关于有限群的可解性

利用极大子群的几乎正规的概念得到了有限群为可解群的若干充要条件．     

阅读与语篇理解的相关理论分析

阅读是外语教学和学习中的重要环节。阅读不仅是读者知识积累和经验积淀的反映,而且可以构架起作者和读者之间沟通的桥梁。掌握了语篇生成的理论基础和技巧,读者就能在阅读中驾轻就熟地理解作者的意图和观点,从而达到理解语篇的目的。     

衔接 连贯与阅读理解

衔接是语篇特征的重要内容.尽管语义上连贯的语篇可以没有形式上的衔接纽带,但在大多数自然出现的语篇中是存在衔接纽带的,这些纽带不仅会增强语篇的连贯性,而且对阅读理解有着很大的帮助作用.分析衔接与阅读的关系以及如何应用照应、替代、省略、连接和词汇衔接纽带促进并加深对语篇的理解.     

反Fuzzy子群的直积

1990年R.Biswas在[2]中提出了反Fuzzy子群的概念.在此基础上本文相应定义了反Fuzzy子群的直积.接着讨论了其包括正规、共轭情况下的若干性质.此外,还给出了直积μ_1×μ_2是G×G上的反Fuzzy子群的一个充要条件.     

英文段落的推展模式及内部衔接机制初探

从篇章语言学的角度看 ,英文段落依层级的概括性推展 ,并呈典型的直线性发展模式。英文作者主要利用词汇相等链、链式结构和编列结构衔接段中各语句来推展段落 ,从而使段落具有交际功能。英文语篇结构模式对英文习得者来说具有实际指导意义     

“换词求雅”的修辞作用及衔接功能

“换词求雅”作为一种消极修辞手段,在避免重复、增加文章的文体色彩方面起到了积极的作用。不仅如此,作为一种衔接手段,“换词求雅”也在保证语篇的衔接与连贯上发挥着不可替代的作用。本文通过对这种辞手段的应用原则及分类方法的阐述,试图表明“换词求雅”并不是为了语言花哨的目的而存在的,无论从修辞还是语法意义角度上看,这一修辞方法的作用都是其他方法难以替代的。     

2阶子群均共轭置换的4p2及4pq阶群

子群H为G的共轭置换子群是指日满足对G中任意元素g均成立HgH=HHg，记为H〈c-pG．本文利用共轭置换来刻画2阶子群均共轭置换的有限群，得到具有该特性的4p2及4pg阶群的结构分类．     

Development of predictive signatures for treatment selection in precision medicine with survival outcomes

For survival endpoints in subgroup selection, a score conversion model is often used to convert the set of biomarkers for each patient into a univariate score and using the median of the univariate scores to divide the patients into biomarker‐positive and biomarker‐negative subgroups. However, this may lead to bias in patient subgroup identification regarding the 2 issues: (1) treatment is equally effective for all patients and/or there is no subgroup difference; (2) the median value of the univariate scores as a cutoff may be inappropriate if the sizes of the 2 subgroups are differ substantially. We utilize a univariate composite score method to convert the set of patient's candidate biomarkers to a univariate response score. We propose applying the likelihood ratio test (LRT) to assess homogeneity of the sampled patients to address the first issue. In the context of identification of the subgroup of responders in adaptive design to demonstrate improvement of treatment efficacy (adaptive power), we suggest that subgroup selection is carried out if the LRT is significant. For the second issue, we utilize a likelihood‐based change‐point algorithm to find an optimal cutoff. Our simulation study shows that type I error generally is controlled, while the overall adaptive power to detect treatment effects sacrifices approximately 4.5% for the simulation designs considered by performing the LRT; furthermore, the change‐point algorithm outperforms the median cutoff considerably when the subgroup sizes differ substantially.     

Model averaging for treatment effect estimation in subgroups

In many clinical trials, biological, pharmacological, or clinical information is used to define candidate subgroups of patients that might have a differential treatment effect. Once the trial results are available, interest will focus on subgroups with an increased treatment effect. Estimating a treatment effect for these groups, together with an adequate uncertainty statement is challenging, owing to the resulting “random high” / selection bias. In this paper, we will investigate Bayesian model averaging to address this problem. The general motivation for the use of model averaging is to realize that subgroup selection can be viewed as model selection, so that methods to deal with model selection uncertainty, such as model averaging, can be used also in this setting. Simulations are used to evaluate the performance of the proposed approach. We illustrate it on an example early‐phase clinical trial.     

A simulation study of methods for selecting subgroup‐specific doses in phase 1 trials

Patient heterogeneity may complicate dose‐finding in phase 1 clinical trials if the dose‐toxicity curves differ between subgroups. Conducting separate trials within subgroups may lead to infeasibly small sample sizes in subgroups having low prevalence. Alternatively,it is not obvious how to conduct a single trial while accounting for heterogeneity. To address this problem,we consider a generalization of the continual reassessment method on the basis of a hierarchical Bayesian dose‐toxicity model that borrows strength between subgroups under the assumption that the subgroups are exchangeable. We evaluate a design using this model that includes subgroup‐specific dose selection and safety rules. A simulation study is presented that includes comparison of this method to 3 alternative approaches,on the basis of nonhierarchical models,that make different types of assumptions about within‐subgroup dose‐toxicity curves. The simulations show that the hierarchical model‐based method is recommended in settings where the dose‐toxicity curves are exchangeable between subgroups. We present practical guidelines for application and provide computer programs for trial simulation and conduct.