Explore the vast range of titles available.

The localization of brain sources based on EEG measurements is a topic that has attracted a lot of attention in the last decades and many different source localization algorithms have been proposed. However, their performance is limited in the case of several simultaneously active brain regions and low signal-to-noise ratios. To overcome these problems, tensor-based preprocessing can be applied, which consists in constructing a space–time–frequency (STF) or space–time–wave–vector (STWV) tensor and decomposing it using the Canonical Polyadic (CP) decomposition. In this paper, we present a new algorithm for the accurate localization of extended sources based on the results of the tensor decomposition. Furthermore, we conduct a detailed study of the tensor-based preprocessing methods, including an analysis of their theoretical foundation, their computational complexity, and their performance for realistic simulated data in comparison to conventional source localization algorithms such as sLORETA, cortical LORETA (cLORETA), and 4-ExSo-MUSIC. Our objective consists, on the one hand, in demonstrating the gain in performance that can be achieved by tensor-based preprocessing, and, on the other hand, in pointing out the limits and drawbacks of this method. Finally, we validate the STF and STWV techniques on real measurements to demonstrate their usefulness for practical applications. •Localization of spatially distributed sources using tensor-based preprocessing•Performance analysis of two tensor-based preprocessing methods•Numerical complexity of tensor-based and conventional algorithms•Performance comparison on realistic simulated EEG data•Validation on real EEG data from an epilepsy patient

Throughout most of human history, events and phenomena of interest have been characterized using space and time as their major characteristic dimensions, in either absolute or relative conceptualizations. Space-time analysis seeks to understand when and where (and sometimes why) things occur. In the context of several of the most recent and substantial advances in individual movement data analysis (time geography in particular) and spatial panel data analysis, we focus on quantitative space-time analytics. Based on more than 700 articles (from 1949 to 2013) we obtained through a key word search on the Web of Knowledge and through the authors' personal archives, this article provides a synthetic overview about the quantitative methodology for space-time analysis. Particularly, we highlight space-time pattern revelation (e.g., various clustering metrics, path comparison indexes, space-time tests), space-time statistical models (e.g., survival analysis, latent trajectory models), and simulation methods (e.g., cellular automaton, agent-based models) as well as their empirical applications in multiple disciplines. This article systematically presents the strengths and weaknesses of a set of prevalent methods used for space-time analysis and points to the major challenges, new opportunities, and future directions of space-time analysis.

This is Part II of a two-part article that serves as a chronological catalog of the methods and solutions in the Space–Time Computational Flow Analysis (STCFA). In Part I , we focused on the methods and solutions in finite element analysis. Here, we focus on the methods and solutions in isogeometric analysis (IGA). The methods we cover include the ST-IGA and ST Slip Interface method. The first-of-its-kind solutions we cover include the flapping-wing aerodynamics with the wing motion coming from an actual locust, ventricle-valve-aorta flow analysis with patient-specific aorta and realistic ventricle and leaflet geometries and motion, and car and tire aerodynamics with near-actual car body and tire geometries, road contact, and tire deformation. These and the other first-of-its-kind solutions covered show how the STCFA brought solutions in so many classes of challenging flow problems.

Background Decision making in the health area usually involves several factors, options and data. In addition, it should take into account technological, social and spatial aspects, among others. Decision making methodologies need to address this set of information , and there is a small group of them with focus on epidemiological purposes, in particular Spatial Decision Support Systems (SDSS). Methods Makes uses a Multiple Criteria Decision Making (MCDM) method as a combining rule of results from a set of SDSS, where each one of them analyzes specific aspects of a complex problem. Specifically, each geo-object of the geographic region is processed, according to its own spatial information, by an SDSS using spatial and non-spatial data, inferential statistics and spatial and spatio-temporal analysis, which are then grouped together by a fuzzy rule-based system that will produce a georeferenced map. This means that, each SDSS provides an initial evaluation for each variable of the problem. The results are combined by the weighted linear combination (WLC) as a criterion in a MCDM problem, producing a final decision map about the priority levels for fight against a disease. In fact, the WLC works as a combining rule for those initial evaluations in a weighted manner, more than a MCDM, i.e., it combines those initial evaluations in order to build the final decision map. Results An example of using this new approach with real epidemiological data of tuberculosis in a Brazilian municipality is provided. As a result, the new approach provides a final map with four priority levels: “non-priority”, “non-priority tendency”, “priority tendency” and “priority”, for the fight against diseases. Conclusion The new approach may help public managers in the planning and direction of health actions, in the reorganization of public services, especially with regard to their levels of priorities.

Objectives Respiratory tract diseases (RTDs) are among the top five leading causes of death worldwide. Mortality rates due to respiratory tract diseases (MRRTDs) follow a spatial pattern and this may suggest a potential link between environmental risk factors and MRRTDs. Spatial analysis of RTDs mortality data in an urban setting can provide new knowledge on spatial variation of potential risk factors for RTDs. This will enable health professionals and urban planners to design tailored interventions. We aim to release the datasets of MRRTDs in the city of Tehran, Iran, between 2008 and 2018. Data description The Research data include four datasets; (a) mortality dataset which includes records of deaths and their attributes (age, gender, date of death and district name where death occurred), (b) population data for 22 districts (age groups with 5 years interval and gender by each district). Furthermore, two spatial datasets about the city are introduced; (c) the digital boundaries of districts and (d) urban suburbs of Tehran.

