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One of the primary goals of coastal water quality monitoring is to characterize spatial variation. Generally, this monitoring takes place at a limited number of fixed sampling points. The alternative sampling methodology explored in this paper involves high density sampling from an onboard flow-through water analysis system (Dataflow). Dataflow has the potential to provide better spatial resolution of water quality features because it generates many closely spaced ( < 10 m) measurements. Regardless of the measurement technique, parameter values at unsampled locations must be interpolated from nearby measurement points in order to generate a comprehensive picture of spatial variations. Standard Euclidean interpolations in coastal settings tend to yield inaccurate results because they extend through barriers in the landscape such as peninsulas, islands, and submerged banks. We recently developed a method for non-Euclidean interpolation by inverse path distance weighting (IPDW) in order to account for these barriers. The algorithms were implemented as part of an R package and made available from R repositories. The combination of IPDW with Dataflow provided more accurate estimates of salinity patterning relative to Euclidean inverse distance weighting (IDW). IPDW was notably more accurate than IDW in the presence of intense spatial gradients.

The spatio-temporal arrangement of interacting populations often influences the maintenance of species diversity and is a subject of intense research. Here, we study the spatio-temporal patterns arising from the cyclic competition between three species in two dimensions. Inspired by recent experiments, we consider a generic metapopulation model comprising "rock-paper-scissors" interactions via dominance removal and replacement, reproduction, mutations, pair-exchange and hopping of individuals. By combining analytical and numerical methods, we obtain the model's phase diagram near its Hopf bifurcation and quantitatively characterize the properties of the spiraling patterns arising in each phase. The phases characterizing the cyclic competition away far from the Hopf bifurcation (at low mutation rate) are also investigated. Our analytical approach relies on the careful analysis of the properties of the complex Ginzburg-Landau equation derived through a controlled (perturbative) multiscale expansion around the model's Hopf bifurcation. Our results allows us to clarify when spatial "rock-paper-scissors" competition leads to stable spiral waves and under which circumstances they are influenced by nonlinear mobility.

This paper reviews developments in statistics for spatial point processes obtained within roughly the last decade. These developments include new classes of spatial point process models such as determinantal point processes, models incorporating both regularity and aggregation, and models where points are randomly distributed around latent geometric structures. Regarding parametric inference the main focus is on various types of estimating functions derived from so-called innovation measures. Optimality of such estimating functions is discussed as well as computational issues. Maximum likelihood inference for determinantal point processes and Bayesian inference are briefly considered too. Concerning non-parametric inference, we consider extensions of functional summary statistics to the case of inhomogeneous point processes as well as new approaches to simulation based inference.

Persistent homology, while ostensibly measuring changes in topology, captures multiscale geometrical information. It is a natural tool for the analysis of point patterns. In this paper we explore the statistical power of the (persistent homology) rank functions. For a point pattern $X$ we construct a filtration of spaces by taking the union of balls of radius $a$ centered on points in $X$, $X_a = \cup_{x\in X}B(x,a)$. The rank function ${\beta}_k(X):{\{(a,b)\in \mathbb{R}^2: a\leq b\}} \to \mathbb{R}$ is then defined by ${\beta}_k(X)(a,b) = rank ( \iota_*:H_k(X_a) \to H_k(X_b))$ where $\iota_*$ is the induced map on homology from the inclusion map on spaces. We consider the rank functions as lying in a Hilbert space and show that under reasonable conditions the rank functions from multiple simulations or experiments will lie in an affine subspace. This enables us to perform functional principal component analysis which we apply to experimental data from colloids at different effective temperatures and of sphere packings with different volume fractions. We also investigate the potential of rank functions in providing a test of complete spatial randomness of 2D point patterns using the distances to an empirically computed mean rank function of binomial point patterns in the unit square.

The precise layout of gene expression patterns is a crucial step in development. Formation of a sharp boundary between high and low expression domains requires a genetic mechanism which is both sensitive and robust to fluctuations, a demand that may not be easily achieved by morphogens alone. Recently it has been demonstrated that small RNAs (and, in particular, microRNAs) play many roles in embryonic development. While some RNAs are essential for embryogenesis, others are limited to fine-tuning a predetermined gene expression pattern. Here we explore the possibility that small RNAs participate in sharpening a gene expression profile that was crudely established by a morphogen. To this end we study a model where small RNAs interact with a target gene and diffusively move from cell to cell. Though diffusion generally smears spatial expression patterns, we find that intercellular mobility of small RNAs is actually critical in sharpening the interface between target expression domains in a robust manner. We discuss the applicability of our results, as examples, to the case of leaf polarity establishment in maize and Hox patterning in the early {\it Drosophila} embryo. Our findings point out the functional significance of some mechanistic properties, such as mobility of small RNAs and the irreversibility of their interactions. These properties are yet to be established directly for most classes of small RNAs. An indirect yet simple experimental test of the proposed mechanism is suggested in some detail.

