丹巴的墨尔多文化

墨尔多是嘉绒地区的一位名山神,墨尔多神山是嘉绒地区的第一大神山。坐落在丹巴墨尔多神山下的墨尔多庙是一个供奉神祗十分复杂的寺庙,所供神祗有佛教,有苯教,也有道教,呈三教合流状态。墨尔多庙供奉神祗的多样性,是丹巴地区文化多元性的反映。     

高速公路沥青路面防排水深化设计

针对水损坏是我国高速公路沥青路面较普遍出现的早期损坏现象,通过分析其产生的主要原因,提出了沥青路面结构防排水设计的重点,并结合怀新(怀化至新晃)高速公路的设计实践,确定了沥青路面结构防排水深化设计方案,为解决沥青路面早期损坏问题提供参考.     

悲喜交集的生命之歌——评迟子建的《逝川》

在短篇小说《逝川》中,迟子建写了一条河,一个人,一种鱼。这条河,无疑是一条生命之河,一条时间之河,在这条河边,主人公吉喜经历了充满生机却又不乏辛酸的人生。吉喜与泪鱼的对照,显示出吉喜强韧的生命力。这条河流淌着的,是一曲悲喜交集的生命之歌。     

沥青路面平整度分析及控制措施探讨

沥青路面平整度是影响行车安全性和舒适性的重要因素.根据多条高速公路的施工经验,通过分析各种可能的影响因素,对提高平整度提出相应的控制措施,许扶高速路面的施工实践,证明这些措施行之有效.     

老旧沥青公路路面结构层水稳冷再生施工技术

以老旧沥青公路路面结构层水稳冷再生施工技术为研究对象,通过分析水稳冷再生施工技术的应用原理和应用过程,得出路面结构层水稳冷再生施工技术在老旧沥青公路改造和修复施工应用时,具有工程量比较小、施工工期短、可大幅度节约成本等优势。但施工工序比较多,需对各道工序严格控制,以提升路面结构层水稳冷再生施工技术应用水平的结论。基于此,文章以固原市硝口村309国道为例,分析了此项技术的应用过程,并提出几点心得体会。老旧沥青公路路面修复难度较大,传统全部铲除再铺筑,费时费力。采用路面结构层水稳冷再生施工技术,可有效解决这一问题,既能恢复老旧沥青公路的性能,还能降低修复成本,值得大范围推广应用。     

JL55防水涂料的研究

JL55防水涂料是用废橡胶和沥青混炼而成的，其具有固体含量大、涂刷次数少、耐热耐高温安定、耐低温而不冷脆、抗膨润、耐久、不透水等特点，是一种较好的屋面防水涂料。     

FTIR研究TC—114对环氧树脂固化的催化作用

用FTIR“原位”技术,研究TC—114偶联剂对环氧E—44和二氨基二苯基甲烷(DDM)固化反应的催化作用,得出固化程度与时间的关系曲线.结果表明,催化作用明显.     

A Mixture IRTree Model for Extreme Response Style: Accounting for Response Process Uncertainty

This paper presents a mixture item response tree (IRTree) model for extreme response style. Unlike traditional applications of single IRTree models, a mixture approach provides a way of representing the mixture of respondents following different underlying response processes (between individuals), as well as the uncertainty present at the individual level (within an individual). Simulation analyses reveal the potential of the mixture approach in identifying subgroups of respondents exhibiting response behavior reflective of different underlying response processes. Application to real data from the Students Like Learning Mathematics (SLM) scale of Trends in International Mathematics and Science Study (TIMSS) 2015 demonstrates the superior comparative fit of the mixture representation, as well as the consequences of applying the mixture on the estimation of content and response style traits. We argue that methodology applied to investigate response styles should attend to the inherent uncertainty of response style influence due to the likely influence of both response styles and the content trait on the selection of extreme response categories.     

Testing Measurement Invariance Across Unobserved Groups: The Role of Covariates in Factor Mixture Modeling

Factor mixture modeling (FMM) has been increasingly used to investigate unobserved population heterogeneity. This study examined the issue of covariate effects with FMM in the context of measurement invariance testing. Specifically, the impact of excluding and misspecifying covariate effects on measurement invariance testing and class enumeration was investigated via Monte Carlo simulations. Data were generated based on FMM models with (1) a zero covariate effect, (2) a covariate effect on the latent class variable, and (3) covariate effects on both the latent class variable and the factor. For each population model, different analysis models that excluded or misspecified covariate effects were fitted. Results highlighted the importance of including proper covariates in measurement invariance testing and evidenced the utility of a model comparison approach in searching for the correct specification of covariate effects and the level of measurement invariance. This approach was demonstrated using an empirical data set. Implications for methodological and applied research are discussed.     

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.