Functional inverted Wishart for Bayesian multivariate spatial modeling with application to regional climatology model data

PubMed Central

Duan, L. L.; Szczesniak, R. D.; Wang, X.

2018-01-01

Modern environmental and climatological studies produce multiple outcomes at high spatial resolutions. Multivariate spatial modeling is an established means to quantify cross-correlation among outcomes. However, existing models typically suffer from poor computational efficiency and lack the flexibility to simultaneously estimate auto- and cross-covariance structures. In this article, we undertake a novel construction of covariance by utilizing spectral convolution and by imposing an inverted Wishart prior on the cross-correlation structure. The cross-correlation structure with this functional inverted Wishart prior flexibly accommodates not only positive but also weak or negative associations among outcomes while preserving spatial resolution. Furthermore, the proposed model is computationally efficient and produces easily interpretable results, including the individual autocovariances and full cross-correlation matrices, as well as a partial cross-correlation matrix reflecting the outcome correlation after excluding the effects caused by spatial convolution. The model is examined using simulated data sets under different scenarios. It is also applied to the data from the North American Regional Climate Change Assessment Program, examining long-term associations between surface outcomes for air temperature, pressure, humidity, and radiation, on the land area of the North American West Coast. Results and predictive performance are compared with findings from approaches using convolution only or coregionalization. PMID:29576735

A Patch Density Recommendation based on Convergence Studies for Vehicle Panel Vibration Response resulting from Excitation by a Diffuse Acoustic Field

NASA Technical Reports Server (NTRS)

Smith, Andrew; LaVerde, Bruce; Jones, Douglas; Towner, Robert; Hunt, Ron

2013-01-01

Fluid structural interaction problems that estimate panel vibration from an applied pressure field excitation are quite dependent on the spatial correlation of the pressure field. There is a danger of either over estimating a low frequency response or under predicting broad band panel response in the more modally dense bands if the pressure field spatial correlation is not accounted for adequately. Even when the analyst elects to use a fitted function for the spatial correlation an error may be introduced if the choice of patch density is not fine enough to represent the more continuous spatial correlation function throughout the intended frequency range of interest. Both qualitative and quantitative illustrations evaluating the adequacy of different patch density assumptions to approximate the fitted spatial correlation function are provided. The actual response of a typical vehicle panel system is then evaluated in a convergence study where the patch density assumptions are varied over the same finite element model. The convergence study results are presented illustrating the impact resulting from a poor choice of patch density. The fitted correlation function used in this study represents a Diffuse Acoustic Field (DAF) excitation of the panel to produce vibration response.

Investigating the effect of previous treatments on wheat biomass over multiple spatial frequencies

NASA Astrophysics Data System (ADS)

Milne, A. E.; Castellanos, M. T.; Cartagena, M. C.; Tarquis, A. M.; Lark, R. M.

2010-09-01

In this study we use the maximum overlap discrete packet transform (MODWPT) to investigate residual effects on wheat biomass of fertigation treatments applied to a previous crop. The wheat crop covered nine subplots from a previous experiment on melon response to fertigation. Each subplot had previously received a different level of applied nitrogen. Many factors affect wheat biomass, causing it to vary at different spatial frequencies. We hypothesize that these will include residual effects from fertilizer application (at relatively low spatial frequencies) and the local influence of individual plants from the previous melon crop (at high frequency). To test this hypothesis we use the MODWPT to identify the dominant spatial frequencies of wheat biomass variation, and analyse the relationship to both the previous fertilizer application and the location of individual melon plants in the previous crop. The MODWPT is particularly appropriate for this because it allows us first to identify the key spatial frequencies in the wheat biomass objectively and to analyse them, and their relationship to hypothesized driving factors without any assumptions of uniformity (stationarity) of wheat-biomass variation. The results showed that the applied nitrogen dominated the wheat biomass response, and that there was a noticeable component of wheat-biomass variation at the spatial frequency that corresponds to the melon cropping. We expected wheat biomass to be negatively correlated with the position of melons in the previous crop, due to uptake of the applied nitrogen. The MODWPT, which allows us to detect changes in correlation between variables at different frequencies, showed that such a relationship was found across part of the experiment but not uniformly.

Time-varying correlations in global real estate markets: A multivariate GARCH with spatial effects approach

NASA Astrophysics Data System (ADS)

Gu, Huaying; Liu, Zhixue; Weng, Yingliang

2017-04-01

The present study applies the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) with spatial effects approach for the analysis of the time-varying conditional correlations and contagion effects among global real estate markets. A distinguishing feature of the proposed model is that it can simultaneously capture the spatial interactions and the dynamic conditional correlations compared with the traditional MGARCH models. Results reveal that the estimated dynamic conditional correlations have exhibited significant increases during the global financial crisis from 2007 to 2009, thereby suggesting contagion effects among global real estate markets. The analysis further indicates that the returns of the regional real estate markets that are in close geographic and economic proximities exhibit strong co-movement. In addition, evidence of significantly positive leverage effects in global real estate markets is also determined. The findings have significant implications on global portfolio diversification opportunities and risk management practices.

Denoising Algorithm for CFA Image Sensors Considering Inter-Channel Correlation.

PubMed

Lee, Min Seok; Park, Sang Wook; Kang, Moon Gi

2017-05-28

In this paper, a spatio-spectral-temporal filter considering an inter-channel correlation is proposed for the denoising of a color filter array (CFA) sequence acquired by CCD/CMOS image sensors. Owing to the alternating under-sampled grid of the CFA pattern, the inter-channel correlation must be considered in the direct denoising process. The proposed filter is applied in the spatial, spectral, and temporal domain, considering the spatio-tempo-spectral correlation. First, nonlocal means (NLM) spatial filtering with patch-based difference (PBD) refinement is performed by considering both the intra-channel correlation and inter-channel correlation to overcome the spatial resolution degradation occurring with the alternating under-sampled pattern. Second, a motion-compensated temporal filter that employs inter-channel correlated motion estimation and compensation is proposed to remove the noise in the temporal domain. Then, a motion adaptive detection value controls the ratio of the spatial filter and the temporal filter. The denoised CFA sequence can thus be obtained without motion artifacts. Experimental results for both simulated and real CFA sequences are presented with visual and numerical comparisons to several state-of-the-art denoising methods combined with a demosaicing method. Experimental results confirmed that the proposed frameworks outperformed the other techniques in terms of the objective criteria and subjective visual perception in CFA sequences.

In vivo correlation mapping microscopy

NASA Astrophysics Data System (ADS)

McGrath, James; Alexandrov, Sergey; Owens, Peter; Subhash, Hrebesh; Leahy, Martin

2016-04-01

To facilitate regular assessment of the microcirculation in vivo, noninvasive imaging techniques such as nailfold capillaroscopy are required in clinics. Recently, a correlation mapping technique has been applied to optical coherence tomography (OCT), which extends the capabilities of OCT to microcirculation morphology imaging. This technique, known as correlation mapping optical coherence tomography, has been shown to extract parameters, such as capillary density and vessel diameter, and key clinical markers associated with early changes in microvascular diseases. However, OCT has limited spatial resolution in both the transverse and depth directions. Here, we extend this correlation mapping technique to other microscopy modalities, including confocal microscopy, and take advantage of the higher spatial resolution offered by these modalities. The technique is achieved as a processing step on microscopy images and does not require any modification to the microscope hardware. Results are presented which show that this correlation mapping microscopy technique can extend the capabilities of conventional microscopy to enable mapping of vascular networks in vivo with high spatial resolution in both the transverse and depth directions.

The relationship between the spatial scaling of biodiversity and ecosystem stability

PubMed Central

Delsol, Robin; Loreau, Michel; Haegeman, Bart

2018-01-01

Aim Ecosystem stability and its link with biodiversity have mainly been studied at the local scale. Here we present a simple theoretical model to address the joint dependence of diversity and stability on spatial scale, from local to continental. Methods The notion of stability we use is based on the temporal variability of an ecosystem-level property, such as primary productivity. In this way, our model integrates the well-known species–area relationship (SAR) with a recent proposal to quantify the spatial scaling of stability, called the invariability–area relationship (IAR). Results We show that the link between the two relationships strongly depends on whether the temporal fluctuations of the ecosystem property of interest are more correlated within than between species. If fluctuations are correlated within species but not between them, then the IAR is strongly constrained by the SAR. If instead individual fluctuations are only correlated by spatial proximity, then the IAR is unrelated to the SAR. We apply these two correlation assumptions to explore the effects of species loss and habitat destruction on stability, and find a rich variety of multi-scale spatial dependencies, with marked differences between the two assumptions. Main conclusions The dependence of ecosystem stability on biodiversity across spatial scales is governed by the spatial decay of correlations within and between species. Our work provides a point of reference for mechanistic models and data analyses. More generally, it illustrates the relevance of macroecology for ecosystem functioning and stability. PMID:29651225

Thermodynamically consistent Langevin dynamics with spatially correlated noise predicting frictionless regime and transient attraction effect

NASA Astrophysics Data System (ADS)

Majka, M.; Góra, P. F.

2016-10-01

While the origins of temporal correlations in Langevin dynamics have been thoroughly researched, the understanding of spatially correlated noise (SCN) is rather incomplete. In particular, very little is known about the relation between friction and SCN. In this article, starting from the microscopic, deterministic model, we derive the analytical formula for the spatial correlation function in the particle-bath interactions. This expression shows that SCN is the inherent component of binary mixtures, originating from the effective (entropic) interactions. Further, employing this spatial correlation function, we postulate the thermodynamically consistent Langevin equation driven by the Gaussian SCN and calculate the adequate fluctuation-dissipation relation. The thermodynamical consistency is achieved by introducing the spatially variant friction coefficient, which can be also derived analytically. This coefficient exhibits a number of intriguing properties, e.g., the singular behavior for certain types of interactions. Eventually, we apply this new theory to the system of two charged particles in the presence of counter-ions. Such particles interact via the screened-charge Yukawa potential and the inclusion of SCN leads to the emergence of the anomalous frictionless regime. In this regime the particles can experience active propulsion leading to the transient attraction effect. This effect suggests a nonequilibrium mechanism facilitating the molecular binding of the like-charged particles.

Perception and psychological evaluation for visual and auditory environment based on the correlation mechanisms

NASA Astrophysics Data System (ADS)

Fujii, Kenji

2002-06-01

In this dissertation, the correlation mechanism in modeling the process in the visual perception is introduced. It has been well described that the correlation mechanism is effective for describing subjective attributes in auditory perception. The main result is that it is possible to apply the correlation mechanism to the process in temporal vision and spatial vision, as well as in audition. (1) The psychophysical experiment was performed on subjective flicker rates for complex waveforms. A remarkable result is that the phenomenon of missing fundamental is found in temporal vision as analogous to the auditory pitch perception. This implies the existence of correlation mechanism in visual system. (2) For spatial vision, the autocorrelation analysis provides useful measures for describing three primary perceptual properties of visual texture: contrast, coarseness, and regularity. Another experiment showed that the degree of regularity is a salient cue for texture preference judgment. (3) In addition, the autocorrelation function (ACF) and inter-aural cross-correlation function (IACF) were applied for analysis of the temporal and spatial properties of environmental noise. It was confirmed that the acoustical properties of aircraft noise and traffic noise are well described. These analyses provided useful parameters extracted from the ACF and IACF in assessing the subjective annoyance for noise. Thesis advisor: Yoichi Ando Copies of this thesis written in English can be obtained from Junko Atagi, 6813 Mosonou, Saijo-cho, Higashi-Hiroshima 739-0024, Japan. E-mail address: atagi\\@urban.ne.jp.

Correlates of individual, and age-related, differences in short-term learning.

PubMed

Zhang, Zhiyong; Davis, Hasker P; Salthouse, Timothy A; Tucker-Drob, Elliot M

2007-07-01

Latent growth models were applied to data on multitrial verbal and spatial learning tasks from two independent studies. Although significant individual differences in both initial level of performance and subsequent learning were found in both tasks, age differences were found only in mean initial level, and not in mean learning. In neither task was fluid or crystallized intelligence associated with learning. Although there were moderate correlations among the level parameters across the verbal and spatial tasks, the learning parameters were not significantly correlated with one another across task modalities. These results are inconsistent with the existence of a general (e.g., material-independent) learning ability.

Dempster-Shafer theory of evidence: A new approach to spatially model wildfire risk potential in central Chile.

PubMed

González, Cristián; Castillo, Miguel; García-Chevesich, Pablo; Barrios, Juan

2018-02-01

A spatial modeling was applied to Chilean wildfire occurrence, through the Dempster-Shafer's evidence theory and considering the 2006-2010 period for the Valparaiso Region (central Chile), a representative area for this experiment. Results indicate strong spatial correlation between documented wildfires and cumulative evidence maps, resulting in a powerful tool for future wildfire risk prevention programs. Copyright © 2017 Elsevier B.V. All rights reserved.

Spatially explicit models for inference about density in unmarked or partially marked populations

USGS Publications Warehouse

Chandler, Richard B.; Royle, J. Andrew

2013-01-01

Recently developed spatial capture–recapture (SCR) models represent a major advance over traditional capture–recapture (CR) models because they yield explicit estimates of animal density instead of population size within an unknown area. Furthermore, unlike nonspatial CR methods, SCR models account for heterogeneity in capture probability arising from the juxtaposition of animal activity centers and sample locations. Although the utility of SCR methods is gaining recognition, the requirement that all individuals can be uniquely identified excludes their use in many contexts. In this paper, we develop models for situations in which individual recognition is not possible, thereby allowing SCR concepts to be applied in studies of unmarked or partially marked populations. The data required for our model are spatially referenced counts made on one or more sample occasions at a collection of closely spaced sample units such that individuals can be encountered at multiple locations. Our approach includes a spatial point process for the animal activity centers and uses the spatial correlation in counts as information about the number and location of the activity centers. Camera-traps, hair snares, track plates, sound recordings, and even point counts can yield spatially correlated count data, and thus our model is widely applicable. A simulation study demonstrated that while the posterior mean exhibits frequentist bias on the order of 5–10% in small samples, the posterior mode is an accurate point estimator as long as adequate spatial correlation is present. Marking a subset of the population substantially increases posterior precision and is recommended whenever possible. We applied our model to avian point count data collected on an unmarked population of the northern parula (Parula americana) and obtained a density estimate (posterior mode) of 0.38 (95% CI: 0.19–1.64) birds/ha. Our paper challenges sampling and analytical conventions in ecology by demonstrating that neither spatial independence nor individual recognition is needed to estimate population density—rather, spatial dependence can be informative about individual distribution and density.

Social and spatial processes associated with childhood diarrheal disease in Matlab, Bangladesh.

PubMed

Perez-Heydrich, Carolina; Furgurson, Jill M; Giebultowicz, Sophia; Winston, Jennifer J; Yunus, Mohammad; Streatfield, Peter Kim; Emch, Michael

2013-01-01

We develop novel methods for conceptualizing geographic space and social networks to evaluate their respective and combined contributions to childhood diarrheal incidence. After defining maternal networks according to direct familial linkages between females, and road networks using satellite imagery of the study area, we use a spatial econometrics model to evaluate the significance of correlation terms relating childhood diarrheal incidence to the incidence observed within respective networks. Disease was significantly clustered within road networks across time, but only inconsistently correlated within maternal networks. These methods could be widely applied to systems in which both social and spatial processes jointly influence health outcomes. Copyright © 2012 Elsevier Ltd. All rights reserved.

Langevin Dynamics with Spatial Correlations as a Model for Electron-Phonon Coupling

NASA Astrophysics Data System (ADS)

Tamm, A.; Caro, M.; Caro, A.; Samolyuk, G.; Klintenberg, M.; Correa, A. A.

2018-05-01

Stochastic Langevin dynamics has been traditionally used as a tool to describe nonequilibrium processes. When utilized in systems with collective modes, traditional Langevin dynamics relaxes all modes indiscriminately, regardless of their wavelength. We propose a generalization of Langevin dynamics that can capture a differential coupling between collective modes and the bath, by introducing spatial correlations in the random forces. This allows modeling the electronic subsystem in a metal as a generalized Langevin bath endowed with a concept of locality, greatly improving the capabilities of the two-temperature model. The specific form proposed here for the spatial correlations produces a physical wave-vector and polarization dependency of the relaxation produced by the electron-phonon coupling in a solid. We show that the resulting model can be used for describing the path to equilibration of ions and electrons and also as a thermostat to sample the equilibrium canonical ensemble. By extension, the family of models presented here can be applied in general to any dense system, solids, alloys, and dense plasmas. As an example, we apply the model to study the nonequilibrium dynamics of an electron-ion two-temperature Ni crystal.

Spatial distribution of nuclei in progressive nucleation: Modeling and application

NASA Astrophysics Data System (ADS)

Tomellini, Massimo

2018-04-01

Phase transformations ruled by non-simultaneous nucleation and growth do not lead to random distribution of nuclei. Since nucleation is only allowed in the untransformed portion of space, positions of nuclei are correlated. In this article an analytical approach is presented for computing pair-correlation function of nuclei in progressive nucleation. This quantity is further employed for characterizing the spatial distribution of nuclei through the nearest neighbor distribution function. The modeling is developed for nucleation in 2D space with power growth law and it is applied to describe electrochemical nucleation where correlation effects are significant. Comparison with both computer simulations and experimental data lends support to the model which gives insights into the transition from Poissonian to correlated nearest neighbor probability density.

Latent spatial models and sampling design for landscape genetics

USGS Publications Warehouse

Hanks, Ephraim M.; Hooten, Mevin B.; Knick, Steven T.; Oyler-McCance, Sara J.; Fike, Jennifer A.; Cross, Todd B.; Schwartz, Michael K.

2016-01-01

We propose a spatially-explicit approach for modeling genetic variation across space and illustrate how this approach can be used to optimize spatial prediction and sampling design for landscape genetic data. We propose a multinomial data model for categorical microsatellite allele data commonly used in landscape genetic studies, and introduce a latent spatial random effect to allow for spatial correlation between genetic observations. We illustrate how modern dimension reduction approaches to spatial statistics can allow for efficient computation in landscape genetic statistical models covering large spatial domains. We apply our approach to propose a retrospective spatial sampling design for greater sage-grouse (Centrocercus urophasianus) population genetics in the western United States.

Spatial scan statistics for detection of multiple clusters with arbitrary shapes.

PubMed

Lin, Pei-Sheng; Kung, Yi-Hung; Clayton, Murray

2016-12-01

In applying scan statistics for public health research, it would be valuable to develop a detection method for multiple clusters that accommodates spatial correlation and covariate effects in an integrated model. In this article, we connect the concepts of the likelihood ratio (LR) scan statistic and the quasi-likelihood (QL) scan statistic to provide a series of detection procedures sufficiently flexible to apply to clusters of arbitrary shape. First, we use an independent scan model for detection of clusters and then a variogram tool to examine the existence of spatial correlation and regional variation based on residuals of the independent scan model. When the estimate of regional variation is significantly different from zero, a mixed QL estimating equation is developed to estimate coefficients of geographic clusters and covariates. We use the Benjamini-Hochberg procedure (1995) to find a threshold for p-values to address the multiple testing problem. A quasi-deviance criterion is used to regroup the estimated clusters to find geographic clusters with arbitrary shapes. We conduct simulations to compare the performance of the proposed method with other scan statistics. For illustration, the method is applied to enterovirus data from Taiwan. © 2016, The International Biometric Society.

A spatially adaptive spectral re-ordering technique for lossless coding of hyper-spectral images

NASA Technical Reports Server (NTRS)

Memon, Nasir D.; Galatsanos, Nikolas

1995-01-01

In this paper, we propose a new approach, applicable to lossless compression of hyper-spectral images, that alleviates some limitations of linear prediction as applied to this problem. According to this approach, an adaptive re-ordering of the spectral components of each pixel is performed prior to prediction and encoding. This re-ordering adaptively exploits, on a pixel-by pixel basis, the presence of inter-band correlations for prediction. Furthermore, the proposed approach takes advantage of spatial correlations, and does not introduce any coding overhead to transmit the order of the spectral bands. This is accomplished by using the assumption that two spatially adjacent pixels are expected to have similar spectral relationships. We thus have a simple technique to exploit spectral and spatial correlations in hyper-spectral data sets, leading to compression performance improvements as compared to our previously reported techniques for lossless compression. We also look at some simple error modeling techniques for further exploiting any structure that remains in the prediction residuals prior to entropy coding.

A consistent hierarchy of generalized kinetic equation approximations to the master equation applied to surface catalysis.

PubMed

Herschlag, Gregory J; Mitran, Sorin; Lin, Guang

2015-06-21

We develop a hierarchy of approximations to the master equation for systems that exhibit translational invariance and finite-range spatial correlation. Each approximation within the hierarchy is a set of ordinary differential equations that considers spatial correlations of varying lattice distance; the assumption is that the full system will have finite spatial correlations and thus the behavior of the models within the hierarchy will approach that of the full system. We provide evidence of this convergence in the context of one- and two-dimensional numerical examples. Lower levels within the hierarchy that consider shorter spatial correlations are shown to be up to three orders of magnitude faster than traditional kinetic Monte Carlo methods (KMC) for one-dimensional systems, while predicting similar system dynamics and steady states as KMC methods. We then test the hierarchy on a two-dimensional model for the oxidation of CO on RuO2(110), showing that low-order truncations of the hierarchy efficiently capture the essential system dynamics. By considering sequences of models in the hierarchy that account for longer spatial correlations, successive model predictions may be used to establish empirical approximation of error estimates. The hierarchy may be thought of as a class of generalized phenomenological kinetic models since each element of the hierarchy approximates the master equation and the lowest level in the hierarchy is identical to a simple existing phenomenological kinetic models.

Proceedings of the Third Annual Symposium on Mathematical Pattern Recognition and Image Analysis

NASA Technical Reports Server (NTRS)

Guseman, L. F., Jr.

1985-01-01

Topics addressed include: multivariate spline method; normal mixture analysis applied to remote sensing; image data analysis; classifications in spatially correlated environments; probability density functions; graphical nonparametric methods; subpixel registration analysis; hypothesis integration in image understanding systems; rectification of satellite scanner imagery; spatial variation in remotely sensed images; smooth multidimensional interpolation; and optimal frequency domain textural edge detection filters.

Implementations of geographically weighted lasso in spatial data with multicollinearity (Case study: Poverty modeling of Java Island)

NASA Astrophysics Data System (ADS)

Setiyorini, Anis; Suprijadi, Jadi; Handoko, Budhi

2017-03-01

Geographically Weighted Regression (GWR) is a regression model that takes into account the spatial heterogeneity effect. In the application of the GWR, inference on regression coefficients is often of interest, as is estimation and prediction of the response variable. Empirical research and studies have demonstrated that local correlation between explanatory variables can lead to estimated regression coefficients in GWR that are strongly correlated, a condition named multicollinearity. It later results on a large standard error on estimated regression coefficients, and, hence, problematic for inference on relationships between variables. Geographically Weighted Lasso (GWL) is a method which capable to deal with spatial heterogeneity and local multicollinearity in spatial data sets. GWL is a further development of GWR method, which adds a LASSO (Least Absolute Shrinkage and Selection Operator) constraint in parameter estimation. In this study, GWL will be applied by using fixed exponential kernel weights matrix to establish a poverty modeling of Java Island, Indonesia. The results of applying the GWL to poverty datasets show that this method stabilizes regression coefficients in the presence of multicollinearity and produces lower prediction and estimation error of the response variable than GWR does.

Correlative Stochastic Optical Reconstruction Microscopy and Electron Microscopy

PubMed Central

Kim, Doory; Deerinck, Thomas J.; Sigal, Yaron M.; Babcock, Hazen P.; Ellisman, Mark H.; Zhuang, Xiaowei

2015-01-01

Correlative fluorescence light microscopy and electron microscopy allows the imaging of spatial distributions of specific biomolecules in the context of cellular ultrastructure. Recent development of super-resolution fluorescence microscopy allows the location of molecules to be determined with nanometer-scale spatial resolution. However, correlative super-resolution fluorescence microscopy and electron microscopy (EM) still remains challenging because the optimal specimen preparation and imaging conditions for super-resolution fluorescence microscopy and EM are often not compatible. Here, we have developed several experiment protocols for correlative stochastic optical reconstruction microscopy (STORM) and EM methods, both for un-embedded samples by applying EM-specific sample preparations after STORM imaging and for embedded and sectioned samples by optimizing the fluorescence under EM fixation, staining and embedding conditions. We demonstrated these methods using a variety of cellular targets. PMID:25874453

Use of forecasting signatures to help distinguish periodicity, randomness, and chaos in ripples and other spatial patterns

USGS Publications Warehouse

Rubin, D.M.

1992-01-01

Forecasting of one-dimensional time series previously has been used to help distinguish periodicity, chaos, and noise. This paper presents two-dimensional generalizations for making such distinctions for spatial patterns. The techniques are evaluated using synthetic spatial patterns and then are applied to a natural example: ripples formed in sand by blowing wind. Tests with the synthetic patterns demonstrate that the forecasting techniques can be applied to two-dimensional spatial patterns, with the same utility and limitations as when applied to one-dimensional time series. One limitation is that some combinations of periodicity and randomness exhibit forecasting signatures that mimic those of chaos. For example, sine waves distorted with correlated phase noise have forecasting errors that increase with forecasting distance, errors that, are minimized using nonlinear models at moderate embedding dimensions, and forecasting properties that differ significantly between the original and surrogates. Ripples formed in sand by flowing air or water typically vary in geometry from one to another, even when formed in a flow that is uniform on a large scale; each ripple modifies the local flow or sand-transport field, thereby influencing the geometry of the next ripple downcurrent. Spatial forecasting was used to evaluate the hypothesis that such a deterministic process - rather than randomness or quasiperiodicity - is responsible for the variation between successive ripples. This hypothesis is supported by a forecasting error that increases with forecasting distance, a greater accuracy of nonlinear relative to linear models, and significant differences between forecasts made with the original ripples and those made with surrogate patterns. Forecasting signatures cannot be used to distinguish ripple geometry from sine waves with correlated phase noise, but this kind of structure can be ruled out by two geometric properties of the ripples: Successive ripples are highly correlated in wavelength, and ripple crests display dislocations such as branchings and mergers. ?? 1992 American Institute of Physics.

Contamination and spatial variation of heavy metals in the soil-rice system in Nanxun County, Southeastern China.

PubMed

Zhao, Keli; Fu, Weijun; Ye, Zhengqian; Zhang, Chaosheng

2015-01-28

There is an increasing concern about heavy metal contamination in farmland in China and worldwide. In order to reveal the spatial features of heavy metals in the soil-rice system, soil and rice samples were collected from Nanxun, Southeastern China. Compared with the guideline values, elevated concentrations of heavy metals in soils were observed, while heavy metals in rice still remained at a safe level. Heavy metals in soils and rice had moderate to strong spatial dependence (nugget/sill ratios: 13.2% to 49.9%). The spatial distribution of copper (Cu), nickel (Ni), lead (Pb) and zinc (Zn) in soils illustrated that their high concentrations were located in the southeast part. The high concentrations of cadmium (Cd) in soils were observed in the northeast part. The accumulation of all the studied metals is related to the long-term application of agrochemicals and industrial activities. Heavy metals in rice showed different spatial distribution patterns. Cross-correlograms were produced to quantitatively determine the spatial correlation between soil properties and heavy metals composition in rice. The pH and soil organic matter had significant spatial correlations with the concentration of heavy metals in rice. Most of the selected variables had clear spatial correlation ranges for heavy metals in rice, which could be further applied to divide agricultural management zones.

Contamination and Spatial Variation of Heavy Metals in the Soil-Rice System in Nanxun County, Southeastern China

PubMed Central

Zhao, Keli; Fu, Weijun; Ye, Zhengqian; Zhang, Chaosheng

2015-01-01

There is an increasing concern about heavy metal contamination in farmland in China and worldwide. In order to reveal the spatial features of heavy metals in the soil-rice system, soil and rice samples were collected from Nanxun, Southeastern China. Compared with the guideline values, elevated concentrations of heavy metals in soils were observed, while heavy metals in rice still remained at a safe level. Heavy metals in soils and rice had moderate to strong spatial dependence (nugget/sill ratios: 13.2% to 49.9%). The spatial distribution of copper (Cu), nickel (Ni), lead (Pb) and zinc (Zn) in soils illustrated that their high concentrations were located in the southeast part. The high concentrations of cadmium (Cd) in soils were observed in the northeast part. The accumulation of all the studied metals is related to the long-term application of agrochemicals and industrial activities. Heavy metals in rice showed different spatial distribution patterns. Cross-correlograms were produced to quantitatively determine the spatial correlation between soil properties and heavy metals composition in rice. The pH and soil organic matter had significant spatial correlations with the concentration of heavy metals in rice. Most of the selected variables had clear spatial correlation ranges for heavy metals in rice, which could be further applied to divide agricultural management zones. PMID:25635917

Statistical and Economic Techniques for Site-specific Nematode Management.

PubMed

Liu, Zheng; Griffin, Terry; Kirkpatrick, Terrence L

2014-03-01

Recent advances in precision agriculture technologies and spatial statistics allow realistic, site-specific estimation of nematode damage to field crops and provide a platform for the site-specific delivery of nematicides within individual fields. This paper reviews the spatial statistical techniques that model correlations among neighboring observations and develop a spatial economic analysis to determine the potential of site-specific nematicide application. The spatial econometric methodology applied in the context of site-specific crop yield response contributes to closing the gap between data analysis and realistic site-specific nematicide recommendations and helps to provide a practical method of site-specifically controlling nematodes.

Large-scale cortical correlation structure of spontaneous oscillatory activity

PubMed Central

Hipp, Joerg F.; Hawellek, David J.; Corbetta, Maurizio; Siegel, Markus; Engel, Andreas K.

2013-01-01

Little is known about the brain-wide correlation of electrophysiological signals. Here we show that spontaneous oscillatory neuronal activity exhibits frequency-specific spatial correlation structure in the human brain. We developed an analysis approach that discounts spurious correlation of signal power caused by the limited spatial resolution of electrophysiological measures. We applied this approach to source estimates of spontaneous neuronal activity reconstructed from magnetoencephalography (MEG). Overall, correlation of power across cortical regions was strongest in the alpha to beta frequency range (8–32 Hz) and correlation patterns depended on the underlying oscillation frequency. Global hubs resided in the medial temporal lobe in the theta frequency range (4–6 Hz), in lateral parietal areas in the alpha to beta frequency range (8–23 Hz), and in sensorimotor areas for higher frequencies (32–45 Hz). Our data suggest that interactions in various large-scale cortical networks may be reflected in frequency specific power-envelope correlations. PMID:22561454

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cable, J.W.

The diffuse scattering of neutrons from magnetic materials provides unique and important information regarding the spatial correlations of the atoms and the spins. Such measurements have been extensively applied to magnetically ordered systems, such as the ferromagnetic binary alloys, for which the observed correlations describe the magnetic moment fluctuations associated with local environment effects. With the advent of polarization analysis, these techniques are increasingly being applied to study disordered paramagnetic systems such as the spin-glasses and the diluted magnetic semiconductors. The spin-pair correlations obtained are essential in understanding the exchange interactions of such systems. In this paper, we describe recentmore » neutron diffuse scattering results on the atom-pair and spin-pair correlations in some of these disordered magnetic systems. 56 refs.« less

Spatial accuracy of a simplified disaggregation method for traffic emissions applied in seven mid-sized Chilean cities

NASA Astrophysics Data System (ADS)

Ossés de Eicker, Margarita; Zah, Rainer; Triviño, Rubén; Hurni, Hans

The spatial accuracy of top-down traffic emission inventory maps obtained with a simplified disaggregation method based on street density was assessed in seven mid-sized Chilean cities. Each top-down emission inventory map was compared against a reference, namely a more accurate bottom-up emission inventory map from the same study area. The comparison was carried out using a combination of numerical indicators and visual interpretation. Statistically significant differences were found between the seven cities with regard to the spatial accuracy of their top-down emission inventory maps. In compact cities with a simple street network and a single center, a good accuracy of the spatial distribution of emissions was achieved with correlation values>0.8 with respect to the bottom-up emission inventory of reference. In contrast, the simplified disaggregation method is not suitable for complex cities consisting of interconnected nuclei, resulting in correlation values<0.5. Although top-down disaggregation of traffic emissions generally exhibits low accuracy, the accuracy is significantly higher in compact cities and might be further improved by applying a correction factor for the city center. Therefore, the method can be used by local environmental authorities in cities with limited resources and with little knowledge on the pollution situation to get an overview on the spatial distribution of the emissions generated by traffic activities.

Combining HJ CCD, GF-1 WFV and MODIS Data to Generate Daily High Spatial Resolution Synthetic Data for Environmental Process Monitoring.

PubMed

Wu, Mingquan; Huang, Wenjiang; Niu, Zheng; Wang, Changyao

2015-08-20

The limitations of satellite data acquisition mean that there is a lack of satellite data with high spatial and temporal resolutions for environmental process monitoring. In this study, we address this problem by applying the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and the Spatial and Temporal Data Fusion Approach (STDFA) to combine Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field of view camera (GF-1 WFV) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to generate daily high spatial resolution synthetic data for land surface process monitoring. Actual HJ CCD and GF-1 WFV data were used to evaluate the precision of the synthetic images using the correlation analysis method. Our method was tested and validated for two study areas in Xinjiang Province, China. The results show that both the ESTARFM and STDFA can be applied to combine HJ CCD and MODIS reflectance data, and GF-1 WFV and MODIS reflectance data, to generate synthetic HJ CCD data and synthetic GF-1 WFV data that closely match actual data with correlation coefficients (r) greater than 0.8989 and 0.8643, respectively. Synthetic red- and near infrared (NIR)-band data generated by ESTARFM are more suitable for the calculation of Normalized Different Vegetation Index (NDVI) than the data generated by STDFA.

Combining HJ CCD, GF-1 WFV and MODIS Data to Generate Daily High Spatial Resolution Synthetic Data for Environmental Process Monitoring

PubMed Central

Wu, Mingquan; Huang, Wenjiang; Niu, Zheng; Wang, Changyao

2015-01-01

The limitations of satellite data acquisition mean that there is a lack of satellite data with high spatial and temporal resolutions for environmental process monitoring. In this study, we address this problem by applying the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and the Spatial and Temporal Data Fusion Approach (STDFA) to combine Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field of view camera (GF-1 WFV) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to generate daily high spatial resolution synthetic data for land surface process monitoring. Actual HJ CCD and GF-1 WFV data were used to evaluate the precision of the synthetic images using the correlation analysis method. Our method was tested and validated for two study areas in Xinjiang Province, China. The results show that both the ESTARFM and STDFA can be applied to combine HJ CCD and MODIS reflectance data, and GF-1 WFV and MODIS reflectance data, to generate synthetic HJ CCD data and synthetic GF-1 WFV data that closely match actual data with correlation coefficients (r) greater than 0.8989 and 0.8643, respectively. Synthetic red- and near infrared (NIR)-band data generated by ESTARFM are more suitable for the calculation of Normalized Different Vegetation Index (NDVI) than the data generated by STDFA. PMID:26308017

Latitude delineates patterns of biogeography in terrestrial Streptomyces.

PubMed

Choudoir, Mallory J; Doroghazi, James R; Buckley, Daniel H

2016-12-01

The biogeography of Streptomyces was examined at regional spatial scales to identify factors that govern patterns of microbial diversity. Streptomyces are spore forming filamentous bacteria which are widespread in soil. Streptomyces strains were isolated from perennial grass habitats sampled across a spatial scale of more than 6000 km. Previous analysis of this geographically explicit culture collection provided evidence for a latitudinal diversity gradient in Streptomyces species. Here the hypothesis that this latitudinal diversity gradient is a result of evolutionary dynamics associated with historical demographic processes was evaluated. Historical demographic phenomena have genetic consequences that can be evaluated through analysis of population genetics. Population genetic approaches were applied to analyze population structure in six of the most numerically abundant and geographically widespread Streptomyces phylogroups from our culture collection. Streptomyces population structure varied at regional spatial scales, and allelic diversity correlated with geographic distance. In addition, allelic diversity and gene flow are partitioned by latitude. Finally, it was found that nucleotide diversity within phylogroups was negatively correlated with latitude. These results indicate that phylogroup diversification is constrained by dispersal limitation at regional spatial scales, and they are consistent with the hypothesis that historical demographic processes have influenced the contemporary biogeography of Streptomyces. © 2016 Society for Applied Microbiology and John Wiley & Sons Ltd.

Recurrence quantification analysis applied to spatiotemporal pattern analysis in high-density mapping of human atrial fibrillation.

PubMed

Zeemering, Stef; Bonizzi, Pietro; Maesen, Bart; Peeters, Ralf; Schotten, Ulrich

2015-01-01

Spatiotemporal complexity of atrial fibrillation (AF) patterns is often quantified by annotated intracardiac contact mapping. We introduce a new approach that applies recurrence plot (RP) construction followed by recurrence quantification analysis (RQA) to epicardial atrial electrograms, recorded with a high-density grid of electrodes. In 32 patients with no history of AF (aAF, n=11), paroxysmal AF (PAF, n=12) and persistent AF (persAF, n=9), RPs were constructed using a phase space electrogram embedding dimension equal to the estimated AF cycle length. Spatial information was incorporated by 1) averaging the recurrence over all electrodes, and 2) by applying principal component analysis (PCA) to the matrix of embedded electrograms and selecting the first principal component as a representation of spatial diversity. Standard RQA parameters were computed on the constructed RPs and correlated to the number of fibrillation waves per AF cycle (NW). Averaged RP RQA parameters showed no correlation with NW. Correlations improved when applying PCA, with maximum correlation achieved between RP threshold and NW (RR1%, r=0.68, p <; 0.001) and RP determinism (DET, r=-0.64, p <; 0.001). All studied RQA parameters based on the PCA RP were able to discriminate between persAF and aAF/PAF (DET persAF 0.40 ± 0.11 vs. 0.59 ± 0.14/0.62 ± 0.16, p <; 0.01). RP construction and RQA combined with PCA provide a quick and reliable tool to visualize dynamical behaviour and to assess the complexity of contact mapping patterns in AF.

Development of a digital astronomical intensity interferometer: laboratory results with thermal light

NASA Astrophysics Data System (ADS)

Matthews, Nolan; Kieda, David; LeBohec, Stephan

2018-06-01

We present measurements of the second-order spatial coherence function of thermal light sources using Hanbury-Brown and Twiss interferometry with a digital correlator. We demonstrate that intensity fluctuations between orthogonal polarizations, or at detector separations greater than the spatial coherence length of the source, are uncorrelated but can be used to reduce systematic noise. The work performed here can readily be applied to existing and future Imaging Air-Cherenkov Telescopes used as star light collectors for stellar intensity interferometry to measure spatial properties of astronomical objects.

Spatial and spectral interpolation of ground-motion intensity measure observations

USGS Publications Warehouse

Worden, Charles; Thompson, Eric M.; Baker, Jack W.; Bradley, Brendon A.; Luco, Nicolas; Wilson, David

2018-01-01

Following a significant earthquake, ground‐motion observations are available for a limited set of locations and intensity measures (IMs). Typically, however, it is desirable to know the ground motions for additional IMs and at locations where observations are unavailable. Various interpolation methods are available, but because IMs or their logarithms are normally distributed, spatially correlated, and correlated with each other at a given location, it is possible to apply the conditional multivariate normal (MVN) distribution to the problem of estimating unobserved IMs. In this article, we review the MVN and its application to general estimation problems, and then apply the MVN to the specific problem of ground‐motion IM interpolation. In particular, we present (1) a formulation of the MVN for the simultaneous interpolation of IMs across space and IM type (most commonly, spectral response at different oscillator periods) and (2) the inclusion of uncertain observation data in the MVN formulation. These techniques, in combination with modern empirical ground‐motion models and correlation functions, provide a flexible framework for estimating a variety of IMs at arbitrary locations.

Approximation of Confidence Limits on Sample Semivariograms From Single Realizations of Spatially Correlated Random Fields

NASA Astrophysics Data System (ADS)

Shafer, J. M.; Varljen, M. D.

1990-08-01

A fundamental requirement for geostatistical analyses of spatially correlated environmental data is the estimation of the sample semivariogram to characterize spatial correlation. Selecting an underlying theoretical semivariogram based on the sample semivariogram is an extremely important and difficult task that is subject to a great deal of uncertainty. Current standard practice does not involve consideration of the confidence associated with semivariogram estimates, largely because classical statistical theory does not provide the capability to construct confidence limits from single realizations of correlated data, and multiple realizations of environmental fields are not found in nature. The jackknife method is a nonparametric statistical technique for parameter estimation that may be used to estimate the semivariogram. When used in connection with standard confidence procedures, it allows for the calculation of closely approximate confidence limits on the semivariogram from single realizations of spatially correlated data. The accuracy and validity of this technique was verified using a Monte Carlo simulation approach which enabled confidence limits about the semivariogram estimate to be calculated from many synthetically generated realizations of a random field with a known correlation structure. The synthetically derived confidence limits were then compared to jackknife estimates from single realizations with favorable results. Finally, the methodology for applying the jackknife method to a real-world problem and an example of the utility of semivariogram confidence limits were demonstrated by constructing confidence limits on seasonal sample variograms of nitrate-nitrogen concentrations in shallow groundwater in an approximately 12-mi2 (˜30 km2) region in northern Illinois. In this application, the confidence limits on sample semivariograms from different time periods were used to evaluate the significance of temporal change in spatial correlation. This capability is quite important as it can indicate when a spatially optimized monitoring network would need to be reevaluated and thus lead to more robust monitoring strategies.

Correlated Imaging – A Grand Challenge in Chemical Analysis

PubMed Central

Masyuko, Rachel; Lanni, Eric; Sweedler, Jonathan V.; Bohn, Paul W.

2013-01-01

Correlated chemical imaging is an emerging strategy for acquisition of images by combining information from multiplexed measurement platforms to track, visualize, and interpret in situ changes in the structure, organization, and activities of interesting chemical systems, frequently spanning multiple decades in space and time. Acquiring and correlating information from complementary imaging experiments has the potential to expose complex chemical behavior in ways that are simply not available from single methods applied in isolation, thereby greatly amplifying the information gathering power of imaging experiments. However, in order to correlate image information across platforms, a number of issues must be addressed. First, signals are obtained from disparate experiments with fundamentally different figures of merit, including pixel size, spatial resolution, dynamic range, and acquisition rates. In addition, images are often acquired on different instruments in different locations, so the sample must be registered spatially so that the same area of the sample landscape is addressed. The signals acquired must be correlated in both spatial and temporal domains, and the resulting information has to be presented in a way that is readily understood. These requirements pose special challenges for image cross-correlation that go well beyond those posed in single technique imaging approaches. The special opportunities and challenges that attend correlated imaging are explored by specific reference to correlated mass spectrometric and Raman imaging, a topic of substantial and growing interest. PMID:23431559

Relationship between sugarcane rust severity and soil properties in louisiana.

PubMed

Johnson, Richard M; Grisham, Michael P; Richard, Edward P

2007-06-01

ABSTRACT The extent of spatial and temporal variability of sugarcane rust (Puccinia melanocephala) infestation was related to variation in soil properties in five commercial fields of sugarcane (interspecific hybrids of Saccharum spp., cv. LCP 85-384) in southern Louisiana. Sugarcane fields were grid-soil sampled at several intensities and rust ratings were collected at each point over 6 to 7 weeks. Soil properties exhibited significant variability (coefficients of variation = 9 to 70.1%) and were spatially correlated in 39 of 40 cases with a range of spatial correlation varying from 39 to 201 m. Rust ratings were spatially correlated in 32 of 33 cases, with a range varying from 29 to 241 m. Rust ratings were correlated with several soil properties, most notably soil phosphorus (r = 0.40 to 0.81) and soil sulfur (r = 0.36 to 0.68). Multiple linear regression analysis resulted in coefficients of determination that ranged from 0.22 to 0.73, and discriminant analysis further improved the overall predictive ability of rust models. Finally, contour plots of soil properties and rust levels clearly suggested a link between these two parameters. These combined data suggest that sugarcane growers that apply fertilizer in excess of plant requirements will increase the incidence and severity of rust infestations in their fields.

Laboratory demonstration of Stellar Intensity Interferometry using a software correlator

NASA Astrophysics Data System (ADS)

Matthews, Nolan; Kieda, David

2017-06-01

In this talk I will present measurements of the spatial coherence function of laboratory thermal (black-body) sources using Hanbury-Brown and Twiss interferometry with a digital off-line correlator. Correlations in the intensity fluctuations of a thermal source, such as a star, allow retrieval of the second order coherence function which can be used to perform high resolution imaging and source geometry characterization. We also demonstrate that intensity fluctuations between orthogonal polarization states are uncorrelated but can be used to reduce systematic noise. The work performed here can readily be applied to existing and future Imaging Air-Cherenkov telescopes to measure spatial properties of stellar sources. Some possible candidates for astronomy applications include close binary star systems, fast rotators, Cepheid variables, and potentially even exoplanet characterization.

The SPAtial EFficiency metric (SPAEF): multiple-component evaluation of spatial patterns for optimization of hydrological models

NASA Astrophysics Data System (ADS)

Koch, Julian; Cüneyd Demirel, Mehmet; Stisen, Simon

2018-05-01

The process of model evaluation is not only an integral part of model development and calibration but also of paramount importance when communicating modelling results to the scientific community and stakeholders. The modelling community has a large and well-tested toolbox of metrics to evaluate temporal model performance. In contrast, spatial performance evaluation does not correspond to the grand availability of spatial observations readily available and to the sophisticate model codes simulating the spatial variability of complex hydrological processes. This study makes a contribution towards advancing spatial-pattern-oriented model calibration by rigorously testing a multiple-component performance metric. The promoted SPAtial EFficiency (SPAEF) metric reflects three equally weighted components: correlation, coefficient of variation and histogram overlap. This multiple-component approach is found to be advantageous in order to achieve the complex task of comparing spatial patterns. SPAEF, its three components individually and two alternative spatial performance metrics, i.e. connectivity analysis and fractions skill score, are applied in a spatial-pattern-oriented model calibration of a catchment model in Denmark. Results suggest the importance of multiple-component metrics because stand-alone metrics tend to fail to provide holistic pattern information. The three SPAEF components are found to be independent, which allows them to complement each other in a meaningful way. In order to optimally exploit spatial observations made available by remote sensing platforms, this study suggests applying bias insensitive metrics which further allow for a comparison of variables which are related but may differ in unit. This study applies SPAEF in the hydrological context using the mesoscale Hydrologic Model (mHM; version 5.8), but we see great potential across disciplines related to spatially distributed earth system modelling.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Jiali; Swati, F. N. U.; Stein, Michael L.

Regional climate models (RCMs) are a standard tool for downscaling climate forecasts to finer spatial scales. The evaluation of RCMs against observational data is an important step in building confidence in the use of RCMs for future prediction. In addition to model performance in climatological means and marginal distributions, a model’s ability to capture spatio-temporal relationships is important. This study develops two approaches: (1) spatial correlation/variogram for a range of spatial lags, with total monthly precipitation and non-seasonal precipitation components used to assess the spatial variations of precipitation; and (2) spatio-temporal correlation for a wide range of distances, directions, andmore » time lags, with daily precipitation occurrence used to detect the dynamic features of precipitation. These measures of spatial and spatio-temporal dependence are applied to a high-resolution RCM run and to the National Center for Environmental Prediction (NCEP)-U.S. Department of Energy (DOE) AMIP II reanalysis data (NCEP-R2), which provides initial and lateral boundary conditions for the RCM. The RCM performs better than NCEP-R2 in capturing both the spatial variations of total and non-seasonal precipitation components and the spatio-temporal correlations of daily precipitation occurrences, which are related to dynamic behaviors of precipitating systems. The improvements are apparent not just at resolutions finer than that of NCEP-R2, but also when the RCM and observational data are aggregated to the resolution of NCEP-R2.« less

Bayesian spatial modelling and the significance of agricultural land use to scrub typhus infection in Taiwan.

PubMed

Wardrop, Nicola A; Kuo, Chi-Chien; Wang, Hsi-Chieh; Clements, Archie C A; Lee, Pei-Fen; Atkinson, Peter M

2013-11-01

Scrub typhus is transmitted by the larval stage of trombiculid mites. Environmental factors, including land cover and land use, are known to influence breeding and survival of trombiculid mites and, thus, also the spatial heterogeneity of scrub typhus risk. Here, a spatially autoregressive modelling framework was applied to scrub typhus incidence data from Taiwan, covering the period 2003 to 2011, to provide increased understanding of the spatial pattern of scrub typhus risk and the environmental and socioeconomic factors contributing to this pattern. A clear spatial pattern in scrub typhus incidence was observed within Taiwan, and incidence was found to be significantly correlated with several land cover classes, temperature, elevation, normalized difference vegetation index, rainfall, population density, average income and the proportion of the population that work in agriculture. The final multivariate regression model included statistically significant correlations between scrub typhus incidence and average income (negatively correlated), the proportion of land that contained mosaics of cropland and vegetation (positively correlated) and elevation (positively correlated). These results highlight the importance of land cover on scrub typhus incidence: mosaics of cropland and vegetation represent a transitional land cover type which can provide favourable habitats for rodents and, therefore, trombiculid mites. In Taiwan, these transitional land cover areas tend to occur in less populated and mountainous areas, following the frontier establishment and subsequent partial abandonment of agricultural cultivation, due to demographic and socioeconomic changes. Future land use policy decision-making should ensure that potential public health outcomes, such as modified risk of scrub typhus, are considered.

Evolution of genuine cross-correlation strength of focal onset seizures.

PubMed

Müller, Markus F; Baier, Gerold; Jiménez, Yurytzy López; Marín García, Arlex O; Rummel, Christian; Schindler, Kaspar

2011-10-01

To quantify the evolution of genuine zero-lag cross-correlations of focal onset seizures, we apply a recently introduced multivariate measure to broad band and to narrow-band EEG data. For frequency components below 12.5 Hz, the strength of genuine cross-correlations decreases significantly during the seizure and the immediate postseizure period, while higher frequency bands show a tendency of elevated cross-correlations during the same period. We conclude that in terms of genuine zero-lag cross-correlations, the electrical brain activity as assessed by scalp electrodes shows a significant spatial fragmentation, which might promote seizure offset.

A Methodology for the Assessment of Unconventional (Continuous) Resources with an Application to the Greater Natural Buttes Gas Field, Utah

DOE Office of Scientific and Technical Information (OSTI.GOV)

Olea, Ricardo A., E-mail: olea@usgs.gov; Cook, Troy A.; Coleman, James L.

2010-12-15

The Greater Natural Buttes tight natural gas field is an unconventional (continuous) accumulation in the Uinta Basin, Utah, that began production in the early 1950s from the Upper Cretaceous Mesaverde Group. Three years later, production was extended to the Eocene Wasatch Formation. With the exclusion of 1100 non-productive ('dry') wells, we estimate that the final recovery from the 2500 producing wells existing in 2007 will be about 1.7 trillion standard cubic feet (TSCF) (48.2 billion cubic meters (BCM)). The use of estimated ultimate recovery (EUR) per well is common in assessments of unconventional resources, and it is one of themore » main sources of information to forecast undiscovered resources. Each calculated recovery value has an associated drainage area that generally varies from well to well and that can be mathematically subdivided into elemental subareas of constant size and shape called cells. Recovery per 5-acre cells at Greater Natural Buttes shows spatial correlation; hence, statistical approaches that ignore this correlation when inferring EUR values for untested cells do not take full advantage of all the information contained in the data. More critically, resulting models do not match the style of spatial EUR fluctuations observed in nature. This study takes a new approach by applying spatial statistics to model geographical variation of cell EUR taking into account spatial correlation and the influence of fractures. We applied sequential indicator simulation to model non-productive cells, while spatial mapping of cell EUR was obtained by applying sequential Gaussian simulation to provide multiple versions of reality (realizations) having equal chances of being the correct model. For each realization, summation of EUR in cells not drained by the existing wells allowed preparation of a stochastic prediction of undiscovered resources, which range between 2.6 and 3.4 TSCF (73.6 and 96.3 BCM) with a mean of 2.9 TSCF (82.1 BCM) for Greater Natural Buttes. A second approach illustrates the application of multiple-point simulation to assess a hypothetical frontier area for which there is no production information but which is regarded as being similar to Greater Natural Buttes.« less

Novel Multidimensional Cross-Correlation Data Comparison Techniques for Spectroscopic Discernment in a Volumetrically Sensitive, Moderating Type Neutron Spectrometer

NASA Astrophysics Data System (ADS)

Hoshor, Cory; Young, Stephan; Rogers, Brent; Currie, James; Oakes, Thomas; Scott, Paul; Miller, William; Caruso, Anthony

2014-03-01

A novel application of the Pearson Cross-Correlation to neutron spectral discernment in a moderating type neutron spectrometer is introduced. This cross-correlation analysis will be applied to spectral response data collected through both MCNP simulation and empirical measurement by the volumetrically sensitive spectrometer for comparison in 1, 2, and 3 spatial dimensions. The spectroscopic analysis methods discussed will be demonstrated to discern various common spectral and monoenergetic neutron sources.

Initial Data of Digital Correlation ECE with a Giga Hertz Sampling Digitizer

NASA Astrophysics Data System (ADS)

Tsuchiya, Hayato; Inagaki, Shigeru; Tokuzawa, Tokihiko; Nagayama, Yoshio

2015-03-01

The proposed Digital Correlation ECE (DCECE) technique is applied in Large Helical Device. DCECE is realized by the use of the Giga Hertz Sampling Digitizer. The waveform of intermediate frequency band of ECE, whose frequency is several giga hertz, can be discretized and saved directly. The discretized IF data can be used for the analysis of correlation ECE with arbitrary parameter of spatial resolution and temporal resolution. In this paper, the characteristic of DCECE and initial Data in LHD is introduced.

Spatial fluctuations in transient creep deformation

NASA Astrophysics Data System (ADS)

Laurson, Lasse; Rosti, Jari; Koivisto, Juha; Miksic, Amandine; Alava, Mikko J.

2011-07-01

We study the spatial fluctuations of transient creep deformation of materials as a function of time, both by digital image correlation (DIC) measurements of paper samples and by numerical simulations of a crystal plasticity or discrete dislocation dynamics model. This model has a jamming or yielding phase transition, around which power law or Andrade creep is found. During primary creep, the relative strength of the strain rate fluctuations increases with time in both cases—the spatially averaged creep rate obeys the Andrade law epsilont ~ t - 0.7, while the time dependence of the spatial fluctuations of the local creep rates is given by Δepsilont ~ t - 0.5. A similar scaling for the fluctuations is found in the logarithmic creep regime that is typically observed for lower applied stresses. We review briefly some classical theories of Andrade creep from the point of view of such spatial fluctuations. We consider these phenomenological, time-dependent creep laws in terms of a description based on a non-equilibrium phase transition separating evolving and frozen states of the system when the externally applied load is varied. Such an interpretation is discussed further by the data collapse of the local deformations in the spirit of absorbing state/depinning phase transitions, as well as deformation-deformation correlations and the width of the cumulative strain distributions. The results are also compared with the order parameter fluctuations observed close to the depinning transition of the 2d linear interface model or the quenched Edwards-Wilkinson equation.

Blob-hole correlation model for edge turbulence and comparisons with NSTX gas puff imaging data

NASA Astrophysics Data System (ADS)

Myra, J. R.; Zweben, S. J.; Russell, D. A.

2018-07-01

Gas puff imaging (GPI) observations made in NSTX (Zweben et al 2017 Phys. Plasmas 24 102509) have revealed two-point spatial correlations of edge and scrape-off layer (SOL) turbulence in the plane perpendicular to the magnetic field. A common feature is the occurrence of dipole-like patterns with significant regions of negative correlation. In this paper, we explore the possibility that these dipole patterns may be due to blob-hole pairs. Statistical methods are applied to determine the two-point spatial correlation that results from a model of blob-hole pair formation. It is shown that the model produces dipole correlation patterns that are qualitatively similar to the GPI data in several respects. Effects of the reference location (confined surfaces or SOL), a superimposed random background, hole velocity and lifetime, and background sheared flows are explored and discussed with respect to experimental observations. Additional analysis of the experimental GPI dataset is performed to further test this blob-hole correlation model. A time delay two-point spatial correlation study did not reveal inward propagation of the negative correlation structures that were postulated to correspond to holes in the data nor did it suggest that the negative correlation structures are due to neutral shadowing. However, tracking of the highest and lowest values (extrema) of the normalized GPI fluctuations shows strong evidence for mean inward propagation of minima and outward propagation of maxima, in qualitative agreement with theoretical expectations. Other properties of the experimentally observed extrema are discussed.

Hippocampal structure and human cognition: key role of spatial processing and evidence supporting the efficiency hypothesis in females

PubMed Central

Colom, Roberto; Stein, Jason L.; Rajagopalan, Priya; Martínez, Kenia; Hermel, David; Wang, Yalin; Álvarez-Linera, Juan; Burgaleta, Miguel; Quiroga, MªÁngeles; Shih, Pei Chun; Thompson, Paul M.

2014-01-01

Here we apply a method for automated segmentation of the hippocampus in 3D high-resolution structural brain MRI scans. One hundred and four healthy young adults completed twenty one tasks measuring abstract, verbal, and spatial intelligence, along with working memory, executive control, attention, and processing speed. After permutation tests corrected for multiple comparisons across vertices (p < .05) significant relationships were found for spatial intelligence, spatial working memory, and spatial executive control. Interactions with sex revealed significant relationships with the general factor of intelligence (g), along with abstract and spatial intelligence. These correlations were mainly positive for males but negative for females, which might support the efficiency hypothesis in women. Verbal intelligence, attention, and processing speed were not related to hippocampal structural differences. PMID:25632167

An examination of gender bias on the eighth-grade MEAP science test as it relates to the Hunter Gatherer Theory of Spatial Sex Differences

NASA Astrophysics Data System (ADS)

Armstrong-Hall, Judy Gail

The purpose of this study was to apply the Hunter-Gatherer Theory of sex spatial skills to responses to individual questions by eighth grade students on the Science component of the Michigan Educational Assessment Program (MEAP) to determine if sex bias was inherent in the test. The Hunter-Gatherer Theory on Spatial Sex Differences, an original theory, that suggested a spatial dimorphism concept with female spatial skill of pattern recall of unconnected items and male spatial skills requiring mental movement. This is the first attempt to apply the Hunter-Gatherer Theory on Spatial Sex Differences to a standardized test. An overall hypothesis suggested that the Hunter-Gatherer Theory of Spatial Sex Differences could predict that males would perform better on problems involving mental movement and females would do better on problems involving the pattern recall of unconnected items. Responses to questions on the 1994-95 MEAP requiring the use of male spatial skills and female spatial skills were analyzed for 5,155 eighth grade students. A panel composed of five educators and a theory developer determined which test items involved the use of male and female spatial skills. A MANOVA, using a random sample of 20% of the 5,155 students to compare male and female correct scores, was statistically significant, with males having higher scores on male spatial skills items and females having higher scores on female spatial skills items. Pearson product moment correlation analyses produced a positive correlation for both male and female performance on both types of spatial skills. The Hunter-Gatherer Theory of Spatial Sex Differences appears to be able to predict that males could perform better on the problems involving mental movement and females could perform better on problems involving the pattern recall of unconnected items. Recommendations for further research included: examination of male/female spatial skill differences at early elementary and high school levels to determine impact of gender on difficulties in solving spatial problems; investigation of the relationship between dominant female spatial skills for students diagnosed with ADHD; study effects of teaching male spatial skills to female students starting in early elementary school to determine the effect on standardized testing.

Modeling of blob-hole correlations in GPI edge turbulence data

NASA Astrophysics Data System (ADS)

Myra, J. R.; Russell, D. A.; Zweben, S. J.

2017-10-01

Gas-puff imaging (GPI) observations made on NSTX have revealed two-point spatial correlation patterns in the plane perpendicular to the magnetic field. A common feature is the occurrence of dipole-like patterns with significant regions of negative correlation. In this work, we explore the possibility that these dipole patterns may be due to blob-hole pairs. Statistical methods are applied to determine the two-point spatial correlation that results from a model of blob-hole pair formation. It is shown that the model produces dipole correlation patterns that are qualitatively similar to the GPI data in many respects. Effects of the reference location (confined surfaces or scrape-off layer), a superimposed random background, hole velocity and lifetime, and background sheared flows are explored. The possibility of using the model to ascertain new information about edge turbulence is discussed. Work supported by the U.S. Department of Energy Office of Science, Office of Fusion Energy Sciences under Award Number DE-FG02-02ER54678.

Relationship Study on Land Use Spatial Distribution Structure and Energy-Related Carbon Emission Intensity in Different Land Use Types of Guangdong, China, 1996–2008

PubMed Central

Huang, Yi; Yang, Lei

2013-01-01

This study attempts to discuss the relationship between land use spatial distribution structure and energy-related carbon emission intensity in Guangdong during 1996–2008. We quantized the spatial distribution structure of five land use types including agricultural land, industrial land, residential and commercial land, traffic land, and other land through applying spatial Lorenz curve and Gini coefficient. Then the corresponding energy-related carbon emissions in each type of land were calculated in the study period. Through building the reasonable regression models, we found that the concentration degree of industrial land is negatively correlated with carbon emission intensity in the long term, whereas the concentration degree is positively correlated with carbon emission intensity in agricultural land, residential and commercial land, traffic land, and other land. The results also indicate that land use spatial distribution structure affects carbon emission intensity more intensively than energy efficiency and production efficiency do. These conclusions provide valuable reference to develop comprehensive policies for energy conservation and carbon emission reduction in a new perspective. PMID:23476128

Relationship study on land use spatial distribution structure and energy-related carbon emission intensity in different land use types of Guangdong, China, 1996-2008.

PubMed

Huang, Yi; Xia, Bin; Yang, Lei

2013-01-01

This study attempts to discuss the relationship between land use spatial distribution structure and energy-related carbon emission intensity in Guangdong during 1996-2008. We quantized the spatial distribution structure of five land use types including agricultural land, industrial land, residential and commercial land, traffic land, and other land through applying spatial Lorenz curve and Gini coefficient. Then the corresponding energy-related carbon emissions in each type of land were calculated in the study period. Through building the reasonable regression models, we found that the concentration degree of industrial land is negatively correlated with carbon emission intensity in the long term, whereas the concentration degree is positively correlated with carbon emission intensity in agricultural land, residential and commercial land, traffic land, and other land. The results also indicate that land use spatial distribution structure affects carbon emission intensity more intensively than energy efficiency and production efficiency do. These conclusions provide valuable reference to develop comprehensive policies for energy conservation and carbon emission reduction in a new perspective.

New insights into the endophenotypic status of cognition in bipolar disorder: genetic modelling study of twins and siblings.

PubMed

Georgiades, Anna; Rijsdijk, Fruhling; Kane, Fergus; Rebollo-Mesa, Irene; Kalidindi, Sridevi; Schulze, Katja K; Stahl, Daniel; Walshe, Muriel; Sahakian, Barbara J; McDonald, Colm; Hall, Mei-Hua; Murray, Robin M; Kravariti, Eugenia

2016-06-01

Twin studies have lacked statistical power to apply advanced genetic modelling techniques to the search for cognitive endophenotypes for bipolar disorder. To quantify the shared genetic variability between bipolar disorder and cognitive measures. Structural equation modelling was performed on cognitive data collected from 331 twins/siblings of varying genetic relatedness, disease status and concordance for bipolar disorder. Using a parsimonious AE model, verbal episodic and spatial working memory showed statistically significant genetic correlations with bipolar disorder (rg = |0.23|-|0.27|), which lost statistical significance after covarying for affective symptoms. Using an ACE model, IQ and visual-spatial learning showed statistically significant genetic correlations with bipolar disorder (rg = |0.51|-|1.00|), which remained significant after covarying for affective symptoms. Verbal episodic and spatial working memory capture a modest fraction of the bipolar diathesis. IQ and visual-spatial learning may tap into genetic substrates of non-affective symptomatology in bipolar disorder. © The Royal College of Psychiatrists 2016.

Spatial and temporal variation of sources contributing to quasi-ultrafine particulate matter PM0.36 in Augsburg, Germany.

PubMed

Li, Fengxia; Schnelle-Kreis, Jürgen; Cyrys, Josef; Wolf, Kathrin; Karg, Erwin; Gu, Jianwei; Orasche, Jürgen; Abbaszade, Gülcin; Peters, Annette; Zimmermann, Ralf

2018-08-01

to study the sources contributing to quasi-ultrafine particle (UFP) organic carbon and the spatial temporal variability of the sources. 24h quasi-UFP (particulate matter <0.36μm in this study) was sampled at a reference site continuously and at one of 5 other sites (T1, T2, T3, T4 and B1) in parallel in Augsburg, Germany from April 11th, 2014 to February 22nd, 2015, attempting to conduct 2-week campaigns at each site in 3 different seasons. Positive matrix factorization (PMF) was applied to measured organic tracers for source apportionment analyses. Pearson correlation coefficient r and coefficient of divergence (COD) were calculated to investigate spatial temporal variation of source contributions. 5 sources were identified comprising biomass burning (BB), traffic emissions (Traffic), biogenic secondary organic aerosol (bioSOA), isoprene originated secondary organic aerosol (isoSOA) and biomass burning related secondary organic aerosol (bbSOA). In general, good temporal correlation and uniform distribution within the study area are found for bioSOA and bbSOA, probably resulting from regional formation/transport. Lower temporal correlation and spatial heterogeneity of isoSOA were found at the city background site with local influence from green space and less traffic impact. BB demonstrated very good temporal correlation, but higher contributions at sites influenced by local residential heating emissions were observed. Traffic showed the least seasonality and lower correlation over time among the sources. However, it demonstrated low spatial heterogeneity of absolute contribution, and only a few days of elevated contribution was found at T3 when wind came directly from the street nearby. temporal correlation and spatial variability of sources contributing to the organic fraction of quasi-UFP vary among sites and source types and show source-specific characteristics. Therefore, caution should be taken when using one monitor site measurement to assess human exposure in health effect studies of quasi-UFP. Copyright © 2018 Elsevier B.V. All rights reserved.

Applying Metrological Techniques to Satellite Fundamental Climate Data Records

NASA Astrophysics Data System (ADS)

Woolliams, Emma R.; Mittaz, Jonathan PD; Merchant, Christopher J.; Hunt, Samuel E.; Harris, Peter M.

2018-02-01

Quantifying long-term environmental variability, including climatic trends, requires decadal-scale time series of observations. The reliability of such trend analysis depends on the long-term stability of the data record, and understanding the sources of uncertainty in historic, current and future sensors. We give a brief overview on how metrological techniques can be applied to historical satellite data sets. In particular we discuss the implications of error correlation at different spatial and temporal scales and the forms of such correlation and consider how uncertainty is propagated with partial correlation. We give a form of the Law of Propagation of Uncertainties that considers the propagation of uncertainties associated with common errors to give the covariance associated with Earth observations in different spectral channels.

Gutzwiller Monte Carlo approach for a critical dissipative spin model

NASA Astrophysics Data System (ADS)

Casteels, Wim; Wilson, Ryan M.; Wouters, Michiel

2018-06-01

We use the Gutzwiller Monte Carlo approach to simulate the dissipative X Y Z model in the vicinity of a dissipative phase transition. This approach captures classical spatial correlations together with the full on-site quantum behavior while neglecting nonlocal quantum effects. By considering finite two-dimensional lattices of various sizes, we identify a ferromagnetic and two paramagnetic phases, in agreement with earlier studies. The greatly reduced numerical complexity of the Gutzwiller Monte Carlo approach facilitates efficient simulation of relatively large lattice sizes. The inclusion of the spatial correlations allows to capture parts of the phase diagram that are completely missed by the widely applied Gutzwiller decoupling of the density matrix.

Astrophysical data analysis with information field theory

NASA Astrophysics Data System (ADS)

Enßlin, Torsten

2014-12-01

Non-parametric imaging and data analysis in astrophysics and cosmology can be addressed by information field theory (IFT), a means of Bayesian, data based inference on spatially distributed signal fields. IFT is a statistical field theory, which permits the construction of optimal signal recovery algorithms. It exploits spatial correlations of the signal fields even for nonlinear and non-Gaussian signal inference problems. The alleviation of a perception threshold for recovering signals of unknown correlation structure by using IFT will be discussed in particular as well as a novel improvement on instrumental self-calibration schemes. IFT can be applied to many areas. Here, applications in in cosmology (cosmic microwave background, large-scale structure) and astrophysics (galactic magnetism, radio interferometry) are presented.

Changes in precipitation isotope-climate relationships from temporal grouping and aggregation of weekly-resolved USNIP data: impacts on paleoclimate and environmental applications

NASA Astrophysics Data System (ADS)

Akers, P. D.; Welker, J. M.

2015-12-01

Spatial variations in precipitation isotopes have been the focus of much recent research, but relatively less work has explored changes at various temporal scales. This is partly because most spatially-diverse and long-term isotope databases are offered at a monthly resolution, while daily or event-level records are spatially and temporally limited by cost and logistics. A subset of 25 United States Network for Isotopes in Precipitation (USNIP) sites with weekly-resolution in the east-central United States was analyzed for site-specific relationships between δ18O and δD (the local meteoric water line/LMWL), δ18O and surface temperature, and δ18O and precipitation amount. Weekly data were then aggregated into monthly and seasonal data to examine the effect of aggregation on correlation and slope values for each of the relationships. Generally, increasing aggregation improved correlations (>25% for some sites) due to a reduced effect of extreme values, but estimates on regression variable error increased (>100%) because of reduced sample sizes. Aggregation resulted in small, but significant drops (5-25%) in relationship slope values for some sites. Weekly data were also grouped by month and season to explore changes in relationships throughout the year. Significant subannual variability exists in slope values and correlations even for sites with very strong overall correlations. LMWL slopes are highest in winter and lowest in summer, while the δ18O-surface temperature relationship is strongest in spring. Despite these overall trends, a high level of month-to-month and season-to-season variability is the norm for these sites. Researchers blindly applying overall relationships drawn from monthly-resolved databases to paleoclimate or environmental research risk assuming these relationships apply at all temporal resolutions. When possible, researchers should match the temporal resolution used to calculate an isotopic relationship with the temporal resolution of their applied proxy.

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

PubMed

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

2018-08-15

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

Binary zone-plate array for a parallel joint transform correlator applied to face recognition.

PubMed

Kodate, K; Hashimoto, A; Thapliya, R

1999-05-10

Taking advantage of small aberrations, high efficiency, and compactness, we developed a new, to our knowledge, design procedure for a binary zone-plate array (BZPA) and applied it to a parallel joint transform correlator for the recognition of the human face. Pairs of reference and unknown images of faces are displayed on a liquid-crystal spatial light modulator (SLM), Fourier transformed by the BZPA, intensity recorded on an optically addressable SLM, and inversely Fourier transformed to obtain correlation signals. Consideration of the bandwidth allows the relations among the channel number, the numerical aperture of the zone plates, and the pattern size to be determined. Experimentally a five-channel parallel correlator was implemented and tested successfully with a 100-person database. The design and the fabrication of a 20-channel BZPA for phonetic character recognition are also included.

Classification of fMRI resting-state maps using machine learning techniques: A comparative study

NASA Astrophysics Data System (ADS)

Gallos, Ioannis; Siettos, Constantinos

2017-11-01

We compare the efficiency of Principal Component Analysis (PCA) and nonlinear learning manifold algorithms (ISOMAP and Diffusion maps) for classifying brain maps between groups of schizophrenia patients and healthy from fMRI scans during a resting-state experiment. After a standard pre-processing pipeline, we applied spatial Independent component analysis (ICA) to reduce (a) noise and (b) spatial-temporal dimensionality of fMRI maps. On the cross-correlation matrix of the ICA components, we applied PCA, ISOMAP and Diffusion Maps to find an embedded low-dimensional space. Finally, support-vector-machines (SVM) and k-NN algorithms were used to evaluate the performance of the algorithms in classifying between the two groups.

Geometry and scaling laws of excursion and iso-sets of enstrophy and dissipation in isotropic turbulence

NASA Astrophysics Data System (ADS)

Elsas, José Hugo; Szalay, Alexander S.; Meneveau, Charles

2018-04-01

Motivated by interest in the geometry of high intensity events of turbulent flows, we examine the spatial correlation functions of sets where turbulent events are particularly intense. These sets are defined using indicator functions on excursion and iso-value sets. Their geometric scaling properties are analysed by examining possible power-law decay of their radial correlation function. We apply the analysis to enstrophy, dissipation and velocity gradient invariants Q and R and their joint spatial distributions, using data from a direct numerical simulation of isotropic turbulence at Reλ ≈ 430. While no fractal scaling is found in the inertial range using box-counting in the finite Reynolds number flow considered here, power-law scaling in the inertial range is found in the radial correlation functions. Thus, a geometric characterisation in terms of these sets' correlation dimension is possible. Strong dependence on the enstrophy and dissipation threshold is found, consistent with multifractal behaviour. Nevertheless, the lack of scaling of the box-counting analysis precludes direct quantitative comparisons with earlier work based on multifractal formalism. Surprising trends, such as a lower correlation dimension for strong dissipation events compared to strong enstrophy events, are observed and interpreted in terms of spatial coherence of vortices in the flow.

Spatial patterns and broad-scale weather cues of beech mast seeding in Europe.

PubMed

Vacchiano, Giorgio; Hacket-Pain, Andrew; Turco, Marco; Motta, Renzo; Maringer, Janet; Conedera, Marco; Drobyshev, Igor; Ascoli, Davide

2017-07-01

Mast seeding is a crucial population process in many tree species, but its spatio-temporal patterns and drivers at the continental scale remain unknown . Using a large dataset (8000 masting observations across Europe for years 1950-2014) we analysed the spatial pattern of masting across the entire geographical range of European beech, how it is influenced by precipitation, temperature and drought, and the temporal and spatial stability of masting-weather correlations. Beech masting exhibited a general distance-dependent synchronicity and a pattern structured in three broad geographical groups consistent with continental climate regimes. Spearman's correlations and logistic regression revealed a general pattern of beech masting correlating negatively with temperature in the summer 2 yr before masting, and positively with summer temperature 1 yr before masting (i.e. 2T model). The temperature difference between the two previous summers (DeltaT model) was also a good predictor. Moving correlation analysis applied to the longest eight chronologies (74-114 yr) revealed stable correlations between temperature and masting, confirming consistency in weather cues across space and time. These results confirm widespread dependency of masting on temperature and lend robustness to the attempts to reconstruct and predict mast years using temperature data. © 2017 The Authors. New Phytologist © 2017 New Phytologist Trust.

High Incidence of Breast Cancer in Light-Polluted Areas with Spatial Effects in Korea.

PubMed

Kim, Yun Jeong; Park, Man Sik; Lee, Eunil; Choi, Jae Wook

2016-01-01

We have reported a high prevalence of breast cancer in light-polluted areas in Korea. However, it is necessary to analyze the spatial effects of light polluted areas on breast cancer because light pollution levels are correlated with region proximity to central urbanized areas in studied cities. In this study, we applied a spatial regression method (an intrinsic conditional autoregressive [iCAR] model) to analyze the relationship between the incidence of breast cancer and artificial light at night (ALAN) levels in 25 regions including central city, urbanized, and rural areas. By Poisson regression analysis, there was a significant correlation between ALAN, alcohol consumption rates, and the incidence of breast cancer. We also found significant spatial effects between ALAN and the incidence of breast cancer, with an increase in the deviance information criterion (DIC) from 374.3 to 348.6 and an increase in R2 from 0.574 to 0.667. Therefore, spatial analysis (an iCAR model) is more appropriate for assessing ALAN effects on breast cancer. To our knowledge, this study is the first to show spatial effects of light pollution on breast cancer, despite the limitations of an ecological study. We suggest that a decrease in ALAN could reduce breast cancer more than expected because of spatial effects.

Text mining by Tsallis entropy

NASA Astrophysics Data System (ADS)

Jamaati, Maryam; Mehri, Ali

2018-01-01

Long-range correlations between the elements of natural languages enable them to convey very complex information. Complex structure of human language, as a manifestation of natural languages, motivates us to apply nonextensive statistical mechanics in text mining. Tsallis entropy appropriately ranks the terms' relevance to document subject, taking advantage of their spatial correlation length. We apply this statistical concept as a new powerful word ranking metric in order to extract keywords of a single document. We carry out an experimental evaluation, which shows capability of the presented method in keyword extraction. We find that, Tsallis entropy has reliable word ranking performance, at the same level of the best previous ranking methods.

A method to estimate the effect of deformable image registration uncertainties on daily dose mapping

PubMed Central

Murphy, Martin J.; Salguero, Francisco J.; Siebers, Jeffrey V.; Staub, David; Vaman, Constantin

2012-01-01

Purpose: To develop a statistical sampling procedure for spatially-correlated uncertainties in deformable image registration and then use it to demonstrate their effect on daily dose mapping. Methods: Sequential daily CT studies are acquired to map anatomical variations prior to fractionated external beam radiotherapy. The CTs are deformably registered to the planning CT to obtain displacement vector fields (DVFs). The DVFs are used to accumulate the dose delivered each day onto the planning CT. Each DVF has spatially-correlated uncertainties associated with it. Principal components analysis (PCA) is applied to measured DVF error maps to produce decorrelated principal component modes of the errors. The modes are sampled independently and reconstructed to produce synthetic registration error maps. The synthetic error maps are convolved with dose mapped via deformable registration to model the resulting uncertainty in the dose mapping. The results are compared to the dose mapping uncertainty that would result from uncorrelated DVF errors that vary randomly from voxel to voxel. Results: The error sampling method is shown to produce synthetic DVF error maps that are statistically indistinguishable from the observed error maps. Spatially-correlated DVF uncertainties modeled by our procedure produce patterns of dose mapping error that are different from that due to randomly distributed uncertainties. Conclusions: Deformable image registration uncertainties have complex spatial distributions. The authors have developed and tested a method to decorrelate the spatial uncertainties and make statistical samples of highly correlated error maps. The sample error maps can be used to investigate the effect of DVF uncertainties on daily dose mapping via deformable image registration. An initial demonstration of this methodology shows that dose mapping uncertainties can be sensitive to spatial patterns in the DVF uncertainties. PMID:22320766

Detection of non-classical space-time correlations with a novel type of single-photon camera.

PubMed

Just, Felix; Filipenko, Mykhaylo; Cavanna, Andrea; Michel, Thilo; Gleixner, Thomas; Taheri, Michael; Vallerga, John; Campbell, Michael; Tick, Timo; Anton, Gisela; Chekhova, Maria V; Leuchs, Gerd

2014-07-14

During the last decades, multi-pixel detectors have been developed capable of registering single photons. The newly developed hybrid photon detector camera has a remarkable property that it has not only spatial but also temporal resolution. In this work, we apply this device to the detection of non-classical light from spontaneous parametric down-conversion and use two-photon correlations for the absolute calibration of its quantum efficiency.

Path-integral Monte Carlo method for Rényi entanglement entropies.

PubMed

Herdman, C M; Inglis, Stephen; Roy, P-N; Melko, R G; Del Maestro, A

2014-07-01

We introduce a quantum Monte Carlo algorithm to measure the Rényi entanglement entropies in systems of interacting bosons in the continuum. This approach is based on a path-integral ground state method that can be applied to interacting itinerant bosons in any spatial dimension with direct relevance to experimental systems of quantum fluids. We demonstrate how it may be used to compute spatial mode entanglement, particle partitioned entanglement, and the entanglement of particles, providing insights into quantum correlations generated by fluctuations, indistinguishability, and interactions. We present proof-of-principle calculations and benchmark against an exactly soluble model of interacting bosons in one spatial dimension. As this algorithm retains the fundamental polynomial scaling of quantum Monte Carlo when applied to sign-problem-free models, future applications should allow for the study of entanglement entropy in large-scale many-body systems of interacting bosons.