Spatial Patterns of the Western Spruce Budworm (Lepidoptera: Tortricidae) and Optimal Sample Sizes for Estimating Population Density or Classifying Expected Defoliation in the Pacific Northwest

Under conditions of high-velocity motion the textures in an observer's field of view are transformed into blur patterns, patterns of blur lines formed by elements in the terrain appearing to streak as a result of the visual system's persistence. Under these conditions most of the usual visual correlates of depth perception fail to operate but new motion-related information becomes available in the form of blur-line divergence and curvature. The experiments reported here determine human ability to process this rapidly moving divergence information. Using simulated blur patterns, divergence thresholds were determined for nine segments of the visual field at three velocities; observers were also required to use the divergence information for orientation of a surface in correspondence with their own perceptions of the inclination of the plane of motion.

The 17-year (1982-1998) trend in surface temperature shows a general cooling over the Antarctic continent, warming of the sea ice zone, with moderate changes over the oceans. Warming of the peripheral seas is associated with negative trends in the regional sea ice extent. Effects of the Southern Hemisphere Annular Mode (SAM) and the extrapolar Southern Oscillation (SO) on surface temperature are quantified through regression analysis. Positive polarities of the SAM are associated with cold anomalies over most of Antarctica, with the most notable exception of the Antarctic Peninsula. Positive temperature anomalies and ice edge retreat in the Pacific sector are associated with El Nino episodes. Over the past two decades, the drift towards high polarity in the SAM and negative polarity in the SO indices couple to produce a spatial pattern with warmer temperatures in the Antarctic Peninsula and peripheral seas, and cooler temperatures over much of East Antarctica.

We performed simultaneous spectral analysis of 26.6 ksec of Chandra and 102.3 ksec of Suzaku X-ray data of the starburst galaxy NGC 3079. The spectra are extracted from four regions: 0.5' (2.25 kpc) circle, an inner 0.5'-1' (2.25--4.5 kpc) ring, and an outer 1'-2' (4.5--9 kpc) ring from Chandra, and 4' (18 kpc) circle from Suzaku, all centered on the nucleus. Fitting with thermal plasma models yields interstellar medium (ISM) temperatures of 0.65+0.05-0.04, 0.45+0.07-0.06, and 0.24+0.03-0.02 keV in the three regions, respectively. The combination of Chandra's high angular resolution and Suzaku's good spectral sensitivity enable us to spatially resolve and measure the abundances of the metals O, Ne, Mg, and Fe within the hot ISM. In particular, the abundance patterns of O/Fe, Ne/Fe, Mg/Fe, and Si/Fe in the central regions (

Statistical pattern classification methods based on data-random graphs were introduced recently. In this approach, a random directed graph is constructed from the data using the relative positions of the data points from various classes. Different random graphs result from different definitions of the proximity region associated with each data point and different graph statistics can be employed for data reduction. The approach used in this article is based on a parameterized family of proximity maps determining an associated family of data-random digraphs. The relative arc density of the digraph is used as the summary statistic, providing an alternative to the domination number employed previously. An important advantage of the relative arc density is that, properly re-scaled, it is a U-statistic, facilitating analytic study of its asymptotic distribution using standard U-statistic central limit theory. The approach is illustrated with an application to the testing of spatial patterns of segregation and association. Knowledge of the asymptotic distribution allows evaluation of the Pitman and Hodges-Lehmann asymptotic efficacy, and selection of the proximity map parameter to optimize efficacy. Notice that the approach presented here also has the advantage of validity for data in any dimension.

Statistical pattern classification methods based on data-random graphs were introduced recently. In this approach, a random directed graph is constructed from the data using the relative positions of the data points from various classes. Different random graphs result from different definitions of the proximity region associated with each data point and different graph statistics can be employed for data reduction. The approach used in this article is based on a parameterized family of proximity maps determining an associated family of data-random digraphs. The relative arc density of the digraph is used as the summary statistic, providing an alternative to the domination number employed previously. An important advantage of the relative arc density is that, properly re-scaled, it is a U-statistic, facilitating analytic study of its asymptotic distribution using standard U-statistic central limit theory. The approach is illustrated with an application to the testing of spatial patterns of segregation and association. Knowledge of the asymptotic distribution allows evaluation of the Pitman and Hodges-Lehmann asymptotic efficacy, and selection of the proximity map parameter to optimize efficacy. Notice that the approach presented here also has the advantage of validity for data in any dimension.

