Quantum Monte Carlo simulation of the ferroelectric or ferrielectric nanowire with core shell morphology

By using the Quantum Monte Carlo simulation; the electric properties of a nanowire, consisting of a ferroelectric core of spin-1/2 surrounded by a ferroelectric shell of spin-1/2 with ferro- or anti-ferroelectric interfacial coupling have been studied within the framework of the Transverse Ising Model (TIM). We have examined the effects of the shell coupling J_s, the interfacial coupling J_Int, the transverse field Ω, and the temperature T on the hysteresis behavior and on the electric properties of the system. The remanent polarization and the coercive field as a function of the transverse field and the temperature are examined. A number of characteristic behavior have been found such as the appearance of triple hysteresis loops for appropriate values of the system parameters.     

A three-component model on the structure of colloidal solution with size-asymmetric electrolytes

ABSTRACT

A three-component model on the structure of colloidal solution with size asymmetric electrolytes is attempted here using density functional theory and Monte Carlo simulation. The solvent is represented as an individual component along with that as a dielectric continuum. The theory uses a weighted density approximation for the hard-sphere contribution to the free energy, whereas the ionic contribution is evaluated through a perturbation expansion around the bulk density. The theory is found to reproduce the simulation data quite well for a wide range of parametric conditions. The present study reflects the importance of the presence of the solvent in determining the structural behaviour of spherical double layers.     

Thermodynamic properties of a hard-core Lennard-Jones fluid from computer simulation and the coupling parameter series expansion

ABSTRACT

The thermodynamic properties of a fluid with an interaction potential consisting in a hard-sphere core plus a Lennard-Jones tail have been obtained by Monte Carlo (MC) NVT simulation as a function of the density along several isotherms. In addition, the liquid–vapour coexistence has been determined by means of histogram-reweighting MC. These data have been used to analyse the performance of perturbation theory. To this end, the first three perturbation terms of the inverse temperature expansion of the Helmholtz free energy have been obtained by means of MC NVT simulations to test the convergence of the perturbation series and to compare with the predictions of the coupling parameter series expansion. Then, the predictions of the latter theory for the thermodynamic properties have been compared with the simulations, revealing the overall excellent performance of this perturbation theory for this model fluid, except in the vicinity of the critical point.     

A directed topic model applied to call center improvement

We propose subject matter expert refined topic (SMERT) allocation, a generative probabilistic model applicable to clustering freestyle text. SMERT models are three‐level hierarchical Bayesian models in which each item is modeled as a finite mixture over a set of topics. In addition to discrete data inputs, we introduce binomial inputs. These ‘high‐level’ data inputs permit the ‘boosting’ or affirming of terms in the topic definitions and the ‘zapping’ of other terms. We also present a collapsed Gibbs sampler for efficient estimation. The methods are illustrated using real world data from a call center. Also, we compare SMERT with three alternative approaches and two criteria. Copyright © 2015 John Wiley & Sons, Ltd.     

Model selection of a switching mechanism for financial time series

The threshold autoregressive model with generalized autoregressive conditionally heteroskedastic (GARCH) specification is a popular nonlinear model that captures the well‐known asymmetric phenomena in financial market data. The switching mechanisms of hysteretic autoregressive GARCH models are different from threshold autoregressive model with GARCH as regime switching may be delayed when the hysteresis variable lies in a hysteresis zone. This paper conducts a Bayesian model comparison among competing models by designing an adaptive Markov chain Monte Carlo sampling scheme. We illustrate the performance of three kinds of criteria by comparing models with fat‐tailed and/or skewed errors: deviance information criteria, Bayesian predictive information, and an asymptotic version of Bayesian predictive information. A simulation study highlights the properties of the three Bayesian criteria and the accuracy as well as their favorable performance as model selection tools. We demonstrate the proposed method in an empirical study of 12 international stock markets, providing evidence to strongly support for both models with skew fat‐tailed innovations. Copyright © 2016 John Wiley & Sons, Ltd.     

