Random Structure Model Research Articles

Framework to construct and interpret latent class trajectory modelling

ObjectivesLatent class trajectory modelling (LCTM) is a relatively new methodology in epidemiology to describe life-course exposures, which simplifies heterogeneous populations into homogeneous patterns or classes. However, for a given dataset,...

Household fear of deportation in relation to chronic stressors and salivary proinflammatory cytokines in Mexican-origin families post-SB 1070

Sociologists recognize that immigration enforcement policies are forms of institutionalized racism that can produce adverse health effects in both undocumented and documented Latinos and Mexican-origin persons in the United States. Despite this important advancement, little research examines the relationship between fear of immigration enforcement and biobehavioral health in mixed-status Mexican-origin families. This study applies an embodiment of racism approach to examine how household fear of deportation (FOD) is related to differences in salivary proinflammatory cytokines (IL-1β, IL-6, IL-8, and TNFα) in healthy Mexican-origin families with at least one immigrant, living in Phoenix, AZ. Participants were 111 individuals (n=46 adults, 72% female; n=65 children, 49% female) from 30 low-income, mixed-status families. During a home visit, anthropometric measures and saliva were collected from each family member and a household survey was administered. Saliva was assayed for salivary IL-1β, IL-6, IL-8, and TNFα. Random effects multilevel structural equation models estimated the relationship between household FOD and a salivary proinflammatory cytokine latent variable between families, while controlling for other chronic stressors (economic/occupational, immigration, parental, and family conflict). Household FOD (β=0.68, p=0.04) and family conflict chronic stress (β=1.96, p=0.03) were strongly related to elevated levels of proinflammatory cytokines between families. These results were consistent in non-mixed and mixed-status families. Future research is needed to characterize what aspects of living with an undocumented family member shape the physical health outcomes of persons with authorized status or US-citizenship.

Light propagation in Swiss-cheese models of random close-packed Szekeres structures: Effects of anisotropy and comparisons with perturbative results

Light propagation in two Swiss cheese models based on anisotropic Szekeres structures is studied and compared with light propagation in Swiss cheese models based on the Szekeres models' underlying LTB models. The study shows that the anisotropy of the Szekeres models has only a small effect on quantities such as redshift-distance relations, projected shear and expansion rate along individual light rays. \newline\indent The average angular diameter distance to the last scattering surface is computed for each model. Contrary to earlier studies, the results obtained here are (mostly) in agreement with perturbative results. In particular, a small negative shift, $\delta D_A:=\frac{D_A-D_{A,bg}}{D_{A,bg}}$, in the angular diameter distance is obtained upon line-of-sight averaging in three of the four models. The results are, however, not statistically significant. In the fourth model, there is a small positive shift which has an especially small statistical significance. The line-of-sight averaged inverse magnification at $z = 1100$ is consistent with $1$ to a high level of confidence for all models, indicating that the area of the surface corresponding to $z = 1100$ is close to that of the background.

New surface plasmon polariton waveguide based on GaN nanowires

Lasers are nowadays widely used in industry, in hospitals and in many devices that we have at home. Random laser development is challenging given its high threshold and low integration. Surface plasmon polariton (SPP) can improve random laser characteristics because of its ability to control diffraction. In this study, we establish a random laser structural model with silicon-based parcel GaN nanowires. The GaN nanowire gain and enhanced surface plasmon increase population inversion level. Our laser model is based on random particle scattering feedback mechanism, nanowire use, and surface plasmon enhancement effect, which causes stochastic laser emergence. Analysis shows that the SPP mode and nanowire waveguides coupled in the dielectric layer of low refractive index can store light energy like a capacitor under low refractive index clearance. The waveguide mode field area and limiting factors show that the modeled laser can achieve sub-wavelength constraints of the output light field. We also investigate emergent laser performance for a more limited light field capacity and lower threshold.

