulations still remain unclear. Here we used multimodal electroencephalography-functional near-infrared spectroscopy (EEG-fNIRS measurements to observe electrical and hemodynamic responses during sedation simultaneously. Forty healthy volunteers were instructed to push the button to administer sedatives in response to auditory stimuli every 9-11 s. To generally illustrate brain activity at repetitive transition points at the loss of consciousness (LOC and the recovery of consciousness (ROC, patient-controlled sedation was performed using two different sedatives (midazolam (MDZ and propofol (PPF under two surgical conditions. Once consciousness was lost via sedatives, we observed gradually increasing EEG power at lower frequencies (15 Hz, as well as spatially increased EEG powers in the delta and lower alpha bands, and particularly also in the upper alpha rhythm, at the frontal and parieto-occipital areas over time. During ROC from unconsciousness, these spatio-temporal changes were reversed. Interestingly, the level of consciousness was switched on/off at significantly higher effect-site concentrations of sedatives in the brain according to the use of surgical stimuli, but the spatio-temporal EEG patterns were similar, regardless of the sedative used. We also observed sudden phase shifts in fronto-parietal connectivity at the LOC and the ROC as critical points. fNIRS measurement also revealed mild hemodynamic fluctuations. Compared with general anesthesia, our results provide insights into critical hallmarks of sedative-induced (unconsciousness, which have similar spatio-temporal EEG-fNIRS patterns regardless of the stage and

Next Place Prediction Based on Spatiotemporal Pattern Mining of Mobile Device Logs.

Science.gov (United States)

Lee, Sungjun; Lim, Junseok; Park, Jonghun; Kim, Kwanho

2016-01-23

Due to the recent explosive growth of location-aware services based on mobile devices, predicting the next places of a user is of increasing importance to enable proactive information services. In this paper, we introduce a data-driven framework that aims to predict the user's next places using his/her past visiting patterns analyzed from mobile device logs. Specifically, the notion of the spatiotemporal-periodic (STP) pattern is proposed to capture the visits with spatiotemporal periodicity by focusing on a detail level of location for each individual. Subsequently, we present algorithms that extract the STP patterns from a user's past visiting behaviors and predict the next places based on the patterns. The experiment results obtained by using a real-world dataset show that the proposed methods are more effective in predicting the user's next places than the previous approaches considered in most cases.

Long-term archives reveal shifting extinction selectivity in China's postglacial mammal fauna

Science.gov (United States)

Crees, Jennifer J.; Li, Zhipeng; Bielby, Jon; Yuan, Jing

2017-01-01

Ecosystems have been modified by human activities for millennia, and insights about ecology and extinction risk based only on recent data are likely to be both incomplete and biased. We synthesize multiple long-term archives (over 250 archaeological and palaeontological sites dating from the early Holocene to the Ming Dynasty and over 4400 historical records) to reconstruct the spatio-temporal dynamics of Holocene–modern range change across China, a megadiverse country experiencing extensive current-day biodiversity loss, for 34 mammal species over three successive postglacial time intervals. Our combined zooarchaeological, palaeontological, historical and current-day datasets reveal that both phylogenetic and spatial patterns of extinction selectivity have varied through time in China, probably in response both to cumulative anthropogenic impacts (an ‘extinction filter’ associated with vulnerable species and accessible landscapes being affected earlier by human activities) and also to quantitative and qualitative changes in regional pressures. China has experienced few postglacial global species-level mammal extinctions, and most species retain over 50% of their maximum estimated Holocene range despite millennia of increasing regional human pressures, suggesting that the potential still exists for successful species conservation and ecosystem restoration. Data from long-term archives also demonstrate that herbivores have experienced more historical extinctions in China, and carnivores have until recently displayed greater resilience. Accurate assessment of patterns of biodiversity loss and the likely predictive power of current-day correlates of faunal vulnerability and resilience is dependent upon novel perspectives provided by long-term archives. PMID:29167363

Deep recurrent neural network reveals a hierarchy of process memory during dynamic natural vision.

