Drude's model calculation rule on electrical transport in Sb-doped SnO2 thin films, deposited via sol-gel

The evaluation of free carrier concentration based on Drude's theory can be performed by the use of optical transmittance in the range 800-2000 nm (near infrared) for Sb-doped SnO2 thin films. In this article, we estimate the free carrier concentration for these films, which are deposited via sol-gel dip-coating. At approximately 900 mn, there is a separation among transmittance curves of doped and undoped samples. The plasma resonance phenomena approach leads to free carrier concentration of about 5 x 1020 cm(-3). The increase in the Sb concentration increases the film conductivity; however, the magnitude of measured resistivity is still very high. The only way to combine such a high free carrier concentration with a rather low conductivity is to have a very low mobility. It becomes possible when the crystallite dimensions are taken into account. We obtain grains with 5 nm of average size by estimating the grain size from X-ray diffraction data, and by using line broadening in the diffraction pattern. The low conductivity is due to very intense scattering at the grain boundary, which is created by the presence of a large amount of nanoscopic crystallites. Such a result is in accordance with X-ray photoemission spectroscopy data that pointed to Sb incorporation proportional to the free electron concentration, evaluated according to Drude's model. (c) 2006 Elsevier Ltd. All rights reserved.

Robust Bayesian analysis of heavy-tailed stochastic volatility models using scale mixtures of normal distributions

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

Evaluation of in vitro gas production of roughages and their mixtures using the curves subtraction method

The in vitro gas production of four single roughages and their paired combinations (1:1 on dry matter basis) were evaluated. Two roughage samples (100 mg) per treatment were fermented with ruminal fluid during a 48 h incubation period. Total 48 h gas volumes of fermentation dry matter (DM), neutral detergent fiber (NDF) and soluble compounds in neutral detergent (NDS) were for sugarcane = 16.8, 11.2, 6.9 mL; sugarcane + corn silage = 20.1, 12.6, 9.1 mL; sugarcane + 60-day elephantgrass = 16.5, 17.6 mL; sugarcane + 180-day elephantgrass = 13.8, 8.2, 5.9 mL; corn silage = 18.8, 16.8, 4.7 mL; corn silage + 60-day elephantgrass = 16.3, 15.4, 2.4 mL; corn silage + 180-day elephantgrass = 16.1, 11.8, 4.2 mL; 60-day elephantgrass = 16.9, 19.0 mL and 180-day elephantgrass = fermented 10.7, 12.2 mL, respectively. The NDS gas production was not possible to estimate for sugarcane + 60-day elephantgrass, 60-day elephantgrass and 180-day elephantgrass. The present data shows that the curves subtraction method can be an option to evaluate the contribution of the soluble fractions in roughages to digestion kinetics. However, this method underestimates the NDS gas contribution when roughages are low in crude protein and soluble carbohydrates. It is advisable to directly apply the two-compartmental mathematical model to the digestion curves for roughage DM, when determining the NDS gas volume and the digestion rate. This method is more straightforward and accurate when compared to the curve subtraction method. Non-structural carbohydrates combined with fiber and protein promoted a positive associative effect in sugarcane + corn silage (50:50) mixture. Therefore, it can be concluded that the soluble fraction of roughages greatly contributes to gas production. (C) 2004 Elsevier B.V. All rights reserved.

Bayesian outlier analysis in binary regression

We propose alternative approaches to analyze residuals in binary regression models based on random effect components. Our preferred model does not depend upon any tuning parameter, being completely automatic. Although the focus is mainly on accommodation of outliers, the proposed methodology is also able to detect them. Our approach consists of evaluating the posterior distribution of random effects included in the linear predictor. The evaluation of the posterior distributions of interest involves cumbersome integration, which is easily dealt with through stochastic simulation methods. We also discuss different specifications of prior distributions for the random effects. The potential of these strategies is compared in a real data set. The main finding is that the inclusion of extra variability accommodates the outliers, improving the adjustment of the model substantially, besides correctly indicating the possible outliers.