Remodeling census population with spatial information from Landsat TM imagery

USGS Publications Warehouse

Yuan, Y.; Smith, R.M.; Limp, W.F.

1997-01-01

In geographic information systems (GIS) studies there has been some difficulty integrating socioeconomic and physiogeographic data. One important type of socioeconomic data, census data, offers a wide range of socioeconomic information, but is aggregated within arbitrary enumeration districts (EDs). Values reflect either raw counts or, when standardized, the mean densities in the EDs. On the other hand, remote sensing imagery, an important type of physiogeographic data, provides large quantities of information with more spatial details than census data. Based on the dasymetric mapping principle, this study applies multivariable regression to examine the correlation between population counts from census and land cover types. The land cover map is classified from LandSat TM imagery. The correlation is high. Census population counts are remodeled to a GIS raster layer based on the discovered correlations coupled with scaling techniques, which offset influences from other than land cover types. The GIS raster layer depicts the population distribution with much more spatial detail than census data offer. The resulting GIS raster layer is ready to be analyzed or integrated with other GIS data. ?? 1998 Elsevier Science Ltd. All rights reserved.

Extension of the spatial autocorrelation (SPAC) method to mixed-component correlations of surface waves

USGS Publications Warehouse

Haney, Matthew M.; Mikesell, T. Dylan; van Wijk, Kasper; Nakahara, Hisashi

2012-01-01

Using ambient seismic noise for imaging subsurface structure dates back to the development of the spatial autocorrelation (SPAC) method in the 1950s. We present a theoretical analysis of the SPAC method for multicomponent recordings of surface waves to determine the complete 3 × 3 matrix of correlations between all pairs of three-component motions, called the correlation matrix. In the case of isotropic incidence, when either Rayleigh or Love waves arrive from all directions with equal power, the only non-zero off-diagonal terms in the matrix are the vertical–radial (ZR) and radial–vertical (RZ) correlations in the presence of Rayleigh waves. Such combinations were not considered in the development of the SPAC method. The method originally addressed the vertical–vertical (ZZ), RR and TT correlations, hence the name spatial autocorrelation. The theoretical expressions we derive for the ZR and RZ correlations offer additional ways to measure Rayleigh wave dispersion within the SPAC framework. Expanding on the results for isotropic incidence, we derive the complete correlation matrix in the case of generally anisotropic incidence. We show that the ZR and RZ correlations have advantageous properties in the presence of an out-of-plane directional wavefield compared to ZZ and RR correlations. We apply the results for mixed-component correlations to a data set from Akutan Volcano, Alaska and find consistent estimates of Rayleigh wave phase velocity from ZR compared to ZZ correlations. This work together with the recently discovered connections between the SPAC method and time-domain correlations of ambient noise provide further insights into the retrieval of surface wave Green’s functions from seismic noise.

Technical Note: Atmospheric CO2 inversions on the mesoscale using data-driven prior uncertainties: methodology and system evaluation

NASA Astrophysics Data System (ADS)

Kountouris, Panagiotis; Gerbig, Christoph; Rödenbeck, Christian; Karstens, Ute; Koch, Thomas Frank; Heimann, Martin

2018-03-01

Atmospheric inversions are widely used in the optimization of surface carbon fluxes on a regional scale using information from atmospheric CO2 dry mole fractions. In many studies the prior flux uncertainty applied to the inversion schemes does not directly reflect the true flux uncertainties but is used to regularize the inverse problem. Here, we aim to implement an inversion scheme using the Jena inversion system and applying a prior flux error structure derived from a model-data residual analysis using high spatial and temporal resolution over a full year period in the European domain. We analyzed the performance of the inversion system with a synthetic experiment, in which the flux constraint is derived following the same residual analysis but applied to the model-model mismatch. The synthetic study showed a quite good agreement between posterior and true fluxes on European, country, annual and monthly scales. Posterior monthly and country-aggregated fluxes improved their correlation coefficient with the known truth by 7 % compared to the prior estimates when compared to the reference, with a mean correlation of 0.92. The ratio of the SD between the posterior and reference and between the prior and reference was also reduced by 33 % with a mean value of 1.15. We identified temporal and spatial scales on which the inversion system maximizes the derived information; monthly temporal scales at around 200 km spatial resolution seem to maximize the information gain.

Predicting crash frequency for multi-vehicle collision types using multivariate Poisson-lognormal spatial model: A comparative analysis.

PubMed

Hosseinpour, Mehdi; Sahebi, Sina; Zamzuri, Zamira Hasanah; Yahaya, Ahmad Shukri; Ismail, Noriszura

2018-06-01

According to crash configuration and pre-crash conditions, traffic crashes are classified into different collision types. Based on the literature, multi-vehicle crashes, such as head-on, rear-end, and angle crashes, are more frequent than single-vehicle crashes, and most often result in serious consequences. From a methodological point of view, the majority of prior studies focused on multivehicle collisions have employed univariate count models to estimate crash counts separately by collision type. However, univariate models fail to account for correlations which may exist between different collision types. Among others, multivariate Poisson lognormal (MVPLN) model with spatial correlation is a promising multivariate specification because it not only allows for unobserved heterogeneity (extra-Poisson variation) and dependencies between collision types, but also spatial correlation between adjacent sites. However, the MVPLN spatial model has rarely been applied in previous research for simultaneously modelling crash counts by collision type. Therefore, this study aims at utilizing a MVPLN spatial model to estimate crash counts for four different multi-vehicle collision types, including head-on, rear-end, angle, and sideswipe collisions. To investigate the performance of the MVPLN spatial model, a two-stage model and a univariate Poisson lognormal model (UNPLN) spatial model were also developed in this study. Detailed information on roadway characteristics, traffic volume, and crash history were collected on 407 homogeneous segments from Malaysian federal roads. The results indicate that the MVPLN spatial model outperforms the other comparing models in terms of goodness-of-fit measures. The results also show that the inclusion of spatial heterogeneity in the multivariate model significantly improves the model fit, as indicated by the Deviance Information Criterion (DIC). The correlation between crash types is high and positive, implying that the occurrence of a specific collision type is highly associated with the occurrence of other crash types on the same road segment. These results support the utilization of the MVPLN spatial model when predicting crash counts by collision manner. In terms of contributing factors, the results show that distinct crash types are attributed to different subsets of explanatory variables. Copyright © 2018 Elsevier Ltd. All rights reserved.

Probing density and spin correlations in two-dimensional Hubbard model with ultracold fermions

NASA Astrophysics Data System (ADS)

Chan, Chun Fai; Drewes, Jan Henning; Gall, Marcell; Wurz, Nicola; Cocchi, Eugenio; Miller, Luke; Pertot, Daniel; Brennecke, Ferdinand; Koehl, Michael

2017-04-01

Quantum gases of interacting fermionic atoms in optical lattices is a promising candidate to study strongly correlated quantum phases of the Hubbard model such as the Mott-insulator, spin-ordered phases, or in particular d-wave superconductivity. We experimentally realise the two-dimensional Hubbard model by loading a quantum degenerate Fermi gas of 40 K atoms into a three-dimensional optical lattice geometry. High-resolution absorption imaging in combination with radiofrequency spectroscopy is applied to spatially resolve the atomic distribution in a single 2D layer. We investigate in local measurements of spatial correlations in both the density and spin sector as a function of filling, temperature and interaction strength. In the density sector, we compare the local density fluctuations and the global thermodynamic quantities, and in the spin sector, we observe the onset of non-local spin correlation, signalling the emergence of the anti-ferromagnetic phase. We would report our recent experimental endeavours to investigate further down in temperature in the spin sector.

Contribution of Small-Scale Correlated Fluctuations of Microstructural Properties of a Spatially Extended Geophysical Target Under the Assessment of Radar Backscatter

NASA Technical Reports Server (NTRS)

Yurchak, Boris S.

2010-01-01

The study of the collective effects of radar scattering from an aggregation of discrete scatterers randomly distributed in a space is important for better understanding the origin of the backscatter from spatially extended geophysical targets (SEGT). We consider the microstructure irregularities of a SEGT as the essential factor that affect radar backscatter. To evaluate their contribution this study uses the "slice" approach: particles close to the front of incident radar wave are considered to reflect incident electromagnetic wave coherently. The radar equation for a SEGT is derived. The equation includes contributions to the total backscatter from correlated small-scale fluctuations of the slice's reflectivity. The correlation contribution changes in accordance with an earlier proposed idea by Smith (1964) based on physical consideration. The slice approach applied allows parameterizing the features of the SEGT's inhomogeneities.

Compressive self-interference Fresnel digital holography with faithful reconstruction

NASA Astrophysics Data System (ADS)

Wan, Yuhong; Man, Tianlong; Han, Ying; Zhou, Hongqiang; Wang, Dayong

2017-05-01

We developed compressive self-interference digital holographic approach that allows retrieving three-dimensional information of the spatially incoherent objects from single-shot captured hologram. The Fresnel incoherent correlation holography is combined with parallel phase-shifting technique to instantaneously obtain spatial-multiplexed phase-shifting holograms. The recording scheme is regarded as compressive forward sensing model, thus the compressive-sensing-based reconstruction algorithm is implemented to reconstruct the original object from the under sampled demultiplexed sub-holograms. The concept was verified by simulations and experiments with simulating use of the polarizer array. The proposed technique has great potential to be applied in 3D tracking of spatially incoherent samples.

Implication of the first decision on visual information-sampling in the spatial frequency domain in pulmonary nodule recognition

NASA Astrophysics Data System (ADS)

Pietrzyk, Mariusz W.; Manning, David; Donovan, Tim; Dix, Alan

2010-02-01

Aim: To investigate the impact on visual sampling strategy and pulmonary nodule recognition of image-based properties of background locations in dwelled regions where the first overt decision was made. . Background: Recent studies in mammography show that the first overt decision (TP or FP) has an influence on further image reading including the correctness of the following decisions. Furthermore, the correlation between the spatial frequency properties of the local background following decision sites and the first decision correctness has been reported. Methods: Subjects with different radiological experience were eye tracked during detection of pulmonary nodules from PA chest radiographs. Number of outcomes and the overall quality of performance are analysed in terms of the cases where correct or incorrect decisions were made. JAFROC methodology is applied. The spatial frequency properties of selected local backgrounds related to a certain decisions were studied. ANOVA was used to compare the logarithmic values of energy carried by non redundant stationary wavelet packet coefficients. Results: A strong correlation has been found between the number of TP as a first decision and the JAFROC score (r = 0.74). The number of FP as a first decision was found negatively correlated with JAFROC (r = -0.75). Moreover, the differential spatial frequency profiles outcomes depend on the first choice correctness.

Probing quantum correlation functions through energy-absorption interferometry

NASA Astrophysics Data System (ADS)

Withington, S.; Thomas, C. N.; Goldie, D. J.

2017-08-01

An interferometric technique is described for determining the spatial forms of the individual degrees of freedom through which a many-body system can absorb energy from its environment. The method separates out the spatial forms of the coherent excitations present at any single frequency; it is not necessary to sweep the frequency and then infer the spatial forms of possible excitations from resonant absorption features. The system under test is excited with two external sources, which create generalized forces, and the fringe in the total power dissipated is measured as the relative phase between the sources is varied. If the complex fringe visibility is measured for different pairs of source locations, the anti-Hermitian part of the complex-valued nonlocal correlation tensor can be determined, which can then be decomposed to give the natural dynamical modes of the system and their relative responsivities. If each source in the interferometer creates a different kind of force, the spatial forms of the individual excitations that are responsible for cross-correlated response can be found. The technique is related to holography, but measures the state of coherence to which the system is maximally sensitive. It can be applied across a wide range of wavelengths, in a variety of ways, to homogeneous media, thin films, patterned structures, and components such as sensors, detectors, and energy-harvesting absorbers.

Parameterizing the Spatial Markov Model From Breakthrough Curve Data Alone

NASA Astrophysics Data System (ADS)

Sherman, Thomas; Fakhari, Abbas; Miller, Savannah; Singha, Kamini; Bolster, Diogo

2017-12-01

The spatial Markov model (SMM) is an upscaled Lagrangian model that effectively captures anomalous transport across a diverse range of hydrologic systems. The distinct feature of the SMM relative to other random walk models is that successive steps are correlated. To date, with some notable exceptions, the model has primarily been applied to data from high-resolution numerical simulations and correlation effects have been measured from simulated particle trajectories. In real systems such knowledge is practically unattainable and the best one might hope for is breakthrough curves (BTCs) at successive downstream locations. We introduce a novel methodology to quantify velocity correlation from BTC data alone. By discretizing two measured BTCs into a set of arrival times and developing an inverse model, we estimate velocity correlation, thereby enabling parameterization of the SMM in studies where detailed Lagrangian velocity statistics are unavailable. The proposed methodology is applied to two synthetic numerical problems, where we measure all details and thus test the veracity of the approach by comparison of estimated parameters with known simulated values. Our results suggest that our estimated transition probabilities agree with simulated values and using the SMM with this estimated parameterization accurately predicts BTCs downstream. Our methodology naturally allows for estimates of uncertainty by calculating lower and upper bounds of velocity correlation, enabling prediction of a range of BTCs. The measured BTCs fall within the range of predicted BTCs. This novel method to parameterize the SMM from BTC data alone is quite parsimonious, thereby widening the SMM's practical applicability.

Blob-hole correlation model for edge turbulence and comparisons with NSTX gas puff imaging data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Myra, J. R.; Zweben, S. J.; Russell, D. A.

We report that gas puff imaging (GPI) observations made in NSTX [Zweben S J, et al., 2017 Phys. Plasmas 24 102509] have revealed two-point spatial correlations of edge and scrape-off layer turbulence in the plane perpendicular to the magnetic field. A common feature is the occurrence of dipole-like patterns with significant regions of negative correlation. In this paper, we explore the possibility that these dipole patterns may be due to blob-hole pairs. Statistical methods are applied to determine the two-point spatial correlation that results from a model of blob-hole pair formation. It is shown that the model produces dipole correlationmore » patterns that are qualitatively similar to the GPI data in several respects. Effects of the reference location (confined surfaces or scrape-off layer), a superimposed random background, hole velocity and lifetime, and background sheared flows are explored and discussed with respect to experimental observations. Additional analysis of the experimental GPI dataset is performed to further test this blob-hole correlation model. A time delay two-point spatial correlation study did not reveal inward propagation of the negative correlation structures that were postulated to correspond to holes in the data nor did it suggest that the negative correlation structures are due to neutral shadowing. However, tracking of the highest and lowest values (extrema) of the normalized GPI fluctuations shows strong evidence for mean inward propagation of minima and outward propagation of maxima, in qualitative agreement with theoretical expectations. Finally, other properties of the experimentally observed extrema are discussed.« less

Blob-hole correlation model for edge turbulence and comparisons with NSTX gas puff imaging data

DOE PAGES

Myra, J. R.; Zweben, S. J.; Russell, D. A.

2018-05-15

We report that gas puff imaging (GPI) observations made in NSTX [Zweben S J, et al., 2017 Phys. Plasmas 24 102509] have revealed two-point spatial correlations of edge and scrape-off layer turbulence in the plane perpendicular to the magnetic field. A common feature is the occurrence of dipole-like patterns with significant regions of negative correlation. In this paper, we explore the possibility that these dipole patterns may be due to blob-hole pairs. Statistical methods are applied to determine the two-point spatial correlation that results from a model of blob-hole pair formation. It is shown that the model produces dipole correlationmore » patterns that are qualitatively similar to the GPI data in several respects. Effects of the reference location (confined surfaces or scrape-off layer), a superimposed random background, hole velocity and lifetime, and background sheared flows are explored and discussed with respect to experimental observations. Additional analysis of the experimental GPI dataset is performed to further test this blob-hole correlation model. A time delay two-point spatial correlation study did not reveal inward propagation of the negative correlation structures that were postulated to correspond to holes in the data nor did it suggest that the negative correlation structures are due to neutral shadowing. However, tracking of the highest and lowest values (extrema) of the normalized GPI fluctuations shows strong evidence for mean inward propagation of minima and outward propagation of maxima, in qualitative agreement with theoretical expectations. Finally, other properties of the experimentally observed extrema are discussed.« less

Directional semivariogram analysis to identify and rank controls on the spatial variability of fracture networks

NASA Astrophysics Data System (ADS)

Hanke, John R.; Fischer, Mark P.; Pollyea, Ryan M.

2018-03-01

In this study, the directional semivariogram is deployed to investigate the spatial variability of map-scale fracture network attributes in the Paradox Basin, Utah. The relative variability ratio (R) is introduced as the ratio of integrated anisotropic semivariogram models, and R is shown to be an effective metric for quantifying the magnitude of spatial variability for any two azimuthal directions. R is applied to a GIS-based data set comprising roughly 1200 fractures, in an area which is bounded by a map-scale anticline and a km-scale normal fault. This analysis reveals that proximity to the fault strongly influences the magnitude of spatial variability for both fracture intensity and intersection density within 1-2 km. Additionally, there is significant anisotropy in the spatial variability, which is correlated with trends of the anticline and fault. The direction of minimum spatial correlation is normal to the fault at proximal distances, and gradually rotates and becomes subparallel to the fold axis over the same 1-2 km distance away from the fault. We interpret these changes to reflect varying scales of influence of the fault and the fold on fracture network development: the fault locally influences the magnitude and variability of fracture network attributes, whereas the fold sets the background level and structure of directional variability.

Structure-specific magnetic field inhomogeneities and its effect on the correlation time.

PubMed

Ziener, Christian H; Bauer, Wolfgang R; Melkus, Gerd; Weber, Thomas; Herold, Volker; Jakob, Peter M

2006-12-01

We describe the relationship between the correlation time and microscopic spatial inhomogeneities in the static magnetic field. The theory takes into account diffusion of nuclear spins in the inhomogeneous field created by magnetized objects. A simple general expression for the correlation time is obtained. It is shown that the correlation time is dependent on a characteristic length, the diffusion coefficient of surrounding medium, the permeability of the surface and the volume fraction of the magnetized objects. For specific geometries (spheres and cylinders), exact analytical expressions for the correlation time are given. The theory can be applied to contrast agents (magnetically labeled cells), capillary network, BOLD effect and so forth.

Intracellular applications of fluorescence correlation spectroscopy: prospects for neuroscience.

PubMed

Kim, Sally A; Schwille, Petra

2003-10-01

Based on time-averaging fluctuation analysis of small fluorescent molecular ensembles in equilibrium, fluorescence correlation spectroscopy has recently been applied to investigate processes in the intracellular milieu. The exquisite sensitivity of fluorescence correlation spectroscopy provides access to a multitude of measurement parameters (rates of diffusion, local concentration, states of aggregation and molecular interactions) in real time with fast temporal and high spatial resolution. The introduction of dual-color cross-correlation, imaging, two-photon excitation, and coincidence analysis coupled with fluorescence correlation spectroscopy has expanded the utility of the technique to encompass a wide range of promising applications in living cells that may provide unprecedented insight into understanding the molecular mechanisms of intracellular neurobiological processes.

The spatiotemporal characteristics of environmental hazards caused by offshore oil and gas operations in the Gulf of Mexico.

PubMed

Meng, Qingmin

2016-09-15

Marine ecosystems are home to a host of numerous species ranging from tiny planktonic organisms, fishes, and birds, to large mammals such as the whales, manatees, and seals. However, human activities such as offshore oil and gas operations increasingly threaten marine and coastal ecosystems, for which there has been little exploration into the spatial and temporal risks of offshore oil operations. Using the Gulf of Mexico, one of the world's hottest spots of offshore oil and gas mining, as the study area, we propose a spatiotemporal approach that integrates spatial statistics and geostatistics in a geographic information system environment to provide insight to environmental management and decision making for oil and gas operators, coastal communities, local governments, and the federal government. We use the records from 1995 to 2015 of twelve types of hazards caused by offshore oil and gas operations, and analyze them spatially over a five year period. The spatial clusters of these hazards are analyzed and mapped using Getis-Ord Gi and local Moran's I statistics. We then design a spatial correlation coefficient matrix for multivariate spatial correlation, which is the ratio of the cross variogram of two types of hazards to the product of the variograms of the two hazards, showing a primary understanding of the degrees of spatial correlation among the twelve types hazards. To the best of our knowledge, it is the first application of spatiotemporal analysis methods to environmental hazards caused by offshore oil and gas operations; the proposed methods can be applied to other regions for the management and monitoring of environmental hazards caused by offshore oil operations. Copyright © 2016 Elsevier B.V. All rights reserved.

A geostatistical state-space model of animal densities for stream networks.

PubMed

Hocking, Daniel J; Thorson, James T; O'Neil, Kyle; Letcher, Benjamin H

2018-06-21

Population dynamics are often correlated in space and time due to correlations in environmental drivers as well as synchrony induced by individual dispersal. Many statistical analyses of populations ignore potential autocorrelations and assume that survey methods (distance and time between samples) eliminate these correlations, allowing samples to be treated independently. If these assumptions are incorrect, results and therefore inference may be biased and uncertainty under-estimated. We developed a novel statistical method to account for spatio-temporal correlations within dendritic stream networks, while accounting for imperfect detection in the surveys. Through simulations, we found this model decreased predictive error relative to standard statistical methods when data were spatially correlated based on stream distance and performed similarly when data were not correlated. We found that increasing the number of years surveyed substantially improved the model accuracy when estimating spatial and temporal correlation coefficients, especially from 10 to 15 years. Increasing the number of survey sites within the network improved the performance of the non-spatial model but only marginally improved the density estimates in the spatio-temporal model. We applied this model to Brook Trout data from the West Susquehanna Watershed in Pennsylvania collected over 34 years from 1981 - 2014. We found the model including temporal and spatio-temporal autocorrelation best described young-of-the-year (YOY) and adult density patterns. YOY densities were positively related to forest cover and negatively related to spring temperatures with low temporal autocorrelation and moderately-high spatio-temporal correlation. Adult densities were less strongly affected by climatic conditions and less temporally variable than YOY but with similar spatio-temporal correlation and higher temporal autocorrelation. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Fine-Scale Mapping by Spatial Risk Distribution Modeling for Regional Malaria Endemicity and Its Implications under the Low-to-Moderate Transmission Setting in Western Cambodia

PubMed Central

Okami, Suguru; Kohtake, Naohiko

2016-01-01

The disease burden of malaria has decreased as malaria elimination efforts progress. The mapping approach that uses spatial risk distribution modeling needs some adjustment and reinvestigation in accordance with situational changes. Here we applied a mathematical modeling approach for standardized morbidity ratio (SMR) calculated by annual parasite incidence using routinely aggregated surveillance reports, environmental data such as remote sensing data, and non-environmental anthropogenic data to create fine-scale spatial risk distribution maps of western Cambodia. Furthermore, we incorporated a combination of containment status indicators into the model to demonstrate spatial heterogeneities of the relationship between containment status and risks. The explanatory model was fitted to estimate the SMR of each area (adjusted Pearson correlation coefficient R2 = 0.774; Akaike information criterion AIC = 149.423). A Bayesian modeling framework was applied to estimate the uncertainty of the model and cross-scale predictions. Fine-scale maps were created by the spatial interpolation of estimated SMRs at each village. Compared with geocoded case data, corresponding predicted values showed conformity [Spearman’s rank correlation r = 0.662 in the inverse distance weighed interpolation and 0.645 in ordinal kriging (95% confidence intervals of 0.414–0.827 and 0.368–0.813, respectively), Welch’s t-test; Not significant]. The proposed approach successfully explained regional malaria risks and fine-scale risk maps were created under low-to-moderate malaria transmission settings where reinvestigations of existing risk modeling approaches were needed. Moreover, different representations of simulated outcomes of containment status indicators for respective areas provided useful insights for tailored interventional planning, considering regional malaria endemicity. PMID:27415623

Optical implementation of neocognitron and its applications to radar signature discrimination

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin; Stoner, William W.

1991-01-01

A feature-extraction-based optoelectronic neural network is introduced. The system implementation approach applies the principle of the neocognitron paradigm first introduced by Fukushima et al. (1983). A multichannel correlator is used as a building block of a generic single layer of the neocognitron for shift-invariant feature correlation. Multilayer processing is achieved by iteratively feeding back the output of the feature correlator to the input spatial light modulator. Successful pattern recognition with intraclass fault tolerance and interclass discrimination is achieved using this optoelectronic neocognitron. Detailed system analysis is described. Experimental demonstration of radar signature processing is also provided.

Support vector machine in crash prediction at the level of traffic analysis zones: Assessing the spatial proximity effects.

PubMed

Dong, Ni; Huang, Helai; Zheng, Liang

2015-09-01

In zone-level crash prediction, accounting for spatial dependence has become an extensively studied topic. This study proposes Support Vector Machine (SVM) model to address complex, large and multi-dimensional spatial data in crash prediction. Correlation-based Feature Selector (CFS) was applied to evaluate candidate factors possibly related to zonal crash frequency in handling high-dimension spatial data. To demonstrate the proposed approaches and to compare them with the Bayesian spatial model with conditional autoregressive prior (i.e., CAR), a dataset in Hillsborough county of Florida was employed. The results showed that SVM models accounting for spatial proximity outperform the non-spatial model in terms of model fitting and predictive performance, which indicates the reasonableness of considering cross-zonal spatial correlations. The best model predictive capability, relatively, is associated with the model considering proximity of the centroid distance by choosing the RBF kernel and setting the 10% of the whole dataset as the testing data, which further exhibits SVM models' capacity for addressing comparatively complex spatial data in regional crash prediction modeling. Moreover, SVM models exhibit the better goodness-of-fit compared with CAR models when utilizing the whole dataset as the samples. A sensitivity analysis of the centroid-distance-based spatial SVM models was conducted to capture the impacts of explanatory variables on the mean predicted probabilities for crash occurrence. While the results conform to the coefficient estimation in the CAR models, which supports the employment of the SVM model as an alternative in regional safety modeling. Copyright © 2015 Elsevier Ltd. All rights reserved.

Estimating the spatial distribution of field-applied mushroom compost in the Brandywine-Christina River Basin using multispectral remote sensing

NASA Astrophysics Data System (ADS)

Moxey, Kelsey A.

The world's greatest concentration of mushroom farms is settled within the Brandywine-Christina River Basin in Chester County in southeastern Pennsylvania. This industry produces a nutrient-rich byproduct known as spent mushroom compost, which has been traditionally applied to local farm fields as an organic fertilizer and soil amendment. While mushroom compost has beneficial properties, the possible over-application to farm fields could potentially degrade stream water quality. The goal of this study was to estimate the spatial extent and intensity of field-applied mushroom compost. We applied a remote sensing approach using Landsat multispectral imagery. We utilized the soil line technique, using the red and near-infrared bands, to estimate differences in soil wetness as a result of increased soil organic matter content from mushroom compost. We validated soil wetness estimates by examining the spectral response of references sites. We performed a second independent validation analysis using expert knowledge from agricultural extension agents. Our results showed that the soil line based wetness index worked well. The spectral validation illustrated that compost changes the spectral response of soil because of changes in wetness. The independent expert validation analysis produced a strong significant correlation between our remotely-sensed wetness estimates and the empirical ratings of compost application intensities. Overall, the methodology produced realistic spatial distributions of field-applied compost application intensities across the study area. These spatial distributions will be used for follow-up studies to assess the effect of spent mushroom compost on stream water quality.

Spatial weighting approach in numerical method for disaggregation of MDGs indicators

NASA Astrophysics Data System (ADS)

Permai, S. D.; Mukhaiyar, U.; Satyaning PP, N. L. P.; Soleh, M.; Aini, Q.

2018-03-01

Disaggregation use to separate and classify the data based on certain characteristics or on administrative level. Disaggregated data is very important because some indicators not measured on all characteristics. Detailed disaggregation for development indicators is important to ensure that everyone benefits from development and support better development-related policymaking. This paper aims to explore different methods to disaggregate national employment-to-population ratio indicator to province- and city-level. Numerical approach applied to overcome the problem of disaggregation unavailability by constructing several spatial weight matrices based on the neighbourhood, Euclidean distance and correlation. These methods can potentially be used and further developed to disaggregate development indicators into lower spatial level even by several demographic characteristics.

Effects of Heterogeniety on Spatial Pattern Analysis of Wild Pistachio Trees in Zagros Woodlands, Iran

NASA Astrophysics Data System (ADS)

Erfanifard, Y.; Rezayan, F.

2014-10-01

Vegetation heterogeneity biases second-order summary statistics, e.g., Ripley's K-function, applied for spatial pattern analysis in ecology. Second-order investigation based on Ripley's K-function and related statistics (i.e., L- and pair correlation function g) is widely used in ecology to develop hypothesis on underlying processes by characterizing spatial patterns of vegetation. The aim of this study was to demonstrate effects of underlying heterogeneity of wild pistachio (Pistacia atlantica Desf.) trees on the second-order summary statistics of point pattern analysis in a part of Zagros woodlands, Iran. The spatial distribution of 431 wild pistachio trees was accurately mapped in a 40 ha stand in the Wild Pistachio & Almond Research Site, Fars province, Iran. Three commonly used second-order summary statistics (i.e., K-, L-, and g-functions) were applied to analyse their spatial pattern. The two-sample Kolmogorov-Smirnov goodness-of-fit test showed that the observed pattern significantly followed an inhomogeneous Poisson process null model in the study region. The results also showed that heterogeneous pattern of wild pistachio trees biased the homogeneous form of K-, L-, and g-functions, demonstrating a stronger aggregation of the trees at the scales of 0-50 m than actually existed and an aggregation at scales of 150-200 m, while regularly distributed. Consequently, we showed that heterogeneity of point patterns may bias the results of homogeneous second-order summary statistics and we also suggested applying inhomogeneous summary statistics with related null models for spatial pattern analysis of heterogeneous vegetations.

Discrete quantum dot like emitters in monolayer MoSe{sub 2}: Spatial mapping, magneto-optics, and charge tuning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Branny, Artur; Kumar, Santosh; Gerardot, Brian D., E-mail: b.d.gerardot@hw.ac.uk

Transition metal dichalcogenide monolayers such as MoSe{sub 2}, MoS{sub 2}, and WSe{sub 2} are direct bandgap semiconductors with original optoelectronic and spin-valley properties. Here we report on spectrally sharp, spatially localized emission in monolayer MoSe{sub 2}. We find this quantum dot-like emission in samples exfoliated onto gold substrates and also suspended flakes. Spatial mapping shows a correlation between the location of emitters and the existence of wrinkles (strained regions) in the flake. We tune the emission properties in magnetic and electric fields applied perpendicular to the monolayer plane. We extract an exciton g-factor of the discrete emitters close to −4,more » as for 2D excitons in this material. In a charge tunable sample, we record discrete jumps on the meV scale as charges are added to the emitter when changing the applied voltage.« less

An Algorithm of Association Rule Mining for Microbial Energy Prospection

PubMed Central

Shaheen, Muhammad; Shahbaz, Muhammad

2017-01-01

The presence of hydrocarbons beneath earth’s surface produces some microbiological anomalies in soils and sediments. The detection of such microbial populations involves pure bio chemical processes which are specialized, expensive and time consuming. This paper proposes a new algorithm of context based association rule mining on non spatial data. The algorithm is a modified form of already developed algorithm which was for spatial database only. The algorithm is applied to mine context based association rules on microbial database to extract interesting and useful associations of microbial attributes with existence of hydrocarbon reserve. The surface and soil manifestations caused by the presence of hydrocarbon oxidizing microbes are selected from existing literature and stored in a shared database. The algorithm is applied on the said database to generate direct and indirect associations among the stored microbial indicators. These associations are then correlated with the probability of hydrocarbon’s existence. The numerical evaluation shows better accuracy for non-spatial data as compared to conventional algorithms at generating reliable and robust rules. PMID:28393846

[Reflections on physical spaces and mental spaces].

PubMed

Chen, Hung-Yi

2013-08-01

This article analyzes certain reciprocal impacts from physical spaces to mental spaces. If the epistemological construction and the spatial imagination from the subject of cogito or the social collectivities are able to influence the construction and creation of the physical spaces of that subject, then the context of that physical space may also affect the cognitive or social subject's mental cognition. This article applies the methodology of iconology from art history (E. Panofsky) and sociology (P. Bourdieu) to explore correlations between the creation of imaginative and physical spaces from the collective consciousness and mental cognition. The author uses Gilles Deleuses's opinion regarding the 17th-century Baroque style and contemporary social collective symptoms as an explanation. From these theoretical studies, the author analyzes the differences of spatial epistemology generated by Taiwan's special geological text. Finally, the author applies Michel Foucault's studies on spatial context to assess the possible application of this thesis of reciprocal impacts from mental spaces to physical spaces in a nursing context.

Damage evolution analysis of coal samples under cyclic loading based on single-link cluster method

NASA Astrophysics Data System (ADS)

Zhang, Zhibo; Wang, Enyuan; Li, Nan; Li, Xuelong; Wang, Xiaoran; Li, Zhonghui

2018-05-01

In this paper, the acoustic emission (AE) response of coal samples under cyclic loading is measured. The results show that there is good positive relation between AE parameters and stress. The AE signal of coal samples under cyclic loading exhibits an obvious Kaiser Effect. The single-link cluster (SLC) method is applied to analyze the spatial evolution characteristics of AE events and the damage evolution process of coal samples. It is found that a subset scale of the SLC structure becomes smaller and smaller when the number of cyclic loading increases, and there is a negative linear relationship between the subset scale and the degree of damage. The spatial correlation length ξ of an SLC structure is calculated. The results show that ξ fluctuates around a certain value from the second cyclic loading process to the fifth cyclic loading process, but spatial correlation length ξ clearly increases in the sixth loading process. Based on the criterion of microcrack density, the coal sample failure process is the transformation from small-scale damage to large-scale damage, which is the reason for changes in the spatial correlation length. Through a systematic analysis, the SLC method is an effective method to research the damage evolution process of coal samples under cyclic loading, and will provide important reference values for studying coal bursts.

The spatial pattern of suicide in the US in relation to deprivation, fragmentation and rurality.

PubMed

Congdon, Peter

2011-01-01

Analysis of geographical patterns of suicide and psychiatric morbidity has demonstrated the impact of latent ecological variables (such as deprivation, rurality). Such latent variables may be derived by conventional multivariate techniques from sets of observed indices (for example, by principal components), by composite variable methods or by methods which explicitly consider the spatial framework of areas and, in particular, the spatial clustering of latent risks and outcomes. This article considers a latent random variable approach to explaining geographical contrasts in suicide in the US; and it develops a spatial structural equation model incorporating deprivation, social fragmentation and rurality. The approach allows for such latent spatial constructs to be correlated both within and between areas. Potential effects of area ethnic mix are also included. The model is applied to male and female suicide deaths over 2002–06 in 3142 US counties.

A Spatial Poisson Hurdle Model for Exploring Geographic Variation in Emergency Department Visits

PubMed Central

Neelon, Brian; Ghosh, Pulak; Loebs, Patrick F.

2012-01-01

Summary We develop a spatial Poisson hurdle model to explore geographic variation in emergency department (ED) visits while accounting for zero inflation. The model consists of two components: a Bernoulli component that models the probability of any ED use (i.e., at least one ED visit per year), and a truncated Poisson component that models the number of ED visits given use. Together, these components address both the abundance of zeros and the right-skewed nature of the nonzero counts. The model has a hierarchical structure that incorporates patient- and area-level covariates, as well as spatially correlated random effects for each areal unit. Because regions with high rates of ED use are likely to have high expected counts among users, we model the spatial random effects via a bivariate conditionally autoregressive (CAR) prior, which introduces dependence between the components and provides spatial smoothing and sharing of information across neighboring regions. Using a simulation study, we show that modeling the between-component correlation reduces bias in parameter estimates. We adopt a Bayesian estimation approach, and the model can be fit using standard Bayesian software. We apply the model to a study of patient and neighborhood factors influencing emergency department use in Durham County, North Carolina. PMID:23543242

Microplasma array patterning of reactive oxygen and nitrogen species onto polystyrene

NASA Astrophysics Data System (ADS)

Szili, Endre J.; Dedrick, James; Oh, Jun-Seok; Bradley, James W.; Boswell, Roderick W.; Charles, Christine; Short, Robert D.; Al-Bataineh, Sameer A.

2017-02-01

We investigate an approach for the patterning of reactive oxygen and nitrogen species (RONS) onto polystyrene using atmospheric-pressure microplasma arrays. The spectrally integrated and time-resolved optical emission from the array is characterised with respect to the applied voltage, applied-voltage frequency and pressure; and the array is used to achieve spatially resolved modification of polystyrene at three pressures: 500 Torr, 760 Torr and 1000 Torr. As determined by time-of-flight secondary ion mass spectrometry (ToF-SIMS), regions over which surface modification occurs are clearly restricted to areas that are exposed to individual microplasma cavities. Analysis of the negative-ion ToF-SIMS mass spectra from the centre of the modified microspots shows that the level of oxidation is dependent on the operating pressure, and closely correlated with the spatial distribution of the optical emission. The functional groups that are generated by the microplasma array on the polystyrene surface are shown to readily participate in an oxidative reaction in phosphate buffered saline solution (pH 7.4). Patterns of oxidised and chemically reactive functionalities could potentially be applied to the future development of biomaterial surfaces, where spatial control over biomolecule or cell function is needed.

A Robust and Multi-Weighted Approach to Estimating Topographically Correlated Tropospheric Delays in Radar Interferograms

PubMed Central

Zhu, Bangyan; Li, Jiancheng; Chu, Zhengwei; Tang, Wei; Wang, Bin; Li, Dawei

2016-01-01

Spatial and temporal variations in the vertical stratification of the troposphere introduce significant propagation delays in interferometric synthetic aperture radar (InSAR) observations. Observations of small amplitude surface deformations and regional subsidence rates are plagued by tropospheric delays, and strongly correlated with topographic height variations. Phase-based tropospheric correction techniques assuming a linear relationship between interferometric phase and topography have been exploited and developed, with mixed success. Producing robust estimates of tropospheric phase delay however plays a critical role in increasing the accuracy of InSAR measurements. Meanwhile, few phase-based correction methods account for the spatially variable tropospheric delay over lager study regions. Here, we present a robust and multi-weighted approach to estimate the correlation between phase and topography that is relatively insensitive to confounding processes such as regional subsidence over larger regions as well as under varying tropospheric conditions. An expanded form of robust least squares is introduced to estimate the spatially variable correlation between phase and topography by splitting the interferograms into multiple blocks. Within each block, correlation is robustly estimated from the band-filtered phase and topography. Phase-elevation ratios are multiply- weighted and extrapolated to each persistent scatter (PS) pixel. We applied the proposed method to Envisat ASAR images over the Southern California area, USA, and found that our method mitigated the atmospheric noise better than the conventional phase-based method. The corrected ground surface deformation agreed better with those measured from GPS. PMID:27420066

A Robust and Multi-Weighted Approach to Estimating Topographically Correlated Tropospheric Delays in Radar Interferograms.

PubMed

Zhu, Bangyan; Li, Jiancheng; Chu, Zhengwei; Tang, Wei; Wang, Bin; Li, Dawei

2016-07-12

Spatial and temporal variations in the vertical stratification of the troposphere introduce significant propagation delays in interferometric synthetic aperture radar (InSAR) observations. Observations of small amplitude surface deformations and regional subsidence rates are plagued by tropospheric delays, and strongly correlated with topographic height variations. Phase-based tropospheric correction techniques assuming a linear relationship between interferometric phase and topography have been exploited and developed, with mixed success. Producing robust estimates of tropospheric phase delay however plays a critical role in increasing the accuracy of InSAR measurements. Meanwhile, few phase-based correction methods account for the spatially variable tropospheric delay over lager study regions. Here, we present a robust and multi-weighted approach to estimate the correlation between phase and topography that is relatively insensitive to confounding processes such as regional subsidence over larger regions as well as under varying tropospheric conditions. An expanded form of robust least squares is introduced to estimate the spatially variable correlation between phase and topography by splitting the interferograms into multiple blocks. Within each block, correlation is robustly estimated from the band-filtered phase and topography. Phase-elevation ratios are multiply- weighted and extrapolated to each persistent scatter (PS) pixel. We applied the proposed method to Envisat ASAR images over the Southern California area, USA, and found that our method mitigated the atmospheric noise better than the conventional phase-based method. The corrected ground surface deformation agreed better with those measured from GPS.

Genetic Variance in Processing Speed Drives Variation in Aging of Spatial and Memory Abilities

ERIC Educational Resources Information Center

Finkel, Deborah; Reynolds, Chandra A.; McArdle, John J.; Hamagami, Fumiaki; Pedersen, Nancy L.

2009-01-01

Previous analyses have identified a genetic contribution to the correlation between declines with age in processing speed and higher cognitive abilities. The goal of the current analysis was to apply the biometric dual change score model to consider the possibility of temporal dynamics underlying the genetic covariance between aging trajectories…

Correlates of Individual, and Age-Related, Differences in Short-Term Learning

ERIC Educational Resources Information Center

Zhang, Zhiyong; Davis, Hasker P.; Salthouse, Timothy A.; Tucker-Drob, Elliot M.

2007-01-01

Latent growth models were applied to data on multitrial verbal and spatial learning tasks from two independent studies. Although significant individual differences in both initial level of performance and subsequent learning were found in both tasks, age differences were found only in mean initial level, and not in mean learning. In neither task…

Phase correlation imaging of unlabeled cell dynamics

NASA Astrophysics Data System (ADS)

Ma, Lihong; Rajshekhar, Gannavarpu; Wang, Ru; Bhaduri, Basanta; Sridharan, Shamira; Mir, Mustafa; Chakraborty, Arindam; Iyer, Rajashekar; Prasanth, Supriya; Millet, Larry; Gillette, Martha U.; Popescu, Gabriel

2016-09-01

We present phase correlation imaging (PCI) as a novel approach to study cell dynamics in a spatially-resolved manner. PCI relies on quantitative phase imaging time-lapse data and, as such, functions in label-free mode, without the limitations associated with exogenous markers. The correlation time map outputted in PCI informs on the dynamics of the intracellular mass transport. Specifically, we show that PCI can extract quantitatively the diffusion coefficient map associated with live cells, as well as standard Brownian particles. Due to its high sensitivity to mass transport, PCI can be applied to studying the integrity of actin polymerization dynamics. Our results indicate that the cyto-D treatment blocking the actin polymerization has a dominant effect at the large spatial scales, in the region surrounding the cell. We found that PCI can distinguish between senescent and quiescent cells, which is extremely difficult without using specific markers currently. We anticipate that PCI will be used alongside established, fluorescence-based techniques to enable valuable new studies of cell function.

A method to classify schizophrenia using inter-task spatial correlations of functional brain images.

PubMed

Michael, Andrew M; Calhoun, Vince D; Andreasen, Nancy C; Baum, Stefi A

2008-01-01

The clinical heterogeneity of schizophrenia (scz) and the overlap of self reported and observed symptoms with other mental disorders makes its diagnosis a difficult task. At present no laboratory-based or image-based diagnostic tool for scz exists and such tools are desired to support existing methods for more precise diagnosis. Functional magnetic resonance imaging (fMRI) is currently employed to identify and correlate cognitive processes related to scz and its symptoms. Fusion of multiple fMRI tasks that probe different cognitive processes may help to better understand hidden networks of this complex disorder. In this paper we utilize three different fMRI tasks and introduce an approach to classify subjects based on inter-task spatial correlations of brain activation. The technique was applied to groups of patients and controls and its validity was checked with the leave-one-out method. We show that the classification rate increases when information from multiple tasks are combined.

Diffuse correlation tomography in the transport regime: A theoretical study of the sensitivity to Brownian motion.

PubMed

Tricoli, Ugo; Macdonald, Callum M; Durduran, Turgut; Da Silva, Anabela; Markel, Vadim A

2018-02-01

Diffuse correlation tomography (DCT) uses the electric-field temporal autocorrelation function to measure the mean-square displacement of light-scattering particles in a turbid medium over a given exposure time. The movement of blood particles is here estimated through a Brownian-motion-like model in contrast to ordered motion as in blood flow. The sensitivity kernel relating the measurable field correlation function to the mean-square displacement of the particles can be derived by applying a perturbative analysis to the correlation transport equation (CTE). We derive an analytical expression for the CTE sensitivity kernel in terms of the Green's function of the radiative transport equation, which describes the propagation of the intensity. We then evaluate the kernel numerically. The simulations demonstrate that, in the transport regime, the sensitivity kernel provides sharper spatial information about the medium as compared with the correlation diffusion approximation. Also, the use of the CTE allows one to explore some additional degrees of freedom in the data such as the collimation direction of sources and detectors. Our results can be used to improve the spatial resolution of DCT, in particular, with applications to blood flow imaging in regions where the Brownian motion is dominant.

Diffuse correlation tomography in the transport regime: A theoretical study of the sensitivity to Brownian motion

NASA Astrophysics Data System (ADS)

Tricoli, Ugo; Macdonald, Callum M.; Durduran, Turgut; Da Silva, Anabela; Markel, Vadim A.

2018-02-01

Diffuse correlation tomography (DCT) uses the electric-field temporal autocorrelation function to measure the mean-square displacement of light-scattering particles in a turbid medium over a given exposure time. The movement of blood particles is here estimated through a Brownian-motion-like model in contrast to ordered motion as in blood flow. The sensitivity kernel relating the measurable field correlation function to the mean-square displacement of the particles can be derived by applying a perturbative analysis to the correlation transport equation (CTE). We derive an analytical expression for the CTE sensitivity kernel in terms of the Green's function of the radiative transport equation, which describes the propagation of the intensity. We then evaluate the kernel numerically. The simulations demonstrate that, in the transport regime, the sensitivity kernel provides sharper spatial information about the medium as compared with the correlation diffusion approximation. Also, the use of the CTE allows one to explore some additional degrees of freedom in the data such as the collimation direction of sources and detectors. Our results can be used to improve the spatial resolution of DCT, in particular, with applications to blood flow imaging in regions where the Brownian motion is dominant.

Spatial problem-solving strategies of middle school students: Wayfinding with geographic information systems

NASA Astrophysics Data System (ADS)

Wigglesworth, John C.

2000-06-01

Geographic Information Systems (GIS) is a powerful computer software package that emphasizes the use of maps and the management of spatially referenced environmental data archived in a systems data base. Professional applications of GIS have been in place since the 1980's, but only recently has GIS gained significant attention in the K--12 classroom. Students using GIS are able to manipulate and query data in order to solve all manners of spatial problems. Very few studies have examined how this technological innovation can support classroom learning. In particular, there has been little research on how experience in using the software correlates with a child's spatial cognition and his/her ability to understand spatial relationships. This study investigates the strategies used by middle school students to solve a wayfinding (route-finding) problem using the ArcView GIS software. The research design combined an individual background questionnaire, results from the Group Assessment of Logical Thinking (GALT) test, and analysis of reflective think-aloud sessions to define the characteristics of the strategies students' used to solve this particular class of spatial problem. Three uniquely different spatial problem solving strategies were identified. Visual/Concrete Wayfinders used a highly visual strategy; Logical/Abstract Wayfinders used GIS software tools to apply a more analytical and systematic approach; Transitional Wayfinders used an approach that showed evidence of one that was shifting from a visual strategy to one that was more analytical. The triangulation of data sources indicates that this progression of wayfinding strategy can be correlated both to Piagetian stages of logical thought and to experience with the use of maps. These findings suggest that GIS teachers must be aware that their students' performance will lie on a continuum that is based on cognitive development, spatial ability, and prior experience with maps. To be most effective, GIS teaching strategies and curriculum development should also represent a progression that correlates to the learners' current skills and experience.

The Use of Geostatistics in the Study of Floral Phenology of Vulpia geniculata (L.) Link

PubMed Central

León Ruiz, Eduardo J.; García Mozo, Herminia; Domínguez Vilches, Eugenio; Galán, Carmen

2012-01-01

Traditionally phenology studies have been focused on changes through time, but there exist many instances in ecological research where it is necessary to interpolate among spatially stratified samples. The combined use of Geographical Information Systems (GIS) and Geostatistics can be an essential tool for spatial analysis in phenological studies. Geostatistics are a family of statistics that describe correlations through space/time and they can be used for both quantifying spatial correlation and interpolating unsampled points. In the present work, estimations based upon Geostatistics and GIS mapping have enabled the construction of spatial models that reflect phenological evolution of Vulpia geniculata (L.) Link throughout the study area during sampling season. Ten sampling points, scattered troughout the city and low mountains in the “Sierra de Córdoba” were chosen to carry out the weekly phenological monitoring during flowering season. The phenological data were interpolated by applying the traditional geostatitical method of Kriging, which was used to ellaborate weekly estimations of V. geniculata phenology in unsampled areas. Finally, the application of Geostatistics and GIS to create phenological maps could be an essential complement in pollen aerobiological studies, given the increased interest in obtaining automatic aerobiological forecasting maps. PMID:22629169

The use of geostatistics in the study of floral phenology of Vulpia geniculata (L.) link.

PubMed

León Ruiz, Eduardo J; García Mozo, Herminia; Domínguez Vilches, Eugenio; Galán, Carmen

2012-01-01

Traditionally phenology studies have been focused on changes through time, but there exist many instances in ecological research where it is necessary to interpolate among spatially stratified samples. The combined use of Geographical Information Systems (GIS) and Geostatistics can be an essential tool for spatial analysis in phenological studies. Geostatistics are a family of statistics that describe correlations through space/time and they can be used for both quantifying spatial correlation and interpolating unsampled points. In the present work, estimations based upon Geostatistics and GIS mapping have enabled the construction of spatial models that reflect phenological evolution of Vulpia geniculata (L.) Link throughout the study area during sampling season. Ten sampling points, scattered throughout the city and low mountains in the "Sierra de Córdoba" were chosen to carry out the weekly phenological monitoring during flowering season. The phenological data were interpolated by applying the traditional geostatitical method of Kriging, which was used to elaborate weekly estimations of V. geniculata phenology in unsampled areas. Finally, the application of Geostatistics and GIS to create phenological maps could be an essential complement in pollen aerobiological studies, given the increased interest in obtaining automatic aerobiological forecasting maps.

A Comparison of Weights Matrices on Computation of Dengue Spatial Autocorrelation

NASA Astrophysics Data System (ADS)

Suryowati, K.; Bekti, R. D.; Faradila, A.

2018-04-01

Spatial autocorrelation is one of spatial analysis to identify patterns of relationship or correlation between locations. This method is very important to get information on the dispersal patterns characteristic of a region and linkages between locations. In this study, it applied on the incidence of Dengue Hemorrhagic Fever (DHF) in 17 sub districts in Sleman, Daerah Istimewa Yogyakarta Province. The link among location indicated by a spatial weight matrix. It describe the structure of neighbouring and reflects the spatial influence. According to the spatial data, type of weighting matrix can be divided into two types: point type (distance) and the neighbourhood area (contiguity). Selection weighting function is one determinant of the results of the spatial analysis. This study use queen contiguity based on first order neighbour weights, queen contiguity based on second order neighbour weights, and inverse distance weights. Queen contiguity first order and inverse distance weights shows that there is the significance spatial autocorrelation in DHF, but not by queen contiguity second order. Queen contiguity first and second order compute 68 and 86 neighbour list

Spatial Accessibility and Availability Measures and Statistical Properties in the Food Environment

PubMed Central

Van Meter, E.; Lawson, A.B.; Colabianchi, N.; Nichols, M.; Hibbert, J.; Porter, D.; Liese, A.D.

2010-01-01

Spatial accessibility is of increasing interest in the health sciences. This paper addresses the statistical use of spatial accessibility and availability indices. These measures are evaluated via an extensive simulation based on cluster models for local food outlet density. We derived Monte Carlo critical values for several statistical tests based on the indices. In particular we are interested in the ability to make inferential comparisons between different study areas where indices of accessibility and availability are to be calculated. We derive tests of mean difference as well as tests for differences in Moran's I for spatial correlation for each of the accessibility and availability indices. We also apply these new statistical tests to a data example based on two counties in South Carolina for various accessibility and availability measures calculated for food outlets, stores, and restaurants. PMID:21499528

Smoothing effect for spatially distributed renewable resources and its impact on power grid robustness.

PubMed

Nagata, Motoki; Hirata, Yoshito; Fujiwara, Naoya; Tanaka, Gouhei; Suzuki, Hideyuki; Aihara, Kazuyuki

2017-03-01

In this paper, we show that spatial correlation of renewable energy outputs greatly influences the robustness of the power grids against large fluctuations of the effective power. First, we evaluate the spatial correlation among renewable energy outputs. We find that the spatial correlation of renewable energy outputs depends on the locations, while the influence of the spatial correlation of renewable energy outputs on power grids is not well known. Thus, second, by employing the topology of the power grid in eastern Japan, we analyze the robustness of the power grid with spatial correlation of renewable energy outputs. The analysis is performed by using a realistic differential-algebraic equations model. The results show that the spatial correlation of the energy resources strongly degrades the robustness of the power grid. Our results suggest that we should consider the spatial correlation of the renewable energy outputs when estimating the stability of power grids.

Aperture synthesis for microwave radiometers in space

NASA Technical Reports Server (NTRS)

Levine, D. M.; Good, J. C.

1983-01-01

A technique is described for obtaining passive microwave measurements from space with high spatial resolution for remote sensing applications. The technique involves measuring the product of the signal from pairs of antennas at many different antenna spacings, thereby mapping the correlation function of antenna voltage. The intensity of radiation at the source can be obtained from the Fourier transform of this correlation function. Theory is presented to show how the technique can be applied to large extended sources such as the Earth when observed from space. Details are presented for a system with uniformly spaced measurements.

A High-Dimensional, Multivariate Copula Approach to Modeling Multivariate Agricultural Price Relationships and Tail Dependencies

Treesearch

Xuan Chi; Barry Goodwin

2012-01-01

Spatial and temporal relationships among agricultural prices have been an important topic of applied research for many years. Such research is used to investigate the performance of markets and to examine linkages up and down the marketing chain. This research has empirically evaluated price linkages by using correlation and regression models and, later, linear and...

Structural covariance networks across healthy young adults and their consistency.

PubMed

Guo, Xiaojuan; Wang, Yan; Guo, Taomei; Chen, Kewei; Zhang, Jiacai; Li, Ke; Jin, Zhen; Yao, Li

2015-08-01

To investigate structural covariance networks (SCNs) as measured by regional gray matter volumes with structural magnetic resonance imaging (MRI) from healthy young adults, and to examine their consistency and stability. Two independent cohorts were included in this study: Group 1 (82 healthy subjects aged 18-28 years) and Group 2 (109 healthy subjects aged 20-28 years). Structural MRI data were acquired at 3.0T and 1.5T using a magnetization prepared rapid-acquisition gradient echo sequence for these two groups, respectively. We applied independent component analysis (ICA) to construct SCNs and further applied the spatial overlap ratio and correlation coefficient to evaluate the spatial consistency of the SCNs between these two datasets. Seven and six independent components were identified for Group 1 and Group 2, respectively. Moreover, six SCNs including the posterior default mode network, the visual and auditory networks consistently existed across the two datasets. The overlap ratios and correlation coefficients of the visual network reached the maximums of 72% and 0.71. This study demonstrates the existence of consistent SCNs corresponding to general functional networks. These structural covariance findings may provide insight into the underlying organizational principles of brain anatomy. © 2014 Wiley Periodicals, Inc.

Impact of environmental variables on Dubas bug infestation rate: A case study from the Sultanate of Oman

PubMed Central

Al-Kindi, Khalifa M.; Andrew, Nigel; Welch, Mitchell

2017-01-01

Date palm cultivation is economically important in the Sultanate of Oman, with significant financial investment coming from both the government and from private individuals. However, a global infestation of Dubas bug (Ommatissus lybicus Bergevin) has impacted the Middle East region, and infestations of date palms have been widespread. In this study, spatial analysis and geostatistical techniques were used to model the spatial distribution of Dubas bug infestations to (a) identify correlations between Dubas bug densities and different environmental variables, and (b) predict the locations of future Dubas bug infestations in Oman. Firstly, we considered individual environmental variables and their correlations with infestation locations. Then, we applied more complex predictive models and regression analysis techniques to investigate the combinations of environmental factors most conducive to the survival and spread of the Dubas bug. Environmental variables including elevation, geology, and distance to drainage pathways were found to significantly affect Dubas bug infestations. In contrast, aspect and hillshade did not significantly impact on Dubas bug infestations. Understanding their distribution and therefore applying targeted controls on their spread is important for effective mapping, control and management (e.g., resource allocation) of Dubas bug infestations. PMID:28558069

Impact of environmental variables on Dubas bug infestation rate: A case study from the Sultanate of Oman.

PubMed

Al-Kindi, Khalifa M; Kwan, Paul; Andrew, Nigel; Welch, Mitchell

2017-01-01

Date palm cultivation is economically important in the Sultanate of Oman, with significant financial investment coming from both the government and from private individuals. However, a global infestation of Dubas bug (Ommatissus lybicus Bergevin) has impacted the Middle East region, and infestations of date palms have been widespread. In this study, spatial analysis and geostatistical techniques were used to model the spatial distribution of Dubas bug infestations to (a) identify correlations between Dubas bug densities and different environmental variables, and (b) predict the locations of future Dubas bug infestations in Oman. Firstly, we considered individual environmental variables and their correlations with infestation locations. Then, we applied more complex predictive models and regression analysis techniques to investigate the combinations of environmental factors most conducive to the survival and spread of the Dubas bug. Environmental variables including elevation, geology, and distance to drainage pathways were found to significantly affect Dubas bug infestations. In contrast, aspect and hillshade did not significantly impact on Dubas bug infestations. Understanding their distribution and therefore applying targeted controls on their spread is important for effective mapping, control and management (e.g., resource allocation) of Dubas bug infestations.

Imaging subsurface hydrothermal structure using a dense geophone array in Yellowstone

NASA Astrophysics Data System (ADS)

Wu, S. M.; Lin, F. C.; Farrell, J.; Smith, R. B.

2016-12-01

The recent development of ambient noise cross-correlation and the availability of large N seismic arrays allow for the study of detailed shallow crustal structure. In this study, we apply multi-component noise cross-correlation to explore shallow hydrothermal structure near Old Faithful geyser in Yellowstone National Park using a temporary geophone array. The array was composed of 133 three-component 5-Hz geophones and was deployed for two weeks during November 2015. The average station spacing is 50 meters and the full aperture of the array is around 1 km with good azimuthal and spatial coverage. The Upper Geyser Basin, where Old Faithful is located, has the largest concentration of geysers in the world. This unique active hydrothermal environment and hence the extremely inhomogeneous noise source distribution makes the construction of empirical Green's functions difficult based on the traditional noise cross-correlation method. In this presentation, we show examples of the constructed cross-correlation functions and demonstrate their spatial and temporal relationships with known hydrothermal activity. We also demonstrate how useful seismic signals can be extracted from these cross-correlation functions and used for subsurface imaging. In particular, we will discuss the existence of a recharge cavity beneath Old Faithful revealed by the noise cross-correlations. In addition, we also investigated the temporal structure variation based on time-lapse noise cross-correlations and these preliminary results will also be discussed.

Parameterizing the Spatial Markov Model from Breakthrough Curve Data Alone

NASA Astrophysics Data System (ADS)

Sherman, T.; Bolster, D.; Fakhari, A.; Miller, S.; Singha, K.

2017-12-01

The spatial Markov model (SMM) uses a correlated random walk and has been shown to effectively capture anomalous transport in porous media systems; in the SMM, particles' future trajectories are correlated to their current velocity. It is common practice to use a priori Lagrangian velocity statistics obtained from high resolution simulations to determine a distribution of transition probabilities (correlation) between velocity classes that govern predicted transport behavior; however, this approach is computationally cumbersome. Here, we introduce a methodology to quantify velocity correlation from Breakthrough (BTC) curve data alone; discretizing two measured BTCs into a set of arrival times and reverse engineering the rules of the SMM allows for prediction of velocity correlation, thereby enabling parameterization of the SMM in studies where Lagrangian velocity statistics are not available. The introduced methodology is applied to estimate velocity correlation from BTCs measured in high resolution simulations, thus allowing for a comparison of estimated parameters with known simulated values. Results show 1) estimated transition probabilities agree with simulated values and 2) using the SMM with estimated parameterization accurately predicts BTCs downstream. Additionally, we include uncertainty measurements by calculating lower and upper estimates of velocity correlation, which allow for prediction of a range of BTCs. The simulated BTCs fall in the range of predicted BTCs. This research proposes a novel method to parameterize the SMM from BTC data alone, thereby reducing the SMM's computational costs and widening its applicability.

Analysis of students geometry skills viewed from spatial intelligence

NASA Astrophysics Data System (ADS)

Riastuti, Nova; Mardiyana, Pramudya, Ikrar

2017-12-01

Geometry is one of the difficult materials for students because students must have the ability to visualize, describe the picture, draw a figure, and know the kinds of figures. This study aimisto describe the students geometry skills in resolving geometry problems viewed from spatial intelligence. This research uses a descriptive qualitative method has aim to identify students geometry skills by 6 students in eight grade of Ngawi regency, Indonesia. The subjects were 2 students with high spatial intelligence, 2 students with medium spatial intelligence, and 2 students with low spatial intelligence. Datas were collected based on written test and interview. The result of this research showed that the students geometry skills viewed from spatial intelligence includes. The results of this study indicate that there was a correlation between students' spatial intelligence with geometric skills. Students had different geometric skills in each category of spatial intelligence, although there were similarities in some geometry skill indicators. Students with low spatial intelligence had less geometry skills, thus requiring special attention from teachers. Mathematics teachers are expected to provide more practice questions that reinforce students' geometry skills including visual skills, descriptive skills, drawing skills, logical skills, applied skills.

Comparison of Spatial Correlation Parameters between Full and Model Scale Launch Vehicles

NASA Technical Reports Server (NTRS)

Kenny, Jeremy; Giacomoni, Clothilde

2016-01-01

The current vibro-acoustic analysis tools require specific spatial correlation parameters as input to define the liftoff acoustic environment experienced by the launch vehicle. Until recently these parameters have not been very well defined. A comprehensive set of spatial correlation data were obtained during a scale model acoustic test conducted in 2014. From these spatial correlation data, several parameters were calculated: the decay coefficient, the diffuse to propagating ratio, and the angle of incidence. Spatial correlation data were also collected on the EFT-1 flight of the Delta IV vehicle which launched on December 5th, 2014. A comparison of the spatial correlation parameters from full scale and model scale data will be presented.

Rainfall Observed Over Bangladesh 2000-2008: A Comparison of Spatial Interpolation Methods

NASA Astrophysics Data System (ADS)

Pervez, M.; Henebry, G. M.

2010-12-01

In preparation for a hydrometeorological study of freshwater resources in the greater Ganges-Brahmaputra region, we compared the results of four methods of spatial interpolation applied to point measurements of daily rainfall over Bangladesh during a seven year period (2000-2008). Two univariate (inverse distance weighted and spline-regularized and tension) and two multivariate geostatistical (ordinary kriging and kriging with external drift) methods were used to interpolate daily observations from a network of 221 rain gauges across Bangladesh spanning an area of 143,000 sq km. Elevation and topographic index were used as the covariates in the geostatistical methods. The validity of the interpolated maps was analyzed through cross-validation. The quality of the methods was assessed through the Pearson and Spearman correlations and root mean square error measurements of accuracy in cross-validation. Preliminary results indicated that the univariate methods performed better than the geostatistical methods at daily scales, likely due to the relatively dense sampled point measurements and a weak correlation between the rainfall and covariates at daily scales in this region. Inverse distance weighted produced the better results than the spline. For the days with extreme or high rainfall—spatially and quantitatively—the correlation between observed and interpolated estimates appeared to be high (r2 ~ 0.6 RMSE ~ 10mm), although for low rainfall days the correlations were poor (r2 ~ 0.1 RMSE ~ 3mm). The performance quality of these methods was influenced by the density of the sample point measurements, the quantity of the observed rainfall along with spatial extent, and an appropriate search radius defining the neighboring points. Results indicated that interpolated rainfall estimates at daily scales may introduce uncertainties in the successive hydrometeorological analysis. Interpolations at 5-day, 10-day, 15-day, and monthly time scales are currently under investigation.

Poverty, health and satellite-derived vegetation indices: their inter-spatial relationship in West Africa

PubMed Central

Sedda, Luigi; Tatem, Andrew J.; Morley, David W.; Atkinson, Peter M.; Wardrop, Nicola A.; Pezzulo, Carla; Sorichetta, Alessandro; Kuleszo, Joanna; Rogers, David J.

2015-01-01

Background Previous analyses have shown the individual correlations between poverty, health and satellite-derived vegetation indices such as the normalized difference vegetation index (NDVI). However, generally these analyses did not explore the statistical interconnections between poverty, health outcomes and NDVI. Methods In this research aspatial methods (principal component analysis) and spatial models (variography, factorial kriging and cokriging) were applied to investigate the correlations and spatial relationships between intensity of poverty, health (expressed as child mortality and undernutrition), and NDVI for a large area of West Africa. Results This research showed that the intensity of poverty (and hence child mortality and nutrition) varies inversely with NDVI. From the spatial point-of-view, similarities in the spatial variation of intensity of poverty and NDVI were found. Conclusions These results highlight the utility of satellite-based metrics for poverty models including health and ecological components and, in general for large scale analysis, estimation and optimisation of multidimensional poverty metrics. However, it also stresses the need for further studies on the causes of the association between NDVI, health and poverty. Once these relationships are confirmed and better understood, the presence of this ecological component in poverty metrics has the potential to facilitate the analysis of the impacts of climate change on the rural populations afflicted by poverty and child mortality. PMID:25733559

Fiber transport of spatially entangled photons

NASA Astrophysics Data System (ADS)

Löffler, W.; Eliel, E. R.; Woerdman, J. P.; Euser, T. G.; Scharrer, M.; Russell, P.

2012-03-01

High-dimensional entangled photons pairs are interesting for quantum information and cryptography: Compared to the well-known 2D polarization case, the stronger non-local quantum correlations could improve noise resistance or security, and the larger amount of information per photon increases the available bandwidth. One implementation is to use entanglement in the spatial degree of freedom of twin photons created by spontaneous parametric down-conversion, which is equivalent to orbital angular momentum entanglement, this has been proven to be an excellent model system. The use of optical fiber technology for distribution of such photons has only very recently been practically demonstrated and is of fundamental and applied interest. It poses a big challenge compared to the established time and frequency domain methods: For spatially entangled photons, fiber transport requires the use of multimode fibers, and mode coupling and intermodal dispersion therein must be minimized not to destroy the spatial quantum correlations. We demonstrate that these shortcomings of conventional multimode fibers can be overcome by using a hollow-core photonic crystal fiber, which follows the paradigm to mimic free-space transport as good as possible, and are able to confirm entanglement of the fiber-transported photons. Fiber transport of spatially entangled photons is largely unexplored yet, therefore we discuss the main complications, the interplay of intermodal dispersion and mode mixing, the influence of external stress and core deformations, and consider the pros and cons of various fiber types.

Technical Note: Detection of gas bubble leakage via correlation of water column multibeam images

NASA Astrophysics Data System (ADS)

Schneider von Deimling, J.; Papenberg, C.

2011-07-01

Hydroacoustic detection of natural gas release from the seafloor has been conducted in the past by using singlebeam echosounders. In contrast modern multibeam swath mapping systems allow much wider coverage, higher resolution, and offer 3-D spatial correlation. However, up to the present, the extremely high data rate hampers water column backscatter investigations. More sophisticated visualization and processing techniques for water column backscatter analysis are still under development. We here present such water column backscattering data gathered with a 50 kHz prototype multibeam system. Water column backscattering data is presented in videoframes grabbed over 75 s and a "re-sorted" singlebeam presentation. Thus individual gas bubbles rising from the 24 m deep seafloor clearly emerge in the acoustic images and rise velocities can be determined. A sophisticated processing scheme is introduced to identify those rising gas bubbles in the hydroacoustic data. It applies a cross-correlation technique similar to that used in Particle Imaging Velocimetry (PIV) to the acoustic backscatter images. Tempo-spatial drift patterns of the bubbles are assessed and match very well measured and theoretical rise patterns. The application of this processing scheme to our field data gives impressive results with respect to unambiguous bubble detection and remote bubble rise velocimetry. The method can identify and exclude the main driver for misinterpretations, i.e. fish-mediated echoes. Even though image-based cross-correlation techniques are well known in the field of fluid mechanics for high resolution and non-inversive current flow field analysis, this technique was never applied in the proposed sense for an acoustic bubble detector.

Revisiting Temporal Markov Chains for Continuum modeling of Transport in Porous Media

NASA Astrophysics Data System (ADS)

Delgoshaie, A. H.; Jenny, P.; Tchelepi, H.

2017-12-01

The transport of fluids in porous media is dominated by flow­-field heterogeneity resulting from the underlying permeability field. Due to the high uncertainty in the permeability field, many realizations of the reference geological model are used to describe the statistics of the transport phenomena in a Monte Carlo (MC) framework. There has been strong interest in working with stochastic formulations of the transport that are different from the standard MC approach. Several stochastic models based on a velocity process for tracer particle trajectories have been proposed. Previous studies have shown that for high variances of the log-conductivity, the stochastic models need to account for correlations between consecutive velocity transitions to predict dispersion accurately. The correlated velocity models proposed in the literature can be divided into two general classes of temporal and spatial Markov models. Temporal Markov models have been applied successfully to tracer transport in both the longitudinal and transverse directions. These temporal models are Stochastic Differential Equations (SDEs) with very specific drift and diffusion terms tailored for a specific permeability correlation structure. The drift and diffusion functions devised for a certain setup would not necessarily be suitable for a different scenario, (e.g., a different permeability correlation structure). The spatial Markov models are simple discrete Markov chains that do not require case specific assumptions. However, transverse spreading of contaminant plumes has not been successfully modeled with the available correlated spatial models. Here, we propose a temporal discrete Markov chain to model both the longitudinal and transverse dispersion in a two-dimensional domain. We demonstrate that these temporal Markov models are valid for different correlation structures without modification. Similar to the temporal SDEs, the proposed model respects the limited asymptotic transverse spreading of the plume in two-dimensional problems.

Joint transform correlators with spatially incoherent illumination

NASA Astrophysics Data System (ADS)

Bykovsky, Yuri A.; Karpiouk, Andrey B.; Markilov, Anatoly A.; Rodin, Vladislav G.; Starikov, Sergey N.

1997-03-01

Two variants of joint transform correlators with monochromatic spatially incoherent illumination are considered. The Fourier-holograms of the reference and recognized images are recorded simultaneously or apart in a time on the same spatial light modulator directly by monochromatic spatially incoherent light. To create the signal of mutual correlation of the images it is necessary to execute nonlinear transformation when the hologram is illuminated by coherent light. In the first scheme of the correlator this aim was achieved by using double pas of a restoring coherent wave through the hologram. In the second variant of the correlator the non-linearity of the characteristic of the spatial light modulator for hologram recording was used. Experimental schemes and results on processing teste images by both variants of joint transform correlators with monochromatic spatially incoherent illumination. The use of spatially incoherent light on the input of joint transform correlators permits to reduce the requirements to optical quality of elements, to reduce accuracy requirements on elements positioning and to expand a number of devices suitable to input images in correlators.

Evaluating spatial and temporal variability in growth and mortality for recreational fisheries with limited catch data

USGS Publications Warehouse

Li, Yan; Wagner, Tyler; Jiao, Yan; Lorantas, Robert M.; Murphy, Cheryl

2018-01-01

Understanding the spatial and temporal variability in life-history traits among populations is essential for the management of recreational fisheries. However, valuable freshwater recreational fish species often suffer from a lack of catch information. In this study, we demonstrated the use of an approach to estimate the spatial and temporal variability in growth and mortality in the absence of catch data and apply the method to riverine smallmouth bass (Micropterus dolomieu) populations in Pennsylvania, USA. Our approach included a growth analysis and a length-based analysis that estimates mortality. Using a hierarchical Bayesian approach, we examined spatial variability in growth and mortality by assuming parameters vary spatially but remain constant over time and temporal variability by assuming parameters vary spatially and temporally. The estimated growth and mortality of smallmouth bass showed substantial variability over time and across rivers. We explored the relationships of the estimated growth and mortality with spring water temperature and spring flow. Growth rate was likely to be positively correlated with these two factors, while young mortality was likely to be positively correlated with spring flow. The spatially and temporally varying growth and mortality suggest that smallmouth bass populations across rivers may respond differently to management plans and disturbance such as environmental contamination and land-use change. The analytical approach can be extended to other freshwater recreational species that also lack of catch data. The approach could also be useful in developing population assessments with erroneous catch data or be used as a model sensitivity scenario to verify traditional models even when catch data are available.

A resampling procedure for generating conditioned daily weather sequences

USGS Publications Warehouse

Clark, Martyn P.; Gangopadhyay, Subhrendu; Brandon, David; Werner, Kevin; Hay, Lauren E.; Rajagopalan, Balaji; Yates, David

2004-01-01

A method is introduced to generate conditioned daily precipitation and temperature time series at multiple stations. The method resamples data from the historical record “nens” times for the period of interest (nens = number of ensemble members) and reorders the ensemble members to reconstruct the observed spatial (intersite) and temporal correlation statistics. The weather generator model is applied to 2307 stations in the contiguous United States and is shown to reproduce the observed spatial correlation between neighboring stations, the observed correlation between variables (e.g., between precipitation and temperature), and the observed temporal correlation between subsequent days in the generated weather sequence. The weather generator model is extended to produce sequences of weather that are conditioned on climate indices (in this case the Niño 3.4 index). Example illustrations of conditioned weather sequences are provided for a station in Arizona (Petrified Forest, 34.8°N, 109.9°W), where El Niño and La Niña conditions have a strong effect on winter precipitation. The conditioned weather sequences generated using the methods described in this paper are appropriate for use as input to hydrologic models to produce multiseason forecasts of streamflow.

A spatial epidemiological analysis of self-rated mental health in the slums of Dhaka

PubMed Central

2011-01-01

Background The deprived physical environments present in slums are well-known to have adverse health effects on their residents. However, little is known about the health effects of the social environments in slums. Moreover, neighbourhood quantitative spatial analyses of the mental health status of slum residents are still rare. The aim of this paper is to study self-rated mental health data in several slums of Dhaka, Bangladesh, by accounting for neighbourhood social and physical associations using spatial statistics. We hypothesised that mental health would show a significant spatial pattern in different population groups, and that the spatial patterns would relate to spatially-correlated health-determining factors (HDF). Methods We applied a spatial epidemiological approach, including non-spatial ANOVA/ANCOVA, as well as global and local univariate and bivariate Moran's I statistics. The WHO-5 Well-being Index was used as a measure of self-rated mental health. Results We found that poor mental health (WHO-5 scores < 13) among the adult population (age ≥15) was prevalent in all slum settlements. We detected spatially autocorrelated WHO-5 scores (i.e., spatial clusters of poor and good mental health among different population groups). Further, we detected spatial associations between mental health and housing quality, sanitation, income generation, environmental health knowledge, education, age, gender, flood non-affectedness, and selected properties of the natural environment. Conclusions Spatial patterns of mental health were detected and could be partly explained by spatially correlated HDF. We thereby showed that the socio-physical neighbourhood was significantly associated with health status, i.e., mental health at one location was spatially dependent on the mental health and HDF prevalent at neighbouring locations. Furthermore, the spatial patterns point to severe health disparities both within and between the slums. In addition to examining health outcomes, the methodology used here is also applicable to residuals of regression models, such as helping to avoid violating the assumption of data independence that underlies many statistical approaches. We assume that similar spatial structures can be found in other studies focussing on neighbourhood effects on health, and therefore argue for a more widespread incorporation of spatial statistics in epidemiological studies. PMID:21599932

A Spectral Method for Spatial Downscaling

PubMed Central

Reich, Brian J.; Chang, Howard H.; Foley, Kristen M.

2014-01-01

Summary Complex computer models play a crucial role in air quality research. These models are used to evaluate potential regulatory impacts of emission control strategies and to estimate air quality in areas without monitoring data. For both of these purposes, it is important to calibrate model output with monitoring data to adjust for model biases and improve spatial prediction. In this article, we propose a new spectral method to study and exploit complex relationships between model output and monitoring data. Spectral methods allow us to estimate the relationship between model output and monitoring data separately at different spatial scales, and to use model output for prediction only at the appropriate scales. The proposed method is computationally efficient and can be implemented using standard software. We apply the method to compare Community Multiscale Air Quality (CMAQ) model output with ozone measurements in the United States in July 2005. We find that CMAQ captures large-scale spatial trends, but has low correlation with the monitoring data at small spatial scales. PMID:24965037

Spatial Decomposition of Translational Water–Water Correlation Entropy in Binding Pockets

PubMed Central

2015-01-01

A number of computational tools available today compute the thermodynamic properties of water at surfaces and in binding pockets by using inhomogeneous solvation theory (IST) to analyze explicit-solvent simulations. Such methods enable qualitative spatial mappings of both energy and entropy around a solute of interest and can also be applied quantitatively. However, the entropy estimates of existing methods have, to date, been almost entirely limited to the first-order terms in the IST’s entropy expansion. These first-order terms account for localization and orientation of water molecules in the field of the solute but not for the modification of water–water correlations by the solute. Here, we present an extension of the Grid Inhomogeneous Solvation Theory (GIST) approach which accounts for water–water translational correlations. The method involves rewriting the two-point density of water in terms of a conditional density and utilizes the efficient nearest-neighbor entropy estimation approach. Spatial maps of this second order term, for water in and around the synthetic host cucurbit[7]uril and in the binding pocket of the enzyme Factor Xa, reveal mainly negative contributions, indicating solute-induced water–water correlations relative to bulk water; particularly strong signals are obtained for sites at the entrances of cavities or pockets. This second-order term thus enters with the same, negative, sign as the first order translational and orientational terms. Numerical and convergence properties of the methodology are examined. PMID:26636620

A model relating Eulerian spatial and temporal velocity correlations

NASA Astrophysics Data System (ADS)

Cholemari, Murali R.; Arakeri, Jaywant H.

2006-03-01

In this paper we propose a model to relate Eulerian spatial and temporal velocity autocorrelations in homogeneous, isotropic and stationary turbulence. We model the decorrelation as the eddies of various scales becoming decorrelated. This enables us to connect the spatial and temporal separations required for a certain decorrelation through the ‘eddy scale’. Given either the spatial or the temporal velocity correlation, we obtain the ‘eddy scale’ and the rate at which the decorrelation proceeds. This leads to a spatial separation from the temporal correlation and a temporal separation from the spatial correlation, at any given value of the correlation relating the two correlations. We test the model using experimental data from a stationary axisymmetric turbulent flow with homogeneity along the axis.

Method and system for spatially variable rate application of agricultural chemicals based on remotely sensed vegetation data

NASA Technical Reports Server (NTRS)

Lewis, Mark David (Inventor); Seal, Michael R. (Inventor); Hood, Kenneth Brown (Inventor); Johnson, James William (Inventor)

2007-01-01

Remotely sensed spectral image data are used to develop a Vegetation Index file which represents spatial variations of actual crop vigor throughout a field that is under cultivation. The latter information is processed to place it in a format that can be used by farm personnel to correlate and calibrate it with actually observed crop conditions existing at control points within the field. Based on the results, farm personnel formulate a prescription request, which is forwarded via email or FTP to a central processing site, where the prescription is prepared. The latter is returned via email or FTP to on-side farm personnel, who can load it into a controller on a spray rig that directly applies inputs to the field at a spatially variable rate.