Shear deformation and strength of the interphase between the soil–rock mixture and the benched bedrock slope surface

A series of benched excavations were typically carried out on the bedrock slope surface to improve the stability of the soil–rock mixture (S–RM) fill slope. It is difficult to devise an in situ, large-scale direct shear test for the interphase between the S–RM fill and the benched bedrock slope surface. This study introduced a comprehensive approach to investigate the shear deformation and strength of the interphase. First the soil–rock distribution characteristics were analyzed by test pitting, image analysis, and sieve test. Then the PFC2D random structure models with different rock block size distributions were built, and large-scale numerical shear tests for the interphase were performed after calibrating model parameters through laboratory tests. The stress evolution, damage evolution and failure, deformation localization (based on a principle proposed in this paper), rotation of rock blocks, and shear strength were systematically investigated. It was found that as the rock block proportion and rock block size (rock block proportion of 50 %) increase, the fluctuations of the post-peak shear stress–displacement curves of the interphase become more obvious, and the shear band/localized failure path network becomes wider. Generally, smaller rock blocks are of greater rotation angles in the shear band. The peak shear stress and internal friction angle of the interphase increase, while the cohesion decreases with growth of the rock block proportion. However, all these three parameters increase as the rock block size (rock block proportion of 50 %) increases.

Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structures.

The natural habitat of most Bayesian methods is data represented by exchangeable sequences of observations, for which de Finetti's theorem provides the theoretical foundation. Dirichlet process clustering, Gaussian process regression, and many other parametric and nonparametric Bayesian models fall within the remit of this framework; many problems arising in modern data analysis do not. This article provides an introduction to Bayesian models of graphs, matrices, and other data that can be modeled by random structures. We describe results in probability theory that generalize de Finetti's theorem to such data and discuss their relevance to nonparametric Bayesian modeling. With the basic ideas in place, we survey example models available in the literature; applications of such models include collaborative filtering, link prediction, and graph and network analysis. We also highlight connections to recent developments in graph theory and probability, and sketch the more general mathematical foundation of Bayesian methods for other types of data beyond sequences and arrays.

Mesoscopic Numerical Computation of Compressive Strength and Damage Mechanism of Rubber Concrete

Evaluations of both macroscopic and mesoscopic strengths of materials have been the topic of a great deal of recent research. This paper presents the results of a study, based on the Walraven equation of the production of a mesoscopic random aggregate structure containing various rubber contents and aggregate sizes. On a mesoscopic scale, the damage mechanism in the rubber concrete and the effects of the rubber content and aggregate-mortar interface on the rubber concrete’s compressive resistance property were studied. The results indicate that the random aggregate structural model very closely approximates the experimental results in terms of the fracture distribution and damage characteristics under uniaxial compression. The aggregate-mortar interface mechanical properties have a substantial impact on the test sample’s strength and fracture distribution. As the rubber content increases, the compressive strength and elastic modulus of the test sample decrease proportionally. This paper presents graphics of the entire process from fracture propagation to structural failure of the test piece by means of the mesoscopic finite-element method, which provides a theoretical reference for studying the damage mechanism in rubber concrete and performing parametric calculations.

The effects of production outsourcing on factory cost performance: an empirical study

Purpose– The purpose of this paper is to investigate the performance implications associated with production outsourcing. Specifically, the paper analyzes the cost of goods sold for firms who outsource core manufacturing processes, using empirical data from a variety of industries. The paper seeks to better understand the influence of outsourcing on factory cost by looking at these in the context of related strategies, such as supplier integration, information technology (IT) implementation, and manufacturing process decisions.Design/methodology/approach– The paper draws on transaction cost economics, manufacturing strategy, and supply chain management literature to aid in predicting the performance to be expected when outsourcing production activities. Furthermore, the paper investigates the moderating effects of manufacturing strategies, supplier integration, and IT expenditures on outsourcing. The primary model is a two-way panel model for the cross-sectional and longitudinal data drawn from the MPI Census of Manufacturers Survey of US manufacturing plants.Findings– The analysis indicates that production outsourcing tends to shift costs among cost of goods sold (COGS) categories, but does not consistently reduce them as measured by overall COGS. The effects of production outsourcing on both the cost of labor and the cost of materials are strong, tending to decrease labor, and increase materials. Additionally, this study shows that a high level of supplier integration has a notable moderating influence on overall COGS, but that process strategies do not. Finally, this analysis indicates that IT expenditures were not influential as a moderator variable when outsourcing, but did have a marked influence on overall COGS, as well as on labor and materials costs.Originality/value– This research investigates the effects of outsourcing on the components of COGS, a level of analysis that is typically not looked at relative to outsourcing. This research also provides methodological contributions with the development of a nested random effects structural model for use with a secondary data source.