Genetic correlations between categorical morphological traits in Nelore cattle by applying Bayesian analysis under a threshold animal model

P>In this study, Bayesian analysis under a threshold animal model was used to estimate genetic correlations between morphological traits (body structure, finishing precocity and muscling) in Nelore cattle evaluated at weaning and yearling. Visual scores obtained from 7651 Nelore cattle at weaning and from 4155 animals at yearling, belonging to the Brazilian Nelore Program, were used. Genetic parameters for the morphological traits were estimated by two-trait Bayesian analysis under a threshold animal model. The genetic correlations between the morphological traits evaluated at two ages of the animal (weaning and yearling) were positive and high for body structure (0.91), finishing precocity (0.96) and muscling (0.94). These results indicate that the traits are mainly determined by the same set of genes of additive action and that direct selection at weaning will also result in genetic progress for the same traits at yearling. Thus, selection of the best genotypes during only one phase of life of the animal is suggested. However, genetic differences between morphological traits were better detected during the growth phase to yearling. Direct selection for body structure, finishing precocity and muscling at only one age, preferentially at yearling, is recommended as genetic differences between traits can be detected at this age.

Planning accelerated life tests under Exponentiated-Weibull-Arrhenius model

Purpose - The purpose of this paper is to present designs for an accelerated life test (ALT). Design/methodology/approach - Bayesian methods and simulation Monte Carlo Markov Chain (MCMC) methods were used. Findings - In the paper a Bayesian method based on MCMC for ALT under EW distribution (for life time) and Arrhenius models (relating the stress variable and parameters) was proposed. The paper can conclude that it is a reasonable alternative to the classical statistical methods since the implementation of the proposed method is simple, not requiring advanced computational understanding and inferences on the parameters can be made easily. By the predictive density of a future observation, a procedure was developed to plan ALT and also to verify if the conformance fraction of the manufactured process reaches some desired level of quality. This procedure is useful for statistical process control in many industrial applications. Research limitations/implications - The results may be applied in a semiconductor manufacturer. Originality/value - The Exponentiated-Weibull-Arrhenius model has never before been used to plan an ALT. © Emerald Group Publishing Limited.

A useful empirical bayesian method to analyse industrial data from saturated factorial designs

The use of saturated two-level designs is very popular, especially in industrial applications where the cost of experiments is too high. Standard classical approaches are not appropriate to analyze data from saturated designs, since we could only get the estimates of the main factor effects and we would not have degrees of freedom to estimate the variance of the error. In this paper, we propose the use of empirical Bayesian procedures to get inferences for data obtained from saturated designs. The proposed methodology is illustrated assuming a simulated data set. © 2013 Growing Science Ltd. All rights reserved.

Genetic Correlation for Pre-Weaning and Post-Weaning Traits of Mediterranean Buffaloes from Brazil, Estimated By Bayesian Inference

The aim of this study was to estimate genetic, environmental and phenotypic correlation between birth weight (BW) and weight at 205 days age (W205), BW and weight at 365 days age (W365) and W205-W365, using Bayesian inference. The Brazilian Program for Genetic Improvement of Buffaloes provided the data that included 3,883 observations from Mediterranean breed buffaloes. With the purpose to estimate variance and covariance, bivariate analyses were performed using Gibbs sampler that is included in the MTGSAM software. The model for BW, W205 and W365 included additive direct and maternal genetic random effects, maternal environmental random effect and contemporary group as fixed effect. The convergence diagnosis was achieved using Geweke, a method that uses an algorithm implemented in R software through the package Bayesian Output Analysis. The calculated direct genetic correlations were 0.34 (BW-W205), 0.25 (BW-W365) and 0.74 (W205-W365). The environmental correlations were 0.12, 0.11 and 0.72 between BW-W205, BW-W365 and W205-W365, respectively. The phenotypic correlations were low for BW-W205 (0.01) and BW-W365 (0.04), differently than the obtained for W205-W365 with a value of 0.67. The results indicate that BW trait have low genetic, environmental and phenotypic association with the two others traits. The genetic correlation between W205 and W365 was high and suggests that the selection for weight at around 205 days could be beneficial to accelerate the genetic gain.