Method and apparatus for spatially variable rate application of agricultural chemicals based on remotely sensed vegetation data

NASA Technical Reports Server (NTRS)

Hood, Kenneth Brown (Inventor); Johnson, James William (Inventor); Seal, Michael R. (Inventor); Lewis, Mark David (Inventor)

2004-01-01

Remotely sensed spectral image data are used to develop a Vegetation Index file which represents spatial variations of actual crop vigor throughout a field that is under cultivation. The latter information is processed to place it in a format that can be used by farm personnel to correlate and calibrate it with actually observed crop conditions existing at control points within the field. Based on the results, farm personnel formulate a prescription request, which is forwarded via email or FTP to a central processing site, where the prescription is prepared. The latter is returned via email or FTP to on-side farm personnel, who can load it into a controller on a spray rig that directly applies inputs to the field at a spatially variable rate.

Novel probabilistic models of spatial genetic ancestry with applications to stratification correction in genome-wide association studies.

PubMed

Bhaskar, Anand; Javanmard, Adel; Courtade, Thomas A; Tse, David

2017-03-15

Genetic variation in human populations is influenced by geographic ancestry due to spatial locality in historical mating and migration patterns. Spatial population structure in genetic datasets has been traditionally analyzed using either model-free algorithms, such as principal components analysis (PCA) and multidimensional scaling, or using explicit spatial probabilistic models of allele frequency evolution. We develop a general probabilistic model and an associated inference algorithm that unify the model-based and data-driven approaches to visualizing and inferring population structure. Our spatial inference algorithm can also be effectively applied to the problem of population stratification in genome-wide association studies (GWAS), where hidden population structure can create fictitious associations when population ancestry is correlated with both the genotype and the trait. Our algorithm Geographic Ancestry Positioning (GAP) relates local genetic distances between samples to their spatial distances, and can be used for visually discerning population structure as well as accurately inferring the spatial origin of individuals on a two-dimensional continuum. On both simulated and several real datasets from diverse human populations, GAP exhibits substantially lower error in reconstructing spatial ancestry coordinates compared to PCA. We also develop an association test that uses the ancestry coordinates inferred by GAP to accurately account for ancestry-induced correlations in GWAS. Based on simulations and analysis of a dataset of 10 metabolic traits measured in a Northern Finland cohort, which is known to exhibit significant population structure, we find that our method has superior power to current approaches. Our software is available at https://github.com/anand-bhaskar/gap . abhaskar@stanford.edu or ajavanma@usc.edu. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Sampling design for spatially distributed hydrogeologic and environmental processes

USGS Publications Warehouse

Christakos, G.; Olea, R.A.

1992-01-01

A methodology for the design of sampling networks over space is proposed. The methodology is based on spatial random field representations of nonhomogeneous natural processes, and on optimal spatial estimation techniques. One of the most important results of random field theory for physical sciences is its rationalization of correlations in spatial variability of natural processes. This correlation is extremely important both for interpreting spatially distributed observations and for predictive performance. The extent of site sampling and the types of data to be collected will depend on the relationship of subsurface variability to predictive uncertainty. While hypothesis formulation and initial identification of spatial variability characteristics are based on scientific understanding (such as knowledge of the physics of the underlying phenomena, geological interpretations, intuition and experience), the support offered by field data is statistically modelled. This model is not limited by the geometric nature of sampling and covers a wide range in subsurface uncertainties. A factorization scheme of the sampling error variance is derived, which possesses certain atttactive properties allowing significant savings in computations. By means of this scheme, a practical sampling design procedure providing suitable indices of the sampling error variance is established. These indices can be used by way of multiobjective decision criteria to obtain the best sampling strategy. Neither the actual implementation of the in-situ sampling nor the solution of the large spatial estimation systems of equations are necessary. The required values of the accuracy parameters involved in the network design are derived using reference charts (readily available for various combinations of data configurations and spatial variability parameters) and certain simple yet accurate analytical formulas. Insight is gained by applying the proposed sampling procedure to realistic examples related to sampling problems in two dimensions. ?? 1992.

Spatio-temporal analysis of annual rainfall in Crete, Greece

NASA Astrophysics Data System (ADS)

Varouchakis, Emmanouil A.; Corzo, Gerald A.; Karatzas, George P.; Kotsopoulou, Anastasia

2018-03-01

Analysis of rainfall data from the island of Crete, Greece was performed to identify key hydrological years and return periods as well as to analyze the inter-annual behavior of the rainfall variability during the period 1981-2014. The rainfall spatial distribution was also examined in detail to identify vulnerable areas of the island. Data analysis using statistical tools and spectral analysis were applied to investigate and interpret the temporal course of the available rainfall data set. In addition, spatial analysis techniques were applied and compared to determine the rainfall spatial distribution on the island of Crete. The analysis presented that in contrast to Regional Climate Model estimations, rainfall rates have not decreased, while return periods vary depending on seasonality and geographic location. A small but statistical significant increasing trend was detected in the inter-annual rainfall variations as well as a significant rainfall cycle almost every 8 years. In addition, statistically significant correlation of the island's rainfall variability with the North Atlantic Oscillation is identified for the examined period. On the other hand, regression kriging method combining surface elevation as secondary information improved the estimation of the annual rainfall spatial variability on the island of Crete by 70% compared to ordinary kriging. The rainfall spatial and temporal trends on the island of Crete have variable characteristics that depend on the geographical area and on the hydrological period.

Solving Large-scale Spatial Optimization Problems in Water Resources Management through Spatial Evolutionary Algorithms

NASA Astrophysics Data System (ADS)

Wang, J.; Cai, X.

2007-12-01

A water resources system can be defined as a large-scale spatial system, within which distributed ecological system interacts with the stream network and ground water system. Water resources management, the causative factors and hence the solutions to be developed have a significant spatial dimension. This motivates a modeling analysis of water resources management within a spatial analytical framework, where data is usually geo- referenced and in the form of a map. One of the important functions of Geographic information systems (GIS) is to identify spatial patterns of environmental variables. The role of spatial patterns in water resources management has been well established in the literature particularly regarding how to design better spatial patterns for satisfying the designated objectives of water resources management. Evolutionary algorithms (EA) have been demonstrated to be successful in solving complex optimization models for water resources management due to its flexibility to incorporate complex simulation models in the optimal search procedure. The idea of combining GIS and EA motivates the development and application of spatial evolutionary algorithms (SEA). SEA assimilates spatial information into EA, and even changes the representation and operators of EA. In an EA used for water resources management, the mathematical optimization model should be modified to account the spatial patterns; however, spatial patterns are usually implicit, and it is difficult to impose appropriate patterns to spatial data. Also it is difficult to express complex spatial patterns by explicit constraints included in the EA. The GIS can help identify the spatial linkages and correlations based on the spatial knowledge of the problem. These linkages are incorporated in the fitness function for the preference of the compatible vegetation distribution. Unlike a regular GA for spatial models, the SEA employs a special hierarchical hyper-population and spatial genetic operators to represent spatial variables in a more efficient way. The hyper-population consists of a set of populations, which correspond to the spatial distributions of the individual agents (organisms). Furthermore spatial crossover and mutation operators are designed in accordance with the tree representation and then applied to both organisms and populations. This study applies the SEA to a specific problem of water resources management- maximizing the riparian vegetation coverage in accordance with the distributed groundwater system in an arid region. The vegetation coverage is impacted greatly by the nonlinear feedbacks and interactions between vegetation and groundwater and the spatial variability of groundwater. The SEA is applied to search for an optimal vegetation configuration compatible to the groundwater flow. The results from this example demonstrate the effectiveness of the SEA. Extension of the algorithm for other water resources management problems is discussed.

Neural correlates of virtual route recognition in congenital blindness.

PubMed

Kupers, Ron; Chebat, Daniel R; Madsen, Kristoffer H; Paulson, Olaf B; Ptito, Maurice

2010-07-13

Despite the importance of vision for spatial navigation, blind subjects retain the ability to represent spatial information and to move independently in space to localize and reach targets. However, the neural correlates of navigation in subjects lacking vision remain elusive. We therefore used functional MRI (fMRI) to explore the cortical network underlying successful navigation in blind subjects. We first trained congenitally blind and blindfolded sighted control subjects to perform a virtual navigation task with the tongue display unit (TDU), a tactile-to-vision sensory substitution device that translates a visual image into electrotactile stimulation applied to the tongue. After training, participants repeated the navigation task during fMRI. Although both groups successfully learned to use the TDU in the virtual navigation task, the brain activation patterns showed substantial differences. Blind but not blindfolded sighted control subjects activated the parahippocampus and visual cortex during navigation, areas that are recruited during topographical learning and spatial representation in sighted subjects. When the navigation task was performed under full vision in a second group of sighted participants, the activation pattern strongly resembled the one obtained in the blind when using the TDU. This suggests that in the absence of vision, cross-modal plasticity permits the recruitment of the same cortical network used for spatial navigation tasks in sighted subjects.

A STED-FLIM microscope applied to imaging the natural killer cell immune synapse

NASA Astrophysics Data System (ADS)

Lenz, M. O.; Brown, A. C. N.; Auksorius, E.; Davis, D. M.; Dunsby, C.; Neil, M. A. A.; French, P. M. W.

2011-03-01

We present a stimulated emission depletion (STED) fluorescence lifetime imaging (FLIM) microscope, excited by a microstructured optical fibre supercontinuum source that is pumped by a femtosecond Ti:Sapphire-laser, which is also used for depletion. Implemented using a piezo-scanning stage on a laser scanning confocal fluorescence microscope system with FLIM realised using time correlated single photon counting (TCSPC), this provides convenient switching between confocal and STED-FLIM with spatial resolution down to below 60 nm. We will present our design considerations to make a robust instrument for biological applications including a comparison between fixed phase plate and spatial light modulator (SLM) approaches to shape the STED beam and the correlation of STED and confocal FLIM microscopy. Following our previous application of FLIM-FRET to study intercellular signalling at the immunological synapse (IS), we are employing STED microscopy to characterize the spatial distribution of cellular molecules with subdiffraction resolution at the IS. In particular, we are imaging cytoskeletal structure at the Natural Killer cell activated immune synapse. We will also present our progress towards multilabel STED microscopy to determine how relative spatial molecular organization, previously undetectable by conventional microscopy techniques, is important for NK cell cytotoxic function. Keywords: STED, Stimulated Emission Depletion Microscopy, Natural Killer (NK) cell, Fluorescence lifetime imaging, FLIM, Super resolution microscopy.

Mapping ionospheric observations using combined techniques for Europe region

NASA Astrophysics Data System (ADS)

Tomasik, Lukasz; Gulyaeva, Tamara; Stanislawska, Iwona; Swiatek, Anna; Pozoga, Mariusz; Dziak-Jankowska, Beata

An k nearest neighbours algorithm (KNN) was used for filling the gaps of the missing F2-layer critical frequency is proposed and applied. This method uses TEC data calculated from EGNOS Vertical Delay Estimate (VDE ≈0.78 TECU) and several GNSS stations and its spatial correlation whit data from selected ionosondes. For mapping purposes two-dimensional similarity function in KNN method was proposed.

Cest Analysis: Automated Change Detection from Very-High Remote Sensing Images

NASA Astrophysics Data System (ADS)

Ehlers, M.; Klonus, S.; Jarmer, T.; Sofina, N.; Michel, U.; Reinartz, P.; Sirmacek, B.

2012-08-01

A fast detection, visualization and assessment of change in areas of crisis or catastrophes are important requirements for coordination and planning of help. Through the availability of new satellites and/or airborne sensors with very high spatial resolutions (e.g., WorldView, GeoEye) new remote sensing data are available for a better detection, delineation and visualization of change. For automated change detection, a large number of algorithms has been proposed and developed. From previous studies, however, it is evident that to-date no single algorithm has the potential for being a reliable change detector for all possible scenarios. This paper introduces the Combined Edge Segment Texture (CEST) analysis, a decision-tree based cooperative suite of algorithms for automated change detection that is especially designed for the generation of new satellites with very high spatial resolution. The method incorporates frequency based filtering, texture analysis, and image segmentation techniques. For the frequency analysis, different band pass filters can be applied to identify the relevant frequency information for change detection. After transforming the multitemporal images via a fast Fourier transform (FFT) and applying the most suitable band pass filter, different methods are available to extract changed structures: differencing and correlation in the frequency domain and correlation and edge detection in the spatial domain. Best results are obtained using edge extraction. For the texture analysis, different 'Haralick' parameters can be calculated (e.g., energy, correlation, contrast, inverse distance moment) with 'energy' so far providing the most accurate results. These algorithms are combined with a prior segmentation of the image data as well as with morphological operations for a final binary change result. A rule-based combination (CEST) of the change algorithms is applied to calculate the probability of change for a particular location. CEST was tested with high-resolution satellite images of the crisis areas of Darfur (Sudan). CEST results are compared with a number of standard algorithms for automated change detection such as image difference, image ratioe, principal component analysis, delta cue technique and post classification change detection. The new combined method shows superior results averaging between 45% and 15% improvement in accuracy.

An algorithm for spatial heirarchy clustering

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Velasco, F. R. D.

1981-01-01

A method for utilizing both spectral and spatial redundancy in compacting and preclassifying images is presented. In multispectral satellite images, a high correlation exists between neighboring image points which tend to occupy dense and restricted regions of the feature space. The image is divided into windows of the same size where the clustering is made. The classes obtained in several neighboring windows are clustered, and then again successively clustered until only one region corresponding to the whole image is obtained. By employing this algorithm only a few points are considered in each clustering, thus reducing computational effort. The method is illustrated as applied to LANDSAT images.

Analysis of spatial correlation in predictive models of forest variables that use LiDAR auxiliary information

Treesearch

F. Mauro; Vicente J. Monleon; H. Temesgen; L.A. Ruiz

2017-01-01

Accounting for spatial correlation of LiDAR model errors can improve the precision of model-based estimators. To estimate spatial correlation, sample designs that provide close observations are needed, but their implementation might be prohibitively expensive. To quantify the gains obtained by accounting for the spatial correlation of model errors, we examined (

Social stressors and air pollution across New York City communities: a spatial approach for assessing correlations among multiple exposures.

PubMed

Shmool, Jessie L C; Kubzansky, Laura D; Newman, Ogonnaya Dotson; Spengler, John; Shepard, Peggy; Clougherty, Jane E

2014-11-06

Recent toxicological and epidemiological evidence suggests that chronic psychosocial stress may modify pollution effects on health. Thus, there is increasing interest in refined methods for assessing and incorporating non-chemical exposures, including social stressors, into environmental health research, towards identifying whether and how psychosocial stress interacts with chemical exposures to influence health and health disparities. We present a flexible, GIS-based approach for examining spatial patterns within and among a range of social stressors, and their spatial relationships with air pollution, across New York City, towards understanding their combined effects on health. We identified a wide suite of administrative indicators of community-level social stressors (2008-2010), and applied simultaneous autoregressive models and factor analysis to characterize spatial correlations among social stressors, and between social stressors and air pollutants, using New York City Community Air Survey (NYCCAS) data (2008-2009). Finally, we provide an exploratory ecologic analysis evaluating possible modification of the relationship between nitrogen dioxide (NO2) and childhood asthma Emergency Department (ED) visit rates by social stressors, to demonstrate how the methods used to assess stressor exposure (and/or consequent psychosocial stress) may alter model results. Administrative indicators of a range of social stressors (e.g., high crime rate, residential crowding rate) were not consistently correlated (rho = - 0.44 to 0.89), nor were they consistently correlated with indicators of socioeconomic position (rho = - 0.54 to 0.89). Factor analysis using 26 stressor indicators suggested geographically distinct patterns of social stressors, characterized by three factors: violent crime and physical disorder, crowding and poor access to resources, and noise disruption and property crimes. In an exploratory ecologic analysis, these factors were differentially associated with area-average NO2 and childhood asthma ED visits. For example, only the 'violent crime and disorder' factor was significantly associated with asthma ED visits, and only the 'crowding and resource access' factor modified the association between area-level NO2 and asthma ED visits. This spatial approach enabled quantification of complex spatial patterning and confounding between chemical and non-chemical exposures, and can inform study design for epidemiological studies of separate and combined effects of multiple urban exposures.

Speckle-field propagation in 'frozen' turbulence: brightness function approach

NASA Astrophysics Data System (ADS)

Dudorov, Vadim V.; Vorontsov, Mikhail A.; Kolosov, Valeriy V.

2006-08-01

Speckle-field long- and short-exposure spatial correlation characteristics for target-in-the-loop (TIL) laser beam propagation and scattering in atmospheric turbulence are analyzed through the use of two different approaches: the conventional Monte Carlo (MC) technique and the recently developed brightness function (BF) method. Both the MC and the BF methods are applied to analysis of speckle-field characteristics averaged over target surface roughness realizations under conditions of 'frozen' turbulence. This corresponds to TIL applications where speckle-field fluctuations associated with target surface roughness realization updates occur within a time scale that can be significantly shorter than the characteristic atmospheric turbulence time. Computational efficiency and accuracy of both methods are compared on the basis of a known analytical solution for the long-exposure mutual correlation function. It is shown that in the TIL propagation scenarios considered the BF method provides improved accuracy and requires significantly less computational time than the conventional MC technique. For TIL geometry with a Gaussian outgoing beam and Lambertian target surface, both analytical and numerical estimations for the speckle-field long-exposure correlation length are obtained. Short-exposure speckle-field correlation characteristics corresponding to propagation in 'frozen' turbulence are estimated using the BF method. It is shown that atmospheric turbulence-induced static refractive index inhomogeneities do not significantly affect the characteristic correlation length of the speckle field, whereas long-exposure spatial correlation characteristics are strongly dependent on turbulence strength.

Speckle-field propagation in 'frozen' turbulence: brightness function approach.

PubMed

Dudorov, Vadim V; Vorontsov, Mikhail A; Kolosov, Valeriy V

2006-08-01

Speckle-field long- and short-exposure spatial correlation characteristics for target-in-the-loop (TIL) laser beam propagation and scattering in atmospheric turbulence are analyzed through the use of two different approaches: the conventional Monte Carlo (MC) technique and the recently developed brightness function (BF) method. Both the MC and the BF methods are applied to analysis of speckle-field characteristics averaged over target surface roughness realizations under conditions of 'frozen' turbulence. This corresponds to TIL applications where speckle-field fluctuations associated with target surface roughness realization updates occur within a time scale that can be significantly shorter than the characteristic atmospheric turbulence time. Computational efficiency and accuracy of both methods are compared on the basis of a known analytical solution for the long-exposure mutual correlation function. It is shown that in the TIL propagation scenarios considered the BF method provides improved accuracy and requires significantly less computational time than the conventional MC technique. For TIL geometry with a Gaussian outgoing beam and Lambertian target surface, both analytical and numerical estimations for the speckle-field long-exposure correlation length are obtained. Short-exposure speckle-field correlation characteristics corresponding to propagation in 'frozen' turbulence are estimated using the BF method. It is shown that atmospheric turbulence-induced static refractive index inhomogeneities do not significantly affect the characteristic correlation length of the speckle field, whereas long-exposure spatial correlation characteristics are strongly dependent on turbulence strength.

Implementing a noise protected logical qubit in methyl groups via microwave irradiation

NASA Astrophysics Data System (ADS)

Annabestani, Razieh; Cory, David G.

2018-02-01

We propose a proof-of-principle experiment to encode one logical qubit in noise protected subspace of three identical spins in a methyl group. The symmetry analysis of the wavefunction shows that this fermionic system exhibits a symmetry correlation between the spatial degree of freedom and the spin degree of freedom. We show that one can use this correlation to populate the noiseless subsystem by relying on the interaction between the electric dipole moment of the methyl group with a circularly polarized microwave field. Logical gates are implemented by controlling both the intensity and phase of the applied field.

Robust mosiacs of close-range high-resolution images

NASA Astrophysics Data System (ADS)

Song, Ran; Szymanski, John E.

2008-03-01

This paper presents a robust algorithm which relies only on the information contained within the captured images for the construction of massive composite mosaic images from close-range and high-resolution originals, such as those obtained when imaging architectural and heritage structures. We first apply Harris algorithm to extract a selection of corners and, then, employ both the intensity correlation and the spatial correlation between the corresponding corners for matching them. Then we estimate the eight-parameter projective transformation matrix by the genetic algorithm. Lastly, image fusion using a weighted blending function together with intensity compensation produces an effective seamless mosaic image.

Considering built environment and spatial correlation in modeling pedestrian injury severity.

PubMed

Prato, Carlo G; Kaplan, Sigal; Patrier, Alexandre; Rasmussen, Thomas K

2018-01-02

This study looks at mitigating and aggravating factors that are associated with the injury severity of pedestrians when they have crashes with another road user and overcomes existing limitations in the literature by focusing attention on the built environment and considering spatial correlation across crashes. Reports for 6,539 pedestrian crashes occurred in Denmark between 2006 and 2015 were merged with geographic information system resources containing detailed information about the built environment and exposure at the crash locations. A linearized spatial logit model estimated the probability of pedestrians sustaining a severe or fatal injury conditional on the occurrence of a crash with another road user. This study confirms previous findings about older pedestrians and intoxicated pedestrians being the most vulnerable road users and crashes with heavy vehicles and in roads with higher speed limits being related to the most severe outcomes. This study provides novel perspectives by showing positive spatial correlations of crashes with the same severity outcomes and emphasizing the role of the built environment in the proximity of the crash. This study emphasizes the need for thinking about traffic calming measures, illumination solutions, road maintenance programs, and speed limit reductions. Moreover, this study emphasizes the role of the built environment, because shopping areas, residential areas, and walking traffic density are positively related to a reduction in pedestrian injury severity. Often, these areas have in common a larger pedestrian mass that is more likely to make other road users more aware and attentive, whereas the same does not seem to apply to areas with lower pedestrian density.

Hierarchical additive modeling of nonlinear association with spatial correlations--an application to relate alcohol outlet density and neighborhood assault rates.

PubMed

Yu, Qingzhao; Li, Bin; Scribner, Richard Allen

2009-06-30

Previous studies have suggested a link between alcohol outlets and assaults. In this paper, we explore the effects of alcohol availability on assaults at the census tract level over time. In addition, we use a natural experiment to check whether a sudden loss of alcohol outlets is associated with deeper decreasing in assault violence. Several features of the data raise statistical challenges: (1) the association between covariates (for example, the alcohol outlet density of each census tract) and the assault rates may be complex and therefore cannot be described using a linear model without covariates transformation, (2) the covariates may be highly correlated with each other, (3) there are a number of observations that have missing inputs, and (4) there is spatial association in assault rates at the census tract level. We propose a hierarchical additive model, where the nonlinear correlations and the complex interaction effects are modeled using the multiple additive regression trees and the residual spatial association in the assault rates that cannot be explained in the model are smoothed using a conditional autoregressive (CAR) method. We develop a two-stage algorithm that connects the nonparametric trees with CAR to look for important covariates associated with the assault rates, while taking into account the spatial association of assault rates in adjacent census tracts. The proposed method is applied to the Los Angeles assault data (1990-1999). To assess the efficiency of the method, the results are compared with those obtained from a hierarchical linear model. Copyright (c) 2009 John Wiley & Sons, Ltd.

Spatial versus sequential correlations for random access coding

NASA Astrophysics Data System (ADS)

Tavakoli, Armin; Marques, Breno; Pawłowski, Marcin; Bourennane, Mohamed

2016-03-01

Random access codes are important for a wide range of applications in quantum information. However, their implementation with quantum theory can be made in two very different ways: (i) by distributing data with strong spatial correlations violating a Bell inequality or (ii) using quantum communication channels to create stronger-than-classical sequential correlations between state preparation and measurement outcome. Here we study this duality of the quantum realization. We present a family of Bell inequalities tailored to the task at hand and study their quantum violations. Remarkably, we show that the use of spatial and sequential quantum correlations imposes different limitations on the performance of quantum random access codes: Sequential correlations can outperform spatial correlations. We discuss the physics behind the observed discrepancy between spatial and sequential quantum correlations.

The roosting spatial network of a bird-predator bat.

PubMed

Fortuna, Miguel A; Popa-Lisseanu, Ana G; Ibáñez, Carlos; Bascompte, Jordi

2009-04-01

The use of roosting sites by animal societies is important in conservation biology, animal behavior, and epidemiology. The giant noctule bat (Nyctalus lasiopterus) constitutes fission-fusion societies whose members spread every day in multiple trees for shelter. To assess how the pattern of roosting use determines the potential for information exchange or disease spreading, we applied the framework of complex networks. We found a social and spatial segregation of the population in well-defined modules or compartments, formed by groups of bats sharing the same trees. Inside each module, we revealed an asymmetric use of trees by bats representative of a nested pattern. By applying a simple epidemiological model, we show that there is a strong correlation between network structure and the rate and shape of infection dynamics. This modular structure slows down the spread of diseases and the exchange of information through the entire network. The implication for management is complex, affecting differently the cohesion inside and among colonies and the transmission of parasites and diseases. Network analysis can hence be applied to quantifying the conservation status of individual trees used by species depending on hollows for shelter.

Spatio-Temporal Variation of Longevity Clusters and the Influence of Social Development Level on Lifespan in a Chinese Longevous Area (1982–2010)

PubMed Central

Qin, Jian; Xia, Tianlong; Li, You; Liang, Xue; Wei, Peng; Long, Bingshuang; Lei, Mingzhi; Wei, Xiao; Tang, Xianyan; Zhang, Zhiyong

2017-01-01

The study aims to determine the spatial and temporal variation of a longevous region and explore the correlation between longevity and socioeconomic development. Population data at the township level were obtained from the last four population censuses (1982–2010). Five main lifespan indicators and the Human Development Index (HDI) were calculated. Getis-Ord G*, Gravity modeling, and Pearson’s r between lifespan indicators and HDI were applied. In this study, a stable longevous gathering area was discovered in Hechi during different periods. Under the influence of social and economic development, more longevous areas appeared. However, the effects of genetic and natural environmental factors on longevity were always dominant in this remote and mountainous city. Furthermore, longevity indicators lacked any significant correlation with life expectancy. No significant positive correlation was detected between lifespan indicators and HDI. Thus, we conclude that lifespan indicators can determine the spatial distribution and variation pattern of longevity from multiple dimensions. The geographical scope of longevity in Hechi City is gradually expanding, and significant spatial clustering was detected in southwestern, southern, and eastern parts of Hechi. This study also found that social economic development is likely to have a certain impact on new longevous areas, but their role on extreme longevity is not significant. PMID:28753971

Correlation analysis of fracture arrangement in space

NASA Astrophysics Data System (ADS)

Marrett, Randall; Gale, Julia F. W.; Gómez, Leonel A.; Laubach, Stephen E.

2018-03-01

We present new techniques that overcome limitations of standard approaches to documenting spatial arrangement. The new techniques directly quantify spatial arrangement by normalizing to expected values for randomly arranged fractures. The techniques differ in terms of computational intensity, robustness of results, ability to detect anti-correlation, and use of fracture size data. Variation of spatial arrangement across a broad range of length scales facilitates distinguishing clustered and periodic arrangements-opposite forms of organization-from random arrangements. Moreover, self-organized arrangements can be distinguished from arrangements due to extrinsic organization. Traditional techniques for analysis of fracture spacing are hamstrung because they account neither for the sequence of fracture spacings nor for possible coordination between fracture size and position, attributes accounted for by our methods. All of the new techniques reveal fractal clustering in a test case of veins, or cement-filled opening-mode fractures, in Pennsylvanian Marble Falls Limestone. The observed arrangement is readily distinguishable from random and periodic arrangements. Comparison of results that account for fracture size with results that ignore fracture size demonstrates that spatial arrangement is dominated by the sequence of fracture spacings, rather than coordination of fracture size with position. Fracture size and position are not completely independent in this example, however, because large fractures are more clustered than small fractures. Both spatial and size organization of veins here probably emerged from fracture interaction during growth. The new approaches described here, along with freely available software to implement the techniques, can be applied with effect to a wide range of structures, or indeed many other phenomena such as drilling response, where spatial heterogeneity is an issue.

Electrophysiological correlates of stimulus-driven reorienting deficits after interference with right parietal cortex during a spatial attention task: a TMS-EEG study

PubMed Central

Capotosto, Paolo; Corbetta, Maurizio; Romani, Gian Luca; Babiloni, Claudio

2013-01-01

Transcranial magnetic stimulation (TMS) interference over right intraparietal sulcus (IPS) causally disrupts behaviorally and electroencephalographic (EEG) rhythmic correlates of endogenous spatial orienting prior to visual target presentation (Capotosto et al. 2009; 2011). Here we combine data from our previous studies to examine whether right parietal TMS during spatial orienting also impairs stimulus-driven re-orienting or the ability to efficiently process unattended stimuli, i.e. stimuli outside the current focus of attention. Healthy subjects (N=24) performed a Posner spatial cueing task while their EEG activity was being monitored. Repetitive TMS (rTMS) was applied for 150 milliseconds (ms) simultaneously to the presentation of a central arrow directing spatial attention to the location of an upcoming visual target. Right IPS-rTMS impaired target detection, especially for stimuli presented at unattended locations; it also caused a modulation of the amplitude of parieto-occipital positive ERPs peaking at about 480 ms (P3) post-target. The P3 significantly decreased for unattended targets, and significantly increased for attended targets after right IPS-rTMS as compared to Sham stimulation. Similar effects were obtained for left IPS stimulation albeit in a smaller group of subjects. We conclude that disruption of anticipatory processes in right IPS has prolonged effects that persist during target processing. The P3 decrement may reflect interference with post-decision processes that are part of stimulus-driven re-orienting. Right IPS is a node of functional interaction between endogenous spatial orienting and stimulus-driven re-orienting processes in human vision. PMID:22905824

Symmetric Phase-Only Filtering in Particle-Image Velocimetry

NASA Technical Reports Server (NTRS)

Wemet, Mark P.

2008-01-01

Symmetrical phase-only filtering (SPOF) can be exploited to obtain substantial improvements in the results of data processing in particle-image velocimetry (PIV). In comparison with traditional PIV data processing, SPOF PIV data processing yields narrower and larger amplitude correlation peaks, thereby providing more-accurate velocity estimates. The higher signal-to-noise ratios associated with the higher amplitude correlation peaks afford greater robustness and reliability of processing. SPOF also affords superior performance in the presence of surface flare light and/or background light. SPOF algorithms can readily be incorporated into pre-existing algorithms used to process digitized image data in PIV, without significantly increasing processing times. A summary of PIV and traditional PIV data processing is prerequisite to a meaningful description of SPOF PIV processing. In PIV, a pulsed laser is used to illuminate a substantially planar region of a flowing fluid in which particles are entrained. An electronic camera records digital images of the particles at two instants of time. The components of velocity of the fluid in the illuminated plane can be obtained by determining the displacements of particles between the two illumination pulses. The objective in PIV data processing is to compute the particle displacements from the digital image data. In traditional PIV data processing, to which the present innovation applies, the two images are divided into a grid of subregions and the displacements determined from cross-correlations between the corresponding sub-regions in the first and second images. The cross-correlation process begins with the calculation of the Fourier transforms (or fast Fourier transforms) of the subregion portions of the images. The Fourier transforms from the corresponding subregions are multiplied, and this product is inverse Fourier transformed, yielding the cross-correlation intensity distribution. The average displacement of the particles across a subregion results in a displacement of the correlation peak from the center of the correlation plane. The velocity is then computed from the displacement of the correlation peak and the time between the recording of the two images. The process as described thus far is performed for all the subregions. The resulting set of velocities in grid cells amounts to a velocity vector map of the flow field recorded on the image plane. In traditional PIV processing, surface flare light and bright background light give rise to a large, broad correlation peak, at the center of the correlation plane, that can overwhelm the true particle- displacement correlation peak. This has made it necessary to resort to tedious image-masking and background-subtraction procedures to recover the relatively small amplitude particle-displacement correlation peak. SPOF is a variant of phase-only filtering (POF), which, in turn, is a variant of matched spatial filtering (MSF). In MSF, one projects a first image (denoted the input image) onto a second image (denoted the filter) as part of a computation to determine how much and what part of the filter is present in the input image. MSF is equivalent to cross-correlation. In POF, the frequency-domain content of the MSF filter is modified to produce a unitamplitude (phase-only) object. POF is implemented by normalizing the Fourier transform of the filter by its magnitude. The advantage of POFs is that they yield correlation peaks that are sharper and have higher signal-to-noise ratios than those obtained through traditional MSF. In the SPOF, these benefits of POF can be extended to PIV data processing. The SPOF yields even better performance than the POF approach, which is uniquely applicable to PIV type image data. In SPOF as now applied to PIV data processing, a subregion of the first image is treated as the input image and the corresponding subregion of the second image is treated as the filter. The Fourier transforms from both the firs and second- image subregions are normalized by the square roots of their respective magnitudes. This scheme yields optimal performance because the amounts of normalization applied to the spatial-frequency contents of the input and filter scenes are just enough to enhance their high-spatial-frequency contents while reducing their spurious low-spatial-frequency content. As a result, in SPOF PIV processing, particle-displacement correlation peaks can readily be detected above spurious background peaks, without need for masking or background subtraction.

Scanless nonlinear optical microscope for image reconstruction and space-time correlation analysis

NASA Astrophysics Data System (ADS)

Ceffa, N. G.; Radaelli, F.; Pozzi, P.; Collini, M.; Sironi, L.; D'alfonso, L.; Chirico, G.

2017-06-01

Optical Microscopy has been applied to life science from its birth and reached widespread application due to its major advantages: limited perturbation of the biological tissue and the easy accessibility of the light sources. However, as the spatial and time resolution requirements and the time stability of the microscopes increase, researchers are struggling against some of its limitations: limited transparency and the refractivity of the living tissue to light and the field perturbations induced by the path in the tissue. We have developed a compact stand-alone, completely scan-less, optical setup that allows to acquire non-linear excitation images and to measure the sample dynamics simultaneously on an ensemble of arbitrary chosen regions of interests. The image is obtained by shining a square array of spots on the sample obtained by a spatial light modulator and by shifting it (10 ms refresh time) on the sample. The final image is computed from the superposition of (100-1000) images. Filtering procedures can be applied to the raw images of the excitation array before building the image. We discuss results that show how this setup can be used for the correction of wave front aberrations induced by turbid samples (such as living tissues) and for the computation of space-time cross-correlations in complex networks.

Spatially Correlated Gene Expression in Bacterial Groups: The Role of Lineage History, Spatial Gradients, and Cell-Cell Interactions.

PubMed

van Vliet, Simon; Dal Co, Alma; Winkler, Annina R; Spriewald, Stefanie; Stecher, Bärbel; Ackermann, Martin

2018-04-25

Gene expression levels in clonal bacterial groups have been found to be spatially correlated. These correlations can partly be explained by the shared lineage history of nearby cells, although they could also arise from local cell-cell interactions. Here, we present a quantitative framework that allows us to disentangle the contributions of lineage history, long-range spatial gradients, and local cell-cell interactions to spatial correlations in gene expression. We study pathways involved in toxin production, SOS stress response, and metabolism in Escherichia coli microcolonies and find for all pathways that shared lineage history is the main cause of spatial correlations in gene expression levels. However, long-range spatial gradients and local cell-cell interactions also contributed to spatial correlations in SOS response, amino acid biosynthesis, and overall metabolic activity. Together, our data show that the phenotype of a cell is influenced by its lineage history and population context, raising the question of whether bacteria can arrange their activities in space to perform functions they cannot achieve alone. Copyright © 2018 Elsevier Inc. All rights reserved.

Augmented GNSS Differential Corrections Minimum Mean Square Error Estimation Sensitivity to Spatial Correlation Modeling Errors

PubMed Central

Kassabian, Nazelie; Presti, Letizia Lo; Rispoli, Francesco

2014-01-01

Railway signaling is a safety system that has evolved over the last couple of centuries towards autonomous functionality. Recently, great effort is being devoted in this field, towards the use and exploitation of Global Navigation Satellite System (GNSS) signals and GNSS augmentation systems in view of lower railway track equipments and maintenance costs, that is a priority to sustain the investments for modernizing the local and regional lines most of which lack automatic train protection systems and are still manually operated. The objective of this paper is to assess the sensitivity of the Linear Minimum Mean Square Error (LMMSE) algorithm to modeling errors in the spatial correlation function that characterizes true pseudorange Differential Corrections (DCs). This study is inspired by the railway application; however, it applies to all transportation systems, including the road sector, that need to be complemented by an augmentation system in order to deliver accurate and reliable positioning with integrity specifications. A vector of noisy pseudorange DC measurements are simulated, assuming a Gauss-Markov model with a decay rate parameter inversely proportional to the correlation distance that exists between two points of a certain environment. The LMMSE algorithm is applied on this vector to estimate the true DC, and the estimation error is compared to the noise added during simulation. The results show that for large enough correlation distance to Reference Stations (RSs) distance separation ratio values, the LMMSE brings considerable advantage in terms of estimation error accuracy and precision. Conversely, the LMMSE algorithm may deteriorate the quality of the DC measurements whenever the ratio falls below a certain threshold. PMID:24922454

Spatiotemporal investigation of long-term seasonal temperature variability in Libya

NASA Astrophysics Data System (ADS)

Elsharkawy, S. G.; Elmallah, E. S.

2016-09-01

Throughout this work, spatial and temporal variations of seasonal surface air temperature have been investigated. Moreover, the effects of relative internal (teleconnection) and external (solar) forcing on surface air temperature variability have been examined. Seasonal temperature time series covering 30 different meteorological locations and lasting over the last century are considered. These locations are classified into two groups based on their spatial distribution. One represents Coast Libya Surface Air Temperature (CLSAT), contains 19 locations, and the other represents Desert Libya Surface Air Temperature (DLSAT), contains 11 locations. Average temperature departure test is applied to investigate the nature of temperature variations. Temperature trends are analyzed using the nonparametric Mann-Kendall test and their coefficients are calculated using Sen's slope estimate. Cross-correlation and spectral analysis techniques are also applied. Our results showed temperature deviation from average within a band of ± 2°C at coast region, while ± 4°C at desert region. Extreme behavior intensions between summer and winter temperatures at coast region are noticed. Segmentation process declared reversal cooling/warming behavior within temperature records for all seasons. Desert region shows warming trend for all seasons with higher coefficients than obtained at coast region. Results obtained for spectral analysis show different short and medium signals and concluded that not only the spectral properties are different for different geographical regions but also different for different climatic seasons on regional scale as well. Cross-correlation results showed that highest influence for Rz upon coastal temperature is always in conjunction with highest influence of NAO upon coastal temperature during the period 1981-2010. Desert region does not obey this phenomenon, where highest temperature-NAO correlations at desert during autumn and winter seasons are not accompanied with highest correlations for temperature-Rz.

Task-evoked brain functional magnetic susceptibility mapping by independent component analysis (χICA).

PubMed

Chen, Zikuan; Calhoun, Vince D

2016-03-01

Conventionally, independent component analysis (ICA) is performed on an fMRI magnitude dataset to analyze brain functional mapping (AICA). By solving the inverse problem of fMRI, we can reconstruct the brain magnetic susceptibility (χ) functional states. Upon the reconstructed χ dataspace, we propose an ICA-based brain functional χ mapping method (χICA) to extract task-evoked brain functional map. A complex division algorithm is applied to a timeseries of fMRI phase images to extract temporal phase changes (relative to an OFF-state snapshot). A computed inverse MRI (CIMRI) model is used to reconstruct a 4D brain χ response dataset. χICA is implemented by applying a spatial InfoMax ICA algorithm to the reconstructed 4D χ dataspace. With finger-tapping experiments on a 7T system, the χICA-extracted χ-depicted functional map is similar to the SPM-inferred functional χ map by a spatial correlation of 0.67 ± 0.05. In comparison, the AICA-extracted magnitude-depicted map is correlated with the SPM magnitude map by 0.81 ± 0.05. The understanding of the inferiority of χICA to AICA for task-evoked functional map is an ongoing research topic. For task-evoked brain functional mapping, we compare the data-driven ICA method with the task-correlated SPM method. In particular, we compare χICA with AICA for extracting task-correlated timecourses and functional maps. χICA can extract a χ-depicted task-evoked brain functional map from a reconstructed χ dataspace without the knowledge about brain hemodynamic responses. The χICA-extracted brain functional χ map reveals a bidirectional BOLD response pattern that is unavailable (or different) from AICA. Copyright © 2016 Elsevier B.V. All rights reserved.

Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks.

PubMed

Wu, Siqi; Joseph, Antony; Hammonds, Ann S; Celniker, Susan E; Yu, Bin; Frise, Erwin

2016-04-19

Spatial gene expression patterns enable the detection of local covariability and are extremely useful for identifying local gene interactions during normal development. The abundance of spatial expression data in recent years has led to the modeling and analysis of regulatory networks. The inherent complexity of such data makes it a challenge to extract biological information. We developed staNMF, a method that combines a scalable implementation of nonnegative matrix factorization (NMF) with a new stability-driven model selection criterion. When applied to a set ofDrosophilaearly embryonic spatial gene expression images, one of the largest datasets of its kind, staNMF identified 21 principal patterns (PP). Providing a compact yet biologically interpretable representation ofDrosophilaexpression patterns, PP are comparable to a fate map generated experimentally by laser ablation and show exceptional promise as a data-driven alternative to manual annotations. Our analysis mapped genes to cell-fate programs and assigned putative biological roles to uncharacterized genes. Finally, we used the PP to generate local transcription factor regulatory networks. Spatially local correlation networks were constructed for six PP that span along the embryonic anterior-posterior axis. Using a two-tail 5% cutoff on correlation, we reproduced 10 of the 11 links in the well-studied gap gene network. The performance of PP with theDrosophiladata suggests that staNMF provides informative decompositions and constitutes a useful computational lens through which to extract biological insight from complex and often noisy gene expression data.

StereoGene: rapid estimation of genome-wide correlation of continuous or interval feature data.

PubMed

Stavrovskaya, Elena D; Niranjan, Tejasvi; Fertig, Elana J; Wheelan, Sarah J; Favorov, Alexander V; Mironov, Andrey A

2017-10-15

Genomics features with similar genome-wide distributions are generally hypothesized to be functionally related, for example, colocalization of histones and transcription start sites indicate chromatin regulation of transcription factor activity. Therefore, statistical algorithms to perform spatial, genome-wide correlation among genomic features are required. Here, we propose a method, StereoGene, that rapidly estimates genome-wide correlation among pairs of genomic features. These features may represent high-throughput data mapped to reference genome or sets of genomic annotations in that reference genome. StereoGene enables correlation of continuous data directly, avoiding the data binarization and subsequent data loss. Correlations are computed among neighboring genomic positions using kernel correlation. Representing the correlation as a function of the genome position, StereoGene outputs the local correlation track as part of the analysis. StereoGene also accounts for confounders such as input DNA by partial correlation. We apply our method to numerous comparisons of ChIP-Seq datasets from the Human Epigenome Atlas and FANTOM CAGE to demonstrate its wide applicability. We observe the changes in the correlation between epigenomic features across developmental trajectories of several tissue types consistent with known biology and find a novel spatial correlation of CAGE clusters with donor splice sites and with poly(A) sites. These analyses provide examples for the broad applicability of StereoGene for regulatory genomics. The StereoGene C ++ source code, program documentation, Galaxy integration scripts and examples are available from the project homepage http://stereogene.bioinf.fbb.msu.ru/. favorov@sensi.org. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Intensity correlation imaging with sunlight-like source

NASA Astrophysics Data System (ADS)

Wang, Wentao; Tang, Zhiguo; Zheng, Huaibin; Chen, Hui; Yuan, Yuan; Liu, Jinbin; Liu, Yanyan; Xu, Zhuo

2018-05-01

We show a method of intensity correlation imaging of targets illuminated by a sunlight-like source both theoretically and experimentally. With a Faraday anomalous dispersion optical filter (FADOF), we have modulated the coherence time of a thermal source up to 0.167 ns. And we carried out measurements of temporal and spatial correlations, respectively, with an intensity interferometer setup. By skillfully using the even Fourier fitting on the very sparse sampling data, the images of targets are successfully reconstructed from the low signal-noise-ratio(SNR) interference pattern by applying an iterative phase retrieval algorithm. The resulting imaging quality is as well as the one obtained by the theoretical fitting. The realization of such a case will bring this technique closer to geostationary satellite imaging illuminated by sunlight.

What Do They Have in Common? Drivers of Streamflow Spatial Correlation and Prediction of Flow Regimes in Ungauged Locations

NASA Astrophysics Data System (ADS)

Betterle, A.; Radny, D.; Schirmer, M.; Botter, G.

2017-12-01

The spatial correlation of daily streamflows represents a statistical index encapsulating the similarity between hydrographs at two arbitrary catchment outlets. In this work, a process-based analytical framework is utilized to investigate the hydrological drivers of streamflow spatial correlation through an extensive application to 78 pairs of stream gauges belonging to 13 unregulated catchments in the eastern United States. The analysis provides insight on how the observed heterogeneity of the physical processes that control flow dynamics ultimately affect streamflow correlation and spatial patterns of flow regimes. Despite the variability of recession properties across the study catchments, the impact of heterogeneous drainage rates on the streamflow spatial correlation is overwhelmed by the spatial variability of frequency and intensity of effective rainfall events. Overall, model performances are satisfactory, with root mean square errors between modeled and observed streamflow spatial correlation below 10% in most cases. We also propose a method for estimating streamflow correlation in the absence of discharge data, which proves useful to predict streamflow regimes in ungauged areas. The method consists in setting a minimum threshold on the modeled flow correlation to individuate hydrologically similar sites. Catchment outlets that are most correlated (ρ>0.9) are found to be characterized by analogous streamflow distributions across a broad range of flow regimes.

Integrative Spatial Data Analytics for Public Health Studies of New York State

PubMed Central

Chen, Xin; Wang, Fusheng

2016-01-01

Increased accessibility of health data made available by the government provides unique opportunity for spatial analytics with much higher resolution to discover patterns of diseases, and their correlation with spatial impact indicators. This paper demonstrated our vision of integrative spatial analytics for public health by linking the New York Cancer Mapping Dataset with datasets containing potential spatial impact indicators. We performed spatial based discovery of disease patterns and variations across New York State, and identify potential correlations between diseases and demographic, socio-economic and environmental indicators. Our methods were validated by three correlation studies: the correlation between stomach cancer and Asian race, the correlation between breast cancer and high education population, and the correlation between lung cancer and air toxics. Our work will allow public health researchers, government officials or other practitioners to adequately identify, analyze, and monitor health problems at the community or neighborhood level for New York State. PMID:28269834

Low spatial frequency characterization of holographic recording materials applied to correlation

NASA Astrophysics Data System (ADS)

Márquez, A.; Neipp, C.; Beléndez, A.; Campos, J.; Pascual, I.; Yzuel, M. J.; Fimia, A.

2003-09-01

Accurate recording of computer-generated holograms (CGH) on a phase material is not a trivial task. The range of available phase materials is large, and their suitability depends on the fabrication technique chosen to produce the hologram. We are particularly interested in low-cost fabrication techniques, easily available for any lab. In this work we present the results obtained with a wide variety of phase holographic recording materials, characterized at low spatial frequencies (leq32 lp mm-1) which is the range associated with the technique we use to produce the CGHs. We have considered bleached emulsion, silver halide sensitized gelatin (SHSG) and dichromated gelatin. Some interesting differences arise between the behaviour of these materials in the usual holographic range (>1000 lp mm-1), and the low-frequency range intended for digital holography. The ultimate goal of this paper is to establish the suitability of different phase materials as the media to generate correlation filters for optical pattern recognition. In all the materials considered, the phase filters generated ensure the discrimination of the target in the recognition process. Taking into account all the experimental results, we can say that SHSG is the best material to generate phase CGHs with low spatial frequencies.

Nonmonotonic spatial structure of interneuronal correlations in prefrontal microcircuits

PubMed Central

Safavi, Shervin; Dwarakanath, Abhilash; Kapoor, Vishal; Werner, Joachim; Hatsopoulos, Nicholas G.; Logothetis, Nikos K.; Panagiotaropoulos, Theofanis I.

2018-01-01

Correlated fluctuations of single neuron discharges, on a mesoscopic scale, decrease as a function of lateral distance in early sensory cortices, reflecting a rapid spatial decay of lateral connection probability and excitation. However, spatial periodicities in horizontal connectivity and associational input as well as an enhanced probability of lateral excitatory connections in the association cortex could theoretically result in nonmonotonic correlation structures. Here, we show such a spatially nonmonotonic correlation structure, characterized by significantly positive long-range correlations, in the inferior convexity of the macaque prefrontal cortex. This functional connectivity kernel was more pronounced during wakefulness than anesthesia and could be largely attributed to the spatial pattern of correlated variability between functionally similar neurons during structured visual stimulation. These results suggest that the spatial decay of lateral functional connectivity is not a common organizational principle of neocortical microcircuits. A nonmonotonic correlation structure could reflect a critical topological feature of prefrontal microcircuits, facilitating their role in integrative processes. PMID:29588415

Self-avoiding walk on a square lattice with correlated vacancies

NASA Astrophysics Data System (ADS)

Cheraghalizadeh, J.; Najafi, M. N.; Mohammadzadeh, H.; Saber, A.

2018-04-01

The self-avoiding walk on the square site-diluted correlated percolation lattice is considered. The Ising model is employed to realize the spatial correlations of the metric space. As a well-accepted result, the (generalized) Flory's mean-field relation is tested to measure the effect of correlation. After exploring a perturbative Fokker-Planck-like equation, we apply an enriched Rosenbluth Monte Carlo method to study the problem. To be more precise, the winding angle analysis is also performed from which the diffusivity parameter of Schramm-Loewner evolution theory (κ ) is extracted. We find that at the critical Ising (host) system, the exponents are in agreement with Flory's approximation. For the off-critical Ising system, we find also a behavior for the fractal dimension of the walker trace in terms of the correlation length of the Ising system ξ (T ) , i.e., DFSAW(T ) -DFSAW(Tc) ˜1/√{ξ (T ) } .

Decorrelation Times of Photospheric Fields and Flows

NASA Technical Reports Server (NTRS)

Welsch, B. T.; Kusano, K.; Yamamoto, T. T.; Muglach, K.

2012-01-01

We use autocorrelation to investigate evolution in flow fields inferred by applying Fourier Local Correlation Tracking (FLCT) to a sequence of high-resolution (0.3 "), high-cadence (approx = 2 min) line-of-sight magnetograms of NOAA active region (AR) 10930 recorded by the Narrowband Filter Imager (NFI) of the Solar Optical Telescope (SOT) aboard the Hinode satellite over 12 - 13 December 2006. To baseline the timescales of flow evolution, we also autocorrelated the magnetograms, at several spatial binnings, to characterize the lifetimes of active region magnetic structures versus spatial scale. Autocorrelation of flow maps can be used to optimize tracking parameters, to understand tracking algorithms f susceptibility to noise, and to estimate flow lifetimes. Tracking parameters varied include: time interval Delta t between magnetogram pairs tracked, spatial binning applied to the magnetograms, and windowing parameter sigma used in FLCT. Flow structures vary over a range of spatial and temporal scales (including unresolved scales), so tracked flows represent a local average of the flow over a particular range of space and time. We define flow lifetime to be the flow decorrelation time, tau . For Delta t > tau, tracking results represent the average velocity over one or more flow lifetimes. We analyze lifetimes of flow components, divergences, and curls as functions of magnetic field strength and spatial scale. We find a significant trend of increasing lifetimes of flow components, divergences, and curls with field strength, consistent with Lorentz forces partially governing flows in the active photosphere, as well as strong trends of increasing flow lifetime and decreasing magnitudes with increases in both spatial scale and Delta t.

Profiles of Motor Laterality in Young Athletes' Performance of Complex Movements: Merging the MOTORLAT and PATHoops Tools

PubMed Central

Castañer, Marta; Andueza, Juan; Hileno, Raúl; Puigarnau, Silvia; Prat, Queralt; Camerino, Oleguer

2018-01-01

Laterality is a key aspect of the analysis of basic and specific motor skills. It is relevant to sports because it involves motor laterality profiles beyond left-right preference and spatial orientation of the body. The aim of this study was to obtain the laterality profiles of young athletes, taking into account the synergies between the support and precision functions of limbs and body parts in the performance of complex motor skills. We applied two instruments: (a) MOTORLAT, a motor laterality inventory comprising 30 items of basic, specific, and combined motor skills, and (b) the Precision and Agility Tapping over Hoops (PATHoops) task, in which participants had to perform a path by stepping in each of 14 hoops arranged on the floor, allowing the observation of their feet, left-right preference and spatial orientation. A total of 96 young athletes performed the PATHoops task and the 30 MOTORLAT items, allowing us to obtain data about limb dominance and spatial orientation of the body in the performance of complex motor skills. Laterality profiles were obtained by means of a cluster analysis and a correlational analysis and a contingency analysis were applied between the motor skills and spatial orientation actions performed. The results obtained using MOTORLAT show that the combined motor skills criterion (for example, turning while jumping) differentiates athletes' uses of laterality, showing a clear tendency toward mixed laterality profiles in the performance of complex movements. In the PATHoops task, the best spatial orientation strategy was “same way” (same foot and spatial wing) followed by “opposite way” (opposite foot and spatial wing), in keeping with the research assumption that actions unfolding in a horizontal direction in front of an observer's eyes are common in a variety of sports. PMID:29930527

Spatial-temporal-spectral EEG patterns of BOLD functional network connectivity dynamics

NASA Astrophysics Data System (ADS)

Lamoš, Martin; Mareček, Radek; Slavíček, Tomáš; Mikl, Michal; Rektor, Ivan; Jan, Jiří

2018-06-01

Objective. Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during electroencephalogram (EEG) data analysis may leave part of the neural activity unrecognized. We propose an approach that blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity. Approach. The blind decomposition of EEG spectrogram by parallel factor analysis has been shown to be a useful technique for uncovering patterns of neural activity. The simultaneously acquired BOLD fMRI data were decomposed by independent component analysis. Dynamic functional connectivity was computed on the component’s time series using a sliding window correlation, and between-network connectivity states were then defined based on the values of the correlation coefficients. ANOVA tests were performed to assess the relationships between the dynamics of between-network connectivity states and the fluctuations of EEG spectral patterns. Main results. We found three patterns related to the dynamics of between-network connectivity states. The first pattern has dominant peaks in the alpha, beta, and gamma bands and is related to the dynamics between the auditory, sensorimotor, and attentional networks. The second pattern, with dominant peaks in the theta and low alpha bands, is related to the visual and default mode network. The third pattern, also with peaks in the theta and low alpha bands, is related to the auditory and frontal network. Significance. Our previous findings revealed a relationship between EEG spectral pattern fluctuations and the hemodynamics of large-scale brain networks. In this study, we suggest that the relationship also exists at the level of functional connectivity dynamics among large-scale brain networks when no standard spatial and spectral constraints are applied on the EEG data.

Geostatistics and spatial analysis in biological anthropology.

PubMed

Relethford, John H

2008-05-01

A variety of methods have been used to make evolutionary inferences based on the spatial distribution of biological data, including reconstructing population history and detection of the geographic pattern of natural selection. This article provides an examination of geostatistical analysis, a method used widely in geology but which has not often been applied in biological anthropology. Geostatistical analysis begins with the examination of a variogram, a plot showing the relationship between a biological distance measure and the geographic distance between data points and which provides information on the extent and pattern of spatial correlation. The results of variogram analysis are used for interpolating values of unknown data points in order to construct a contour map, a process known as kriging. The methods of geostatistical analysis and discussion of potential problems are applied to a large data set of anthropometric measures for 197 populations in Ireland. The geostatistical analysis reveals two major sources of spatial variation. One pattern, seen for overall body and craniofacial size, shows an east-west cline most likely reflecting the combined effects of past population dispersal and settlement. The second pattern is seen for craniofacial height and shows an isolation by distance pattern reflecting rapid spatial changes in the midlands region of Ireland, perhaps attributable to the genetic impact of the Vikings. The correspondence of these results with other analyses of these data and the additional insights generated from variogram analysis and kriging illustrate the potential utility of geostatistical analysis in biological anthropology. (c) 2008 Wiley-Liss, Inc.

A catchment-scale model to predict spatial and temporal burden of E. coli on pasture from grazing livestock.

PubMed

Oliver, David M; Bartie, Phil J; Louise Heathwaite, A; Reaney, Sim M; Parnell, Jared A Q; Quilliam, Richard S

2018-03-01

Effective management of diffuse microbial water pollution from agriculture requires a fundamental understanding of how spatial patterns of microbial pollutants, e.g. E. coli, vary over time at the landscape scale. The aim of this study was to apply the Visualising Pathogen &Environmental Risk (ViPER) model, developed to predict E. coli burden on agricultural land, in a spatially distributed manner to two contrasting catchments in order to map and understand changes in E. coli burden contributed to land from grazing livestock. The model was applied to the River Ayr and Lunan Water catchments, with significant correlations observed between area of improved grassland and the maximum total E. coli per 1km 2 grid cell (Ayr: r=0.57; p<0.001, Lunan: r=0.32; p<0.001). There was a significant difference in the predicted maximum E. coli burden between seasons in both catchments, with summer and autumn predicted to accrue higher E. coli contributions relative to spring and winter (P<0.001), driven largely by livestock presence. The ViPER model thus describes, at the landscape scale, spatial nuances in the vulnerability of E. coli loading to land as driven by stocking density and livestock grazing regimes. Resulting risk maps therefore provide the underpinning evidence to inform spatially-targeted decision-making with respect to managing sources of E. coli in agricultural environments. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

Spatial correlation of probabilistic earthquake ground motion and loss

USGS Publications Warehouse

Wesson, R.L.; Perkins, D.M.

2001-01-01

Spatial correlation of annual earthquake ground motions and losses can be used to estimate the variance of annual losses to a portfolio of properties exposed to earthquakes A direct method is described for the calculations of the spatial correlation of earthquake ground motions and losses. Calculations for the direct method can be carried out using either numerical quadrature or a discrete, matrix-based approach. Numerical results for this method are compared with those calculated from a simple Monte Carlo simulation. Spatial correlation of ground motion and loss is induced by the systematic attenuation of ground motion with distance from the source, by common site conditions, and by the finite length of fault ruptures. Spatial correlation is also strongly dependent on the partitioning of the variability, given an event, into interevent and intraevent components. Intraevent variability reduces the spatial correlation of losses. Interevent variability increases spatial correlation of losses. The higher the spatial correlation, the larger the variance in losses to a port-folio, and the more likely extreme values become. This result underscores the importance of accurately determining the relative magnitudes of intraevent and interevent variability in ground-motion studies, because of the strong impact in estimating earthquake losses to a portfolio. The direct method offers an alternative to simulation for calculating the variance of losses to a portfolio, which may reduce the amount of calculation required.

Vehicle-borne IED detection using the ULTOR correlation processor

NASA Astrophysics Data System (ADS)

Burcham, Joel D.; Vachon, Joyce E.

2006-05-01

Advanced Optical Systems, Inc. developed the ULTOR(r) system, a real-time correlation processor that looks for improvised explosive devices (IED) by examining imagery of vehicles. The system determines the level of threat an approaching vehicle may represent. The system works on incoming video collected at different wavelengths, including visible, infrared, and synthetic aperture radar. Sensors that attach to ULTOR can be located wherever necessary to improve the safety around a checkpoint. When a suspect vehicle is detected, ULTOR can track the vehicle, alert personnel, check for previous instances of the vehicle, and update other networked systems with the threat information. The ULTOR processing engine focuses on the spatial frequency information available in the image. It correlates the imagery with templates that specify the criteria defining a suspect vehicle. It can perform full field correlations at a rate of 180 Hz or better. Additionally, the spatial frequency information is applied to a trained neural network to identify suspect vehicles. We have performed various laboratory and field experiments to verify the performance of the ULTOR system in a counter IED environment. The experiments cover tracking specific targets in video clips to demonstrating real-time ULTOR system performance. The selected targets in the experiments include various automobiles in both visible and infrared video.

Functional overestimation due to spatial smoothing of fMRI data.

PubMed

Liu, Peng; Calhoun, Vince; Chen, Zikuan

2017-11-01

Pearson correlation (simply correlation) is a basic technique for neuroimage function analysis. It has been observed that the spatial smoothing may cause functional overestimation, which however remains a lack of complete understanding. Herein, we present a theoretical explanation from the perspective of correlation scale invariance. For a task-evoked spatiotemporal functional dataset, we can extract the functional spatial map by calculating the temporal correlations (tcorr) of voxel timecourses against the task timecourse. From the relationship between image noise level (changed through spatial smoothing) and the tcorr map calculation, we show that the spatial smoothing causes a noise reduction, which in turn smooths the tcorr map and leads to a spatial expansion on neuroactivity blob estimation. Through numerical simulations and subject experiments, we show that the spatial smoothing of fMRI data may overestimate activation spots in the correlation functional map. Our results suggest a small spatial smoothing (with a smoothing kernel with a full width at half maximum (FWHM) of no more than two voxels) on fMRI data processing for correlation-based functional mapping COMPARISON WITH EXISTING METHODS: In extreme noiselessness, the correlation of scale-invariance property defines a meaningless binary tcorr map. In reality, a functional activity blob in a tcorr map is shaped due to the spoilage of image noise on correlative responses. We may reduce data noise level by smoothing processing, which poses a smoothing effect on correlation. This logic allows us to understand the noise dependence and the smoothing effect of correlation-based fMRI data analysis. Copyright © 2017 Elsevier B.V. All rights reserved.

Posterior parietal cortex mediates encoding and maintenance processes in change blindness.

PubMed

Tseng, Philip; Hsu, Tzu-Yu; Muggleton, Neil G; Tzeng, Ovid J L; Hung, Daisy L; Juan, Chi-Hung

2010-03-01

It is commonly accepted that right posterior parietal cortex (PPC) plays an important role in updating spatial representations, directing visuospatial attention, and planning actions. However, recent studies suggest that right PPC may also be involved in processes that are more closely associated with our visual awareness as its activation level positively correlates with successful conscious change detection (Beck, D.M., Rees, G., Frith, C.D., & Lavie, N. (2001). Neural correlates of change detection and change blindness. Nature Neuroscience, 4, 645-650.). Furthermore, disruption of its activity increases the occurrences of change blindness, thus suggesting a causal role for right PPC in change detection (Beck, D.M., Muggleton, N., Walsh, V., & Lavie, N. (2006). Right parietal cortex plays a critical role in change blindness. Cerebral Cortex, 16, 712-717.). In the context of a 1-shot change detection paradigm, we applied transcranial magnetic stimulation (TMS) during different time intervals to elucidate the temporally precise involvement of PPC in change detection. While subjects attempted to detect changes between two image sets separated by a brief time interval, TMS was applied either during the presentation of picture 1 when subjects were encoding and maintaining information into visual short-term memory, or picture 2 when subjects were retrieving information relating to picture 1 and comparing it to picture 2. Our results show that change blindness occurred more often when TMS was applied during the viewing of picture 1, which implies that right PPC plays a crucial role in the processes of encoding and maintaining information in visual short-term memory. In addition, since our stimuli did not involve changes in spatial locations, our findings also support previous studies suggesting that PPC may be involved in the processes of encoding non-spatial visual information (Todd, J.J. & Marois, R. (2004). Capacity limit of visual short-term memory in human posterior parietal cortex. Nature, 428, 751-754.). Copyright (c) 2009 Elsevier Ltd. All rights reserved.

Decay and Spatial Diffusion of Turbulent Kinetic Energy In The Presence of a Linear Kinetic Energy Gradient

NASA Astrophysics Data System (ADS)

Meneveau, Charles

2015-11-01

A topic that elicited the interest of John Lumley is pressure transport in turbulence. In 1978 (JL, in Advances in Applied Mechanics, pages 123-176) he showed that pressure transport likely acts in the opposite direction to the spatial flux of kinetic energy due to triple velocity correlations. Here we examine a flow in which the interplay of turbulent decay and spatial transport is particularly relevant. Specifically, using a specially designed active grid and screens placed in the Corrsin wind tunnel, such a flow is realized. Data are acquired using X-wire thermal anemometry at different spanwise and downstream locations. In order to resolve the dissipation rate accurately, measurements are also acquired using the NSTAP probe developed and manufactured by Princeton researchers and kindly provided to us (M. Hultmark, Y. Fan, L. Smits). The results show power-law decay with downstream distance, with a decay exponent that becomes larger in the high kinetic energy side of the flow. Measurements of the dissipation enable us to obtain the spanwise gradient of the spatial flux. One possible explanation for the observations is upgrading transport of kinetic energy due to pressure-velocity correlations, although its magnitude required to close the budget appears very large. Absence of simultaneous pressure velocity measurement preclude us to fully elucidate the observed trends. In collaboration with Adrien Thormann, Johns Hopkins University. Financial support: National Science Foundation.

Nonparametric Bayesian models for a spatial covariance.

PubMed

Reich, Brian J; Fuentes, Montserrat

2012-01-01

A crucial step in the analysis of spatial data is to estimate the spatial correlation function that determines the relationship between a spatial process at two locations. The standard approach to selecting the appropriate correlation function is to use prior knowledge or exploratory analysis, such as a variogram analysis, to select the correct parametric correlation function. Rather that selecting a particular parametric correlation function, we treat the covariance function as an unknown function to be estimated from the data. We propose a flexible prior for the correlation function to provide robustness to the choice of correlation function. We specify the prior for the correlation function using spectral methods and the Dirichlet process prior, which is a common prior for an unknown distribution function. Our model does not require Gaussian data or spatial locations on a regular grid. The approach is demonstrated using a simulation study as well as an analysis of California air pollution data.

Quantitative characterization of the spatial distribution of particles in materials: Application to materials processing

NASA Technical Reports Server (NTRS)

Parse, Joseph B.; Wert, J. A.

1991-01-01

Inhomogeneities in the spatial distribution of second phase particles in engineering materials are known to affect certain mechanical properties. Progress in this area has been hampered by the lack of a convenient method for quantitative description of the spatial distribution of the second phase. This study intends to develop a broadly applicable method for the quantitative analysis and description of the spatial distribution of second phase particles. The method was designed to operate on a desktop computer. The Dirichlet tessellation technique (geometrical method for dividing an area containing an array of points into a set of polygons uniquely associated with the individual particles) was selected as the basis of an analysis technique implemented on a PC. This technique is being applied to the production of Al sheet by PM processing methods; vacuum hot pressing, forging, and rolling. The effect of varying hot working parameters on the spatial distribution of aluminum oxide particles in consolidated sheet is being studied. Changes in distributions of properties such as through-thickness near-neighbor distance correlate with hot-working reduction.

LOX/hydrocarbon fuel carbon formation and mixing data analysis

NASA Technical Reports Server (NTRS)

Fang, J.

1983-01-01

By applying the Priem-Heidmann Generalized-Length vaporization correlation, the computer model developed by the present study predicts the spatial variation of propellant vaporization rate using the injector cold flow results to define the streamtubes. The calculations show that the overall and local propellant vaporization rate and mixture ratio change drastically as the injection element type or the injector operating condition is changed. These results are compared with the regions of carbon formation observed in the photographic combustion testing. The correlation shows that the fuel vaporization rate and the local mixture ratio produced by the injector element have first order effects on the degree of carbon formation.

Spatial Multiplexing of Atom-Photon Entanglement Sources using Feedforward Control and Switching Networks.

PubMed

Tian, Long; Xu, Zhongxiao; Chen, Lirong; Ge, Wei; Yuan, Haoxiang; Wen, Yafei; Wang, Shengzhi; Li, Shujing; Wang, Hai

2017-09-29

The light-matter quantum interface that can create quantum correlations or entanglement between a photon and one atomic collective excitation is a fundamental building block for a quantum repeater. The intrinsic limit is that the probability of preparing such nonclassical atom-photon correlations has to be kept low in order to suppress multiexcitation. To enhance this probability without introducing multiexcitation errors, a promising scheme is to apply multimode memories to the interface. Significant progress has been made in temporal, spectral, and spatial multiplexing memories, but the enhanced probability for generating the entangled atom-photon pair has not been experimentally realized. Here, by using six spin-wave-photon entanglement sources, a switching network, and feedforward control, we build a multiplexed light-matter interface and then demonstrate a ∼sixfold (∼fourfold) probability increase in generating entangled atom-photon (photon-photon) pairs. The measured compositive Bell parameter for the multiplexed interface is 2.49±0.03 combined with a memory lifetime of up to ∼51  μs.

Research on Quantum Algorithms at the Institute for Quantum Information

DTIC Science & Technology

2009-10-17

accuracy threshold theorem for the one-way quantum computer. Their proof is based on a novel scheme, in which a noisy cluster state in three spatial...detected. The proof applies to independent stochastic noise but (in contrast to proofs of the quantum accuracy threshold theorem based on concatenated...proved quantum threshold theorems for long-range correlated non-Markovian noise, for leakage faults, for the one-way quantum computer, for postselected

Exploring the Mechanisms of Ecological Land Change Based on the Spatial Autoregressive Model: A Case Study of the Poyang Lake Eco-Economic Zone, China

PubMed Central

Xie, Hualin; Liu, Zhifei; Wang, Peng; Liu, Guiying; Lu, Fucai

2013-01-01

Ecological land is one of the key resources and conditions for the survival of humans because it can provide ecosystem services and is particularly important to public health and safety. It is extremely valuable for effective ecological management to explore the evolution mechanisms of ecological land. Based on spatial statistical analyses, we explored the spatial disparities and primary potential drivers of ecological land change in the Poyang Lake Eco-economic Zone of China. The results demonstrated that the global Moran’s I value is 0.1646 during the 1990 to 2005 time period and indicated significant positive spatial correlation (p < 0.05). The results also imply that the clustering trend of ecological land changes weakened in the study area. Some potential driving forces were identified by applying the spatial autoregressive model in this study. The results demonstrated that the higher economic development level and industrialization rate were the main drivers for the faster change of ecological land in the study area. This study also tested the superiority of the spatial autoregressive model to study the mechanisms of ecological land change by comparing it with the traditional linear regressive model. PMID:24384778

Modification of turbulence and turbulent transport associated with a confinement transition in LAPD

NASA Astrophysics Data System (ADS)

Carter, Troy

2009-11-01

Azimuthal flow is driven in the edge of the Large Plasma Device (LAPD) through biasing a section of the vacuum vessel relative to the plasma source cathode. As the applied bias exceeds a threshold, a transition in radial particle confinement is observed, evidenced by a dramatic steepening in the density profile, similar to the L- to H-mode transition in toroidal confinement devices. The threshold behavior and dynamic behavior of radial transport is related to flow penetration and the degree of spatial overlap between the flow shear and density gradient profiles. An investigation of the changes in turbulence and turbulent particle transport associated with the confinement transition is presented. Two-dimensional cross-correlation measurements show that the spatial coherence of edge turbulence in LAPD changes significantly with biasing. The azimuthal correlation in the turbulence increases dramatically, while the radial correlation length is little altered. Turbulent amplitude is reduced at the transition, particularly in electric field fluctuations, but the dominant change observed is in the cross-phase between density and electric field fluctuations. The changes in cross-phase lead to a suppression and then apparent reversal of turbulent particle flux as the threshold is exceeded.

Projection correlation based view interpolation for cone beam CT: primary fluence restoration in scatter measurement with a moving beam stop array.

PubMed

Yan, Hao; Mou, Xuanqin; Tang, Shaojie; Xu, Qiong; Zankl, Maria

2010-11-07

Scatter correction is an open problem in x-ray cone beam (CB) CT. The measurement of scatter intensity with a moving beam stop array (BSA) is a promising technique that offers a low patient dose and accurate scatter measurement. However, when restoring the blocked primary fluence behind the BSA, spatial interpolation cannot well restore the high-frequency part, causing streaks in the reconstructed image. To address this problem, we deduce a projection correlation (PC) to utilize the redundancy (over-determined information) in neighbouring CB views. PC indicates that the main high-frequency information is contained in neighbouring angular projections, instead of the current projection itself, which provides a guiding principle that applies to high-frequency information restoration. On this basis, we present the projection correlation based view interpolation (PC-VI) algorithm; that it outperforms the use of only spatial interpolation is validated. The PC-VI based moving BSA method is developed. In this method, PC-VI is employed instead of spatial interpolation, and new moving modes are designed, which greatly improve the performance of the moving BSA method in terms of reliability and practicability. Evaluation is made on a high-resolution voxel-based human phantom realistically including the entire procedure of scatter measurement with a moving BSA, which is simulated by analytical ray-tracing plus Monte Carlo simulation with EGSnrc. With the proposed method, we get visually artefact-free images approaching the ideal correction. Compared with the spatial interpolation based method, the relative mean square error is reduced by a factor of 6.05-15.94 for different slices. PC-VI does well in CB redundancy mining; therefore, it has further potential in CBCT studies.

Spatial and temporal variability of throughfall and soil moisture in a deciduous forest in the low mountain ranges (Hesse, Germany)

NASA Astrophysics Data System (ADS)

Chifflard, Peter; Weishaupt, Philipp; Reiss, Martin

2017-04-01

Spatial and temporal patterns of throughfall can affect the heterogeneity of ecological, biogeochemical and hydrological processes at a forest floor and further the underlying soil. Previous research suggests different factors controlling the spatial and temporal patterns of throughfall, but most studies focus on coniferous forest, where the vegetation coverage is more or less constant over time. In deciduous forests the leaf area index varies due to the leaf fall in autumn which implicates a specific spatial and temporal variability of throughfall and furthermore of the soil moisture. Therefore, in the present study, the measurements of throughfall and soil moisture in a deciduous forest in the low mountain ranges focused especially on the period of leaf fall. The aims of this study were: 1) to detect the spatial and temporal variability of both the throughfall and the soil moisture, 2) to examine the temporal stability of the spatial patterns of the throughfall and soil moisture and 3) relate the soil moisture patterns to the throughfall patterns and further to the canopy characteristics. The study was carried out in a small catchment on middle Hesse (Germany) which is covered by beech forest. Annual mean air temperature is 9.4°C (48.9˚F) and annual mean precipitation is 650 mm. Base materials for soil genesis is greywacke and clay shale from Devonian deposits. The soil type at the study plot is a shallow cambisol. The study plot covers an area of about 150 m2 where 77 throughfall samplers where installed. The throughfall and the soil moisture (FDR-method, 20 cm depth) was measured immediately after every rainfall event at the 77 measurement points. During the period of October to December 2015 altogether 7 events were investigated. The geostatistical method kriging was used to interpolate between the measurements points to visualize the spatial patterns of each investigated parameter. Time-stability-plots were applied to examine temporal scatters of each investigated parameter. The spearmen and pearson correlation coefficients were applied to detect the relationship between the different investigated parameters. First results show that the spatial variability of throughfall decreases if the total amount of the throughfall increases. The soil moisture shows a similar behavior. It`s spatial variability decreases if higher soil moisture values were measured. Concerning the temporal stability of throughfall it can be shown that it is very high during the leaf-free period, although the rainfall events have different total througfall amounts. The soil moisture patterns consists of a low temporal stability and additionally only during one event a significant correlations between throughfall and soil moisture patterns exists. This implies that other factors than the throughfall patterns control the spatial patterns of soil moisture.

Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness

PubMed Central

Li, Jin; Tran, Maggie; Siwabessy, Justy

2016-01-01

Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia’s marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage at limited locations. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e., hard90 and hard70). We developed optimal predictive models to predict seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), knowledge informed AVI (KIAVI), Boruta and regularized RF (RRF) were tested based on predictive accuracy. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were examined. Finally, spatial predictions generated using the most accurate models were visually examined and analysed. This study confirmed that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness of four classes can be predicted with a high degree of accuracy; 3) the typical approach used to pre-select predictive variables by excluding highly correlated variables needs to be re-examined; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving predictive models; 5) FS methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to ‘small p and large n’ problems in environmental sciences. Additionally, automated computational programs for AVI need to be developed to increase its computational efficiency and caution should be taken when applying filter FS methods in selecting predictive models. PMID:26890307

Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness.

PubMed

Li, Jin; Tran, Maggie; Siwabessy, Justy

2016-01-01

Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia's marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage at limited locations. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e., hard90 and hard70). We developed optimal predictive models to predict seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), knowledge informed AVI (KIAVI), Boruta and regularized RF (RRF) were tested based on predictive accuracy. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were examined. Finally, spatial predictions generated using the most accurate models were visually examined and analysed. This study confirmed that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness of four classes can be predicted with a high degree of accuracy; 3) the typical approach used to pre-select predictive variables by excluding highly correlated variables needs to be re-examined; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving predictive models; 5) FS methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to 'small p and large n' problems in environmental sciences. Additionally, automated computational programs for AVI need to be developed to increase its computational efficiency and caution should be taken when applying filter FS methods in selecting predictive models.

Spatial Analysis of Ambient PM2.5 Exposure and Bladder Cancer Mortality in Taiwan

PubMed Central

Yeh, Hsin-Ling; Hsu, Shang-Wei; Chang, Yu-Chia; Chan, Ta-Chien; Tsou, Hui-Chen; Chang, Yen-Chen; Chiang, Po-Huang

2017-01-01

Fine particulate matter (PM2.5) is an air pollutant that is receiving intense regulatory attention in Taiwan. In previous studies, the effect of air pollution on bladder cancer has been explored. This study was conducted to elucidate the effect of atmospheric PM2.5 and other local risk factors on bladder cancer mortality based on available 13-year mortality data. Geographically weighted regression (GWR) was applied to estimate and interpret the spatial variability of the relationships between bladder cancer mortality and ambient PM2.5 concentrations, and other variables were covariates used to adjust for the effect of PM2.5. After applying a GWR model, the concentration of ambient PM2.5 showed a positive correlation with bladder cancer mortality in males in northern Taiwan and females in most of the townships in Taiwan. This is the first time PM2.5 has been identified as a risk factor for bladder cancer based on the statistical evidence provided by GWR analysis. PMID:28489042

Estimating Function Approaches for Spatial Point Processes

NASA Astrophysics Data System (ADS)

Deng, Chong

Spatial point pattern data consist of locations of events that are often of interest in biological and ecological studies. Such data are commonly viewed as a realization from a stochastic process called spatial point process. To fit a parametric spatial point process model to such data, likelihood-based methods have been widely studied. However, while maximum likelihood estimation is often too computationally intensive for Cox and cluster processes, pairwise likelihood methods such as composite likelihood, Palm likelihood usually suffer from the loss of information due to the ignorance of correlation among pairs. For many types of correlated data other than spatial point processes, when likelihood-based approaches are not desirable, estimating functions have been widely used for model fitting. In this dissertation, we explore the estimating function approaches for fitting spatial point process models. These approaches, which are based on the asymptotic optimal estimating function theories, can be used to incorporate the correlation among data and yield more efficient estimators. We conducted a series of studies to demonstrate that these estmating function approaches are good alternatives to balance the trade-off between computation complexity and estimating efficiency. First, we propose a new estimating procedure that improves the efficiency of pairwise composite likelihood method in estimating clustering parameters. Our approach combines estimating functions derived from pairwise composite likeli-hood estimation and estimating functions that account for correlations among the pairwise contributions. Our method can be used to fit a variety of parametric spatial point process models and can yield more efficient estimators for the clustering parameters than pairwise composite likelihood estimation. We demonstrate its efficacy through a simulation study and an application to the longleaf pine data. Second, we further explore the quasi-likelihood approach on fitting second-order intensity function of spatial point processes. However, the original second-order quasi-likelihood is barely feasible due to the intense computation and high memory requirement needed to solve a large linear system. Motivated by the existence of geometric regular patterns in the stationary point processes, we find a lower dimension representation of the optimal weight function and propose a reduced second-order quasi-likelihood approach. Through a simulation study, we show that the proposed method not only demonstrates superior performance in fitting the clustering parameter but also merits in the relaxation of the constraint of the tuning parameter, H. Third, we studied the quasi-likelihood type estimating funciton that is optimal in a certain class of first-order estimating functions for estimating the regression parameter in spatial point process models. Then, by using a novel spectral representation, we construct an implementation that is computationally much more efficient and can be applied to more general setup than the original quasi-likelihood method.