Statistical pattern classification methods based on data-random graphs were introduced recently. In this approach, a random directed graph is constructed from the data using the relative positions of the data points from various classes. Different random graphs result from different definitions of the proximity region associated with each data point and different graph statistics can be employed for data reduction. The approach used in this article is based on a parameterized family of proximity maps determining an associated family of data-random digraphs. The relative arc density of the digraph is used as the summary statistic, providing an alternative to the domination number employed previously. An important advantage of the relative arc density is that, properly re-scaled, it is a U-statistic, facilitating analytic study of its asymptotic distribution using standard U-statistic central limit theory. The approach is illustrated with an application to the testing of spatial patterns of segregation and association. Knowledge of the asymptotic distribution allows evaluation of the Pitman and Hodges-Lehmann asymptotic efficacy, and selection of the proximity map parameter to optimize efficacy. Notice that the approach presented here also has the advantage of validity for data in any dimension.

Statistical pattern classification methods based on data-random graphs were introduced recently. In this approach, a random directed graph is constructed from the data using the relative positions of the data points from various classes. Different random graphs result from different definitions of the proximity region associated with each data point and different graph statistics can be employed for data reduction. The approach used in this article is based on a parameterized family of proximity maps determining an associated family of data-random digraphs. The relative arc density of the digraph is used as the summary statistic, providing an alternative to the domination number employed previously. An important advantage of the relative arc density is that, properly re-scaled, it is a U-statistic, facilitating analytic study of its asymptotic distribution using standard U-statistic central limit theory. The approach is illustrated with an application to the testing of spatial patterns of segregation and association. Knowledge of the asymptotic distribution allows evaluation of the Pitman and Hodges-Lehmann asymptotic efficacy, and selection of the proximity map parameter to optimize efficacy. Notice that the approach presented here also has the advantage of validity for data in any dimension.

Statistical pattern classification methods based on data-random graphs were introduced recently. In this approach, a random directed graph is constructed from the data using the relative positions of the data points from various classes. Different random graphs result from different definitions of the proximity region associated with each data point and different graph statistics can be employed for data reduction. The approach used in this article is based on a parameterized family of proximity maps determining an associated family of data-random digraphs. The relative arc density of the digraph is used as the summary statistic, providing an alternative to the domination number employed previously. An important advantage of the relative arc density is that, properly re-scaled, it is a U-statistic, facilitating analytic study of its asymptotic distribution using standard U-statistic central limit theory. The approach is illustrated with an application to the testing of spatial patterns of segregation and association. Knowledge of the asymptotic distribution allows evaluation of the Pitman and Hodges-Lehmann asymptotic efficacy, and selection of the proximity map parameter to optimize efficacy. Notice that the approach presented here also has the advantage of validity for data in any dimension.

Statistical pattern classification methods based on data-random graphs were introduced recently. In this approach, a random directed graph is constructed from the data using the relative positions of the data points from various classes. Different random graphs result from different definitions of the proximity region associated with each data point and different graph statistics can be employed for data reduction. The approach used in this article is based on a parameterized family of proximity maps determining an associated family of data-random digraphs. The relative arc density of the digraph is used as the summary statistic, providing an alternative to the domination number employed previously. An important advantage of the relative arc density is that, properly re-scaled, it is a U-statistic, facilitating analytic study of its asymptotic distribution using standard U-statistic central limit theory. The approach is illustrated with an application to the testing of spatial patterns of segregation and association. Knowledge of the asymptotic distribution allows evaluation of the Pitman and Hodges-Lehmann asymptotic efficacy, and selection of the proximity map parameter to optimize efficacy. Notice that the approach presented here also has the advantage of validity for data in any dimension.

Statistical pattern classification methods based on data-random graphs were introduced recently. In this approach, a random directed graph is constructed from the data using the relative positions of the data points from various classes. Different random graphs result from different definitions of the proximity region associated with each data point and different graph statistics can be employed for data reduction. The approach used in this article is based on a parameterized family of proximity maps determining an associated family of data-random digraphs. The relative arc density of the digraph is used as the summary statistic, providing an alternative to the domination number employed previously. An important advantage of the relative arc density is that, properly re-scaled, it is a U-statistic, facilitating analytic study of its asymptotic distribution using standard U-statistic central limit theory. The approach is illustrated with an application to the testing of spatial patterns of segregation and association. Knowledge of the asymptotic distribution allows evaluation of the Pitman and Hodges-Lehmann asymptotic efficacy, and selection of the proximity map parameter to optimize efficacy. Notice that the approach presented here also has the advantage of validity for data in any dimension.

