A Bayesian spatio-temporal model of panel design data: airborne particle number concentration in Brisbane, Australia

This paper outlines a methodology for semi-parametric spatio-temporal modelling of data which is dense in time but sparse in space, obtained from a split panel design, the most feasible approach to covering space and time with limited equipment. The data are hourly averaged particle number concentration (PNC) and were collected, as part of the Ultrafine Particles from Transport Emissions and Child Health (UPTECH) project. Two weeks of continuous measurements were taken at each of a number of government primary schools in the Brisbane Metropolitan Area. The monitoring equipment was taken to each school sequentially. The school data are augmented by data from long term monitoring stations at three locations in Brisbane, Australia. Fitting the model helps describe the spatial and temporal variability at a subset of the UPTECH schools and the long-term monitoring sites. The temporal variation is modelled hierarchically with penalised random walk terms, one common to all sites and a term accounting for the remaining temporal trend at each site. Parameter estimates and their uncertainty are computed in a computationally efficient approximate Bayesian inference environment, R-INLA. The temporal part of the model explains daily and weekly cycles in PNC at the schools, which can be used to estimate the exposure of school children to ultrafine particles (UFPs) emitted by vehicles. At each school and long-term monitoring site, peaks in PNC can be attributed to the morning and afternoon rush hour traffic and new particle formation events. The spatial component of the model describes the school to school variation in mean PNC at each school and within each school ground. It is shown how the spatial model can be expanded to identify spatial patterns at the city scale with the inclusion of more spatial locations.Comment: Draft of this paper presented at ISBA 2012 as poster, part of UPTECH projec

Taxonomic rearrangements of the genera Thiocapsa and Amoebobacter on the basis of 16S rDNA sequence analyses and description of Thiolamprovum gen. nov.

Complete nucleotide sequences of the 16S rDNAs were determined from Thiocapsa and Amoebobacter species, including all available type strains and some additional isolates. The distance-matrix analysis and the dendrogram for estimating the genetic relationships revealed that the investigated strains were found in two major clusters within the Chromatiaceae. One cluster comprises all Amoebobacter species, Thiocapsa roseopersicina and several isolates related to Thiocapsa roseopersicina. Representatives of the species Amoebobacter roseus, Amoebobacter pendens and Thiocapsa roseopersicina, the so called ‘Thiocapsa roseopersicina group’, are very closely related, justifying their inclusion into one genus, Thiocapsa, for which an emended description is presented. Amoebobacter purpureus and Amoebobacter pedioformis formed two separate lines of descent with less than 93% (89·6–92·9%) similarity to strains of the ‘Thiocapsa roseopersicina group’. Therefore, they will be considered as two separate genera. As a consequence, an emended description is presented for the genus Amoebobacter, with Amoebobacter purpureus as the new type species and A. pedioformis is transferred to Thiolamprovum pedioforme gen. nov., comb. nov. Two species, Thiocapsa pfennigii and Thiocapsa halophila, which have been classified with the genus Thiocapsa because of their morphological properties, were found within another major cluster of the Chromatiaceae and are only distantly phylogenetically related to the first cluster with 88·4–90·6% and 90·4–92·2% sequence similarity, respectively

Density of States for a Specified Correlation Function and the Energy Landscape

The degeneracy of two-phase disordered microstructures consistent with a specified correlation function is analyzed by mapping it to a ground-state degeneracy. We determine for the first time the associated density of states via a Monte Carlo algorithm. Our results are described in terms of the roughness of the energy landscape, defined on a hypercubic configuration space. The use of a Hamming distance in this space enables us to define a roughness metric, which is calculated from the correlation function alone and related quantitatively to the structural degeneracy. This relation is validated for a wide variety of disordered systems.Comment: Accepted for publication in Physical Review Letter

L'insémination artificielle chez la lapine techniques utilisées, quelques résultats

International audienc

Modeling Heterogeneous Materials via Two-Point Correlation Functions: I. Basic Principles

Heterogeneous materials abound in nature and man-made situations. Examples include porous media, biological materials, and composite materials. Diverse and interesting properties exhibited by these materials result from their complex microstructures, which also make it difficult to model the materials. In this first part of a series of two papers, we collect the known necessary conditions on the standard two-point correlation function S2(r) and formulate a new conjecture. In particular, we argue that given a complete two-point correlation function space, S2(r) of any statistically homogeneous material can be expressed through a map on a selected set of bases of the function space. We provide new examples of realizable two-point correlation functions and suggest a set of analytical basis functions. Moreover, we devise an efficient and isotropy- preserving construction algorithm, namely, the Lattice-Point algorithm to generate realizations of materials from their two- point correlation functions based on the Yeong-Torquato technique. Subsequent analysis can be performed on the generated images to obtain desired macroscopic properties. These developments are integrated here into a general scheme that enables one to model and categorize heterogeneous materials via two-point correlation functions.Comment: 37 pages, 26 figure

First Passage Time in a Two-Layer System

As a first step in the first passage problem for passive tracer in stratified porous media, we consider the case of a two-dimensional system consisting of two layers with different convection velocities. Using a lattice generating function formalism and a variety of analytic and numerical techniques, we calculate the asymptotic behavior of the first passage time probability distribution. We show analytically that the asymptotic distribution is a simple exponential in time for any choice of the velocities. The decay constant is given in terms of the largest eigenvalue of an operator related to a half-space Green's function. For the anti-symmetric case of opposite velocities in the layers, we show that the decay constant for system length $L$ crosses over from $L^{-2}$ behavior in diffusive limit to $L^{-1}$ behavior in the convective regime, where the crossover length $L^*$ is given in terms of the velocities. We also have formulated a general self-consistency relation, from which we have developed a recursive approach which is useful for studying the short time behavior.Comment: LaTeX, 28 pages, 7 figures not include

Image decompositions and transformations as peaks and wells

10 pages to be submitted to ISMM2011International audienceAn image may be decomposed as a difference between an image of peaks and an image of wells. Applying a morphological operator to these two components before reconstructing a final image produces interesting filters for grey tone or binary images. This decomposition depends upon the point of view from where the image is considered