Development of a local size hierarchy causes regular spacing of trees in an even-aged Abies forest: analyses using spatial autocorrelation and the mark correlation function.

PubMed

Suzuki, Satoshi N; Kachi, Naoki; Suzuki, Jun-Ichirou

2008-09-01

During the development of an even-aged plant population, the spatial distribution of individuals often changes from a clumped pattern to a random or regular one. The development of local size hierarchies in an Abies forest was analysed for a period of 47 years following a large disturbance in 1959. In 1980 all trees in an 8 x 8 m plot were mapped and their height growth after the disturbance was estimated. Their mortality and growth were then recorded at 1- to 4-year intervals between 1980 and 2006. Spatial distribution patterns of trees were analysed by the pair correlation function. Spatial correlations between tree heights were analysed with a spatial autocorrelation function and the mark correlation function. The mark correlation function was able to detect a local size hierarchy that could not be detected by the spatial autocorrelation function alone. The small-scale spatial distribution pattern of trees changed from clumped to slightly regular during the 47 years. Mortality occurred in a density-dependent manner, which resulted in regular spacing between trees after 1980. The spatial autocorrelation and mark correlation functions revealed the existence of tree patches consisting of large trees at the initial stage. Development of a local size hierarchy was detected within the first decade after the disturbance, although the spatial autocorrelation was not negative. Local size hierarchies that developed persisted until 2006, and the spatial autocorrelation became negative at later stages (after about 40 years). This is the first study to detect local size hierarchies as a prelude to regular spacing using the mark correlation function. The results confirm that use of the mark correlation function together with the spatial autocorrelation function is an effective tool to analyse the development of a local size hierarchy of trees in a forest.

A method for functional network connectivity among spatially independent resting-state components in schizophrenia.

PubMed

Jafri, Madiha J; Pearlson, Godfrey D; Stevens, Michael; Calhoun, Vince D

2008-02-15

Functional connectivity of the brain has been studied by analyzing correlation differences in time courses among seed voxels or regions with other voxels of the brain in healthy individuals as well as in patients with brain disorders. The spatial extent of strongly temporally coherent brain regions co-activated during rest has also been examined using independent component analysis (ICA). However, the weaker temporal relationships among ICA component time courses, which we operationally define as a measure of functional network connectivity (FNC), have not yet been studied. In this study, we propose an approach for evaluating FNC and apply it to functional magnetic resonance imaging (fMRI) data collected from persons with schizophrenia and healthy controls. We examined the connectivity and latency among ICA component time courses to test the hypothesis that patients with schizophrenia would show increased functional connectivity and increased lag among resting state networks compared to controls. Resting state fMRI data were collected and the inter-relationships among seven selected resting state networks (identified using group ICA) were evaluated by correlating each subject's ICA time courses with one another. Patients showed higher correlation than controls among most of the dominant resting state networks. Patients also had slightly more variability in functional connectivity than controls. We present a novel approach for quantifying functional connectivity among brain networks identified with spatial ICA. Significant differences between patient and control connectivity in different networks were revealed possibly reflecting deficiencies in cortical processing in patients.

A Method for Functional Network Connectivity Among Spatially Independent Resting-State Components in Schizophrenia

PubMed Central

Jafri, Madiha J; Pearlson, Godfrey D; Stevens, Michael; Calhoun, Vince D

2011-01-01

Functional connectivity of the brain has been studied by analyzing correlation differences in time courses among seed voxels or regions with other voxels of the brain in patients versus controls. The spatial extent of strongly temporally coherent brain regions co-activated during rest has also been examined using independent component analysis (ICA). However, the weaker temporal relationships among ICA component time courses, which we operationally define as a measure of functional network connectivity (FNC), have not yet been studied. In this study, we propose an approach for evaluating FNC and apply it to functional magnetic resonance imaging (fMRI) data collected from persons with schizophrenia and healthy controls. We examined the connectivity and latency among ICA component time courses to test the hypothesis that patients with schizophrenia would show increased functional connectivity and increased lag among resting state networks compared to controls. Resting state fMRI data were collected and the inter-relationships among seven selected resting state networks (identified using group ICA) were evaluated by correlating each subject’s ICA time courses with one another. Patients showed higher correlation than controls among most of the dominant resting state networks. Patients also had slightly more variability in functional connectivity than controls. We present a novel approach for quantifying functional connectivity among brain networks identified with spatial ICA. Significant differences between patient and control connectivity in different networks were revealed possibly reflecting deficiencies in cortical processing in patients. PMID:18082428

Accounting for connectivity and spatial correlation in the optimal placement of wildlife habitat

Treesearch

John Hof; Curtis H. Flather

1996-01-01

This paper investigates optimization approaches to simultaneously modelling habitat fragmentation and spatial correlation between patch populations. The problem is formulated with habitat connectivity affecting population means and variances, with spatial correlations accounted for in covariance calculations. Population with a pre-specifled confidence level is then...

Ergodic channel capacity of spatial correlated multiple-input multiple-output free space optical links using multipulse pulse-position modulation

NASA Astrophysics Data System (ADS)

Wang, Huiqin; Wang, Xue; Cao, Minghua

2017-02-01

The spatial correlation extensively exists in the multiple-input multiple-output (MIMO) free space optical (FSO) communication systems due to the channel fading and the antenna space limitation. Wilkinson's method was utilized to investigate the impact of spatial correlation on the MIMO FSO communication system employing multipulse pulse-position modulation. Simulation results show that the existence of spatial correlation reduces the ergodic channel capacity, and the reception diversity is more competent to resist this kind of performance degradation.

Modeling The Hydrology And Water Allocation Under Climate Change In Rural River Basins: A Case Study From Nam Ngum River Basin, Laos

NASA Astrophysics Data System (ADS)

Jayasekera, D. L.; Kaluarachchi, J.; Kim, U.

2011-12-01

Rural river basins with sufficient water availability to maintain economic livelihoods can be affected with seasonal fluctuations of precipitation and sometimes by droughts. In addition, climate change impacts can also alter future water availability. General Circulation Models (GCMs) provide credible quantitative estimates of future climate conditions but such estimates are often characterized by bias and coarse scale resolution making it necessary to downscale the outputs for use in regional hydrologic models. This study develops a methodology to downscale and project future monthly precipitation in moderate scale basins where data are limited. A stochastic framework for single-site and multi-site generation of weekly rainfall is developed while preserving the historical temporal and spatial correlation structures. The spatial correlations in the simulated occurrences and the amounts are induced using spatially correlated yet serially independent random numbers. This method is applied to generate weekly precipitation data for a 100-year period in the Nam Ngum River Basin (NNRB) that has a land area of 16,780 km2 located in Lao P.D.R. This method is developed and applied using precipitation data from 1961 to 2000 for 10 selected weather stations that represents the basin rainfall characteristics. Bias-correction method, based on fitted theoretical probability distribution transformations, is applied to improve monthly mean frequency, intensity and the amount of raw GCM precipitation predicted at a given weather station using CGCM3.1 and ECHAM5 for SRES A2 emission scenario. Bias-correction procedure adjusts GCM precipitation to approximate the long-term frequency and the intensity distribution observed at a given weather station. Index of agreement and mean absolute error are determined to assess the overall ability and performance of the bias correction method. The generated precipitation series aggregated at monthly time step was perturbed by the change factors estimated using the corrected GCM and baseline scenarios for future time periods of 2011-2050 and 2051-2090. A network based hydrologic and water resources model, WEAP, was used to simulate the current water allocation and management practices to identify the impacts of climate change in the 20th century. The results of this work are used to identify the multiple challenges faced by stakeholders and planners in water allocation for competing demands in the presence of climate change impacts.

Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Siqi; Joseph, Antony; Hammonds, Ann S.

Spatial gene expression patterns enable the detection of local covariability and are extremely useful for identifying local gene interactions during normal development. The abundance of spatial expression data in recent years has led to the modeling and analysis of regulatory networks. The inherent complexity of such data makes it a challenge to extract biological information. We developed staNMF, a method that combines a scalable implementation of nonnegative matrix factorization (NMF) with a new stability-driven model selection criterion. When applied to a set of Drosophila early embryonic spatial gene expression images, one of the largest datasets of its kind, staNMF identifiedmore » 21 principal patterns (PP). Providing a compact yet biologically interpretable representation of Drosophila expression patterns, PP are comparable to a fate map generated experimentally by laser ablation and show exceptional promise as a data-driven alternative to manual annotations. Our analysis mapped genes to cell-fate programs and assigned putative biological roles to uncharacterized genes. Finally, we used the PP to generate local transcription factor regulatory networks. Spatially local correlation networks were constructed for six PP that span along the embryonic anterior-posterior axis. Using a two-tail 5% cutoff on correlation, we reproduced 10 of the 11 links in the well-studied gap gene network. In conclusion, the performance of PP with the Drosophila data suggests that staNMF provides informative decompositions and constitutes a useful computational lens through which to extract biological insight from complex and often noisy gene expression data.« less

Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks

DOE PAGES

Wu, Siqi; Joseph, Antony; Hammonds, Ann S.; ...

2016-04-06

Spatial gene expression patterns enable the detection of local covariability and are extremely useful for identifying local gene interactions during normal development. The abundance of spatial expression data in recent years has led to the modeling and analysis of regulatory networks. The inherent complexity of such data makes it a challenge to extract biological information. We developed staNMF, a method that combines a scalable implementation of nonnegative matrix factorization (NMF) with a new stability-driven model selection criterion. When applied to a set of Drosophila early embryonic spatial gene expression images, one of the largest datasets of its kind, staNMF identifiedmore » 21 principal patterns (PP). Providing a compact yet biologically interpretable representation of Drosophila expression patterns, PP are comparable to a fate map generated experimentally by laser ablation and show exceptional promise as a data-driven alternative to manual annotations. Our analysis mapped genes to cell-fate programs and assigned putative biological roles to uncharacterized genes. Finally, we used the PP to generate local transcription factor regulatory networks. Spatially local correlation networks were constructed for six PP that span along the embryonic anterior-posterior axis. Using a two-tail 5% cutoff on correlation, we reproduced 10 of the 11 links in the well-studied gap gene network. In conclusion, the performance of PP with the Drosophila data suggests that staNMF provides informative decompositions and constitutes a useful computational lens through which to extract biological insight from complex and often noisy gene expression data.« less

The cluster-cluster correlation function. [of galaxies

NASA Technical Reports Server (NTRS)

Postman, M.; Geller, M. J.; Huchra, J. P.

1986-01-01

The clustering properties of the Abell and Zwicky cluster catalogs are studied using the two-point angular and spatial correlation functions. The catalogs are divided into eight subsamples to determine the dependence of the correlation function on distance, richness, and the method of cluster identification. It is found that the Corona Borealis supercluster contributes significant power to the spatial correlation function to the Abell cluster sample with distance class of four or less. The distance-limited catalog of 152 Abell clusters, which is not greatly affected by a single system, has a spatial correlation function consistent with the power law Xi(r) = 300r exp -1.8. In both the distance class four or less and distance-limited samples the signal in the spatial correlation function is a power law detectable out to 60/h Mpc. The amplitude of Xi(r) for clusters of richness class two is about three times that for richness class one clusters. The two-point spatial correlation function is sensitive to the use of estimated redshifts.

Spatial and temporal analysis of drought variability at several time scales in Syria during 1961-2012

NASA Astrophysics Data System (ADS)

Mathbout, Shifa; Lopez-Bustins, Joan A.; Martin-Vide, Javier; Bech, Joan; Rodrigo, Fernando S.

2018-02-01

This paper analyses the observed spatiotemporal characteristics of drought phenomenon in Syria using the Standardised Precipitation Index (SPI) and the Standardised Precipitation Evapotranspiration Index (SPEI). Temporal variability of drought is calculated for various time scales (3, 6, 9, 12, and 24 months) for 20 weather stations over the 1961-2012 period. The spatial patterns of drought were identified by applying a Principal Component Analysis (PCA) to the SPI and SPEI values at different time scales. The results revealed three heterogeneous and spatially well-defined regions with different temporal evolution of droughts: 1) Northeastern (inland desert); 2) Southern (mountainous landscape); 3) Northwestern (Mediterranean coast). The evolutionary characteristics of drought during 1961-2012 were analysed including spatial and temporal variability of SPI and SPEI, the frequency distribution, and the drought duration. The results of the non-parametric Mann-Kendall test applied to the SPI and SPEI series indicate prevailing significant negative trends (drought) at all stations. Both drought indices have been correlated both on spatial and temporal scales and they are highly comparable, especially, over a 12 and 24 month accumulation period. We concluded that the temporal and spatial characteristics of the SPI and SPEI can be used for developing a drought intensity - areal extent - and frequency curve that assesses the variability of regional droughts in Syria. The analysis of both indices suggests that all three regions had a severe drought in the 1990s, which had never been observed before in the country. Furthermore, the 2007-2010 drought was the driest period in the instrumental record, happening just before the onset of the recent conflict in Syria.

Impact of spatial variation in snow water equivalent and snow ablation on spring snowcover depletion over an alpine ridge

NASA Astrophysics Data System (ADS)

Schirmer, Michael; Harder, Phillip; Pomeroy, John

2016-04-01

The spatial and temporal dynamics of mountain snowmelt are controlled by the spatial distribution of snow accumulation and redistribution and the pattern of melt energy applied to this snowcover. In order to better quantify the spatial variations of accumulation and ablation, Structure-from-Motion techniques were applied to sequential aerial photographs of an alpine ridge in the Canadian Rocky Mountains taken from an Unmanned Aerial Vehicle (UAV). Seven spatial maps of snow depth and changes to depth during late melt (May-July) were generated at very high resolutions covering an area of 800 x 600 m. The accuracy was assessed with over 100 GPS measurements and RMSE were found to be less than 10 cm. Low resolution manual measurements of density permitted calculation of snow water equivalent (SWE) and change in SWE (ablation rate). The results indicate a highly variable initial SWE distribution, which was five times more variable than the spatial variation in ablation rate. Spatial variation in ablation rate was still substantial, with a factor of two difference between north and south aspects and small scale variations due to local dust deposition. However, the impact of spatial variations in ablation rate on the snowcover depletion curve could not be discerned. The reason for this is that only a weak spatial correlation developed between SWE and ablation rate. These findings suggest that despite substantial variations in ablation rate, snowcover depletion curve calculations should emphasize the spatial variation of initial SWE rather than the variation in ablation rate. While there is scientific evidence from other field studies that support this, there are also studies that suggest that spatial variations in ablation rate can influence snowcover depletion curves in complex terrain, particularly in early melt. The development of UAV photogrammetry has provided an opportunity for further detailed measurement of ablation rates, SWE and snowcover depletion over complex terrain and UAV field studies are recommended to clarify the relative importance of SWE and melt variability on snowcover depletion in various environmental conditions.

Spatial Distribution of the Coefficient of Variation and Bayesian Forecast for the Paleo-Earthquakes in Japan

NASA Astrophysics Data System (ADS)

Nomura, Shunichi; Ogata, Yosihiko

2016-04-01

We propose a Bayesian method of probability forecasting for recurrent earthquakes of inland active faults in Japan. Renewal processes with the Brownian Passage Time (BPT) distribution are applied for over a half of active faults in Japan by the Headquarters for Earthquake Research Promotion (HERP) of Japan. Long-term forecast with the BPT distribution needs two parameters; the mean and coefficient of variation (COV) for recurrence intervals. The HERP applies a common COV parameter for all of these faults because most of them have very few specified paleoseismic events, which is not enough to estimate reliable COV values for respective faults. However, different COV estimates are proposed for the same paleoseismic catalog by some related works. It can make critical difference in forecast to apply different COV estimates and so COV should be carefully selected for individual faults. Recurrence intervals on a fault are, on the average, determined by the long-term slip rate caused by the tectonic motion but fluctuated by nearby seismicities which influence surrounding stress field. The COVs of recurrence intervals depend on such stress perturbation and so have spatial trends due to the heterogeneity of tectonic motion and seismicity. Thus we introduce a spatial structure on its COV parameter by Bayesian modeling with a Gaussian process prior. The COVs on active faults are correlated and take similar values for closely located faults. It is found that the spatial trends in the estimated COV values coincide with the density of active faults in Japan. We also show Bayesian forecasts by the proposed model using Markov chain Monte Carlo method. Our forecasts are different from HERP's forecast especially on the active faults where HERP's forecasts are very high or low.

Hybrid inversions of CO2 fluxes at regional scale applied to network design

NASA Astrophysics Data System (ADS)

Kountouris, Panagiotis; Gerbig, Christoph; -Thomas Koch, Frank

2013-04-01

Long term observations of atmospheric greenhouse gas measuring stations, located at representative regions over the continent, improve our understanding of greenhouse gas sources and sinks. These mixing ratio measurements can be linked to surface fluxes by atmospheric transport inversions. Within the upcoming years new stations are to be deployed, which requires decision making tools with respect to the location and the density of the network. We are developing a method to assess potential greenhouse gas observing networks in terms of their ability to recover specific target quantities. As target quantities we use CO2 fluxes aggregated to specific spatial and temporal scales. We introduce a high resolution inverse modeling framework, which attempts to combine advantages from pixel based inversions with those of a carbon cycle data assimilation system (CCDAS). The hybrid inversion system consists of the Lagrangian transport model STILT, the diagnostic biosphere model VPRM and a Bayesian inversion scheme. We aim to retrieve the spatiotemporal distribution of net ecosystem exchange (NEE) at a high spatial resolution (10 km x 10 km) by inverting for spatially and temporally varying scaling factors for gross ecosystem exchange (GEE) and respiration (R) rather than solving for the fluxes themselves. Thus the state space includes parameters for controlling photosynthesis and respiration, but unlike in a CCDAS it allows for spatial and temporal variations, which can be expressed as NEE(x,y,t) = λG(x,y,t) GEE(x,y,t) + λR(x,y,t) R(x,y,t) . We apply spatially and temporally correlated uncertainties by using error covariance matrices with non-zero off-diagonal elements. Synthetic experiments will test our system and select the optimal a priori error covariance by using different spatial and temporal correlation lengths on the error statistics of the a priori covariance and comparing the optimized fluxes against the 'known truth'. As 'known truth' we use independent fluxes generated from a different biosphere model (BIOME-BGC). Initially we perform single-station inversions for Ochsenkopf tall tower located in Germany. Further expansion of the inversion framework to multiple stations and its application to network design will address the questions of how well a set of network stations can constrain a given target quantity, and whether there are objective criteria to select an optimal configuration for new stations that maximizes the uncertainty reduction.

Estimation of geotechnical parameters on the basis of geophysical methods and geostatistics

NASA Astrophysics Data System (ADS)

Brom, Aleksander; Natonik, Adrianna

2017-12-01

The paper presents possible implementation of ordinary cokriging and geophysical investigation on humidity data acquired in geotechnical studies. The Author describes concept of geostatistics, terminology of geostatistical modelling, spatial correlation functions, principles of solving cokriging systems, advantages of (co-)kriging in comparison with other interpolation methods, obstacles in this type of attempt. Cross validation and discussion of results was performed with an indication of prospect of applying similar procedures in various researches..

Far-infrared image restoration analysis of the protostellar cluster in S140

NASA Technical Reports Server (NTRS)

Lester, D. F.; Harvey, P. M.; Joy, M.; Ellis, H. B., Jr.

1986-01-01

Image restoration techniques are applied to one-dimensional scans at 50 and 100 microns of the protostellar cluster in S140. These measurements resolve the surrounding nebula clearly, and Fourier methods are used to match the effective beam profiles at these wavelengths. This allows the radial distribution of temperature and dust column density to be derived at a diffraction limited spatial resolution of 23 arcsec (0.1 pc). Evidence for heating of the S140 molecular cloud by a nearby ionization front is established, and the dissociation of molecules inside the ionization front is spatially well correlated with the heating of the dust. The far-infrared spectral distribution of the three near-infrared sources within 10 arcsesc of the cluster center is presented.

Phase transitions in coupled map lattices and in associated probabilistic cellular automata.

PubMed

Just, Wolfram

2006-10-01

Analytical tools are applied to investigate piecewise linear coupled map lattices in terms of probabilistic cellular automata. The so-called disorder condition of probabilistic cellular automata is closely related with attracting sets in coupled map lattices. The importance of this condition for the suppression of phase transitions is illustrated by spatially one-dimensional systems. Invariant densities and temporal correlations are calculated explicitly. Ising type phase transitions are found for one-dimensional coupled map lattices acting on repelling sets and for a spatially two-dimensional Miller-Huse-like system with stable long time dynamics. Critical exponents are calculated within a finite size scaling approach. The relevance of detailed balance of the resulting probabilistic cellular automaton for the critical behavior is pointed out.

Analysis of Particle Image Velocimetry (PIV) Data for Application to Subsonic Jet Noise Studies

NASA Technical Reports Server (NTRS)

Blackshire, James L.

1997-01-01

Global velocimetry measurements were taken using Particle Image Velocimetry (PIV) in the subsonic flow exiting a 1 inch circular nozzle in an attempt to better understand the turbulence characteristics of its shear layer region. This report presents the results of the PIV analysis and data reduction portions of the test and details the processing that was done. Custom data analysis and data validation algorithms were developed and applied to a data ensemble consisting of over 750 PIV 70 mm photographs taken in the 0.85 mach flow facility. Results are presented detailing spatial characteristics of the flow including ensemble mean and standard deviation, turbulence intensities and Reynold's stress levels, and 2-point spatial correlations.

Estimating the spatial distribution of soil moisture based on Bayesian maximum entropy method with auxiliary data from remote sensing

NASA Astrophysics Data System (ADS)

Gao, Shengguo; Zhu, Zhongli; Liu, Shaomin; Jin, Rui; Yang, Guangchao; Tan, Lei

2014-10-01

Soil moisture (SM) plays a fundamental role in the land-atmosphere exchange process. Spatial estimation based on multi in situ (network) data is a critical way to understand the spatial structure and variation of land surface soil moisture. Theoretically, integrating densely sampled auxiliary data spatially correlated with soil moisture into the procedure of spatial estimation can improve its accuracy. In this study, we present a novel approach to estimate the spatial pattern of soil moisture by using the BME method based on wireless sensor network data and auxiliary information from ASTER (Terra) land surface temperature measurements. For comparison, three traditional geostatistic methods were also applied: ordinary kriging (OK), which used the wireless sensor network data only, regression kriging (RK) and ordinary co-kriging (Co-OK) which both integrated the ASTER land surface temperature as a covariate. In Co-OK, LST was linearly contained in the estimator, in RK, estimator is expressed as the sum of the regression estimate and the kriged estimate of the spatially correlated residual, but in BME, the ASTER land surface temperature was first retrieved as soil moisture based on the linear regression, then, the t-distributed prediction interval (PI) of soil moisture was estimated and used as soft data in probability form. The results indicate that all three methods provide reasonable estimations. Co-OK, RK and BME can provide a more accurate spatial estimation by integrating the auxiliary information Compared to OK. RK and BME shows more obvious improvement compared to Co-OK, and even BME can perform slightly better than RK. The inherent issue of spatial estimation (overestimation in the range of low values and underestimation in the range of high values) can also be further improved in both RK and BME. We can conclude that integrating auxiliary data into spatial estimation can indeed improve the accuracy, BME and RK take better advantage of the auxiliary information compared to Co-OK, and BME outperforms RK by integrating the auxiliary data in a probability form.

[Temporal and spatial characteristics of ecological risk in Shunyi, Beijing, China based on landscape structure.

PubMed

Qing, Feng Ting; Peng, Yu

2016-05-01

Based on the remote sensing data in 1997, 2001, 2005, 2009 and 2013, this article classified the landscape types of Shunyi, and the ecological risk index was built based on landscape disturbance index and landscape fragility. The spatial auto-correlation and geostatistical analysis by GS + and ArcGIS was used to study temporal and spatial changes of ecological risk. The results showed that eco-risk degree in the study region had positive spatial correlation which decreased with the increasing grain size. Within a certain grain range (<12 km), the spatial auto-correlation had an obvious dependence on scale. The random variation of spatial heterogeneity was less than spatial auto-correlation variation from 1997 to 2013, which meant the auto-correlation had a dominant role in spatial heterogeneity. The ecological risk of Shunyi was mainly at moderate level during the study period. The area of the district with higher and lower ecological risk increased, while that of mode-rate ecological risk decreased. The area with low ecological risk was mainly located in the airport region and forest of southeast Shunyi, while that with high ecological risk was mainly concentrated in the water landscape, such as the banks of Chaobai River.

Using temporal detrending to observe the spatial correlation of traffic.

PubMed

Ermagun, Alireza; Chatterjee, Snigdhansu; Levinson, David

2017-01-01

This empirical study sheds light on the spatial correlation of traffic links under different traffic regimes. We mimic the behavior of real traffic by pinpointing the spatial correlation between 140 freeway traffic links in a major sub-network of the Minneapolis-St. Paul freeway system with a grid-like network topology. This topology enables us to juxtapose the positive and negative correlation between links, which has been overlooked in short-term traffic forecasting models. To accurately and reliably measure the correlation between traffic links, we develop an algorithm that eliminates temporal trends in three dimensions: (1) hourly dimension, (2) weekly dimension, and (3) system dimension for each link. The spatial correlation of traffic links exhibits a stronger negative correlation in rush hours, when congestion affects route choice. Although this correlation occurs mostly in parallel links, it is also observed upstream, where travelers receive information and are able to switch to substitute paths. Irrespective of the time-of-day and day-of-week, a strong positive correlation is witnessed between upstream and downstream links. This correlation is stronger in uncongested regimes, as traffic flow passes through consecutive links more quickly and there is no congestion effect to shift or stall traffic. The extracted spatial correlation structure can augment the accuracy of short-term traffic forecasting models.

Using temporal detrending to observe the spatial correlation of traffic

PubMed Central

2017-01-01

This empirical study sheds light on the spatial correlation of traffic links under different traffic regimes. We mimic the behavior of real traffic by pinpointing the spatial correlation between 140 freeway traffic links in a major sub-network of the Minneapolis—St. Paul freeway system with a grid-like network topology. This topology enables us to juxtapose the positive and negative correlation between links, which has been overlooked in short-term traffic forecasting models. To accurately and reliably measure the correlation between traffic links, we develop an algorithm that eliminates temporal trends in three dimensions: (1) hourly dimension, (2) weekly dimension, and (3) system dimension for each link. The spatial correlation of traffic links exhibits a stronger negative correlation in rush hours, when congestion affects route choice. Although this correlation occurs mostly in parallel links, it is also observed upstream, where travelers receive information and are able to switch to substitute paths. Irrespective of the time-of-day and day-of-week, a strong positive correlation is witnessed between upstream and downstream links. This correlation is stronger in uncongested regimes, as traffic flow passes through consecutive links more quickly and there is no congestion effect to shift or stall traffic. The extracted spatial correlation structure can augment the accuracy of short-term traffic forecasting models. PMID:28472093

Using Bayesian hierarchical models to better understand nitrate sources and sinks in agricultural watersheds.

PubMed

Xia, Yongqiu; Weller, Donald E; Williams, Meghan N; Jordan, Thomas E; Yan, Xiaoyuan

2016-11-15

Export coefficient models (ECMs) are often used to predict nutrient sources and sinks in watersheds because ECMs can flexibly incorporate processes and have minimal data requirements. However, ECMs do not quantify uncertainties in model structure, parameters, or predictions; nor do they account for spatial and temporal variability in land characteristics, weather, and management practices. We applied Bayesian hierarchical methods to address these problems in ECMs used to predict nitrate concentration in streams. We compared four model formulations, a basic ECM and three models with additional terms to represent competing hypotheses about the sources of error in ECMs and about spatial and temporal variability of coefficients: an ADditive Error Model (ADEM), a SpatioTemporal Parameter Model (STPM), and a Dynamic Parameter Model (DPM). The DPM incorporates a first-order random walk to represent spatial correlation among parameters and a dynamic linear model to accommodate temporal correlation. We tested the modeling approach in a proof of concept using watershed characteristics and nitrate export measurements from watersheds in the Coastal Plain physiographic province of the Chesapeake Bay drainage. Among the four models, the DPM was the best--it had the lowest mean error, explained the most variability (R 2  = 0.99), had the narrowest prediction intervals, and provided the most effective tradeoff between fit complexity (its deviance information criterion, DIC, was 45.6 units lower than any other model, indicating overwhelming support for the DPM). The superiority of the DPM supports its underlying hypothesis that the main source of error in ECMs is their failure to account for parameter variability rather than structural error. Analysis of the fitted DPM coefficients for cropland export and instream retention revealed some of the factors controlling nitrate concentration: cropland nitrate exports were positively related to stream flow and watershed average slope, while instream nitrate retention was positively correlated with nitrate concentration. By quantifying spatial and temporal variability in sources and sinks, the DPM provides new information to better target management actions to the most effective times and places. Given the wide use of ECMs as research and management tools, our approach can be broadly applied in other watersheds and to other materials. Copyright © 2016 Elsevier Ltd. All rights reserved.

Analysis of Correlation between Ionospheric Spatial Gradients and Space Weather Intensity under Nominal Conditions for Ground-Based Augmentation Systems

NASA Astrophysics Data System (ADS)

Lee, J.

2013-12-01

Ground-Based Augmentation Systems (GBAS) support aircraft precision approach and landing by providing differential GPS corrections to aviation users. For GBAS applications, most of ionospheric errors are removed by applying the differential corrections. However, ionospheric correction errors may exist due to ionosphere spatial decorrelation between GBAS ground facility and users. Thus, the standard deviation of ionosphere spatial decorrelation (σvig) is estimated and included in the computation of error bounds on user position solution. The σvig of 4mm/km, derived for the Conterminous United States (CONUS), bounds one-sigma ionospheric spatial gradients under nominal conditions (including active, but not stormy condition) with an adequate safety margin [1]. The conservatism residing in the current σvig by fixing it to a constant value for all non-stormy conditions could be mitigated by subdividing ionospheric conditions into several classes and using different σvig for each class. This new concept, real-time σvig adaptation, will be possible if the level of ionospheric activity can be well classified based on space weather intensity. This paper studies correlation between the statistics of nominal ionospheric spatial gradients and space weather indices. The analysis was carried out using two sets of data collected from Continuous Operating Reference Station (CORS) Network; 9 consecutive (nominal and ionospherically active) days in 2004 and 19 consecutive (relatively 'quiet') days in 2010. Precise ionospheric delay estimates are obtained using the simplified truth processing method and vertical ionospheric gradients are computed using the well-known 'station pair method' [2]. The remaining biases which include carrier-phase leveling errors and Inter-frequency Bias (IFB) calibration errors are reduced by applying linear slip detection thresholds. The σvig was inflated to overbound the distribution of vertical ionospheric gradients with the required confidence level. Using the daily maximum values of σvig, day-to-day variations of spatial gradients are compared to those of two space weather indices; Disturbance, Storm Time (Dst) index and Interplanetary Magnetic Field Bz (IMF Bz). The day-to-day variations of both space weather indices showed a good agreement with those of daily maximum σvig. The results demonstrate that ionospheric gradient statistics are highly correlated with space weather indices on nominal and off-nominal days. Further investigation on this relationship would facilitate prediction of upcoming ionospheric behavior based on space weather information and adjusting σvig in real time. Consequently it will improve GBAS availability by adding external information to operation. [1] Lee, J., S. Pullen, S. Datta-Barua, and P. Enge (2007), Assessment of ionosphere spatial decorrelation for GPS-based aircraft landing systems, J. Aircraft, 44(5), 1662-1669, doi:10.2514/1.28199. [2] Jung, S., and J. Lee (2012), Long-term ionospheric anomaly monitoring for ground based augmentation systems, Radio Sci., 47, RS4006, doi:10.1029/2012RS005016.

Co-occurrence correlations of heavy metals in sediments revealed using network analysis.

PubMed

Liu, Lili; Wang, Zhiping; Ju, Feng; Zhang, Tong

2015-01-01

In this study, the correlation-based study was used to identify the co-occurrence correlations among metals in marine sediment of Hong Kong, based on the long-term (from 1991 to 2011) temporal and spatial monitoring data. 14 stations out of the total 45 marine sediment monitoring stations were selected from three representative areas, including Deep Bay, Victoria Harbour and Mirs Bay. Firstly, Spearman's rank correlation-based network analysis was conducted as the first step to identify the co-occurrence correlations of metals from raw metadata, and then for further analysis using the normalized metadata. The correlations patterns obtained by network were consistent with those obtained by the other statistic normalization methods, including annual ratios, R-squared coefficient and Pearson correlation coefficient. Both Deep Bay and Victoria Harbour have been polluted by heavy metals, especially for Pb and Cu, which showed strong co-occurrence with other heavy metals (e.g. Cr, Ni, Zn and etc.) and little correlations with the reference parameters (Fe or Al). For Mirs Bay, which has better marine sediment quality compared with Deep Bay and Victoria Harbour, the co-occurrence patterns revealed by network analysis indicated that the metals in sediment dominantly followed the natural geography process. Besides the wide applications in biology, sociology and informatics, it is the first time to apply network analysis in the researches of environment pollutions. This study demonstrated its powerful application for revealing the co-occurrence correlations among heavy metals in marine sediments, which could be further applied for other pollutants in various environment systems. Copyright © 2014 Elsevier Ltd. All rights reserved.

Automatic Classification of Extensive Aftershock Sequences Using Empirical Matched Field Processing

NASA Astrophysics Data System (ADS)

Gibbons, Steven J.; Harris, David B.; Kværna, Tormod; Dodge, Douglas A.

2013-04-01

The aftershock sequences that follow large earthquakes create considerable problems for data centers attempting to produce comprehensive event bulletins in near real-time. The greatly increased number of events which require processing can overwhelm analyst resources and reduce the capacity for analyzing events of monitoring interest. This exacerbates a potentially reduced detection capability at key stations, due the noise generated by the sequence, and a deterioration in the quality of the fully automatic preliminary event bulletins caused by the difficulty in associating the vast numbers of closely spaced arrivals over the network. Considerable success has been enjoyed by waveform correlation methods for the automatic identification of groups of events belonging to the same geographical source region, facilitating the more time-efficient analysis of event ensembles as opposed to individual events. There are, however, formidable challenges associated with the automation of correlation procedures. The signal generated by a very large earthquake seldom correlates well enough with the signals generated by far smaller aftershocks for a correlation detector to produce statistically significant triggers at the correct times. Correlation between events within clusters of aftershocks is significantly better, although the issues of when and how to initiate new pattern detectors are still being investigated. Empirical Matched Field Processing (EMFP) is a highly promising method for detecting event waveforms suitable as templates for correlation detectors. EMFP is a quasi-frequency-domain technique that calibrates the spatial structure of a wavefront crossing a seismic array in a collection of narrow frequency bands. The amplitude and phase weights that result are applied in a frequency-domain beamforming operation that compensates for scattering and refraction effects not properly modeled by plane-wave beams. It has been demonstrated to outperform waveform correlation as a classifier of ripple-fired mining blasts since the narrowband procedure is insensitive to differences in the source-time functions. For sequences in which the spectral content and time-histories of the signals from the main shock and aftershocks vary greatly, the spatial structure calibrated by EMFP is an invariant that permits reliable detection of events in the specific source region. Examples from the 2005 Kashmir and 2011 Van earthquakes demonstrate how EMFP templates from the main events detect arrivals from the aftershock sequences with high sensitivity and exceptionally low false alarm rates. Classical waveform correlation detectors are demonstrated to fail for these examples. Even arrivals with SNR below unity can produce significant EMFP triggers as the spatial pattern of the incoming wavefront is identified, leading to robust detections at a greater number of stations and potentially more reliable automatic bulletins. False EMFP triggers are readily screened by scanning a space of phase shifts relative to the imposed template. EMFP has the potential to produce a rapid and robust overview of the evolving aftershock sequence such that correlation and subspace detectors can be applied semi-autonomously, with well-chosen parameter specifications, to identify and classify clusters of very closely spaced aftershocks.

Spatial correlation in precipitation trends in the Brazilian Amazon

NASA Astrophysics Data System (ADS)

Buarque, Diogo Costa; Clarke, Robin T.; Mendes, Carlos Andre Bulhoes

2010-06-01

A geostatistical analysis of variables derived from Amazon daily precipitation records (trends in annual precipitation totals, trends in annual maximum precipitation accumulated over 1-5 days, trend in length of dry spell, trend in number of wet days per year) gave results that are consistent with those previously reported. Averaged over the Brazilian Amazon region as a whole, trends in annual maximum precipitations were slightly negative, the trend in the length of dry spell was slightly positive, and the trend in the number of wet days in the year was slightly negative. For trends in annual maximum precipitation accumulated over 1-5 days, spatial correlation between trends was found to extend up to a distance equivalent to at least half a degree of latitude or longitude, with some evidence of anisotropic correlation. Time trends in annual precipitation were found to be spatially correlated up to at least ten degrees of separation, in both W-E and S-N directions. Anisotropic spatial correlation was strongly evident in time trends in length of dry spell with much stronger evidence of spatial correlation in the W-E direction, extending up to at least five degrees of separation, than in the S-N. Because the time trends analyzed are shown to be spatially correlated, it is argued that methods at present widely used to test the statistical significance of climate trends over time lead to erroneous conclusions if spatial correlation is ignored, because records from different sites are assumed to be statistically independent.

Improving Geoscience Students' Spatial Thinking Skills: Applying Cognitive Science Research in the Classroom

NASA Astrophysics Data System (ADS)

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

2011-12-01

Spatial thinking skills are critical to success in many subdisciplines of the geosciences (and beyond). There are many components of spatial thinking, such as mental rotation, penetrative visualization, disembedding, perspective taking, and navigation. Undergraduate students in introductory and upper-level geoscience courses bring a wide variety of spatial skill levels to the classroom, as measured by psychometric tests of many of these components of spatial thinking. Furthermore, it is not unusual for individual students to excel in some of these areas while struggling in others. Although pre- and post-test comparisons show that student skill levels typically improve over the course of an academic term, average gains are quite modest. This suggests that it may be valuable to develop interventions to help undergraduate students develop a range of spatial skills that can be used to solve geoscience problems. Cognitive science research suggests a number of strong strategies for building students' spatial skills. Practice is essential, and time on task is correlated to improvement. Progressive alignment may be used to scaffold students' successes on simpler problems, allowing them to see how more complex problems are related to those they can solve. Gesturing has proven effective in moving younger students from incorrect problem-solving strategies to correct strategies in other disciplines. These principles can be used to design instructional materials to improve undergraduate geoscience students' spatial skills; we will present some examples of such materials.

A comparative analysis of two highly spatially resolved European atmospheric emission inventories

NASA Astrophysics Data System (ADS)

Ferreira, J.; Guevara, M.; Baldasano, J. M.; Tchepel, O.; Schaap, M.; Miranda, A. I.; Borrego, C.

2013-08-01

A reliable emissions inventory is highly important for air quality modelling applications, especially at regional or local scales, which require high resolutions. Consequently, higher resolution emission inventories have been developed that are suitable for regional air quality modelling. This research performs an inter-comparative analysis of different spatial disaggregation methodologies of atmospheric emission inventories. This study is based on two different European emission inventories with different spatial resolutions: 1) the EMEP (European Monitoring and Evaluation Programme) inventory and 2) an emission inventory developed by the TNO (Netherlands Organisation for Applied Scientific Research). These two emission inventories were converted into three distinct gridded emission datasets as follows: (i) the EMEP emission inventory was disaggregated by area (EMEParea) and (ii) following a more complex methodology (HERMES-DIS - High-Elective Resolution Modelling Emissions System - DISaggregation module) to understand and evaluate the influence of different disaggregation methods; and (iii) the TNO gridded emissions, which are based on different emission data sources and different disaggregation methods. A predefined common grid with a spatial resolution of 12 × 12 km2 was used to compare the three datasets spatially. The inter-comparative analysis was performed by source sector (SNAP - Selected Nomenclature for Air Pollution) with emission totals for selected pollutants. It included the computation of difference maps (to focus on the spatial variability of emission differences) and a linear regression analysis to calculate the coefficients of determination and to quantitatively measure differences. From the spatial analysis, greater differences were found for residential/commercial combustion (SNAP02), solvent use (SNAP06) and road transport (SNAP07). These findings were related to the different spatial disaggregation that was conducted by the TNO and HERMES-DIS for the first two sectors and to the distinct data sources that were used by the TNO and HERMES-DIS for road transport. Regarding the regression analysis, the greatest correlation occurred between the EMEParea and HERMES-DIS because the latter is derived from the first, which does not occur for the TNO emissions. The greatest correlations were encountered for agriculture NH3 emissions, due to the common use of the CORINE Land Cover database for disaggregation. The point source emissions (energy industries, industrial processes, industrial combustion and extraction/distribution of fossil fuels) resulted in the lowest coefficients of determination. The spatial variability of SOx differed among the emissions that were obtained from the different disaggregation methods. In conclusion, HERMES-DIS and TNO are two distinct emission inventories, both very well discretized and detailed, suitable for air quality modelling. However, the different databases and distinct disaggregation methodologies that were used certainly result in different spatial emission patterns. This fact should be considered when applying regional atmospheric chemical transport models. Future work will focus on the evaluation of air quality models performance and sensitivity to these spatial discrepancies in emission inventories. Air quality modelling will benefit from the availability of appropriate resolution, consistent and reliable emission inventories.

Exploiting the spatial locality of electron correlation within the parametric two-electron reduced-density-matrix method

NASA Astrophysics Data System (ADS)

DePrince, A. Eugene; Mazziotti, David A.

2010-01-01

The parametric variational two-electron reduced-density-matrix (2-RDM) method is applied to computing electronic correlation energies of medium-to-large molecular systems by exploiting the spatial locality of electron correlation within the framework of the cluster-in-molecule (CIM) approximation [S. Li et al., J. Comput. Chem. 23, 238 (2002); J. Chem. Phys. 125, 074109 (2006)]. The 2-RDMs of individual molecular fragments within a molecule are determined, and selected portions of these 2-RDMs are recombined to yield an accurate approximation to the correlation energy of the entire molecule. In addition to extending CIM to the parametric 2-RDM method, we (i) suggest a more systematic selection of atomic-orbital domains than that presented in previous CIM studies and (ii) generalize the CIM method for open-shell quantum systems. The resulting method is tested with a series of polyacetylene molecules, water clusters, and diazobenzene derivatives in minimal and nonminimal basis sets. Calculations show that the computational cost of the method scales linearly with system size. We also compute hydrogen-abstraction energies for a series of hydroxyurea derivatives. Abstraction of hydrogen from hydroxyurea is thought to be a key step in its treatment of sickle cell anemia; the design of hydroxyurea derivatives that oxidize more rapidly is one approach to devising more effective treatments.

Common mode error in Antarctic GPS coordinate time series on its effect on bedrock-uplift estimates

NASA Astrophysics Data System (ADS)

Liu, Bin; King, Matt; Dai, Wujiao

2018-05-01

Spatially-correlated common mode error always exists in regional, or-larger, GPS networks. We applied independent component analysis (ICA) to GPS vertical coordinate time series in Antarctica from 2010 to 2014 and made a comparison with the principal component analysis (PCA). Using PCA/ICA, the time series can be decomposed into a set of temporal components and their spatial responses. We assume the components with common spatial responses are common mode error (CME). An average reduction of ˜40% about the RMS values was achieved in both PCA and ICA filtering. However, the common mode components obtained from the two approaches have different spatial and temporal features. ICA time series present interesting correlations with modeled atmospheric and non-tidal ocean loading displacements. A white noise (WN) plus power law noise (PL) model was adopted in the GPS velocity estimation using maximum likelihood estimation (MLE) analysis, with ˜55% reduction of the velocity uncertainties after filtering using ICA. Meanwhile, spatiotemporal filtering reduces the amplitude of PL and periodic terms in the GPS time series. Finally, we compare the GPS uplift velocities, after correction for elastic effects, with recent models of glacial isostatic adjustment (GIA). The agreements of the GPS observed velocities and four GIA models are generally improved after the spatiotemporal filtering, with a mean reduction of ˜0.9 mm/yr of the WRMS values, possibly allowing for more confident separation of various GIA model predictions.

An insight on correlations between kinematic rupture parameters from dynamic ruptures on rough faults

NASA Astrophysics Data System (ADS)

Thingbijam, Kiran Kumar; Galis, Martin; Vyas, Jagdish; Mai, P. Martin

2017-04-01

We examine the spatial interdependence between kinematic parameters of earthquake rupture, which include slip, rise-time (total duration of slip), acceleration time (time-to-peak slip velocity), peak slip velocity, and rupture velocity. These parameters were inferred from dynamic rupture models obtained by simulating spontaneous rupture on faults with varying degree of surface-roughness. We observe that the correlations between these parameters are better described by non-linear correlations (that is, on logarithm-logarithm scale) than by linear correlations. Slip and rise-time are positively correlated while these two parameters do not correlate with acceleration time, peak slip velocity, and rupture velocity. On the other hand, peak slip velocity correlates positively with rupture velocity but negatively with acceleration time. Acceleration time correlates negatively with rupture velocity. However, the observed correlations could be due to weak heterogeneity of the slip distributions given by the dynamic models. Therefore, the observed correlations may apply only to those parts of rupture plane with weak slip heterogeneity if earthquake-rupture associate highly heterogeneous slip distributions. Our findings will help to improve pseudo-dynamic rupture generators for efficient broadband ground-motion simulations for seismic hazard studies.

Performance on a virtual reality angled laparoscope task correlates with spatial ability of trainees.

PubMed

Rosenthal, Rachel; Hamel, Christian; Oertli, Daniel; Demartines, Nicolas; Gantert, Walter A

2010-08-01

The aim of the present study was to investigate whether trainees' performance on a virtual reality angled laparoscope navigation task correlates with scores obtained on a validated conventional test of spatial ability. 56 participants of a surgery workshop performed an angled laparoscope navigation task on the Xitact LS 500 virtual reality Simulator. Performance parameters were correlated with the score of a validated paper-and-pencil test of spatial ability. Performance at the conventional spatial ability test significantly correlated with performance at the virtual reality task for overall task score (p < 0.001), task completion time (p < 0.001) and economy of movement (p = 0.035), not for endoscope travel speed (p = 0.947). In conclusion, trainees' performance in a standardized virtual reality camera navigation task correlates with their innate spatial ability. This VR session holds potential to serve as an assessment tool for trainees.

Real-life memory and spatial navigation in patients with focal epilepsy: ecological validity of a virtual reality supermarket task.

PubMed

Grewe, P; Lahr, D; Kohsik, A; Dyck, E; Markowitsch, H J; Bien, C G; Botsch, M; Piefke, M

2014-02-01

Ecological assessment and training of real-life cognitive functions such as visual-spatial abilities in patients with epilepsy remain challenging. Some studies have applied virtual reality (VR) paradigms, but external validity of VR programs has not sufficiently been proven. Patients with focal epilepsy (EG, n=14) accomplished an 8-day program in a VR supermarket, which consisted of learning and buying items on a shopping list. Performance of the EG was compared with that of healthy controls (HCG, n=19). A comprehensive neuropsychological examination was administered. Real-life performance was investigated in a real supermarket. Learning in the VR supermarket was significantly impaired in the EG on different VR measures. Delayed free recall of products did not differ between the EG and the HCG. Virtual reality scores were correlated with neuropsychological measures of visual-spatial cognition, subjective estimates of memory, and performance in the real supermarket. The data indicate that our VR approach allows for the assessment of real-life visual-spatial memory and cognition in patients with focal epilepsy. The multimodal, active, and complex VR paradigm may particularly enhance visual-spatial cognitive resources. Copyright © 2013 Elsevier Inc. All rights reserved.

Creating a spatial multi-criteria decision support system for energy related integrated environmental impact assessment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wanderer, Thomas, E-mail: thomas.wanderer@dlr.de; Herle, Stefan, E-mail: stefan.herle@rwth-aachen.de

2015-04-15

By their spatially very distributed nature, profitability and impacts of renewable energy resources are highly correlated with the geographic locations of power plant deployments. A web-based Spatial Decision Support System (SDSS) based on a Multi-Criteria Decision Analysis (MCDA) approach has been implemented for identifying preferable locations for solar power plants based on user preferences. The designated areas found serve for the input scenario development for a subsequent integrated Environmental Impact Assessment. The capabilities of the SDSS service get showcased for Concentrated Solar Power (CSP) plants in the region of Andalusia, Spain. The resulting spatial patterns of possible power plant sitesmore » are an important input to the procedural chain of assessing impacts of renewable energies in an integrated effort. The applied methodology and the implemented SDSS are applicable for other renewable technologies as well. - Highlights: • The proposed tool facilitates well-founded CSP plant siting decisions. • Spatial MCDA methods are implemented in a WebGIS environment. • GIS-based SDSS can contribute to a modern integrated impact assessment workflow. • The conducted case study proves the suitability of the methodology.« less

Teacher spatial skills are linked to differences in geometry instruction.

PubMed

Otumfuor, Beryl Ann; Carr, Martha

2017-12-01

Spatial skills have been linked to better performance in mathematics. The purpose of this study was to examine the relationship between teacher spatial skills and their instruction, including teacher content and pedagogical knowledge, use of pictorial representations, and use of gestures during geometry instruction. Fifty-six middle school teachers participated in the study. The teachers were administered spatial measures of mental rotations and spatial visualization. Next, a single geometry class was videotaped. Correlational analyses revealed that spatial skills significantly correlate with teacher's use of representational gestures and content and pedagogical knowledge during instruction of geometry. Spatial skills did not independently correlate with the use of pointing gestures or the use of pictorial representations. However, an interaction term between spatial skills and content and pedagogical knowledge did correlate significantly with the use of pictorial representations. Teacher experience as measured by the number of years of teaching and highest degree did not appear to affect the relationships among the variables with the exception of the relationship between spatial skills and teacher content and pedagogical knowledge. Teachers with better spatial skills are also likely to use representational gestures and to show better content and pedagogical knowledge during instruction. Spatial skills predict pictorial representation use only as a function of content and pedagogical knowledge. © 2017 The British Psychological Society.

Value-at-Risk forecasts by a spatiotemporal model in Chinese stock market

NASA Astrophysics Data System (ADS)

Gong, Pu; Weng, Yingliang

2016-01-01

This paper generalizes a recently proposed spatial autoregressive model and introduces a spatiotemporal model for forecasting stock returns. We support the view that stock returns are affected not only by the absolute values of factors such as firm size, book-to-market ratio and momentum but also by the relative values of factors like trading volume ranking and market capitalization ranking in each period. This article studies a new method for constructing stocks' reference groups; the method is called quartile method. Applying the method empirically to the Shanghai Stock Exchange 50 Index, we compare the daily volatility forecasting performance and the out-of-sample forecasting performance of Value-at-Risk (VaR) estimated by different models. The empirical results show that the spatiotemporal model performs surprisingly well in terms of capturing spatial dependences among individual stocks, and it produces more accurate VaR forecasts than the other three models introduced in the previous literature. Moreover, the findings indicate that both allowing for serial correlation in the disturbances and using time-varying spatial weight matrices can greatly improve the predictive accuracy of a spatial autoregressive model.

[Spatial point patterns of Antarctic krill fishery in the northern Antarctic Peninsula].

PubMed

Yang, Xiao Ming; Li, Yi Xin; Zhu, Guo Ping

2016-12-01

As a key species in the Antarctic ecosystem, the spatial distribution of Antarctic krill (thereafter krill) often tends to present aggregation characteristics, which therefore reflects the spatial patterns of krill fishing operation. Based on the fishing data collected from Chinese krill fishing vessels, of which vessel A was professional krill fishing vessel and Vessel B was a fishing vessel which shifted between Chilean jack mackerel (Trachurus murphyi) fishing ground and krill fishing ground. In order to explore the characteristics of spatial distribution pattern and their ecological effects of two obvious different fishing fleets under a high and low nominal catch per unit effort (CPUE), from the viewpoint of spatial point pattern, the present study analyzed the spatial distribution characteristics of krill fishery in the northern Antarctic Peninsula from three aspects: (1) the two vessels' point pattern characteristics of higher CPUEs and lower CPUEs at different scales; (2) correlation of the bivariate point patterns between these points of higher CPUE and lower CPUE; and (3) correlation patterns of CPUE. Under the analysis derived from the Ripley's L function and mark correlation function, the results showed that the point patterns of the higher/lo-wer catch available were similar, both showing an aggregation distribution in this study windows at all scale levels. The aggregation intensity of krill fishing was nearly maximum at 15 km spatial scale, and kept stably higher values at the scale of 15-50 km. The aggregation intensity of krill fishery point patterns could be described in order as higher CPUE of vessel A > lower CPUE of vessel B >higher CPUE of vessel B > higher CPUE of vessel B. The relationship of the higher and lo-wer CPUEs of vessel A showed positive correlation at the spatial scale of 0-75 km, and presented stochastic relationship after 75 km scale, whereas vessel B showed positive correlation at all spatial scales. The point events of higher and lower CPUEs were synchronized, showing significant correlations at most of spatial scales because of the dynamics nature and complex of krill aggregation patterns. The distribution of vessel A's CPUEs was positively correlated at scales of 0-44 km, but negatively correlated at the scales of 44-80 km. The distribution of vessel B's CPUEs was negatively correlated at the scales of 50-70 km, but no significant correlations were found at other scales. The CPUE mark point patterns showed a negative correlation, which indicated that intraspecific competition for space and prey was significant. There were significant differences in spatial point pattern distribution between vessel A with higher fishing capacity and vessel B with lower fishing capacity. The results showed that the professional krill fishing vessel is suitable to conduct the analysis of spatial point pattern and scientific fishery survey.

Spatio-temporal Reconstruction of Neural Sources Using Indirect Dominant Mode Rejection.

PubMed

Jafadideh, Alireza Talesh; Asl, Babak Mohammadzadeh

2018-04-27

Adaptive minimum variance based beamformers (MVB) have been successfully applied to magnetoencephalogram (MEG) and electroencephalogram (EEG) data to localize brain activities. However, the performance of these beamformers falls down in situations where correlated or interference sources exist. To overcome this problem, we propose indirect dominant mode rejection (iDMR) beamformer application in brain source localization. This method by modifying measurement covariance matrix makes MVB applicable in source localization in the presence of correlated and interference sources. Numerical results on both EEG and MEG data demonstrate that presented approach accurately reconstructs time courses of active sources and localizes those sources with high spatial resolution. In addition, the results of real AEF data show the good performance of iDMR in empirical situations. Hence, iDMR can be reliably used for brain source localization especially when there are correlated and interference sources.

One-way EPR steering and genuine multipartite EPR steering

NASA Astrophysics Data System (ADS)

He, Qiongyi; Reid, Margaret D.

2012-11-01

We propose criteria and experimental strategies to realise the Einstein-Podolsky-Rosen (EPR) steering nonlocality. One-way steering can be obtained where there is asymmetry of thermal noise on each system. We also present EPR steering inequalities that act as signatures and suggest how to optimise EPR correlations in specific schemes so that the genuine multipartite EPR steering nonlocality (EPR paradox) can also possibly be realised. The results presented here also apply to the spatially separated macroscopic atomic ensembles.

Acoustic Observation of the Time Dependence of the Roughness of Sandy Seafloors

DTIC Science & Technology

2009-11-25

relations between acoustic and roughness temporal correlations are developed and applied. Manuscript received April 23, 2007; revised June 04. 2008 and...Fourier transform of the relief function as follows: (F(K2, t2)F*(Klt h)) = W(KU tu t2)6{Ki - K2) . (6) The presence of the Dirac delta function is only...appropriate if /(R, t) is stationary with infinite extent in the spatial coordi- nates. As a result of the windowing assumed here, the delta func

Simulating Non-Fickian Transport across Péclet Regimes by doing Lévy Flights in the Rank Space of Velocity

NASA Astrophysics Data System (ADS)

Most, S.; Dentz, M.; Bolster, D.; Bijeljic, B.; Nowak, W.

2017-12-01

Transport in real porous media shows non-Fickian characteristics. In the Lagrangian perspective this leads to skewed distributions of particle arrival times. The skewness is triggered by particles' memory of velocity that persists over a characteristic length. Capturing process memory is essential to represent non-Fickianity thoroughly. Classical non-Fickian models (e.g., CTRW models) simulate the effects of memory but not the mechanisms leading to process memory. CTRWs have been applied successfully in many studies but nonetheless they have drawbacks. In classical CTRWs each particle makes a spatial transition for which each particle adapts a random transit time. Consecutive transit times are drawn independently from each other, and this is only valid for sufficiently large spatial transitions. If we want to apply a finer numerical resolution than that, we have to implement memory into the simulation. Recent CTRW methods use transitions matrices to simulate correlated transit times. However, deriving such transition matrices require transport data of a fine-scale transport simulation, and the obtained transition matrix is solely valid for this single Péclet regime. The CTRW method we propose overcomes all three drawbacks: 1) We simulate transport without restrictions in transition length. 2) We parameterize our CTRW without requiring a transport simulation. 3) Our parameterization scales across Péclet regimes. We do so by sampling the pore-scale velocity distribution to generate correlated transit times as a Lévy flight on the CDF-axis of velocities with reflection at 0 and 1. The Lévy flight is parametrized only by the correlation length. We explicitly model memory including the evolution and decay of non-Fickianity, so it extends from local via pre-asymptotic to asymptotic scales.

Correlation time and diffusion coefficient imaging: application to a granular flow system.

PubMed

Caprihan, A; Seymour, J D

2000-05-01

A parametric method for spatially resolved measurements for velocity autocorrelation functions, R(u)(tau) = , expressed as a sum of exponentials, is presented. The method is applied to a granular flow system of 2-mm oil-filled spheres rotated in a half-filled horizontal cylinder, which is an Ornstein-Uhlenbeck process with velocity autocorrelation function R(u)(tau) = e(- ||tau ||/tau(c)), where tau(c) is the correlation time and D = tau(c) is the diffusion coefficient. The pulsed-field-gradient NMR method consists of applying three different gradient pulse sequences of varying motion sensitivity to distinguish the range of correlation times present for particle motion. Time-dependent apparent diffusion coefficients are measured for these three sequences and tau(c) and D are then calculated from the apparent diffusion coefficient images. For the cylinder rotation rate of 2.3 rad/s, the axial diffusion coefficient at the top center of the free surface was 5.5 x 10(-6) m(2)/s, the correlation time was 3 ms, and the velocity fluctuation or granular temperature was 1.8 x 10(-3) m(2)/s(2). This method is also applicable to study transport in systems involving turbulence and porous media flows. Copyright 2000 Academic Press.

Spatiotemporal Filtering Using Principal Component Analysis and Karhunen-Loeve Expansion Approaches for Regional GPS Network Analysis

NASA Technical Reports Server (NTRS)

Dong, D.; Fang, P.; Bock, F.; Webb, F.; Prawirondirdjo, L.; Kedar, S.; Jamason, P.

2006-01-01

Spatial filtering is an effective way to improve the precision of coordinate time series for regional GPS networks by reducing so-called common mode errors, thereby providing better resolution for detecting weak or transient deformation signals. The commonly used approach to regional filtering assumes that the common mode error is spatially uniform, which is a good approximation for networks of hundreds of kilometers extent, but breaks down as the spatial extent increases. A more rigorous approach should remove the assumption of spatially uniform distribution and let the data themselves reveal the spatial distribution of the common mode error. The principal component analysis (PCA) and the Karhunen-Loeve expansion (KLE) both decompose network time series into a set of temporally varying modes and their spatial responses. Therefore they provide a mathematical framework to perform spatiotemporal filtering.We apply the combination of PCA and KLE to daily station coordinate time series of the Southern California Integrated GPS Network (SCIGN) for the period 2000 to 2004. We demonstrate that spatially and temporally correlated common mode errors are the dominant error source in daily GPS solutions. The spatial characteristics of the common mode errors are close to uniform for all east, north, and vertical components, which implies a very long wavelength source for the common mode errors, compared to the spatial extent of the GPS network in southern California. Furthermore, the common mode errors exhibit temporally nonrandom patterns.

Spatial variation in the bacterial and denitrifying bacterial community in a biofilter treating subsurface agricultural drainage.

PubMed

Andrus, J Malia; Porter, Matthew D; Rodríguez, Luis F; Kuehlhorn, Timothy; Cooke, Richard A C; Zhang, Yuanhui; Kent, Angela D; Zilles, Julie L

2014-02-01

Denitrifying biofilters can remove agricultural nitrates from subsurface drainage, reducing nitrate pollution that contributes to coastal hypoxic zones. The performance and reliability of natural and engineered systems dependent upon microbially mediated processes, such as the denitrifying biofilters, can be affected by the spatial structure of their microbial communities. Furthermore, our understanding of the relationship between microbial community composition and function is influenced by the spatial distribution of samples.In this study we characterized the spatial structure of bacterial communities in a denitrifying biofilter in central Illinois. Bacterial communities were assessed using automated ribosomal intergenic spacer analysis for bacteria and terminal restriction fragment length polymorphism of nosZ for denitrifying bacteria.Non-metric multidimensional scaling and analysis of similarity (ANOSIM) analyses indicated that bacteria showed statistically significant spatial structure by depth and transect,while denitrifying bacteria did not exhibit significant spatial structure. For determination of spatial patterns, we developed a package of automated functions for the R statistical environment that allows directional analysis of microbial community composition data using either ANOSIM or Mantel statistics.Applying this package to the biofilter data, the flow path correlation range for the bacterial community was 6.4 m at the shallower, periodically in undated depth and 10.7 m at the deeper, continually submerged depth. These spatial structures suggest a strong influence of hydrology on the microbial community composition in these denitrifying biofilters. Understanding such spatial structure can also guide optimal sample collection strategies for microbial community analyses.

Coexistence of species with different dispersal across landscapes: a critical role of spatial correlation in disturbance.

PubMed

Liao, Jinbao; Ying, Zhixia; Woolnough, Daelyn A; Miller, Adam D; Li, Zhenqing; Nijs, Ivan

2016-05-11

Disturbance is key to maintaining species diversity in plant communities. Although the effects of disturbance frequency and extent on species diversity have been studied, we do not yet have a mechanistic understanding of how these aspects of disturbance interact with spatial structure of disturbance to influence species diversity. Here we derive a novel pair approximation model to explore competitive outcomes in a two-species system subject to spatially correlated disturbance. Generally, spatial correlation in disturbance favoured long-range dispersers, while distance-limited dispersers were greatly suppressed. Interestingly, high levels of spatial aggregation of disturbance promoted long-term species coexistence that is not possible in the absence of disturbance, but only when the local disperser was intrinsically competitively superior. However, spatial correlation in disturbance led to different competitive outcomes, depending on the disturbed area. Concerning ecological conservation and management, we theoretically demonstrate that introducing a spatially correlated disturbance to the system or altering an existing disturbance regime can be a useful strategy either to control species invasion or to promote species coexistence. Disturbance pattern analysis may therefore provide new insights into biodiversity conservation. © 2016 The Author(s).

Effects of Spatial Variability of Soil Properties on the Triggering of Rainfall-Induced Shallow Landslides

NASA Astrophysics Data System (ADS)

Fan, Linfeng; Lehmann, Peter; Or, Dani

2015-04-01

Naturally-occurring spatial variations in soil properties (e.g., soil depth, moisture, and texture) affect key hydrological processes and potentially the mechanical response of soil to hydromechanical loading (relative to the commonly-assumed uniform soil mantle). We quantified the effects of soil spatial variability on the triggering of rainfall-induced shallow landslides at the hillslope- and catchment-scales, using a physically-based landslide triggering model that considers interacting soil columns with mechanical strength thresholds (represented by the Fiber Bundle Model). The spatial variations in soil properties are represented as Gaussian random distributions and the level of variation is characterized by the coefficient of variation and correlation lengths of soil properties (i.e., soil depth, soil texture and initial water content in this study). The impacts of these spatial variations on landslide triggering characteristics were measured by comparing the times to triggering and landslide volumes for heterogeneous soil properties and homogeneous cases. Results at hillslope scale indicate that for spatial variations of an individual property (without cross correlation), the increasing of coefficient of variation introduces weak spots where mechanical damage is accelerated and leads to earlier onset of landslide triggering and smaller volumes. Increasing spatial correlation length of soil texture and initial water content also induces early landslide triggering and small released volumes due to the transition of failure mode from brittle to ductile failure. In contrast, increasing spatial correlation length of soil depth "reduces" local steepness and postpones landslide triggering. Cross-correlated soil properties generally promote landslide initiation, but depending on the internal structure of spatial distribution of each soil property, landslide triggering may be reduced. The effects of cross-correlation between initial water content and soil texture were investigated in detail at the catchment scale by incorporating correlations of both variables with topography. Results indicate that the internal structure of the spatial distribution of each soil property together with their interplays determine the overall performance of the coupled spatial variability. This study emphasizes the importance of both the randomness and spatial structure of soil properties on landslide triggering and characteristics.

Pair correlation functions for identifying spatial correlation in discrete domains

NASA Astrophysics Data System (ADS)

Gavagnin, Enrico; Owen, Jennifer P.; Yates, Christian A.

2018-06-01

Identifying and quantifying spatial correlation are important aspects of studying the collective behavior of multiagent systems. Pair correlation functions (PCFs) are powerful statistical tools that can provide qualitative and quantitative information about correlation between pairs of agents. Despite the numerous PCFs defined for off-lattice domains, only a few recent studies have considered a PCF for discrete domains. Our work extends the study of spatial correlation in discrete domains by defining a new set of PCFs using two natural and intuitive definitions of distance for a square lattice: the taxicab and uniform metric. We show how these PCFs improve upon previous attempts and compare between the quantitative data acquired. We also extend our definitions of the PCF to other types of regular tessellation that have not been studied before, including hexagonal, triangular, and cuboidal. Finally, we provide a comprehensive PCF for any tessellation and metric, allowing investigation of spatial correlation in irregular lattices for which recognizing correlation is less intuitive.

Mapping brain activity in gradient-echo functional MRI using principal component analysis

NASA Astrophysics Data System (ADS)

Khosla, Deepak; Singh, Manbir; Don, Manuel

1997-05-01

The detection of sites of brain activation in functional MRI has been a topic of immense research interest and many technique shave been proposed to this end. Recently, principal component analysis (PCA) has been applied to extract the activated regions and their time course of activation. This method is based on the assumption that the activation is orthogonal to other signal variations such as brain motion, physiological oscillations and other uncorrelated noises. A distinct advantage of this method is that it does not require any knowledge of the time course of the true stimulus paradigm. This technique is well suited to EPI image sequences where the sampling rate is high enough to capture the effects of physiological oscillations. In this work, we propose and apply tow methods that are based on PCA to conventional gradient-echo images and investigate their usefulness as tools to extract reliable information on brain activation. The first method is a conventional technique where a single image sequence with alternating on and off stages is subject to a principal component analysis. The second method is a PCA-based approach called the common spatial factor analysis technique (CSF). As the name suggests, this method relies on common spatial factors between the above fMRI image sequence and a background fMRI. We have applied these methods to identify active brain ares during visual stimulation and motor tasks. The results from these methods are compared to those obtained by using the standard cross-correlation technique. We found good agreement in the areas identified as active across all three techniques. The results suggest that PCA and CSF methods have good potential in detecting the true stimulus correlated changes in the presence of other interfering signals.

Correlates of county-level nonviral sexually transmitted infection hot spots in the US: application of hot spot analysis and spatial logistic regression.

PubMed

Chang, Brian A; Pearson, William S; Owusu-Edusei, Kwame

2017-04-01

We used a combination of hot spot analysis (HSA) and spatial regression to examine county-level hot spot correlates for the most commonly reported nonviral sexually transmitted infections (STIs) in the 48 contiguous states in the United States (US). We obtained reported county-level total case rates of chlamydia, gonorrhea, and primary and secondary (P&S) syphilis in all counties in the 48 contiguous states from national surveillance data and computed temporally smoothed rates using 2008-2012 data. Covariates were obtained from county-level multiyear (2008-2012) American Community Surveys from the US census. We conducted HSA to identify hot spot counties for all three STIs. We then applied spatial logistic regression with the spatial error model to determine the association between the identified hot spots and the covariates. HSA indicated that ≥84% of hot spots for each STI were in the South. Spatial regression results indicated that, a 10-unit increase in the percentage of Black non-Hispanics was associated with ≈42% (P < 0.01) [≈22% (P < 0.01), for Hispanics] increase in the odds of being a hot spot county for chlamydia and gonorrhea, and ≈27% (P < 0.01) [≈11% (P < 0.01) for Hispanics] for P&S syphilis. Compared with the other regions (West, Midwest, and Northeast), counties in the South were 6.5 (P < 0.01; chlamydia), 9.6 (P < 0.01; gonorrhea), and 4.7 (P < 0.01; P&S syphilis) times more likely to be hot spots. Our study provides important information on hot spot clusters of nonviral STIs in the entire United States, including associations between hot spot counties and sociodemographic factors. Published by Elsevier Inc.

Stochastic Evolution of Augmented Born-Infeld Equations

NASA Astrophysics Data System (ADS)

Holm, Darryl D.

2018-06-01

This paper compares the results of applying a recently developed method of stochastic uncertainty quantification designed for fluid dynamics to the Born-Infeld model of nonlinear electromagnetism. The similarities in the results are striking. Namely, the introduction of Stratonovich cylindrical noise into each of their Hamiltonian formulations introduces stochastic Lie transport into their dynamics in the same form for both theories. Moreover, the resulting stochastic partial differential equations retain their unperturbed form, except for an additional term representing induced Lie transport by the set of divergence-free vector fields associated with the spatial correlations of the cylindrical noise. The explanation for this remarkable similarity lies in the method of construction of the Hamiltonian for the Stratonovich stochastic contribution to the motion in both cases, which is done via pairing spatial correlation eigenvectors for cylindrical noise with the momentum map for the deterministic motion. This momentum map is responsible for the well-known analogy between hydrodynamics and electromagnetism. The momentum map for the Maxwell and Born-Infeld theories of electromagnetism treated here is the 1-form density known as the Poynting vector. Two appendices treat the Hamiltonian structures underlying these results.

Geography of Genetic Structure in Barley Wild Relative Hordeum vulgare subsp. spontaneum in Jordan.

PubMed

Thormann, Imke; Reeves, Patrick; Reilley, Ann; Engels, Johannes M M; Lohwasser, Ulrike; Börner, Andreas; Pillen, Klaus; Richards, Christopher M

2016-01-01

Informed collecting, conservation, monitoring and utilization of genetic diversity requires knowledge of the distribution and structure of the variation occurring in a species. Hordeum vulgare subsp. spontaneum (K. Koch) Thell., a primary wild relative of barley, is an important source of genetic diversity for barley improvement and co-occurs with the domesticate within the center of origin. We studied the current distribution of genetic diversity and population structure in H. vulgare subsp. spontaneum in Jordan and investigated whether it is correlated with either spatial or climatic variation inferred from publically available climate layers commonly used in conservation and ecogeographical studies. The genetic structure of 32 populations collected in 2012 was analyzed with 37 SSRs. Three distinct genetic clusters were identified. Populations were characterized by admixture and high allelic richness, and genetic diversity was concentrated in the northern part of the study area. Genetic structure, spatial location and climate were not correlated. This may point out a limitation in using large scale climatic data layers to predict genetic diversity, especially as it is applied to regional genetic resources collections in H. vulgare subsp. spontaneum.

Geography of Genetic Structure in Barley Wild Relative Hordeum vulgare subsp. spontaneum in Jordan

PubMed Central

Reeves, Patrick; Reilley, Ann; Engels, Johannes M. M.; Lohwasser, Ulrike; Börner, Andreas; Pillen, Klaus; Richards, Christopher M.

2016-01-01

Informed collecting, conservation, monitoring and utilization of genetic diversity requires knowledge of the distribution and structure of the variation occurring in a species. Hordeum vulgare subsp. spontaneum (K. Koch) Thell., a primary wild relative of barley, is an important source of genetic diversity for barley improvement and co-occurs with the domesticate within the center of origin. We studied the current distribution of genetic diversity and population structure in H. vulgare subsp. spontaneum in Jordan and investigated whether it is correlated with either spatial or climatic variation inferred from publically available climate layers commonly used in conservation and ecogeographical studies. The genetic structure of 32 populations collected in 2012 was analyzed with 37 SSRs. Three distinct genetic clusters were identified. Populations were characterized by admixture and high allelic richness, and genetic diversity was concentrated in the northern part of the study area. Genetic structure, spatial location and climate were not correlated. This may point out a limitation in using large scale climatic data layers to predict genetic diversity, especially as it is applied to regional genetic resources collections in H. vulgare subsp. spontaneum. PMID:27513459

Hierarchical relaxation methods for multispectral pixel classification as applied to target identification

NASA Astrophysics Data System (ADS)

Cohen, E. A., Jr.

1985-02-01

This report provides insights into the approaches toward image modeling as applied to target detection. The approach is that of examining the energy in prescribed wave-bands which emanate from a target and correlating the emissions. Typically, one might be looking at two or three infrared bands, possibly together with several visual bands. The target is segmented, using both first and second order modeling, into a set of interesting components and these components are correlated so as to enhance the classification process. A Markov-type model is used to provide an a priori assessment of the spatial relationships among critical parts of the target, and a stochastic model using the output of an initial probabilistic labeling is invoked. The tradeoff between this stochastic model and the Markov model is then optimized to yield a best labeling for identification purposes. In an identification of friend or foe (IFF) context, this methodology could be of interest, for it provides the ingredients for such a higher level of understanding.

Velocity-vorticity correlation structures (VVCS) in spatially developing compressible turbulent boundary layer

NASA Astrophysics Data System (ADS)

Li, Shi-Yao; She, Zhen-Su; Chen, Jun

2017-11-01

A velocity-vorticity correlation structure (VVCS) analysis is applied to the direct numerical simulation (DNS) of compressible turbulent boundary layer (CTBL) at Mach numbers, Ma = 2.25 , 4.50 and 6.0 . It is shown that the VVCS analysis captures the geometry variation in the streamwise direction during the transition and in the wall-normal direction in the fully developed regime. Specifically, before transition, the VVCS captures the instability wave number, while in the transition region it displays a distinct scaling change of the dimensions. The fully developed turbulence regime is characterized by a nearly constant spatial extension of the VVCS. Particularly, after turbulence is well developed, a multi-layer structure in the wall normal direction is observed in the maximum correlation coefficient and in the length scales of the VVCS, as expected from a recent symmetry-based theory, the ensemble structure dynamics (SED). The most interesting outcome is an observed linear dependence of the length scale of the VVCS from y+ 50 to 200, which is a direct support to Townsend's attached-eddy theory. In conclusion, the VVCS analysis quantifies the geometrical characteristics of the coherent structures in turbulent compressible shear flows throughout the whole domain. Supported by NSFC (11172006, 11221062, 11452002) and by MOST (China) 973 project (2009CB724100).

Characterization of the Spatial Structure of Local Functional Connectivity Using Multidistance Average Correlation Measures.

PubMed

Macià, Dídac; Pujol, Jesus; Blanco-Hinojo, Laura; Martínez-Vilavella, Gerard; Martín-Santos, Rocío; Deus, Joan

2018-06-01

There is ample evidence from basic research in neuroscience of the importance of local corticocortical networks. Millimetric resolution is achievable with current functional magnetic resonance imaging (fMRI) scanners and sequences, and consequently a number of "local" activity similarity measures have been defined to describe patterns of segregation and integration at this spatial scale. We have introduced the use of IsoDistant Average Correlation (IDAC), easily defined as the average fMRI temporal correlation of a given voxel with other voxels placed at increasingly separated isodistant intervals, to characterize the curve of local fMRI signal similarities. IDAC curves can be statistically compared using parametric multivariate statistics. Furthermore, by using red-green-blue color coding to display jointly IDAC values belonging to three different distance lags, IDAC curves can also be displayed as multidistance IDAC maps. We applied IDAC analysis to a sample of 41 subjects scanned under two different conditions, a resting state and an auditory-visual continuous stimulation. Multidistance IDAC mapping was able to discriminate between gross anatomofunctional cortical areas and, moreover, was sensitive to modulation between the two brain conditions in areas known to activate and deactivate during audiovisual tasks. Unlike previous fMRI local similarity measures already in use, our approach draws special attention to the continuous smooth pattern of local functional connectivity.

Concentrated Animal Feeding Operations, Row Crops and their Relationship to Nitrate in Eastern Iowa Rivers

PubMed Central

Weldon, Mark B.; Hornbuckle, Keri C.

2009-01-01

Concentrated animal feeding operations (CAFO) and fertilizer application to row crops may contribute to poor water quality in surface waters. To test this hypothesis, we evaluated nutrient concentrations and fluxes in four Eastern Iowa watersheds sampled between 1996-2004. We found that these watersheds contribute nearly 10% of annual nitrate flux entering the Gulf of Mexico, while representing only 1.5% of the contributing drainage basin. Mass budget analysis shows stream flow to be a major loss of nitrogen (18% of total N output), second only to crop harvest (63%). The major watershed inputs of nitrogen include applied fertilizer for corn (54% of total N input) and nitrogen fixation by soybeans (26%). Despite the relatively small input from animal manure (~5%), the results of spatial analysis indicate that row crop and CAFO densities are significantly and independently correlated to higher nitrate concentration in streams. Pearson correlation coefficients of 0.59 and 0.89 were found between nitrate concentration and row crop and CAFO density, respectively. Multiple linear regression analysis produced a correlation for nitrate concentration with an R2 value of 85%. High spatial density of row crops and CAFOs are linked to the highest river nitrate concentrations (up to 15 mg/l normalized over five years). PMID:16749677

Relationship between symptom dimensions and white matter alterations in obsessive-compulsive disorder.

PubMed

Yagi, Michiyo; Hirano, Yoshiyuki; Nakazato, Michiko; Nemoto, Kiyotaka; Ishikawa, Kazuhiro; Sutoh, Chihiro; Miyata, Haruko; Matsumoto, Junko; Matsumoto, Koji; Masuda, Yoshitada; Obata, Takayuki; Iyo, Masaomi; Shimizu, Eiji; Nakagawa, Akiko

2017-06-01

To investigate the relationship between the severities of symptom dimensions in obsessive-compulsive disorder (OCD) and white matter alterations. We applied tract-based spatial statistics for diffusion tensor imaging (DTI) acquired by 3T magnetic resonance imaging. First, we compared fractional anisotropy (FA) between 20 OCD patients and 30 healthy controls (HC). Then, applying whole brain analysis, we searched the brain regions showing correlations between the severities of symptom dimensions assessed by Obsessive-Compulsive Inventory-Revised and FA in all participants. Finally, we calculated the correlations between the six symptom dimensions and multiple DTI measures [FA, axial diffusivity (AD), radial diffusivity (RD), mean diffusivity (MD)] in a region-of-interest (ROI) analysis and explored the differences between OCD patients and HC. There were no between-group differences in FA or brain region correlations between the severities of symptom dimensions and FA in any of the participants. ROI analysis revealed negative correlations between checking severity and left inferior frontal gyrus white matter and left middle temporal gyrus white matter and a positive correlation between ordering severity and right precuneus in FA in OCD compared with HC. We also found negative correlations between ordering severity and right precuneus in RD, between obsessing severities and right supramarginal gyrus in AD and MD, and between hoarding severity and right insular gyrus in AD. Our study supported the hypothesis that the severities of respective symptom dimensions are associated with different patterns of white matter alterations.

