Relationships between Young Sisters as Revealed in their Overt Responses

The jackknife and bootstrap methods are becoming increasingly popular in research. Although the two approaches have similar goals and use similar strategies, information is lacking with regard to the comparability of their results. In the present study, this issue was systematically investigated for a case of canonical correlation analysis. Bootstrap, jackknife, and Monte Carlo experiments were carried out for 4 sample sizes (n = 200, 100, 50, 20). The jackknife analyses were also varied as regards the number of jackknife observations deleted in each analysis. Some meaningful discrepancies were observed between the bootstrap and jackknife results, especially under small sample-size conditions. Based on the comparisons made with Monte Carlo estimates, the empirical results suggest that the bootstrap technique provides less biased and more consistent results than the jackknife technique does.     

Identifying Unobserved Hazard Functions in Discrete-Time Survival Mixture Analysis

This Monte Carlo simulation adds to the growing body of enumeration index performance research in continuous response variable mixture models by addressing the issue of the performance of these indexes in discrete-time survival mixture analysis (DTSMA) models. Results showed that although all enumeration indexes performed very well in identifying a homogeneous DTSMA model (i.e., k = 1 hazard function in the sample data), the findings also showed that the enumeration indexes performed poorly in identifying the correct number of unobserved hazard functions present in a heterogeneous (i.e., k = 3) DTSMA model. More important, the performance of the enumeration indexes for the heterogeneous DTSMA models did not improve as the sample size, the effect of time-invariant covariates, or adjacent hazard function separation distance increased, which is inconsistent with some previous Monte Carlo simulation results. The limitations of this Monte Carlo simulation study and future empirical investigation possibilities are both discussed.     

Vapor-liquid equilibrium simulation of binary and ternary mixtures of CH4, C2H4 and iso-C4H10

The vapor-liquid equilibrium（VLE） properties for the binary and ternary mixtures of CH4,C2H4 and isoC4H10 are of great importance in the recovery of ethylene from mixture containing CH4 and C2H4 with iso-C4H10 as solvent.Hence,Gibbs ensemble Monte Carlo（GEMC） simulations were used to estimate vapor-liquid equilibrium for the binary and ternary mixtures of CH4,C2H4 and iso-C4H10 with the united atom potential NERD model.The selected simulation conditions are based on the experiment in the literature.The results of this work were shown to be in satisfactory agreement with available experimental data and predictions of Peng-Robinson equation of state.The structure of simulated liquid phase is also characterized by radial distribution function（RDF）,which contributes to further understanding of the VLE curve of these systems.RDF is not sensitive to the pressure and temperature range.With the increase of pressure or the decrease of temperature,the molecules tend to gather together.     

Monte Carlo Confidence Intervals for Complex Functions of Indirect Effects

One challenge in mediation analysis is to generate a confidence interval (CI) with high coverage and power that maintains a nominal significance level for any well-defined function of indirect and direct effects in the general context of structural equation modeling (SEM). This study discusses a proposed Monte Carlo extension that finds the CIs for any well-defined function of the coefficients of SEM such as the product of k coefficients and the ratio of the contrasts of indirect effects, using the Monte Carlo method. Finally, we conduct a small-scale simulation study to compare CIs produced by the Monte Carlo, nonparametric bootstrap, and asymptotic-delta methods. Based on our simulation study, we recommend researchers use the Monte Carlo method to test a complex function of indirect effects.     

Vapour–Liquid Equilibrium for <Emphasis Type="Italic">N</Emphasis>, <Emphasis Type="Italic">N</Emphasis>-Dimethylformamide + Benzene + Thiophene via Gibbs Ensemble Molecular Simulation

The selection and design of an optimal solvent for extractive distillation require reliable vapour–liquid phase equilibrium data and knowledge of extraction mechanisms. Compared with time-consuming experiments, molecular simulation presents great potential in research on the properties of fluids. Therefore, in this work, Gibbs ensemble Monte Carlo was applied to successfully predict the vapour–liquid phase equilibrium data of binary and ternary systems containing benzene, thiophene and N, N-dimethylformamide (DMF) at P = 101.3 kPa. The explicit hydrogen version of the transferable potentials for phase equilibria potential model was chosen for benzene and thiophene, whereas the OPLS potential model was selected for DMF. The predicted phase diagrams were compared with experimental data and the UNIQUAC thermodynamic model. A good agreement was obtained, which corroborated the validity of the potential models. In addition, the extraction mechanism was explored by radial distribution function (RDF) of the liquid-phase structure. The RDFs showed that thiophene and benzene shared a similar liquid-phase structure because of the intermolecular interaction. The distinct difference between the RDFs of DMF/benzene and those of DMF/thiophene is that the oxygen atom of DMF is more associated with hydrogen atoms of thiophene than that of benzene, which may be responsible for the extraction effect of DMF.     