Science.gov (United States)

Shi, Junxing; Wen, Haiguang; Zhang, Yizhen; Han, Kuan; Liu, Zhongming

2018-05-01

The human visual cortex extracts both spatial and temporal visual features to support perception and guide behavior. Deep convolutional neural networks (CNNs) provide a computational framework to model cortical representation and organization for spatial visual processing, but unable to explain how the brain processes temporal information. To overcome this limitation, we extended a CNN by adding recurrent connections to different layers of the CNN to allow spatial representations to be remembered and accumulated over time. The extended model, or the recurrent neural network (RNN), embodied a hierarchical and distributed model of process memory as an integral part of visual processing. Unlike the CNN, the RNN learned spatiotemporal features from videos to enable action recognition. The RNN better predicted cortical responses to natural movie stimuli than the CNN, at all visual areas, especially those along the dorsal stream. As a fully observable model of visual processing, the RNN also revealed a cortical hierarchy of temporal receptive window, dynamics of process memory, and spatiotemporal representations. These results support the hypothesis of process memory, and demonstrate the potential of using the RNN for in-depth computational understanding of dynamic natural vision. © 2018 Wiley Periodicals, Inc.

Symptom perception in gastroesophageal reflux disease is dependent on spatiotemporal reflux characteristics

NARCIS (Netherlands)

Weusten, B. L.; Akkermans, L. M.; vanBerge-Henegouwen, G. P.; Smout, A. J.

1995-01-01

BACKGROUND/AIMS: The mechanisms responsible for the development of symptoms in gastroesophageal reflux disease (GERD) are poorly understood. The aims of this study were to identify differences in spatiotemporal reflux characteristics (proximal extent and duration of reflux episodes, ascending

Suitable landscape classification systems for quantifying spatiotemporal development of riverine ecosystem services

NARCIS (Netherlands)

Koopman, K.R.; Augustijn, Dionysius C.M.; Breure, A.M.; Lenders, H.J.R.; Leuven, R.S.E.W.

River systems provide numerous ecosystem services that contribute to human well-being. Biophysical quantification of spatiotemporal development of ecosystem services is useful for environmental impact assessments or scenario analyses of river management and could be done by linking biophysical

Multiple scroll wave chimera states

Science.gov (United States)

Maistrenko, Volodymyr; Sudakov, Oleksandr; Osiv, Oleksiy; Maistrenko, Yuri

2017-06-01

We report the appearance of three-dimensional (3D) multiheaded chimera states that display cascades of self-organized spatiotemporal patterns of coexisting coherence and incoherence. We demonstrate that the number of incoherent chimera domains can grow additively under appropriate variations of the system parameters generating thereby head-adding cascades of the scroll wave chimeras. The phenomenon is derived for the Kuramoto model of N 3 identical phase oscillators placed in the unit 3D cube with periodic boundary conditions, parameters being the coupling radius r and phase lag α. To obtain the multiheaded chimeras, we perform the so-called `cloning procedure' as follows: choose a sample single-headed 3D chimera state, make appropriate scale transformation, and put some number of copies of them into the unit cube. After that, start numerical simulations with slightly perturbed initial conditions and continue them for a sufficiently long time to confirm or reject the state existence and stability. In this way it is found, that multiple scroll wave chimeras including those with incoherent rolls, Hopf links and trefoil knots admit this sort of multiheaded regeneration. On the other hand, multiple 3D chimeras without spiral rotations, like coherent and incoherent balls, tubes, crosses, and layers appear to be unstable and are destroyed rather fast even for arbitrarily small initial perturbations.

Dying like rabbits: general determinants of spatio-temporal variability in survival.

Science.gov (United States)