Genetic Parameters for Growth Traits of Mediterranean Buffaloes from Brazil, Estimated By Bayesian Inference

Quantitative analysis of growth genetic parameters is not available for many breeds of buffaloes making selection and breeding decisions an empirical process that lacks robustness. The objective of this study was to estimate heritability for birth weight (BW), weight at 205 days (W205) and 365 days (W365) of age using Bayesian inference. The Brazilian Program for Genetic Improvement of Buffaloes provided the data. For the traits BW, W205 and W365 of Brazilian Mediterranean buffaloes 5169, 3792 and 3883 observations have been employed for the analysis, respectively. In order to obtain the estimates of variance, univariate analyses were conducted using the Gibbs sampler included in the MTGSAM software. The model for BW, W205 and W365 included additive direct and maternal genetic random effects, random maternal permanent environmental effect and contemporary group that was treated as a fixed effect. The convergence diagnosis was performed employing Geweke, a method that uses an algorithm from the Bayesian Output Analysis package that was implemented using R software environment. The average values for weight traits were 37.6 +/- 4.7 kg for BW, 192.7 +/- 40.3 kg for W205 and 298.6 +/- 67.4 kg for W365. The heritability posterior distributions for direct and maternal effects were symmetric and close to those expected in a normal distribution. Direct heritability estimates obtained using the modes were 0.30 (BW), 0.52 (W205) and 0.54 (W365). The maternal heritability coefficient estimates were 0.31, 0.19 and 0.21 for BW, W205 and W365, respectively. Our data suggests that all growth traits and mainly W205 and W365, have clear potential for yield improvement through direct genetic selection.

Genetic Parameters for Milk Yield and Lactation Length of Crossbred Buffaloes from Brazil by Bayesian Inference

The objective of the study was to estimate heritability and repeatability for milk yield (MY) and lactation length (LL) in buffaloes using Bayesian inference. The Brazilian genetic improvement program of buffalo provided the data that included 628 females, from four herds, born between 1980 and 2003. In order to obtain the estimates of variance, univariate analyses were performed with the Gibbs sampler, using the MTGSAM software. The model for MY and LL included direct genetic additive and permanent environment as random effects, and contemporary groups, milking frequency and calving number as fixed effects. The convergence diagnosis was performed with the Geweke method using an algorithm implemented in R software through the package Bayesian Output Analysis. Average for milk yield and lactation length was 1,546.1 +/- 483.8 kg and 252.3 +/- 42.5 days, respectively. The heritability coefficients were 0.31 (mode), 0.35 (mean) and 0.34 (median) for MY and 0.11 (mode), 0.10 (mean) and 0.10 (median) for LL. The repeatability coefficient (mode) were 0.50 and 0.15 for MY and LL, respectively. Milk yield is the only trait with clear potential for genetic improvement by direct genetic selection. The repeatability for MY indicates that selection based on the first lactation could contribute for an improvement in this trait.

3D stability orbits close to 433 Eros using an effective polyhedral model method

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Bayesian age and growth analysis of the shortfin mako shark Isurus oxyrinchus in the Western South Atlantic Ocean using a flexible model

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Search for a standard model Higgs boson produced in association with a top-quark pair and decaying to bottom quarks using a matrix element method

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Phonon confinement model to measure the average sizes of anatase nanoparticles synthesized by a solvothermal method using H2O2

In this work, crystalline titanium dioxide (TiO2) nanoparticles with variable average crystallite sizes (e.g., 8 nm) and surface areas (e.g., 192 m² g-1) were synthesized in pure anatase phase using H2O2 to reduce the hydrolysis rate of the titanium ions. An isopropanol (IP) solution was employed as the reaction medium. The TiO2 nanoparticles were characterized by powder X-ray diffraction analysis (XRD), Raman spectroscopy and transmission electron microscopy (TEM). By changing the synthesis parameters it was possible to control nanoparticle size and avoid the coalescence process. A dependence of the Raman wavenumber on the nanocrystal sizes was determined, which is quite useful for a quick check of the size of TiO2 nanocrystals.

The FGM bivariate lifetime copula model: a bayesian approach

In this paper, we propose a bivariate distribution for the bivariate survival times based on Farlie-Gumbel-Morgenstern copula to model the dependence on a bivariate survival data. The proposed model allows for the presence of censored data and covariates. For inferential purpose a Bayesian approach via Markov Chain Monte Carlo (MCMC) is considered. Further, some discussions on the model selection criteria are given. In order to examine outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. The newly developed procedures are illustrated via a simulation study and a real dataset.