Quantifying spatial phylogenetic structures of fully stem-mapped plant communities

Analysis of the phylogenetic similarity of co-occurring species at different spatial scales is increasingly used for decoding community assembly rules. Here, we integrated the analysis of phylobetadiversity and marked point pattern analysis to yield a new metric, the phylogenetic mark correlation function, kd(r), to quantify spatial phylogenetic structure of fully stem-mapped communities. kd(r) is defined as the expected phylogenetic distance of two heterospecifics separated by spatial distance r, and normalized with the expected phylogenetic distance of two heterospecifics taken randomly from a study area. It measures spatial phylogenetic turnover relative to spatial species turnover and is closely related with the spatially explicit Simpson index. We used simulated fully stem-mapped plant communities with known spatial phylogenetic structures to assess type I and II errors of the phylogenetic mark correlation function kd(r) under a null model of random phylogenetic spatial structure, and to test the ability of the kd(r) to detect scale-dependent signals of phylogenetic spatial structure. We also compared the performance of the kd(r) with two existing measures of phylobetadiversity that have been previously used to analyse fully stem-mapped plots. Finally, we explored the spatial phylogenetic structure of a 24-ha fully stem-mapped subtropical forest in China. Simulation tests showed that the new metric yielded correct type I and type II errors and accurately detected the spatial scales at which various processes (e.g. habitat filtering and competition) were invoked to generate spatial phylogenetic structures. The power of the kd(r) was not affected by a phylogenetic signal in species abundance and different topologies of the phylogenetic tree. Replacing phylogenetic distance by functional distance allows for application of the kd(r) to estimate spatial correlations in functional community structure. Thus, the kd(r) allows trait and phylogenetic structure to be analysed in the same framework. The phylogenetic mark correlation function is a powerful and accurate tool for revealing scale-dependent phylogenetic/functional footprints in community assemblages and allows ecologists to keep up with the increasingly available data of fully stem-mapped plots, functional traits and community phylogenies.

Three Dimensional Random Structural Model of Karst Rock and its Application

Karst caves determine the engineering characteristics of the whole rock in a large extent. A methed to simulate karst caves is described in this paper. Firstly, three-dimensional mathematic description of the geological mass in which karst caves lies is given. Secondly, the determination of the shape and size of ellipsoids which are adopted to simulate karst caves is introduced in detail. The mathematic modeling of three-dimensional random caves is discussed simultaneously. Thirdly, a real three-dimensional random structural model of karst rock based on actual observation records is provided. Lastly, the above model used for the leakage analysis of the karst rock is discussed. The research results illustrates that the Mathematic model for three-dimensional random caves of karst rock put forward in this paper can be very helpful to leakage analysis on Karst rock.

Transition energies and direct-indirect band gap crossing in zinc-blende AlxGa1−xN