This article is from Fems Microbiology Ecology , volume 85 . Abstract The composition of prokaryotic communities was determined in the meso- and bathypelagic waters funneled through the Romanche Fracture Zone (RFZ, 2°7′S, 31°79′W to 0°6′N, 14°33′W) in the tropical Atlantic. Distinct water masses were identified based on their physical and chemical characteristics. The bacterial and archaeal communities were depth-stratified with a total of 116 and 25 operational taxonomic units (OTUs), respectively, distributed among the distinct water masses as revealed by terminal restriction fragment length polymorphism, and cloning and sequencing. The relative abundance of Thaumarchaeota, determined by catalyzed reporter deposition-fluorescence in situ hybridization, was significantly higher in deeper layers (Antarctic Bottom Water, AABW, > 4000 m depth), contributing up to 31% to the total prokaryotic community, than in the mesopelagic and lower euphotic layer. Although the contribution of SAR11 to bacterial abundance did not increase with depth, SAR202, SAR324, SAR406 and Alteromonas did increase with depth. Terminal restriction fragment length polymorphism analysis revealed successional changes in the bacterial and archaeal community composition of the North Atlantic Deep Water (NADW) with a passage time through the RFZ of c. 4 months but not in the under- and overlying water masses. Our results indicate that specific water masses harbor distinct bacterial and archaeal communities and that the prokaryotic community of the NADW undergoes successional changes in this conduit between the western and eastern Atlantic basin. Apparently, in the absence of major input of organic matter to specific deep-water masses, the indigenous prokaryotic community adapts to subtle physical and biogeochemical changes in the water mass within a time frame of weeks, similar to the reported seasonal changes in surface water prokaryotic communities.

The analysis of spatial point patterns that occur in the network domain have recently gained much attraction and various intensity functions and measures have been proposed. However, the linkage of spatial network statistics to regression models has not been approached so far. This paper presents a new regression approach which treats a generic intensity function of a planar point pattern that occurred on a network as the outcome of a set of different covariates and various graph statistics. Different to all alternative approaches, our model is the first which permits the statistical analysis of complex regression data in the context of network intensity functions for spatial point patterns. The potential of our new technique to model the structural dependencies of network intensity functions on various covariates and graph statistics is illustrated using call-in data on neighbour and community disturbances in an urban context.

We propose a model equation for the dynamics of tree density in mesic savannas. It considers long-range competition among trees and the effect of fire acting as a local facilitation mechanism. Despite short-range facilitation is taken to the local-range limit, the standard full spectrum of spatial structures obtained in general vegetation models is recovered. Long-range competition is thus the key ingredient for the development of patterns. The long time coexistence between trees and grass, and how fires affect the survival of trees as well as the maintenance of the patterns is studied. The influence of demographic noise is analyzed. The stochastic system, under the parameter constraints typical of mesic savannas, shows irregular patterns characteristics of realistic situations. The coexistence of trees and grass still remains at reasonable noise intensities.

We propose a model equation for the dynamics of tree density in mesic savannas. It considers long-range competition among trees and the effect of fire acting as a local facilitation mechanism. Despite short-range facilitation is taken to the local-range limit, the standard full spectrum of spatial structures obtained in general vegetation models is recovered. Long-range competition is thus the key ingredient for the development of patterns. The long time coexistence between trees and grass, and how fires affect the survival of trees as well as the maintenance of the patterns is studied. The influence of demographic noise is analyzed. The stochastic system, under the parameter constraints typical of mesic savannas, shows irregular patterns characteristics of realistic situations. The coexistence of trees and grass still remains at reasonable noise intensities.

The spatial and temporal patterns of land settlements during poly-crises - financial, climatic, and humanitarian - are reviewed. Research gaps are identified, socio-spatial and governance implications are examined, and indicators for policy responses and resilience building are suggested.

