加热炉工艺对集装箱钢边部缺陷影响浅析

用SEM扫描电镜法分析了典型缺陷产生的原因,排除了这些表面缺陷并非由于连铸结晶器保护渣引起,最终通过调整加热炉工艺制度解决了表面缺陷。     

The Use of Incorrect Informative Priors in the Estimation of MIMIC Model Parameters with Small Sample Sizes

Recently, advancements in Bayesian structural equation modeling (SEM), particularly software developments, have allowed researchers to more easily employ it in data analysis. With the potential for greater use, come opportunities to apply Bayesian SEM in a wider array of situations, including for small sample size problems. Effective use of Bayseian estimation hinges on selection of appropriate prior distributions for model parameters. Researchers have suggested that informative priors may be useful with small samples, presuming that the mean of the prior is accurate with respect to the population mean. The purpose of this simulation study was to examine model parameter estimation for the Multiple Indicator Multiple Cause model when an informative prior distribution had an incorrect mean. Results demonstrated that the use of incorrect informative priors with somewhat larger variance than is typical, yields more accurate parameter estimates than do naïve priors, or maximum likelihood estimation. Implications for practice are discussed.     

Drawing Conclusions from Cross-Lagged Relationships: Re-Considering the Role of the Time-Interval

The cross-lagged panel model (CLPM), a discrete-time (DT) SEM model, is frequently used to gather evidence for (reciprocal) Granger-causal relationships when lacking an experimental design. However, it is well known that CLPMs can lead to different parameter estimates depending on the time-interval of observation. Consequently, this can lead to researchers drawing conflicting conclusions regarding the sign and/or dominance of relationships. Multiple authors have suggested the use of continuous-time models to address this issue.

In this article, we demonstrate the exact circumstances under which such conflicting conclusions occur. Specifically, we show that such conflicts are only avoided in general in the case of bivariate, stable, nonoscillating, first-order systems, when comparing models with uniform time-intervals between observations. In addition, we provide a range of tools, proofs, and guidelines regarding the comparison of discrete- and continuous-time parameter estimates.     

Effects of Missing Data Methods in SEM Under Conditions of Incomplete and Nonnormal Data

Using Monte Carlo simulations, this research examined the performance of four missing data methods in SEM under different multivariate distributional conditions. The effects of four independent variables (sample size, missing proportion, distribution shape, and factor loading magnitude) were investigated on six outcome variables: convergence rate, parameter estimate bias, MSE of parameter estimates, standard error coverage, model rejection rate, and model goodness of fit—RMSEA. A three-factor CFA model was used. Findings indicated that FIML outperformed the other methods in MCAR, and MI should be used to increase the plausibility of MAR. SRPI was not comparable to the other three methods in either MCAR or MAR.     

Standardized Parameters in Misspecified Structural Equation Models: Empirical Performance in Point Estimates,Standard Errors,and Confidence Intervals

In practice, models always have misfit, and it is not well known in what situations methods that provide point estimates, standard errors (SEs), or confidence intervals (CIs) of standardized structural equation modeling (SEM) parameters are trustworthy. In this article we carried out simulations to evaluate the empirical performance of currently available methods. We studied maximum likelihood point estimates, as well as SE estimators based on the delta method, nonparametric bootstrap (NP-B), and semiparametric bootstrap (SP-B). For CIs we studied Wald CI based on delta, and percentile and BC_a intervals based on NP-B and SP-B. We conducted simulation studies using both confirmatory factor analysis and SEM models. Depending on (a) whether point estimate, SE, or CI is of interest; (b) amount of model misfit; (c) sample size; and (d) model complexity, different methods can be the one that renders best performance. Based on the simulation results, we discuss how to choose proper methods in practice.     

热电氧化物(Ca_3Co_2O_6)_(1-x)(Ca_3Co_4O_9)_x体系的正电子湮没研究

Ca3Co4O9热电氧化物是一种重要应用前景广泛的绿色能源材料。本文采用“快速加热”技术,获得了热电氧化物(Ca3Co2O6)1-x(Ca3Co4O9)x系列。利用扫描电子显微镜(SEM)、正电子淹没技术表征并测试了样品缺陷特性。结果表明Ca3Co2O6到Ca3Co4O9的晶型转变在两相含量各半时存在突变,我们测量到了在该点正电子寿命、电子浓度和空穴浓度的峰值响应。     

纳米表面合金膜修饰电极的制备、表征及电催化活性研究

以不锈钢(SS)作基底,自行研制不锈钢载纳米表面合金电催化材料(Surface alloy/SS),并运用循环伏安法(CV)和扫描电镜(SEM)等技术对该催化剂进行结构和性能表征。SEM研究表明,所研制的Surface alloy/SS电催化剂是一种由粒度主要约为100nm的颗粒合金组成的薄膜。循环伏安研究表明,所研制的Surface alloy/SS电催化剂在常温常压下对顺丁烯二酸的加氢还原表现出很高的电催化活性。顺丁烯二酸的起始还原电位为-0.4V,与通常用的阴极铅材料相比,正移约200mV。     

教育研究的有力工具:结构方程模型(SEM)

目前结构方程模型（ＳＥＭ）已成为心理学、社会学、组织行为　学各领域有力的研究　手段之一，它是理论发展的重要工具，促使研究者细致、认真地考虑研究的理论构思与变量　结构，使研究更为严密并富有理论与实际意义。     

Test Reliability at the Individual Level

Reliability has a long history as one of the key psychometric properties of a test. However, a given test might not measure people equally reliably. Test scores from some individuals might have considerably greater error than others. This study proposed two approaches using intraindividual variation to estimate test reliability for each person. A simulation study suggested that the parallel tests approach and the structural equation modeling approach recovered the simulated reliability coefficients. Then in an empirical study, where 45 females were measured daily on the Positive and Negative Affect Schedule (PANAS) for 45 consecutive days, separate estimates of reliability were generated for each person. Results showed that reliability estimates of the PANAS varied substantially from person to person. The methods provided in this article apply to tests measuring changeable attributes and require repeated measures across time on each individual. This article also provides a set of parallel forms of PANAS.     

The GRoLTS-Checklist: Guidelines for Reporting on Latent Trajectory Studies

Estimating models within the mixture model framework, like latent growth mixture modeling (LGMM) or latent class growth analysis (LCGA), involves making various decisions throughout the estimation process. This has led to a wide variety in how results of latent trajectory analysis are reported. To overcome this issue, using a 4-round Delphi study, we developed Guidelines for Reporting on Latent Trajectory Studies (GRoLTS). The purpose of GRoLTS is to present criteria that should be included when reporting the results of latent trajectory analysis across research fields. We have gone through a systematic process to identify key components that, according to a panel of experts, are necessary when reporting results for trajectory studies. We applied GRoLTS to 38 papers where LGMM or LCGA was used to study trajectories of posttraumatic stress after a traumatic event.