Spatial correlation in grain misorientation distribution

Grain misorientation was studied in relation to the nearest neighbor's mutual distance using electron back-scattered diffraction measurements. The misorientation correlation function was defined as the probability density for the occurrence of a certain misorientation between pairs of grains separated by a certain distance. Scale-invariant spatial correlation between neighbor grains was manifested by a power law dependence of the preferred misorientation vs. inter-granular distance in various materials after diverse strain paths. The obtained negative scaling exponents were in the range of -2 +/- 0.3 for high-angle grain boundaries. The exponent decreased in the presence of low-angle grain boundaries or dynamic recrystallization, indicating faster decay of correlations. The correlations vanished in annealed materials. The results were interpreted in terms of lattice incompatibility and continuity conditions at the interface between neighboring grains. Grain-size effects on texture development, as well as the implications of such spatial correlations on texture modeling, were discussed.

Spatial correlation of heat release rate and sound emission from turbulent premixed flames

The two-point spatial correlation of the rate of change of fluctuating heat release rate is central to the sound emission from open turbulent flames, and a few attempts have been made to address this correlation in recent studies. In this paper, the two-point correlation and its role in combustion noise are studied by analysing direct numerical simulation (DNS) data of statistically multi-dimensional turbulent premixed flames. The results suggest that this correlation function depends on the separation distance and direction but, not on the positions inside the flame brush. This correlation can be modelled using a combination of Hermite-Gaussian functions of zero and second order, i.e. functions of the form (1-Ax2)e-Bx2 for constants A and B, to include its possible negative values. The integral correlation volume obtained using this model is about 0.2δL3 with the length scale obtained from its cube root being about 0.6δ L, where δ L is the laminar flame thermal thickness. Both of the values are slightly larger than the values reported in an earlier study because of the anisotropy observed for the correlation. This model together with the turbulence-dependent parameter K, the ratio of the root-mean-square (RMS) value of the rate of change of reaction rate to the mean reaction rate, derived from the DNS data is applied to predict the far-field sound emitted from open flames. The calculated noise levels agree well with recently reported measurements and show a sensitivity to K values. © 2012 The Combustion Institute.

Effect of electron-hole spatial correlation on spin relaxation dynamics in InAs submonolayer

Using time-resolved photoluminescence and time-resolved Kerr rotation spectroscopy, we explore the unique electron spin behavior in an InAs submonolayer sandwiched in a GaAs matrix, which shows very different spin characteristics under resonant and non-resonant excitations. While a very long spin relaxation lifetime of a few nanoseconds at low temperature is observed under non-resonant excitation, it decreases dramatically under resonant excitation. These interesting results are attributed to the difference in electron-hole interactions caused by non-geminate or geminate capture of photo-generated electron-hole pairs in the two excitation cases, and provide a direct verification of the electron-hole spatial correlation effect on electron spin relaxation. (c) 2007 Elsevier Ltd. All rights reserved.

Shape and spatial correlation control of InAs-InAlAs-InP(001) nanostructure superlattices

The control of shape and spatial correlation of InAs-InAlAs-InP(001) nanostructure superlattices has been realized by changing the As overpressure during the molecular-beam epitaxy (MBE) growth of InAs layers. InAs quantum wires (QWRs) are obtained under higher As overpressure (1x10(-5) Torr), while elongated InAs quantum dots (QDs) are formed under lower As overpressure (5x10(-6) or 2.5x10(-6) Torr). Correspondingly, spatial correlation changes from vertical anti-correlation in QWR superlattices to vertical correlation in QD superlattices, which is well explained by the different alloy phase separation in InAlAs spacer layers triggered by the InAs nanostrcutures. It was observed that the alloy phase separation in QD superlattices could extend a long distance along the growth direction, indicating the vertical correlation of QD superlattices can be kept in a wide range of spacer layer thickness.

Mineralogy of the clay fraction of alfisols in two slope curvatures: IV - spatial correlation with physical properties

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Designing spatial correlation of quantum dots: towards self-assembled three-dimensional structures

Buried two-dimensional arrays of InP dots were used as a template for the lateral ordering of self-assembled quantum dots. The template strain field can laterally organize compressive (InAs) as well as tensile (GaP) self-assembled nanostructures in a highly ordered square lattice. High-resolution transmission electron microscopy measurements show that the InAs dots are vertically correlated to the InP template, while the GaP dots are vertically anti-correlated, nucleating in the position between two buried InP dots. Finite InP dot size effects are observed to originate InAs clustering but do not affect GaP dot nucleation. The possibility of bilayer formation with different vertical correlations suggests a new path for obtaining three-dimensional pseudocrystals.

Spatial correlation between the composition of the clay fraction and contents of available phosphorus of an Oxisol at hillslope scale

Relief is regarded as the abiotic factor most strongly influencing pedogenic processes at a local scale. The spatial correlations between the composition of the clay fraction (iron - Fe and aluminum - Al oxides, kaolinite and organic matter - OM) and contents of available phosphorus (P) of an Oxisol were evaluated at hillslope scale under sugarcane cultivation. A total of 119 samples were collected at intersection points on a 100. ×. 100. m georeferenced grid of regularly spaced points 10. m apart in the 0.2-0.4. m depth in an area consisting of two landform components namely: component I (an area with a linear hillslope curvature), and component II (one with a concave-convex hillslope curvature). Soil OM and available P contents were subjected to descriptive statistics and geostatistical analyses in order to assess their variability and spatial dependence. All attributes studied were spatially dependent. Available phosphorus had positive spatial correlation with high crystalline goethite, hematite and gibbsite. Identifying small hillslope curvatures is useful with a view to better understanding their relationships with soil organic matter and available phosphorus, as well as kaolinite and Fe and Al oxide attributes. A simple correlation analysis by itself is inadequate to relate attributes, which requires a supplemental, geostatistical technique. © 2012 Elsevier B.V..