The electronic and optical properties of zinc-blende (zb) Al${}_{x}$Ga${}_{1\ensuremath{-}x}$N over the whole alloy composition range are presented in a joint theoretical and experimental study. Because zb-GaN is a direct (${\ensuremath{\Gamma}}_{v}\ensuremath{\rightarrow}{\ensuremath{\Gamma}}_{c}$) semiconductor and zb-AlN shows an indirect (${\ensuremath{\Gamma}}_{v}\ensuremath{\rightarrow}{X}_{c}$) fundamental band gap, the ternary alloy exhibits a concentration-dependent direct-indirect band gap crossing point the position of which is highly controversial. The dielectric functions of zb-Al${}_{x}$Ga${}_{1\ensuremath{-}x}$N alloys are measured employing synchrotron-based ellipsometry in an energy range between 1 and 20 eV. The experimentally determined fundamental energy transitions originating from the $\ensuremath{\Gamma}$, $X$, and $L$ points are identified by comparison to theoretical band-to-band transition energies. In order to determine the direct-indirect band gap crossing point, the measured transition energies at the $X$ point have to be aligned by the calculated position of the highest valence state. Thereby density-functional theory (DFT) based approaches to the electronic structure, ranging from the standard (semi)local generalized gradient approximation (GGA), self-energy corrected local density approximation (LDA-1/2), and meta-GGA DFT (TB-mBJLDA) to hybrid functional DFT and many-body perturbation theory in the $GW$ approximation, are applied to random and special quasirandom structure models of zb-Al${}_{x}$Ga${}_{1\ensuremath{-}x}$N. This study provides interesting insights into the accuracy of the various numerical approaches and contains reliable ab initio data on the electronic structure and fundamental alloy band gaps of zb-Al${}_{x}$Ga${}_{1\ensuremath{-}x}$N. Nonlocal Heyd-Scuseria-Ernzerhof-type hybrid-functional DFT calculations or, alternatively, $GW$ quasiparticle calculations are required to reproduce prominent features of the electronic structure. The direct-indirect band gap crossing point of zb-Al${}_{x}$Ga${}_{1\ensuremath{-}x}$N is found in the Al rich composition range at an Al content between $x=0.64$ and $0.69$ in hybrid functional DFT, which is in good agreement with $x=0.71$ determined from the aligned experimental transition energies. Thus our study solves the long-standing debate on the nature of the fundamental zb-Al${}_{x}$Ga${}_{1\ensuremath{-}x}$N alloy band gap.

Morphology and effective properties of multi-scale random sets: A review

Complex microstructures in materials often involve multi-scale heterogeneous textures, modeled by random sets derived from Mathematical Morphology. Our approach starts from 2D or 3D images; a complete morphological characterization by image analysis is performed, and used for the identification of a model of random structure. Morphological models enter into the prediction of effective properties by estimation, bounds, or from numerical simulations. Simulations of realistic microstructures are introduced in a numerical solver to compute appropriate fields (electric, elastic, …) and to estimate the effective properties by numerical homogenization, accounting for scale dependent statistical fluctuations of the fields. Our approach is illustrated by various examples of multi-scale models: Boolean random sets based on Cox point processes and various random grains (spheres, cylinders), showing a very low percolation threshold, and therefore a high conductivity or high elastic moduli for a low volume fraction of a second phase. Multi-scale iterations of random media provide another source of morphologies with interesting overall properties.

Multi-scale random sets: from morphology to effective properties and to fracture statistics

Complex microstructures in materials often involve multi-scale heterogeneous textures, modelled by random sets derived from Mathematical Morphology. Starting from 2D or 3D images, a complete morphological characterization by image analysis is performed, and used for the identification of a model of random structure. From morphological models, simulations of realistic microstructures are introduced in a numerical solver to compute appropriate fields (electric, elastic stress or strain, …) and to estimate the effective properties by numerical homogenization, accounting for scale dependent statistical fluctuations of the fields. Our approach is illustrated by various examples of multi-scale models: Boolean random sets based on Cox point processes and various random grains (spheres, cylinders), showing a very low percolation threshold, and therefore a high conductivity or high elastic moduli for a low volume fraction of a second phase. Multiscale Cox point processes are also a source of instructive models of fracture statistics, such as multiscale weakest link models.

MODELLING RANDOM MEDIA

Basic random structure models (random sets and random function models) are introduced for the simulation of images and of microstructures. Their implementation requires the use of image analysis tools defined in mathematical morphology.They can be used for solving problems of physics of random media.

Probability density functions of the average and difference intensities of Friedel opposites

Trigonometric series for the average (A) and difference (D) intensities of Friedel opposites were carefully rederived and were normalized to minimize their dependence on sin(theta)/lambda. Probability density functions (hereafter p.d.f.s) of these series were then derived by the Fourier method [Shmueli, Weiss, Kiefer & Wilson (1984). Acta Cryst. A40, 651-660] and their expressions, which admit any chemical composition of the unit-cell contents, were obtained for the space group P1. Histograms of A and D were then calculated for an assumed random-structure model and for 3135 Friedel pairs of a published solved crystal structure, and were compared with the p.d.f.s after the latter were scaled up to the histograms. Good agreement was obtained for the random-structure model and a qualitative one for the published solved structure. The results indicate that the residual discrepancy is mainly due to the presumed statistical independence of the p.d.f.'s characteristic function on the contributions of the interatomic vectors.