Species extinction thresholds in the face of spatially correlated periodic disturbance.

PubMed

Liao, Jinbao; Ying, Zhixia; Hiebeler, David E; Wang, Yeqiao; Takada, Takenori; Nijs, Ivan

2015-10-20

The spatial correlation of disturbance is gaining attention in landscape ecology, but knowledge is still lacking on how species traits determine extinction thresholds under spatially correlated disturbance regimes. Here we develop a pair approximation model to explore species extinction risk in a lattice-structured landscape subject to aggregated periodic disturbance. Increasing disturbance extent and frequency accelerated population extinction irrespective of whether dispersal was local or global. Spatial correlation of disturbance likewise increased species extinction risk, but only for local dispersers. This indicates that models based on randomly simulated disturbances (e.g., mean-field or non-spatial models) may underestimate real extinction rates. Compared to local dispersal, species with global dispersal tolerated more severe disturbance, suggesting that the spatial correlation of disturbance favors long-range dispersal from an evolutionary perspective. Following disturbance, intraspecific competition greatly enhanced the extinction risk of distance-limited dispersers, while it surprisingly did not influence the extinction thresholds of global dispersers, apart from decreasing population density to some degree. As species respond differently to disturbance regimes with different spatiotemporal properties, different regimes may accommodate different species.

Cross correlation calculations and neutron scattering analysis for a portable solid state neutron detection system

NASA Astrophysics Data System (ADS)

Saltos, Andrea

In efforts to perform accurate dosimetry, Oakes et al. [Nucl. Intrum. Mehods. (2013)] introduced a new portable solid state neutron rem meter based on an adaptation of the Bonner sphere and the position sensitive long counter. The system utilizes high thermal efficiency neutron detectors to generate a linear combination of measurement signals that are used to estimate the incident neutron spectra. The inversion problem associated to deduce dose from the counts in individual detector elements is addressed by applying a cross-correlation method which allows estimation of dose with average errors less than 15%. In this work, an evaluation of the performance of this system was extended to take into account new correlation techniques and neutron scattering contribution. To test the effectiveness of correlations, the Distance correlation, Pearson Product-Moment correlation, and their weighted versions were performed between measured spatial detector responses obtained from nine different test spectra, and the spatial response of Library functions generated by MCNPX. Results indicate that there is no advantage of using the Distance Correlation over the Pearson Correlation, and that weighted versions of these correlations do not increase their performance in evaluating dose. Both correlations were proven to work well even at low integrated doses measured for short periods of time. To evaluate the contribution produced by room-return neutrons on the dosimeter response, MCNPX was used to simulate dosimeter responses for five isotropic neutron sources placed inside different sizes of rectangular concrete rooms. Results show that the contribution of scattered neutrons to the response of the dosimeter can be significant, so that for most cases the dose is over predicted with errors as large as 500%. A possible method to correct for the contribution of room-return neutrons is also assessed and can be used as a good initial estimate on how to approach the problem.

Interpolating Non-Parametric Distributions of Hourly Rainfall Intensities Using Random Mixing

NASA Astrophysics Data System (ADS)

Mosthaf, Tobias; Bárdossy, András; Hörning, Sebastian

2015-04-01

The correct spatial interpolation of hourly rainfall intensity distributions is of great importance for stochastical rainfall models. Poorly interpolated distributions may lead to over- or underestimation of rainfall and consequently to wrong estimates of following applications, like hydrological or hydraulic models. By analyzing the spatial relation of empirical rainfall distribution functions, a persistent order of the quantile values over a wide range of non-exceedance probabilities is observed. As the order remains similar, the interpolation weights of quantile values for one certain non-exceedance probability can be applied to the other probabilities. This assumption enables the use of kernel smoothed distribution functions for interpolation purposes. Comparing the order of hourly quantile values over different gauges with the order of their daily quantile values for equal probabilities, results in high correlations. The hourly quantile values also show high correlations with elevation. The incorporation of these two covariates into the interpolation is therefore tested. As only positive interpolation weights for the quantile values assure a monotonically increasing distribution function, the use of geostatistical methods like kriging is problematic. Employing kriging with external drift to incorporate secondary information is not applicable. Nonetheless, it would be fruitful to make use of covariates. To overcome this shortcoming, a new random mixing approach of spatial random fields is applied. Within the mixing process hourly quantile values are considered as equality constraints and correlations with elevation values are included as relationship constraints. To profit from the dependence of daily quantile values, distribution functions of daily gauges are used to set up lower equal and greater equal constraints at their locations. In this way the denser daily gauge network can be included in the interpolation of the hourly distribution functions. The applicability of this new interpolation procedure will be shown for around 250 hourly rainfall gauges in the German federal state of Baden-Württemberg. The performance of the random mixing technique within the interpolation is compared to applicable kriging methods. Additionally, the interpolation of kernel smoothed distribution functions is compared with the interpolation of fitted parametric distributions.

A computed tomography-based spatial normalization for the analysis of [18F] fluorodeoxyglucose positron emission tomography of the brain.

PubMed

Cho, Hanna; Kim, Jin Su; Choi, Jae Yong; Ryu, Young Hoon; Lyoo, Chul Hyoung

2014-01-01

We developed a new computed tomography (CT)-based spatial normalization method and CT template to demonstrate its usefulness in spatial normalization of positron emission tomography (PET) images with [(18)F] fluorodeoxyglucose (FDG) PET studies in healthy controls. Seventy healthy controls underwent brain CT scan (120 KeV, 180 mAs, and 3 mm of thickness) and [(18)F] FDG PET scans using a PET/CT scanner. T1-weighted magnetic resonance (MR) images were acquired for all subjects. By averaging skull-stripped and spatially-normalized MR and CT images, we created skull-stripped MR and CT templates for spatial normalization. The skull-stripped MR and CT images were spatially normalized to each structural template. PET images were spatially normalized by applying spatial transformation parameters to normalize skull-stripped MR and CT images. A conventional perfusion PET template was used for PET-based spatial normalization. Regional standardized uptake values (SUV) measured by overlaying the template volume of interest (VOI) were compared to those measured with FreeSurfer-generated VOI (FSVOI). All three spatial normalization methods underestimated regional SUV values by 0.3-20% compared to those measured with FSVOI. The CT-based method showed slightly greater underestimation bias. Regional SUV values derived from all three spatial normalization methods were correlated significantly (p < 0.0001) with those measured with FSVOI. CT-based spatial normalization may be an alternative method for structure-based spatial normalization of [(18)F] FDG PET when MR imaging is unavailable. Therefore, it is useful for PET/CT studies with various radiotracers whose uptake is expected to be limited to specific brain regions or highly variable within study population.

Feasibility study of imaging spectroscopy to monitor the quality of online welding.

PubMed

Mirapeix, Jesús; García-Allende, P Beatriz; Cobo, Adolfo; Conde, Olga M; López-Higuera, José M

2009-08-20

An online welding quality system based on the use of imaging spectroscopy is proposed and discussed. Plasma optical spectroscopy has already been successfully applied in this context by establishing a direct correlation between some spectroscopic parameters, e.g., the plasma electronic temperature and the resulting seam quality. Given that the use of the so-called hyperspectral devices provides both spatial and spectral information, we propose their use for the particular case of arc welding quality monitoring in an attempt to determine whether this technique would be suitable for this industrial situation. Experimental welding tests are presented, and the ability of the proposed solution to identify simulated defects is proved. Detailed spatial analyses suggest that this additional dimension can be used to improve the performance of the entire system.

Analyses and assessments of span wise gust gradient data from NASA B-57B aircraft

NASA Technical Reports Server (NTRS)

Frost, Walter; Chang, Ho-Pen; Ringnes, Erik A.

1987-01-01

Analysis of turbulence measured across the airfoil of a Cambera B-57 aircraft is reported. The aircraft is instrumented with probes for measuring wind at both wing tips and at the nose. Statistical properties of the turbulence are reported. These consist of the standard deviations of turbulence measured by each individual probe, standard deviations and probability distribution of differences in turbulence measured between probes and auto- and two-point spatial correlations and spectra. Procedures associated with calculations of two-point spatial correlations and spectra utilizing data were addressed. Methods and correction procedures for assuring the accuracy of aircraft measured winds are also described. Results are found, in general, to agree with correlations existing in the literature. The velocity spatial differences fit a Gaussian/Bessel type probability distribution. The turbulence agrees with the von Karman turbulence correlation and with two-point spatial correlations developed from the von Karman correlation.

Spatial-temporal clustering of tornadoes

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Spatial-Temporal Clustering of Tornadoes

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Measuring local volume fraction, long-wavelength correlations, and fractionation in a phase-separating polydisperse fluid

DOE Office of Scientific and Technical Information (OSTI.GOV)

Williamson, J. J., E-mail: johnjosephwilliamson@gmail.com; Evans, R. M. L.

We dynamically simulate fractionation (partitioning of particle species) during spinodal gas-liquid separation of a size-polydisperse colloid, using polydispersity up to ∼40% and a skewed parent size distribution. We introduce a novel coarse-grained Voronoi method to minimise size bias in measuring local volume fraction, along with a variety of spatial correlation functions which detect fractionation without requiring a clear distinction between the phases. These can be applied whether or not a system is phase separated, to determine structural correlations in particle size, and generalise easily to other kinds of polydispersity (charge, shape, etc.). We measure fractionation in both mean size andmore » polydispersity between the phases, its direction differing between model interaction potentials which are identical in the monodisperse case. These qualitative features are predicted by a perturbative theory requiring only a monodisperse reference as input. The results show that intricate fractionation takes place almost from the start of phase separation, so can play a role even in nonequilibrium arrested states. The methods for characterisation of inhomogeneous polydisperse systems could in principle be applied to experiment as well as modelling.« less

Hydroacoustic estimation of zooplankton biomass at two shoal complexes in the Apostle Islands Region of Lake Superior

USGS Publications Warehouse

Holbrook, B.V.; Hrabik, T.R.; Branstrator, D.K.; Yule, D.L.; Stockwell, J.D.

2006-01-01

Hydroacoustics can be used to assess zooplankton populations, however, backscatter must be scaled to be biologically meaningful. In this study, we used a general model to correlate site-specific hydroacoustic backscatter with zooplankton dry weight biomass estimated from net tows. The relationship between zooplankton dry weight and backscatter was significant (p < 0.001) and explained 76% of the variability in the dry weight data. We applied this regression to hydroacoustic data collected monthly in 2003 and 2004 at two shoals in the Apostle Island Region of Lake Superior. After applying the regression model to convert hydroacoustic backscatter to zooplankton dry weight biomass, we used geostatistics to analyze the mean and variance, and ordinary kriging to create spatial zooplankton distribution maps. The mean zooplankton dry weight biomass estimates from plankton net tows and hydroacoustics were not significantly different (p = 0.19) but the hydroacoustic data had a significantly lower coefficient of variation (p < 0.001). The maps of zooplankton distribution illustrated spatial trends in zooplankton dry weight biomass that were not discernable from the overall means.

Spatial extremes modeling applied to extreme precipitation data in the state of Paraná

NASA Astrophysics Data System (ADS)

Olinda, R. A.; Blanchet, J.; dos Santos, C. A. C.; Ozaki, V. A.; Ribeiro, P. J., Jr.

2014-11-01

Most of the mathematical models developed for rare events are based on probabilistic models for extremes. Although the tools for statistical modeling of univariate and multivariate extremes are well developed, the extension of these tools to model spatial extremes includes an area of very active research nowadays. A natural approach to such a modeling is the theory of extreme spatial and the max-stable process, characterized by the extension of infinite dimensions of multivariate extreme value theory, and making it possible then to incorporate the existing correlation functions in geostatistics and therefore verify the extremal dependence by means of the extreme coefficient and the Madogram. This work describes the application of such processes in modeling the spatial maximum dependence of maximum monthly rainfall from the state of Paraná, based on historical series observed in weather stations. The proposed models consider the Euclidean space and a transformation referred to as space weather, which may explain the presence of directional effects resulting from synoptic weather patterns. This method is based on the theorem proposed for de Haan and on the models of Smith and Schlather. The isotropic and anisotropic behavior of these models is also verified via Monte Carlo simulation. Estimates are made through pairwise likelihood maximum and the models are compared using the Takeuchi Information Criterion. By modeling the dependence of spatial maxima, applied to maximum monthly rainfall data from the state of Paraná, it was possible to identify directional effects resulting from meteorological phenomena, which, in turn, are important for proper management of risks and environmental disasters in countries with its economy heavily dependent on agribusiness.

Multidimensional Compressed Sensing MRI Using Tensor Decomposition-Based Sparsifying Transform

PubMed Central

Yu, Yeyang; Jin, Jin; Liu, Feng; Crozier, Stuart

2014-01-01

Compressed Sensing (CS) has been applied in dynamic Magnetic Resonance Imaging (MRI) to accelerate the data acquisition without noticeably degrading the spatial-temporal resolution. A suitable sparsity basis is one of the key components to successful CS applications. Conventionally, a multidimensional dataset in dynamic MRI is treated as a series of two-dimensional matrices, and then various matrix/vector transforms are used to explore the image sparsity. Traditional methods typically sparsify the spatial and temporal information independently. In this work, we propose a novel concept of tensor sparsity for the application of CS in dynamic MRI, and present the Higher-order Singular Value Decomposition (HOSVD) as a practical example. Applications presented in the three- and four-dimensional MRI data demonstrate that HOSVD simultaneously exploited the correlations within spatial and temporal dimensions. Validations based on cardiac datasets indicate that the proposed method achieved comparable reconstruction accuracy with the low-rank matrix recovery methods and, outperformed the conventional sparse recovery methods. PMID:24901331

Numerical analysis of the spatial nonuniformity in a Cs-seeded H{sup -} ion source

DOE Office of Scientific and Technical Information (OSTI.GOV)

Takado, N.; Hanatani, J.; Mizuno, T.

The H{sup -} ion production and transport processes are numerically simulated to clarify the origin of H{sup -} beam nonuniformity. The three-dimensional transport code using the Monte Carlo method has been applied to H{sup 0} atoms and H{sup -} ions in the large 'JAERI 10A negative ion source' under the Cs-seeded condition, in which negative ions are dominantly produced by the surface production process. The results show that a large fraction of hydrogen atoms is produced in the region with high electron temperature. This leads to a spatial nonuniformity of H{sup 0} atom flux to the plasma grid and themore » resultant H{sup -} ion surface production. In addition, most surface-produced H{sup -} ions are extracted even through the high T{sub e} region without destruction. These results indicate a correlation between the production process of the H{sup -} ion and the spatial nonuniformity of the H{sup -} ion beam.« less

Spatial path models with multiple indicators and multiple causes: mental health in US counties.

PubMed

Congdon, Peter

2011-06-01

This paper considers a structural model for the impact on area mental health outcomes (poor mental health, suicide) of spatially structured latent constructs: deprivation, social capital, social fragmentation and rurality. These constructs are measured by multiple observed effect indicators, with the constructs allowed to be correlated both between and within areas. However, in the scheme developed here, particular latent constructs may also be influenced by known variables, or, via path sequences, by other constructs, possibly nonlinearly. For example, area social capital may be measured by effect indicators (e.g. associational density, charitable activity), but influenced as causes by other constructs (e.g. area deprivation), and by observed features of the socio-ethnic structure of areas. A model incorporating these features is applied to suicide mortality and the prevalence of poor mental health in 3141 US counties, which are related to the latent spatial constructs and to observed variables (e.g. county ethnic mix). Copyright © 2011 Elsevier Ltd. All rights reserved.

Resolution improvement in positron emission tomography using anatomical Magnetic Resonance Imaging.

PubMed

Chu, Yong; Su, Min-Ying; Mandelkern, Mark; Nalcioglu, Orhan

2006-08-01

An ideal imaging system should provide information with high-sensitivity, high spatial, and temporal resolution. Unfortunately, it is not possible to satisfy all of these desired features in a single modality. In this paper, we discuss methods to improve the spatial resolution in positron emission imaging (PET) using a priori information from Magnetic Resonance Imaging (MRI). Our approach uses an image restoration algorithm based on the maximization of mutual information (MMI), which has found significant success for optimizing multimodal image registration. The MMI criterion is used to estimate the parameters in the Sharpness-Constrained Wiener filter. The generated filter is then applied to restore PET images of a realistic digital brain phantom. The resulting restored images show improved resolution and better signal-to-noise ratio compared to the interpolated PET images. We conclude that a Sharpness-Constrained Wiener filter having parameters optimized from a MMI criterion may be useful for restoring spatial resolution in PET based on a priori information from correlated MRI.

Analysis of field-scale spatial correlations and variations of soil nutrients using geostatistics.

PubMed

Liu, Ruimin; Xu, Fei; Yu, Wenwen; Shi, Jianhan; Zhang, Peipei; Shen, Zhenyao

2016-02-01

Spatial correlations and soil nutrient variations are important for soil nutrient management. They help to reduce the negative impacts of agricultural nonpoint source pollution. Based on the sampled available nitrogen (AN), available phosphorus (AP), and available potassium (AK), soil nutrient data from 2010, the spatial correlation, was analyzed, and the probabilities of the nutrient's abundance or deficiency were discussed. This paper presents a statistical approach to spatial analysis, the spatial correlation analysis (SCA), which was originally developed for describing heterogeneity in the presence of correlated variation and based on ordinary kriging (OK) results. Indicator kriging (IK) was used to assess the susceptibility of excess of soil nutrients based on crop needs. The kriged results showed there was a distinct spatial variability in the concentration of all three soil nutrients. High concentrations of these three soil nutrients were found near Anzhou. As the distance from the center of town increased, the concentration of the soil nutrients gradually decreased. Spatially, the relationship between AN and AP was negative, and the relationship between AP and AK was not clear. The IK results showed that there were few areas with a risk of AN and AP overabundance. However, almost the entire study region was at risk of AK overabundance. Based on the soil nutrient distribution results, it is clear that the spatial variability of the soil nutrients differed throughout the study region. This spatial soil nutrient variability might be caused by different fertilizer types and different fertilizing practices.

Easy monitoring of velocity fields in microfluidic devices using spatiotemporal image correlation spectroscopy.

PubMed

Travagliati, Marco; Girardo, Salvatore; Pisignano, Dario; Beltram, Fabio; Cecchini, Marco

2013-09-03

Spatiotemporal image correlation spectroscopy (STICS) is a simple and powerful technique, well established as a tool to probe protein dynamics in cells. Recently, its potential as a tool to map velocity fields in lab-on-a-chip systems was discussed. However, the lack of studies on its performance has prevented its use for microfluidics applications. Here, we systematically and quantitatively explore STICS microvelocimetry in microfluidic devices. We exploit a simple experimental setup, based on a standard bright-field inverted microscope (no fluorescence required) and a high-fps camera, and apply STICS to map liquid flow in polydimethylsiloxane (PDMS) microchannels. Our data demonstrates optimal 2D velocimetry up to 10 mm/s flow and spatial resolution down to 5 μm.

A physiologically motivated sparse, compact, and smooth (SCS) approach to EEG source localization.

PubMed

Cao, Cheng; Akalin Acar, Zeynep; Kreutz-Delgado, Kenneth; Makeig, Scott

2012-01-01

Here, we introduce a novel approach to the EEG inverse problem based on the assumption that principal cortical sources of multi-channel EEG recordings may be assumed to be spatially sparse, compact, and smooth (SCS). To enforce these characteristics of solutions to the EEG inverse problem, we propose a correlation-variance model which factors a cortical source space covariance matrix into the multiplication of a pre-given correlation coefficient matrix and the square root of the diagonal variance matrix learned from the data under a Bayesian learning framework. We tested the SCS method using simulated EEG data with various SNR and applied it to a real ECOG data set. We compare the results of SCS to those of an established SBL algorithm.

A variance-decomposition approach to investigating multiscale habitat associations

USGS Publications Warehouse

Lawler, J.J.; Edwards, T.C.

2006-01-01

The recognition of the importance of spatial scale in ecology has led many researchers to take multiscale approaches to studying habitat associations. However, few of the studies that investigate habitat associations at multiple spatial scales have considered the potential effects of cross-scale correlations in measured habitat variables. When cross-scale correlations in such studies are strong, conclusions drawn about the relative strength of habitat associations at different spatial scales may be inaccurate. Here we adapt and demonstrate an analytical technique based on variance decomposition for quantifying the influence of cross-scale correlations on multiscale habitat associations. We used the technique to quantify the variation in nest-site locations of Red-naped Sapsuckers (Sphyrapicus nuchalis) and Northern Flickers (Colaptes auratus) associated with habitat descriptors at three spatial scales. We demonstrate how the method can be used to identify components of variation that are associated only with factors at a single spatial scale as well as shared components of variation that represent cross-scale correlations. Despite the fact that no explanatory variables in our models were highly correlated (r < 0.60), we found that shared components of variation reflecting cross-scale correlations accounted for roughly half of the deviance explained by the models. These results highlight the importance of both conducting habitat analyses at multiple spatial scales and of quantifying the effects of cross-scale correlations in such analyses. Given the limits of conventional analytical techniques, we recommend alternative methods, such as the variance-decomposition technique demonstrated here, for analyzing habitat associations at multiple spatial scales. ?? The Cooper Ornithological Society 2006.

Cross-comparison and evaluation of air pollution field estimation methods

NASA Astrophysics Data System (ADS)

Yu, Haofei; Russell, Armistead; Mulholland, James; Odman, Talat; Hu, Yongtao; Chang, Howard H.; Kumar, Naresh

2018-04-01

Accurate estimates of human exposure is critical for air pollution health studies and a variety of methods are currently being used to assign pollutant concentrations to populations. Results from these methods may differ substantially, which can affect the outcomes of health impact assessments. Here, we applied 14 methods for developing spatiotemporal air pollutant concentration fields of eight pollutants to the Atlanta, Georgia region. These methods include eight methods relying mostly on air quality observations (CM: central monitor; SA: spatial average; IDW: inverse distance weighting; KRIG: kriging; TESS-D: discontinuous tessellation; TESS-NN: natural neighbor tessellation with interpolation; LUR: land use regression; AOD: downscaled satellite-derived aerosol optical depth), one using the RLINE dispersion model, and five methods using a chemical transport model (CMAQ), with and without using observational data to constrain results. The derived fields were evaluated and compared. Overall, all methods generally perform better at urban than rural area, and for secondary than primary pollutants. We found the CM and SA methods may be appropriate only for small domains, and for secondary pollutants, though the SA method lead to large negative spatial correlations when using data withholding for PM2.5 (spatial correlation coefficient R = -0.81). The TESS-D method was found to have major limitations. Results of the IDW, KRIG and TESS-NN methods are similar. They are found to be better suited for secondary pollutants because of their satisfactory temporal performance (e.g. average temporal R2 > 0.85 for PM2.5 but less than 0.35 for primary pollutant NO2). In addition, they are suitable for areas with relatively dense monitoring networks due to their inability to capture spatial concentration variabilities, as indicated by the negative spatial R (lower than -0.2 for PM2.5 when assessed using data withholding). The performance of LUR and AOD methods were similar to kriging. Using RLINE and CMAQ fields without fusing observational data led to substantial errors and biases, though the CMAQ model captured spatial gradients reasonably well (spatial R = 0.45 for PM2.5). Two unique tests conducted here included quantifying autocorrelation of method biases (which can be important in time series analyses) and how well the methods capture the observed interspecies correlations (which would be of particular importance in multipollutant health assessments). Autocorrelation of method biases lasted longest and interspecies correlations of primary pollutants was higher than observations when air quality models were used without data fusing. Use of hybrid methods that combine air quality model outputs with observational data overcome some of these limitations and is better suited for health studies. Results from this study contribute to better understanding the strengths and weaknesses of different methods for estimating human exposures.

Temperature properties in the tropical tropopause layer and their correlations with Outgoing Longwave Radiation: FORMOSAT-3/COSMIC observations

NASA Astrophysics Data System (ADS)

Wang, Kaiti; Wu, Yi-chao; Lin, Jia-Ting; Tan, Pei-Hua

2018-06-01

The properties of temperature at the level of lapse rate minimum (LRM) in the tropical tropopause layer between 20°S and 20°N are investigated using 3-year radio occultation observations based on the FORMOSAT-3/COSMIC mission from November of 2006 to October of 2009. The correlations between this LRM temperature and Outgoing Longwave Radiation (OLR) are analyzed by 5° × 5° grids in longitude and latitude. Two primary regions, one from 60°E to 180°E and the other from 90°W to 30°E, are found to have higher correlations and can be associated with regions of lower OLR values. The patterns of this spatial distributions of regions with higher correlations begin to change more obviously when the altitude ascends to the level of Cold Point Tropopause (CPT). This correlation at the LRM altitude in annual and seasonal scales also shows spatial distributions associated with OLR intensities. The altitudinal dependence of the correlations between temperature and OLR is further analyzed based on grids of high correlations with significance at LRM altitude, for the two primary regions. The results show that for the different time scales in this analysis (3-year, annual, and seasonal), the correlations all gradually decrease above the LRM levels but maintain a significant level to as high as 2.5-3.5 km. Below the LRM level, the correlation decreases with a slower rate as the altitude descends and still keeps significant at the deep 5 km level. These suggest that the vertical temperature profiles could be affected by the convection mechanism for a wide range of altitudes in the troposphere even above LRM altitude. Applying the same analysis on one complete La Niña event during the survey period also reveals similar features.

Inversion of multi-frequency electromagnetic induction data for 3D characterization of hydraulic conductivity

USGS Publications Warehouse

Brosten, Troy R.; Day-Lewis, Frederick D.; Schultz, Gregory M.; Curtis, Gary P.; Lane, John W.

2011-01-01

Electromagnetic induction (EMI) instruments provide rapid, noninvasive, and spatially dense data for characterization of soil and groundwater properties. Data from multi-frequency EMI tools can be inverted to provide quantitative electrical conductivity estimates as a function of depth. In this study, multi-frequency EMI data collected across an abandoned uranium mill site near Naturita, Colorado, USA, are inverted to produce vertical distribution of electrical conductivity (EC) across the site. The relation between measured apparent electrical conductivity (ECa) and hydraulic conductivity (K) is weak (correlation coefficient of 0.20), whereas the correlation between the depth dependent EC obtained from the inversions, and K is sufficiently strong to be used for hydrologic estimation (correlation coefficient of − 0.62). Depth-specific EC values were correlated with co-located K measurements to develop a site-specific ln(EC)–ln(K) relation. This petrophysical relation was applied to produce a spatially detailed map of K across the study area. A synthetic example based on ECa values at the site was used to assess model resolution and correlation loss given variations in depth and/or measurement error. Results from synthetic modeling indicate that optimum correlation with K occurs at ~ 0.5 m followed by a gradual correlation loss of 90% at 2.3 m. These results are consistent with an analysis of depth of investigation (DOI) given the range of frequencies, transmitter–receiver separation, and measurement errors for the field data. DOIs were estimated at 2.0 ± 0.5 m depending on the soil conductivities. A 4-layer model, with varying thicknesses, was used to invert the ECa to maximize available information within the aquifer region for improved correlations with K. Results show improved correlation between K and the corresponding inverted EC at similar depths, underscoring the importance of inversion in using multi-frequency EMI data for hydrologic estimation.

Inversion of multi-frequency electromagnetic induction data for 3D characterization of hydraulic conductivity

USGS Publications Warehouse

Brosten, T.R.; Day-Lewis, F. D.; Schultz, G.M.; Curtis, G.P.; Lane, J.W.

2011-01-01

Electromagnetic induction (EMI) instruments provide rapid, noninvasive, and spatially dense data for characterization of soil and groundwater properties. Data from multi-frequency EMI tools can be inverted to provide quantitative electrical conductivity estimates as a function of depth. In this study, multi-frequency EMI data collected across an abandoned uranium mill site near Naturita, Colorado, USA, are inverted to produce vertical distribution of electrical conductivity (EC) across the site. The relation between measured apparent electrical conductivity (ECa) and hydraulic conductivity (K) is weak (correlation coefficient of 0.20), whereas the correlation between the depth dependent EC obtained from the inversions, and K is sufficiently strong to be used for hydrologic estimation (correlation coefficient of -0.62). Depth-specific EC values were correlated with co-located K measurements to develop a site-specific ln(EC)-ln(K) relation. This petrophysical relation was applied to produce a spatially detailed map of K across the study area. A synthetic example based on ECa values at the site was used to assess model resolution and correlation loss given variations in depth and/or measurement error. Results from synthetic modeling indicate that optimum correlation with K occurs at ~0.5m followed by a gradual correlation loss of 90% at 2.3m. These results are consistent with an analysis of depth of investigation (DOI) given the range of frequencies, transmitter-receiver separation, and measurement errors for the field data. DOIs were estimated at 2.0??0.5m depending on the soil conductivities. A 4-layer model, with varying thicknesses, was used to invert the ECa to maximize available information within the aquifer region for improved correlations with K. Results show improved correlation between K and the corresponding inverted EC at similar depths, underscoring the importance of inversion in using multi-frequency EMI data for hydrologic estimation. ?? 2011.

Comparing Multipollutant Emissions-Based Mobile Source Indicators to Other Single Pollutant and Multipollutant Indicators in Different Urban Areas

PubMed Central

Oakes, Michelle M.; Baxter, Lisa K.; Duvall, Rachelle M.; Madden, Meagan; Xie, Mingjie; Hannigan, Michael P.; Peel, Jennifer L.; Pachon, Jorge E.; Balachandran, Siv; Russell, Armistead; Long, Thomas C.

2014-01-01

A variety of single pollutant and multipollutant metrics can be used to represent exposure to traffic pollutant mixtures and evaluate their health effects. Integrated mobile source indicators (IMSIs) that combine air quality concentration and emissions data have recently been developed and evaluated using data from Atlanta, Georgia. IMSIs were found to track trends in traffic-related pollutants and have similar or stronger associations with health outcomes. In the current work, we apply IMSIs for gasoline, diesel and total (gasoline + diesel) vehicles to two other cities (Denver, Colorado and Houston, Texas) with different emissions profiles as well as to a different dataset from Atlanta. We compare spatial and temporal variability of IMSIs to single-pollutant indicators (carbon monoxide (CO), nitrogen oxides (NOx) and elemental carbon (EC)) and multipollutant source apportionment factors produced by Positive Matrix Factorization (PMF). Across cities, PMF-derived and IMSI gasoline metrics were most strongly correlated with CO (r = 0.31–0.98), while multipollutant diesel metrics were most strongly correlated with EC (r = 0.80–0.98). NOx correlations with PMF factors varied across cities (r = 0.29–0.67), while correlations with IMSIs were relatively consistent (r = 0.61–0.94). In general, single-pollutant metrics were more correlated with IMSIs (r = 0.58–0.98) than with PMF-derived factors (r = 0.07–0.99). A spatial analysis indicated that IMSIs were more strongly correlated (r > 0.7) between two sites in each city than single pollutant and PMF factors. These findings provide confidence that IMSIs provide a transferable, simple approach to estimate mobile source air pollution in cities with differing topography and source profiles using readily available data. PMID:25405595

Analysis of parenchymal patterns using conspicuous spatial frequency features in mammograms applied to the BI-RADS density rating scheme

NASA Astrophysics Data System (ADS)

Perconti, Philip; Loew, Murray

2006-03-01

Automatic classification of the density of breast parenchyma is shown using a measure that is correlated to the human observer performance, and compared against the BI-RADS density rating. Increasingly popular in the United States, the Breast Imaging Reporting and Data System (BI-RADS) is used to draw attention to the increased screening difficulty associated with greater breast density; however, the BI-RADS rating scheme is subjective and is not intended as an objective measure of breast density. So, while popular, BI-RADS does not define density classes using a standardized measure, which leads to increased variability among observers. The adaptive thresholding technique is a more quantitative approach for assessing the percentage breast density, but considerable reader interaction is required. We calculate an objective density rating that is derived using a measure of local feature salience. Previously, this measure was shown to correlate well with radiologists' localization and discrimination of true positive and true negative regions-of-interest. Using conspicuous spatial frequency features, an objective density rating is obtained and correlated with adaptive thresholding, and the subjectively ascertained BI-RADS density ratings. Using 100 cases, obtained from the University of South Florida's DDSM database, we show that an automated breast density measure can be derived that is correlated with the interactive thresholding method for continuous percentage breast density, but not with the BI-RADS density rating categories for the selected cases. Comparison between interactive thresholding and the new salience percentage density resulted in a Pearson correlation of 76.7%. Using a four-category scale equivalent to the BI-RADS density categories, a Spearman correlation coefficient of 79.8% was found.

Spatial Analysis of Slowly Oscillating Electric Activity in the Gut of Mice Using Low Impedance Arrayed Microelectrodes

PubMed Central

Taniguchi, Mizuki; Kajioka, Shunichi; Shozib, Habibul B.; Sawamura, Kenta; Nakayama, Shinsuke

2013-01-01

Smooth and elaborate gut motility is based on cellular cooperation, including smooth muscle, enteric neurons and special interstitial cells acting as pacemaker cells. Therefore, spatial characterization of electric activity in tissues containing these electric excitable cells is required for a precise understanding of gut motility. Furthermore, tools to evaluate spatial electric activity in a small area would be useful for the investigation of model animals. We thus employed a microelectrode array (MEA) system to simultaneously measure a set of 8×8 field potentials in a square area of ∼1 mm2. The size of each recording electrode was 50×50 µm2, however the surface area was increased by fixing platinum black particles. The impedance of microelectrode was sufficiently low to apply a high-pass filter of 0.1 Hz. Mapping of spectral power, and auto-correlation and cross-correlation parameters characterized the spatial properties of spontaneous electric activity in the ileum of wild-type (WT) and W/Wv mice, the latter serving as a model of impaired network of pacemaking interstitial cells. Namely, electric activities measured varied in both size and cooperativity in W/Wv mice, despite the small area. In the ileum of WT mice, procedures suppressing the excitability of smooth muscle and neurons altered the propagation of spontaneous electric activity, but had little change in the period of oscillations. In conclusion, MEA with low impedance electrodes enables to measure slowly oscillating electric activity, and is useful to evaluate both histological and functional changes in the spatio-temporal property of gut electric activity. PMID:24124480

Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach.

PubMed

Xu, Pengpeng; Huang, Helai; Dong, Ni; Wong, S C

2017-01-01

This study was performed to investigate the spatially varying relationships between crash frequency and related risk factors. A Bayesian spatially varying coefficients model was elaborately introduced as a methodological alternative to simultaneously account for the unstructured and spatially structured heterogeneity of the regression coefficients in predicting crash frequencies. The proposed method was appealing in that the parameters were modeled via a conditional autoregressive prior distribution, which involved a single set of random effects and a spatial correlation parameter with extreme values corresponding to pure unstructured or pure spatially correlated random effects. A case study using a three-year crash dataset from the Hillsborough County, Florida, was conducted to illustrate the proposed model. Empirical analysis confirmed the presence of both unstructured and spatially correlated variations in the effects of contributory factors on severe crash occurrences. The findings also suggested that ignoring spatially structured heterogeneity may result in biased parameter estimates and incorrect inferences, while assuming the regression coefficients to be spatially clustered only is probably subject to the issue of over-smoothness. Copyright © 2016 Elsevier Ltd. All rights reserved.

Accounting for irregular support in spatial interpolation - analysing the effect of using alternative distance measures

NASA Astrophysics Data System (ADS)

Skøien, J. O.; Gottschalk, L.; Leblois, E.

2009-04-01

Whereas geostatistical and objective methods mostly have been developed for observations with point support or a regular support, e.g. runoff related data can be assumed to have an irregular support in space, and sometimes also a temporal support. The correlations between observations and between observations and the prediction location are found through an integration of a point variogram or point correlation function, a method known as regularisation. Being a relatively simple method for observations with equal and regular support, it can be computationally demanding if the observations have irregular support. With improved speed of computers, solving such integrations has become easier, but there can still be numerical problems that are not easily solved even with high-resolution computations. This can particularly be a problem in hydrological sciences where catchments are overlapping, the correlations are high, and small numerical errors can give ill-posed covariance matrices. The problem increases with increasing number of spatial and/or temporal dimensions. Gottschalk [1993a; 1993b] suggested to replace the integration by a Taylor expansion, hence reducing the computation time considerably, and also expecting less numerical problems with the covariance matrices. In practice, the integrated correlation/semivariance between observations are replaced by correlations/semivariances using the so called Ghosh-distance. Although Gottschalk and collaborators have used the Ghosh-distance also in other papers [Sauquet, et al., 2000a; Sauquet, et al., 2000b], the properties of the simplification have not been examined in detail. Hence, we will here analyse the replacement of the integration by the use of Ghosh-distances, both in sense of the ability to reproduce regularised semivariogram and correlation values, and the influence on the final interpolated maps. Comparisons will be performed both for real observations with a support (hydrological data) and for more hypothetical observations with regular supports where analytical expressions for the regularised semivariances/correlations in some cases can be derived. The results indicate that the simplification is useful for spatial interpolation when the support of the observations has to be taken into account. The difference in semivariogram value or correlation value between the simplified method and the full integration is limited on short distances, increasing for larger distances. However, this is to some degree taken into account while fitting a model for the point process, so that the results after interpolation are less affected by the simplification. The method is of particular use if computation time is of importance, e.g. in the case of real-time mapping procedures. Gottschalk, L. (1993a) Correlation and covariance of runoff, Stochastic Hydrology and Hydraulics, 7, 85-101. Gottschalk, L. (1993b) Interpolation of runoff applying objective methods, Stochastic Hydrology and Hydraulics, 7, 269-281. Sauquet, E., L. Gottschalk, and E. Leblois (2000a) Mapping average annual runoff: a hierarchical approach applying a stochastic interpolation scheme, Hydrological Sciences Journal, 45, 799-815. Sauquet, E., I. Krasovskaia, and E. Leblois (2000b) Mapping mean monthly runoff pattern using EOF analysis, Hydrology and Earth System Sciences, 4, 79-93.

Quantification of spatial and seasonal variations in the proportional contribution of nitrate sources using a multi-isotope approach and Bayesian isotope mixing model.

PubMed

Meghdadi, Aminreza; Javar, Narmin

2018-04-01

Spatial and seasonal variations in nitrate contamination are a globally concern. While numerous studies have used δ 15 N-NO 3 and δ 18 O-NO 3 to elucidate the dominant sources of nitrate in groundwater, this approach has significant limitations due to the overlap of nitrate isotopic ranges and the occurrence of nitrate isotopic fractionation. This study quantitatively assessed the spatial and seasonal variations in the proportional contributions of nitrate sources from different land uses in the Tarom watershed in North-West Iran. To achieve this aim, orthogonal projection of the hydrochemical and isotopic dataset of the principal component analysis (PCA) as well as correlation coefficient matrix (Corr-PCA) were evaluated to reduce the dimensionality of the inter-correlated dataset. Next, a nitrate isotopic biplot accompanied with a Bayesian isotope mixing model (SIAR) were applied to specify the spatial and seasonal trends in the proportional contribution of three dominant sources of nitrate (fertilizers, animal manure and residential waste) in the watershed. Finally, in order to provide a sensitive framework for nitrate source appointment and overcome the associated limitations of dual nitrate isotope application, the integration of boron isotope (δ 11 B) and strontium isotopic ratio ( 87 Sr/ 86 Sr) was introduced. The results revealed that the mean contribution of residential sewage increased (17%-27.5%), while the mean contribution of fertilizers decreased (28.3%-19%), from late spring to early autumn. Also, fertilizer was the highest contributor (42.1% ± 3.2) during late spring, especially in regions with more than 75% agricultural land. Meanwhile, the mean contribution of sewage was highest in early autumn (32.1% ± 2.8) in the areas with more than 20% residential land. These results were confirmed by coupled application of δ 11 B and 87 Sr/ 86 Sr. This study provides a useful insight for environmental managers to verify groundwater pollution contributors and to better apply remedial solutions. Copyright © 2017 Elsevier Ltd. All rights reserved.

Fission barriers from multidimensionally-constrained covariant density functional theories

NASA Astrophysics Data System (ADS)

Lu, Bing-Nan; Zhao, Jie; Zhao, En-Guang; Zhou, Shan-Gui

2017-11-01

In recent years, we have developed the multidimensionally-constrained covariant density functional theories (MDC-CDFTs) in which both axial and spatial reflection symmetries are broken and all shape degrees of freedom described by βλμ with even μ, such as β20, β22, β30, β32, β40, etc., are included self-consistently. The MDC-CDFTs have been applied to the investigation of potential energy surfaces and fission barriers of actinide nuclei, third minima in potential energy surfaces of light actinides, shapes and potential energy surfaces of superheavy nuclei, octupole correlations between multiple chiral doublet bands in 78Br, octupole correlations in Ba isotopes, the Y32 correlations in N = 150 isotones and Zr isotopes, the spontaneous fission of Fm isotopes, and shapes of hypernuclei. In this contribution we present the formalism of MDC-CDFTs and the application of these theories to the study of fission barriers and potential energy surfaces of actinide nuclei.

Developing a bivariate spatial association measure: An integration of Pearson's r and Moran's I

NASA Astrophysics Data System (ADS)

Lee, Sang-Il

This research is concerned with developing a bivariate spatial association measure or spatial correlation coefficient, which is intended to capture spatial association among observations in terms of their point-to-point relationships across two spatial patterns. The need for parameterization of the bivariate spatial dependence is precipitated by the realization that aspatial bivariate association measures, such as Pearson's correlation coefficient, do not recognize spatial distributional aspects of data sets. This study devises an L statistic by integrating Pearson's r as an aspatial bivariate association measure and Moran's I as a univariate spatial association measure. The concept of a spatial smoothing scalar (SSS) plays a pivotal role in this task.

Applications and limitations of electron correlation microscopy to study relaxation dynamics in supercooled liquids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Pei; He, Li; Besser, Matthew F.

Here, electron correlation microscopy (ECM) is a way to measure structural relaxation times, τ, of liquids with nanometer-scale spatial resolution using coherent electron scattering equivalent of photon correlation spectroscopy. We have applied ECM with a 3.5 nm diameter probe to Pt 57.5Cu 14.7Ni 5.3P 22.5 amorphous nanorods and Pd 40Ni 40P 20 bulk metallic glass (BMG) heated inside the STEM into the supercooled liquid region. These data demonstrate that the ECM technique is limited by the characteristics of the time series, which must be at least 40τ to obtain a well-converged correlation function g 2(t), and the time per frame,more » which must be less than 0.1τ to obtain sufficient sampling. A high-speed direct electron camera enables fast acquisition and affords reliable g 2(t) data even with low signal per frame.« less

Applications and limitations of electron correlation microscopy to study relaxation dynamics in supercooled liquids

DOE PAGES

Zhang, Pei; He, Li; Besser, Matthew F.; ...

2016-09-08

Here, electron correlation microscopy (ECM) is a way to measure structural relaxation times, τ, of liquids with nanometer-scale spatial resolution using coherent electron scattering equivalent of photon correlation spectroscopy. We have applied ECM with a 3.5 nm diameter probe to Pt 57.5Cu 14.7Ni 5.3P 22.5 amorphous nanorods and Pd 40Ni 40P 20 bulk metallic glass (BMG) heated inside the STEM into the supercooled liquid region. These data demonstrate that the ECM technique is limited by the characteristics of the time series, which must be at least 40τ to obtain a well-converged correlation function g 2(t), and the time per frame,more » which must be less than 0.1τ to obtain sufficient sampling. A high-speed direct electron camera enables fast acquisition and affords reliable g 2(t) data even with low signal per frame.« less

Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation

PubMed Central

Carvalho, Carlos; Gomes, Danielo G.; Agoulmine, Nazim; de Souza, José Neuman

2011-01-01

This paper proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN). Prediction of data not sent to the sink node is a technique used to save energy in WSNs by reducing the amount of data traffic. However, it may not be very accurate. Simulations were made involving simple linear regression and multiple linear regression functions to assess the performance of the proposed method. The results show a higher correlation between gathered inputs when compared to time, which is an independent variable widely used for prediction and forecasting. Prediction accuracy is lower when simple linear regression is used, whereas multiple linear regression is the most accurate one. In addition to that, our proposal outperforms some current solutions by about 50% in humidity prediction and 21% in light prediction. To the best of our knowledge, we believe that we are probably the first to address prediction based on multivariate correlation for WSN data reduction. PMID:22346626

Negative Correlation between the Diffusion Coefficient and Transcriptional Activity of the Glucocorticoid Receptor.

PubMed

Mikuni, Shintaro; Yamamoto, Johtaro; Horio, Takashi; Kinjo, Masataka

2017-08-25

The glucocorticoid receptor (GR) is a transcription factor, which interacts with DNA and other cofactors to regulate gene transcription. Binding to other partners in the cell nucleus alters the diffusion properties of GR. Raster image correlation spectroscopy (RICS) was applied to quantitatively characterize the diffusion properties of EGFP labeled human GR (EGFP-hGR) and its mutants in the cell nucleus. RICS is an image correlation technique that evaluates the spatial distribution of the diffusion coefficient as a diffusion map. Interestingly, we observed that the averaged diffusion coefficient of EGFP-hGR strongly and negatively correlated with its transcriptional activities in comparison to that of EGFP-hGR wild type and mutants with various transcriptional activities. This result suggests that the decreasing of the diffusion coefficient of hGR was reflected in the high-affinity binding to DNA. Moreover, the hyper-phosphorylation of hGR can enhance the transcriptional activity by reduction of the interaction between the hGR and the nuclear corepressors.

Unsupervised segmentation of low-contrast multichannel images: discrimination of tissue components in microscopic images of unstained specimens

NASA Astrophysics Data System (ADS)

Kopriva, Ivica; Popović Hadžija, Marijana; Hadžija, Mirko; Aralica, Gorana

2015-06-01

Low-contrast images, such as color microscopic images of unstained histological specimens, are composed of objects with highly correlated spectral profiles. Such images are very hard to segment. Here, we present a method that nonlinearly maps low-contrast color image into an image with an increased number of non-physical channels and a decreased correlation between spectral profiles. The method is a proof-of-concept validated on the unsupervised segmentation of color images of unstained specimens, in which case the tissue components appear colorless when viewed under the light microscope. Specimens of human hepatocellular carcinoma, human liver with metastasis from colon and gastric cancer and mouse fatty liver were used for validation. The average correlation between the spectral profiles of the tissue components was greater than 0.9985, and the worst case correlation was greater than 0.9997. The proposed method can potentially be applied to the segmentation of low-contrast multichannel images with high spatial resolution that arise in other imaging modalities.

Correlative tomography at the cathode/electrolyte interfaces of solid oxide fuel cells

NASA Astrophysics Data System (ADS)

Wankmüller, Florian; Szász, Julian; Joos, Jochen; Wilde, Virginia; Störmer, Heike; Gerthsen, Dagmar; Ivers-Tiffée, Ellen

2017-08-01

This paper introduces a correlative tomography technique. It visualizes the spatial organization of primary and secondary phases at the interface of La0.58Sr0.4Co0.2Fe0.8O3-δ cathode/10 mol% Gadolinia doped Ceria/8 mol% Yttria stabilized Zirconia electrolyte. It uses focused ion beam/scanning electron microscope tomography (FIB/SEM), and combines data sets from Everhart-Thornley and Inlens detector differentiating four primary and two secondary material phases. In addition, grayscale information is correlated to elemental distribution gained by energy dispersive X-ray spectroscopy in a scanning transmission electron microscope. Interdiffusion of GDC into YSZ and SrZrO3 as secondary phases depend (in both amount and spatial organization) on the varied co-sintering temperature of the GDC/YSZ electrolyte. The ion-blocking SrZrO3 forms a continuous layer on top of the temperature-dependent GDC/YSZ interdiffusion zone (ID) at and below a co-sintering temperature of 1200 °C; above it becomes intermittent. 2D FIB/SEM images of primary and secondary phases at 1100, 1200, 1300 and 1400 °C were combined with a 3D FIB/SEM reconstruction (1300 °C). This reveals that ;preferred; oxygen ion transport pathways from the LSCF cathode through GDC and the ID into the YSZ electrolyte only exist in samples sintered above 1200 °C. The applied correlative technique expands our understanding of this multiphase cathode/electrolyte interface region.

Pre-Processed Recursive Lattice Reduction for Complexity Reduction in Spatially and Temporally Correlated MIMO Channels

NASA Astrophysics Data System (ADS)

An, Chan-Ho; Yang, Janghoon; Jang, Seunghun; Kim, Dong Ku

In this letter, a pre-processed lattice reduction (PLR) scheme is developed for the lattice reduction aided (LRA) detection of multiple input multiple-output (MIMO) systems in spatially correlated channel. The PLR computes the LLL-reduced matrix of the equivalent matrix, which is the product of the present channel matrix and unimodular transformation matrix for LR of spatial correlation matrix, rather than the present channel matrix itself. In conjunction with PLR followed by recursive lattice reduction (RLR) scheme [7], pre-processed RLR (PRLR) is shown to efficiently carry out the LR of the channel matrix, especially for the burst packet message in spatially and temporally correlated channel while matching the performance of conventional LRA detection.

Removing the Impact of Correlated PSF Uncertainties in Weak Lensing

NASA Astrophysics Data System (ADS)

Lu, Tianhuan; Zhang, Jun; Dong, Fuyu; Li, Yingke; Liu, Dezi; Fu, Liping; Li, Guoliang; Fan, Zuhui

2018-05-01

Accurate reconstruction of the spatial distributions of the point-spread function (PSF) is crucial for high precision cosmic shear measurements. Nevertheless, current methods are not good at recovering the PSF fluctuations of high spatial frequencies. In general, the residual PSF fluctuations are spatially correlated, and therefore can significantly contaminate the correlation functions of the weak lensing signals. We propose a method to correct for this contamination statistically, without any assumptions on the PSF and galaxy morphologies or their spatial distribution. We demonstrate our idea with the data from the W2 field of CFHTLenS.

Infant mortality in South Africa - distribution, associations and policy implications, 2007: an ecological spatial analysis

PubMed Central

2011-01-01

Background Many sub-Saharan countries are confronted with persistently high levels of infant mortality because of the impact of a range of biological and social determinants. In particular, infant mortality has increased in sub-Saharan Africa in recent decades due to the HIV/AIDS epidemic. The geographic distribution of health problems and their relationship to potential risk factors can be invaluable for cost effective intervention planning. The objective of this paper is to determine and map the spatial nature of infant mortality in South Africa at a sub district level in order to inform policy intervention. In particular, the paper identifies and maps high risk clusters of infant mortality, as well as examines the impact of a range of determinants on infant mortality. A Bayesian approach is used to quantify the spatial risk of infant mortality, as well as significant associations (given spatial correlation between neighbouring areas) between infant mortality and a range of determinants. The most attributable determinants in each sub-district are calculated based on a combination of prevalence and model risk factor coefficient estimates. This integrated small area approach can be adapted and applied in other high burden settings to assist intervention planning and targeting. Results Infant mortality remains high in South Africa with seemingly little reduction since previous estimates in the early 2000's. Results showed marked geographical differences in infant mortality risk between provinces as well as within provinces as well as significantly higher risk in specific sub-districts and provinces. A number of determinants were found to have a significant adverse influence on infant mortality at the sub-district level. Following multivariable adjustment increasing maternal mortality, antenatal HIV prevalence, previous sibling mortality and male infant gender remained significantly associated with increased infant mortality risk. Of these antenatal HIV sero-prevalence, previous sibling mortality and maternal mortality were found to be the most attributable respectively. Conclusions This study demonstrates the usefulness of advanced spatial analysis to both quantify excess infant mortality risk at the lowest administrative unit, as well as the use of Bayesian modelling to quantify determinant significance given spatial correlation. The "novel" integration of determinant prevalence at the sub-district and coefficient estimates to estimate attributable fractions further elucidates the "high impact" factors in particular areas and has considerable potential to be applied in other locations. The usefulness of the paper, therefore, not only suggests where to intervene geographically, but also what specific interventions policy makers should prioritize in order to reduce the infant mortality burden in specific administration areas. PMID:22093084

Modeling trends from North American Breeding Bird Survey data: a spatially explicit approach

USGS Publications Warehouse

Bled, Florent; Sauer, John R.; Pardieck, Keith L.; Doherty, Paul; Royle, J. Andy

2013-01-01

Population trends, defined as interval-specific proportional changes in population size, are often used to help identify species of conservation interest. Efficient modeling of such trends depends on the consideration of the correlation of population changes with key spatial and environmental covariates. This can provide insights into causal mechanisms and allow spatially explicit summaries at scales that are of interest to management agencies. We expand the hierarchical modeling framework used in the North American Breeding Bird Survey (BBS) by developing a spatially explicit model of temporal trend using a conditional autoregressive (CAR) model. By adopting a formal spatial model for abundance, we produce spatially explicit abundance and trend estimates. Analyses based on large-scale geographic strata such as Bird Conservation Regions (BCR) can suffer from basic imbalances in spatial sampling. Our approach addresses this issue by providing an explicit weighting based on the fundamental sample allocation unit of the BBS. We applied the spatial model to three species from the BBS. Species have been chosen based upon their well-known population change patterns, which allows us to evaluate the quality of our model and the biological meaning of our estimates. We also compare our results with the ones obtained for BCRs using a nonspatial hierarchical model (Sauer and Link 2011). Globally, estimates for mean trends are consistent between the two approaches but spatial estimates provide much more precise trend estimates in regions on the edges of species ranges that were poorly estimated in non-spatial analyses. Incorporating a spatial component in the analysis not only allows us to obtain relevant and biologically meaningful estimates for population trends, but also enables us to provide a flexible framework in order to obtain trend estimates for any area.

Temporal and spatial correlation patterns of air pollutants in Chinese cities

PubMed Central

Dai, Yue-Hua

2017-01-01

As a huge threat to the public health, China’s air pollution has attracted extensive attention and continues to grow in tandem with the economy. Although the real-time air quality report can be utilized to update our knowledge on air quality, questions about how pollutants evolve across time and how pollutants are spatially correlated still remain a puzzle. In view of this point, we adopt the PMFG network method to analyze the six pollutants’ hourly data in 350 Chinese cities in an attempt to find out how these pollutants are correlated temporally and spatially. In terms of time dimension, the results indicate that, except for O3, the pollutants have a common feature of the strong intraday patterns of which the daily variations are composed of two contraction periods and two expansion periods. Besides, all the time series of the six pollutants possess strong long-term correlations, and this temporal memory effect helps to explain why smoggy days are always followed by one after another. In terms of space dimension, the correlation structure shows that O3 is characterized by the highest spatial connections. The PMFGs reveal the relationship between this spatial correlation and provincial administrative divisions by filtering the hierarchical structure in the correlation matrix and refining the cliques as the tinny spatial clusters. Finally, we check the stability of the correlation structure and conclude that, except for PM10 and O3, the other pollutants have an overall stable correlation, and all pollutants have a slight trend to become more divergent in space. These results not only enhance our understanding of the air pollutants’ evolutionary process, but also shed lights on the application of complex network methods into geographic issues. PMID:28832599

Biomagnetic monitoring as a validation tool for local air quality models: a case study for an urban street canyon.

PubMed

Hofman, Jelle; Samson, Roeland

2014-09-01

Biomagnetic monitoring of tree leaf deposited particles has proven to be a good indicator of the ambient particulate concentration. The objective of this study is to apply this method to validate a local-scale air quality model (ENVI-met), using 96 tree crown sampling locations in a typical urban street canyon. To the best of our knowledge, the application of biomagnetic monitoring for the validation of pollutant dispersion modeling is hereby presented for the first time. Quantitative ENVI-met validation showed significant correlations between modeled and measured results throughout the entire in-leaf period. ENVI-met performed much better at the first half of the street canyon close to the ring road (r=0.58-0.79, RMSE=44-49%), compared to second part (r=0.58-0.64, RMSE=74-102%). The spatial model behavior was evaluated by testing effects of height, azimuthal position, tree position and distance from the main pollution source on the obtained model results and magnetic measurements. Our results demonstrate that biomagnetic monitoring seems to be a valuable method to evaluate the performance of air quality models. Due to the high spatial and temporal resolution of this technique, biomagnetic monitoring can be applied anywhere in the city (where urban green is present) to evaluate model performance at different spatial scales. Copyright © 2014 Elsevier Ltd. All rights reserved.

Quantifying suspended sediment dynamics in mega deltas using remote sensing data: A case study of the Mekong floodplains

NASA Astrophysics Data System (ADS)

Dang, Thanh Duc; Cochrane, Thomas A.; Arias, Mauricio E.

2018-06-01

Temporal and spatial concentrations of suspended sediment in floodplains are difficult to quantify because in situ measurements can be logistically complex, time consuming and costly. In this research, satellite imagery with long temporal and large spatial coverage (Landsat TM/ETM+) was used to complement in situ suspended sediment measurements to reflect sediment dynamics in a large (70,000 km2) floodplain. Instead of using a single spectral band from Landsat, a Principal Component Analysis was applied to obtain uncorrelated reflectance values for five bands of Landsat TM/ETM+. Significant correlations between the scores of the 1st principal component and the values of continuously gauged suspended sediment concentration, shown via high coefficients of determination of sediment rating curves (R2 ranging from 0.66 to 0.92), permit the application of satellite images to quantify spatial and temporal sediment variation in the Mekong floodplains. Estimated suspended sediment maps show that hydraulic regimes at Chaktomuk (Cambodia), where the Mekong, Bassac, and Tonle Sap rivers diverge, determine the amount of seasonal sediment supplies to the Mekong Delta. The development of flood prevention systems to allow for three rice crops a year in the Vietnam Mekong Delta significantly reduces localized flooding, but also prevents sediment (source of nutrients) from entering fields. A direct consequence of this is the need to apply more artificial fertilizers to boost agricultural productivity, which may trigger environmental problems. Overall, remote sensing is shown to be an effective tool to understand temporal and spatial sediment dynamics in large floodplains.

Resistance and resilience of microbial communities--temporal and spatial insurance against perturbations.

PubMed

Baho, Didier L; Peter, Hannes; Tranvik, Lars J

2012-09-01

Bacteria play fundamental roles for many ecosystem processes; however, little empirical evidence is available on how environmental perturbations affect their composition and function. We investigated how spatial and temporal refuges affect the resistance and resilience of a freshwater bacterioplankton community upon a salinity pulse perturbation in continuous cultures. Attachment to a surface avoided the flushing out of cells and enabled re-colonization of the liquid phase after the perturbation, hence serving as a temporal refuge. A spatial refuge was established by introduction of bacteria from an undisturbed reservoir upstream of the continuous culture vessel, acting analogous to a regional species pool in a metacommunity. The salinity pulse affected bacterial community composition and the rates of respiration and the pattern of potential substrate utilization as well as the correlation between composition and function. Compared with the no-refuge treatment, the temporal refuge shortened return to pre-perturbation conditions, indicating enhanced community resilience. Composition and function were less disturbed in the treatment providing a spatial refuge, suggesting higher resistance. Our results highlight that spatial and temporal dynamics in general and refuges in particular need to be considered for conceptual progress in how microbial metacommunities are shaped by perturbations. © 2012 Society for Applied Microbiology and Blackwell Publishing Ltd.

Revealing Spatial Variation and Correlation of Urban Travels from Big Trajectory Data

NASA Astrophysics Data System (ADS)

Li, X.; Tu, W.; Shen, S.; Yue, Y.; Luo, N.; Li, Q.

2017-09-01

With the development of information and communication technology, spatial-temporal data that contain rich human mobility information are growing rapidly. However, the consistency of multi-mode human travel behind multi-source spatial-temporal data is not clear. To this aim, we utilized a week of taxies' and buses' GPS trajectory data and smart card data in Shenzhen, China to extract city-wide travel information of taxi, bus and metro and tested the correlation of multi-mode travel characteristics. Both the global correlation and local correlation of typical travel indicator were examined. The results show that: (1) Significant differences exist in of urban multi-mode travels. The correlation between bus travels and taxi travels, metro travel and taxi travels are globally low but locally high. (2) There are spatial differences of the correlation relationship between bus, metro and taxi travel. These findings help us understanding urban travels deeply therefore facilitate both the transport policy making and human-space interaction research.

A Generalized Spatial Measure for Resilience of Microbial Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Renslow, Ryan S.; Lindemann, Stephen R.; Song, Hyun-Seob

2016-04-07

The emergent property of resilience is the ability of a system to return to an original state after a disturbance. Resilience may be used as an early warning system for significant or irreversible community transition, i.e., a community with diminishing or low resilience may be close to catastrophic shift in function or an irreversible collapse. Typically, resilience is quantified using recovery time, which may be difficult or impossible to directly measure in microbial systems. A recent study in the literature showed that under certain conditions, a set of spatial-based metrics termed recovery length, can be correlated to recovery time, andmore » thus may be a reasonable alternative measure of resilience. As a limitation, however, this spatial metric of resilience is useful only for step-change perturbations. Building upon the concept of recovery length, we propose a more general form of the spatial metric of resilience that can be applied to any shape of perturbation profiles (i.e., a sharp or smooth gradient). We termed this new spatial measure “perturbation-adjusted spatial metric of resilience” (PASMORE). We demonstrate the applicability of the proposed metric using a mathematical model of a microbial mat. PASMORE can help identify when a system, such as a microbial community, is on the verge of collapse or nearing an irreversible transition across a tipping point.« less

A spectral method for spatial downscaling | Science Inventory ...

EPA Pesticide Factsheets

Complex computer models play a crucial role in air quality research. These models are used to evaluate potential regulatory impacts of emission control strategies and to estimate air quality in areas without monitoring data. For both of these purposes, it is important to calibrate model output with monitoring data to adjust for model biases and improve spatial prediction. In this paper, we propose a new spectral method to study and exploit complex relationships between model output and monitoring data. Spectral methods allow us to estimate the relationship between model output and monitoring data separately at different spatial scales, and to use model output for prediction only at the appropriate scales. The proposed method is computationally efficient and can be implemented using standard software. We apply the method to compare Community Multiscale Air Quality (CMAQ) model output with ozone measurements in the United States in July, 2005. We find that CMAQ captures large-scale spatial trends, but has low correlation with the monitoring data at small spatial scales. The National Exposure Research Laboratory′s (NERL′s)Atmospheric Modeling Division (AMAD) conducts research in support of EPA′s mission to protect human health and the environment. AMAD′s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation′s air quality and for assessing ch

Study of satellite retrieved aerosol optical depth spatial resolution effect on particulate matter concentration prediction

NASA Astrophysics Data System (ADS)

Strandgren, J.; Mei, L.; Vountas, M.; Burrows, J. P.; Lyapustin, A.; Wang, Y.

2014-10-01

The Aerosol Optical Depth (AOD) spatial resolution effect is investigated for the linear correlation between satellite retrieved AOD and ground level particulate matter concentrations (PM2.5). The Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for the Moderate Resolution Imaging Spectroradiometer (MODIS) for obtaining AOD with a high spatial resolution of 1 km and provides a good dataset for the study of the AOD spatial resolution effect on the particulate matter concentration prediction. 946 Environmental Protection Agency (EPA) ground monitoring stations across the contiguous US have been used to investigate the linear correlation between AOD and PM2.5 using AOD at different spatial resolutions (1, 3 and 10 km) and for different spatial scales (urban scale, meso-scale and continental scale). The main conclusions are: (1) for both urban, meso- and continental scale the correlation between PM2.5 and AOD increased significantly with increasing spatial resolution of the AOD, (2) the correlation between AOD and PM2.5 decreased significantly as the scale of study region increased for the eastern part of the US while vice versa for the western part of the US, (3) the correlation between PM2.5 and AOD is much more stable and better over the eastern part of the US compared to western part due to the surface characteristics and atmospheric conditions like the fine mode fraction.

Electroconvulsive therapy-induced brain plasticity determines therapeutic outcome in mood disorders

PubMed Central

Dukart, Juergen; Regen, Francesca; Kherif, Ferath; Colla, Michael; Bajbouj, Malek; Heuser, Isabella; Frackowiak, Richard S.; Draganski, Bogdan

2014-01-01

There remains much scientific, clinical, and ethical controversy concerning the use of electroconvulsive therapy (ECT) for psychiatric disorders stemming from a lack of information and knowledge about how such treatment might work, given its nonspecific and spatially unfocused nature. The mode of action of ECT has even been ascribed to a “barbaric” form of placebo effect. Here we show differential, highly specific, spatially distributed effects of ECT on regional brain structure in two populations: patients with unipolar or bipolar disorder. Unipolar and bipolar disorders respond differentially to ECT and the associated local brain-volume changes, which occur in areas previously associated with these diseases, correlate with symptom severity and the therapeutic effect. Our unique evidence shows that electrophysical therapeutic effects, although applied generally, take on regional significance through interactions with brain pathophysiology. PMID:24379394

Detection of anomalous crop condition and soil variability mapping using a 26 year Landsat record and the Palmer crop moisture index

NASA Astrophysics Data System (ADS)

Venteris, E. R.; Tagestad, J. D.; Downs, J. L.; Murray, C. J.

2015-07-01

Cost-effective and reliable vegetation monitoring methods are needed for applications ranging from traditional agronomic mapping, to verifying the safety of geologic injection activities. A particular challenge is defining baseline crop conditions and subsequent anomalies from long term imagery records (Landsat) in the face of large spatiotemporal variability. We develop a new method for defining baseline crop response (near peak growth) using the normalized difference vegetation index (NDVI) from 26 years (1986-2011) of Landsat data for 400 km2 surrounding a planned geologic carbon sequestration site near Jacksonville, Illinois. The normal score transform (yNDVI) was applied on a field by field basis to accentuate spatial patterns and level differences due to planting times. We tested crop type and soil moisture (Palmer crop moisture index (CMI)) as predictors of expected crop condition. Spatial patterns in yNDVI were similar between corn and soybeans - the two major crops. Linear regressions between yNDVI and the cumulative CMI (CCMI) exposed complex interactions between crop condition, field location (topography and soils), and annual moisture. Wet toposequence positions (depressions) were negatively correlated to CCMI and dry positions (crests) positively correlated. However, only 21% of the landscape showed a statistically significant (p < 0.05) linear relationship. To map anomalous crop conditions, we defined a tolerance interval based on yNDVI statistics. Tested on an independent image (2013), 63 of 1483 possible fields showed unusual crop condition. While the method is not directly suitable for crop health assessment, the spatial patterns in correlation between yNDVI and CCMI have potential applications for pest damage detection and edaphological soil mapping, especially in the developing world.

Geostatistical mapping of effluent-affected sediment distribution on the Palos Verdes shelf

USGS Publications Warehouse

Murray, C.J.; Lee, H.J.; Hampton, M.A.

2002-01-01

Geostatistical techniques were used to study the spatial continuity of the thickness of effluent-affected sediment in the offshore Palos Verdes Margin area. The thickness data were measured directly from cores and indirectly from high-frequency subbottom profiles collected over the Palos Verdes Margin. Strong spatial continuity of the sediment thickness data was identified, with a maximum range of correlation in excess of 1.4 km. The spatial correlation showed a marked anisotropy, and was more than twice as continuous in the alongshore direction as in the cross-shelf direction. Sequential indicator simulation employing models fit to the thickness data variograms was used to map the distribution of the sediment, and to quantify the uncertainty in those estimates. A strong correlation between sediment thickness data and measurements of the mass of the contaminant p,p???-DDE per unit area was identified. A calibration based on the bivariate distribution of the thickness and p,p???-DDE data was applied using Markov-Bayes indicator simulation to extend the geostatistical study and map the contamination levels in the sediment. Integrating the map grids produced by the geostatistical study of the two variables indicated that 7.8 million m3 of effluent-affected sediment exist in the map area, containing approximately 61-72 Mg (metric tons) of p,p???-DDE. Most of the contaminated sediment (about 85% of the sediment and 89% of the p,p???-DDE) occurs in water depths < 100 m. The geostatistical study also indicated that the samples available for mapping are well distributed and the uncertainty of the estimates of the thickness and contamination level of the sediments is lowest in areas where the contaminated sediment is most prevalent. ?? 2002 Elsevier Science Ltd. All rights reserved.

Correspondence between large-scale ictal and interictal epileptic networks revealed by single photon emission computed tomography (SPECT) and electroencephalography (EEG)-functional magnetic resonance imaging (fMRI).

PubMed

Tousseyn, Simon; Dupont, Patrick; Goffin, Karolien; Sunaert, Stefan; Van Paesschen, Wim

2015-03-01

Epilepsy is increasingly recognized as a network disorder, but the spatial relationship between ictal and interictal networks is still largely unexplored. In this work, we compared hemodynamic changes related to seizures and interictal spikes on a whole brain scale. Twenty-eight patients with refractory focal epilepsy (14 temporal and 14 extratemporal lobe) underwent both subtraction ictal single photon emission computed tomography (SPECT) coregistered to magnetic resonance imaging (MRI) (SISCOM) and spike-related electroencephalography (EEG-functional MRI (fMRI). SISCOM visualized relative perfusion changes during seizures, whereas EEG-fMRI mapped blood oxygen level-dependent (BOLD) changes related to spikes. Similarity between statistical maps of both modalities was analyzed per patient using the following two measures: (1) correlation between unthresholded statistical maps (Pearson's correlation coefficient) and (2) overlap between thresholded images (Dice coefficient). Overlap was evaluated at a regional level, for hyperperfusions and activations and for hypoperfusions and deactivations separately, using different thresholds. Nonparametric permutation tests were applied to assess statistical significance (p ≤ 0.05). We found significant and positive correlations between hemodynamic changes related to seizures and spikes in 27 (96%) of 28 cases (median correlation coefficient 0.29 [range -0.12 to 0.62]). In 20 (71%) of 28 cases, spatial overlap between hyperperfusion on SISCOM and activation on EEG-fMRI was significantly larger than expected by chance. Congruent changes were not restricted to the territory of the presumed epileptogenic zone, but could be seen at distant sites (e.g., cerebellum and basal ganglia). Overlap between ictal hypoperfusion and interictal deactivation was statistically significant in 22 (79%) of 28 patients. Despite the high rate of congruence, discrepancies were observed for both modalities. We conclude that hemodynamic changes related to seizures and spikes varied spatially with the same sign and within a common network. Overlap was present in regions nearby and distant from discharge origin. Wiley Periodicals, Inc. © 2015 International League Against Epilepsy.

Requirements for a reliable millennium temperature reconstruction

NASA Astrophysics Data System (ADS)

Christiansen, Bo; Ljungqvist, Fredrik

2014-05-01

Quantitative temperature reconstructions are hampered by several problems. Proxy records are sparse which is witnessed by the fact that roughly half of all available high-resolution millennia-long proxy data have been published in the last five years. Moreover, proxies are inhomogeneously distributed around the globe and they often have coarse temporal resolution. The period of overlap between proxies and instrumental observations - the calibration period - is brief and dominated by a strong warming trend. Furthermore, proxies are often only weakly correlated to temperature and it is common that some form of screening procedure is applied to select only informative proxies. We study the influence of these limitations on the reliability of temperature reconstructions for the previous millennium. This influence depends on the spatial and temporal correlation structure of the surface temperature field. It also depends on the reconstruction methodology. We use gridded surface temperature data from GISTEMP and HadCRUT4 to investigate the geographical distribution of the spatial decorrelation length and of the temporal decorrelation time. The spatial decorrelation length varies with more than a factor of 5 with the largest values in the region dominated by the El Nino-Southern Oscillation. The temporal decorrelation time varies less with typical values of 1-2 years over land and 2-5 years over ocean. We also investigate the correlations between proxies and local temperatures (using the 91 proxies from Christiansen and Ljungqvist 2012) and between local temperatures and the NH mean temperature. These correlations have typical values around 0.3 but cover a wide range from weakly negative to larger than 0.8. The results outlined above allow us to identify regions where the effect of the lack of proxies is most important. They also inform us on the consequences of the short calibration period and on the influence of the recent trend. Finally, we demonstrate the effect of a weak proxy/temperature relationship on three different simple reconstruction methodologies. We show that the size and strength of this effect depends strongly on the chosen methodology.

Exploratory study and application of the angular wavelet analysis for assessing the spatial distribution of breakdown spots in Pt/HfO2/Pt structures

NASA Astrophysics Data System (ADS)

Muñoz-Gorriz, J.; Monaghan, S.; Cherkaoui, K.; Suñé, J.; Hurley, P. K.; Miranda, E.

2017-12-01

The angular wavelet analysis is applied for assessing the spatial distribution of breakdown spots in Pt/HfO2/Pt capacitors with areas ranging from 104 to 105 μm2. The breakdown spot lateral sizes are in the range from 1 to 3 μm, and they appear distributed on the top metal electrode as a point pattern. The spots are generated by ramped and constant voltage stresses and are the consequence of microexplosions caused by the formation of shorts spanning the dielectric film. This kind of pattern was analyzed in the past using the conventional spatial analysis tools such as intensity plots, distance histograms, pair correlation function, and nearest neighbours. Here, we show that the wavelet analysis offers an alternative and complementary method for testing whether or not the failure site distribution departs from a complete spatial randomness process in the angular domain. The effect of using different wavelet functions, such as the Haar, Sine, French top hat, Mexican hat, and Morlet, as well as the roles played by the process intensity, the location of the voltage probe, and the aspect ratio of the device, are all discussed.

Remote sensing and spatial statistical techniques for modelling Ommatissus lybicus (Hemiptera: Tropiduchidae) habitat and population densities

PubMed Central

Kwan, Paul; Welch, Mitchell

2017-01-01

In order to understand the distribution and prevalence of Ommatissus lybicus (Hemiptera: Tropiduchidae) as well as analyse their current biographical patterns and predict their future spread, comprehensive and detailed information on the environmental, climatic, and agricultural practices are essential. The spatial analytical techniques such as Remote Sensing and Spatial Statistics Tools, can help detect and model spatial links and correlations between the presence, absence and density of O. lybicus in response to climatic, environmental, and human factors. The main objective of this paper is to review remote sensing and relevant analytical techniques that can be applied in mapping and modelling the habitat and population density of O. lybicus. An exhaustive search of related literature revealed that there are very limited studies linking location-based infestation levels of pests like the O. lybicus with climatic, environmental, and human practice related variables. This review also highlights the accumulated knowledge and addresses the gaps in this area of research. Furthermore, it makes recommendations for future studies, and gives suggestions on monitoring and surveillance methods in designing both local and regional level integrated pest management strategies of palm tree and other affected cultivated crops. PMID:28875085

Remote sensing and spatial statistical techniques for modelling Ommatissus lybicus (Hemiptera: Tropiduchidae) habitat and population densities.

PubMed

Al-Kindi, Khalifa M; Kwan, Paul; R Andrew, Nigel; Welch, Mitchell

2017-01-01

In order to understand the distribution and prevalence of Ommatissus lybicus (Hemiptera: Tropiduchidae) as well as analyse their current biographical patterns and predict their future spread, comprehensive and detailed information on the environmental, climatic, and agricultural practices are essential. The spatial analytical techniques such as Remote Sensing and Spatial Statistics Tools, can help detect and model spatial links and correlations between the presence, absence and density of O. lybicus in response to climatic, environmental, and human factors. The main objective of this paper is to review remote sensing and relevant analytical techniques that can be applied in mapping and modelling the habitat and population density of O. lybicus . An exhaustive search of related literature revealed that there are very limited studies linking location-based infestation levels of pests like the O. lybicus with climatic, environmental, and human practice related variables. This review also highlights the accumulated knowledge and addresses the gaps in this area of research. Furthermore, it makes recommendations for future studies, and gives suggestions on monitoring and surveillance methods in designing both local and regional level integrated pest management strategies of palm tree and other affected cultivated crops.

Global and 3D Spatial Assessment of Neuroinflammation in Rodent Models of Multiple Sclerosis

PubMed Central

Gupta, Shashank; Utoft, Regine; Hasseldam, Henrik; Schmidt-Christensen, Anja; Hannibal, Tine Dahlbaek; Hansen, Lisbeth; Fransén-Pettersson, Nina; Agarwal-Gupta, Noopur; Rozell, Björn; Andersson, Åsa; Holmberg, Dan

2013-01-01

Multiple Sclerosis (MS) is a progressive autoimmune inflammatory and demyelinating disease of the central nervous system (CNS). T cells play a key role in the progression of neuroinflammation in MS and also in the experimental autoimmune encephalomyelitis (EAE) animal models for the disease. A technology for quantitative and 3 dimensional (3D) spatial assessment of inflammation in this and other CNS inflammatory conditions is much needed. Here we present a procedure for 3D spatial assessment and global quantification of the development of neuroinflammation based on Optical Projection Tomography (OPT). Applying this approach to the analysis of rodent models of MS, we provide global quantitative data of the major inflammatory component as a function of the clinical course. Our data demonstrates a strong correlation between the development and progression of neuroinflammation and clinical disease in several mouse and a rat model of MS refining the information regarding the spatial dynamics of the inflammatory component in EAE. This method provides a powerful tool to investigate the effect of environmental and genetic forces and for assessing the therapeutic effects of drug therapy in animal models of MS and other neuroinflammatory/neurodegenerative disorders. PMID:24124545

Suppressive mechanisms in visual motion processing: from perception to intelligence

PubMed Central

Tadin, Duje

2015-01-01

Perception operates on an immense amount of incoming information that greatly exceeds the brain's processing capacity. Because of this fundamental limitation, the ability to suppress irrelevant information is a key determinant of perceptual efficiency. Here, I will review a series of studies investigating suppressive mechanisms in visual motion processing, namely perceptual suppression of large, background-like motions. These spatial suppression mechanisms are adaptive, operating only when sensory inputs are sufficiently robust to guarantee visibility. Converging correlational and causal evidence links these behavioral results with inhibitory center-surround mechanisms, namely those in cortical area MT. Spatial suppression is abnormally weak in several special populations, including the elderly and those with schizophrenia—a deficit that is evidenced by better-than-normal direction discriminations of large moving stimuli. Theoretical work shows that this abnormal weakening of spatial suppression should result in motion segregation deficits, but direct behavioral support of this hypothesis is lacking. Finally, I will argue that the ability to suppress information is a fundamental neural process that applies not only to perception but also to cognition in general. Supporting this argument, I will discuss recent research that shows individual differences in spatial suppression of motion signals strongly predict individual variations in IQ scores. PMID:26299386

Assessing the role of spatial correlations during collective cell spreading

PubMed Central

Treloar, Katrina K.; Simpson, Matthew J.; Binder, Benjamin J.; McElwain, D. L. Sean; Baker, Ruth E.

2014-01-01

Spreading cell fronts are essential features of development, repair and disease processes. Many mathematical models used to describe the motion of cell fronts, such as Fisher's equation, invoke a mean–field assumption which implies that there is no spatial structure, such as cell clustering, present. Here, we examine the presence of spatial structure using a combination of in vitro circular barrier assays, discrete random walk simulations and pair correlation functions. In particular, we analyse discrete simulation data using pair correlation functions to show that spatial structure can form in a spreading population of cells either through sufficiently strong cell–to–cell adhesion or sufficiently rapid cell proliferation. We analyse images from a circular barrier assay describing the spreading of a population of MM127 melanoma cells using the same pair correlation functions. Our results indicate that the spreading melanoma cell populations remain very close to spatially uniform, suggesting that the strength of cell–to–cell adhesion and the rate of cell proliferation are both sufficiently small so as not to induce any spatial patterning in the spreading populations. PMID:25026987

Is the Spatial Distribution of Mankind's Most Basic Economic Traits Determined by Climate and Soil Alone?

