Magnetic properties and magnetoresistance effect of SnFe2O4 spinel nanoparticles: Experimental,ab initio and Monte Carlo simulation

In this contribution, SnFe₂O₄ nanoparticles were prepared by the solvothermal method, the structural properties were performed using X-Ray Diffraction (DRX) to prove the success of tin ferrite formation and to determine de crystals parameters. The size and morphological study were build using Scanning Electron Microscopy (SEM) and Transmission Electron microscopy (TEM), the results showed that the size of particles is uniform with a range of particles (5–7 nm). The magnetic properties were carried out using the SQUID device, the SnFe2O4 nanoparticles have a magnetic transition at 750 K. In addition, the hysteresis loops at low temperature displayed Ms and Mr equals to 23 emu/g and 6 emu/g, respectively. The magnetoresistance properties were investigated, the SnFe₂O₄ nanoparticles present a large magnetoresistance effect (80%). The experimental results are supplemented by model calculations utilizing density functional theory and Monte-Carlo simulations.     

An exploration of corrosion inhibition of mild steel in sulphuric acid solution through experimental study and Monte Carlo simulations

Abstract

4-[4-(1H-imidazol-1-yl)methyl)-1H-1,2,3-triazol-1-yl]methylbenzophenone (ITBP) and 4[4-(1H-1,2,4-triazol-1-yl)methyl)-1H-1,2,3-triazol-1-yl]methylbenzophenone (TTBP) are synthesized as new heterocyclic compounds of the triazole derivative family and tested successfully as potential inhibitors for MS in 1?M H₂SO₄ corrosive medium by using gravimetric analysis, electrochemical impedance spectroscopy, potentiodynamic polarization, and energy dispersive X-ray spectroscopy (EDX). Polarization curves show that the tested inhibitors are mixed-type inhibitors. Scanning electron microscopy (SEM) affirmed the existence of an adsorbed film on the steel surface. Monte Carlo simulations were in excellent agreement with the experimental tests.

Abbreviation: PDP: Potentiodynamic Polarization; EIS: Electrochemical impedance spectroscopy; DFT: Density functional theory; MC: Monte Carlo     

基于最小维修的石化安全设备区间截尾寿命数据分析

在石化安全关键设备寿命截尾数据的可靠性评估过程中，为避免传统可靠性评估方法未考虑维修对故障间隔时间的影响，以及中值填充法误差较大的缺点，本文提出了一种逆变换区间截尾数据填充方法。首先，在可修系统基于最小维修的假设下，通过非齐次泊松过程描述系统的故障趋势，利用逆变换填充得到监测区间的缺失数据；随后根据蒙特卡罗期望最大化算法，获得填充数据的参数估计值。通过蜡油加氢装置紧急泄压阀的案例分析，该算法能够很好地融合已有数据，得到的形状参数估计值偏差与中值填充法相比缩小4%以上，从而验证了该算法的有效性；这有助于安全关键设备的定量化完整性管理，保障石化装置平稳运行。     

Robust nonlinear feedback–feedforward control of a coupled kinetic Monte Carlo–finite difference simulation

Robust nonlinear feedforward–feedback controllers are designed for a multiscale system that dynamically couples kinetic Monte Carlo (KMC) and finite difference (FD) simulation codes. The coupled codes simulate the copper electrodeposition process for manufacturing on-chip copper interconnects in electronic devices. The control objective is to regulate the current density subject to the condition that the steady-state fluctuation of the overpotential remains bounded within ±0.01 V. The controller designs incorporate a low-order stochastic model that captures the input–output behavior of the coupled KMC–FD code. The controllers achieve the objectives and the closed-loop responses implemented on the low-order model and the coupled KMC–FD code match well within stochastic variations. The nonlinear feedforward control reduces the rise time of the controller response while the feedback control ensures robustness in the presence of model uncertainty.     

Monte Carlo simulation of gas phase polymerization of 1,3‐butadiene. Part II: Kinetics optimization and confirmation

Studies on the deactivations and initiations of gas phase polymerizations of 1,3‐butadiene have been achieved by Monte Carlo simulation. Initiation and deactivation control the reaction before and after the peak of the polymerization rate, respectively. The influence of polymerization temperature has been studied. Monte Carlo modeling of polymerization kinetics and mechanism was confirmed by the agreement of experimental data and simulation results of polymerizations run with a temporary evacuation of monomer. The balance of catalysts and active chains is established by both initiation and chain transfer reactions with cocatalyst, which causes a ‘pseudo‐stability’ stage. © 2003 Society of Chemical Industry     

A stochastic model for localized disturbances and its applications

A stochastic model for local disturbances, particularly for a temporal harmonic with random modulations in amplitude and/or phase, is proposed in this paper. Results for the second moment responses of a linear single-degree-of-freedom system to this type of stochastic loading demonstrate a significant change in response characteristics due to a small uncertainty. A local phenomenon may last much longer and resonance may be smeared to a broad range. Integrated with wavelet transform, the proposed approach may be used to model a random process with non-stationary frequency content. Especially, it can be effectively used for Monte Carlo simulation to generate large size of samples that have similar characteristics in time and frequency domains as a pre-selected mother sample has. The technique has a great potential for the case where uncertainty study is warranted but the available samples are limited.     