Field-scale spatial correlation between contents of iron oxides and CO2 emission in an Oxisol cultivated with sugarcane

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Impact of vaccination on the spatial correlation and persistence of measles dynamics.

The onset of measles vaccination in England and Wales in 1968 coincided with a marked drop in the temporal correlation of epidemic patterns between major cities. We analyze a variety of hypotheses for the mechanisms driving this change. Straightforward stochastic models suggest that the interaction between a lowered susceptible population (and hence increased demographic noise) and nonlinear dynamics is sufficient to cause the observed drop in correlation. The decorrelation of epidemics could potentially lessen the chance of global extinction and so inhibit attempts at measles eradication.

Measuring the degree of spatial correlation between histological features in thin sections of brain tissue

Histological features visible in thin sections of brain tissue, such as neuronal perikarya, blood vessels, or pathological lesions may exhibit a degree of spatial association or correlation. In neurodegenerative disorders such as AD, Pick's disease, and CJD, information on whether different types of pathological lesion are spatially correlated may be useful in elucidating disease pathogenesis. In the present article the statistical methods available for studying spatial association in histological sections are reviewed. These include tests of interspecific association between two or more histological features using χ2 contingency tables, measurement of 'complete' and 'absolute' association, and more complex methods that use grids of contiguous samples. In addition, the use of correlation matrices and stepwise multiple regression methods are described. The advantages and limitations of each method are reviewed and possible future developments discussed.

Measuring the degree of spatial correlation between pathological lesions in Alzheimer's disease

The pathological lesions characteristic of Alzheimer's disease (AD), viz., senile plaques (SP) and neurofibrillary tangles (NFT) may not be randomly distributed with reference to each other but exhibit a degree of sptial association or correlation, information on the degree of association between SP and NFT or between the lesions and normal histological features, such as neuronal perikarya and blood vessels, may be valuable in elucidating the pathogenesis of AD. This article reviews the statistical methods available for studying the degree of spatial association in histological sections of AD tissue. These include tests of interspecific association between two or more histological features using chi-square contingency tables, measurement of 'complete' and 'absolute' association, and more complex methods that use grids of contiguous samples. In addition, analyses of association using correlation matrices and stepwise multiple regression methods are described. The advantages and limitations of each method are reviewed and possible future developments discussed.

Spatial prediction of N2O emissions in pasture : a Bayesian model averaging analysis

Nitrous oxide (N2O) is one of the greenhouse gases that can contribute to global warming. Spatial variability of N2O can lead to large uncertainties in prediction. However, previous studies have often ignored the spatial dependency to quantify the N2O - environmental factors relationships. Few researches have examined the impacts of various spatial correlation structures (e.g. independence, distance-based and neighbourhood based) on spatial prediction of N2O emissions. This study aimed to assess the impact of three spatial correlation structures on spatial predictions and calibrate the spatial prediction using Bayesian model averaging (BMA) based on replicated, irregular point-referenced data. The data were measured in 17 chambers randomly placed across a 271 m(2) field between October 2007 and September 2008 in the southeast of Australia. We used a Bayesian geostatistical model and a Bayesian spatial conditional autoregressive (CAR) model to investigate and accommodate spatial dependency, and to estimate the effects of environmental variables on N2O emissions across the study site. We compared these with a Bayesian regression model with independent errors. The three approaches resulted in different derived maps of spatial prediction of N2O emissions. We found that incorporating spatial dependency in the model not only substantially improved predictions of N2O emission from soil, but also better quantified uncertainties of soil parameters in the study. The hybrid model structure obtained by BMA improved the accuracy of spatial prediction of N2O emissions across this study region.

An investigation of the impact of various geographical scales for the specification of spatial dependence

Ecological studies are based on characteristics of groups of individuals, which are common in various disciplines including epidemiology. It is of great interest for epidemiologists to study the geographical variation of a disease by accounting for the positive spatial dependence between neighbouring areas. However, the choice of scale of the spatial correlation requires much attention. In view of a lack of studies in this area, this study aims to investigate the impact of differing definitions of geographical scales using a multilevel model. We propose a new approach -- the grid-based partitions and compare it with the popular census region approach. Unexplained geographical variation is accounted for via area-specific unstructured random effects and spatially structured random effects specified as an intrinsic conditional autoregressive process. Using grid-based modelling of random effects in contrast to the census region approach, we illustrate conditions where improvements are observed in the estimation of the linear predictor, random effects, parameters, and the identification of the distribution of residual risk and the aggregate risk in a study region. The study has found that grid-based modelling is a valuable approach for spatially sparse data while the SLA-based and grid-based approaches perform equally well for spatially dense data.