Mathematical models for academic webs: Linear relationship or non-linear power law?

Previous studies of academic web interlinking have tended to hypothesise that the relationship between the research of a university and links to or from its web site should follow a linear trend, yet the typical distribution of web data, in general, seems to be a non-linear power law. This paper assesses whether a linear trend or a power law is the most appropriate method with which to model the relationship between research and web site size or outlinks. Following linear regression, analysis of the confidence intervals for the logarithmic graphs, and analysis of the outliers, the results suggest that a linear trend is more appropriate than a non-linear power law.     

How children's creative visual imagination and creative thinking relate to their representation of space

This article explores how creative visual imagination and creative thinking can help children to construct mental models of space. A mixed-method study, integrating quantitative and qualitative approaches and involving 98 five-year-old children (54 girls and 44 boys) demonstrated that creative visual imagination, rather than creative thinking, is linked with the knowledge and understanding of space. These results support the notion that creative visual imagination contributes to improvements in children's understanding of basic astronomical concepts. Interestingly, creative thinking was not associated with understanding and knowledge of cosmic phenomena in models taking into account creative imagination. The qualitative analysis showed differences in children's representations of space. We discuss the consequences of these findings for science education, particularly the teaching of elementary astronomy.     

阅读模式及其对英语阅读教学的启示

介绍了现代语言心理学关于四种阅读模式的研究，进而从阅读能力，阅读策略，阅读图式，阅读情境四个方面，探讨了英语阅读教学观念和方法的更新。     

论广告英语的语言特点

本探讨了广告英语的语义特点、词汇特点及句法特点。     

A New Perspective on the Effects of Covariates in Mixture Models

In this simulation study, we explored the effect of introducing covariates to a growth mixture model when covariates were also generated by a mixture model. We varied the association between the latent classes underlying the growth trajectories and the covariates, the degree of separation between the latent classes underlying the covariates, the number of covariates included, and amount of missing data in the growth data. We found that adding covariates to the growth mixture model generally hurt class recovery except where the latent classes underlying the growth trajectories and the covariates were the same or very strongly associated, and there was a large degree of separation between the classes underlying the covariates. We found that when covariates were introduced, entropy might no longer be an accurate indicator of the distinctiveness of the growth trajectory classes.     

Bayesian Analysis of Mixture Structural Equation Models With an Unknown Number of Components

Multivariate heterogenous data with latent variables are common in many fields such as biological, medical, behavioral, and social-psychological sciences. Mixture structural equation models are multivariate techniques used to examine heterogeneous interrelationships among latent variables. In the analysis of mixture models, determination of the number of mixture components is always an important and challenging issue. This article aims to develop a full Bayesian approach with the use of reversible jump Markov chain Monte Carlo method to analyze mixture structural equation models with an unknown number of components. The proposed procedure can simultaneously and efficiently select the number of mixture components and conduct parameter estimation. Simulation studies show the satisfactory empirical performance of the method. The proposed method is applied to study risk factors of osteoporotic fractures in older people.     

Automated Latent Growth Curve Model Fitting: A Segmentation and Knot Selection Approach

Latent growth curve models are widely used in the social and behavioral sciences to study complex developmental patterns of change over time. The trajectories of these developmental patterns frequently exhibit distinct segments in the studied variables. Latent growth models with piecewise functions for repeated measurements of variables have become increasingly popular for modeling such developmental trajectories. A major problem with using piecewise models is determining the precise location of the point where the change in the process has occurred and uncovering the related number of segments. The purpose of this paper is to introduce an optimization procedure that can be used to determine both the segments and location of the knots in piecewise linear latent growth models. The procedure is illustrated using empirical data in order to detect the number of segments and change points. The results demonstrate the capabilities of the procedure for fitting latent growth curve models.     

Examining Chi-Square Test Statistics Under Conditions of Large Model Size and Ordinal Data

This study examined the effect of model size on the chi-square test statistics obtained from ordinal factor analysis models. The performance of six robust chi-square test statistics were compared across various conditions, including number of observed variables (p), number of factors, sample size, model (mis)specification, number of categories, and threshold distribution. Results showed that the unweighted least squares (ULS) robust chi-square statistics generally outperform the diagonally weighted least squares (DWLS) robust chi-square statistics. The ULSM estimator performed the best overall. However, when fitting ordinal factor analysis models with a large number of observed variables and small sample size, the ULSM-based chi-square tests may yield empirical variances that are noticeably larger than the theoretical values and inflated Type I error rates. On the other hand, when the number of observed variables is very large, the mean- and variance-corrected chi-square test statistics (e.g., based on ULSMV and WLSMV) could produce empirical variances conspicuously smaller than the theoretical values and Type I error rates lower than the nominal level, and demonstrate lower power rates to reject misspecified models. Recommendations for applied researchers and future empirical studies involving large models are provided.     

「Vテモラウ」的意义和用法——具有事先请求的情况

「Vテモラウ」的意义与其先行动词的意义和モラウ的意义这两个因素有关。本文首先综合先行研究的观点分析提取独立动词「モラウ」的语义特征,然后,从「Vテモラウ」的先行动词的特征和「モラウ」的语义特征两方面分析「Vテモラウ」的句义和用法,确定具有事先请求的情况的「Vテモラウ」句的意义下位分类存在请求义、指示义、表达愿望义、许可义。     

中医方剂领域本体建模和语义推理实现

针对目前中医领域知识共享的现状,结合对中医方剂领域知识特点的分析,采用骨架法构建基于OWL语言的方剂本体模型。并以中医诊断和开方为应用场景,提出基于SWRL规则结合Jess推理引擎的方法,尝试中医领域本体的语义推理。