Bayesian Variable Selection on Model Spaces Constrained by Heredity Conditions

This article investigates Bayesian variable selection when there is a hierarchical dependence structure on the inclusion of predictors in the model. In particular, we study the type of dependence found in polynomial response surfaces of orders two and higher, whose model spaces are required to satisfy weak or strong heredity conditions. These conditions restrict the inclusion of higher-order terms depending upon the inclusion of lower-order parent terms. We develop classes of priors on the model space, investigate their theoretical and finite sample properties, and provide a Metropolis–Hastings algorithm for searching the space of models. The tools proposed allow fast and thorough exploration of model spaces that account for hierarchical polynomial structure in the predictors and provide control of the inclusion of false positives in high posterior probability models.     

Zero Expectile Processes and Bayesian Spatial Regression

We introduce new classes of stationary spatial processes with asymmetric, sub-Gaussian marginal distributions using the idea of expectiles. We derive theoretical properties of the proposed processes. Moreover, we use the proposed spatial processes to formulate a spatial regression model for point-referenced data where the spatially correlated errors have skewed marginal distribution. We introduce a Bayesian computational procedure for model fitting and inference for this class of spatial regression models. We compare the performance of the proposed method with the traditional Gaussian process-based spatial regression through simulation studies and by applying it to a dataset on air pollution in California.     

Expanding the number of phthalates monitored in house dust

Phthalates have been used as plasticisers for several decades in various industry and consumer products. A method was developed for the determination of 13 not commonly monitored phthalates in household dust. The method was based on solvent extraction using sonication, sample clean-up by solid phase extraction (SPE), and analysis using isotope dilution gas chromatography-tandem mass spectrometry (GC/MS/MS). The method was applied to the analysis of dust samples collected using two vacuum sampling techniques from 38 urban Canadian homes: a sample of fresh or ‘active’ dust (FD) collected by technicians and a composite sample taken from the household vacuum cleaner (HD). Spearman rank correlations between HD and FD samples were significant for six phthalates with median concentrations above their method detection limits (MDLs), suggesting that the HD samples provide comparable results with FD samples. Seven phthalates were detected and quantified in a Canada-wide set of 126 household dust samples, among which six phthalates were detected at frequencies higher than 87%, with median (range) concentrations of 1.9 (<0.42–240) (μg/g) for diisohexyl phthalate (DIHxP), 3.8 (<0.16–260) (μg/g) for di-n-heptyl phthalate (DHepP), 6.6 (<1.1–1170) (μg/g) for diisooctyl phthalate (DIOP), 1.1 (<0.12–390) (μg/g) for di-n-octyl phthalate (DOP), 6.3 (<0.16–430) (μg/g) for dinonyl phthalate (DNP), and 1.8 (<0.18–850) (μg/g) for di-n-decyl phthalate (DDP). High detection frequencies and widely scattered concentration levels of these phthalates in this preliminary set of 126 samples suggested a high variability in potential exposure to phthalates in Canadian homes. NIST SRM 2585 (organic contaminants in house dust) was also analysed; eight phthalates were detected, with concentrations ranging from 6.0 μg/g for DOP to 79 μg/g for DIHxP. The results from SRM 2585 may contribute to the certification of phthalate concentration values in this SRM.     

Uncertainty evaluation for the quantification of low masses of benzo[a]pyrene: Comparison between the Law of Propagation of Uncertainty and the Monte Carlo method

A proper evaluation of the uncertainty associated to the quantification of micropollutants in the environment, like Polycyclic Aromatic Hydrocarbons (PAHs), is crucial for the reliability of the measurement results. The present work describes a comparison between the uncertainty evaluation carried out according to the GUM uncertainty framework and the Monte Carlo (MC) method. This comparison was carried out starting from real data sets obtained from the quantification of benzo[a]pyrene (BaP), spiked on filters commonly used for airborne particulate matter sampling. BaP was chosen as target analyte as it is listed in the current European legislation as marker of the carcinogenic risk for the whole class of PAHs.