Transport: general interest

Transport: general interest

Layouts of the campuses of nine American, English and Canadian universities show how five types of growth patterns (axial, linear, radial, precinctual and cellular/grid) provide for flexibility in expansion. The site of the campus, a major influence in growth patterns, is discussed from the aspects of location, accessibility, physical characteristics, and climate. Pedestrian and vehicular movement patterns are also illustrated and discussed. Plans and descriptive diagrams develop the theme throughout. (RK)

Layouts of the campuses of nine American, English and Canadian universities show how five types of growth patterns (axial, linear, radial, precinctual and cellular/grid) provide for flexibility in expansion. The site of the campus, a major influence in growth patterns, is discussed from the aspects of location, accessibility, physical characteristics, and climate. Pedestrian and vehicular movement patterns are also illustrated and discussed. Plans and descriptive diagrams develop the theme throughout. (RK)

Layouts of the campuses of nine American, English and Canadian universities show how five types of growth patterns (axial, linear, radial, precinctual and cellular/grid) provide for flexibility in expansion. The site of the campus, a major influence in growth patterns, is discussed from the aspects of location, accessibility, physical characteristics, and climate. Pedestrian and vehicular movement patterns are also illustrated and discussed. Plans and descriptive diagrams develop the theme throughout. (RK)

Layouts of the campuses of nine American, English and Canadian universities show how five types of growth patterns (axial, linear, radial, precinctual and cellular/grid) provide for flexibility in expansion. The site of the campus, a major influence in growth patterns, is discussed from the aspects of location, accessibility, physical characteristics, and climate. Pedestrian and vehicular movement patterns are also illustrated and discussed. Plans and descriptive diagrams develop the theme throughout. (RK)

Conference proceedings

Complex spatial and temporal patterns of gene expression underlie embryo differentiation, yet methods do not yet exist for the efficient genome-wide determination of spatial expression patterns during development. In situ imaging of transcripts and proteins is the gold-standard, but it is difficult and time consuming to apply to an entire genome, even when highly automated. Sequencing, in contrast, is fast and genome-wide, but is generally applied to homogenized tissues, thereby discarding spatial information. It is likely that these methods will ultimately converge, and we will be able to sequence RNAs in situ, simultaneously determining their identity and location. As a step along this path, we developed methods to cryosection individual blastoderm stage Drosophila melanogaster embryos along the anterior-posterior axis and sequence the mRNA isolated from each 25 micron slice. The spatial patterns of gene expression we infer closely match patterns previously determined by in situ hybridization and microscopy. We applied this method to generate a genome-wide timecourse of spatial gene expression from shortly after fertilization through gastrulation. We identify numerous genes with spatial patterns that have not yet been described in the several ongoing systematic in situ based projects. This simple experiment demonstrates the potential for combining careful anatomical dissection with high-throughput sequencing to obtain spatially resolved gene expression on a genome-wide scale.

We consider pattern selection process in a wide aperture VCSEL near threshold. We show that for a square geometry of the laser aperture, the patterns formed at lasing threshold can be very complicated because of a possible misalignment between directions of an intrinsic spatial anisotropy of VCSEL and lateral boundaries of its aperture. The analogy with quantum billiard structures is established, and fingerprints of wave chaos are found. Influence of localized inhomogeneous in the pump current is also considered.

In May 1991, coincident with an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) overflight, a ground-based study covering 9 hectares of an annual grassland was completed. There were two goals of this ground study: (1) obtain ecologically and physiologically meaningful data for relating AVIRIS images to canopy structure, biochemistry, and physiology; and (2) evaluate the suitability of the 20-m AVIRIS pixel size for depicting detailed spatial patterns of productivity.

In May 1991, coincident with an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) overflight, a ground-based study covering 9 hectares of an annual grassland was completed. There were two goals of this ground study: (1) obtain ecologically and physiologically meaningful data for relating AVIRIS images to canopy structure, biochemistry, and physiology; and (2) evaluate the suitability of the 20-m AVIRIS pixel size for depicting detailed spatial patterns of productivity.

A cellular automaton in which cells represent agents playing the Prisoner's Dilemma (PD) game following the simple "win-stay, loose-shift" strategy is studied. Individuals with binary behavior, such as they can either cooperate (C) or defect (D), play repeatedly with their neighbors (Von Neumann's and Moore's neighborhoods). Their utilities in each round of the game are given by a rescaled payoff matrix described by a single parameter Tau, which measures the ratio of 'temptation to defect' to 'reward for cooperation'. Depending on the region of the parameter space Tau, the system self-organizes - after a transient - into dynamical equilibrium states characterized by different definite fractions of C agents (2 states for the Von Neumann neighborhood and 4 for Moore neighborhood). For some ranges of Tau the cluster size distributions, the power spectrums P(f) and the perimeter-area curves follow power-law scalings. Percolation below threshold is also found for D agent clusters. We also analyze the asynchronous dynamics version of this model and compare results.