用计算机模拟薄膜生长过程的KMC方法

以有表面活性剂介入的Cu薄膜为例,用Kinetic Monte Carlo方法对薄膜生长的随机过程进行了计算机模拟,并对所提出的模型和Kinetic Monte Carlo算法进行了详尽描述．在实现动力学生长过程及物理模型向计算机语言及算法的转化过程中,不仅构建了模拟薄膜生长过程的主体流程图,并且将主体流程图分解成了原子吸附事件流程图、原子迁移事件过程流程图、“小事件”列表刷新流程图和事件列表刷新示意图四个部分,分别对其进行说明和阐述．可以看出在薄膜领域中,计算机模拟在理论和实验这两方面的研究中都发挥了重要作用,Kinetic Monte Carlo方法也已经成了解决薄膜生长相关问题的必要工具．     

FenO（n=3～13）团簇稳定结构和电磁特性的DFT计算

基于密度泛函理论,采用广义梯度近似下的交换关联泛函PW91和双数值极化函数基组DNP,对氧原子掺杂的铁团簇FenO（n=3～13）的稳定性和电磁特性进行了研究.相比于纯铁团簇Fen（n=3～13）,氧原子的掺入提高了团簇的结构稳定性和化学惰性;铁氧团簇的结合能随着原子数的增加而增加,除Fe10O外,团簇中氧原子的位置总是位于团簇的表面;通过计算FenO结合能的二阶差分发现,Fe6O,Fe9O,Fe11O团簇是相对稳定的幻数团簇,氧原子的加入使团簇的幻数发生了改变.在FenO团簇中总磁距与氧原子电荷转移的多少有关,当n＞7后,电荷转移量增加,磁距也随之增大.     

Monte Carlo模拟在聚合反应速率常数估算中的运用

1 Introduction It is i mportant to study polymerization mechanisminpolymerization engineering. Obtaining kinetic data isthe key step for establishing polymerization mechanismand understanding elementary reactions . The kineticdata contains two groups of data ,i.e.,the rate andreaction order .The rate ,especially the rate constant ,is i mportant and to be esti mated assuming that the or-der of the reactionis known.Furthermore ,esti mationof rate constants for polymerization is an active anddif…     

Optimization of time-delayed feedback control of seismically excited building structures

An optimization method for time-delayed feedback control of partially observable linear building structures subjected to seismic excitation is proposed. A time-delayed control problem of partially observable linear building structure under horizontal ground acceleration excitation is formulated and converted into that of completely observable linear structure by using separation principle. The time-delayed control forces are approximately expressed in terms of control forces without time delay. The control system is then governed by Itoe stochastic differential equations for the conditional means of system states and then transformed into those for the conditional means of modal energies by using the stochastic averaging method for quasi-Hamiltonian systems. The control law is assumed to be modal velocity feedback control with time delay and the unknown control gains are determined by the modal performance indices. A three-storey building structure is taken as example to illustrate the proposal method and the numerical results are confirmed by using Monte Carlo simulation.     

线性聚合物链三级相互作用蒙特卡洛方法

基于三级相互作用近似的旋转异构态模型,建立了一个蒙特卡洛模拟方法。使一个样本的产生按其在玻耳兹曼分布中的出现机率进行。然后以此方法计算了聚甲撑链的无扰均方末端距和均方回转半径,其结果和矩阵代数方法计算结果是一致的。     

Unequal Covariate Group Means and the Analysis of Covariance

The study, using a Monte Carlo technique, was designed to investigate the effect of the differences in covariate means among the treatment groups on the significance level and the power of the F-test of the analysis of covariance. The results show that the covariate group means differences have little effect on the significance level if the covariate is highly correlated with the criterion variable. However, if the correlation is .4 or less, larger sample sizes are required. The effect on the power is more sensitive for smaller experiments. The larger the differences among covariate group means, the lower the actual power becomes compared to the approximate theoretical power.     

Performance of the Grade of Membership Model Under a Variety of Sample Sizes,Group Size Ratios,and Differential Group Response Probabilities for Dichotomous Indicators

Social scientists are frequently interested in identifying latent subgroups within the population, based on a set of observed variables. One of the more common tools for this purpose is latent class analysis (LCA), which models a scenario involving k finite and mutually exclusive classes within the population. An alternative approach to this problem is presented by the grade of membership (GoM) model, in which individuals are assumed to have partial membership in multiple population subgroups. In this respect, it differs from the hard groupings associated with LCA. The current Monte Carlo simulation study extended on prior work on the GoM by investigating its ability to recover underlying subgroups in the population for a variety of sample sizes, latent group size ratios, and differing group response profiles. In addition, this study compared the performance of GoM with that of LCA. Results demonstrated that when the underlying process conforms to the GoM model form, the GoM approach yielded more accurate classification results than did LCA. In addition, it was found that the GoM modeling paradigm yielded accurate results for samples as small as 200, even when latent subgroups were very unequal in size. Implications for practice were discussed.     