Simulation of Strength Distribution in Ground Ceramics by Incorporating Residual Stress Effect

Strength of ground ceramics may be affected by residual stress as well as surface flaws induced by grinding. Strength prediction for ground ceramics is convenient for mechanical design of ceramic components. In this article, a numerical procedure based on fracture mechanics was proposed to estimate strength distribution of ground ceramics by considering grinding-induced residual stress. Bending strength and residual stress of ground ceramics were measured for three grinding-conditions. By comparison of simulated results with experimental ones, it was revealed that strength characteristics in experiments were well simulated by using the proposed procedure.     

Dynamic crack propagation with cohesive elements: a methodology to address mesh dependency

In this paper, two brittle fracture problems are numerically simulated: the failure of a ceramic ring under centrifugal loading and crack branching in a PMMA strip. A three‐dimensional finite element package in which cohesive elements are dynamically inserted has been developed. The cohesive elements' strength is chosen to follow a modified weakest link Weibull distribution. The probability of introducing a weak cohesive element is set to increase with the cohesive element size. This reflects the physically based effect according to which larger elements are more likely to contain defects. The calculations illustrate how the area dependence of the Weibull model can be used to effectively address mesh dependency. On the other hand, regular Weibull distributions have failed to reduce mesh dependency for the examples shown in this paper. The ceramic ring calculations revealed that two distinct phenomena appear depending on the magnitude of the Weibull modulus. For low Weibull modulus, the fragmentation of the ring is dominated by heterogeneities. Whereas many cracks were generated, few of them could propagate to the outer surface. Monte Carlo simulations revealed that for highly heterogeneous rings, the number of small fragments was large and that few large fragments were generated. For high Weibull modulus, signifying that the ring is close to being homogeneous, the fragmentation process was very different. Monte Carlo simulations highlighted that a larger number of large fragments are generated due to crack branching. Copyright © 2003 John Wiley & Sons, Ltd.     

A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm

The estimation of the differences among groups in observational studies is frequently inaccurate owing to a bias caused by differences in the distributions of covariates. In order to estimate the average treatment effects when the treatment variable is binary, Rosenbaum and Rubin [1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41-55] proposed an adjustment method for pre-treatment variables using propensity scores. Imbens [2000. The role of the propensity score in estimating dose-response functions. Biometrika 87, 706-710] extended the propensity score methodology for estimation of average treatment effects with multivalued treatments.However, these studies focused only on estimating the marginal mean structure. In many substantive sciences such as the biological and social sciences, a general estimation method is required to deal with more complex analyses other than regression, such as testing group differences on latent variables. For latent variable models, the EM algorithm or the traditional Monte Carlo methods are necessary. However, in propensity score adjustment, these methods cannot be used because the full distribution is not specified.In this paper, we propose a quasi-Bayesian estimation method for general parametric models that integrate out the distributions of covariates using propensity scores. Although the proposed Bayes estimates are shown to be consistent, they can be calculated by existing Markov chain Monte Carlo methods such as Gibbs sampler. The proposed method is useful to estimate parameters in latent variable models, while the previous methods were unable to provide valid estimates for complex models such as latent variable models.We also illustrated the procedure using the data obtained from the US National Longitudinal Survey of Children and Youth (NLSY1979-2002) for estimating the effect of maternal smoking during pregnancy on the development of the child's cognitive functioning.     

Identification of the minimum effective dose for right-censored survival data

In this paper, we consider identifying the minimum effective dose (MED) in a dose-response study when survival data are subject to random right-censorship, where the MED is defined to be the smallest dose level under study that has survival advantage over the zero-dose control. To this end, we suggest single-step-down testing procedures based on three different types of weighted logrank statistics, respectively. The comparative results of a Monte Carlo error rate and power/bias study for a variety of survival and censoring distributions are then presented and discussed. The application of the testing procedures for identifying the MED is finally illustrated by using a numerical example of prostate cancer data.