PubMed Central

Beck, Jan; Sieber, Andrea

2010-01-01

Background Several authors, most prominently Jared Diamond (1997, Guns, Germs and Steel), have investigated biogeographic determinants of human history and civilization. The timing of the transition to an agricultural lifestyle, associated with steep population growth and consequent societal change, has been suggested to be affected by the availability of suitable organisms for domestication. These factors were shown to quantitatively explain some of the current global inequalities of economy and political power. Here, we advance this approach one step further by looking at climate and soil as sole determining factors. Methodology/Principal Findings As a simplistic ‘null model’, we assume that only climate and soil conditions affect the suitability of four basic landuse types – agriculture, sedentary animal husbandry, nomadic pastoralism and hunting-and-gathering. Using ecological niche modelling (ENM), we derive spatial predictions of the suitability for these four landuse traits and apply these to the Old World and Australia. We explore two aspects of the properties of these predictions, conflict potential and population density. In a calculation of overlap of landuse suitability, we map regions of potential conflict between landuse types. Results are congruent with a number of real, present or historical, regions of conflict between ethnic groups associated with different landuse traditions. Furthermore, we found that our model of agricultural suitability explains a considerable portion of population density variability. We mapped residuals from this correlation, finding geographically highly structured deviations that invite further investigation. We also found that ENM of agricultural suitability correlates with a metric of local wealth generation (Gross Domestic Product, Purchasing Power Parity). Conclusions/Significance From simplified assumptions on the links between climate, soil and landuse we are able to provide good predictions on complex features of human geography. The spatial distribution of deviations from ENM predictions identifies those regions requiring further investigation of potential explanations. Our findings and methodological approaches may be of applied interest, e.g., in the context of climate change. PMID:20463959

Is the spatial distribution of mankind's most basic economic traits determined by climate and soil alone?

PubMed

Beck, Jan; Sieber, Andrea

2010-05-05

Several authors, most prominently Jared Diamond (1997, Guns, Germs and Steel), have investigated biogeographic determinants of human history and civilization. The timing of the transition to an agricultural lifestyle, associated with steep population growth and consequent societal change, has been suggested to be affected by the availability of suitable organisms for domestication. These factors were shown to quantitatively explain some of the current global inequalities of economy and political power. Here, we advance this approach one step further by looking at climate and soil as sole determining factors. As a simplistic 'null model', we assume that only climate and soil conditions affect the suitability of four basic landuse types - agriculture, sedentary animal husbandry, nomadic pastoralism and hunting-and-gathering. Using ecological niche modelling (ENM), we derive spatial predictions of the suitability for these four landuse traits and apply these to the Old World and Australia. We explore two aspects of the properties of these predictions, conflict potential and population density. In a calculation of overlap of landuse suitability, we map regions of potential conflict between landuse types. Results are congruent with a number of real, present or historical, regions of conflict between ethnic groups associated with different landuse traditions. Furthermore, we found that our model of agricultural suitability explains a considerable portion of population density variability. We mapped residuals from this correlation, finding geographically highly structured deviations that invite further investigation. We also found that ENM of agricultural suitability correlates with a metric of local wealth generation (Gross Domestic Product, Purchasing Power Parity). From simplified assumptions on the links between climate, soil and landuse we are able to provide good predictions on complex features of human geography. The spatial distribution of deviations from ENM predictions identifies those regions requiring further investigation of potential explanations. Our findings and methodological approaches may be of applied interest, e.g., in the context of climate change.

SAR Interferometry: On the Coherence Estimation in non Stationary Scenes

NASA Astrophysics Data System (ADS)

Ballatore, P.

2005-05-01

The possibility of producing good quality satellite SAR interferometry allows observations of terrain mass movement as small as millimetric scales, with applicability in researches about landslides, volcanoes, seismology and others. SAR interferometric images is characterized by the presence of random speckle, whose pattern does not correspond to the underlying image structure. However the local brightness of speckle reflects the local echogenicity of the underlying scatters. Specifically, the coherence between interferometric pair is generally considered as an indicator of interferogram quality. Moreover, it leads to useful image segmentations and it can be employed in data mining and database browsing algorithms. SAR coherence is generally computed by substituting the ensemble averages with the spatial averages, by assuming ergodicity in the estimation window sub-areas. Nevertheless, the actual results may depend on the spatial size scale of the sampling window used for the computation. This is especially true in the cases of fast coherence estimator algorithms, which make use of the correlation coefficient's square root (Rignon and van Zyl, IEEE Trans. Geosci.Remote Sensing, vol. 31, n. 4, pp. 896-906, 1993; Guarnieri and Prati, IEEE Trans. Geosci. Remote Sensing, vol. 35, n. 3, pp. 660-669, 1997). In fact, the correlation coefficient is increased by image texture, due to non stationary absolute values within single sample estimation windows. For example, this can happen in the case of mountainous lands, and, specifically, in the case of the Italian Southern Appennini region around Benevento city, which is of specific geophysical attention for its numerous seismic and landslide terrain movements. In these cases, dedicated techniques are applied for compensating texture effects. This presentation shows an example of interferometric coherence image depending on the spatial size of sampling window. Moreover, the different methodologies present in literature for texture effect control are briefly summarized and applied to our specific exemplary case. A quantitative comparison among resulting coherences is illustrated and discussed in terms of different experimental applicability.

Spatial Correlations in Natural Scenes Modulate Response Reliability in Mouse Visual Cortex

PubMed Central

Rikhye, Rajeev V.

2015-01-01

Intrinsic neuronal variability significantly limits information encoding in the primary visual cortex (V1). Certain stimuli can suppress this intertrial variability to increase the reliability of neuronal responses. In particular, responses to natural scenes, which have broadband spatiotemporal statistics, are more reliable than responses to stimuli such as gratings. However, very little is known about which stimulus statistics modulate reliable coding and how this occurs at the neural ensemble level. Here, we sought to elucidate the role that spatial correlations in natural scenes play in reliable coding. We developed a novel noise-masking method to systematically alter spatial correlations in natural movies, without altering their edge structure. Using high-speed two-photon calcium imaging in vivo, we found that responses in mouse V1 were much less reliable at both the single neuron and population level when spatial correlations were removed from the image. This change in reliability was due to a reorganization of between-neuron correlations. Strongly correlated neurons formed ensembles that reliably and accurately encoded visual stimuli, whereas reducing spatial correlations reduced the activation of these ensembles, leading to an unreliable code. Together with an ensemble-specific normalization model, these results suggest that the coordinated activation of specific subsets of neurons underlies the reliable coding of natural scenes. SIGNIFICANCE STATEMENT The natural environment is rich with information. To process this information with high fidelity, V1 neurons have to be robust to noise and, consequentially, must generate responses that are reliable from trial to trial. While several studies have hinted that both stimulus attributes and population coding may reduce noise, the details remain unclear. Specifically, what features of natural scenes are important and how do they modulate reliability? This study is the first to investigate the role of spatial correlations, which are a fundamental attribute of natural scenes, in shaping stimulus coding by V1 neurons. Our results provide new insights into how stimulus spatial correlations reorganize the correlated activation of specific ensembles of neurons to ensure accurate information processing in V1. PMID:26511254

Complex Networks Dynamics Based on Events-Phase Synchronization and Intensity Correlation Applied to The Anomaly Patterns and Extremes in The Tropical African Climate System

NASA Astrophysics Data System (ADS)

Oluoch, K.; Marwan, N.; Trauth, M.; Loew, A.; Kurths, J.

2012-04-01

The African continent lie almost entirely within the tropics and as such its (tropical) climate systems are predominantly governed by the heterogeneous, spatial and temporal variability of the Hadley and Walker circulations. The variabilities in these meridional and zonal circulations lead to intensification or suppression of the intensities, durations and frequencies of the Inter-tropical Convergence Zone (ICTZ) migration, trade winds and subtropical high-pressure regions and the continental monsoons. The above features play a central role in determining the African rainfall spatial and temporal variability patterns. The current understanding of these climate features and their influence on the rainfall patterns is not sufficiently understood. Like many real-world systems, atmospheric-oceanic processes exhibit non-linear properties that can be better explored using non-linear (NL) methods of time-series analysis. Over the recent years, the complex network approach has evolved as a powerful new player in understanding spatio-temporal dynamics and evolution of complex systems. Together with NL techniques, it is continuing to find new applications in many areas of science and technology including climate research. We would like to use these two powerful methods to understand the spatial structure and dynamics of African rainfall anomaly patterns and extremes. The method of event synchronization (ES) developed by Quiroga et al., 2002 and first applied to climate networks by Malik et al., 2011 looks at correlations with a dynamic time lag and as such, it is a more intuitive way to correlate a complex and heterogeneous system like climate networks than a fixed time delay most commonly used. On the other hand, the short comings of ES is its lack of vigorous test statistics for the significance level of the correlations, and the fact that only the events' time indices are synchronized while all information about how the relative intensities propagate within network framework is lost. The new method we present is motivated by the ES and borrows ideas from signal processing where a signal is represented by its intensity and frequency. Even though the anomaly signals are not periodic, the idea of phase synchronization is not far fetched. It brings into one umbrella, the traditionally known linear Intensity correlation methods like Pearson correlation, spear-man's rank or non-linear ones like mutual information with the ES for non-linear temporal synchronization. The intensity correlation is only performed where there is a temporal synchronization. The former just measures how constant the intensity differences are. In other words, how monotonic are the two functions. The overall measure of correlation and synchronization is the product of the two coefficients. Complex networks constructed by this technique has all the advantages inherent in each of the techniques it borrows. But, it is more superior and able to uncover many known and unknown dynamical features in rainfall field or any variable of interest. The main aim of this work is to develop a method that can identify the footprints of coherent or incoherent structures within the ICTZ, the African and the Indian monsoons and the ENSO signal on the tropical African continent and their temporal evolution.

Implementation of a flow-dependent background error correlation length scale formulation in the NEMOVAR OSTIA system

NASA Astrophysics Data System (ADS)

Fiedler, Emma; Mao, Chongyuan; Good, Simon; Waters, Jennifer; Martin, Matthew

2017-04-01

OSTIA is the Met Office's Operational Sea Surface Temperature (SST) and Ice Analysis system, which produces L4 (globally complete, gridded) analyses on a daily basis. Work is currently being undertaken to replace the original OI (Optimal Interpolation) data assimilation scheme with NEMOVAR, a 3D-Var data assimilation method developed for use with the NEMO ocean model. A dual background error correlation length scale formulation is used for SST in OSTIA, as implemented in NEMOVAR. Short and long length scales are combined according to the ratio of the decomposition of the background error variances into short and long spatial correlations. The pre-defined background error variances vary spatially and seasonally, but not on shorter time-scales. If the derived length scales applied to the daily analysis are too long, SST features may be smoothed out. Therefore a flow-dependent component to determining the effective length scale has also been developed. The total horizontal gradient of the background SST field is used to identify regions where the length scale should be shortened. These methods together have led to an improvement in the resolution of SST features compared to the previous OI analysis system, without the introduction of spurious noise. This presentation will show validation results for feature resolution in OSTIA using the OI scheme, the dual length scale NEMOVAR scheme, and the flow-dependent implementation.

Regional effects of agricultural conservation practices on nutrient transport in the Upper Mississippi River Basin

USGS Publications Warehouse

Garcia, Ana Maria.; Alexander, Richard B.; Arnold, Jeffrey G.; Norfleet, Lee; White, Michael J.; Robertson, Dale M.; Schwarz, Gregory E.

2016-01-01

Despite progress in the implementation of conservation practices, related improvements in water quality have been challenging to measure in larger river systems. In this paper we quantify these downstream effects by applying the empirical U.S. Geological Survey water-quality model SPARROW to investigate whether spatial differences in conservation intensity were statistically correlated with variations in nutrient loads. In contrast to other forms of water quality data analysis, the application of SPARROW controls for confounding factors such as hydrologic variability, multiple sources and environmental processes. A measure of conservation intensity was derived from the USDA-CEAP regional assessment of the Upper Mississippi River and used as an explanatory variable in a model of the Upper Midwest. The spatial pattern of conservation intensity was negatively correlated (p = 0.003) with the total nitrogen loads in streams in the basin. Total phosphorus loads were weakly negatively correlated with conservation (p = 0.25). Regional nitrogen reductions were estimated to range from 5 to 34% and phosphorus reductions from 1 to 10% in major river basins of the Upper Mississippi region. The statistical associations between conservation and nutrient loads are consistent with hydrological and biogeochemical processes such as denitrification. The results provide empirical evidence at the regional scale that conservation practices have had a larger statistically detectable effect on nitrogen than on phosphorus loadings in streams and rivers of the Upper Mississippi Basin.

Regional Effects of Agricultural Conservation Practices on Nutrient Transport in the Upper Mississippi River Basin.

PubMed

García, Ana María; Alexander, Richard B; Arnold, Jeffrey G; Norfleet, Lee; White, Michael J; Robertson, Dale M; Schwarz, Gregory

2016-07-05

Despite progress in the implementation of conservation practices, related improvements in water quality have been challenging to measure in larger river systems. In this paper we quantify these downstream effects by applying the empirical U.S. Geological Survey water-quality model SPARROW to investigate whether spatial differences in conservation intensity were statistically correlated with variations in nutrient loads. In contrast to other forms of water quality data analysis, the application of SPARROW controls for confounding factors such as hydrologic variability, multiple sources and environmental processes. A measure of conservation intensity was derived from the USDA-CEAP regional assessment of the Upper Mississippi River and used as an explanatory variable in a model of the Upper Midwest. The spatial pattern of conservation intensity was negatively correlated (p = 0.003) with the total nitrogen loads in streams in the basin. Total phosphorus loads were weakly negatively correlated with conservation (p = 0.25). Regional nitrogen reductions were estimated to range from 5 to 34% and phosphorus reductions from 1 to 10% in major river basins of the Upper Mississippi region. The statistical associations between conservation and nutrient loads are consistent with hydrological and biogeochemical processes such as denitrification. The results provide empirical evidence at the regional scale that conservation practices have had a larger statistically detectable effect on nitrogen than on phosphorus loadings in streams and rivers of the Upper Mississippi Basin.

Anisotropic patterns of liver cancer prevalence in Guangxi in Southwest China: is local climate a contributing factor?

PubMed

Deng, Wei; Long, Long; Tang, Xian-Yan; Huang, Tian-Ren; Li, Ji-Lin; Rong, Min-Hua; Li, Ke-Zhi; Liu, Hai-Zhou

2015-01-01

Geographic information system (GIS) technology has useful applications for epidemiology, enabling the detection of spatial patterns of disease dispersion and locating geographic areas at increased risk. In this study, we applied GIS technology to characterize the spatial pattern of mortality due to liver cancer in the autonomous region of Guangxi Zhuang in southwest China. A database with liver cancer mortality data for 1971-1973, 1990-1992, and 2004-2005, including geographic locations and climate conditions, was constructed, and the appropriate associations were investigated. It was found that the regions with the highest mortality rates were central Guangxi with Guigang City at the center, and southwest Guangxi centered in Fusui County. Regions with the lowest mortality rates were eastern Guangxi with Pingnan County at the center, and northern Guangxi centered in Sanjiang and Rongshui counties. Regarding climate conditions, in the 1990s the mortality rate of liver cancer positively correlated with average temperature and average minimum temperature, and negatively correlated with average precipitation. In 2004 through 2005, mortality due to liver cancer positively correlated with the average minimum temperature. Regions of high mortality had lower average humidity and higher average barometric pressure than did regions of low mortality. Our results provide information to benefit development of a regional liver cancer prevention program in Guangxi, and provide important information and a reference for exploring causes of liver cancer.

Correlation of near-infrared spectroscopy and transcranial magnetic stimulation of the motor cortex in overt reading and musical tasks.

PubMed

Lo, Y L; Zhang, H H; Wang, C C; Chin, Z Y; Fook-Chong, S; Gabriel, C; Guan, C T

2009-01-01

In overt reading and singing tasks, actual vocalization of words in a rhythmic fashion is performed. During execution of these tasks, the role of underlying vascular processes in relation to cortical excitability changes in a spatial manner is uncertain. Our objective was to investigate cortical excitability changes during reading and singing with transcranial magnetic stimulation (TMS), as well as vascular changes with nearinfrared spectroscopy (NIRS). Findings with TMS and NIRS were correlated. TMS and NIRS recordings were performed in 5 normal subjects while they performed reading and singing tasks separately. TMS was applied over the left motor cortex at 9 positions 2.5 cm apart. NIRS recordings were made over these identical positions. Although both TMS and NIRS showed significant mean cortical excitability and hemodynamic changes from baseline during vocalization tasks, there was no significant spatial correlation of these changes evaluated with the 2 techniques over the left motor cortex. Our findings suggest that increased left-sided cortical excitability from overt vocalization tasks in the corresponding "hand area" were the result of "functional connectivity," rather than an underlying "vascular overflow mechanism" from the adjacent speech processing or face/mouth areas. Our findings also imply that functional neurophysiological and vascular methods may evaluate separate underlying processes, although subjects performed identical vocalization tasks. Future research combining similar methodologies should embrace this aspect and harness their separate capabilities.

Coarse-Grained Theory of Biological Charge Transfer with Spatially and Temporally Correlated Noise.

PubMed

Liu, Chaoren; Beratan, David N; Zhang, Peng

2016-04-21

System-environment interactions are essential in determining charge-transfer (CT) rates and mechanisms. We developed a computationally accessible method, suitable to simulate CT in flexible molecules (i.e., DNA) with hundreds of sites, where the system-environment interactions are explicitly treated with numerical noise modeling of time-dependent site energies and couplings. The properties of the noise are tunable, providing us a flexible tool to investigate the detailed effects of correlated thermal fluctuations on CT mechanisms. The noise is parametrizable by molecular simulation and quantum calculation results of specific molecular systems, giving us better molecular resolution in simulating the system-environment interactions than sampling fluctuations from generic spectral density functions. The spatially correlated thermal fluctuations among different sites are naturally built-in in our method but are not readily incorporated using approximate spectral densities. Our method has quantitative accuracy in systems with small redox potential differences (

A hierarchical spatial model of avian abundance with application to Cerulean Warblers

USGS Publications Warehouse

Thogmartin, Wayne E.; Sauer, John R.; Knutson, Melinda G.

2004-01-01

Surveys collecting count data are the primary means by which abundance is indexed for birds. These counts are confounded, however, by nuisance effects including observer effects and spatial correlation between counts. Current methods poorly accommodate both observer and spatial effects because modeling these spatially autocorrelated counts within a hierarchical framework is not practical using standard statistical approaches. We propose a Bayesian approach to this problem and provide as an example of its implementation a spatial model of predicted abundance for the Cerulean Warbler (Dendroica cerulea) in the Prairie-Hardwood Transition of the upper midwestern United States. We used an overdispersed Poisson regression with fixed and random effects, fitted by Markov chain Monte Carlo methods. We used 21 years of North American Breeding Bird Survey counts as the response in a loglinear function of explanatory variables describing habitat, spatial relatedness, year effects, and observer effects. The model included a conditional autoregressive term representing potential correlation between adjacent route counts. Categories of explanatory habitat variables in the model included land cover composition and configuration, climate, terrain heterogeneity, and human influence. The inherent hierarchy in the model was from counts occurring, in part, as a function of observers within survey routes within years. We found that the percentage of forested wetlands, an index of wetness potential, and an interaction between mean annual precipitation and deciduous forest patch size best described Cerulean Warbler abundance. Based on a map of relative abundance derived from the posterior parameter estimates, we estimated that only 15% of the species' population occurred on federal land, necessitating active engagement of public landowners and state agencies in the conservation of the breeding habitat for this species. Models of this type can be applied to any data in which the response is counts, such as animal counts, activity (e.g.,nest) counts, or species richness. The most noteworthy practical application of this spatial modeling approach is the ability to map relative species abundance. The functional relationships that we elucidated for the Cerulean Warbler provide a basis for the development of management programs and may serve to focus management and monitoring on areas and habitat variables important to Cerulean Warblers.

Model-based estimators of density and connectivity to inform conservation of spatially structured populations

USGS Publications Warehouse

Morin, Dana J.; Fuller, Angela K.; Royle, J. Andrew; Sutherland, Chris

2017-01-01

Conservation and management of spatially structured populations is challenging because solutions must consider where individuals are located, but also differential individual space use as a result of landscape heterogeneity. A recent extension of spatial capture–recapture (SCR) models, the ecological distance model, uses spatial encounter histories of individuals (e.g., a record of where individuals are detected across space, often sequenced over multiple sampling occasions), to estimate the relationship between space use and characteristics of a landscape, allowing simultaneous estimation of both local densities of individuals across space and connectivity at the scale of individual movement. We developed two model-based estimators derived from the SCR ecological distance model to quantify connectivity over a continuous surface: (1) potential connectivity—a metric of the connectivity of areas based on resistance to individual movement; and (2) density-weighted connectivity (DWC)—potential connectivity weighted by estimated density. Estimates of potential connectivity and DWC can provide spatial representations of areas that are most important for the conservation of threatened species, or management of abundant populations (i.e., areas with high density and landscape connectivity), and thus generate predictions that have great potential to inform conservation and management actions. We used a simulation study with a stationary trap design across a range of landscape resistance scenarios to evaluate how well our model estimates resistance, potential connectivity, and DWC. Correlation between true and estimated potential connectivity was high, and there was positive correlation and high spatial accuracy between estimated DWC and true DWC. We applied our approach to data collected from a population of black bears in New York, and found that forested areas represented low levels of resistance for black bears. We demonstrate that formal inference about measures of landscape connectivity can be achieved from standard methods of studying animal populations which yield individual encounter history data such as camera trapping. Resulting biological parameters including resistance, potential connectivity, and DWC estimate the spatial distribution and connectivity of the population within a statistical framework, and we outline applications to many possible conservation and management problems.

Correlations between soil respiration and soil properties in sugarcane areas under green and slash-and-burn management systems

NASA Astrophysics Data System (ADS)

Rodrigo Panosso, Alan; Milori, Débora M. B. P.; Marques Júnior, José; Martin-Neto, Ladislau; La Scala, Newton, Jr.

2010-05-01

Soil management causes changes in soil physical, chemical, and biological properties that consequently affect its CO2 emission. In this work we studied soil respiration (FCO2) in areas with sugarcane production in southern Brazil under two different sugarcane management systems: green (G), consisting of mechanized harvesting that produces a large amount of crop residues left on the soil surface, and slash-and-burn (SB), in which the residues are burned before manual harvest, leaving no residues on the soil surface. The study was conducted after the harvest period in two side-by-side grids installed in adjacent areas, having 20 measurement points each. The objective of this work was to determinate whether soil physical and chemical properties within each plot were useful in order to explain the spatial variability of FCO2, supposedly influence by each management system. Most of the soil physical properties studied showed no significant differences between management systems, but on the other hand most of the chemical properties differed significantly when SB and G areas were compared. Total FCO2 was 31% higher in the SB plot (729 g CO2 m-2) when compared to the G plot (557 g CO2 m-2) throughout the 70-day period after harvest studied. This seems to be related to the sensitivity of FCO2 to precipitation events, as respiration in this plot increased significantly with increases in soil moisture. Despite temporal variability showed to be positively related to soil moisture, inside each management system there was a negative correlation (p<0.01) between the spatial changes of FCO2 and soil moisture (MS), R= -0.56 and -0.59 for G and SB respectively. There was no spatial correlation between FCO2 and soil organic matter in each management system, however, the humification index (Hum) of organic matter was negatively linear correlated with FCO2 in SB (R= -0.53, p<0.05) while positively linear correlated in G area (R=0.42, p<0.10). The multiple regression model analysis applied in each management system indicates that 63% of the FCO2 spatial variability in G managed could be explained by the model: FCO2(G)= 4.11978 -0.07672MS + 0.0045Hum +1.5352K -0.04474FWP, where K and FWP are potassium content and free water porosity in G area, respectively. On the other hand, 75% of FCO2 spatial variability in SB managed plot was accounted by the model: FCO2(SB) = 10.66774 -0.08624MS -0.02904Hum -2.42548K. Therefore, soil moisture, humification index of organic matter and potassium level were the main properties able to explain the spatial variability of FCO2 in both sugarcane management systems. This result indicates that changes in sugarcane management systems could result in changes on the soil chemical properties, mostly, especially humification index of organic matter. It seems that in conversion from slash-and-burn to green harvest system, free water porosity turns to be an important aspect in order to explain part of FCO2 spatial variability in green managed system.

Spatial correlation of the dynamic propensity of a glass-forming liquid

NASA Astrophysics Data System (ADS)

Razul, M. Shajahan G.; Matharoo, Gurpreet S.; Poole, Peter H.

2011-06-01

We present computer simulation results on the dynamic propensity (as defined by Widmer-Cooper et al 2004 Phys. Rev. Lett. 93 135701) in a Kob-Andersen binary Lennard-Jones liquid system consisting of 8788 particles. We compute the spatial correlation function for the dynamic propensity as a function of both the reduced temperature T, and the time scale on which the particle displacements are measured. For T <= 0.6, we find that non-zero correlations occur at the largest length scale accessible in our system. We also show that a cluster-size analysis of particles with extremal values of the dynamic propensity, as well as 3D visualizations, reveal spatially correlated regions that approach the size of our system as T decreases, consistently with the behavior of the spatial correlation function. Next, we define and examine the 'coordination propensity', the isoconfigurational average of the coordination number of the minority B particles around the majority A particles. We show that a significant correlation exists between the spatial fluctuations of the dynamic and coordination propensities. In addition, we find non-zero correlations of the coordination propensity occurring at the largest length scale accessible in our system for all T in the range 0.466 < T < 1.0. We discuss the implications of these results for understanding the length scales of dynamical heterogeneity in glass-forming liquids.

Laser correlation velocimetry performance in diesel applications: spatial selectivity and velocity sensitivity

NASA Astrophysics Data System (ADS)

Hespel, Camille; Blaisot, Jean-Bernard; Gazon, Matthieu; Godard, Gilles

2012-07-01

The characterization of diesel jets in the near field of the nozzle exit still presents challenges for experimenters. Detailed velocity measurements are needed to characterize diesel injector performance and also to establish boundary conditions for CFD codes. The present article examines the efficiency of laser correlation velocimetry (LCV) applied to diesel spray characterization. A new optical configuration based on a long-distance microscope was tested, and special care was taken to examine the spatial selectivity of the technique. Results show that the depth of the measurement volume (along the laser beam) of LCV extends beyond the depth of field of the imaging setup. The LCV results were also found to be particularly sensitive to high-speed elements of a spray. Results from high-pressure diesel jets in a back-pressure environment indicate that this technique is particularly suited to the very near field of the nozzle exit, where the flow is the narrowest and where the velocity distribution is not too large. It is also shown that the performance of the LCV technique is controlled by the filtering and windowing parameters used in the processing of the raw signals.

Relating remotely sensed optical variability to marine benthic biodiversity.

PubMed

Herkül, Kristjan; Kotta, Jonne; Kutser, Tiit; Vahtmäe, Ele

2013-01-01

Biodiversity is important in maintaining ecosystem viability, and the availability of adequate biodiversity data is a prerequisite for the sustainable management of natural resources. As such, there is a clear need to map biodiversity at high spatial resolutions across large areas. Airborne and spaceborne optical remote sensing is a potential tool to provide such biodiversity data. The spectral variation hypothesis (SVH) predicts a positive correlation between spectral variability (SV) of a remotely sensed image and biodiversity. The SVH has only been tested on a few terrestrial plant communities. Our study is the first attempt to apply the SVH in the marine environment using hyperspectral imagery recorded by Compact Airborne Spectrographic Imager (CASI). All coverage-based diversity measures of benthic macrophytes and invertebrates showed low but statistically significant positive correlations with SV whereas the relationship between biomass-based diversity measures and SV were weak or lacking. The observed relationships did not vary with spatial scale. SV had the highest independent effect among predictor variables in the statistical models of coverage-derived total benthic species richness and Shannon index. Thus, the relevance of SVH in marine benthic habitats was proved and this forms a prerequisite for the future use of SV in benthic biodiversity assessments.

Seniority and orbital symmetry as tools for establishing a full configuration interaction hierarchy.

PubMed

Bytautas, Laimutis; Henderson, Thomas M; Jiménez-Hoyos, Carlos A; Ellis, Jason K; Scuseria, Gustavo E

2011-07-28

We explore the concept of seniority number (defined as the number of unpaired electrons in a determinant) when applied to the problem of electron correlation in atomic and molecular systems. Although seniority is a good quantum number only for certain model Hamiltonians (such as the pairing Hamiltonian), we show that it provides a useful partitioning of the electronic full configuration interaction (FCI) wave function into rapidly convergent Hilbert subspaces whose weight diminishes as its seniority number increases. The primary focus of this study is the adequate description of static correlation effects. The examples considered are the ground states of the helium, beryllium, and neon atoms, the symmetric dissociation of the N(2) and CO(2) molecules, as well as the symmetric dissociation of an H(8) hydrogen chain. It is found that the symmetry constraints that are normally placed on the spatial orbitals greatly affect the convergence rate of the FCI expansion. The energy relevance of the seniority zero sector (determinants with all paired electrons) increases dramatically if orbitals of broken spatial symmetry (as those commonly used for Hubbard Hamiltonian studies) are allowed in the wave function construction. © 2011 American Institute of Physics

Nonlinear bivariate dependency of price-volume relationships in agricultural commodity futures markets: A perspective from Multifractal Detrended Cross-Correlation Analysis

NASA Astrophysics Data System (ADS)

He, Ling-Yun; Chen, Shu-Peng

2011-01-01

Nonlinear dependency between characteristic financial and commodity market quantities (variables) is crucially important, especially between trading volume and market price. Studies on nonlinear dependency between price and volume can provide practical insights into market trading characteristics, as well as the theoretical understanding of market dynamics. Actually, nonlinear dependency and its underlying dynamical mechanisms between price and volume can help researchers and technical analysts in understanding the market dynamics by integrating the market variables, instead of investigating them in the current literature. Therefore, for investigating nonlinear dependency of price-volume relationships in agricultural commodity futures markets in China and the US, we perform a new statistical test to detect cross-correlations and apply a new methodology called Multifractal Detrended Cross-Correlation Analysis (MF-DCCA), which is an efficient algorithm to analyze two spatially or temporally correlated time series. We discuss theoretically the relationship between the bivariate cross-correlation exponent and the generalized Hurst exponents for time series of respective variables. We also perform an empirical study and find that there exists a power-law cross-correlation between them, and that multifractal features are significant in all the analyzed agricultural commodity futures markets.

Spatial cross-correlation of undisturbed, natural shortleaf pine stands in northern Georgia

Treesearch

Robin M. Reich; Raymond L. Czaplewski; William A. Bechtold

1994-01-01

In this study a cross-correlation statistic is used to analyse the spatial relationship among stand characteristics of natural, undisturbed shortleaf pine stands sampled during 1961-72 and 1972-82 in northern Georgia. Stand characteristics included stand age, site index, tree density, hardwood competition, and mortality. In each time period, the spatial cross-...

Correlation of gravestone decay and air quality 1960-2010

NASA Astrophysics Data System (ADS)

Mooers, H. D.; Carlson, M. J.; Harrison, R. M.; Inkpen, R. J.; Loeffler, S.

2017-03-01

Evaluation of spatial and temporal variability in surface recession of lead-lettered Carrara marble gravestones provides a quantitative measure of acid flux to the stone surfaces and is closely related to local land use and air quality. Correlation of stone decay, land use, and air quality for the period after 1960 when reliable estimates of atmospheric pollution are available is evaluated. Gravestone decay and SO2 measurements are interpolated spatially using deterministic and geostatistical techniques. A general lack of spatial correlation was identified and therefore a land-use-based technique for correlation of stone decay and air quality is employed. Decadally averaged stone decay is highly correlated with land use averaged spatially over an optimum radius of ≈7 km even though air quality, determined by records from the UK monitoring network, is not highly correlated with gravestone decay. The relationships among stone decay, air-quality, and land use is complicated by the relatively low spatial density of both gravestone decay and air quality data and the fact that air quality data is available only as annual averages and therefore seasonal dependence cannot be evaluated. However, acid deposition calculated from gravestone decay suggests that the deposition efficiency of SO2 has increased appreciably since 1980 indicating an increase in the SO2 oxidation process possibly related to reactions with ammonia.

Horizontal Residual Mean Circulation: Evaluation of Spatial Correlations in Coarse Resolution Ocean Models

NASA Astrophysics Data System (ADS)

Li, Y.; McDougall, T. J.

2016-02-01

Coarse resolution ocean models lack knowledge of spatial correlations between variables on scales smaller than the grid scale. Some researchers have shown that these spatial correlations play a role in the poleward heat flux. In order to evaluate the poleward transport induced by the spatial correlations at a fixed horizontal position, an equation is obtained to calculate the approximate transport from velocity gradients. The equation involves two terms that can be added to the quasi-Stokes streamfunction (based on temporal correlations) to incorporate the contribution of spatial correlations. Moreover, these new terms do not need to be parameterized and is ready to be evaluated by using model data directly. In this study, data from a high resolution ocean model have been used to estimate the accuracy of this HRM approach for improving the horizontal property fluxes in coarse-resolution ocean models. A coarse grid is formed by sub-sampling and box-car averaging the fine grid scale. The transport calculated on the coarse grid is then compared to the transport on original high resolution grid scale accumulated over a corresponding number of grid boxes. The preliminary results have shown that the estimate on coarse resolution grids roughly match the corresponding transports on high resolution grids.

Examination of the Spatial Correlation of Statistics Information in the Ultrasonic Echo from Diseased Liver

NASA Astrophysics Data System (ADS)

Yamaguchi, Tadashi; Hachiya, Hiroyuki; Kamiyama, Naohisa; Moriyasu, Fuminori

2002-05-01

To realize a quantitative diagnosis of liver cirrhosis, we have been analyzing the characteristics of echo amplitude in B-mode images. Realizing the distinction between liver diseases such as liver cirrhosis and chronic hepatitis is required in the field of medical ultrasonics. In this study, we examine the spatial correlation, with the coefficient of correlation between the frames and the amplitude characteristics of each frame, using the volumetric data of RF echo signals from normal and diseased liver. It is found that there is a relationship between the tissue structure of liver and the spatial correlation of echo information.

Temporal and spatial analysis of psittacosis in association with poultry farming in the Netherlands, 2000-2015.

PubMed

Hogerwerf, Lenny; Holstege, Manon M C; Benincà, Elisa; Dijkstra, Frederika; van der Hoek, Wim

2017-07-26

Human psittacosis is a highly under diagnosed zoonotic disease, commonly linked to psittacine birds. Psittacosis in birds, also known as avian chlamydiosis, is endemic in poultry, but the risk for people living close to poultry farms is unknown. Therefore, our study aimed to explore the temporal and spatial patterns of human psittacosis infections and identify possible associations with poultry farming in the Netherlands. We analysed data on 700 human cases of psittacosis notified between 01-01-2000 and 01-09-2015. First, we studied the temporal behaviour of psittacosis notifications by applying wavelet analysis. Then, to identify possible spatial patterns, we applied spatial cluster analysis. Finally, we investigated the possible spatial association between psittacosis notifications and data on the Dutch poultry sector at municipality level using a multivariable model. We found a large spatial cluster that covered a highly poultry-dense area but additional clusters were found in areas that had a low poultry density. There were marked geographical differences in the awareness of psittacosis and the amount and the type of laboratory diagnostics used for psittacosis, making it difficult to draw conclusions about the correlation between the large cluster and poultry density. The multivariable model showed that the presence of chicken processing plants and slaughter duck farms in a municipality was associated with a higher rate of human psittacosis notifications. The significance of the associations was influenced by the inclusion or exclusion of farm density in the model. Our temporal and spatial analyses showed weak associations between poultry-related variables and psittacosis notifications. Because of the low number of psittacosis notifications available for analysis, the power of our analysis was relative low. Because of the exploratory nature of this research, the associations found cannot be interpreted as evidence for airborne transmission of psittacosis from poultry to the general population. Further research is needed to determine the prevalence of C. psittaci in Dutch poultry. Also, efforts to promote PCR-based testing for C. psittaci and genotyping for source tracing are important to reduce the diagnostic deficit, and to provide better estimates of the human psittacosis burden, and the possible role of poultry.

Bilateral filtering using the full noise covariance matrix applied to x-ray phase-contrast computed tomography.

PubMed

Allner, S; Koehler, T; Fehringer, A; Birnbacher, L; Willner, M; Pfeiffer, F; Noël, P B

2016-05-21

The purpose of this work is to develop an image-based de-noising algorithm that exploits complementary information and noise statistics from multi-modal images, as they emerge in x-ray tomography techniques, for instance grating-based phase-contrast CT and spectral CT. Among the noise reduction methods, image-based de-noising is one popular approach and the so-called bilateral filter is a well known algorithm for edge-preserving filtering. We developed a generalization of the bilateral filter for the case where the imaging system provides two or more perfectly aligned images. The proposed generalization is statistically motivated and takes the full second order noise statistics of these images into account. In particular, it includes a noise correlation between the images and spatial noise correlation within the same image. The novel generalized three-dimensional bilateral filter is applied to the attenuation and phase images created with filtered backprojection reconstructions from grating-based phase-contrast tomography. In comparison to established bilateral filters, we obtain improved noise reduction and at the same time a better preservation of edges in the images on the examples of a simulated soft-tissue phantom, a human cerebellum and a human artery sample. The applied full noise covariance is determined via cross-correlation of the image noise. The filter results yield an improved feature recovery based on enhanced noise suppression and edge preservation as shown here on the example of attenuation and phase images captured with grating-based phase-contrast computed tomography. This is supported by quantitative image analysis. Without being bound to phase-contrast imaging, this generalized filter is applicable to any kind of noise-afflicted image data with or without noise correlation. Therefore, it can be utilized in various imaging applications and fields.

Performance Comparison of Big Data Analytics With NEXUS and Giovanni

NASA Astrophysics Data System (ADS)

Jacob, J. C.; Huang, T.; Lynnes, C.

2016-12-01

NEXUS is an emerging data-intensive analysis framework developed with a new approach for handling science data that enables large-scale data analysis. It is available through open source. We compare performance of NEXUS and Giovanni for 3 statistics algorithms applied to NASA datasets. Giovanni is a statistics web service at NASA Distributed Active Archive Centers (DAACs). NEXUS is a cloud-computing environment developed at JPL and built on Apache Solr, Cassandra, and Spark. We compute global time-averaged map, correlation map, and area-averaged time series. The first two algorithms average over time to produce a value for each pixel in a 2-D map. The third algorithm averages spatially to produce a single value for each time step. This talk is our report on benchmark comparison findings that indicate 15x speedup with NEXUS over Giovanni to compute area-averaged time series of daily precipitation rate for the Tropical Rainfall Measuring Mission (TRMM with 0.25 degree spatial resolution) for the Continental United States over 14 years (2000-2014) with 64-way parallelism and 545 tiles per granule. 16-way parallelism with 16 tiles per granule worked best with NEXUS for computing an 18-year (1998-2015) TRMM daily precipitation global time averaged map (2.5 times speedup) and 18-year global map of correlation between TRMM daily precipitation and TRMM real time daily precipitation (7x speedup). These and other benchmark results will be presented along with key lessons learned in applying the NEXUS tiling approach to big data analytics in the cloud.

Displacement fields from point cloud data: Application of particle imaging velocimetry to landslide geodesy

USGS Publications Warehouse

Aryal, Arjun; Brooks, Benjamin A.; Reid, Mark E.; Bawden, Gerald W.; Pawlak, Geno

2012-01-01

Acquiring spatially continuous ground-surface displacement fields from Terrestrial Laser Scanners (TLS) will allow better understanding of the physical processes governing landslide motion at detailed spatial and temporal scales. Problems arise, however, when estimating continuous displacement fields from TLS point-clouds because reflecting points from sequential scans of moving ground are not defined uniquely, thus repeat TLS surveys typically do not track individual reflectors. Here, we implemented the cross-correlation-based Particle Image Velocimetry (PIV) method to derive a surface deformation field using TLS point-cloud data. We estimated associated errors using the shape of the cross-correlation function and tested the method's performance with synthetic displacements applied to a TLS point cloud. We applied the method to the toe of the episodically active Cleveland Corral Landslide in northern California using TLS data acquired in June 2005–January 2007 and January–May 2010. Estimated displacements ranged from decimeters to several meters and they agreed well with independent measurements at better than 9% root mean squared (RMS) error. For each of the time periods, the method provided a smooth, nearly continuous displacement field that coincides with independently mapped boundaries of the slide and permits further kinematic and mechanical inference. For the 2010 data set, for instance, the PIV-derived displacement field identified a diffuse zone of displacement that preceded by over a month the development of a new lateral shear zone. Additionally, the upslope and downslope displacement gradients delineated by the dense PIV field elucidated the non-rigid behavior of the slide.

Age-associated patterns in gray matter volume, cerebral perfusion and BOLD oscillations in children and adolescents.

PubMed

Bray, Signe

2017-05-01

Healthy brain development involves changes in brain structure and function that are believed to support cognitive maturation. However, understanding how structural changes such as grey matter thinning relate to functional changes is challenging. To gain insight into structure-function relationships in development, the present study took a data driven approach to define age-related patterns of variation in gray matter volume (GMV), cerebral blood flow (CBF) and blood-oxygen level dependent (BOLD) signal variation (fractional amplitude of low-frequency fluctuations; fALFF) in 59 healthy children aged 7-18 years, and examined relationships between modalities. Principal components analysis (PCA) was applied to each modality in parallel, and participant scores for the top components were assessed for age associations. We found that decompositions of CBF, GMV and fALFF all included components for which scores were significantly associated with age. The dominant patterns in GMV and CBF showed significant (GMV) or trend level (CBF) associations with age and a strong spatial overlap, driven by increased signal intensity in default mode network (DMN) regions. GMV, CBF and fALFF additionally showed components accounting for 3-5% of variability with significant age associations. However, these patterns were relatively spatially independent, with small-to-moderate overlap between modalities. Independence of age effects was further demonstrated by correlating individual subject maps between modalities: CBF was significantly less correlated with GMV and fALFF in older children relative to younger. These spatially independent effects of age suggest that the parallel decline observed in global GMV and CBF may not reflect spatially synchronized processes. Hum Brain Mapp 38:2398-2407, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Simultaneous narrowband ultrasonic strain-flow imaging

NASA Astrophysics Data System (ADS)

Tsou, Jean K.; Mai, Jerome J.; Lupotti, Fermin A.; Insana, Michael F.

2004-04-01

We are summarizing new research aimed at forming spatially and temporally registered combinations of strain and color-flow images using echo data recorded from a commercial ultrasound system. Applications include diagnosis of vascular diseases and tumor malignancies. The challenge is to meet the diverse needs of each measurement. The approach is to first apply eigenfilters that separate echo components from moving tissues and blood flow, and then estimate blood velocity and tissue displacement from the filtered-IQ-signal phase modulations. At the cost of a lower acquisition frame rate, we find the autocorrelation strain estimator yields higher resolution strain estimate than the cross-correlator since estimates are made from ensembles at a single point in space. The technique is applied to in vivo carotid imaging, to demonstrate the sensitivity for strain-flow vascular imaging.

Spatio-temporal patterns of key exploited marine species in the Northwestern Mediterranean Sea.

PubMed

Morfin, Marie; Fromentin, Jean-Marc; Jadaud, Angélique; Bez, Nicolas

2012-01-01

This study analyzes the temporal variability/stability of the spatial distributions of key exploited species in the Gulf of Lions (Northwestern Mediterranean Sea). To do so, we analyzed data from the MEDITS bottom-trawl scientific surveys from 1994 to 2010 at 66 fixed stations and selected 12 key exploited species. We proposed a geostatistical approach to handle zero-inflated and non-stationary distributions and to test for the temporal stability of the spatial structures. Empirical Orthogonal Functions and other descriptors were then applied to investigate the temporal persistence and the characteristics of the spatial patterns. The spatial structure of the distribution (i.e. the pattern of spatial autocorrelation) of the 12 key species studied remained highly stable over the time period sampled. The spatial distributions of all species obtained through kriging also appeared to be stable over time, while each species displayed a specific spatial distribution. Furthermore, adults were generally more densely concentrated than juveniles and occupied areas included in the distribution of juveniles. Despite the strong persistence of spatial distributions, we also observed that the area occupied by each species was correlated to its abundance: the more abundant the species, the larger the occupation area. Such a result tends to support MacCall's basin theory, according to which density-dependence responses would drive the expansion of those 12 key species in the Gulf of Lions. Further analyses showed that these species never saturated their habitats, suggesting that they are below their carrying capacity; an assumption in agreement with the overexploitation of several of these species. Finally, the stability of their spatial distributions over time and their potential ability to diffuse outside their main habitats give support to Marine Protected Areas as a potential pertinent management tool.

Impact of spatial proxies on the representation of bottom-up emission inventories: A satellite-based analysis

NASA Astrophysics Data System (ADS)

Geng, Guannan; Zhang, Qiang; Martin, Randall V.; Lin, Jintai; Huo, Hong; Zheng, Bo; Wang, Siwen; He, Kebin

2017-03-01

Spatial proxies used in bottom-up emission inventories to derive the spatial distributions of emissions are usually empirical and involve additional levels of uncertainty. Although uncertainties in current emission inventories have been discussed extensively, uncertainties resulting from improper spatial proxies have rarely been evaluated. In this work, we investigate the impact of spatial proxies on the representation of gridded emissions by comparing six gridded NOx emission datasets over China developed from the same magnitude of emissions and different spatial proxies. GEOS-Chem-modeled tropospheric NO2 vertical columns simulated from different gridded emission inventories are compared with satellite-based columns. The results show that differences between modeled and satellite-based NO2 vertical columns are sensitive to the spatial proxies used in the gridded emission inventories. The total population density is less suitable for allocating NOx emissions than nighttime light data because population density tends to allocate more emissions to rural areas. Determining the exact locations of large emission sources could significantly strengthen the correlation between modeled and observed NO2 vertical columns. Using vehicle population and an updated road network for the on-road transport sector could substantially enhance urban emissions and improve the model performance. When further applying industrial gross domestic product (IGDP) values for the industrial sector, modeled NO2 vertical columns could better capture pollution hotspots in urban areas and exhibit the best performance of the six cases compared to satellite-based NO2 vertical columns (slope = 1.01 and R2 = 0. 85). This analysis provides a framework for information from satellite observations to inform bottom-up inventory development. In the future, more effort should be devoted to the representation of spatial proxies to improve spatial patterns in bottom-up emission inventories.

Applying complex networks to evaluate precipitation patterns over South America

NASA Astrophysics Data System (ADS)

Ciemer, Catrin; Boers, Niklas; Barbosa, Henrique; Kurths, Jürgen; Rammig, Anja

2016-04-01

The climate of South America exhibits pronounced differences between the wet- and the dry-season, which are accompanied by specific synoptic events like changes in the location of the South American Low Level Jet (SALLJ) and the establishment of the South American Convergence Zone (SACZ). The onset of these events can be related to the presence of typical large-scale precipitation patterns over South America, as previous studies have shown[1,2]. The application of complex network methods to precipitation data recently received increased scientific attention for the special case of extreme events, as it is possible with such methods to analyze the spatiotemporal correlation structure as well as possible teleconnections of these events[3,4]. In these approaches the correlation between precipitation datasets is calculated by means of Event Synchronization which restricts their applicability to extreme precipitation events. In this work, we propose a method which is able to consider not only extreme precipitation but complete time series. A direct application of standard similarity measures in order to correlate precipitation time series is impossible due to their intricate statistical properties as the large amount of zeros. Therefore, we introduced and evaluated a suitable modification of Pearson's correlation coefficient to construct spatial correlation networks of precipitation. By analyzing the characteristics of spatial correlation networks constructed on the basis of this new measure, we are able to determine coherent areas of similar precipitation patterns, spot teleconnections of correlated areas, and detect central regions for precipitation correlation. By analyzing the change of the network over the year[5], we are also able to determine local and global changes in precipitation correlation patterns. Additionally, global network characteristics as the network connectivity yield indications for beginning and end of wet- and dry season. In order to identify large-scale synoptic events like the SACZ and SALLJ onset, detecting the changes of correlation over time between certain regions is of significant relevance. [1] Nieto-Ferreira et al. Quarterly Journal of the Royal Meteorological Society (2011) [2] Vera et al. Bulletin of the American Meteorological Society (2006) [3] Quiroga et al. Physical review E (2002) [4] Boers et al. nature communications (2014) [5] Radebach et al. Physical review E (2013)

Tracing Crop Nitrogen Dynamics on the Field-Scale by Combining Multisensoral EO Data with an Integrated Process Model- A Validation Experiment for Cereals in Southern Germany

NASA Astrophysics Data System (ADS)

Hank, Tobias B.; Bach, Heike; Danner, Martin; Hodrius, Martina; Mauser, Wolfram

2016-08-01

Nitrogen, being the basic element for the construction of plant proteins and pigments, is one of the most important production factors for agricultural cultivation. High resolution and near real-time information on nitrogen status in the soil thus is of highest interest for economically and ecologically optimized fertilizer planning and application. Unfortunately, nitrogen storage in the soil column cannot be directly observed with Earth Observation (EO) instruments. Advanced EO supported process modelling approaches therefore must be applied that allow tracing the spatiotemporal dynamics of nitrogen transformation, translocation and transport in the soil and in the canopy. Before these models can be applied as decision support tools for smart farming, they must be carefully parameterized and validated. This study applies an advanced land surface process model (PROMET) to selected winter cereal fields in Southern Germany and correlates the model outputs to destructively sampled nitrogen data from the growing season of 2015 (17 sampling dates, 8 sample locations). The spatial parametrization of the process model thereby is supported by assimilating eight satellite images (5 times Landsat 8 OLI and 3 times RapidEye). It was found that the model is capable of realistically tracing the temporal and spatial dynamics of aboveground nitrogen uptake and allocation (R2 = 0.84, RMSE 31.3 kg ha-1).

Fast and effective characterization of 3D region of interest in medical image data

NASA Astrophysics Data System (ADS)

Kontos, Despina; Megalooikonomou, Vasileios

2004-05-01

We propose a framework for detecting, characterizing and classifying spatial Regions of Interest (ROIs) in medical images, such as tumors and lesions in MRI or activation regions in fMRI. A necessary step prior to classification is efficient extraction of discriminative features. For this purpose, we apply a characterization technique especially designed for spatial ROIs. The main idea of this technique is to extract a k-dimensional feature vector using concentric spheres in 3D (or circles in 2D) radiating out of the ROI's center of mass. These vectors form characterization signatures that can be used to represent the initial ROIs. We focus on classifying fMRI ROIs obtained from a study that explores neuroanatomical correlates of semantic processing in Alzheimer's disease (AD). We detect a ROI highly associated with AD and apply the feature extraction technique with different experimental settings. We seek to distinguish control from patient samples. We study how classification can be performed using the extracted signatures as well as how different experimental parameters affect classification accuracy. The obtained classification accuracy ranged from 82% to 87% (based on the selected ROI) suggesting that the proposed classification framework can be potentially useful in supporting medical decision-making.

Development and calibration of a new gamma camera detector using large square Photomultiplier Tubes

NASA Astrophysics Data System (ADS)

Zeraatkar, N.; Sajedi, S.; Teimourian Fard, B.; Kaviani, S.; Akbarzadeh, A.; Farahani, M. H.; Sarkar, S.; Ay, M. R.

2017-09-01

Large area scintillation detectors applied in gamma cameras as well as Single Photon Computed Tomography (SPECT) systems, have a major role in in-vivo functional imaging. Most of the gamma detectors utilize hexagonal arrangement of Photomultiplier Tubes (PMTs). In this work we applied large square-shaped PMTs with row/column arrangement and positioning. The Use of large square PMTs reduces dead zones in the detector surface. However, the conventional center of gravity method for positioning may not introduce an acceptable result. Hence, the digital correlated signal enhancement (CSE) algorithm was optimized to obtain better linearity and spatial resolution in the developed detector. The performance of the developed detector was evaluated based on NEMA-NU1-2007 standard. The acquired images using this method showed acceptable uniformity and linearity comparing to three commercial gamma cameras. Also the intrinsic and extrinsic spatial resolutions with low-energy high-resolution (LEHR) collimator at 10 cm from surface of the detector were 3.7 mm and 7.5 mm, respectively. The energy resolution of the camera was measured 9.5%. The performance evaluation demonstrated that the developed detector maintains image quality with a reduced number of used PMTs relative to the detection area.

JPEG2000-coded image error concealment exploiting convex sets projections.

PubMed

Atzori, Luigi; Ginesu, Giaime; Raccis, Alessio

2005-04-01

Transmission errors in JPEG2000 can be grouped into three main classes, depending on the affected area: LL, high frequencies at the lower decomposition levels, and high frequencies at the higher decomposition levels. The first type of errors are the most annoying but can be concealed exploiting the signal spatial correlation like in a number of techniques proposed in the past; the second are less annoying but more difficult to address; the latter are often imperceptible. In this paper, we address the problem of concealing the second class or errors when high bit-planes are damaged by proposing a new approach based on the theory of projections onto convex sets. Accordingly, the error effects are masked by iteratively applying two procedures: low-pass (LP) filtering in the spatial domain and restoration of the uncorrupted wavelet coefficients in the transform domain. It has been observed that a uniform LP filtering brought to some undesired side effects that negatively compensated the advantages. This problem has been overcome by applying an adaptive solution, which exploits an edge map to choose the optimal filter mask size. Simulation results demonstrated the efficiency of the proposed approach.

Technical Note: Detection of gas bubble leakage via correlation of water column multibeam images

NASA Astrophysics Data System (ADS)

Schneider von Deimling, J.; Papenberg, C.

2012-03-01

Hydroacoustic detection of natural gas release from the seafloor has been conducted in the past by using singlebeam echosounders. In contrast, modern multibeam swath mapping systems allow much wider coverage, higher resolution, and offer 3-D spatial correlation. Up to the present, the extremely high data rate hampers water column backscatter investigations and more sophisticated visualization and processing techniques are needed. Here, we present water column backscatter data acquired with a 50 kHz prototype multibeam system over a period of 75 seconds. Display types are of swath-images as well as of a "re-sorted" singlebeam presentation. Thus, individual and/or groups of gas bubbles rising from the 24 m deep seafloor clearly emerge in the acoustic images, making it possible to estimate rise velocities. A sophisticated processing scheme is introduced to identify those rising gas bubbles in the hydroacoustic data. We apply a cross-correlation technique adapted from particle imaging velocimetry (PIV) to the acoustic backscatter images. Temporal and spatial drift patterns of the bubbles are assessed and are shown to match very well to measured and theoretical rise patterns. The application of this processing to our field data gives clear results with respect to unambiguous bubble detection and remote bubble rise velocimetry. The method can identify and exclude the main source of misinterpretations, i.e. fish-mediated echoes. Although image-based cross-correlation techniques are well known in the field of fluid mechanics for high resolution and non-inversive current flow field analysis, we present the first application of this technique as an acoustic bubble detector.

Correlation between the habitats productivity and species richness (amphibians and reptiles) in Portugal through remote sensed data

NASA Astrophysics Data System (ADS)

Teodoro, A. C.; Sillero, N.; Alves, S.; Duarte, L.

2013-10-01

Several biogeographic theories propose that the species richness depends on the structure and ecosystems diversity. The habitat productivity, a surrogate for these variables, can be evaluated through satellite imagery, namely using vegetation indexes (e.g. NDVI). We analyzed the correlation between species richness (from the Portuguese Atlas of Amphibians and Reptiles) and NDVI (from Landsat, MODIS, and Vegetation images). The species richness database contains more than 80000 records, collected from bibliographic sources (at 1 or 10 km of spatial resolution) and fieldwork sampling stations (recorded with GPS devices). Several study areas were chosen for Landsat images (three subsets), and all Portugal for MODIS and Vegetation images. The Landsat subareas had different climatic and habitat characteristics, located in the north, center and south of Portugal. Different species richness datasets were used depending on the image spatial resolution: data with metric resolution were used for Landsat, and with 1 km resolution, for MODIS and Vegetation images. The NDVI indexes and all the images were calculated/processed in an open source software (Quantum GIS). Several plug-ins were applied in order to automatize several procedures. We did not find any correlation between the species richness of amphibians and reptiles (not even after separating both groups by species of Atlantic and Mediterranean affinity) and the NDVI calculated with Landsat, MODIS and Vegetation images. Our results may fail to find a relationship because as the species richness is not correlated with only one variable (NDVI), and thus other environmental variables must be considered.

SERAPHIM: studying environmental rasters and phylogenetically informed movements.

PubMed

Dellicour, Simon; Rose, Rebecca; Faria, Nuno R; Lemey, Philippe; Pybus, Oliver G

2016-10-15

SERAPHIM ("Studying Environmental Rasters and PHylogenetically Informed Movements") is a suite of computational methods developed to study phylogenetic reconstructions of spatial movement in an environmental context. SERAPHIM extracts the spatio-temporal information contained in estimated phylogenetic trees and uses this information to calculate summary statistics of spatial spread and to visualize dispersal history. Most importantly, SERAPHIM enables users to study the impact of customized environmental variables on the spread of the study organism. Specifically, given an environmental raster, SERAPHIM computes environmental "weights" for each phylogeny branch, which represent the degree to which the environmental variable impedes (or facilitates) lineage movement. Correlations between movement duration and these environmental weights are then assessed, and the statistical significances of these correlations are evaluated using null distributions generated by a randomization procedure. SERAPHIM can be applied to any phylogeny whose nodes are annotated with spatial and temporal information. At present, such phylogenies are most often found in the field of emerging infectious diseases, but will become increasingly common in other biological disciplines as population genomic data grows. SERAPHIM 1.0 is freely available from http://evolve.zoo.ox.ac.uk/ R package, source code, example files, tutorials and a manual are also available from this website. simon.dellicour@kuleuven.be or oliver.pybus@zoo.ox.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Assessment of the performance of CORDEX-SA experiments in simulating seasonal mean temperature over the Himalayan region for the present climate: Part I

NASA Astrophysics Data System (ADS)

Nengker, T.; Choudhary, A.; Dimri, A. P.

2018-04-01

The ability of an ensemble of five regional climate models (hereafter RCMs) under Coordinated Regional Climate Downscaling Experiments-South Asia (hereafter, CORDEX-SA) in simulating the key features of present day near surface mean air temperature (Tmean) climatology (1970-2005) over the Himalayan region is studied. The purpose of this paper is to understand the consistency in the performance of models across the ensemble, space and seasons. For this a number of statistical measures like trend, correlation, variance, probability distribution function etc. are applied to evaluate the performance of models against observation and simultaneously the underlying uncertainties between them for four different seasons. The most evident finding from the study is the presence of a large cold bias (-6 to -8 °C) which is systematically seen across all the models and across space and time over the Himalayan region. However, these RCMs with its fine resolution perform extremely well in capturing the spatial distribution of the temperature features as indicated by a consistently high spatial correlation (greater than 0.9) with the observation in all seasons. In spite of underestimation in simulated temperature and general intensification of cold bias with increasing elevation the models show a greater rate of warming than the observation throughout entire altitudinal stretch of study region. During winter, the simulated rate of warming gets even higher at high altitudes. Moreover, a seasonal response of model performance and its spatial variability to elevation is found.

Robust optical flow using adaptive Lorentzian filter for image reconstruction under noisy condition

NASA Astrophysics Data System (ADS)

Kesrarat, Darun; Patanavijit, Vorapoj

2017-02-01

In optical flow for motion allocation, the efficient result in Motion Vector (MV) is an important issue. Several noisy conditions may cause the unreliable result in optical flow algorithms. We discover that many classical optical flows algorithms perform better result under noisy condition when combined with modern optimized model. This paper introduces effective robust models of optical flow by using Robust high reliability spatial based optical flow algorithms using the adaptive Lorentzian norm influence function in computation on simple spatial temporal optical flows algorithm. Experiment on our proposed models confirm better noise tolerance in optical flow's MV under noisy condition when they are applied over simple spatial temporal optical flow algorithms as a filtering model in simple frame-to-frame correlation technique. We illustrate the performance of our models by performing an experiment on several typical sequences with differences in movement speed of foreground and background where the experiment sequences are contaminated by the additive white Gaussian noise (AWGN) at different noise decibels (dB). This paper shows very high effectiveness of noise tolerance models that they are indicated by peak signal to noise ratio (PSNR).

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

PubMed

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

2015-08-07

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

Dynamics and spatio-temporal variability of environmental factors in Eastern Australia using functional principal component analysis

USGS Publications Warehouse

Szabo, J.K.; Fedriani, E.M.; Segovia-Gonzalez, M. M.; Astheimer, L.B.; Hooper, M.J.

2010-01-01

This paper introduces a new technique in ecology to analyze spatial and temporal variability in environmental variables. By using simple statistics, we explore the relations between abiotic and biotic variables that influence animal distributions. However, spatial and temporal variability in rainfall, a key variable in ecological studies, can cause difficulties to any basic model including time evolution. The study was of a landscape scale (three million square kilometers in eastern Australia), mainly over the period of 19982004. We simultaneously considered qualitative spatial (soil and habitat types) and quantitative temporal (rainfall) variables in a Geographical Information System environment. In addition to some techniques commonly used in ecology, we applied a new method, Functional Principal Component Analysis, which proved to be very suitable for this case, as it explained more than 97% of the total variance of the rainfall data, providing us with substitute variables that are easier to manage and are even able to explain rainfall patterns. The main variable came from a habitat classification that showed strong correlations with rainfall values and soil types. ?? 2010 World Scientific Publishing Company.

Space-time codependence of retinal ganglion cells can be explained by novel and separable components of their receptive fields.

PubMed

Cowan, Cameron S; Sabharwal, Jasdeep; Wu, Samuel M

2016-09-01

Reverse correlation methods such as spike-triggered averaging consistently identify the spatial center in the linear receptive fields (RFs) of retinal ganglion cells (GCs). However, the spatial antagonistic surround observed in classical experiments has proven more elusive. Tests for the antagonistic surround have heretofore relied on models that make questionable simplifying assumptions such as space-time separability and radial homogeneity/symmetry. We circumvented these, along with other common assumptions, and observed a linear antagonistic surround in 754 of 805 mouse GCs. By characterizing the RF's space-time structure, we found the overall linear RF's inseparability could be accounted for both by tuning differences between the center and surround and differences within the surround. Finally, we applied this approach to characterize spatial asymmetry in the RF surround. These results shed new light on the spatiotemporal organization of GC linear RFs and highlight a major contributor to its inseparability. © 2016 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of the American Physiological Society and The Physiological Society.

Simon and Garner effects with color and location: Evidence for two independent routes by which irrelevant location influences performance.

PubMed

Fitousi, Daniel

2016-11-01

Classic theories of attention assume that the processing of a target's featural dimension (e.g., color) is contingent on the processing of its spatial location. The present study challenges this maxim. Three experiments evaluated the dimensional independence of spatial location and color using a combined Simon (Simon & Rudell Journal of Applied Psychology: 51, 300-304, 1967) and Garner (Garner, 1974) design. The results showed that when the stimulus's spatial location was rendered more discriminable than its color (Experiment 1 and 2), both Simon and Garner effects were obtained, and location interfered with color judgments to a larger extent than color intruded on location. However, when baseline discriminabilities of location and color were matched (Experiment 3), no Garner interference was obtained from location to color, yet Simon effects still emerged, proving resilient to manipulations of discriminability. Further correlational and distributional analyses showed that Garner and Simon effects have dissociable effects. A triple-route model is proposed to account for the results, according to which irrelevant location can influence performance via two independent location routes/codes.

Spatial Correlation Of Streamflows: An Analytical Approach

NASA Astrophysics Data System (ADS)

Betterle, A.; Schirmer, M.; Botter, G.

2016-12-01

The interwoven space and time variability of climate and landscape properties results in complex and non-linear hydrological response of streamflow dynamics. Understanding how meteorologic and morphological characteristics of catchments affect similarity/dissimilarity of streamflow timeseries at their outlets represents a scientific challenge with application in water resources management, ecological studies and regionalization approaches aimed to predict streamflows in ungauged areas. In this study, we establish an analytical approach to estimate the spatial correlation of daily streamflows in two arbitrary locations within a given hydrologic district or river basin at seasonal and annual time scales. The method is based on a stochastic description of the coupled streamflow dynamics at the outlet of two catchments. The framework aims to express the correlation of daily streamflows at two locations along a river network as a function of a limited number of physical parameters characterizing the main underlying hydrological drivers, that include climate conditions, precipitation regime and catchment drainage rates. The proposed method portrays how heterogeneity of climate and landscape features affect the spatial variability of flow regimes along river systems. In particular, we show that frequency and intensity of synchronous effective rainfall events in the relevant contributing catchments are the main driver of the spatial correlation of daily discharge, whereas only pronounced differences in the drainage rate of the two basins bear a significant effect on the streamflow correlation. The topological arrangement of the two outlets also influences the underlying streamflow correlation, as we show that nested catchments tend to maximize the spatial correlation of flow regimes. The application of the method to a set of catchments in the South-Eastern US suggests the potential of the proposed tool for the characterization of spatial connections of flow regimes in the absence of discharge measurements.

Mapping spatial patterns of stream power and channel change along a gravel-bed river in northern Yellowstone

NASA Astrophysics Data System (ADS)

Lea, Devin M.; Legleiter, Carl J.

2016-01-01

Stream power represents the rate of energy expenditure along a river and can be calculated using topographic data acquired via remote sensing or field surveys. This study sought to quantitatively relate temporal changes in the form of Soda Butte Creek, a gravel-bed river in northeastern Yellowstone National Park, to stream power gradients along an 8-km reach. Aerial photographs from 1994 to 2012 and ground-based surveys were used to develop a locational probability map and morphologic sediment budget to assess lateral channel mobility and changes in net sediment flux. A drainage area-to-discharge relationship and DEM developed from LiDAR data were used to obtain the discharge and slope values needed to calculate stream power. Local and lagged relationships between mean stream power gradient at median peak discharge and volumes of erosion, deposition, and net sediment flux were quantified via spatial cross-correlation analyses. Similarly, autocorrelations of locational probabilities and sediment fluxes were used to examine spatial patterns of sediment sources and sinks. Energy expended above critical stream power was calculated for each time period to relate the magnitude and duration of peak flows to the total volumetric change in each time increment. Collectively, we refer to these methods as the stream power gradient (SPG) framework. The results of this study were compromised by methodological limitations of the SPG framework and revealed some complications likely to arise when applying this framework to small, wandering, gravel-bed rivers. Correlations between stream power gradients and sediment flux were generally weak, highlighting the inability of relatively simple statistical approaches to link sub-budget cell-scale sediment dynamics to larger-scale driving forces such as stream power gradients. Improving the moderate spatial resolution techniques used in this study and acquiring very-high resolution data from recently developed methods in fluvial remote sensing could help improve understanding of the spatial organization of stream power, sediment transport, and channel change in dynamic natural rivers.

'spup' - an R package for uncertainty propagation in spatial environmental modelling

NASA Astrophysics Data System (ADS)

Sawicka, Kasia; Heuvelink, Gerard

2016-04-01

Computer models have become a crucial tool in engineering and environmental sciences for simulating the behaviour of complex static and dynamic systems. However, while many models are deterministic, the uncertainty in their predictions needs to be estimated before they are used for decision support. Currently, advances in uncertainty propagation and assessment have been paralleled by a growing number of software tools for uncertainty analysis, but none has gained recognition for a universal applicability, including case studies with spatial models and spatial model inputs. Due to the growing popularity and applicability of the open source R programming language we undertook a project to develop an R package that facilitates uncertainty propagation analysis in spatial environmental modelling. In particular, the 'spup' package provides functions for examining the uncertainty propagation starting from input data and model parameters, via the environmental model onto model predictions. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques, as well as several uncertainty visualization functions. Uncertain environmental variables are represented in the package as objects whose attribute values may be uncertain and described by probability distributions. Both numerical and categorical data types are handled. Spatial auto-correlation within an attribute and cross-correlation between attributes is also accommodated for. For uncertainty propagation the package has implemented the MC approach with efficient sampling algorithms, i.e. stratified random sampling and Latin hypercube sampling. The design includes facilitation of parallel computing to speed up MC computation. The MC realizations may be used as an input to the environmental models called from R, or externally. Selected static and interactive visualization methods that are understandable by non-experts with limited background in statistics can be used to summarize and visualize uncertainty about the measured input, model parameters and output of the uncertainty propagation. We demonstrate that the 'spup' package is an effective and easy tool to apply and can be used in multi-disciplinary research and model-based decision support.

'spup' - an R package for uncertainty propagation analysis in spatial environmental modelling

NASA Astrophysics Data System (ADS)

Sawicka, Kasia; Heuvelink, Gerard

2017-04-01

Computer models have become a crucial tool in engineering and environmental sciences for simulating the behaviour of complex static and dynamic systems. However, while many models are deterministic, the uncertainty in their predictions needs to be estimated before they are used for decision support. Currently, advances in uncertainty propagation and assessment have been paralleled by a growing number of software tools for uncertainty analysis, but none has gained recognition for a universal applicability and being able to deal with case studies with spatial models and spatial model inputs. Due to the growing popularity and applicability of the open source R programming language we undertook a project to develop an R package that facilitates uncertainty propagation analysis in spatial environmental modelling. In particular, the 'spup' package provides functions for examining the uncertainty propagation starting from input data and model parameters, via the environmental model onto model predictions. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques, as well as several uncertainty visualization functions. Uncertain environmental variables are represented in the package as objects whose attribute values may be uncertain and described by probability distributions. Both numerical and categorical data types are handled. Spatial auto-correlation within an attribute and cross-correlation between attributes is also accommodated for. For uncertainty propagation the package has implemented the MC approach with efficient sampling algorithms, i.e. stratified random sampling and Latin hypercube sampling. The design includes facilitation of parallel computing to speed up MC computation. The MC realizations may be used as an input to the environmental models called from R, or externally. Selected visualization methods that are understandable by non-experts with limited background in statistics can be used to summarize and visualize uncertainty about the measured input, model parameters and output of the uncertainty propagation. We demonstrate that the 'spup' package is an effective and easy tool to apply and can be used in multi-disciplinary research and model-based decision support.

Effect of intranasal manganese administration on neurotransmission and spatial learning in rats

DOE Office of Scientific and Technical Information (OSTI.GOV)

Blecharz-Klin, Kamilla; Piechal, Agnieszka; Joniec-Maciejak, Ilona

2012-11-15

The effect of intranasal manganese chloride (MnCl{sub 2}·4H{sub 2}O) exposure on spatial learning, memory and motor activity was estimated in Morris water maze task in adult rats. Three-month-old male Wistar rats received for 2 weeks MnCl{sub 2}·4H{sub 2}O at two doses the following: 0.2 mg/kg b.w. (Mn0.2) or 0.8 mg/kg b.w. (Mn0.8) per day. Control (Con) and manganese-exposed groups were observed for behavioral performance and learning in water maze. ANOVA for repeated measurements did not show any significant differences in acquisition in the water maze between the groups. However, the results of the probe trial on day 5, exhibited spatialmore » memory deficits following manganese treatment. After completion of the behavioral experiment, the regional brain concentrations of neurotransmitters and their metabolites were determined via HPLC in selected brain regions, i.e. prefrontal cortex, hippocampus and striatum. ANOVA demonstrated significant differences in the content of monoamines and metabolites between the treatment groups compared to the controls. Negative correlations between platform crossings on the previous platform position in Southeast (SE) quadrant during the probe trial and neurotransmitter turnover suggest that impairment of spatial memory and cognitive performance after manganese (Mn) treatment is associated with modulation of the serotonergic, noradrenergic and dopaminergic neurotransmission in the brain. These findings show that intranasally applied Mn can impair spatial memory with significant changes in the tissue level and metabolism of monoamines in several brain regions. -- Highlights: ► Intranasal exposure to manganese in rats impairs spatial memory in the water maze. ► Regional changes in levels of neurotransmitters in the brain have been identified. ► Cognitive disorder correlates with modulation of 5-HT, NA and DA neurotransmission.« less

An Integrated Approach for the Assessment of the Natural and Anthropogenic Controls on Land Subsidence in the Kingdom of Saudi Arabia

NASA Astrophysics Data System (ADS)

Othman, A.; Sultan, M.; Ahmed, M.; Alharbi, T.; Gebremichael, E.; Emil, M.

2015-12-01

Recent land subsidence incidences in the Kingdom of Saudi Arabia (KSA) resulted in loss in life and property. In this study, an integrated approach is adopted to accomplish the following: (1) map the spatial distribution of areas that are witnessing land subsidence, (2) quantify the rates of land subsidence, and (3) identify the factors causing the observed subsidence. A three-fold approach is applied: (1) use of interferometric techniques to assess the spatial distribution of land subsidence and to quantify the rates of subsidence, (2) generate a GIS database to encompass all relevant data and derived products, and (3) correlate findings from the radar exercise with relevant spatial and temporal datasets (e.g., remote sensing, geology, fluid extraction rates, distribution of urban areas, etc.). Three main areas were selected: (1) central and northern parts of the KSA, (2) areas surrounding the Ghawar oil/gas field, and (3) the Harrat Lunayyir volcanic field. Applications of two-pass, three-pass, and SBAS radar interferometric techniques over central KSA revealed the following: (1) subsidence rates of up to -15 mm/yr were detected; the spatial distribution of the subsided areas that were extracted using the various interferometric techniques are similar, (2) subsided areas correlated spatially with the distribution of: (a) areas with high groundwater extraction rates as evidenced from the analysis of field and Gravity Recovery and Climate Experiment (GRACE) data, (b) agricultural plantations as evidenced from the analysis of field and temporal Landsat data, (c) urban areas (e.g., Buraydah City), (d) outcrops of carbonates and anhydrite formations (e.g., Khuff and Jilh formations), (3) subsidence could be related to more than one parameter. Similar research activities are underway in northern KSA and in areas surrounding the Ghawar oil/gas and the Harrat Lunayyir volcanic fields to assess the distribution and factors controlling land deformation in those areas.

Mapping turbidity in the Charles River, Boston using a high-resolution satellite.

PubMed

Hellweger, Ferdi L; Miller, Will; Oshodi, Kehinde Sarat

2007-09-01

The usability of high-resolution satellite imagery for estimating spatial water quality patterns in urban water bodies is evaluated using turbidity in the lower Charles River, Boston as a case study. Water turbidity was surveyed using a boat-mounted optical sensor (YSI) at 5 m spatial resolution, resulting in about 4,000 data points. The ground data were collected coincidently with a satellite imagery acquisition (IKONOS), which consists of multispectral (R, G, B) reflectance at 1 m resolution. The original correlation between the raw ground and satellite data was poor (R2 = 0.05). Ground data were processed by removing points affected by contamination (e.g., sensor encounters a particle floc), which were identified visually. Also, the ground data were corrected for the memory effect introduced by the sensor's protective casing using an analytical model. Satellite data were processed to remove pixels affected by permanent non-water features (e.g., shoreline). In addition, water pixels within a certain buffer distance from permanent non-water features were removed due to contamination by the adjacency effect. To determine the appropriate buffer distance, a procedure that explicitly considers the distance of pixels to the permanent non-water features was applied. Two automatic methods for removing the effect of temporary non-water features (e.g., boats) were investigated, including (1) creating a water-only mask based on an unsupervised classification and (2) removing (filling) all local maxima in reflectance. After the various processing steps, the correlation between the ground and satellite data was significantly better (R2 = 0.70). The correlation was applied to the satellite image to develop a map of turbidity in the lower Charles River, which reveals large-scale patterns in water clarity. However, the adjacency effect prevented the application of this method to near-shore areas, where high-resolution patterns were expected (e.g., outfall plumes).

An Empirical Bayes Approach to Spatial Analysis

NASA Technical Reports Server (NTRS)

Morris, C. N.; Kostal, H.

1983-01-01

Multi-channel LANDSAT data are collected in several passes over agricultural areas during the growing season. How empirical Bayes modeling can be used to develop crop identification and discrimination techniques that account for spatial correlation in such data is considered. The approach models the unobservable parameters and the data separately, hoping to take advantage of the fact that the bulk of spatial correlation lies in the parameter process. The problem is then framed in terms of estimating posterior probabilities of crop types for each spatial area. Some empirical Bayes spatial estimation methods are used to estimate the logits of these probabilities.

A non-parametric automatic blending methodology to estimate rainfall fields from rain gauge and radar data

NASA Astrophysics Data System (ADS)

Velasco-Forero, Carlos A.; Sempere-Torres, Daniel; Cassiraga, Eduardo F.; Jaime Gómez-Hernández, J.

2009-07-01

Quantitative estimation of rainfall fields has been a crucial objective from early studies of the hydrological applications of weather radar. Previous studies have suggested that flow estimations are improved when radar and rain gauge data are combined to estimate input rainfall fields. This paper reports new research carried out in this field. Classical approaches for the selection and fitting of a theoretical correlogram (or semivariogram) model (needed to apply geostatistical estimators) are avoided in this study. Instead, a non-parametric technique based on FFT is used to obtain two-dimensional positive-definite correlograms directly from radar observations, dealing with both the natural anisotropy and the temporal variation of the spatial structure of the rainfall in the estimated fields. Because these correlation maps can be automatically obtained at each time step of a given rainfall event, this technique might easily be used in operational (real-time) applications. This paper describes the development of the non-parametric estimator exploiting the advantages of FFT for the automatic computation of correlograms and provides examples of its application on a case study using six rainfall events. This methodology is applied to three different alternatives to incorporate the radar information (as a secondary variable), and a comparison of performances is provided. In particular, their ability to reproduce in estimated rainfall fields (i) the rain gauge observations (in a cross-validation analysis) and (ii) the spatial patterns of radar fields are analyzed. Results seem to indicate that the methodology of kriging with external drift [KED], in combination with the technique of automatically computing 2-D spatial correlograms, provides merged rainfall fields with good agreement with rain gauges and with the most accurate approach to the spatial tendencies observed in the radar rainfall fields, when compared with other alternatives analyzed.

Pilot study for supervised target detection applied to spatially registered multiparametric MRI in order to non-invasively score prostate cancer.

PubMed

Mayer, Rulon; Simone, Charles B; Skinner, William; Turkbey, Baris; Choykey, Peter

2018-03-01

Gleason Score (GS) is a validated predictor of prostate cancer (PCa) disease progression and outcomes. GS from invasive needle biopsies suffers from significant inter-observer variability and possible sampling error, leading to underestimating disease severity ("underscoring") and can result in possible complications. A robust non-invasive image-based approach is, therefore, needed. Use spatially registered multi-parametric MRI (MP-MRI), signatures, and supervised target detection algorithms (STDA) to non-invasively GS PCa at the voxel level. This study retrospectively analyzed 26 MP-MRI from The Cancer Imaging Archive. The MP-MRI (T2, Diffusion Weighted, Dynamic Contrast Enhanced) were spatially registered to each other, combined into stacks, and stitched together to form hypercubes. Multi-parametric (or multi-spectral) signatures derived from a training set of registered MP-MRI were transformed using statistics-based Whitening-Dewhitening (WD). Transformed signatures were inserted into STDA (having conical decision surfaces) applied to registered MP-MRI determined the tumor GS. The MRI-derived GS was quantitatively compared to the pathologist's assessment of the histology of sectioned whole mount prostates from patients who underwent radical prostatectomy. In addition, a meta-analysis of 17 studies of needle biopsy determined GS with confusion matrices and was compared to the MRI-determined GS. STDA and histology determined GS are highly correlated (R = 0.86, p < 0.02). STDA more accurately determined GS and reduced GS underscoring of PCa relative to needle biopsy as summarized by meta-analysis (p < 0.05). This pilot study found registered MP-MRI, STDA, and WD transforms of signatures shows promise in non-invasively GS PCa and reducing underscoring with high spatial resolution. Copyright © 2018 Elsevier Ltd. All rights reserved.