The distribution patterns of mafic-ultramafic xenoliths within Archaean orthogneiss terrain furnish an essential key for the elucidation of granite-greenstone relations. Most greenstone belts constitute mega-xenoliths rather than primary basin structures. Transition along strike and across strike between stratigraphically low greenstone sequences and xenolith chains demonstrate their contemporaneity. These terrains represent least deformed cratonic islands within an otherwise penetratively foliated deformed gneiss-greenstone crust. Whereas early greenstone sequences are invariably intruded by tonalitic/trondhjemitic/granodioritic gneisses, stratigraphically higher successions may locally overlap older gneiss terrains and their entrained xenoliths unconformably. The contiguity of xenolith patterns suggests their derivation as relics of regional mafic-ultramafic volcanic crustal units and places limits on horizontal movements between individual crustal blocks.

The provision of accurate methods for predicting the climate response to anthropogenic and natural forcings is a key contemporary scientific challenge. Using a simplified and efficient open-source general circulation model of the atmosphere featuring O($10^5$) degrees of freedom, we show how it is possible to approach such a problem using nonequilibrium statistical mechanics. Response theory allows one to practically compute the time-dependent measure supported on the pullback attractor of the climate system, whose dynamics is non-autonomous as a result of time-dependent forcings. We propose a simple yet efficient method for predicting - at any lead time and in an ensemble sense - the change in climate properties resulting from increase in the concentration of CO$_2$ using test perturbation model runs. We assess strengths and limitations of the response theory in predicting the changes in the globally averaged values of surface temperature and of the yearly total precipitation, as well as in their spatial patterns. The quality of the predictions obtained for the surface temperature fields is rather good, while in the case of precipitation a good skill is observed only for the global average. We also show how it is possible to define accurately concepts like the the inertia of the climate system or to predict when climate change is detectable given a scenario of forcing. Our analysis can be extended for dealing with more complex portfolios of forcings and can be adapted to treat, in principle, any climate observable. Our conclusion is that climate change is indeed a problem that can be effectively seen through a statistical mechanical lens, and that there is great potential for optimizing the current coordinated modelling exercises run for the preparation of the subsequent reports of the Intergovernmental Panel for Climate Change.

This paper presents an initial exploration of high frequency records of extreme wind speed in two steps. The first consists in finding the suitable extreme distribution for $120$ measuring stations in Switzerland, by comparing three known distributions: Weibull, Gamma, and Generalized extreme value. This comparison serves as a basis for the second step which applies a spatial modelling by using Extreme Learning Machine. The aim is to model distribution parameters by employing a high dimensional input space of topographical information. The knowledge of probability distribution gives a comprehensive information and a global overview of wind phenomena. Through this study, a flexible and a simple modelling approach is presented, which can be generalized to almost extreme environmental data for risk assessment and to model renewable energy.

This paper presents an initial exploration of high frequency records of extreme wind speed in two steps. The first consists in finding the suitable extreme distribution for $120$ measuring stations in Switzerland, by comparing three known distributions: Weibull, Gamma, and Generalized extreme value. This comparison serves as a basis for the second step which applies a spatial modelling by using Extreme Learning Machine. The aim is to model distribution parameters by employing a high dimensional input space of topographical information. The knowledge of probability distribution gives a comprehensive information and a global overview of wind phenomena. Through this study, a flexible and a simple modelling approach is presented, which can be generalized to almost extreme environmental data for risk assessment and to model renewable energy.

We introduce a new biologically-motivated model of sequential spatial memory which is based on the principle of winnerless competition (WLC). We implement this mechanism in a two-layer neural network structure and present the learning dynamics which leads to the formation of a WLC network. After learning, the system is capable of associative retrieval of pre-recorded sequences of spatial patterns.

This paper addresses the question of how population diffusion affects the formation of the spatial patterns in the spatial epidemic model by Turing mechanisms. In particular, we present theoretical analysis to results of the numerical simulations in two dimensions. Moreover, there is a critical value for the system within the linear regime. Below the critical value the spatial patterns are impermanent, whereas above it stationary spot and stripe patterns can coexist over time. We have observed the striking formation of spatial patterns during the evolution, but the isolated ordered spot patterns don't emerge in the space.

This paper presents an original ABC algorithm, "ABC Shadow", that can be applied to sample posterior densities that are continuously differentiable. The proposed method uses the ideas given by the auxiliary variable MH of (M\o ller and Waagepetersen, 2004). The obtained algorithm solves the main condition to be fulfilled by any ABC algorithm, in order to be useful in practice. This condition requires enough samples in the parameter space region, induced by the observed statistics (Blum, 2010). The algorithm is tuned on the posterior of a Gaussian model which is entirely known, and then it is applied for the statistical analysis of several spatial patterns. These patterns are issued or assumed to be outcomes of point processes. The considered models are: Strauss, Candy and area-interaction.