Tablado, Zulima; Revilla, Eloy; Palomares, Francisco

2012-01-01

1. Identifying general patterns of how and why survival rates vary across space and time is necessary to truly understand population dynamics of a species. However, this is not an easy task given the complexity and interactions of processes involved, and the interpopulation differences in main survival determinants. 2. Here, using European rabbits (Oryctolagus cuniculus) as a model and information from local studies, we investigated whether we could make inferences about trends and drivers of survival of a species that are generalizable to large spatio-temporal scales. To do this, we first focused on overall survival and then examined cause-specific mortalities, mainly predation and diseases, which may lead to those patterns. 3. Our results show that within the large-scale variability in rabbit survival, there exist general patterns that are explained by the integration of factors previously known to be important at the local level (i.e. age, climate, diseases, predation or density dependence). We found that both inter- and intrastudy survival rates increased in magnitude and decreased in variability as rabbits grow old, although this tendency was less pronounced in populations with epidemic diseases. Some causes leading to these higher mortalities in young rabbits could be the stronger effect of rainfall at those ages, as well as, other death sources like malnutrition or infanticide. 4. Predation is also greater for newborns and juveniles, especially in population without diseases. Apart from the effect of diseases, predation patterns also depended on factors, such as, density, season, and type and density of predators. Finally, we observed that infectious diseases also showed general relationships with climate, breeding (i.e. new susceptible rabbits) and age, although the association type varied between myxomatosis and rabbit haemorrhagic disease. 5. In conclusion, large-scale patterns of spatio-temporal variability in rabbit survival emerge from the combination

Probabilistic M/EEG source imaging from sparse spatio-temporal event structure

DEFF Research Database (Denmark)

Stahlhut, Carsten; Attias, Hagai T.; Wipf, David

While MEG and EEG source imaging methods have to tackle a severely ill-posed problem their success can be stated as their ability to constrain the solutions using appropriate priors. In this paper we propose a hierarchical Bayesian model facilitating spatio-temporal patterns through the use of bo...

Spatio-Temporal Encoding in Medical Ultrasound Imaging

DEFF Research Database (Denmark)

Gran, Fredrik

2005-01-01

In this dissertation two methods for spatio-temporal encoding in medical ultrasound imaging are investigated. The first technique is based on a frequency division approach. Here, the available spectrum of the transducer is divided into a set of narrow bands. A waveform is designed for each band...... the signal to noise ratio and simultaneously the penetration depth so that the medical doctor can image deeper lying structures. The method is tested both experimentally and in simulation and has also evaluated for the purpose of blood flow estimation. The work presented is based on four papers which...

A spatio-temporal autocorrelation change detection approach using hyper-temporal satellite data

CSIR Research Space (South Africa)

Kleynhans, W

2013-07-01

Full Text Available -1 IEEE International Geoscience and Remote Sensing Symposium, Melbourne, Australia 21-26 July 2013 A SPATIO-TEMPORAL AUTOCORRELATION CHANGE DETECTION APPROACH USING HYPER-TEMPORAL SATELLITE DATA yzW. Kleynhans, yz,B.P Salmon,zK. J. Wessels...

Regulation of Spatiotemporal Patterns by Biological Variability: General Principles and Applications to Dictyostelium discoideum.

Directory of Open Access Journals (Sweden)

Miriam Grace

2015-11-01

Full Text Available Spatiotemporal patterns often emerge from local interactions in a self-organizing fashion. In biology, the resulting patterns are also subject to the influence of the systematic differences between the system's constituents (biological variability. This regulation of spatiotemporal patterns by biological variability is the topic of our review. We discuss several examples of correlations between cell properties and the self-organized spatiotemporal patterns, together with their relevance for biology. Our guiding, illustrative example will be spiral waves of cAMP in a colony of Dictyostelium discoideum cells. Analogous processes take place in diverse situations (such as cardiac tissue, where spiral waves occur in potentially fatal ventricular fibrillation so a deeper understanding of this additional layer of self-organized pattern formation would be beneficial to a wide range of applications. One of the most striking differences between pattern-forming systems in physics or chemistry and those in biology is the potential importance of variability. In the former, system components are essentially identical with random fluctuations determining the details of the self-organization process and the resulting patterns. In biology, due to variability, the properties of potentially very few cells can have a driving influence on the resulting asymptotic collective state of the colony. Variability is one means of implementing a few-element control on the collective mode. Regulatory architectures, parameters of signaling cascades, and properties of structure formation processes can be "reverse-engineered" from observed spatiotemporal patterns, as different types of regulation and forms of interactions between the constituents can lead to markedly different correlations. The power of this biology-inspired view of pattern formation lies in building a bridge between two scales: the patterns as a collective state of a very large number of cells on the one hand