Spatial abilities, Earth science conceptual understanding, and psychological gender of university non-science majors

NASA Astrophysics Data System (ADS)

Black, Alice A. (Jill)

Research has shown the presence of many Earth science misconceptions and conceptual difficulties that may impede concept understanding, and has also identified a number of categories of spatial ability. Although spatial ability has been linked to high performance in science, some researchers believe it has been overlooked in traditional education. Evidence exists that spatial ability can be improved. This correlational study investigated the relationship among Earth science conceptual understanding, three types of spatial ability, and psychological gender, a self-classification that reflects socially-accepted personality and gender traits. A test of Earth science concept understanding, the Earth Science Concepts (ESC) test, was developed and field tested from 2001 to 2003 in 15 sections of university classes. Criterion validity was .60, significant at the .01 level. Spearman/Brown reliability was .74 and Kuder/Richardson reliability was .63. The Purdue Visualization of Rotations (PVOR) (mental rotation), the Group Embedded Figures Test (GEFT) (spatial perception), the Differential Aptitude Test: Space Relations (DAT) (spatial visualization), and the Bem Inventory (BI) (psychological gender) were administered to 97 non-major university students enrolled in undergraduate science classes. Spearman correlations revealed moderately significant correlations at the .01 level between ESC scores and each of the three spatial ability test scores. Stepwise regression analysis indicated that PVOR scores were the best predictor of ESC scores, and showed that spatial ability scores accounted for 27% of the total variation in ESC scores. Spatial test scores were moderately or weakly correlated with each other. No significant correlations were found among BI scores and other test scores. Scantron difficulty analysis of ESC items produced difficulty ratings ranging from 33.04 to 96.43, indicating the percentage of students who answered incorrectly. Mean score on the ESC was 34%, indicating that the non-majors tested exhibited many Earth science misconceptions and conceptual difficulties. A number of significant results were found when independent t-tests and correlations were conducted among test scores and demographic variables. The number of previous university Earth science courses was significantly related to ESC scores. Preservice elementary/middle majors differed significantly in several ways from other non-majors, and several earlier results were not supported. Results of this study indicate that an important opportunity may exist to improve Earth science conceptual understanding by focusing on spatial ability, a cognitive ability that has heretofore not been directly addressed in schools.

Multi-scale integration and predictability in resting state brain activity

PubMed Central

Kolchinsky, Artemy; van den Heuvel, Martijn P.; Griffa, Alessandra; Hagmann, Patric; Rocha, Luis M.; Sporns, Olaf; Goñi, Joaquín

2014-01-01

The human brain displays heterogeneous organization in both structure and function. Here we develop a method to characterize brain regions and networks in terms of information-theoretic measures. We look at how these measures scale when larger spatial regions as well as larger connectome sub-networks are considered. This framework is applied to human brain fMRI recordings of resting-state activity and DSI-inferred structural connectivity. We find that strong functional coupling across large spatial distances distinguishes functional hubs from unimodal low-level areas, and that this long-range functional coupling correlates with structural long-range efficiency on the connectome. We also find a set of connectome regions that are both internally integrated and coupled to the rest of the brain, and which resemble previously reported resting-state networks. Finally, we argue that information-theoretic measures are useful for characterizing the functional organization of the brain at multiple scales. PMID:25104933

Determining conduction patterns on a sparse electrode grid: Implications for the analysis of clinical arrhythmias

NASA Astrophysics Data System (ADS)

Vidmar, David; Narayan, Sanjiv M.; Krummen, David E.; Rappel, Wouter-Jan

2016-11-01

We present a general method of utilizing bioelectric recordings from a spatially sparse electrode grid to compute a dynamic vector field describing the underlying propagation of electrical activity. This vector field, termed the wave-front flow field, permits quantitative analysis of the magnitude of rotational activity (vorticity) and focal activity (divergence) at each spatial point. We apply this method to signals recorded during arrhythmias in human atria and ventricles using a multipolar contact catheter and show that the flow fields correlate with corresponding activation maps. Further, regions of elevated vorticity and divergence correspond to sites identified as clinically significant rotors and focal sources where therapeutic intervention can be effective. These flow fields can provide quantitative insights into the dynamics of normal and abnormal conduction in humans and could potentially be used to enhance therapies for cardiac arrhythmias.

Estimation of the vortex length scale and intensity from two-dimensional samples

NASA Technical Reports Server (NTRS)

Reuss, D. L.; Cheng, W. P.

1992-01-01

A method is proposed for estimating flow features that influence flame wrinkling in reciprocating internal combustion engines, where traditional statistical measures of turbulence are suspect. Candidate methods were tested in a computed channel flow where traditional turbulence measures are valid and performance can be rationally evaluated. Two concepts are tested. First, spatial filtering is applied to the two-dimensional velocity distribution and found to reveal structures corresponding to the vorticity field. Decreasing the spatial-frequency cutoff of the filter locally changes the character and size of the flow structures that are revealed by the filter. Second, vortex length scale and intensity is estimated by computing the ensemble-average velocity distribution conditionally sampled on the vorticity peaks. The resulting conditionally sampled 'average vortex' has a peak velocity less than half the rms velocity and a size approximately equal to the two-point-correlation integral-length scale.

Prediction and prevention of parasitic diseases using a landscape genomics framework

PubMed Central

Schwabl, Philipp; Llewellyn, Martin; Landguth, Erin L.; Andersson, Björn; Kitron, Uriel; Costales, Jaime A.; Ocaña, Sofía; Grijalva, Mario J.

2016-01-01

Summary Substantial heterogeneity exists in the dispersal, distribution and transmission of parasitic species. Understanding and predicting how such features are governed by the ecological variation of landscape they inhabit is the central goal of spatial epidemiology. Genetic data can further inform functional connectivity among parasite, host and vector populations in a landscape. Gene flow correlates with the spread of epidemiologically relevant phenotypes among parasite and vector populations (e.g., virulence, drug and pesticide resistance), as well as invasion and re-invasion risk where parasite transmission is absent due to current or past intervention measures. However, the formal integration of spatial and genetic data (‘landscape genetics’) is scarcely ever applied to parasites. Here, we discuss the specific challenges and practical prospects for the use of landscape genetics and genomics to understand the biology and control of parasitic disease and present a practical framework for doing so. PMID:27863902

Monitoring Everglades freshwater marsh water level using L-band synthetic aperture radar backscatter

USGS Publications Warehouse

Kim, Jin-Woo; Lu, Zhong; Jones, John W.; Shum, C.K.; Lee, Hyongki; Jia, Yuanyuan

2014-01-01

The Florida Everglades plays a significant role in controlling floods, improving water quality, supporting ecosystems, and maintaining biodiversity in south Florida. Adaptive restoration and management of the Everglades requires the best information possible regarding wetland hydrology. We developed a new and innovative approach to quantify spatial and temporal variations in wetland water levels within the Everglades, Florida. We observed high correlations between water level measured at in situ gages and L-band SAR backscatter coefficients in the freshwater marsh, though C-band SAR backscatter has no close relationship with water level. Here we illustrate the complementarity of SAR backscatter coefficient differencing and interferometry (InSAR) for improved estimation of high spatial resolution water level variations in the Everglades. This technique has a certain limitation in applying to swamp forests with dense vegetation cover, but we conclude that this new method is promising in future applications to wetland hydrology research.

Compact optical duplicate system for satellite-ground laser communications: application of averaging effects

NASA Astrophysics Data System (ADS)

Nakayama, Tomoko; Takayama, Yoshihisa; Fujikawa, Chiemi; Watanabe, Eriko; Kodate, Kashiko

2014-09-01

In recent years, there has been considerable interest in satellite-ground laser communication due to an increase in the quantity of data exchanged between satellites and the ground. However, improving the quality of this data communication is necessary as laser communication is vulnerable to air fluctuation. We first verify the spatial and temporal averaging effects using light beam intensity images acquired from middle-range transmission experiments between two ground positions and the superposition of these images using simulations. Based on these results, we propose a compact and lightweight optical duplicate system as a multi-beam generation device with which it is easy to apply the spatial averaging effect. Although an optical duplicate system is already used for optical correlation operations, we present optimum design solutions, design a compact optical duplicate system for satellite-ground laser communications, and demonstrate the efficacy of this system using simulations.

Optics. Spatially structured photons that travel in free space slower than the speed of light.

PubMed

Giovannini, Daniel; Romero, Jacquiline; Potoček, Václav; Ferenczi, Gergely; Speirits, Fiona; Barnett, Stephen M; Faccio, Daniele; Padgett, Miles J

2015-02-20

That the speed of light in free space is constant is a cornerstone of modern physics. However, light beams have finite transverse size, which leads to a modification of their wave vectors resulting in a change to their phase and group velocities. We study the group velocity of single photons by measuring a change in their arrival time that results from changing the beam's transverse spatial structure. Using time-correlated photon pairs, we show a reduction in the group velocity of photons in both a Bessel beam and photons in a focused Gaussian beam. In both cases, the delay is several micrometers over a propagation distance of ~1 meter. Our work highlights that, even in free space, the invariance of the speed of light only applies to plane waves. Copyright © 2015, American Association for the Advancement of Science.

Prediction and Prevention of Parasitic Diseases Using a Landscape Genomics Framework.

PubMed

Schwabl, Philipp; Llewellyn, Martin S; Landguth, Erin L; Andersson, Björn; Kitron, Uriel; Costales, Jaime A; Ocaña, Sofía; Grijalva, Mario J

2017-04-01

Substantial heterogeneity exists in the dispersal, distribution and transmission of parasitic species. Understanding and predicting how such features are governed by the ecological variation of landscape they inhabit is the central goal of spatial epidemiology. Genetic data can further inform functional connectivity among parasite, host and vector populations in a landscape. Gene flow correlates with the spread of epidemiologically relevant phenotypes among parasite and vector populations (e.g., virulence, drug and pesticide resistance), as well as invasion and re-invasion risk where parasite transmission is absent due to current or past intervention measures. However, the formal integration of spatial and genetic data ('landscape genetics') is scarcely ever applied to parasites. Here, we discuss the specific challenges and practical prospects for the use of landscape genetics and genomics to understand the biology and control of parasitic disease and present a practical framework for doing so. Copyright © 2016 Elsevier Ltd. All rights reserved.

Collective behavior in the spatial spreading of obesity

PubMed Central

Gallos, Lazaros K.; Barttfeld, Pablo; Havlin, Shlomo; Sigman, Mariano; Makse, Hernán A.

2012-01-01

Obesity prevalence is increasing in many countries at alarming levels. A difficulty in the conception of policies to reverse these trends is the identification of the drivers behind the obesity epidemics. Here, we implement a spatial spreading analysis to investigate whether obesity shows spatial correlations, revealing the effect of collective and global factors acting above individual choices. We find a regularity in the spatial fluctuations of their prevalence revealed by a pattern of scale-free long-range correlations. The fluctuations are anomalous, deviating in a fundamental way from the weaker correlations found in the underlying population distribution indicating the presence of collective behavior, i.e., individual habits may have negligible influence in shaping the patterns of spreading. Interestingly, we find the same scale-free correlations in economic activities associated with food production. These results motivate future interventions to investigate the causality of this relation providing guidance for the implementation of preventive health policies. PMID:22822425

Stochastic Geometric Models with Non-stationary Spatial Correlations in Lagrangian Fluid Flows

NASA Astrophysics Data System (ADS)

Gay-Balmaz, François; Holm, Darryl D.

2018-01-01

Inspired by spatiotemporal observations from satellites of the trajectories of objects drifting near the surface of the ocean in the National Oceanic and Atmospheric Administration's "Global Drifter Program", this paper develops data-driven stochastic models of geophysical fluid dynamics (GFD) with non-stationary spatial correlations representing the dynamical behaviour of oceanic currents. Three models are considered. Model 1 from Holm (Proc R Soc A 471:20140963, 2015) is reviewed, in which the spatial correlations are time independent. Two new models, called Model 2 and Model 3, introduce two different symmetry breaking mechanisms by which the spatial correlations may be advected by the flow. These models are derived using reduction by symmetry of stochastic variational principles, leading to stochastic Hamiltonian systems, whose momentum maps, conservation laws and Lie-Poisson bracket structures are used in developing the new stochastic Hamiltonian models of GFD.

Quantitative analysis of spatial variability of geotechnical parameters

NASA Astrophysics Data System (ADS)

Fang, Xing

2018-04-01

Geotechnical parameters are the basic parameters of geotechnical engineering design, while the geotechnical parameters have strong regional characteristics. At the same time, the spatial variability of geotechnical parameters has been recognized. It is gradually introduced into the reliability analysis of geotechnical engineering. Based on the statistical theory of geostatistical spatial information, the spatial variability of geotechnical parameters is quantitatively analyzed. At the same time, the evaluation of geotechnical parameters and the correlation coefficient between geotechnical parameters are calculated. A residential district of Tianjin Survey Institute was selected as the research object. There are 68 boreholes in this area and 9 layers of mechanical stratification. The parameters are water content, natural gravity, void ratio, liquid limit, plasticity index, liquidity index, compressibility coefficient, compressive modulus, internal friction angle, cohesion and SP index. According to the principle of statistical correlation, the correlation coefficient of geotechnical parameters is calculated. According to the correlation coefficient, the law of geotechnical parameters is obtained.

Collective behavior in the spatial spreading of obesity

NASA Astrophysics Data System (ADS)

Gallos, Lazaros K.; Barttfeld, Pablo; Havlin, Shlomo; Sigman, Mariano; Makse, Hernán A.

2012-06-01

Obesity prevalence is increasing in many countries at alarming levels. A difficulty in the conception of policies to reverse these trends is the identification of the drivers behind the obesity epidemics. Here, we implement a spatial spreading analysis to investigate whether obesity shows spatial correlations, revealing the effect of collective and global factors acting above individual choices. We find a regularity in the spatial fluctuations of their prevalence revealed by a pattern of scale-free long-range correlations. The fluctuations are anomalous, deviating in a fundamental way from the weaker correlations found in the underlying population distribution indicating the presence of collective behavior, i.e., individual habits may have negligible influence in shaping the patterns of spreading. Interestingly, we find the same scale-free correlations in economic activities associated with food production. These results motivate future interventions to investigate the causality of this relation providing guidance for the implementation of preventive health policies.

Stochastic Geometric Models with Non-stationary Spatial Correlations in Lagrangian Fluid Flows

NASA Astrophysics Data System (ADS)

Gay-Balmaz, François; Holm, Darryl D.

2018-06-01

Inspired by spatiotemporal observations from satellites of the trajectories of objects drifting near the surface of the ocean in the National Oceanic and Atmospheric Administration's "Global Drifter Program", this paper develops data-driven stochastic models of geophysical fluid dynamics (GFD) with non-stationary spatial correlations representing the dynamical behaviour of oceanic currents. Three models are considered. Model 1 from Holm (Proc R Soc A 471:20140963, 2015) is reviewed, in which the spatial correlations are time independent. Two new models, called Model 2 and Model 3, introduce two different symmetry breaking mechanisms by which the spatial correlations may be advected by the flow. These models are derived using reduction by symmetry of stochastic variational principles, leading to stochastic Hamiltonian systems, whose momentum maps, conservation laws and Lie-Poisson bracket structures are used in developing the new stochastic Hamiltonian models of GFD.

Spatial correlations and exact solution of the problem of the boson peak profile in amorphous media

NASA Astrophysics Data System (ADS)

Kirillov, Sviatoslav A.; A. Voyiatzis, George; Kolomiyets, Tatiana M.; H. Anastasiadis, Spiros

1999-11-01

Based on a model correlation function which covers spatial correlations from Gaussian to exponential, we have arrived at an exact analytic solution of the problem of the Boson peak profile in amorphous media. Probe fits made for polyisoprene and triacetin prove the working ability of the formulae obtained.

Spatial Autocorrelation of Cancer Incidence in Saudi Arabia

PubMed Central

Al-Ahmadi, Khalid; Al-Zahrani, Ali

2013-01-01

Little is known about the geographic distribution of common cancers in Saudi Arabia. We explored the spatial incidence patterns of common cancers in Saudi Arabia using spatial autocorrelation analyses, employing the global Moran’s I and Anselin’s local Moran’s I statistics to detect nonrandom incidence patterns. Global ordinary least squares (OLS) regression and local geographically-weighted regression (GWR) were applied to examine the spatial correlation of cancer incidences at the city level. Population-based records of cancers diagnosed between 1998 and 2004 were used. Male lung cancer and female breast cancer exhibited positive statistically significant global Moran’s I index values, indicating a tendency toward clustering. The Anselin’s local Moran’s I analyses revealed small significant clusters of lung cancer, prostate cancer and Hodgkin’s disease among males in the Eastern region and significant clusters of thyroid cancers in females in the Eastern and Riyadh regions. Additionally, both regression methods found significant associations among various cancers. For example, OLS and GWR revealed significant spatial associations among NHL, leukemia and Hodgkin’s disease (r² = 0.49–0.67 using OLS and r² = 0.52–0.68 using GWR) and between breast and prostate cancer (r² = 0.53 OLS and 0.57 GWR) in Saudi Arabian cities. These findings may help to generate etiologic hypotheses of cancer causation and identify spatial anomalies in cancer incidence in Saudi Arabia. Our findings should stimulate further research on the possible causes underlying these clusters and associations. PMID:24351742

Simultaneous detection of landmarks and key-frame in cardiac perfusion MRI using a joint spatial-temporal context model

NASA Astrophysics Data System (ADS)

Lu, Xiaoguang; Xue, Hui; Jolly, Marie-Pierre; Guetter, Christoph; Kellman, Peter; Hsu, Li-Yueh; Arai, Andrew; Zuehlsdorff, Sven; Littmann, Arne; Georgescu, Bogdan; Guehring, Jens

2011-03-01

Cardiac perfusion magnetic resonance imaging (MRI) has proven clinical significance in diagnosis of heart diseases. However, analysis of perfusion data is time-consuming, where automatic detection of anatomic landmarks and key-frames from perfusion MR sequences is helpful for anchoring structures and functional analysis of the heart, leading toward fully automated perfusion analysis. Learning-based object detection methods have demonstrated their capabilities to handle large variations of the object by exploring a local region, i.e., context. Conventional 2D approaches take into account spatial context only. Temporal signals in perfusion data present a strong cue for anchoring. We propose a joint context model to encode both spatial and temporal evidence. In addition, our spatial context is constructed not only based on the landmark of interest, but also the landmarks that are correlated in the neighboring anatomies. A discriminative model is learned through a probabilistic boosting tree. A marginal space learning strategy is applied to efficiently learn and search in a high dimensional parameter space. A fully automatic system is developed to simultaneously detect anatomic landmarks and key frames in both RV and LV from perfusion sequences. The proposed approach was evaluated on a database of 373 cardiac perfusion MRI sequences from 77 patients. Experimental results of a 4-fold cross validation show superior landmark detection accuracies of the proposed joint spatial-temporal approach to the 2D approach that is based on spatial context only. The key-frame identification results are promising.

Brain functional network changes following Prelimbic area inactivation in a spatial memory extinction task.

PubMed

Méndez-Couz, Marta; Conejo, Nélida M; Vallejo, Guillermo; Arias, Jorge L

2015-01-01

Several studies suggest a prefrontal cortex involvement during the acquisition and consolidation of spatial memory, suggesting an active modulating role at late stages of acquisition processes. Recently, we have reported that the prelimbic and infralimbic areas of the prefrontal cortex, among other structures, are also specifically involved in the late phases of spatial memory extinction. This study aimed to evaluate whether the inactivation of the prelimbic area of the prefrontal cortex impaired spatial memory extinction. For this purpose, male Wistar rats were implanted bilaterally with cannulae into the prelimbic region of the prefrontal cortex. Animals were trained during 5 consecutive days in a hidden platform task and tested for reference spatial memory immediately after the last training session. One day after completing the training task, bilateral infusion of the GABAA receptor agonist Muscimol was performed before the extinction protocol was carried out. Additionally, cytochrome c oxidase histochemistry was applied to map the metabolic brain activity related to the spatial memory extinction under prelimbic cortex inactivation. Results show that animals acquired the reference memory task in the water maze, and the extinction task was successfully completed without significant impairment. However, analysis of the functional brain networks involved by cytochrome oxidase activity interregional correlations showed changes in brain networks between the group treated with Muscimol as compared to the saline-treated group, supporting the involvement of the mammillary bodies at a the late stage in the memory extinction process. Copyright © 2015 Elsevier B.V. All rights reserved.

Spatial Variation of Soil Respiration in a Cropland under Winter Wheat and Summer Maize Rotation in the North China Plain.

PubMed

Huang, Ni; Wang, Li; Hu, Yongsen; Tian, Haifeng; Niu, Zheng

2016-01-01

Spatial variation of soil respiration (Rs) in cropland ecosystems must be assessed to evaluate the global terrestrial carbon budget. This study aims to explore the spatial characteristics and controlling factors of Rs in a cropland under winter wheat and summer maize rotation in the North China Plain. We collected Rs data from 23 sample plots in the cropland. At the late jointing stage, the daily mean Rs of summer maize (4.74 μmol CO2 m-2 s-1) was significantly higher than that of winter wheat (3.77μmol CO2 m-2 s-1). However, the spatial variation of Rs in summer maize (coefficient of variation, CV = 12.2%) was lower than that in winter wheat (CV = 18.5%). A similar trend in CV was also observed for environmental factors but not for biotic factors, such as leaf area index, aboveground biomass, and canopy chlorophyll content. Pearson's correlation analyses based on the sampling data revealed that the spatial variation of Rs was poorly explained by the spatial variations of biotic factors, environmental factors, or soil properties alone for winter wheat and summer maize. The similarly non-significant relationship was observed between Rs and the enhanced vegetation index (EVI), which was used as surrogate for plant photosynthesis. EVI was better correlated with field-measured leaf area index than the normalized difference vegetation index and red edge chlorophyll index. All the data from the 23 sample plots were categorized into three clusters based on the cluster analysis of soil carbon/nitrogen and soil organic carbon content. An apparent improvement was observed in the relationship between Rs and EVI in each cluster for both winter wheat and summer maize. The spatial variation of Rs in the cropland under winter wheat and summer maize rotation could be attributed to the differences in spatial variations of soil properties and biotic factors. The results indicate that applying cluster analysis to minimize differences in soil properties among different clusters can improve the role of remote sensing data as a proxy of plant photosynthesis in semi-empirical Rs models and benefit the acquisition of Rs in cropland ecosystems at large scales.

Bibliography of spatial interferometry in optical astronomy

NASA Technical Reports Server (NTRS)

Gezari, Daniel Y.; Roddier, Francois; Roddier, Claude

1990-01-01

The Bibliography of Spatial Interferometry in Optical Astronomy is a guide to the published literature in applications of spatial interferometry techniques to astronomical observations, theory and instrumentation at visible and infrared wavelengths. The key words spatial and optical define the scope of this discipline, distinguishing it from spatial interferometry at radio wavelengths, interferometry in the frequency domain applied to spectroscopy, or more general electro-optics theoretical and laboratory research. The main bibliography is a listing of all technical articles published in the international scientific literature and presented at the major international meetings and workshops attended by the spatial interferometry community. Section B summarizes publications dealing with the basic theoretical concepts and algorithms proposed and applied to optical spatial interferometry and imaging through a turbulent atmosphere. The section on experimental techniques is divided into twelve categories, representing the most clearly identified major areas of experimental research work. Section D, Observations, identifies publications dealing specifically with observations of astronomical sources, in which optical spatial interferometry techniques have been applied.

Dynamic Assessment on the Landscape Patterns and Spatio-temporal Change in the mainstream of Tarim River

NASA Astrophysics Data System (ADS)

Zhang, Hui; Xue, Lianqing; Yang, Changbing; Chen, Xinfang; Zhang, Luochen; Wei, Guanghui

2018-01-01

The Tarim River (TR), as the longest inland river at an arid area in China, is a typical regions of vegetation variation research and plays a crucial role in the sustainable development of regional ecological environment. In this paper, the newest dataset of MODND1M NDVI, at a resolution of 500m, were applied to calculate vegetation index in growing season during the period 2000-2015. Using a vegetation coverage index, a trend line analysis, and the local spatial autocorrelation analysis, this paper investigated the landscape patterns and spatio-temporal variation of vegetation coverage at regional and pixel scales over mainstream of the Tarim River, Xinjiang. The results showed that (1) The bare land area on both sides of Tarim River appeared to have a fluctuated downward trend and there were two obvious valley values in 2005 and 2012. (2) Spatially, the vegetation coverage improved areas is mostly distributed in upstream and the degraded areas is mainly distributed in the left bank of midstream and the end of Tarim River during 2000-2005. (3) The local spatial auto-correlation analysis revealed that vegetation coverage was spatially positive autocorrelated and spatial concentrated. The high-high self-related areas are mainly distributed in upstream, where vegetation cover are relatively good, and the low-low self-related areas are mostly with lower vegetation cover in the lower reaches of Tarim River.

Spatial frequency characteristics at image decision-point locations for observers with different radiological backgrounds in lung nodule detection

NASA Astrophysics Data System (ADS)

Pietrzyk, Mariusz W.; Manning, David J.; Dix, Alan; Donovan, Tim

2009-02-01

Aim: The goal of the study is to determine the spatial frequency characteristics at locations in the image of overt and covert observers' decisions and find out if there are any similarities in different observers' groups: the same radiological experience group or the same accuracy scored level. Background: The radiological task is described as a visual searching decision making procedure involving visual perception and cognitive processing. Humans perceive the world through a number of spatial frequency channels, each sensitive to visual information carried by different spatial frequency ranges and orientations. Recent studies have shown that particular physical properties of local and global image-based elements are correlated with the performance and the level of experience of human observers in breast cancer and lung nodule detections. Neurological findings in visual perception were an inspiration for wavelet applications in vision research because the methodology tries to mimic the brain processing algorithms. Methods: The wavelet approach to the set of postero-anterior chest radiographs analysis has been used to characterize perceptual preferences observers with different levels of experience in the radiological task. Psychophysical methodology has been applied to track eye movements over the image, where particular ROIs related to the observers' fixation clusters has been analysed in the spaces frame by Daubechies functions. Results: Significance differences have been found between the spatial frequency characteristics at the location of different decisions.

How Unique is Any Given Seismogram? - Exploring Correlation Methods to Identify Explosions

NASA Astrophysics Data System (ADS)

Walter, W. R.; Dodge, D. A.; Ford, S. R.; Pyle, M. L.; Hauk, T. F.

2015-12-01

As with conventional wisdom about snowflakes, we would expect it unlikely that any two broadband seismograms would ever be exactly identical. However depending upon the resolution of our comparison metric, we do expect, and often find, bandpassed seismograms that correlate to very high levels (>0.99). In fact regional (e.g. Schaff and Richards, 2011) and global investigations (e.g. Dodge and Walter, 2015) find large numbers of highly correlated seismograms. Decreasing computational costs are increasing the tremendous potential for correlation in lowering detection, location and identification thresholds for explosion monitoring (e.g. Schaff et al., 2012, Gibbons and Ringdal, 2012; Zhang and Wen, 2015). We have shown in the case of Source Physics Experiment (SPE) chemical explosions, templates at local and near regional stations can detect, locate and identify very small explosions, which might be applied to monitoring active test sites (Ford and Walter, 2015). In terms of elastic theory, seismograms are the convolution between source and Green function terms. Thus high correlation implies similar sources, closely located. How do we quantify this physically? For example it is well known that as the template event and target events are increasingly separated spatially, their correlation diminishes, as the difference in the Green function between the two events grows larger. This is related to the event separation in terms of wavelength, the heterogeneity of the Earth structure, and the time-bandwidth of the correlation parameters used, but this has not been well quantified. We are using the historic dataset of nuclear explosions in southern Nevada to explore empirically where and how well these events correlate as a function of location, depth, size, time-bandwidth and other parameters. A goal is to develop more meaningful and physical metrics that go beyond the correlation coefficient and can be applied to explosion monitoring problems, particularly event identification.

A method based on IHS cylindrical transform model for quality assessment of image fusion

NASA Astrophysics Data System (ADS)

Zhu, Xiaokun; Jia, Yonghong

2005-10-01

Image fusion technique has been widely applied to remote sensing image analysis and processing, and methods for quality assessment of image fusion in remote sensing have also become the research issues at home and abroad. Traditional assessment methods combine calculation of quantitative indexes and visual interpretation to compare fused images quantificationally and qualitatively. However, in the existing assessment methods, there are two defects: on one hand, most imdexes lack the theoretic support to compare different fusion methods. On the hand, there is not a uniform preference for most of the quantitative assessment indexes when they are applied to estimate the fusion effects. That is, the spatial resolution and spectral feature could not be analyzed synchronously by these indexes and there is not a general method to unify the spatial and spectral feature assessment. So in this paper, on the basis of the approximate general model of four traditional fusion methods, including Intensity Hue Saturation(IHS) triangle transform fusion, High Pass Filter(HPF) fusion, Principal Component Analysis(PCA) fusion, Wavelet Transform(WT) fusion, a correlation coefficient assessment method based on IHS cylindrical transform is proposed. By experiments, this method can not only get the evaluation results of spatial and spectral features on the basis of uniform preference, but also can acquire the comparison between fusion image sources and fused images, and acquire differences among fusion methods. Compared with the traditional assessment methods, the new methods is more intuitionistic, and in accord with subjective estimation.

Roles of Spatial Scale and Rarity on the Relationship between Butterfly Species Richness and Human Density in South Africa

PubMed Central

Mecenero, Silvia; Altwegg, Res; Colville, Jonathan F.; Beale, Colin M.

2015-01-01

Wildlife and humans tend to prefer the same productive environments, yet high human densities often lead to reduced biodiversity. Species richness is often positively correlated with human population density at broad scales, but this correlation could also be caused by unequal sampling effort leading to higher species tallies in areas of dense human activity. We examined the relationships between butterfly species richness and human population density at five spatial resolutions ranging from 2' to 60' across South Africa. We used atlas-type data and spatial interpolation techniques aimed at reducing the effect of unequal spatial sampling. Our results confirm the general positive correlation between total species richness and human population density. Contrary to our expectations, the strength of this positive correlation did not weaken at finer spatial resolutions. The patterns observed using total species richness were driven mostly by common species. The richness of threatened and restricted range species was not correlated to human population density. None of the correlations we examined were particularly strong, with much unexplained variance remaining, suggesting that the overlap between butterflies and humans is not strong compared to other factors not accounted for in our analyses. Special consideration needs to be made regarding conservation goals and variables used when investigating the overlap between species and humans for biodiversity conservation. PMID:25915899

Roles of Spatial Scale and Rarity on the Relationship between Butterfly Species Richness and Human Density in South Africa.

PubMed

Mecenero, Silvia; Altwegg, Res; Colville, Jonathan F; Beale, Colin M

2015-01-01

Wildlife and humans tend to prefer the same productive environments, yet high human densities often lead to reduced biodiversity. Species richness is often positively correlated with human population density at broad scales, but this correlation could also be caused by unequal sampling effort leading to higher species tallies in areas of dense human activity. We examined the relationships between butterfly species richness and human population density at five spatial resolutions ranging from 2' to 60' across South Africa. We used atlas-type data and spatial interpolation techniques aimed at reducing the effect of unequal spatial sampling. Our results confirm the general positive correlation between total species richness and human population density. Contrary to our expectations, the strength of this positive correlation did not weaken at finer spatial resolutions. The patterns observed using total species richness were driven mostly by common species. The richness of threatened and restricted range species was not correlated to human population density. None of the correlations we examined were particularly strong, with much unexplained variance remaining, suggesting that the overlap between butterflies and humans is not strong compared to other factors not accounted for in our analyses. Special consideration needs to be made regarding conservation goals and variables used when investigating the overlap between species and humans for biodiversity conservation.

Fluorescence correlation spectroscopy of diffusion probed with a Gaussian Lorentzian spatial distribution

NASA Astrophysics Data System (ADS)

Marrocco, Michele

2007-11-01

Fluorescence correlation spectroscopy is fundamental in many physical, chemical and biological studies of molecular diffusion. However, the concept of fluorescence correlation is founded on the assumption that the analytical description of the correlation decay of diffusion can be achieved if the spatial profile of the detected volume obeys a three-dimensional Gaussian distribution. In the present Letter, the analytical result is instead proven for the fundamental Gaussian-Lorentzian profile.

Evaluation of modeling NO2 concentrations driven by satellite-derived and bottom-up emission inventories using in situ measurements over China

NASA Astrophysics Data System (ADS)

Liu, Fei; van der A, Ronald J.; Eskes, Henk; Ding, Jieying; Mijling, Bas

2018-03-01

Chemical transport models together with emission inventories are widely used to simulate NO2 concentrations over China, but validation of the simulations with in situ measurements has been extremely limited. Here we use ground measurements obtained from the air quality monitoring network recently developed by the Ministry of Environmental Protection of China to validate modeling surface NO2 concentrations from the CHIMERE regional chemical transport model driven by the satellite-derived DECSO and the bottom-up MIX emission inventories. We applied a correction factor to the observations to account for the interferences of other oxidized nitrogen compounds (NOz), based on the modeled ratio of NO2 to NOz. The model accurately reproduces the spatial variability in NO2 from in situ measurements, with a spatial correlation coefficient of over 0.7 for simulations based on both inventories. A negative and positive bias is found for the simulation with the DECSO (slope = 0.74 and 0.64 for the daily mean and daytime only) and the MIX (slope = 1.3 and 1.1) inventories, respectively, suggesting an underestimation and overestimation of NOx emissions from corresponding inventories. The bias between observed and modeled concentrations is reduced, with the slope dropping from 1.3 to 1.0 when the spatial distribution of NOx emissions in the DECSO inventory is applied as the spatial proxy for the MIX inventory, which suggests an improvement of the distribution of emissions between urban and suburban or rural areas in the DECSO inventory compared to that used in the bottom-up inventory. A rough estimate indicates that the observed concentrations, from sites predominantly placed in the populated urban areas, may be 10-40 % higher than the corresponding model grid cell mean. This reduces the estimate of the negative bias of the DECSO-based simulation to the range of -30 to 0 % on average and more firmly establishes that the MIX inventory is biased high over major cities. The performance of the model is comparable over seasons, with a slightly worse spatial correlation in summer due to the difficulties in resolving the more active NOx photochemistry and larger concentration gradients in summer by the model. In addition, the model well captures the daytime diurnal cycle but shows more significant disagreement between simulations and measurements during nighttime, which likely produces a positive model bias of about 15 % in the daily mean concentrations. This is most likely related to the uncertainty in vertical mixing in the model at night.

Evaluation of Modeling NO2 Concentrations Driven by Satellite-Derived and Bottom-Up Emission Inventories Using In-Situ Measurements Over China

NASA Technical Reports Server (NTRS)

Liu, Fei; van der A, Ronald J.; Eskes, Henk; Ding, Jieying; Mijling, Bas

2018-01-01

Chemical transport models together with emission inventories are widely used to simulate NO2 concentrations over China, but validation of the simulations with in situ measurements has been extremely limited. Here we use ground measurements obtained from the air quality monitoring network recently developed by the Ministry of Environmental Protection of China to validate modeling surface NO2 concentrations from the CHIMERE regional chemical transport model driven by the satellite-derived DECSO and the bottom-up MIX emission inventories. We applied a correction factor to the observations to account for the interferences of other oxidized nitrogen compounds (NOz), based on the modeled ratio of NO2 to NOz. The model accurately reproduces the spatial variability in NO2 from in situ measurements, with a spatial correlation coefficient of over 0.7 for simulations based on both inventories. A negative and positive bias is found for the simulation with the DECSO (slopeD0.74 and 0.64 for the daily mean and daytime only) and the MIX (slopeD1.3 and 1.1) inventories, respectively, suggesting an underestimation and overestimation of NOx emissions from corresponding inventories. The bias between observed and modeled concentrations is reduced, with the slope dropping from 1.3 to 1.0 when the spatial distribution of NOx emissions in the DECSO inventory is applied as the spatial proxy for the MIX inventory, which suggests an improvement of the distribution of emissions between urban and suburban or rural areas in the DECSO inventory compared to that used in the bottom-up inventory. A rough estimate indicates that the observed concentrations, from sites predominantly placed in the populated urban areas, may be 10-40% higher than the corresponding model grid cell mean. This reduces the estimate of the negative bias of the DECSO-based simulation to the range of -30 to 0% on average and more firmly establishes that the MIX inventory is biased high over major cities. The performance of the model is comparable over seasons, with a slightly worse spatial correlation in summer due to the difficulties in resolving the more active NOx photochemistry and larger concentration gradients in summer by the model. In addition, the model well captures the daytime diurnal cycle but shows more significant disagreement between simulations and measurements during nighttime, which likely produces a positive model bias of about 15% in the daily mean concentrations. This is most likely related to the uncertainty in vertical mixing in the model at night.

Executive and memory correlates of age-related differences in wayfinding performances using a virtual reality application.

PubMed

Taillade, Mathieu; Sauzéon, Hélène; Dejos, Marie; Pala, Prashant Arvind; Larrue, Florian; Wallet, Grégory; Gross, Christian; N'Kaoua, Bernard

2013-01-01

The aim of this study was to evaluate in large-scale spaces wayfinding and spatial learning difficulties for older adults in relation to the executive and memory decline associated with aging. We compared virtual reality (VR)-based wayfinding and spatial memory performances between young and older adults. Wayfinding and spatial memory performances were correlated with classical measures of executive and visuo-spatial memory functions, but also with self-reported estimates of wayfinding difficulties. We obtained a significant effect of age on wayfinding performances but not on spatial memory performances. The overall correlations showed significant correlations between the wayfinding performances and the classical measures of both executive and visuo-spatial memory, but only when the age factor was not partialled out. Also, older adults underestimated their wayfinding difficulties. A significant relationship between the wayfinding performances and self-reported wayfinding difficulty estimates is found, but only when the age effect was partialled out. These results show that, even when older adults have an equivalent spatial knowledge to young adults, they had greater difficulties with the wayfinding task, supporting an executive decline view in age-related wayfinding difficulties. However, the correlation results are in favor of both the memory and executive decline views as mediators of age-related differences in wayfinding performances. This is discussed in terms of the relationships between memory and executive functioning in wayfinding task orchestration. Our results also favor the use of objective assessments of everyday navigation difficulties in virtual applications, instead of self-reported questionnaires, since older adults showed difficulties in estimating their everyday wayfinding problems.

Earthquake scaling laws for rupture geometry and slip heterogeneity

NASA Astrophysics Data System (ADS)

Thingbaijam, Kiran K. S.; Mai, P. Martin; Goda, Katsuichiro

2016-04-01

We analyze an extensive compilation of finite-fault rupture models to investigate earthquake scaling of source geometry and slip heterogeneity to derive new relationships for seismic and tsunami hazard assessment. Our dataset comprises 158 earthquakes with a total of 316 rupture models selected from the SRCMOD database (http://equake-rc.info/srcmod). We find that fault-length does not saturate with earthquake magnitude, while fault-width reveals inhibited growth due to the finite seismogenic thickness. For strike-slip earthquakes, fault-length grows more rapidly with increasing magnitude compared to events of other faulting types. Interestingly, our derived relationship falls between the L-model and W-model end-members. In contrast, both reverse and normal dip-slip events are more consistent with self-similar scaling of fault-length. However, fault-width scaling relationships for large strike-slip and normal dip-slip events, occurring on steeply dipping faults (δ~90° for strike-slip faults, and δ~60° for normal faults), deviate from self-similarity. Although reverse dip-slip events in general show self-similar scaling, the restricted growth of down-dip fault extent (with upper limit of ~200 km) can be seen for mega-thrust subduction events (M~9.0). Despite this fact, for a given earthquake magnitude, subduction reverse dip-slip events occupy relatively larger rupture area, compared to shallow crustal events. In addition, we characterize slip heterogeneity in terms of its probability distribution and spatial correlation structure to develop a complete stochastic random-field characterization of earthquake slip. We find that truncated exponential law best describes the probability distribution of slip, with observable scale parameters determined by the average and maximum slip. Applying Box-Cox transformation to slip distributions (to create quasi-normal distributed data) supports cube-root transformation, which also implies distinctive non-Gaussian slip distributions. To further characterize the spatial correlations of slip heterogeneity, we analyze the power spectral decay of slip applying the 2-D von Karman auto-correlation function (parameterized by the Hurst exponent, H, and correlation lengths along strike and down-slip). The Hurst exponent is scale invariant, H = 0.83 (± 0.12), while the correlation lengths scale with source dimensions (seismic moment), thus implying characteristic physical scales of earthquake ruptures. Our self-consistent scaling relationships allow constraining the generation of slip-heterogeneity scenarios for physics-based ground-motion and tsunami simulations.

Characterization of microscopic deformation through two-point spatial correlation functions

NASA Astrophysics Data System (ADS)

Huang, Guan-Rong; Wu, Bin; Wang, Yangyang; Chen, Wei-Ren

2018-01-01

The molecular rearrangements of most fluids under flow and deformation do not directly follow the macroscopic strain field. In this work, we describe a phenomenological method for characterizing such nonaffine deformation via the anisotropic pair distribution function (PDF). We demonstrate how the microscopic strain can be calculated in both simple shear and uniaxial extension, by perturbation expansion of anisotropic PDF in terms of real spherical harmonics. Our results, given in the real as well as the reciprocal space, can be applied in spectrum analysis of small-angle scattering experiments and nonequilibrium molecular dynamics simulations of soft matter under flow.

Core-Shell Magnetic Morphology of Structurally Uniform Magnetite Nanoparticles

NASA Astrophysics Data System (ADS)

Krycka, K. L.; Booth, R. A.; Hogg, C. R.; Ijiri, Y.; Borchers, J. A.; Chen, W. C.; Watson, S. M.; Laver, M.; Gentile, T. R.; Dedon, L. R.; Harris, S.; Rhyne, J. J.; Majetich, S. A.

2010-05-01

A new development in small-angle neutron scattering with polarization analysis allows us to directly extract the average spatial distributions of magnetic moments and their correlations with three-dimensional directional sensitivity in any magnetic field. Applied to a collection of spherical magnetite nanoparticles 9.0 nm in diameter, this enhanced method reveals uniformly canted, magnetically active shells in a nominally saturating field of 1.2 T. The shell thickness depends on temperature, and it disappears altogether when the external field is removed, confirming that these canted nanoparticle shells are magnetic, rather than structural, in origin.

Characterization of microscopic deformation through two-point spatial correlation functions.

PubMed

Huang, Guan-Rong; Wu, Bin; Wang, Yangyang; Chen, Wei-Ren

2018-01-01

The molecular rearrangements of most fluids under flow and deformation do not directly follow the macroscopic strain field. In this work, we describe a phenomenological method for characterizing such nonaffine deformation via the anisotropic pair distribution function (PDF). We demonstrate how the microscopic strain can be calculated in both simple shear and uniaxial extension, by perturbation expansion of anisotropic PDF in terms of real spherical harmonics. Our results, given in the real as well as the reciprocal space, can be applied in spectrum analysis of small-angle scattering experiments and nonequilibrium molecular dynamics simulations of soft matter under flow.

Verifying the Dependence of Fractal Coefficients on Different Spatial Distributions

NASA Astrophysics Data System (ADS)

Gospodinov, Dragomir; Marekova, Elisaveta; Marinov, Alexander

2010-01-01

A fractal distribution requires that the number of objects larger than a specific size r has a power-law dependence on the size N(r) = C/rD∝r-D where D is the fractal dimension. Usually the correlation integral is calculated to estimate the correlation fractal dimension of epicentres. A `box-counting' procedure could also be applied giving the `capacity' fractal dimension. The fractal dimension can be an integer and then it is equivalent to a Euclidean dimension (it is zero of a point, one of a segment, of a square is two and of a cube is three). In general the fractal dimension is not an integer but a fractional dimension and there comes the origin of the term `fractal'. The use of a power-law to statistically describe a set of events or phenomena reveals the lack of a characteristic length scale, that is fractal objects are scale invariant. Scaling invariance and chaotic behavior constitute the base of a lot of natural hazards phenomena. Many studies of earthquakes reveal that their occurrence exhibits scale-invariant properties, so the fractal dimension can characterize them. It has first been confirmed that both aftershock rate decay in time and earthquake size distribution follow a power law. Recently many other earthquake distributions have been found to be scale-invariant. The spatial distribution of both regional seismicity and aftershocks show some fractal features. Earthquake spatial distributions are considered fractal, but indirectly. There are two possible models, which result in fractal earthquake distributions. The first model considers that a fractal distribution of faults leads to a fractal distribution of earthquakes, because each earthquake is characteristic of the fault on which it occurs. The second assumes that each fault has a fractal distribution of earthquakes. Observations strongly favour the first hypothesis. The fractal coefficients analysis provides some important advantages in examining earthquake spatial distribution, which are:—Simple way to quantify scale-invariant distributions of complex objects or phenomena by a small number of parameters.—It is becoming evident that the applicability of fractal distributions to geological problems could have a more fundamental basis. Chaotic behaviour could underlay the geotectonic processes and the applicable statistics could often be fractal. The application of fractal distribution analysis has, however, some specific aspects. It is usually difficult to present an adequate interpretation of the obtained values of fractal coefficients for earthquake epicenter or hypocenter distributions. That is why in this paper we aimed at other goals—to verify how a fractal coefficient depends on different spatial distributions. We simulated earthquake spatial data by generating randomly points first in a 3D space - cube, then in a parallelepiped, diminishing one of its sides. We then continued this procedure in 2D and 1D space. For each simulated data set we calculated the points' fractal coefficient (correlation fractal dimension of epicentres) and then checked for correlation between the coefficients values and the type of spatial distribution. In that way one can obtain a set of standard fractal coefficients' values for varying spatial distributions. These then can be used when real earthquake data is analyzed by comparing the real data coefficients values to the standard fractal coefficients. Such an approach can help in interpreting the fractal analysis results through different types of spatial distributions.

Airborne electromagnetic data levelling using principal component analysis based on flight line difference

NASA Astrophysics Data System (ADS)

Zhang, Qiong; Peng, Cong; Lu, Yiming; Wang, Hao; Zhu, Kaiguang

2018-04-01

A novel technique is developed to level airborne geophysical data using principal component analysis based on flight line difference. In the paper, flight line difference is introduced to enhance the features of levelling error for airborne electromagnetic (AEM) data and improve the correlation between pseudo tie lines. Thus we conduct levelling to the flight line difference data instead of to the original AEM data directly. Pseudo tie lines are selected distributively cross profile direction, avoiding the anomalous regions. Since the levelling errors of selective pseudo tie lines show high correlations, principal component analysis is applied to extract the local levelling errors by low-order principal components reconstruction. Furthermore, we can obtain the levelling errors of original AEM data through inverse difference after spatial interpolation. This levelling method does not need to fly tie lines and design the levelling fitting function. The effectiveness of this method is demonstrated by the levelling results of survey data, comparing with the results from tie-line levelling and flight-line correlation levelling.

Localization of incipient tip vortex cavitation using ray based matched field inversion method

NASA Astrophysics Data System (ADS)

Kim, Dongho; Seong, Woojae; Choo, Youngmin; Lee, Jeunghoon

2015-10-01

Cavitation of marine propeller is one of the main contributing factors of broadband radiated ship noise. In this research, an algorithm for the source localization of incipient vortex cavitation is suggested. Incipient cavitation is modeled as monopole type source and matched-field inversion method is applied to find the source position by comparing the spatial correlation between measured and replicated pressure fields at the receiver array. The accuracy of source localization is improved by broadband matched-field inversion technique that enhances correlation by incoherently averaging correlations of individual frequencies. Suggested localization algorithm is verified through known virtual source and model test conducted in Samsung ship model basin cavitation tunnel. It is found that suggested localization algorithm enables efficient localization of incipient tip vortex cavitation using a few pressure data measured on the outer hull above the propeller and practically applicable to the typically performed model scale experiment in a cavitation tunnel at the early design stage.

A Big Spatial Data Processing Framework Applying to National Geographic Conditions Monitoring

NASA Astrophysics Data System (ADS)

Xiao, F.

2018-04-01

In this paper, a novel framework for spatial data processing is proposed, which apply to National Geographic Conditions Monitoring project of China. It includes 4 layers: spatial data storage, spatial RDDs, spatial operations, and spatial query language. The spatial data storage layer uses HDFS to store large size of spatial vector/raster data in the distributed cluster. The spatial RDDs are the abstract logical dataset of spatial data types, and can be transferred to the spark cluster to conduct spark transformations and actions. The spatial operations layer is a series of processing on spatial RDDs, such as range query, k nearest neighbor and spatial join. The spatial query language is a user-friendly interface which provide people not familiar with Spark with a comfortable way to operation the spatial operation. Compared with other spatial frameworks, it is highlighted that comprehensive technologies are referred for big spatial data processing. Extensive experiments on real datasets show that the framework achieves better performance than traditional process methods.

a Data Field Method for Urban Remotely Sensed Imagery Classification Considering Spatial Correlation

NASA Astrophysics Data System (ADS)

Zhang, Y.; Qin, K.; Zeng, C.; Zhang, E. B.; Yue, M. X.; Tong, X.

2016-06-01

Spatial correlation between pixels is important information for remotely sensed imagery classification. Data field method and spatial autocorrelation statistics have been utilized to describe and model spatial information of local pixels. The original data field method can represent the spatial interactions of neighbourhood pixels effectively. However, its focus on measuring the grey level change between the central pixel and the neighbourhood pixels results in exaggerating the contribution of the central pixel to the whole local window. Besides, Geary's C has also been proven to well characterise and qualify the spatial correlation between each pixel and its neighbourhood pixels. But the extracted object is badly delineated with the distracting salt-and-pepper effect of isolated misclassified pixels. To correct this defect, we introduce the data field method for filtering and noise limitation. Moreover, the original data field method is enhanced by considering each pixel in the window as the central pixel to compute statistical characteristics between it and its neighbourhood pixels. The last step employs a support vector machine (SVM) for the classification of multi-features (e.g. the spectral feature and spatial correlation feature). In order to validate the effectiveness of the developed method, experiments are conducted on different remotely sensed images containing multiple complex object classes inside. The results show that the developed method outperforms the traditional method in terms of classification accuracies.

China's Air Quality and Respiratory Disease Mortality Based on the Spatial Panel Model.

PubMed

Cao, Qilong; Liang, Ying; Niu, Xueting

2017-09-18

Background : Air pollution has become an important factor restricting China's economic development and has subsequently brought a series of social problems, including the impact of air pollution on the health of residents, which is a topical issue in China. Methods : Taking into account this spatial imbalance, the paper is based on the spatial panel data model PM 2.5 . Respiratory disease mortality in 31 Chinese provinces from 2004 to 2008 is taken as the main variable to study the spatial effect and impact of air quality and respiratory disease mortality on a large scale. Results : It was found that there is a spatial correlation between the mortality of respiratory diseases in Chinese provinces. The spatial correlation can be explained by the spatial effect of PM 2.5 pollutions in the control of other variables. Conclusions : Compared with the traditional non-spatial model, the spatial model is better for describing the spatial relationship between variables, ensuring the conclusions are scientific and can measure the spatial effect between variables.

Analysis of Island Formation Due to RMPs in D3D Plasmas Using SIESTA

NASA Astrophysics Data System (ADS)

Hirshman, Steven; Shafer, Morgan; Seal, Sudip; Canik, John

2015-11-01

By varying the initial helical perturbation amplitude of Resonant Magnetic Perturbations (RMPs) applied to a Doublet III-D (DIII-D) plasma, a variety of meta-stable equilibrium are scanned using the SIESTA MHD equilibrium code. It is found that increasing the perturbation strength at the dominant m =2 resonant surface leads to lower MHD energies and significant increases in the equilibrium island widths at the m =2 (and sidebands) surfaces. Island overlap eventually leads to stochastic magnetic fields which correlate well with the experimentally inferred field line structure. The magnitude and spatial phase (around associated rational surfaces) of resonant (shielding) components of the parallel current is shown to be correlated with the magnetic island topology. Work supported by U.S. DOE under Contract DE-AC05-00OR22725 with UT-Battelle, LLC.

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

PubMed

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

2014-11-01

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

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

PubMed Central

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

2016-01-01

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

Boundary Information Inflow Enhances Correlation in Flocking

NASA Astrophysics Data System (ADS)

Cavagna, Andrea; Giardina, Irene; Ginelli, Francesco

2013-04-01

The most conspicuous trait of collective animal behavior is the emergence of highly ordered structures. Less obvious to the eye, but perhaps more profound a signature of self-organization, is the presence of long-range spatial correlations. Experimental data on starling flocks in 3D show that the exponent ruling the decay of the velocity correlation function, C(r)˜1/rγ, is extremely small, γ≪1. This result can neither be explained by equilibrium field theory nor by off-equilibrium theories and simulations of active systems. Here, by means of numerical simulations and theoretical calculations, we show that a dynamical field applied to the boundary of a set of Heisenberg spins on a 3D lattice gives rise to a vanishing exponent γ, as in starling flocks. The effect of the dynamical field is to create an information inflow from border to bulk that triggers long-range spin-wave modes, thus giving rise to an anomalously long-ranged correlation. The biological origin of this phenomenon can be either exogenous—information produced by environmental perturbations is transferred from boundary to bulk of the flock—or endogenous—the flock keeps itself in a constant state of dynamical excitation that is beneficial to correlation and collective response.

Spatial and Temporal Uncertainty of Crop Yield Aggregations

NASA Technical Reports Server (NTRS)

Porwollik, Vera; Mueller, Christoph; Elliott, Joshua; Chryssanthacopoulos, James; Iizumi, Toshichika; Ray, Deepak K.; Ruane, Alex C.; Arneth, Almut; Balkovic, Juraj; Ciais, Philippe;

Spatially explicit modelling of forest structure and function using airborne lidar and hyperspectral remote sensing data combined with micrometeorological measurements

NASA Astrophysics Data System (ADS)

Thomas, Valerie Anne

This research models canopy-scale photosynthesis at the Groundhog River Flux Site through the integration of high-resolution airborne remote sensing data and micrometeorological measurements collected from a flux tower. Light detection and ranging (lidar) data are analysed to derive models of tree structure, including: canopy height, basal area, crown closure, and average aboveground biomass. Lidar and hyperspectral remote sensing data are used to model canopy chlorophyll (Chl) and carotenoid concentrations (known to be good indicators of photosynthesis). The integration of lidar and hyperspectral data is applied to derive spatially explicit models of the fraction of photosynthetically active radiation (fPAR) absorbed by the canopy as well as a species classification for the site. These products are integrated with flux tower meteorological measurements (i.e., air temperature and global solar radiation) collected on a continuous basis over 2004 to apply the C-Fix model of carbon exchange to the site. Results demonstrate that high resolution lidar and lidar-hyperspectral integration techniques perform well in the boreal mixedwood environment. Lidar models are well correlated with forest structure, despite the complexities introduced in the mixedwood case (e.g., r2=0.84, 0.89, 0.60, and 0.91, for mean dominant height, basal area, crown closure, and average aboveground biomass). Strong relationships are also shown for canopy scale chlorophyll/carotenoid concentration analysis using integrated lidar-hyperspectral techniques (e.g., r2=0.84, 0.84, and 0.82 for Chl(a), Chl(a+b), and Chl(b)). Examination of the spatially explicit models of fPAR reveal distinct spatial patterns which become increasingly apparent throughout the season due to the variation in species groupings (and canopy chlorophyll concentration) within the 1 km radius surrounding the flux tower. Comparison of results from the modified local-scale version of the C-Fix model to tower gross ecosystem productivity (GEP) demonstrate a good correlation to flux tower measured GEP (r2=0.70 for 10 day averages), with the largest deviations occurring in June-July. This research has direct benefits for forest inventory mapping and management practices; mapping of canopy physiology and biochemical constituents related to forest health; and scaling and direct comparison to large resolution satellite models to help bridge the gap between the local-scale measurements at flux towers and predictions derived from continental-scale carbon models.

Long-term Observations of Intense Precipitation Small-scale Spatial Variability in a Semi-arid Catchment

NASA Astrophysics Data System (ADS)

Cropp, E. L.; Hazenberg, P.; Castro, C. L.; Demaria, E. M.

2017-12-01

In the southwestern US, the summertime North American Monsoon (NAM) provides about 60% of the region's annual precipitation. Recent research using high-resolution atmospheric model simulations and retrospective predictions has shown that since the 1950's, and more specifically in the last few decades, the mean daily precipitation in the southwestern U.S. during the NAM has followed a decreasing trend. Furthermore, days with more extreme precipitation have intensified. The current work focuses the impact of these long-term changes on the observed small-scale spatial variability of intense precipitation. Since limited long-term high-resolution observational data exist to support such climatological-induced spatial changes in precipitation frequency and intensity, the current work utilizes observations from the USDA-ARS Walnut Gulch Experimental Watershed (WGEW) in southeastern Arizona. Within this 150 km^2 catchment over 90 rain gauges have been installed since the 1950s, measuring at sub-hourly resolution. We have applied geospatial analyses and the kriging interpolation technique to identify long-term changes in the spatial and temporal correlation and anisotropy of intense precipitation. The observed results will be compared with the previously model simulated results, as well as related to large-scale variations in climate patterns, such as the El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO).

Suppressive mechanisms in visual motion processing: From perception to intelligence.

PubMed

Tadin, Duje

2015-10-01

Perception operates on an immense amount of incoming information that greatly exceeds the brain's processing capacity. Because of this fundamental limitation, the ability to suppress irrelevant information is a key determinant of perceptual efficiency. Here, I will review a series of studies investigating suppressive mechanisms in visual motion processing, namely perceptual suppression of large, background-like motions. These spatial suppression mechanisms are adaptive, operating only when sensory inputs are sufficiently robust to guarantee visibility. Converging correlational and causal evidence links these behavioral results with inhibitory center-surround mechanisms, namely those in cortical area MT. Spatial suppression is abnormally weak in several special populations, including the elderly and individuals with schizophrenia-a deficit that is evidenced by better-than-normal direction discriminations of large moving stimuli. Theoretical work shows that this abnormal weakening of spatial suppression should result in motion segregation deficits, but direct behavioral support of this hypothesis is lacking. Finally, I will argue that the ability to suppress information is a fundamental neural process that applies not only to perception but also to cognition in general. Supporting this argument, I will discuss recent research that shows individual differences in spatial suppression of motion signals strongly predict individual variations in IQ scores. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Nanoscale Spatiotemporal Diffusion Modes Measured by Simultaneous Confocal and Stimulated Emission Depletion Nanoscopy Imaging.

PubMed

Schneider, Falk; Waithe, Dominic; Galiani, Silvia; Bernardino de la Serna, Jorge; Sezgin, Erdinc; Eggeling, Christian

2018-06-19

The diffusion dynamics in the cellular plasma membrane provide crucial insights into molecular interactions, organization, and bioactivity. Beam-scanning fluorescence correlation spectroscopy combined with super-resolution stimulated emission depletion nanoscopy (scanning STED-FCS) measures such dynamics with high spatial and temporal resolution. It reveals nanoscale diffusion characteristics by measuring the molecular diffusion in conventional confocal mode and super-resolved STED mode sequentially for each pixel along the scanned line. However, to directly link the spatial and the temporal information, a method that simultaneously measures the diffusion in confocal and STED modes is needed. Here, to overcome this problem, we establish an advanced STED-FCS measurement method, line interleaved excitation scanning STED-FCS (LIESS-FCS), that discloses the molecular diffusion modes at different spatial positions with a single measurement. It relies on fast beam-scanning along a line with alternating laser illumination that yields, for each pixel, the apparent diffusion coefficients for two different observation spot sizes (conventional confocal and super-resolved STED). We demonstrate the potential of the LIESS-FCS approach with simulations and experiments on lipid diffusion in model and live cell plasma membranes. We also apply LIESS-FCS to investigate the spatiotemporal organization of glycosylphosphatidylinositol-anchored proteins in the plasma membrane of live cells, which, interestingly, show multiple diffusion modes at different spatial positions.

Exploring the effects of photon correlations from thermal sources on bacterial photosynthesis

NASA Astrophysics Data System (ADS)

Manrique, Pedro D.; Caycedo-Soler, Felipe; De Mendoza, Adriana; Rodríguez, Ferney; Quiroga, Luis; Johnson, Neil F.

Thermal light sources can produce photons with strong spatial correlations. We study the role that these correlations might potentially play in bacterial photosynthesis. Our findings show a relationship between the transversal distance between consecutive absorptions and the efficiency of the photosynthetic process. Furthermore, membranes where the clustering of core complexes (so-called RC-LH1) is high, display a range where the organism profits maximally from the spatial correlation of the incoming light. By contrast, no maximum is found for membranes with low core-core clustering. We employ a detailed membrane model with state-of-the-art empirical inputs. Our results suggest that the organization of the membrane's antenna complexes may be well-suited to the spatial correlations present in an natural light source. Future experiments will be needed to test this prediction.

Spatial strategies for managing visitor impacts in National Parks

USGS Publications Warehouse

Leung, Y.-F.; Marion, J.L.

1999-01-01

Resource and social impacts caused by recreationists and tourists have become a management concern in national parks and equivalent protected areas. The need to contain visitor impacts within acceptable limits has prompted park and protected area managers to implement a wide variety of strategies and actions, many of which are spatial in nature. This paper classifies and illustrates the basic spatial strategies for managing visitor impacts in parks and protected areas. A typology of four spatial strategies was proposed based on the recreation and park management literature. Spatial segregation is a common strategy for shielding sensitive resources from visitor impacts or for separating potentially conflicting types of use. Two forms of spatial segregation are zoning and closure. A spatial containment strategy is intended to minimize the aggregate extent of visitor impacts by confining use to limited designated or established Iocations. In contrast, a spatial dispersal strategy seeks to spread visitor use, reducing the frequency of use to levels that avoid or minimize permanent resource impacts or visitor crowding and conflict. Finally, a spatial configuration strategy minimizes impacting visitor behavior though the judicious spatial arrangement of facilities. These four spatial strategics can be implemented separately or in combination at varying spatial scales within a single park. A survey of national park managers provides an empirical example of the diversity of implemented spatial strategies in managing visitor impacts. Spatial segregation is frequently applied in the form of camping restrictions or closures to protect sensitive natural or cultural resources and to separate incompatible visitor activities. Spatial containment is the most widely applied strategy for minimizing the areal extent of resource impacts. Spatial dispersal is commonly applied to reduce visitor crowding or conflicts in popular destination areas but is less frequently applied or effective in minimizing resource impacts. Spatial configuration was only minimally evaluated, as it was not included in the survey. The proposed typology of spatial strategies offers a useful means of organizing and understanding the wide variety of management strategies and actions applied in managing visitor impacts in parks and protected areas. Examples from U.S. national parks demonstrate the diversity of these basic strategies and their flexibility in implementation at various spatial scales. Documentation of these examples helps illustrate their application and inform managers of the multitude of options. Further analysis from the spatial perspective is needed Io extend the applicability of this typology to other recreational activities and management issues.

Pre-processing ambient noise cross-correlations with equalizing the covariance matrix eigenspectrum

NASA Astrophysics Data System (ADS)

Seydoux, Léonard; de Rosny, Julien; Shapiro, Nikolai M.

2017-09-01

Passive imaging techniques from ambient seismic noise requires a nearly isotropic distribution of the noise sources in order to ensure reliable traveltime measurements between seismic stations. However, real ambient seismic noise often partially fulfils this condition. It is generated in preferential areas (in deep ocean or near continental shores), and some highly coherent pulse-like signals may be present in the data such as those generated by earthquakes. Several pre-processing techniques have been developed in order to attenuate the directional and deterministic behaviour of this real ambient noise. Most of them are applied to individual seismograms before cross-correlation computation. The most widely used techniques are the spectral whitening and temporal smoothing of the individual seismic traces. We here propose an additional pre-processing to be used together with the classical ones, which is based on the spatial analysis of the seismic wavefield. We compute the cross-spectra between all available stations pairs in spectral domain, leading to the data covariance matrix. We apply a one-bit normalization to the covariance matrix eigenspectrum before extracting the cross-correlations in the time domain. The efficiency of the method is shown with several numerical tests. We apply the method to the data collected by the USArray, when the M8.8 Maule earthquake occurred on 2010 February 27. The method shows a clear improvement compared with the classical equalization to attenuate the highly energetic and coherent waves incoming from the earthquake, and allows to perform reliable traveltime measurement even in the presence of the earthquake.

Three dimensional simulation of spatial and temporal variability of stratospheric hydrogen chloride

NASA Technical Reports Server (NTRS)

Kaye, Jack A.; Rood, Richard B.; Jackman, Charles H.; Allen, Dale J.; Larson, Edmund M.

1989-01-01

Spatial and temporal variability of atmospheric HCl columns are calculated for January 1979 using a three-dimensional chemistry-transport model designed to provide the best possible representation of stratospheric transport. Large spatial and temporal variability of the HCl columns is shown to be correlated with lower stratospheric potential vorticity and thus to be of dynamical origin. Systematic longitudinal structure is correlated with planetary wave structure. These results can help place spatially and temporally isolated column and profile measurements in a regional and/or global perspective.

Managing the spatial properties and photon correlations in squeezed non-classical twisted light

NASA Astrophysics Data System (ADS)

Zakharov, R. V.; Tikhonova, O. V.

2018-05-01

Spatial photon correlations and mode content of the squeezed vacuum light generated in a system of two separated nonlinear crystals is investigated. The contribution of both the polar and azimuthal modes with non-zero orbital angular momentum is analyzed. The control and engineering of the spatial properties and degree of entanglement of the non-classical squeezed light by changing the distance between crystals and pump parameters is demonstrated. Methods for amplification of certain spatial modes and managing the output mode content and intensity profile of quantum twisted light are suggested.

Kolmogorov-Smirnov test for spatially correlated data

USGS Publications Warehouse

Olea, R.A.; Pawlowsky-Glahn, V.

2009-01-01

The Kolmogorov-Smirnov test is a convenient method for investigating whether two underlying univariate probability distributions can be regarded as undistinguishable from each other or whether an underlying probability distribution differs from a hypothesized distribution. Application of the test requires that the sample be unbiased and the outcomes be independent and identically distributed, conditions that are violated in several degrees by spatially continuous attributes, such as topographical elevation. A generalized form of the bootstrap method is used here for the purpose of modeling the distribution of the statistic D of the Kolmogorov-Smirnov test. The innovation is in the resampling, which in the traditional formulation of bootstrap is done by drawing from the empirical sample with replacement presuming independence. The generalization consists of preparing resamplings with the same spatial correlation as the empirical sample. This is accomplished by reading the value of unconditional stochastic realizations at the sampling locations, realizations that are generated by simulated annealing. The new approach was tested by two empirical samples taken from an exhaustive sample closely following a lognormal distribution. One sample was a regular, unbiased sample while the other one was a clustered, preferential sample that had to be preprocessed. Our results show that the p-value for the spatially correlated case is always larger that the p-value of the statistic in the absence of spatial correlation, which is in agreement with the fact that the information content of an uncorrelated sample is larger than the one for a spatially correlated sample of the same size. ?? Springer-Verlag 2008.

Spatial distribution of uranium and basic water quality parameter in the capital of Bihar and consequent ingestion dose.

PubMed

Kumar, Deepak; Singh, Anshuman; Jha, Rishi Kumar

2018-04-21

Investigation of presence of Uranium (U) in groundwater/drinking water is an active are of research due to its chemical and radiological toxicity as well as long-term health effects. The current study had the objective of estimating U as a naturally occurring radioactive element in groundwater samples and assessment of ingestion dose, when groundwater is the source of drinking water. The random sampling method was chosen for the collection of samples based on population density. The estimation of U was done using LED fluorimeter. Statistical tools were applied to analyze the data and its spatial distribution. The U concentrations in three blocks of urban Patna were well below the permissible limits suggested by different health agencies of the world. A correlation test was performed to analyze the association of U with other physiochemical parameters of water samples. It was found that the sulfate, chloride, calcium, hardness, alkalinity, TDS, salinity, and ORP were positively correlated, whereas fluoride, phosphate, magnesium, dissolved oxygen, and pH were negatively correlated with U concentrations. The ingestion dose due to U, occurring in groundwater, was found to vary from 0.2-27.0 μSv y -1 with a mean of 4.2 μSv y - 1 , which was well below the recommended limit of 0.1 mSv (WHO WHO Chron 38:104-108, 2012).Therefore, the water in this region is fit for drinking purposes.

Improving Photometry and Stellar Signal Preservation with Pixel-Level Systematic Error Correction

NASA Technical Reports Server (NTRS)

Kolodzijczak, Jeffrey J.; Smith, Jeffrey C.; Jenkins, Jon M.

2013-01-01

The Kepler Mission has demonstrated that excellent stellar photometric performance can be achieved using apertures constructed from optimally selected CCD pixels. The clever methods used to correct for systematic errors, while very successful, still have some limitations in their ability to extract long-term trends in stellar flux. They also leave poorly correlated bias sources, such as drifting moiré pattern, uncorrected. We will illustrate several approaches where applying systematic error correction algorithms to the pixel time series, rather than the co-added raw flux time series, provide significant advantages. Examples include, spatially localized determination of time varying moiré pattern biases, greater sensitivity to radiation-induced pixel sensitivity drops (SPSDs), improved precision of co-trending basis vectors (CBV), and a means of distinguishing the stellar variability from co-trending terms even when they are correlated. For the last item, the approach enables physical interpretation of appropriately scaled coefficients derived in the fit of pixel time series to the CBV as linear combinations of various spatial derivatives of the pixel response function (PRF). We demonstrate that the residuals of a fit of soderived pixel coefficients to various PRF-related components can be deterministically interpreted in terms of physically meaningful quantities, such as the component of the stellar flux time series which is correlated with the CBV, as well as, relative pixel gain, proper motion and parallax. The approach also enables us to parameterize and assess the limiting factors in the uncertainties in these quantities.

What Do They Have in Common? Physical Drivers of Streamflow Spatial Correlation and Prediction of Flow Regimes at Ungauged Locations in the Contiguous United States

NASA Astrophysics Data System (ADS)

Betterle, A.; Schirmer, M.; Botter, G.

2017-12-01

Streamflow dynamics strongly influence anthropogenic activities and the ecological functions of riverine and riparian habitats. However, the widespread lack of direct discharge measurements often challenges the set-up of conscious and effective decision-making processes, including droughts and floods protection, water resources management and river restoration practices. By characterizing the spatial correlation of daily streamflow timeseries at two arbitrary locations, this study provides a method to evaluate how spatially variable catchment-scale hydrological process affects the resulting streamflow dynamics along and across river systems. In particular, streamflow spatial correlation is described analytically as a function of morphological, climatic and vegetation properties in the contributing catchments, building on a joint probabilistic description of flow dynamics at pairs of outlets. The approach enables an explicit linkage between similarities of flow dynamics and spatial patterns of hydrologically relevant features of climate and landscape. Therefore, the method is suited to explore spatial patterns of streamflow dynamics across geomorphoclimatic gradients. In particular, we show how the streamflow correlation can be used at the continental scale to individuate catchment pairs with similar hydrological dynamics, thereby providing a useful tool for the estimate of flow duration curves in poorly gauged areas.

Fast depth decision for HEVC inter prediction based on spatial and temporal correlation

NASA Astrophysics Data System (ADS)

Chen, Gaoxing; Liu, Zhenyu; Ikenaga, Takeshi

2016-07-01

High efficiency video coding (HEVC) is a video compression standard that outperforms the predecessor H.264/AVC by doubling the compression efficiency. To enhance the compression accuracy, the partition sizes ranging is from 4x4 to 64x64 in HEVC. However, the manifold partition sizes dramatically increase the encoding complexity. This paper proposes a fast depth decision based on spatial and temporal correlation. Spatial correlation utilize the code tree unit (CTU) Splitting information and temporal correlation utilize the motion vector predictor represented CTU in inter prediction to determine the maximum depth in each CTU. Experimental results show that the proposed method saves about 29.1% of the original processing time with 0.9% of BD-bitrate increase on average.

Prediction of Spatiotemporal Patterns of Neural Activity from Pairwise Correlations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marre, O.; El Boustani, S.; Fregnac, Y.

We designed a model-based analysis to predict the occurrence of population patterns in distributed spiking activity. Using a maximum entropy principle with a Markovian assumption, we obtain a model that accounts for both spatial and temporal pairwise correlations among neurons. This model is tested on data generated with a Glauber spin-glass system and is shown to correctly predict the occurrence probabilities of spatiotemporal patterns significantly better than Ising models only based on spatial correlations. This increase of predictability was also observed on experimental data recorded in parietal cortex during slow-wave sleep. This approach can also be used to generate surrogatesmore » that reproduce the spatial and temporal correlations of a given data set.« less

Spatio-temporal correlations in the Manna model in one, three and five dimensions

NASA Astrophysics Data System (ADS)

Willis, Gary; Pruessner, Gunnar

2018-02-01

Although the paradigm of criticality is centered around spatial correlations and their anomalous scaling, not many studies of self-organized criticality (SOC) focus on spatial correlations. Often, integrated observables, such as avalanche size and duration, are used, not least as to avoid complications due to the unavoidable lack of translational invariance. The present work is a survey of spatio-temporal correlation functions in the Manna Model of SOC, measured numerically in detail in d = 1,3 and 5 dimensions and compared to theoretical results, in particular relating them to “integrated” observables such as avalanche size and duration scaling, that measure them indirectly. Contrary to the notion held by some of SOC models organizing into a critical state by re-arranging their spatial structure avalanche by avalanche, which may be expected to result in large, nontrivial, system-spanning spatial correlations in the quiescent state (between avalanches), correlations of inactive particles in the quiescent state have a small amplitude that does not and cannot increase with the system size, although they display (noisy) power law scaling over a range linear in the system size. Self-organization, however, does take place as the (one-point) density of inactive particles organizes into a particular profile that is asymptotically independent of the driving location, also demonstrated analytically in one dimension. Activity and its correlations, on the other hand, display nontrivial long-ranged spatio-temporal scaling with exponents that can be related to established results, in particular avalanche size and duration exponents. The correlation length and amplitude are set by the system size (confirmed analytically for some observables), as expected in systems displaying finite size scaling. In one dimension, we find some surprising inconsistencies of the dynamical exponent. A (spatially extended) mean field theory (MFT) is recovered, with some corrections, in five dimensions.

Using multi-scale sampling and spatial cross-correlation to investigate patterns of plant species richness

USGS Publications Warehouse

Kalkhan, M.A.; Stohlgren, T.J.

2000-01-01

Land managers need better techniques to assess exoticplant invasions. We used the cross-correlationstatistic, IYZ, to test for the presence ofspatial cross-correlation between pair-wisecombinations of soil characteristics, topographicvariables, plant species richness, and cover ofvascular plants in a 754 ha study site in RockyMountain National Park, Colorado, U.S.A. Using 25 largeplots (1000 m2) in five vegetation types, 8 of 12variables showed significant spatial cross-correlationwith at least one other variable, while 6 of 12variables showed significant spatial auto-correlation. Elevation and slope showed significant spatialcross-correlation with all variables except percentcover of native and exotic species. Percent cover ofnative species had significant spatialcross-correlations with soil variables, but not withexotic species. This was probably because of thepatchy distributions of vegetation types in the studyarea. At a finer resolution, using data from ten1 m2 subplots within each of the 1000 m2 plots, allvariables showed significant spatial auto- andcross-correlation. Large-plot sampling was moreaffected by topographic factors than speciesdistribution patterns, while with finer resolutionsampling, the opposite was true. However, thestatistically and biologically significant spatialcorrelation of native and exotic species could only bedetected with finer resolution sampling. We foundexotic plant species invading areas with high nativeplant richness and cover, and in fertile soils high innitrogen, silt, and clay. Spatial auto- andcross-correlation statistics, along with theintegration of remotely sensed data and geographicinformation systems, are powerful new tools forevaluating the patterns and distribution of native andexotic plant species in relation to landscape structure.

Cardiac-driven Pulsatile Motion of Intracranial Cerebrospinal Fluid Visualized Based on a Correlation Mapping Technique.

PubMed

Yatsushiro, Satoshi; Sunohara, Saeko; Hayashi, Naokazu; Hirayama, Akihiro; Matsumae, Mitsunori; Atsumi, Hideki; Kuroda, Kagayaki

2018-04-10

A correlation mapping technique delineating delay time and maximum correlation for characterizing pulsatile cerebrospinal fluid (CSF) propagation was proposed. After proofing its technical concept, this technique was applied to healthy volunteers and idiopathic normal pressure hydrocephalus (iNPH) patients. A time-resolved three dimensional-phase contrast (3D-PC) sampled the cardiac-driven CSF velocity at 32 temporal points per cardiac period at each spatial location using retrospective cardiac gating. The proposed technique visualized distributions of propagation delay and correlation coefficient of the PC-based CSF velocity waveform with reference to a waveform at a particular point in the CSF space. The delay time was obtained as the amount of time-shift, giving the maximum correlation for the velocity waveform at an arbitrary location with that at the reference location. The validity and accuracy of the technique were confirmed in a flow phantom equipped with a cardiovascular pump. The technique was then applied to evaluate the intracranial CSF motions in young, healthy (N = 13), and elderly, healthy (N = 13) volunteers and iNPH patients (N = 13). The phantom study demonstrated that root mean square error of the delay time was 2.27%, which was less than the temporal resolution of PC measurement used in this study (3.13% of a cardiac cycle). The human studies showed a significant difference (P < 0.01) in the mean correlation coefficient between the young, healthy group and the other two groups. A significant difference (P < 0.05) was also recognized in standard deviation of the correlation coefficients in intracranial CSF space among all groups. The result suggests that the CSF space compliance of iNPH patients was lower than that of healthy volunteers. The correlation mapping technique allowed us to visualize pulsatile CSF velocity wave propagations as still images. The technique may help to classify diseases related to CSF dynamics, such as iNPH.

A scoping review of spatial cluster analysis techniques for point-event data.

PubMed

Fritz, Charles E; Schuurman, Nadine; Robertson, Colin; Lear, Scott

2013-05-01

Spatial cluster analysis is a uniquely interdisciplinary endeavour, and so it is important to communicate and disseminate ideas, innovations, best practices and challenges across practitioners, applied epidemiology researchers and spatial statisticians. In this research we conducted a scoping review to systematically search peer-reviewed journal databases for research that has employed spatial cluster analysis methods on individual-level, address location, or x and y coordinate derived data. To illustrate the thematic issues raised by our results, methods were tested using a dataset where known clusters existed. Point pattern methods, spatial clustering and cluster detection tests, and a locally weighted spatial regression model were most commonly used for individual-level, address location data (n = 29). The spatial scan statistic was the most popular method for address location data (n = 19). Six themes were identified relating to the application of spatial cluster analysis methods and subsequent analyses, which we recommend researchers to consider; exploratory analysis, visualization, spatial resolution, aetiology, scale and spatial weights. It is our intention that researchers seeking direction for using spatial cluster analysis methods, consider the caveats and strengths of each approach, but also explore the numerous other methods available for this type of analysis. Applied spatial epidemiology researchers and practitioners should give special consideration to applying multiple tests to a dataset. Future research should focus on developing frameworks for selecting appropriate methods and the corresponding spatial weighting schemes.