We present here a new method, MMF, for automatically segmenting cosmic structure into its basic components: clusters, filaments, and walls. Importantly, the segmentation is scale independent, so all structures are identified without prejudice as to their size or shape. The method is ideally suited for extracting catalogues of clusters, walls, and filaments from samples of galaxies in redshift surveys or from particles in cosmological N-body simulations: it makes no prior assumptions about the scale or shape of the structures.}

Self-arrangement of individuals into spatial patterns often accompanies and promotes species diversity in ecological systems. Here, we investigate pattern formation arising from cyclic dominance of three species, operating near a bifurcation point. In its vicinity, an Eckhaus instability occurs, leading to convectively unstable "blurred" patterns. At the bifurcation point, stochastic effects dominate and induce counterintuitive effects on diversity: Large patterns, emerging for medium values of individuals' mobility, lead to rapid species extinction, while small patterns (low mobility) promote diversity, and high mobilities render spatial structures irrelevant. We provide a quantitative analysis of these phenomena, employing a complex Ginzburg-Landau equation.

http://uf.catalog.fcla.edu/uf.jsp?st=UF001119952&ix=pm&I=0&V=D&pm=1

http://uf.catalog.fcla.edu/uf.jsp?st=UF001119952&ix=pm&I=0&V=D&pm=1

http://uf.catalog.fcla.edu/uf.jsp?st=UF001119952&ix=pm&I=0&V=D&pm=1

Management sustainability related tuberculosis patient treatment was limited. Tuberculosis analysis was still in the form of data aggregation. This is cross sectional survey using geographical information system, analyzed by descriptive methods, the sample included 162 pulmonary tuberrculosis patient in 2014. The variables were pulmonary tuberrculosis patients and isohyet data. Mycrobacterium tuberculosis will be survive and multiply during rainy season. Rainfall data was an increasing pattern from first quarter to fourth quarter in 2014, however data in 2011, 2012 and 2013, which each quarter was largely experiencing sustained increase and decline. Pulmonary tuberrculosis patients were most prevalent in 2014. It was increase in the rainy season. The most high rainfall intensity (&gt; 2400 mm) in east of Lendah and western of Kokap areas, while the lowest intensity (&lt; 1500 mm) in east of Nanggulan, in the south of Panjatan and Galur areas. It was mostly located in areas with high rainfall intensity (2200 - 2400 mm) which spreads and stretches in Sentolo, Wates, and Panjatan areas. Pulmonary tuberrculosis occurred over the rainy season. Spatial pattern distribution of pulmonary tuberrculosis patients in high rainfall intensity spreads and stretches from east to west areas. Active case monitoring program should be performed by tuberculosis program that concerned in areas of high rainfall intensity.

This article is from PLoS ONE , volume 9 . Abstract To examine the method for estimating the spatial patterns of soil respiration (Rs) in agricultural ecosystems using remote sensing and geographical information system (GIS), Rs rates were measured at 53 sites during the peak growing season of maize in three counties in North China. Through Pearson's correlation analysis, leaf area index (LAI), canopy chlorophyll content, aboveground biomass, soil organic carbon (SOC) content, and soil total nitrogen content were selected as the factors that affected spatial variability in Rs during the peak growing season of maize. The use of a structural equation modeling approach revealed that only LAI and SOC content directly affected Rs. Meanwhile, other factors indirectly affected Rs through LAI and SOC content. When three greenness vegetation indices were extracted from an optical image of an environmental and disaster mitigation satellite in China, enhanced vegetation index (EVI) showed the best correlation with LAI and was thus used as a proxy for LAI to estimate Rs at the regional scale. The spatial distribution of SOC content was obtained by extrapolating the SOC content at the plot scale based on the kriging interpolation method in GIS. When data were pooled for 38 plots, a first-order exponential analysis indicated that approximately 73% of the spatial variability in Rs during the peak growing season of maize can be explained by EVI and SOC content. Further test analysis based on independent data from 15 plots showed that the simple exponential model had acceptable accuracy in estimating the spatial patterns of Rs in maize fields on the basis of remotely sensed EVI and GIS-interpolated SOC content, with R2 of 0.69 and root-mean-square error of 0.51 µmol CO2 m−2 s−1. The conclusions from this study provide valuable information for estimates of Rs during the peak growing season of maize in three counties in North China.