Bayesian spatio-temporal modelling of tobacco-related cancer mortality in Switzerland

Directory of Open Access Journals (Sweden)

Verena Jürgens

2013-05-01

Full Text Available Tobacco smoking is a main cause of disease in Switzerland; lung cancer being the most common cancer mortality in men and the second most common in women. Although disease-specific mortality is decreasing in men, it is steadily increasing in women. The four language regions in this country might play a role in this context as they are influenced in different ways by the cultural and social behaviour of neighbouring countries. Bayesian hierarchical spatio-temporal, negative binomial models were fitted on subgroup-specific death rates indirectly standardized by national references to explore age- and gender-specific spatio-temporal patterns of mortality due to lung cancer and other tobacco-related cancers in Switzerland for the time period 1969-2002. Differences influenced by linguistic region and life in rural or urban areas were also accounted for. Male lung cancer mortality was found to be rather homogeneous in space, whereas women were confirmed to be more affected in urban regions. Compared to the German-speaking part, female mortality was higher in the French-speaking part of the country, a result contradicting other reports of similar comparisons between France and Germany. The spatio-temporal patterns of mortality were similar for lung cancer and other tobacco-related cancers. The estimated mortality maps can support the planning in health care services and evaluation of a national tobacco control programme. Better understanding of spatial and temporal variation of cancer of the lung and other tobacco-related cancers may help in allocating resources for more effective screening, diagnosis and therapy. The methodology can be applied to similar studies in other settings.

Control of spatio-temporal on-off intermittency in random driving diffusively coupled map lattices

International Nuclear Information System (INIS)

Ziabakhsh Deilami, M.; Rahmani Cherati, Z.; Jahed Motlagh, M.R.

2009-01-01

In this paper, we propose feedback methods for controlling spatio-temporal on-off intermittency which is an aperiodic switching between an 'off' state and an 'on' state. Diffusively coupled map lattice with spatially non-uniform random driving is used for showing spatio-temporal on-off intermittency. For this purpose, we apply three different feedbacks. First, we use a linear feedback which is a simple method but has a long transient time. To overcome this problem, two nonlinear feedbacks based on prediction strategy are proposed. An important advantage of the methods is that the feedback signal is vanished when control is realized. Simulation results show that all methods have suppressed the chaotic behavior.

Spatiotemporal air pollution exposure assessment for a Canadian population-based lung cancer case-control study

Directory of Open Access Journals (Sweden)

Hystad Perry

2012-04-01

Full Text Available Abstract Background Few epidemiological studies of air pollution have used residential histories to develop long-term retrospective exposure estimates for multiple ambient air pollutants and vehicle and industrial emissions. We present such an exposure assessment for a Canadian population-based lung cancer case-control study of 8353 individuals using self-reported residential histories from 1975 to 1994. We also examine the implications of disregarding and/or improperly accounting for residential mobility in long-term exposure assessments. Methods National spatial surfaces of ambient air pollution were compiled from recent satellite-based estimates (for PM2.5 and NO2 and a chemical transport model (for O3. The surfaces were adjusted with historical annual air pollution monitoring data, using either spatiotemporal interpolation or linear regression. Model evaluation was conducted using an independent ten percent subset of monitoring data per year. Proximity to major roads, incorporating a temporal weighting factor based on Canadian mobile-source emission estimates, was used to estimate exposure to vehicle emissions. A comprehensive inventory of geocoded industries was used to estimate proximity to major and minor industrial emissions. Results Calibration of the national PM2.5 surface using annual spatiotemporal interpolation predicted historical PM2.5 measurement data best (R2 = 0.51, while linear regression incorporating the national surfaces, a time-trend and population density best predicted historical concentrations of NO2 (R2 = 0.38 and O3 (R2 = 0.56. Applying the models to study participants residential histories between 1975 and 1994 resulted in mean PM2.5, NO2 and O3 exposures of 11.3 μg/m3 (SD = 2.6, 17.7 ppb (4.1, and 26.4 ppb (3.4 respectively. On average, individuals lived within 300 m of a highway for 2.9 years (15% of exposure-years and within 3 km of a major industrial emitter for 6.4 years (32% of exposure-years. Approximately 50