Improving the Root Mean Square Error of Approximation for Nonnormal Conditions in Structural Equation Modeling

In this study, the authors investigated incorporating adjusted model fit information into the root mean square error of approximation (RMSEA) fit index. Through Monte Carlo simulation, the usefulness of this adjusted index was evaluated for assessing model adequacy in structural equation modeling when the multivariate normality assumption underlying maximum likelihood estimation is violated. Adjustment to the RMSEA was considered in 2 forms: a rescaling adjustment via the Satorra-Bentler rescaled goodness-of-fit statistic and a bootstrap adjustment via the Bollen and Stine adjusted model p value. Both properly specified and misspecifed models were examined. The adjusted RMSEA was evaluated in terms of the average index value across study conditions and with respect to model rejection rates under tests of exact fit, close fit, and not-close fit.     

压力对有限深量子阱中基态能的影响

考虑电子有效质量及禁带宽度随流体静压力的变化,讨论有限深量子阱中电子的基态能.对GaAs/ALxGa1-xAs量子阱系统中电子的基态能进行了数值计算,给出基态能随铝组份,阱宽和压力的变化关系.结果显示,基态能随阱宽和压力的增加而减小,随铝组份增加而增大.     

A Scaled F Distribution as an Approximation to the Distribution of Test Statistics in Covariance Structure Analysis

When the assumption of multivariate normality is violated or when a discrepancy function other than (normal theory) maximum likelihood is used in structural equation models, the null distribution of the test statistic may not be χ² distributed. Most existing methods to approximate this distribution only match up to 2 moments. In this article, we propose 2 additional approximation methods: a scaled F distribution that matches 3 moments simultaneously and a direct Monte Carlo–based weighted sum of i.i.d. χ² variates. We also conduct comprehensive simulation studies to compare the new and existing methods for both maximum likelihood and nonmaximum likelihood discrepancy functions and to separately evaluate the effect of sampling uncertainty in the estimated weights of the weighted sum on the performance of the approximation methods.     

具有Stark位移的非线性双光子Jaynes-Cummings模型

Two-photon Jaynes-Cummimgs model is generalized to the case of Kerr medium in this paper,The field and atom are prepared initially in two-photon superposition state and ground state respectively.Nonlinear coefficient affects the dynamic behaviors of the field and atom.Evolutions of the squeezing for the operators of field and atom and the quantum inversion are discussed.In particular,the higher-order squeezing for atomic dipole and the effects of nonlinearity on it,which have not been studied by other authors,are investigated,Increasing the nonlinear coefficient will decrease the squeezing depth of atomic dipole.     

利用原子--腔模拉曼相互作用操纵纠缠态(英文)

基于原子 -腔模拉曼相互作用 ,提出了制备纠缠原子态的方案 .用这种方法 ,能够制备原子Bell态和多原子GHZ态 .此外 ,还能够制备纠缠相干态 .值得注意的是利用这种技术 ,我们能够完成量子态的隐形传送 .比如Bell类型的、纠缠原子态的和纠缠相干态的隐形传送 .最后 ,我们讨论了实验的可行性方案     

蒙特卡罗方法及其在辐射剂量计算中的应用

概述了蒙特卡罗方法的产生与发展，阐述了蒙特卡罗方法的基本特点，最后就蒙特卡罗方法在辐射剂量计算上的应用进行了讨论。     

Separating the Odds: Thresholds for Entropy in Logistic Regression

Abstract

Researchers are often reluctant to rely on classification rates because a model with favorable classification rates but poor separation may not replicate well. In comparison, entropy captures information about borderline cases unlikely to generalize to the population. In logistic regression, the correctness of predicted group membership is known, however, this information has not yet been utilized in entropy calculations. The purpose of this study was to, 1) introduce three new variants of entropy as approximate-model-fit measures, 2) establish rule-of-thumb thresholds to determine whether a theoretical model fits the data, and 3) investigate empirical Type I error and statistical power associated with those thresholds. Results are presented from two Monte Carlo simulations. Simulation results indicated that EFR-rescaled was the most representative of overall model effect size, whereas EFR provided the most intuitive interpretation for all group size ratios. Empirically-derived thresholds are provided.     

3-D Fracture Network Modelling in Hydropower Engineering Based on Optimal Monte Carlo Simulation

Effectively and accurately modelling the spatial relation of fracture surfaces is crucial in the design and construction of large hydropower dams having a complex underlying geology. However, fracture surfaces are randomly formed and vary greatly with respect to their spatial distribution, which makes the construction of accurate 3-D models challenging. In this study, we use an optimal Monte Carlo simulation and dynamic conditioning to construct a fracture network model. We found the optimal Monte Carlo simulation to effectively reduce the error associated with the Monte Carlo method and use dynamic conditioning to ensure the consistency of the model with the actual distribution of fractures on the excavation faces and outcrops. We applied this novel approach to a hydropower station on the Jinshajiang River, China. The simulation results matched the real sampled values well, confirming that the model is capable of effectively and accurately simulating the spatial relations in a fracture network.