This article is from BMC Public Health , volume 14 . Abstract Background: Mobile populations are at a high risk of malaria infection and suspected to carry and spread resistant parasites. The Myanmar National Malaria Control Programme focuses on preventive interventions and vector control measures for the temporary mobile/migrant workers in Myanmar Artemisinin Resistance Containment Zones. Methods: A prospective cross-sectional study was conducted in 2012 in Kawthaung and Bokepyin townships of Tanintharyi Region, Myanmar, covering 192 mobile/migrant aggregates. The objectives were to identify the spatial distribution of the mobile/migrant populations, and to assess knowledge, attitudes, perceptions, and practices concerning malaria prevention and control, and their preferred methods of interventions. The structure of the192 migrant aggregates was investigated using a migrant mapping tool. Individual and household information was collected by structured interviews of 408 respondents from 39 aggregates, supplemented by 12 in-depth interviews of health care providers, authorities, volunteers, and employers. Data were analyzed by triangulating quantitative and qualitative data. Results: The primary reasons for the limitation in access to formal health services for suspected malaria within 24 hours were identified to be scattered distribution of migrant aggregates, variable working hours and the lack of transportation. Only 19.6% of respondents reported working at night from dusk to dawn. Among study populations, 73% reported a perceived risk of contracting malaria and 60% reported to know how to confirm a suspected case of malaria. Moreover, only 15% was able to cite correct antimalarial drugs, and less than 10% believed that non-compliance with antimalarial treatment may be related to the risk of drug resistance. About 50% of study population reported to seeking health care from the public sector, and to sleep under ITNs/LLINs the night before the survey. There was a gap in willingness to buy ITNs/LLINs and affordability (88.5% vs. 60.2%) which may affect their sustained and consistent use. Only 32.4% across all aggregates realized the importance of community participation in effective malaria prevention and control. Conclusions: Community-based innovative approaches through strong collaboration and coordination of multi-stakeholders are desirable for relaying information on ITNs/LLINs, rapid diagnostic test, and artemisinin combination therapy and drug resistance successfully across the social and economic diversity of mobile/migrant aggregates in Myanmar.

This article is from International Journal of Environmental Research and Public Health , volume 8 . Abstract Negative air ions (NAI) produce biochemical reactions that increase the levels of the mood chemical serotonin in the environment. Moreover, they benefit both the psychological well being and the human body’s physiological condition. The aim of this research was to estimate and measure the spatial distributions of negative and positive air ions in a residential garden in central Taiwan. Negative and positive air ions were measured at thirty monitoring locations in the study garden from July 2009 to June 2010. Moreover, Kriging was applied to estimate the spatial distribution of negative and positive air ions, as well as the air ion index in the study area. The measurement results showed that the numbers of NAI and PAI differed greatly during the four seasons, the highest and the lowest negative and positive air ion concentrations were found in the summer and winter, respectively. Moreover, temperature was positively affected negative air ions concentration. No matter what temperature is, the ranges of variogram in NAI/PAI were similar during four seasons. It indicated that spatial patterns of NAI/PAI were independent of the seasons and depended on garden elements and configuration, thus the NAP/PAI was a good estimate of the air quality regarding air ions. Kriging maps depicted that the highest negative and positive air ion concentration was next to the waterfall, whereas the lowest air ions areas were next to the exits of the garden. The results reveal that waterscapes are a source of negative and positive air ions, and that plants and green space are a minor source of negative air ions in the study garden. Moreover, temperature and humidity are positively and negatively affected negative air ions concentration, respectively. The proposed monitoring and mapping approach provides a way to effectively assess the patterns of negative and positive air ions in future landscape design projects.

This article is from Sensors (Basel, Switzerland) , volume 14 . Abstract The main objective of this study was to compare two apparent soil electrical conductivity (ECa) sensors (Veris 2000 XA and DUALEM 1S) for mapping variability of soil properties in a Mediterranean shallow soil. This study also aims at studying the effect of soil cover vegetation on the ECa measurement by the two types of sensors. The study was based on two surveys carried out under two very different situations: in February of 2012, with low soil moisture content (SMC) and with high and differentiated vegetation development (non grazed pasture), and in February of 2013, with high SMC and with short and relatively homogeneous vegetation development (grazed pasture). The greater temporal stability of Veris sensor, despite the wide variation in the SMC and vegetation ground cover indicates the suitability of using this sensor for monitoring soil properties in permanent pastures. The survey carried out with the DUALEM sensor in 2012 might have been affected by the presence of a 0.20 m vegetation layer at the soil surface, masking the soil properties. These differences should be considered in the selection of ECa sensing systems for a particular application.