Magnetic properties of La2CuMnO6 double perovskite ceramic investigated by Monte Carlo simulations

We present a theoretical study of the magnetic properties of the lanthanum copper manganate double perovskite La2CuMnO6 ceramic,using Monte Carlo simulations.We analyze and discuss the ground state phase diagrams in different planes to show the effect of every physical parameter.Based on the Monte Carlo simulations,which combine Metropolis algorithm and Ising model,we explore the thermal behavior of the total magnetization and susceptibility.We also present and discuss the influence of physical parameters such as the external magnetic field,the exchange coupling interactions between magnetic atoms,and the exchange magnetic field on the magnetization of the system.Moreover,the critical temperature of the system is about Tc=70 K,in agreement with the experimental value.Finally,the hysteresis loops of La2CuMnO6 are discussed.

ROBUST ESTIMATION IN PARTIAL LINEAR MIXED MODEL FOR LONGITUDINAL DATA

In this article, robust generalized estimating equation for the analysis of partial linear mixed model for longitudinal data is used. The authors approximate the nonparametric function by a regression spline. Under some regular conditions, the asymptotic properties of the estimators are obtained. To avoid the computation of high-dimensional integral, a robust Monte Carlo Newton-Raphson algorithm is used. Some simulations are carried out to study the performance of the proposed robust estimators. In addition, the authors also study the robustness and the efficiency of the proposed estimators by simulation. Finally, two real longitudinal data sets are analyzed.

Stable and accurate schemes for smoothed dissipative particle dynamics

Smoothed dissipative particle dynamics （SDPD） is a mesoscopic particle method that allows to select the level of resolution at which a fluid is simulated. The numerical integration of its equations of motion still suffers from the lack of numerical schemes satisfying all the desired properties such as energy conservation and stability. Similarities between SDPD and dissipative particle dynamics with energy （DPDE） con- servation, which is another coarse-grained model, enable adaptation of recent numerical schemes developed for DPDE to the SDPD setting. In this article, a Metropolis step in the integration of the fluctuation/dissipation part of SDPD is introduced to improve its stability.

Zoeppritz-based AVO inversion using an improved Markov chain Monte Carlo method

The conventional Markov chain Monte Carlo （MCMC） method is limited to the selected shape and size of proposal distribution and is not easy to start when the initial proposal distribution is far away from the target distribution. To overcome these drawbacks of the conventional MCMC method, two useful improvements in MCMC method, adaptive Metropolis （AM） algorithm and delayed rejection （DR） algorithm, are attempted to be combined. The AM algorithm aims at adapting the proposal distribution by using the generated estimators, and the DR algorithm aims at enhancing the efficiency of the improved MCMC method. Based on the improved MCMC method, a Bayesian amplitude versus offset （AVO） inversion method on the basis of the exact Zoeppritz equation has been developed, with which the P- and S-wave velocities and the density can be obtained directly, and the uncertainty of AVO inversion results has been estimated as well. The study based on the logging data and the seismic data demonstrates the feasibility and robustness of the method and shows that all three parameters are well retrieved. So the exact Zoeppritz-based nonlinear inversion method by using the improved MCMC is not only suitable for reservoirs with strong-contrast interfaces and longoffset ranges but also it is more stable, accurate and antinoise.

Optimization of Well Position and Sampling Frequency for Groundwater Monitoring and Inverse Identification of Contamination Source Conditions Using Bayes’Theorem

Coupling Bayes’Theorem with a two-dimensional(2D)groundwater solute advection-diffusion transport equation allows an inverse model to be established to identify a set of contamination source parameters including source intensity(M),release location(0 X,0 Y)and release time(0 T),based on monitoring well data.To address the issues of insufficient monitoring wells or weak correlation between monitoring data and model parameters,a monitoring well design optimization approach was developed based on the Bayesian formula and information entropy.To demonstrate how the model works,an exemplar problem with an instantaneous release of a contaminant in a confined groundwater aquifer was employed.The information entropy of the model parameters posterior distribution was used as a criterion to evaluate the monitoring data quantity index.The optimal monitoring well position and monitoring frequency were solved by the two-step Monte Carlo method and differential evolution algorithm given a known well monitoring locations and monitoring events.Based on the optimized monitoring well position and sampling frequency,the contamination source was identified by an improved Metropolis algorithm using the Latin hypercube sampling approach.The case study results show that the following parameters were obtained:1)the optimal monitoring well position(D)is at(445,200);and 2)the optimal monitoring frequency(Δt)is 7,providing that the monitoring events is set as 5 times.Employing the optimized monitoring well position and frequency,the mean errors of inverse modeling results in source parameters(M,X0,Y0,T0)were 9.20%,0.25%,0.0061%,and 0.33%,respectively.The optimized monitoring well position and sampling frequency canIt was also learnt that the improved Metropolis-Hastings algorithm(a Markov chain Monte Carlo method)can make the inverse modeling result independent of the initial sampling points and achieves an overall optimization,which significantly improved the accuracy and numerical stability of the inverse modeling results.

Bayesian analysis of series system with dependent causes of failure

Most studies of series system assume the causes of failure are independent,which may not hold in practice.In this paper,dependent causes of failure are considered by using a Marshall-Olkin bivariateWeibull distribution.We derived four reference priors based on several grouping orders.Gibbs sampling combined with the rejection sampling algorithm and Metropolis-Hastings algorithm is developed to obtain the estimates of the unknown parameters.The proposed approach is compared with the maximum-likelihood method via simulation.We find that the root-meansquared errors of the Bayesian estimates are much smaller for the case of small sample size,and that the coverage probabilities of the Bayesian estimates are much closer to the nominal levels.Finally,a real data-set is analysed for illustration.

Conditional inversion to estimate parameters from eddy-flux observations

Aims Data assimilation is a useful tool to extract information from large datasets of the net ecosystem exchange(NEE)of CO_(2) obtained by eddy-flux measurements.However,the number of parameters in ecosystem models that can be constrained by eddy-flux data is limited by conventional inverse analysis that estimates parameter values based on one-time inversion.This study aimed to improve data assimilation to increase the number of constrained parameters.Methods In this study,we developed conditional Bayesian inversion to maximize the number of parameters to be constrained by NEE data in several steps.In each step,we conducted a Bayesian inversion to constrain parameters.The maximum likelihood estimates of the constrained parameters were then used as prior to fix parameter values in the next step of inversion.The conditional inversion was repeated until there were no more parameters that could be further constrained.We applied the conditional inversion to hourly NEE data from Harvard Forest with a physiologically based ecosystem model.Important Findings Results showed that the conventional inversion method constrained 6 of 16 parameters in the model while the conditional inversion method constrained 13 parameters after six steps.The cost function that indicates mismatch between the modeled and observed data decreased with each step of conditional Bayesian inversion.The Bayesian information criterion also decreased,suggesting reduced information loss with each step of conditional Bayesian inversion.A wavelet analysis reflected that model performance under conditional Bayesian inversion was better than that under conventional inversion at multiple time scales,except for seasonal and half-yearly scales.In addition,our analysis also demonstrated that parameter convergence in a subsequent step of the conditional inversion depended on correlations with the parameters constrained in a previous step.Overall,the conditional Bayesian inversion substantially increased the number of parameters to be constrained by NEE data and can be a powerful tool to be used in data assimilation in ecology.