Analysis of Stationary Random Responses for Non-Parametric Probabilistic Systems

The move from conceptual design, through fabrication to observation and measurement on the resulting physical structure is fraught with uncertainty. This, together with the necessary simplifications inherent when using the finite element technique, makes the development of a predictive model for the physical structure sufficiently approximate that the use of random structural models is often to be preferred. In this paper, the random uncertainties of the mass, damping and stiffness matrices in a finite element model are replaced by random matrices, and a highly efficient pseudo excitation method for the dynamic response analysis of non-parametric probability systems subjected to stationary random loads is developed. A numerical example shows that the dynamic responses calculated using a conventional (mean) finite element model may be quite different from those based on a random matrix model. For precise fabrication, the uncertainties of models cannot be ignored and the proposed method should be useful in the analysis of such problems.

Analysis of stationary random responses for non-parametric probabilistic systems

The move from conceptual design, through fabrication to observation and measurement on the resulting physical structure is fraught with uncertainty. This, together with the necessary simplifications inherent when using the finite element technique, makes the development of a predictive model for the physical structure sufficiently approximate that the use of random structural models is often to be preferred. In this paper, the random uncertainties of the mass, damping and stiffness matrices in a finite element model are replaced by random matrices, and a highly efficient pseudo excitation method for the dynamic response analysis of non-parametric probability systems subjected to stationary random loads is developed. A numerical example shows that the dynamic responses calculated using a conventional (mean) finite element model may be quite different from those based on a random matrix model. For precise fabrication, the uncertainties of models cannot be ignored and the proposed method should be useful in the analysis of such problems.

Some applications of generalized linear latent and mixed models in epidemiology: Repeated measures, measurement error and multilevel modeling

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Numerical Simulation of RSA Seepage Based on Random Structure Model

The research about seepage of RSA is little, but the material is familiar. There are many difficulties in laboratory research about the materials, so numerical simulation research in the paper is introduced. The model of the random structure about SRA is set up, and then the study of seepage is given. The conversion of the two boundaries is solved correctly in the computer program. So some conclusions are drawn that the rock blocks the seepage to some degree, and the changing of RSA seepage is little under the effect of the fluctuating of rainfall intensity, namely the changing of the seepage is less than the fluctuating of rainfall intensity. The study is the foundation of the next study about the coupled of stress and seepage of RSA.

Potential of capillary zone electrophoresis for estimation of humate acid-base properties

Capillary zone electrophoresis (CZE) has been applied for fractionation and characterization of soil-derived humic acids (HAs). Humic acids from soddy-podzolic (HA s) and chernozem (HA ch) soils were studied as well as hydrophobic high-molecular-weight (HMW) and hydrophilic low-molecular-weight (LMW) HA s fractions obtained by salting-out with ammonium sulfate at a saturation of 0–40% and >70%, respectively. The possibility of CZE partial fractionation of HAs has been demonstrated. The shape of “humic hump” was shown to depend on the pH of running electrolyte. Almost the whole peak overlapping occurred if alkaline solutions were used for fractionation, but the peak resolution was improved at pH 5–7. Under appropriate fractionation conditions (pH 7), at least three humic acid subfractions with different electrophoretic mobilities were distinguished in the electropherograms of initial HA and HA s fractions. Such a high peak resolution has never been achieved for humic acids before. The presence of three subfractions in the HA is in agreement with gel-filtration analysis and was confirmed by comparison of the electrophoretic behavior of HA s with those of its HMW (hydrophobic) and the LMW (hydrophilic) fractions. The potentiometric titration of HA and its fractions was performed and the p K a of the functional groups were calculated. An attempt was made for the first time to relate the variation of electrophoretic mobility values with acid-base properties of humic acids. It was shown that changes in the humate charge resulting from the variation of the ionization degree of its functional groups as a function of pH can be estimated on the basis of electrophoretic mobility values. Potential of CZE in estimation of HA isoelectric point was demonstrated. The pH value corresponding to the lowest absolute electrophoretic mobility value of about 20 × 10 −5 cm 2 V −1 s −1 can be used for approximate estimation of HA isoelectric point. The data were discussed and agreement with the random coil structural model has been shown.