Plasticity of human spatial cognition: spatial language and cognition covary across cultures.

PubMed

Haun, Daniel B M; Rapold, Christian J; Janzen, Gabriele; Levinson, Stephen C

2011-04-01

The present paper explores cross-cultural variation in spatial cognition by comparing spatial reconstruction tasks by Dutch and Namibian elementary school children. These two communities differ in the way they predominantly express spatial relations in language. Four experiments investigate cognitive strategy preferences across different levels of task-complexity and instruction. Data show a correlation between dominant linguistic spatial frames of reference and performance patterns in non-linguistic spatial memory tasks. This correlation is shown to be stable across an increase of complexity in the spatial array. When instructed to use their respective non-habitual cognitive strategy, participants were not easily able to switch between strategies and their attempts to do so impaired their performance. These results indicate a difference not only in preference but also in competence and suggest that spatial language and non-linguistic preferences and competences in spatial cognition are systematically aligned across human populations. Copyright © 2011 Elsevier B.V. All rights reserved.

Regional-specific Stochastic Simulation of Spatially-distributed Ground-motion Time Histories using Wavelet Packet Analysis

NASA Astrophysics Data System (ADS)

Huang, D.; Wang, G.

2014-12-01

Stochastic simulation of spatially distributed ground-motion time histories is important for performance-based earthquake design of geographically distributed systems. In this study, we develop a novel technique to stochastically simulate regionalized ground-motion time histories using wavelet packet analysis. First, a transient acceleration time history is characterized by wavelet-packet parameters proposed by Yamamoto and Baker (2013). The wavelet-packet parameters fully characterize ground-motion time histories in terms of energy content, time- frequency-domain characteristics and time-frequency nonstationarity. This study further investigates the spatial cross-correlations of wavelet-packet parameters based on geostatistical analysis of 1500 regionalized ground motion data from eight well-recorded earthquakes in California, Mexico, Japan and Taiwan. The linear model of coregionalization (LMC) is used to develop a permissible spatial cross-correlation model for each parameter group. The geostatistical analysis of ground-motion data from different regions reveals significant dependence of the LMC structure on regional site conditions, which can be characterized by the correlation range of Vs30 in each region. In general, the spatial correlation and cross-correlation of wavelet-packet parameters are stronger if the site condition is more homogeneous. Using the regional-specific spatial cross-correlation model and cokriging technique, wavelet packet parameters at unmeasured locations can be best estimated, and regionalized ground-motion time histories can be synthesized. Case studies and blind tests demonstrated that the simulated ground motions generally agree well with the actual recorded data, if the influence of regional-site conditions is considered. The developed method has great potential to be used in computational-based seismic analysis and loss estimation in a regional scale.

Estimation of Lithological Classification in Taipei Basin: A Bayesian Maximum Entropy Method

NASA Astrophysics Data System (ADS)

Wu, Meng-Ting; Lin, Yuan-Chien; Yu, Hwa-Lung

2015-04-01

In environmental or other scientific applications, we must have a certain understanding of geological lithological composition. Because of restrictions of real conditions, only limited amount of data can be acquired. To find out the lithological distribution in the study area, many spatial statistical methods used to estimate the lithological composition on unsampled points or grids. This study applied the Bayesian Maximum Entropy (BME method), which is an emerging method of the geological spatiotemporal statistics field. The BME method can identify the spatiotemporal correlation of the data, and combine not only the hard data but the soft data to improve estimation. The data of lithological classification is discrete categorical data. Therefore, this research applied Categorical BME to establish a complete three-dimensional Lithological estimation model. Apply the limited hard data from the cores and the soft data generated from the geological dating data and the virtual wells to estimate the three-dimensional lithological classification in Taipei Basin. Keywords: Categorical Bayesian Maximum Entropy method, Lithological Classification, Hydrogeological Setting

Spatial abilities and technical skills performance in health care: a systematic review.

PubMed

Langlois, Jean; Bellemare, Christian; Toulouse, Josée; Wells, George A

2015-11-01

The aim of this study was to conduct a systematic review and meta-analysis of the relationship between spatial abilities and technical skills performance in health care in beginners and to compare this relationship with those in intermediate and autonomous learners. Search criteria included 'spatial abilities' and 'technical skills'. Keywords related to these criteria were defined. A literature search was conducted to 20 December, 2013 in Scopus (including MEDLINE) and in several databases on EBSCOhost platforms (CINAHL Plus with Full Text, ERIC, Education Source and PsycINFO). Citations were obtained and reviewed by two independent reviewers. Articles related to retained citations were reviewed and a final list of eligible articles was determined. Articles were assessed for quality using the Scottish Intercollegiate Guidelines Network-50 assessment instrument. Data were extracted from articles in a systematic way. Correlations between spatial abilities test scores and technical skills performance were identified. A series of 8289 citations was obtained. Eighty articles were retained and fully reviewed, yielding 36 eligible articles. The systematic review found a tendency for spatial abilities to be negatively correlated with the duration of technical skills and positively correlated with the quality of technical skills performance in beginners and intermediate learners. Pooled correlations of studies were -0.46 (p = 0.03) and -0.38 (95% confidence interval [CI] -0.53 to -0.21) for duration and 0.33 (95% CI 0.20-0.44) and 0.41 (95% CI 0.26-0.54) for quality of technical skills performance in beginners and intermediate learners, respectively. However, correlations between spatial abilities test scores and technical skills performance were not statistically significant in autonomous learners. Spatial abilities are an important factor to consider in selecting and training individuals in technical skills in health care. © 2015 John Wiley & Sons Ltd.

The Role of Auxiliary Variables in Deterministic and Deterministic-Stochastic Spatial Models of Air Temperature in Poland

NASA Astrophysics Data System (ADS)

Szymanowski, Mariusz; Kryza, Maciej

2017-02-01

Our study examines the role of auxiliary variables in the process of spatial modelling and mapping of climatological elements, with air temperature in Poland used as an example. The multivariable algorithms are the most frequently applied for spatialization of air temperature, and their results in many studies are proved to be better in comparison to those obtained by various one-dimensional techniques. In most of the previous studies, two main strategies were used to perform multidimensional spatial interpolation of air temperature. First, it was accepted that all variables significantly correlated with air temperature should be incorporated into the model. Second, it was assumed that the more spatial variation of air temperature was deterministically explained, the better was the quality of spatial interpolation. The main goal of the paper was to examine both above-mentioned assumptions. The analysis was performed using data from 250 meteorological stations and for 69 air temperature cases aggregated on different levels: from daily means to 10-year annual mean. Two cases were considered for detailed analysis. The set of potential auxiliary variables covered 11 environmental predictors of air temperature. Another purpose of the study was to compare the results of interpolation given by various multivariable methods using the same set of explanatory variables. Two regression models: multiple linear (MLR) and geographically weighted (GWR) method, as well as their extensions to the regression-kriging form, MLRK and GWRK, respectively, were examined. Stepwise regression was used to select variables for the individual models and the cross-validation method was used to validate the results with a special attention paid to statistically significant improvement of the model using the mean absolute error (MAE) criterion. The main results of this study led to rejection of both assumptions considered. Usually, including more than two or three of the most significantly correlated auxiliary variables does not improve the quality of the spatial model. The effects of introduction of certain variables into the model were not climatologically justified and were seen on maps as unexpected and undesired artefacts. The results confirm, in accordance with previous studies, that in the case of air temperature distribution, the spatial process is non-stationary; thus, the local GWR model performs better than the global MLR if they are specified using the same set of auxiliary variables. If only GWR residuals are autocorrelated, the geographically weighted regression-kriging (GWRK) model seems to be optimal for air temperature spatial interpolation.

Spherical disharmonics in the Earth sciences and the spatial solution: Ridges, hotspots, slabs, geochemistry and tomography correlations

NASA Technical Reports Server (NTRS)

Ray, Terrill W.; Anderson, Don L.

1994-01-01

There is increasing use of statistical correlations between geophysical fields and between geochemical and geophysical fields in attempts to understand how the Earth works. Typically, such correlations have been based on spherical harmonic expansions. The expression of functions on the sphere as spherical harmonic series has many pitfalls, especially if the data are nonuniformly and/or sparsely sampled. Many of the difficulties involved in the use of spherical harmonic expansion techniques can be avoided through the use of spatial domain correlations, but this introduces other complications, such as the choice of a sampling lattice. Additionally, many geophysical and geochemical fields fail to satisfy the assumptions of standard statistical significance tests. This is especially problematic when the data values to be correlated with a geophysical field were collected at sample locations which themselves correlate with that field. This paper examines many correlations which have been claimed in the past between geochemistry and mantle tomography and between hotspot, ridge, and slab locations and tomography using both spherical harmonic coefficient correlations and spatial domain correlations. No conclusively significant correlations are found between isotopic geochemistry and mantle tomography. The Crough and Jurdy (short) hotspot location list shows statistically significant correlation with lowermost mantle tomography for degree 2 of the spherical harmonic expansion, but there are no statistically significant correlations in the spatial case. The Vogt (long) hotspot location list does not correlate with tomography anywhere in the mantle using either technique. Both hotspot lists show a strong correlation between hotspot locations and geoid highs when spatially correlated, but no correlations are revealed by spherical harmonic techniques. Ridge locations do not show any statistically significant correlations with tomography, slab locations, or the geoid; the strongest correlation is with lowermost mantle tomography, which is probably spurious. The most striking correlations are between mantle tomography and post-Pangean subducted slabs. The integrated locations of slabs correlate strongly with fast areas near the transition zone and the core-mantle boundary and with slow regions from 1022-1248 km depth. This seems to be consistent with the 'avalanching' downwellings which have been indicated by models of the mantle which include an endothermic phase transition at the 670-km discontinuity, although this is not a unique interpretation. Taken as a whole, these results suggest that slabs and associated cold downwellings are the dominant feature of mantle convection. Hotspot locations are no better correlated with lower mantle tomography than are ridge locations.

Consistency and similarity of MEG- and fMRI-signal time courses during movie viewing.

PubMed

Lankinen, Kaisu; Saari, Jukka; Hlushchuk, Yevhen; Tikka, Pia; Parkkonen, Lauri; Hari, Riitta; Koskinen, Miika

2018-06-01

Movie viewing allows human perception and cognition to be studied in complex, real-life-like situations in a brain-imaging laboratory. Previous studies with functional magnetic resonance imaging (fMRI) and with magneto- and electroencephalography (MEG and EEG) have demonstrated consistent temporal dynamics of brain activity across movie viewers. However, little is known about the similarities and differences of fMRI and MEG or EEG dynamics during such naturalistic situations. We thus compared MEG and fMRI responses to the same 15-min black-and-white movie in the same eight subjects who watched the movie twice during both MEG and fMRI recordings. We analyzed intra- and intersubject voxel-wise correlations within each imaging modality as well as the correlation of the MEG envelopes and fMRI signals. The fMRI signals showed voxel-wise within- and between-subjects correlations up to r = 0.66 and r = 0.37, respectively, whereas these correlations were clearly weaker for the envelopes of band-pass filtered (7 frequency bands below 100 Hz) MEG signals (within-subjects correlation r < 0.14 and between-subjects r < 0.05). Direct MEG-fMRI voxel-wise correlations were unreliable. Notably, applying a spatial-filtering approach to the MEG data uncovered consistent canonical variates that showed considerably stronger (up to r = 0.25) between-subjects correlations than the univariate voxel-wise analysis. Furthermore, the envelopes of the time courses of these variates up to about 10 Hz showed association with fMRI signals in a general linear model. Similarities between envelopes of MEG canonical variates and fMRI voxel time-courses were seen mostly in occipital, but also in temporal and frontal brain regions, whereas intra- and intersubject correlations for MEG and fMRI separately were strongest only in the occipital areas. In contrast to the conventional univariate analysis, the spatial-filtering approach was able to uncover associations between the MEG envelopes and fMRI time courses, shedding light on the similarities of hemodynamic and electromagnetic brain activities during movie viewing. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Attempting to physically explain space-time correlation of extremes

NASA Astrophysics Data System (ADS)

Bernardara, Pietro; Gailhard, Joel

2010-05-01

Spatial and temporal clustering of hydro-meteorological extreme events is scientific evidence. Moreover, the statistical parameters characterizing their local frequencies of occurrence show clear spatial patterns. Thus, in order to robustly assess the hydro-meteorological hazard, statistical models need to be able to take into account spatial and temporal dependencies. Statistical models considering long term correlation for quantifying and qualifying temporal and spatial dependencies are available, such as multifractal approach. Furthermore, the development of regional frequency analysis techniques allows estimating the frequency of occurrence of extreme events taking into account spatial patterns on the extreme quantiles behaviour. However, in order to understand the origin of spatio-temporal clustering, an attempt to find physical explanation should be done. Here, some statistical evidences of spatio-temporal correlation and spatial patterns of extreme behaviour are given on a large database of more than 400 rainfall and discharge series in France. In particular, the spatial distribution of multifractal and Generalized Pareto distribution parameters shows evident correlation patterns in the behaviour of frequency of occurrence of extremes. It is then shown that the identification of atmospheric circulation pattern (weather types) can physically explain the temporal clustering of extreme rainfall events (seasonality) and the spatial pattern of the frequency of occurrence. Moreover, coupling this information with the hydrological modelization of a watershed (as in the Schadex approach) an explanation of spatio-temporal distribution of extreme discharge can also be provided. We finally show that a hydro-meteorological approach (as the Schadex approach) can explain and take into account space and time dependencies of hydro-meteorological extreme events.

Quantitation of spatially-localized proteins in tissue samples using MALDI-MRM imaging.

PubMed

Clemis, Elizabeth J; Smith, Derek S; Camenzind, Alexander G; Danell, Ryan M; Parker, Carol E; Borchers, Christoph H

2012-04-17

MALDI imaging allows the creation of a "molecular image" of a tissue slice. This image is reconstructed from the ion abundances in spectra obtained while rastering the laser over the tissue. These images can then be correlated with tissue histology to detect potential biomarkers of, for example, aberrant cell types. MALDI, however, is known to have problems with ion suppression, making it difficult to correlate measured ion abundance with concentration. It would be advantageous to have a method which could provide more accurate protein concentration measurements, particularly for screening applications or for precise comparisons between samples. In this paper, we report the development of a novel MALDI imaging method for the localization and accurate quantitation of proteins in tissues. This method involves optimization of in situ tryptic digestion, followed by reproducible and uniform deposition of an isotopically labeled standard peptide from a target protein onto the tissue, using an aerosol-generating device. Data is acquired by MALDI multiple reaction monitoring (MRM) mass spectrometry (MS), and accurate peptide quantitation is determined from the ratio of MRM transitions for the endogenous unlabeled proteolytic peptides to the corresponding transitions from the applied isotopically labeled standard peptides. In a parallel experiment, the quantity of the labeled peptide applied to the tissue was determined using a standard curve generated from MALDI time-of-flight (TOF) MS data. This external calibration curve was then used to determine the quantity of endogenous peptide in a given area. All standard curves generate by this method had coefficients of determination greater than 0.97. These proof-of-concept experiments using MALDI MRM-based imaging show the feasibility for the precise and accurate quantitation of tissue protein concentrations over 2 orders of magnitude, while maintaining the spatial localization information for the proteins.

3-D DNA methylation phenotypes correlate with cytotoxicity levels in prostate and liver cancer cell models

PubMed Central

2013-01-01

Background The spatial organization of the genome is being evaluated as a novel indicator of toxicity in conjunction with drug-induced global DNA hypomethylation and concurrent chromatin reorganization. 3D quantitative DNA methylation imaging (3D-qDMI) was applied as a cell-by-cell high-throughput approach to investigate this matter by assessing genome topology through represented immunofluorescent nuclear distribution patterns of 5-methylcytosine (MeC) and global DNA (4,6-diamidino-2-phenylindole = DAPI) in labeled nuclei. Methods Differential progression of global DNA hypomethylation was studied by comparatively dosing zebularine (ZEB) and 5-azacytidine (AZA). Treated and untreated (control) human prostate and liver cancer cells were subjected to confocal scanning microscopy and dedicated 3D image analysis for the following features: differential nuclear MeC/DAPI load and codistribution patterns, cell similarity based on these patterns, and corresponding differences in the topology of low-intensity MeC (LIM) and low in intensity DAPI (LID) sites. Results Both agents generated a high fraction of similar MeC phenotypes across applied concentrations. ZEB exerted similar effects at 10–100-fold higher drug concentrations than its AZA analogue: concentration-dependent progression of global cytosine demethylation, validated by measuring differential MeC levels in repeat sequences using MethyLight, and the concurrent increase in nuclear LIM densities correlated with cellular growth reduction and cytotoxicity. Conclusions 3D-qDMI demonstrated the capability of quantitating dose-dependent drug-induced spatial progression of DNA demethylation in cell nuclei, independent from interphase cell-cycle stages and in conjunction with cytotoxicity. The results support the notion of DNA methylation topology being considered as a potential indicator of causal impacts on chromatin distribution with a conceivable application in epigenetic drug toxicology. PMID:23394161

Thermal modifications of charmonia and bottomonia from spatial correlation functions

NASA Astrophysics Data System (ADS)

Ding, Heng-Tong; Kaczmarek, Olaf; Kruse, Anna-lena; Mukherjee, Swagato; Ohno, Hiroshi; Sandmeyer, Hauke; Shu, Hai-Tao

2018-03-01

We present our study on the thermal modifications of charmonia and bottomonia from spatial correlation functions at zero and nonzero momenta in quenched QCD. To accommodate the heavy quarks on the lattice we performed simulations on very fine lattices at a fixed beta value corresponding to a lattice spacing a-1 = 22:8 GeV on 1923×32, 1923 × 48, 1923 × 56, 1923 × 64 and 1923 × 96 lattices using clover-improved Wilson fermions. These lattices correspond to temperatures of 2.25Tc, 1.50Tc, 1.25Tc, 1.10Tc and 0.75Tc. To increase the signal to noise ratio in the axial-vector and scalar channels we used multi-sources for the measurement of spatial correlation functions. By investigating on the differences between spatial and temporal correlators as well as the temperature dependence of screening masses we will discuss the thermal effects in different channels of quarkonium states. Besides this the dispersion relation of the screening mass at different momenta is also discussed.

Development of advanced acreage estimation methods

NASA Technical Reports Server (NTRS)

Guseman, L. F., Jr. (Principal Investigator)

1980-01-01

The use of the AMOEBA clustering/classification algorithm was investigated as a basis for both a color display generation technique and maximum likelihood proportion estimation procedure. An approach to analyzing large data reduction systems was formulated and an exploratory empirical study of spatial correlation in LANDSAT data was also carried out. Topics addressed include: (1) development of multiimage color images; (2) spectral spatial classification algorithm development; (3) spatial correlation studies; and (4) evaluation of data systems.

On Information Metrics for Spatial Coding.

PubMed

Souza, Bryan C; Pavão, Rodrigo; Belchior, Hindiael; Tort, Adriano B L

2018-04-01

The hippocampal formation is involved in navigation, and its neuronal activity exhibits a variety of spatial correlates (e.g., place cells, grid cells). The quantification of the information encoded by spikes has been standard procedure to identify which cells have spatial correlates. For place cells, most of the established metrics derive from Shannon's mutual information (Shannon, 1948), and convey information rate in bits/s or bits/spike (Skaggs et al., 1993, 1996). Despite their widespread use, the performance of these metrics in relation to the original mutual information metric has never been investigated. In this work, using simulated and real data, we find that the current information metrics correlate less with the accuracy of spatial decoding than the original mutual information metric. We also find that the top informative cells may differ among metrics, and show a surrogate-based normalization that yields comparable spatial information estimates. Since different information metrics may identify different neuronal populations, we discuss current and alternative definitions of spatially informative cells, which affect the metric choice. Copyright © 2018 IBRO. Published by Elsevier Ltd. All rights reserved.

Effect of thermal environment on the temporal, spatial and seasonal occurrence of measles in Ondo state, Nigeria

NASA Astrophysics Data System (ADS)

Omonijo, Akinyemi Gabriel; Matzarakis, Andreas; Oguntoke, Olusegun; Adeofun, Clement Olabinjo

2012-09-01

We investigated the temporal and spatial dynamics, as well as the seasonal occurrence of measles in Ondo state, Nigeria, to better understand the role of the thermal environment in the occurrence of the childhood killer disease measles, which ranks among the top ten leading causes of child deaths worldwide. The linkages between measles and atmospheric environmental factors were examined by correlating human-biometeorological parameters in the study area with reported clinical cases of measles for the period 1998-2008. We also applied stepwise regression analysis in order to determine the human-biometeorological parameters that lead to statistical changes in reported clinical cases of measles. We found that high reported cases of measles are associated with the least populated areas, where rearing and cohabitation of livestock/domestic animals within human communities are common. There was a significant correlation ( P < 0.01) between monthly cases of measles and human-biometeorological parameters except wind speed and vapour pressure. High transmission of measles occurred in the months of January to May during the dry season when human thermal comfort indices are very high. This highlights the importance of the thermal environment in disease demographics since it accounted for more than 40% variation in measles transmission within the study period.

Monitoring Seasonal Evapotranspiration in Vulnerable Agriculture using Time Series VHSR Satellite Data

NASA Astrophysics Data System (ADS)

Dalezios, Nicolas; Spyropoulos, Nicos V.; Tarquis, Ana M.

2015-04-01

The research work stems from the hypothesis that it is possible to perform an estimation of seasonal water needs of olive tree farms under drought periods by cross correlating high spatial, spectral and temporal resolution (~monthly) of satellite data, acquired at well defined time intervals of the phenological cycle of crops, with ground-truth information simultaneously applied during the image acquisitions. The present research is for the first time, demonstrating the coordinated efforts of space engineers, satellite mission control planners, remote sensing scientists and ground teams to record at specific time intervals of the phenological cycle of trees from ground "zero" and from 770 km above the Earth's surface, the status of plants for subsequent cross correlation and analysis regarding the estimation of the seasonal evapotranspiration in vulnerable agricultural environment. The ETo and ETc derived by Penman-Montieth equation and reference Kc tables, compared with new ETd using the Kc extracted from the time series satellite data. Several vegetation indices were also used especially the RedEdge and the chlorophyll one based on WorldView-2 RedEdge and second NIR bands to relate the tree status with water and nutrition needs. Keywords: Evapotransipration, Very High Spatial Resolution - VHSR, time series, remote sensing, vulnerability, agriculture, vegetation indeces.

High-precision spatial localization of mouse vocalizations during social interaction.

PubMed

Heckman, Jesse J; Proville, Rémi; Heckman, Gert J; Azarfar, Alireza; Celikel, Tansu; Englitz, Bernhard

2017-06-07

Mice display a wide repertoire of vocalizations that varies with age, sex, and context. Especially during courtship, mice emit ultrasonic vocalizations (USVs) of high complexity, whose detailed structure is poorly understood. As animals of both sexes vocalize, the study of social vocalizations requires attributing single USVs to individuals. The state-of-the-art in sound localization for USVs allows spatial localization at centimeter resolution, however, animals interact at closer ranges, involving tactile, snout-snout exploration. Hence, improved algorithms are required to reliably assign USVs. We develop multiple solutions to USV localization, and derive an analytical solution for arbitrary vertical microphone positions. The algorithms are compared on wideband acoustic noise and single mouse vocalizations, and applied to social interactions with optically tracked mouse positions. A novel, (frequency) envelope weighted generalised cross-correlation outperforms classical cross-correlation techniques. It achieves a median error of ~1.4 mm for noise and ~4-8.5 mm for vocalizations. Using this algorithms in combination with a level criterion, we can improve the assignment for interacting mice. We report significant differences in mean USV properties between CBA mice of different sexes during social interaction. Hence, the improved USV attribution to individuals lays the basis for a deeper understanding of social vocalizations, in particular sequences of USVs.

Climate change and epidemics in Chinese history: A multi-scalar analysis.

PubMed

Lee, Harry F; Fei, Jie; Chan, Christopher Y S; Pei, Qing; Jia, Xin; Yue, Ricci P H

2017-02-01

This study seeks to provide further insight regarding the relationship of climate-epidemics in Chinese history through a multi-scalar analysis. Based on 5961 epidemic incidents in China during 1370-1909 CE we applied Ordinary Least Square regression and panel data regression to verify the climate-epidemic nexus over a range of spatial scales (country, macro region, and province). Results show that epidemic outbreaks were negatively correlated with the temperature in historical China at various geographic levels, while a stark reduction in the correlational strength was observed at lower geographic levels. Furthermore, cooling drove up epidemic outbreaks in northern and central China, where population pressure reached a clear threshold for amplifying the vulnerability of epidemic outbreaks to climate change. Our findings help to illustrate the modifiable areal unit and the uncertain geographic context problems in climate-epidemics research. Researchers need to consider the scale effect in the course of statistical analyses, which are currently predominantly conducted on a national/single scale; and also the importance of how the study area is delineated, an issue which is rarely discussed in the climate-epidemics literature. Future research may leverage our results and provide a cross-analysis with those derived from spatial analysis. Copyright © 2016 Elsevier Ltd. All rights reserved.

Detection of potato beetle damage using remote sensing from small unmanned aircraft systems

NASA Astrophysics Data System (ADS)

Hunt, E. Raymond; Rondon, Silvia I.

2017-04-01

Colorado potato beetle (CPB) adults and larvae devour leaves of potato and other solanaceous crops and weeds, and may quickly develop resistance to pesticides. With early detection of CPB damage, more options are available for precision integrated pest management, which reduces the amount of pesticides applied in a field. Remote sensing with small unmanned aircraft systems (sUAS) has potential for CPB detection because low flight altitudes allow image acquisition at very high spatial resolution. A five-band multispectral sensor and up-looking incident light sensor were mounted on a six-rotor sUAS, which was flown at altitudes of 60 and 30 m in June 2014. Plants went from visibly undamaged to having some damage in just 1 day. Whole-plot normalized difference vegetation index (NDVI) and the number of pixels classified as damaged (0.70≤NDVI≤0.80) were not correlated with visible CPB damage ranked from least to most. Area of CPB damage estimated using object-based image analysis was highly correlated to the visual ranking of damage. Furthermore, plant height calculated using structure-from-motion point clouds was related to CPB damage, but this method required extensive operator intervention for success. Object-based image analysis has potential for early detection based on high spatial resolution sUAS remote sensing.

Large-scale changes in network interactions as a physiological signature of spatial neglect

PubMed Central

Baldassarre, Antonello; Ramsey, Lenny; Hacker, Carl L.; Callejas, Alicia; Astafiev, Serguei V.; Metcalf, Nicholas V.; Zinn, Kristi; Rengachary, Jennifer; Snyder, Abraham Z.; Carter, Alex R.; Shulman, Gordon L.

2014-01-01

The relationship between spontaneous brain activity and behaviour following focal injury is not well understood. Here, we report a large-scale study of resting state functional connectivity MRI and spatial neglect following stroke in a large (n = 84) heterogeneous sample of first-ever stroke patients (within 1–2 weeks). Spatial neglect, which is typically more severe after right than left hemisphere injury, includes deficits of spatial attention and motor actions contralateral to the lesion, and low general attention due to impaired vigilance/arousal. Patients underwent structural and resting state functional MRI scans, and spatial neglect was measured using the Posner spatial cueing task, and Mesulam and Behavioural Inattention Test cancellation tests. A principal component analysis of the behavioural tests revealed a main factor accounting for 34% of variance that captured three correlated behavioural deficits: visual neglect of the contralesional visual field, visuomotor neglect of the contralesional field, and low overall performance. In an independent sample (21 healthy subjects), we defined 10 resting state networks consisting of 169 brain regions: visual-fovea and visual-periphery, sensory-motor, auditory, dorsal attention, ventral attention, language, fronto-parietal control, cingulo-opercular control, and default mode. We correlated the neglect factor score with the strength of resting state functional connectivity within and across the 10 resting state networks. All damaged brain voxels were removed from the functional connectivity:behaviour correlational analysis. We found that the correlated behavioural deficits summarized by the factor score were associated with correlated multi-network patterns of abnormal functional connectivity involving large swaths of cortex. Specifically, dorsal attention and sensory-motor networks showed: (i) reduced interhemispheric functional connectivity; (ii) reduced anti-correlation with fronto-parietal and default mode networks in the right hemisphere; and (iii) increased intrahemispheric connectivity with the basal ganglia. These patterns of functional connectivity:behaviour correlations were stronger in patients with right- as compared to left-hemisphere damage and were independent of lesion volume. Our findings identify large-scale changes in resting state network interactions that are a physiological signature of spatial neglect and may relate to its right hemisphere lateralization. PMID:25367028

Spatial correlation and irradiance statistics in a multiple-beam terrestrial free-space optical communication link.

PubMed

Anguita, Jaime A; Neifeld, Mark A; Vasic, Bane V

2007-09-10

By means of numerical simulations we analyze the statistical properties of the power fluctuations induced by the incoherent superposition of multiple transmitted laser beams in a terrestrial free-space optical communication link. The measured signals arising from different transmitted optical beams are found to be statistically correlated. This channel correlation increases with receiver aperture and propagation distance. We find a simple scaling rule for the spatial correlation coefficient in terms of the propagation distance and we are able to predict the scintillation reduction in previously reported experiments with good accuracy. We propose an approximation to the probability density function of the received power of a spatially correlated multiple-beam system in terms of the parameters of the single-channel gamma-gamma function. A bit-error-rate evaluation is also presented to demonstrate the improvement of a multibeam system over its single-beam counterpart.

Accurate mask-based spatially regularized correlation filter for visual tracking

NASA Astrophysics Data System (ADS)

Gu, Xiaodong; Xu, Xinping

2017-01-01

Recently, discriminative correlation filter (DCF)-based trackers have achieved extremely successful results in many competitions and benchmarks. These methods utilize a periodic assumption of the training samples to efficiently learn a classifier. However, this assumption will produce unwanted boundary effects, which severely degrade the tracking performance. Correlation filters with limited boundaries and spatially regularized DCFs were proposed to reduce boundary effects. However, their methods used the fixed mask or predesigned weights function, respectively, which was unsuitable for large appearance variation. We propose an accurate mask-based spatially regularized correlation filter for visual tracking. Our augmented objective can reduce the boundary effect even in large appearance variation. In our algorithm, the masking matrix is converted into the regularized function that acts on the correlation filter in frequency domain, which makes the algorithm fast convergence. Our online tracking algorithm performs favorably against state-of-the-art trackers on OTB-2015 Benchmark in terms of efficiency, accuracy, and robustness.

MOnthly TEmperature DAtabase of Spain 1951-2010: MOTEDAS (2): The Correlation Decay Distance (CDD) and the spatial variability of maximum and minimum monthly temperature in Spain during (1981-2010).

NASA Astrophysics Data System (ADS)

Cortesi, Nicola; Peña-Angulo, Dhais; Simolo, Claudia; Stepanek, Peter; Brunetti, Michele; Gonzalez-Hidalgo, José Carlos

2014-05-01

One of the key point in the develop of the MOTEDAS dataset (see Poster 1 MOTEDAS) in the framework of the HIDROCAES Project (Impactos Hidrológicos del Calentamiento Global en España, Spanish Ministery of Research CGL2011-27574-C02-01) is the reference series for which no generalized metadata exist. In this poster we present an analysis of spatial variability of monthly minimum and maximum temperatures in the conterminous land of Spain (Iberian Peninsula, IP), by using the Correlation Decay Distance function (CDD), with the aim of evaluating, at sub-regional level, the optimal threshold distance between neighbouring stations for producing the set of reference series used in the quality control (see MOTEDAS Poster 1) and the reconstruction (see MOREDAS Poster 3). The CDD analysis for Tmax and Tmin was performed calculating a correlation matrix at monthly scale between 1981-2010 among monthly mean values of maximum (Tmax) and minimum (Tmin) temperature series (with at least 90% of data), free of anomalous data and homogenized (see MOTEDAS Poster 1), obtained from AEMEt archives (National Spanish Meteorological Agency). Monthly anomalies (difference between data and mean 1981-2010) were used to prevent the dominant effect of annual cycle in the CDD annual estimation. For each station, and time scale, the common variance r2 (using the square of Pearson's correlation coefficient) was calculated between all neighbouring temperature series and the relation between r2 and distance was modelled according to the following equation (1): Log (r2ij) = b*°dij (1) being Log(rij2) the common variance between target (i) and neighbouring series (j), dij the distance between them and b the slope of the ordinary least-squares linear regression model applied taking into account only the surrounding stations within a starting radius of 50 km and with a minimum of 5 stations required. Finally, monthly, seasonal and annual CDD values were interpolated using the Ordinary Kriging with a spherical variogram over conterminous land of Spain, and converted on a regular 10 km2 grid (resolution similar to the mean distance between stations) to map the results. In the conterminous land of Spain the distance at which couples of stations have a common variance in temperature (both maximum Tmax, and minimum Tmin) above the selected threshold (50%, r Pearson ~0.70) on average does not exceed 400 km, with relevant spatial and temporal differences. The spatial distribution of the CDD shows a clear coastland-to-inland gradient at annual, seasonal and monthly scale, with highest spatial variability along the coastland areas and lower variability inland. The highest spatial variability coincide particularly with coastland areas surrounded by mountain chains and suggests that the orography is one of the most driving factor causing higher interstation variability. Moreover, there are some differences between the behaviour of Tmax and Tmin, being Tmin spatially more homogeneous than Tmax, but its lower CDD values indicate that night-time temperature is more variable than diurnal one. The results suggest that in general local factors affects the spatial variability of monthly Tmin more than Tmax and then higher network density would be necessary to capture the higher spatial variability highlighted for Tmin respect to Tmax. The results suggest that in general local factors affects the spatial variability of Tmin more than Tmax and then higher network density would be necessary to capture the higher spatial variability highlighted for minimum temperature respect to maximum temperature. A conservative distance for reference series could be evaluated in 200 km, that we propose for continental land of Spain and use in the development of MOTEDAS.

Frequency-Based Spatial Correlation Assessments of the Ares I Subscale Acoustic Model Test Firings

NASA Technical Reports Server (NTRS)

Kenny, R. Jeremy; Houston, J.

2012-01-01

The Marshall Space Flight Center has performed a series of test firings to simulate and understand the acoustic environments generated for the Ares I liftoff profiles. Part of the instrumentation package had special sensor groups to assess the acoustic field spatial correlation features for the various test configurations. The spatial correlation characteristics were evaluated for all of the test firings, inclusive of understanding the diffuse to propagating wave amplitude ratios, the acoustic wave decays, and the incident angle of propagating waves across the sensor groups. These parameters were evaluated across the measured frequency spectra and the associated uncertainties for each parameter were estimated.

Statistical analysis of atmospheric turbulence about a simulated block building

NASA Technical Reports Server (NTRS)

Steely, S. L., Jr.

1981-01-01

An array of towers instrumented to measure the three components of wind speed was used to study atmospheric flow about a simulated block building. Two-point spacetime correlations of the longitudinal velocity component were computed along with two-point spatial correlations. These correlations are in good agreement with fundamental concepts of fluid mechanics. The two-point spatial correlations computed directly were compared with correlations predicted by Taylor's hypothesis and excellent agreement was obtained at the higher levels which were out of the building influence. The correlations fall off significantly in the building wake but recover beyond the wake to essentially the same values in the undisturbed, higher regions.

SO2 trajectories in a complex terrain environment using CALPUFF dispersion model, OMI and MODIS data

NASA Astrophysics Data System (ADS)

Sagan, Vasit; Pasken, Robert; Zarauz, Jorge; Krotkov, Nickolay

2018-07-01

Latest improvements in the resolution of atmospheric satellite sensors that measure chemical constituents from space have led to enhanced detection of trace gases. This paper explores the use of sulfur dioxide (SO2) level 2 dataset from OMI instrument, in conjunction with aerosol optical depth and Ångström exponent data from MODIS spectroradiometer, to estimate SO2 loads under clear and turbid atmospheres. The spatial patterns of SO2 loads in polluted atmospheric conditions are compared with a regional pollutant dispersion model (CALPUFF) and field observations near the Andes Peruvian city La Oroya, which is one of the most polluted places in the world. The efficacy of this methodology is further examined incorporating synchronous wind vectors. Results show that the spatial-temporal dynamics of OMI SO2 is in agreement with field measurements and CALPUFF. The SO2 satellite data obtained under optimal viewing conditions and clear skies are also compared with field observations. A correlation is found between in-situ measurements and OMI estimations. The correlation increases for days with predominantly fine aerosols when Ångström exponents are between 0.7 and 1. Moreover, pixel averaging techniques and low and high spatial frequency filtration, applied to OMI SO2 data, results in a more reliable representation of the mean SO2 plume. The paper concludes that anthropogenic SO2 can be monitored from space, even for turbid sky conditions. This demonstrates the potential for the use of satellite products to improve the air quality prediction models.

The sensitivity of ecosystem service models to choices of input data and spatial resolution

USGS Publications Warehouse

Bagstad, Kenneth J.; Cohen, Erika; Ancona, Zachary H.; McNulty, Steven; Sun, Ge

2018-01-01

Although ecosystem service (ES) modeling has progressed rapidly in the last 10–15 years, comparative studies on data and model selection effects have become more common only recently. Such studies have drawn mixed conclusions about whether different data and model choices yield divergent results. In this study, we compared the results of different models to address these questions at national, provincial, and subwatershed scales in Rwanda. We compared results for carbon, water, and sediment as modeled using InVEST and WaSSI using (1) land cover data at 30 and 300 m resolution and (2) three different input land cover datasets. WaSSI and simpler InVEST models (carbon storage and annual water yield) were relatively insensitive to the choice of spatial resolution, but more complex InVEST models (seasonal water yield and sediment regulation) produced large differences when applied at differing resolution. Six out of nine ES metrics (InVEST annual and seasonal water yield and WaSSI) gave similar predictions for at least two different input land cover datasets. Despite differences in mean values when using different data sources and resolution, we found significant and highly correlated results when using Spearman's rank correlation, indicating consistent spatial patterns of high and low values. Our results confirm and extend conclusions of past studies, showing that in certain cases (e.g., simpler models and national-scale analyses), results can be robust to data and modeling choices. For more complex models, those with different output metrics, and subnational to site-based analyses in heterogeneous environments, data and model choices may strongly influence study findings.

Hindcasting of decadal‐timescale estuarine bathymetric change with a tidal‐timescale model

USGS Publications Warehouse

Ganju, Neil K.; Schoellhamer, David H.; Jaffe, Bruce E.

2009-01-01

Hindcasting decadal-timescale bathymetric change in estuaries is prone to error due to limited data for initial conditions, boundary forcing, and calibration; computational limitations further hinder efforts. We developed and calibrated a tidal-timescale model to bathymetric change in Suisun Bay, California, over the 1867–1887 period. A general, multiple-timescale calibration ensured robustness over all timescales; two input reduction methods, the morphological hydrograph and the morphological acceleration factor, were applied at the decadal timescale. The model was calibrated to net bathymetric change in the entire basin; average error for bathymetric change over individual depth ranges was 37%. On a model cell-by-cell basis, performance for spatial amplitude correlation was poor over the majority of the domain, though spatial phase correlation was better, with 61% of the domain correctly indicated as erosional or depositional. Poor agreement was likely caused by the specification of initial bed composition, which was unknown during the 1867–1887 period. Cross-sectional bathymetric change between channels and flats, driven primarily by wind wave resuspension, was modeled with higher skill than longitudinal change, which is driven in part by gravitational circulation. The accelerated response of depth may have prevented gravitational circulation from being represented properly. As performance criteria became more stringent in a spatial sense, the error of the model increased. While these methods are useful for estimating basin-scale sedimentation changes, they may not be suitable for predicting specific locations of erosion or deposition. They do, however, provide a foundation for realistic estuarine geomorphic modeling applications.

a Spatial Analysis on Gis-Hedonic Pricing Model on the Influence of Public Open Space and House Price in Klang Valley, Malaysia

NASA Astrophysics Data System (ADS)

Zainora, A. M.; Norzailawati, M. N.; Tuminah, P.

2016-06-01

Presently, it is noticeable that there is a significant influence of public open space about house price, especially in many developed nations. Literature suggests the relationship between the two aspects give impact on the housing market, however not many studies undertaken in Malaysia. Thus, this research was initiated to analyse the relationship of open space and house price via the techniques of GIS-Hedonic Pricing Model. In this regards, the GIS tool indicates the pattern of the relationship between open space and house price spatially. Meanwhile, Hedonic Pricing Model demonstrates the index of the selected criteria in determining the housing price. This research is a perceptual study of 200 respondents who were the house owners of double-storey terrace houses in four townships, namely Bandar Baru Bangi, Taman Melawati, Subang Jaya and Shah Alam, in Klang Valley. The key research question is whether the relationship between open space and house price exists and the nature of its pattern and intensity. The findings indicate that there is a positive correlation between open space and house price. Correlation analysis reveals that a weak relationship (rs < 0.1) established between the variable of open space and house price (rs = 0.91, N = 200, p = 0.2). Consequently, the rate of house price change is rather small. In overall, this research has achieved its research aims and thus, offers the value added in applying the GIS-Hedonic pricing model in analysing the influence of open space to the house price in the form of spatially and textually.

The ecological risk, source identification, and pollution assessment of heavy metals in road dust: a case study in Rafsanjan, SE Iran.

PubMed

Mirzaei Aminiyan, Milad; Baalousha, Mohammed; Mousavi, Rouhollah; Mirzaei Aminiyan, Farzad; Hosseini, Hamideh; Heydariyan, Amin

2018-05-01

Heavy metal (HM) contamination in road dust is a potential environmental and human health threat. The sources, concentrations, spatial distribution, and ecological risk of As, Cd, Cu, Cr, Ni, Pb, and Zn in road dust in Rafsanjan City, Iran, were investigated. Pollution was assessed using the enrichment factor (EF). The potentially harmful effects of HMs were evaluated by calculating the potential ecological risk factor of individual metals (E r ) and of multiple metals (RI) using the Hakanson method. Correlation and principal component analyses (PCA) were applied to identify HM pollution sources. The concentrations of HMs in road dust were higher (ca. 5-10 folds) than their natural background values. The EF and E r increased according to the following order Cu > Pb > As > Zn > Cd > Cr > Ni and Cu > Cd > Pb > As > Ni > Zn > Cr, respectively. Thus, Cu is regarded as the pollutant of highest concern. Based on potential ecological risk index (RI) spatial distribution, all parts of Rafsanjan are characterized by significantly high potential ecological risk. HM concentration heat maps, PCA, and correlation analysis suggest that Cu, Pb, As, Cd, and Zn may have originated from the same source and follow the same spatial distribution pattern. These metals originated mainly from anthropogenic sources like copper mining and smelting plants, industrial and chemical activities, inordinate application of chemical fertilizers and pesticides in farmlands, and heavy traffic. Ni and Cr are likely to origniate from the industrial activities and traffic load in Rafsanjan City.

Scrub typhus islands in the Taiwan area and the association between scrub typhus disease and forest land use and farmer population density: geographically weighted regression.

PubMed

Tsai, Pui-Jen; Yeh, Hsi-Chyi

2013-04-29

The Taiwan area comprises the main island of Taiwan and several small islands located off the coast of the Southern China. The eastern two-thirds of Taiwan are characterized by rugged mountains covered with tropical and subtropical vegetation. The western region of Taiwan is characterized by flat or gently rolling plains. Geographically, the Taiwan area is diverse in ecology and environment, although scrub typhus threatens local human populations. In this study, we investigate the effects of seasonal and meteorological factors on the incidence of scrub typhus infection among 10 local climate regions. The correlation between the spatial distribution of scrub typhus and cultivated forests in Taiwan, as well as the relationship between scrub typhus incidence and the population density of farm workers is examined. We applied Pearson's product moment correlation to calculate the correlation between the incidence of scrub typhus and meteorological factors among 10 local climate regions. We used the geographically weighted regression (GWR) method, a type of spatial regression that generates parameters disaggregated by the spatial units of analysis, to detail and map each regression point for the response variables of the standardized incidence ratio (SIR)-district scrub typhus. We also applied the GWR to examine the explanatory variables of types of forest-land use and farm worker density in Taiwan in 2005. In the Taiwan Area, scrub typhus endemic areas are located in the southeastern regions and mountainous townships of Taiwan, as well as the Pescadore, Kinmen, and Matou Islands. Among these islands and low-incidence areas in the central western and southwestern regions of Taiwan, we observed a significant correlation between scrub typhus incidence and surface temperature. No similar significant correlation was found in the endemic areas (e.g., the southeastern region and the mountainous area of Taiwan). Precipitation correlates positively with scrub typhus incidence in 3 local climate regions (i.e., Taiwan's central western and southwestern regions, and the Kinmen Islands). Relative humidity correlates positively with incidence in Southwestern Taiwan and the Kinmen Islands. The number of wet days correlates positively with incidence in Southwestern Taiwan. The duration of sunshine correlates positively with incidence in Central Western Taiwan, as well as the Kinmen and Matou Islands. In addition, the 10 local climatic regions can be classified into the following 3 groups, based on the warm-cold seasonal fluctuations in scrub typhus incidence: (a) Type 1, evident in 5 local climate regions (Taiwan's northern, northwestern, northeastern, and southeastern regions, as well as the mountainous area); (b) Type 2 (Taiwan's central western and southwestern regions, and the Pescadore Islands); and (c) Type 3 (the Kinmen and Matou Islands). In the GWR models, the response variable of the SIR-district scrub typhus has a statistically significantly positive association with 2 explanatory variables (farm worker population density and timber management). In addition, other explanatory variables (recreational forests, natural reserves, and "other purpose" areas) show positive or negative signs for parameter estimates in various locations in Taiwan. Negative signs of parameter estimates occurred only for the explanatory variables of national protectorates, plantations, and clear-cut areas. The results of this study show that scrub typhus in Taiwan can be classified into 3 types. Type 1 exhibits no climatic effect, whereas the incidence of Type 2 correlates positively with higher temperatures during the warm season, and the incidence of Type 3 correlates positively with higher surface temperatures and longer hours of sunshine. The results also show that in the mountainous township areas of Taiwan's central and southern regions, as well as in Southeastern Taiwan, higher SIR values for scrub typhus are associated with the following variables: farm worker population density, timber management, and area type (i.e., recreational forest, natural reserve, or other purpose).

Scrub typhus islands in the Taiwan area and the association between scrub typhus disease and forest land use and farmer population density: geographically weighted regression

PubMed Central

2013-01-01

Background The Taiwan area comprises the main island of Taiwan and several small islands located off the coast of the Southern China. The eastern two-thirds of Taiwan are characterized by rugged mountains covered with tropical and subtropical vegetation. The western region of Taiwan is characterized by flat or gently rolling plains. Geographically, the Taiwan area is diverse in ecology and environment, although scrub typhus threatens local human populations. In this study, we investigate the effects of seasonal and meteorological factors on the incidence of scrub typhus infection among 10 local climate regions. The correlation between the spatial distribution of scrub typhus and cultivated forests in Taiwan, as well as the relationship between scrub typhus incidence and the population density of farm workers is examined. Methods We applied Pearson’s product moment correlation to calculate the correlation between the incidence of scrub typhus and meteorological factors among 10 local climate regions. We used the geographically weighted regression (GWR) method, a type of spatial regression that generates parameters disaggregated by the spatial units of analysis, to detail and map each regression point for the response variables of the standardized incidence ratio (SIR)-district scrub typhus. We also applied the GWR to examine the explanatory variables of types of forest-land use and farm worker density in Taiwan in 2005. Results In the Taiwan Area, scrub typhus endemic areas are located in the southeastern regions and mountainous townships of Taiwan, as well as the Pescadore, Kinmen, and Matou Islands. Among these islands and low-incidence areas in the central western and southwestern regions of Taiwan, we observed a significant correlation between scrub typhus incidence and surface temperature. No similar significant correlation was found in the endemic areas (e.g., the southeastern region and the mountainous area of Taiwan). Precipitation correlates positively with scrub typhus incidence in 3 local climate regions (i.e., Taiwan’s central western and southwestern regions, and the Kinmen Islands). Relative humidity correlates positively with incidence in Southwestern Taiwan and the Kinmen Islands. The number of wet days correlates positively with incidence in Southwestern Taiwan. The duration of sunshine correlates positively with incidence in Central Western Taiwan, as well as the Kinmen and Matou Islands. In addition, the 10 local climatic regions can be classified into the following 3 groups, based on the warm-cold seasonal fluctuations in scrub typhus incidence: (a) Type 1, evident in 5 local climate regions (Taiwan’s northern, northwestern, northeastern, and southeastern regions, as well as the mountainous area); (b) Type 2 (Taiwan’s central western and southwestern regions, and the Pescadore Islands); and (c) Type 3 (the Kinmen and Matou Islands). In the GWR models, the response variable of the SIR-district scrub typhus has a statistically significantly positive association with 2 explanatory variables (farm worker population density and timber management). In addition, other explanatory variables (recreational forests, natural reserves, and “other purpose” areas) show positive or negative signs for parameter estimates in various locations in Taiwan. Negative signs of parameter estimates occurred only for the explanatory variables of national protectorates, plantations, and clear-cut areas. Conclusion The results of this study show that scrub typhus in Taiwan can be classified into 3 types. Type 1 exhibits no climatic effect, whereas the incidence of Type 2 correlates positively with higher temperatures during the warm season, and the incidence of Type 3 correlates positively with higher surface temperatures and longer hours of sunshine. The results also show that in the mountainous township areas of Taiwan’s central and southern regions, as well as in Southeastern Taiwan, higher SIR values for scrub typhus are associated with the following variables: farm worker population density, timber management, and area type (i.e., recreational forest, natural reserve, or other purpose). PMID:23627966

China’s Air Quality and Respiratory Disease Mortality Based on the Spatial Panel Model

PubMed Central

Cao, Qilong; Liang, Ying; Niu, Xueting

2017-01-01

Background: Air pollution has become an important factor restricting China’s economic development and has subsequently brought a series of social problems, including the impact of air pollution on the health of residents, which is a topical issue in China. Methods: Taking into account this spatial imbalance, the paper is based on the spatial panel data model PM2.5. Respiratory disease mortality in 31 Chinese provinces from 2004 to 2008 is taken as the main variable to study the spatial effect and impact of air quality and respiratory disease mortality on a large scale. Results: It was found that there is a spatial correlation between the mortality of respiratory diseases in Chinese provinces. The spatial correlation can be explained by the spatial effect of PM2.5 pollutions in the control of other variables. Conclusions: Compared with the traditional non-spatial model, the spatial model is better for describing the spatial relationship between variables, ensuring the conclusions are scientific and can measure the spatial effect between variables. PMID:28927016

Geostatistical analysis of regional hydraulic conductivity variations in the Snake River Plain aquifer, eastern Idaho

USGS Publications Warehouse

Welhan, J.A.; Reed, M.F.

1997-01-01

The regional spatial correlation structure of bulk horizontal hydraulic conductivity (Kb) estimated from published transmissivity data from 79 open boreholes in the fractured basalt aquifer of the eastern Snake River Plain was analyzed with geostatistical methods. The two-dimensional spatial correlation structure of In Kb shows a pronounced 4:1 range anisotropy, with a maximum correlation range in the north-northwest- south-southeast direction of about 6 km. The maximum variogram range of In Kb is similar to the mean length of flow groups exposed at the surface. The In Kb range anisotropy is similar to the mean width/length ratio of late Quaternary and Holocene basalt lava flows and the orientations of the major volcanic structural features on the eastern Snake River Plain. The similarity between In Kb correlation scales and basalt flow dimensions and between basalt flow orientations and correlation range anisotropy suggests that the spatial distribution of zones of high hydraulic conductivity may be controlled by the lateral dimensions, spatial distribution, and interconnection between highly permeable zones which are known to occur between lava flows within flow groups. If hydraulic conductivity and lithology are eventually shown to be cross correlative in this geologic setting, it may be possible to stochastically simulate hydraulic conductivity distributions, which are conditional on a knowledge of volcanic stratigraphy.

An evaluation of potential sampling locations in a reservoir with emphasis on conserved spatial correlation structure.

PubMed

Yenilmez, Firdes; Düzgün, Sebnem; Aksoy, Aysegül

2015-01-01

In this study, kernel density estimation (KDE) was coupled with ordinary two-dimensional kriging (OK) to reduce the number of sampling locations in measurement and kriging of dissolved oxygen (DO) concentrations in Porsuk Dam Reservoir (PDR). Conservation of the spatial correlation structure in the DO distribution was a target. KDE was used as a tool to aid in identification of the sampling locations that would be removed from the sampling network in order to decrease the total number of samples. Accordingly, several networks were generated in which sampling locations were reduced from 65 to 10 in increments of 4 or 5 points at a time based on kernel density maps. DO variograms were constructed, and DO values in PDR were kriged. Performance of the networks in DO estimations were evaluated through various error metrics, standard error maps (SEM), and whether the spatial correlation structure was conserved or not. Results indicated that smaller number of sampling points resulted in loss of information in regard to spatial correlation structure in DO. The minimum representative sampling points for PDR was 35. Efficacy of the sampling location selection method was tested against the networks generated by experts. It was shown that the evaluation approach proposed in this study provided a better sampling network design in which the spatial correlation structure of DO was sustained for kriging.

Beyond Dizziness: Virtual Navigation, Spatial Anxiety and Hippocampal Volume in Bilateral Vestibulopathy

PubMed Central

Kremmyda, Olympia; Hüfner, Katharina; Flanagin, Virginia L.; Hamilton, Derek A.; Linn, Jennifer; Strupp, Michael; Jahn, Klaus; Brandt, Thomas

2016-01-01

Bilateral vestibulopathy (BVP) is defined as the impairment or loss of function of either the labyrinths or the eighth nerves. Patients with total BVP due to bilateral vestibular nerve section exhibit difficulties in spatial memory and navigation and show a loss of hippocampal volume. In clinical practice, most patients do not have a complete loss of function but rather an asymmetrical residual functioning of the vestibular system. The purpose of the current study was to investigate navigational ability and hippocampal atrophy in BVP patients with residual vestibular function. Fifteen patients with BVP and a group of age- and gender- matched healthy controls were examined. Self-reported questionnaires on spatial anxiety and wayfinding were used to assess the applied strategy of wayfinding and quality of life. Spatial memory and navigation were tested directly using a virtual Morris Water Maze Task. The hippocampal volume of these two groups was evaluated by voxel-based morphometry. In the patients, the questionnaire showed a higher spatial anxiety and the Morris Water Maze Task a delayed spatial learning performance. MRI revealed a significant decrease in the gray matter mid-hippocampal volume (Left: p = 0.006, Z = 4.58, Right: p < 0.001, Z = 3.63) and posterior parahippocampal volume (Right: p = 0.005, Z = 4.65, Left: p < 0.001, Z = 3.87) compared to those of healthy controls. In addition, a decrease in hippocampal formation volume correlated with a more dominant route-finding strategy. Our current findings demonstrate that even partial bilateral vestibular loss leads to anatomical and functional changes in the hippocampal formation and objective and subjective behavioral deficits. PMID:27065838

Characterizing groundwater quality ranks for drinking purposes in Sylhet district, Bangladesh, using entropy method, spatial autocorrelation index, and geostatistics.

PubMed

Islam, Abu Reza Md Towfiqul; Ahmed, Nasir; Bodrud-Doza, Md; Chu, Ronghao

2017-12-01

Drinking water is susceptible to the poor quality of contaminated water affecting the health of humans. Thus, it is an essential study to investigate factors affecting groundwater quality and its suitability for drinking uses. In this paper, the entropy theory, multivariate statistics, spatial autocorrelation index, and geostatistics are applied to characterize groundwater quality and its spatial variability in the Sylhet district of Bangladesh. A total of 91samples have been collected from wells (e.g., shallow, intermediate, and deep tube wells at 15-300-m depth) from the study area. The results show that NO 3 - , then SO 4 2- , and As are the most contributed parameters influencing the groundwater quality according to the entropy theory. The principal component analysis (PCA) and correlation coefficient also confirm the results of the entropy theory. However, Na + has the highest spatial autocorrelation and the most entropy, thus affecting the groundwater quality. Based on the entropy-weighted water quality index (EWQI) and groundwater quality index (GWQI) classifications, it is observed that 60.45 and 53.86% of water samples are classified as having an excellent to good qualities, while the remaining samples vary from medium to extremely poor quality domains for drinking purposes. Furthermore, the EWQI classification provides the more reasonable results than GWQIs due to its simplicity, accuracy, and ignoring of artificial weight. A Gaussian semivariogram model has been chosen to the best fit model, and groundwater quality indices have a weak spatial dependence, suggesting that both geogenic and anthropogenic factors play a pivotal role in spatial heterogeneity of groundwater quality oscillations.

Spatial statistics of hydrography and water chemistry in a eutrophic boreal lake based on sounding and water samples.

PubMed

Leppäranta, Matti; Lewis, John E; Heini, Anniina; Arvola, Lauri

2018-06-04

Spatial variability, an essential characteristic of lake ecosystems, has often been neglected in field research and monitoring. In this study, we apply spatial statistical methods for the key physics and chemistry variables and chlorophyll a over eight sampling dates in two consecutive years in a large (area 103 km 2 ) eutrophic boreal lake in southern Finland. In the four summer sampling dates, the water body was vertically and horizontally heterogenic except with color and DOC, in the two winter ice-covered dates DO was vertically stratified, while in the two autumn dates, no significant spatial differences in any of the measured variables were found. Chlorophyll a concentration was one order of magnitude lower under the ice cover than in open water. The Moran statistic for spatial correlation was significant for chlorophyll a and NO 2 +NO 3 -N in all summer situations and for dissolved oxygen and pH in three cases. In summer, the mass centers of the chemicals were within 1.5 km from the geometric center of the lake, and the 2nd moment radius ranged in 3.7-4.1 km respective to 3.9 km for the homogeneous situation. The lateral length scales of the studied variables were 1.5-2.5 km, about 1 km longer in the surface layer. The detected spatial "noise" strongly suggests that besides vertical variation also the horizontal variation in eutrophic lakes, in particular, should be considered when the ecosystems are monitored.

The Effects of City Streets on an Urban Disease Vector

PubMed Central

Barbu, Corentin M.; Hong, Andrew; Manne, Jennifer M.; Small, Dylan S.; Quintanilla Calderón, Javier E.; Sethuraman, Karthik; Quispe-Machaca, Víctor; Ancca-Juárez, Jenny; Cornejo del Carpio, Juan G.; Málaga Chavez, Fernando S.; Náquira, César; Levy, Michael Z.

2013-01-01

With increasing urbanization vector-borne diseases are quickly developing in cities, and urban control strategies are needed. If streets are shown to be barriers to disease vectors, city blocks could be used as a convenient and relevant spatial unit of study and control. Unfortunately, existing spatial analysis tools do not allow for assessment of the impact of an urban grid on the presence of disease agents. Here, we first propose a method to test for the significance of the impact of streets on vector infestation based on a decomposition of Moran's spatial autocorrelation index; and second, develop a Gaussian Field Latent Class model to finely describe the effect of streets while controlling for cofactors and imperfect detection of vectors. We apply these methods to cross-sectional data of infestation by the Chagas disease vector Triatoma infestans in the city of Arequipa, Peru. Our Moran's decomposition test reveals that the distribution of T. infestans in this urban environment is significantly constrained by streets (p<0.05). With the Gaussian Field Latent Class model we confirm that streets provide a barrier against infestation and further show that greater than 90% of the spatial component of the probability of vector presence is explained by the correlation among houses within city blocks. The city block is thus likely to be an appropriate spatial unit to describe and control T. infestans in an urban context. Characteristics of the urban grid can influence the spatial dynamics of vector borne disease and should be considered when designing public health policies. PMID:23341756

3D landslide motion from a UAV-derived time-series of morphological attributes

NASA Astrophysics Data System (ADS)

Valasia Peppa, Maria; Mills, Jon Philip; Moore, Philip; Miller, Pauline; Chambers, Jon

2017-04-01

Landslides are recognised as dynamic and significantly hazardous phenomena. Time-series observations can improve the understanding of a landslide's complex behaviour and aid assessment of its geometry and kinematics. Conventional quantification of landslide motion involves the installation of survey markers into the ground at discrete locations and periodic observations over time. However, such surveying is labour intensive, provides limited spatial resolution, is occasionally hazardous for steep terrain, or even impossible for inaccessible mountainous areas. The emergence of mini unmanned aerial vehicles (UAVs) equipped with off-the-shelf compact cameras, alongside the structure-from-motion (SfM) photogrammetric pipeline and modern pixel-based matching approaches, has expedited the automatic generation of high resolution digital elevation models (DEMs). Moreover, cross-correlation functions applied to finely co-registered consecutive orthomosaics and/or DEMs have been widely used to determine the displacement of moving features in an automated way, resulting in high spatial resolution motion vectors. This research focuses on estimating the 3D displacement field of an active slow moving earth-slide earth-flow landslide located in Lias mudrocks of North Yorkshire, UK, with the ultimate aim of assessing landslide deformation patterns. The landslide extends approximately 290 m E-W and 230 m N-S, with an average slope of 12˚ and 50 m elevation difference from N-S. Cross-correlation functions were applied to an eighteen-month duration, UAV-derived, time-series of morphological attributes in order to determine motion vectors for subsequent landslide analysis. A self-calibrating bundle adjustment was firstly incorporated into the SfM pipeline and utilised to process imagery acquired using a Panasonic Lumix DMC-LX5 compact camera from a mini fixed-wing Quest 300 UAV, with 2 m wingspan and maximum 5 kg payload. Data from six field campaigns were used to generate a DEM time-series at 6 cm spatial resolution. DEMs were georeferenced into a common reference frame using control information from surveyed ground control points. The accuracy of the co-registration was estimated from planimetric and vertical RMS errors at independent checkpoints as 4 cm and 3 cm respectively. Afterwards, various morphological attributes, including shaded relief, curvature and openness were calculated from the UAV-derived DEMs. These attributes are indicative of the local structures of discernible geomorphological features (e.g. scarps, ridges, cracks, etc.), the motion of which can be monitored using the cross-correlation algorithm. Multiple experiments were conducted to test the performance of the cross-correlation function implemented on successive epochs. Two benchmark datasets were used for validation of the cross-correlation results: a) the motion vectors generated from the surveyed 3D position of installed markers; b) the calculated displacements of features, manually tracked from successive UAV-derived orthomosaics. Both benchmark datasets detected a maximum planimetric displacement of approximately 1 m at the foot of the landslide, with a dominant N-S orientation, between December 2014 and May 2016. Preliminary cross-correlation results illustrated a similar planimetric motion in both magnitude and orientation, however user intervention was required to filter spurious displacement vectors.

`spup' - An R Package for Analysis of Spatial Uncertainty Propagation and Application to Trace Gas Emission Simulations

NASA Astrophysics Data System (ADS)

Sawicka, K.; Breuer, L.; Houska, T.; Santabarbara Ruiz, I.; Heuvelink, G. B. M.

2016-12-01

Computer models have become a crucial tool in engineering and environmental sciences for simulating the behaviour of complex static and dynamic systems. However, while many models are deterministic, the uncertainty in their predictions needs to be estimated before they are used for decision support. Advances in uncertainty propagation analysis and assessment have been paralleled by a growing number of software tools for uncertainty analysis, but none has gained recognition for a universal applicability, including case studies with spatial models and spatial model inputs. Due to the growing popularity and applicability of the open source R programming language we undertook a project to develop an R package that facilitates uncertainty propagation analysis in spatial environmental modelling. In particular, the `spup' package provides functions for examining the uncertainty propagation starting from input data and model parameters, via the environmental model onto model predictions. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo techniques, as well as several uncertainty visualization functions. Here we will demonstrate that the 'spup' package is an effective and easy-to-use tool to be applied even in a very complex study case, and that it can be used in multi-disciplinary research and model-based decision support. As an example, we use the ecological LandscapeDNDC model to analyse propagation of uncertainties associated with spatial variability of the model driving forces such as rainfall, nitrogen deposition and fertilizer inputs. The uncertainty propagation is analysed for the prediction of emissions of N2O and CO2 for a German low mountainous, agriculturally developed catchment. The study tests the effect of spatial correlations on spatially aggregated model outputs, and could serve as an advice for developing best management practices and model improvement strategies.

Adding Spatially Correlated Noise to a Median Ionosphere

NASA Astrophysics Data System (ADS)

Holmes, J. M.; Egert, A. R.; Dao, E. V.; Colman, J. J.; Parris, R. T.

2017-12-01

We describe a process for adding spatially correlated noise to a background ionospheric model, in this case the International Reference Ionosphere (IRI). Monthly median models do a good job describing bulk features of the ionosphere in a median sense. It is well known that the ionosphere almost never actually looks like its median. For the purposes of constructing an Operational System Simulation Experiment, it may be desirable to construct an ionosphere more similar to a particular instant, hour, or day than to the monthly median. We will examine selected data from the Global Ionosphere Radio Observatory (GIRO) database and estimate the amount of variance captured by the IRI model. We will then examine spatial and temporal correlations within the residuals. This analysis will be used to construct a temporal-spatial gridded ionosphere that represents a particular instantiation of those statistics.

TU-CD-BRB-05: Radiation Damage Signature of White Matter Fiber Bundles Using Diffusion Tensor Imaging (DTI)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhu, T; Chapman, C; Lawrence, T

2015-06-15

Purpose: To develop an automated and scalable approach and identify temporal, spatial and dosimetric patterns of radiation damage of white matter (WM) fibers following partial brain irradiation. Methods: An automated and scalable approach was developed to extract DTI features of 22 major WM fibers from 33 patients with low-grade/benign tumors treated by radiation therapy (RT). DTI scans of the patients were performed pre-RT, 3- and 6-week during RT, and 1, 6 and 18 months after RT. The automated tractography analysis was applied to 198 datasets as: (1) intra-subject registration of longitudinal DTI, (2) spatial normalization of individual-patient DTI to themore » Johns Hopkins WM Atlas, (3) automatic fiber tracking regulated by the WM Atlas, and (4) segmentation of WM into 22 major tract profiles. Longitudinal percentage changes in fractional anisotropy (FA), and mean, axial and radial diffusivity (MD/AD/RD) of each tract from pre-RT were quantified and correlated to 95%, 90% and 80% percentiles of doses and mean doses received by the tract. Heatmaps were used to identify clusters of significant correlation and reveal temporal, spatial and dosimetric signatures of WM damage. A multivariate linear regression was further carried out to determine influence of clinical factors. Results: Of 22 tracts, AD/MD changes in 12 tracts had significant correlation with doses, especially at 6 and 18 months post-RT, indicating progressive radiation damage after RT. Most interestingly, the DTI-index changes in the elongated tracts were associated with received maximum doses, suggesting a serial-structure behavior; while short association fibers were affected by mean doses, indicating a parallel-structure response. Conclusion: Using an automated DTI-tractography analysis of whole brain WM fibers, we reveal complex radiation damage patterns of WM fibers. Damage in WM fibers that play an important role in the neural network could be associated with late neurocognitive function declines after brain irradiation. NIH NS064973.« less

Preserving subject variability in group fMRI analysis: performance evaluation of GICA vs. IVA

PubMed Central

Michael, Andrew M.; Anderson, Mathew; Miller, Robyn L.; Adalı, Tülay; Calhoun, Vince D.

2014-01-01

Independent component analysis (ICA) is a widely applied technique to derive functionally connected brain networks from fMRI data. Group ICA (GICA) and Independent Vector Analysis (IVA) are extensions of ICA that enable users to perform group fMRI analyses; however a full comparison of the performance limits of GICA and IVA has not been investigated. Recent interest in resting state fMRI data with potentially higher degree of subject variability makes the evaluation of the above techniques important. In this paper we compare component estimation accuracies of GICA and an improved version of IVA using simulated fMRI datasets. We systematically change the degree of inter-subject spatial variability of components and evaluate estimation accuracy over all spatial maps (SMs) and time courses (TCs) of the decomposition. Our results indicate the following: (1) at low levels of SM variability or when just one SM is varied, both GICA and IVA perform well, (2) at higher levels of SM variability or when more than one SMs are varied, IVA continues to perform well but GICA yields SM estimates that are composites of other SMs with errors in TCs, (3) both GICA and IVA remove spatial correlations of overlapping SMs and introduce artificial correlations in their TCs, (4) if number of SMs is over estimated, IVA continues to perform well but GICA introduces artifacts in the varying and extra SMs with artificial correlations in the TCs of extra components, and (5) in the absence or presence of SMs unique to one subject, GICA produces errors in TCs and IVA estimates are accurate. In summary, our simulation experiments (both simplistic and realistic) and our holistic analyses approach indicate that IVA produces results that are closer to ground truth and thereby better preserves subject variability. The improved version of IVA is now packaged into the GIFT toolbox (http://mialab.mrn.org/software/gift). PMID:25018704

Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses

PubMed Central

Nickerson, Lisa D.; Smith, Stephen M.; Öngür, Döst; Beckmann, Christian F.

2017-01-01

Independent Component Analysis (ICA) is one of the most popular techniques for the analysis of resting state FMRI data because it has several advantageous properties when compared with other techniques. Most notably, in contrast to a conventional seed-based correlation analysis, it is model-free and multivariate, thus switching the focus from evaluating the functional connectivity of single brain regions identified a priori to evaluating brain connectivity in terms of all brain resting state networks (RSNs) that simultaneously engage in oscillatory activity. Furthermore, typical seed-based analysis characterizes RSNs in terms of spatially distributed patterns of correlation (typically by means of simple Pearson's coefficients) and thereby confounds together amplitude information of oscillatory activity and noise. ICA and other regression techniques, on the other hand, retain magnitude information and therefore can be sensitive to both changes in the spatially distributed nature of correlations (differences in the spatial pattern or “shape”) as well as the amplitude of the network activity. Furthermore, motion can mimic amplitude effects so it is crucial to use a technique that retains such information to ensure that connectivity differences are accurately localized. In this work, we investigate the dual regression approach that is frequently applied with group ICA to assess group differences in resting state functional connectivity of brain networks. We show how ignoring amplitude effects and how excessive motion corrupts connectivity maps and results in spurious connectivity differences. We also show how to implement the dual regression to retain amplitude information and how to use dual regression outputs to identify potential motion effects. Two key findings are that using a technique that retains magnitude information, e.g., dual regression, and using strict motion criteria are crucial for controlling both network amplitude and motion-related amplitude effects, respectively, in resting state connectivity analyses. We illustrate these concepts using realistic simulated resting state FMRI data and in vivo data acquired in healthy subjects and patients with bipolar disorder and schizophrenia. PMID:28348512

Modelling soil properties in a crop field located in Croatia

NASA Astrophysics Data System (ADS)

Bogunovic, Igor; Pereira, Paulo; Millan, Mesic; Percin, Aleksandra; Zgorelec, Zeljka

2016-04-01

Development of tillage activities had negative effects on soil quality as destruction of soil horizons, compacting and aggregates destruction, increasing soil erosion and loss of organic matter. For a better management in order to mitigate the effects of intensive soil management in land degradation it is fundamental to map the spatial distribution of soil properties (Brevik et al., 2016). The understanding the distribution of the variables in space is very important for a sustainable management, in order to identify areas that need a potential intervention and decrease the economic losses (Galiati et al., 2016). The objective of this work is study the spatial distribution of some topsoil properties as clay, fine silt, coarse silt, fine sand, coarse sand, penetration resistance, moisture and organic matter in a crop field located in Croatia. A grid with 275x25 (625 m2) was designed and a total of 48 samples were collected. Previous to data modelling, data normality was checked using the Shapiro wilk-test. As in previous cases (Pereira et al., 2015), data did not followed the normal distribution, even after a logarithmic (Log), square-root, and box cox transformation. Thus, for modeling proposes, we used the log transformed data, since was the closest to the normality. In order to identify groups among the variables we applied a principal component analysis (PCA), based on the correlation matrix. On average clay content was 15.47% (±3.23), fine silt 24.24% (±4.08), coarse silt 35.34% (±3.12), fine sand 20.93% (±4.68), coarse sand 4.02% (±1.69), penetration resistance 0.66 MPa (±0.28), organic matter 1.51% (±0.25) and soil moisture 32.04% (±3.27). The results showed that the PCA identified three factors explained at least one of the variables. The first factor had high positive loadings in soil clay, fine silt and organic matter and a high negative loading in fine sand. The second factor had high positive loadings in coarse sand and moisture and a high negative loading in coarse silt. Finally, the factor 3 had a positive loading in penetration resistance. The loadings of these three factors were mapped using ordinary kriging method. The analysis of incremental spatial correlation identified that the highest spatial correlation in the factor 1 was identified at 41.87 m, in factor 2 at 75.61 m and factor 3 at 143.9 m. In the case of factor 2, the maximum peak of spatial autocorrelation was significant at a p<0.05. This showed that the variable has a random distribution, as confirmed with the Moran's I spatial correlation analysis. In relation to the other factors the maximum peaks were significantly clustered at a p<0.001. These results suggested that the each factor has a different spatial pattern and the studied soil properties explained by each factor had a different spatial distribution. References Breivik, E., Baumgarten, A., Calzolari, C., Miller, B., Pereira, P., Kabala, C., Jordán, A. Soil mapping, classification, and modelling: history and future directions. Geoderma, 264, Part B, 256-274. Galiati, A., Gristina, L., Crescimanno, Barone, E., Novara, A. (2016) Towards more efficient incentives for agri-environment measures in degraded and eroded vineyards. Land Degradation and Development, DOI: 10.1002/ldr.2389 Pereira, P., Cerdà, A., Úbeda, X., Mataix-Solera, J. Arcenegui, V., Zavala, L. (2015) Modelling the impacts of wildfire on ash thickness in a short-term period, Land Degradation and Development, 26, 180-192.

Experimental determination of the correlation properties of plasma turbulence using 2D BES systems

NASA Astrophysics Data System (ADS)

Fox, M. F. J.; Field, A. R.; van Wyk, F.; Ghim, Y.-c.; Schekochihin, A. A.; the MAST Team

2017-04-01

A procedure is presented to map from the spatial correlation parameters of a turbulent density field (the radial and binormal correlation lengths and wavenumbers, and the fluctuation amplitude) to correlation parameters that would be measured by a beam emission spectroscopy (BES) diagnostic. The inverse mapping is also derived, which results in resolution criteria for recovering correct correlation parameters, depending on the spatial response of the instrument quantified in terms of point-spread functions (PSFs). Thus, a procedure is presented that allows for a systematic comparison between theoretical predictions and experimental observations. This procedure is illustrated using the Mega-Ampere Spherical Tokamak BES system and the validity of the underlying assumptions is tested on fluctuating density fields generated by direct numerical simulations using the gyrokinetic code GS2. The measurement of the correlation time, by means of the cross-correlation time-delay method, is also investigated and is shown to be sensitive to the fluctuating radial component of velocity, as well as to small variations in the spatial properties of the PSFs.

Spatial Reasoning and Understanding the Particulate Nature of Matter: A Middle School Perspective

NASA Astrophysics Data System (ADS)

Cole, Merryn L.

This dissertation employed a mixed-methods approach to examine the relationship between spatial reasoning ability and understanding of chemistry content for both middle school students and their science teachers. Spatial reasoning has been linked to success in learning STEM subjects (Wai, Lubinski, & Benbow, 2009). Previous studies have shown a correlation between understanding of chemistry content and spatial reasoning ability (e.g., Pribyl & Bodner, 1987; Wu & Shah, 2003: Stieff, 2013), raising the importance of developing the spatial reasoning ability of both teachers and students. Few studies examine middle school students' or in-service middle school teachers' understanding of chemistry concepts or its relation to spatial reasoning ability. The first paper in this dissertation addresses the quantitative relationship between mental rotation, a type of spatial reasoning ability, and understanding a fundamental concept in chemistry, the particulate nature of matter. The data showed a significant, positive correlation between scores on the Purdue Spatial Visualization Test of Rotations (PSVT; Bodner & Guay, 1997) and the Particulate Nature of Matter Assessment (ParNoMA; Yezierski, 2003) for middle school students prior to and after chemistry instruction. A significant difference in spatial ability among students choosing different answer choices on ParNoMA questions was also found. The second paper examined the ways in which students of different spatial abilities talked about matter and chemicals differently. Students with higher spatial ability tended to provide more of an explanation, though not necessarily in an articulate matter. In contrast, lower spatial ability students tended to use any keywords that seemed relevant, but provided little or no explanation. The third paper examined the relationship between mental reasoning and understanding chemistry for middle school science teachers. Similar to their students, a significant, positive correlation between scores on the PSVT and the ParNoMA was observed. Teachers who used consistent reasoning in providing definitions and examples for matter and chemistry tended to have higher spatial abilities than those teachers who used inconsistent reasoning on the same questions. This is the first study to explore the relationship between spatial reasoning and understanding of chemistry concepts at the middle school level. Though we are unable to infer cause and effect relationship from correlational data, these results illustrate a need to further investigate this relationship as well as identify the relationship between different spatial abilities (not just mental rotation) and other chemistry concepts.

A straightforward approach for gated STED-FCS to investigate lipid membrane dynamics

PubMed Central

Clausen, Mathias P.; Sezgin, Erdinc; Bernardino de la Serna, Jorge; Waithe, Dominic; Lagerholm, B. Christoffer; Eggeling, Christian

2015-01-01

Recent years have seen the development of multiple technologies to investigate, with great spatial and temporal resolution, the dynamics of lipids in cellular and model membranes. One of these approaches is the combination of far-field super-resolution stimulated-emission-depletion (STED) microscopy with fluorescence correlation spectroscopy (FCS). STED-FCS combines the diffraction-unlimited spatial resolution of STED microscopy with the statistical accuracy of FCS to determine sub-millisecond-fast molecular dynamics with single-molecule sensitivity. A unique advantage of STED-FCS is that the observation spot for the FCS data recordings can be tuned to sub-diffraction scales, i.e. <200 nm in diameter, in a gradual manner to investigate fast diffusion of membrane-incorporated labelled entities. Unfortunately, so far the STED-FCS technology has mostly been applied on a few custom-built setups optimised for far-red fluorescent emitters. Here, we summarise the basics of the STED-FCS technology and highlight how it can give novel details into molecular diffusion modes. Most importantly, we present a straightforward way for performing STED-FCS measurements on an unmodified turnkey commercial system using a time-gated detection scheme. Further, we have evaluated the STED-FCS performance of different commonly used green emitting fluorescent dyes applying freely available, custom-written analysis software. PMID:26123184

Near-field transport imaging applied to photovoltaic materials

DOE PAGES

Xiao, Chuanxiao; Jiang, Chun -Sheng; Moseley, John; ...

2017-05-26

We developed and applied a new analytical technique - near-field transport imaging (NF-TI or simply TI) - to photovoltaic materials. Charge-carrier transport is an important factor in solar cell performance, and TI is an innovative approach that integrates a scanning electron microscope with a near-field scanning optical microscope, providing the possibility to study luminescence associated with recombination and transport with high spatial resolution. In this paper, we describe in detail the technical barriers we had to overcome to develop the technique for routine application and the data-fitting procedure used to calculate minority-carrier diffusion length values. The diffusion length measured bymore » TI agrees well with the results calculated by time-resolved photoluminescence on well-controlled gallium arsenide (GaAs) thin-film samples. We report for the first time on measurements on thin-film cadmium telluride using this technique, including the determination of effective carrier diffusion length, as well as the first near-field imaging of the effect of a single localized defect on carrier transport and recombination in a GaAs heterostructure. Furthermore, by changing the scanning setup, we were able to demonstrate near-field cathodoluminescence (CL), and correlated the results with standard CL measurements. In conclusion, the TI technique shows great potential for mapping transport properties in solar cell materials with high spatial resolution.« less