Compactness of a congressional district is a traditional principle in adjudicating gerrymandering claims in political redistricting. During the last decade, many states have used compactness as an important criterion to constrain the presence of gerrymandering in the redistricting process. In this study, we conducted an array of spatiotemporal analyses aiming to evaluate the changes in compactness between the 112th and 113th Congressional districting plans in California and North Carolina, two states that have been well known for their heavy gerrymandering for years. We employed classic shape-based compactness measures, moment-of-inertia-based measures, and measures of partisan bias to assess the districting plans from multiple angles, including irregularity of district boundaries, spatial dispersion, population-weighted shape dispersion, and partisan symmetry. This new and combined use of spatial measures evidenced remarkable increases on the average compactness scores for California's Congress, suggesting general alleviation of the bipartisan gerrymandering in the previous plan. On the contrary, the partisan gerrymandering in North Carolina intensified in the current map, indicated by the substantial decline in the compactness scores for a majority of the districts. Analysis of partisan bias in the districting plans suggested a very slight bias toward Democrats in California in both districting plans. In North Carolina, the partisan advantage shifted from Democrats to Republicans during redistricting. Comparative analysis between the two families of spatial measures revealed the superiority of the moment of inertia family to the classic shape-based indexes for measuring compactness of congressional districts.

Background Suriname has experienced a significant change in malaria transmission risk and incidence over the past years. The country is now moving toward malaria elimination. The first objective of this study is to describe malaria epidemiological trends in Suriname between 2000 and 2016. The second objective is to identify spatiotemporal malaria trends in notification points between 2007 and 2016. Methods National malaria surveillance data resulting from active and passive screening between 2000 and 2016 were used for the temporal trend analysis. A space–time cluster analysis using SaTScan™ was conducted on Malaria Programme-data from 2007 to 2016 comparing cases (people tested positive) with controls (people tested negative). Results Suriname experienced a period of high malaria incidence during 2000–2005, followed by a steep decline in number of malaria cases from 2005 onwards. Imported malaria cases, mostly of Brazilian nationality and travelling from French Guiana, were major contributors to the reported number of cases, exceeding the national malaria burden (94.2% of the total). Most clusters in notification points are found in the border area between Suriname and French Guiana. Clustering was also found in the migrant clinic in Paramaribo. Conclusions Suriname has successfully reduced malaria to near-elimination level in the last 17 years. However, the high malaria import rate resulting from cross-border moving migrants is a major challenge for reaching elimination. This requires continued investment in the national health system, with a focus on border screening and migrant health. A regional approach to malaria elimination within the Guianas and Brazil is urgently needed.

Aim This paper presents the statistical bases for temporal beta‐diversity analysis, a method to study changes in community composition through time from repeated surveys at several sites. Surveys of that type are presently done by ecologists around the world. A temporal beta‐diversity Index (TBI) is computed for each site, measuring the change in species composition between the first (T1) and second surveys (T2). TBI indices can be decomposed into losses and gains; they can also be tested for significance, allowing one to identify the sites that have changed in composition in exceptional ways. This method will be of value to identify exceptional sites in space–time surveys carried out to study anthropogenic impacts, including climate change. Innovation The null hypothesis of the TBI test is that a species assemblage is not exceptionally different between T1 and T2, compared to assemblages that could have been observed at this site at T1 and T2 under conditions corresponding to H0. Tests of significance of coefficients in a dissimilarity matrix are usually not possible because the values in the matrix are interrelated. Here, however, the dissimilarity between T1 and T2 for a site is computed with different data from the dissimilarities used for the T1–T2 comparison at other sites. It is thus possible to compute a valid test of significance in that case. In addition, the paper shows how TBI dissimilarities can be decomposed into loss and gain components (of species, or abundances‐per‐species) and how a B–C plot can be produced from these components, which informs users about the processes of biodiversity losses and gains through time in space–time survey data. Main conclusion Three applications of the method to different ecological communities are presented. This method is applicable worldwide to all types of communities, marine, and terrestrial. R software is available implementing the method. This paper describes a new method, temporal beta‐diversity analysis, to study the changes in community composition through time from repeated surveys at several sites. (a) A temporal beta‐diversity Index (TBI) is computed for each site, measuring the change in species composition between the first and second surveys. TBI indices can be tested for significance, allowing one to identify the sites that have changed in composition in exceptional ways. (b) It is often of interest to examine the species loss and gain components of the TBI indices because change through time is directional. Graphical procedures are demonstrated. Several examples from the ecological literature are provided.

We present a hydrodynamic stability theory for incompressible viscous fluid flows based on a space–time variational formulation and associated generalized singular value decomposition of the (linearized) Navier–Stokes equations. We first introduce a linear framework applicable to a wide variety of stationary- or time-dependent base flows: we consider arbitrary disturbances in both the initial condition and the dynamics measured in a ‘data’ space–time norm; the theory provides a rigorous, sharp (realizable) and efficiently computed bound for the velocity perturbation measured in a ‘solution’ space–time norm. We next present a generalization of the linear framework in which the disturbances and perturbation are now measured in respective selected space–time semi-norms; the semi-norm theory permits rigorous and sharp quantification of, for example, the growth of initial disturbances or functional outputs. We then develop a (Brezzi–Rappaz–Raviart) nonlinear theory which provides, for disturbances which satisfy a certain (rather stringent) amplitude condition, rigorous finite-amplitude bounds for the velocity and output perturbations. Finally, we demonstrate the application of our linear and nonlinear hydrodynamic stability theory to unsteady moderate Reynolds number flow in an eddy-promoter channel.