残疾人心理健康服务需求的调查分析

本研究使用自编残疾人心理健康服务需求问卷,对864个残疾人进行施测,旨在调查和了解我国残疾人心理健康服务需求的现状,为残疾人心理健康服务体系的建设提供依据。结果表明：超过1／3的残疾人对自身的心理健康水平评价较低,68．4％的残疾人认为心理健康很重要,90．2％的残疾人认为接受心理健康服务很重要,说明残疾人对心理健康服务的认识和接纳程度较高,需求意愿强烈。在具体的服务目标方面,24．5％的残疾人期望通过心理健康服务能够使家庭生活幸福,21．2％的人希望获得良好的社会适应,19．7％的人希望达到人际和谐。服务内容方面,44．7％的人选择了对家庭婚姻问题的心理咨询,39．7％的人选择了提供心理保健知识。在心理服务形式方面,39．9％的人选择求助兼职或专业的心理咨询人员,28．8％的受访者选择了从朋友家人那里寻求支持,而剩余31．4％的受访者选择了什么也不做或自己解决。在具体的形式方面,41．4％的人选择了求助心理热线,38．2％的人选择了专业心理咨询。在选择机构方面,以社区服务站和社区的主页心理服务机构为主,分别占25．5％和25．3％。调查还表明现实生活中残疾人心理健康服务的开展力度较弱。     

试论列宁关于避免执政危险的思想

政党执掌政权只有避免执政危险,才能长久地确保执政地位。十月革命胜利之后,列宁就已经开始思考如何避免执政危险的问题,他深刻地认识到俄共（布）执政面临着急躁冒进的危险、脱离群众的危险、能力不足的危险以及被俄国旧文化所征服的危险。列宁对如何避免这些执政危险进行了初步的思考和探索,并采取了一系列措施,巩固了俄共（布）的执政地位。     

有机硼交联HPG压裂液超低温破胶剂室内研究

对埋藏浅,油层温度低的油藏进行压裂改造,需解决压裂液低温破胶水化的问题。采用活化剂来提高过硫酸铵(APS),酶,胶囊破胶剂的低温破胶能力。通过对有机硼交联的羟丙基瓜胶(HPG)压裂液低温下破胶后粘度损失率的测定,筛选出用于50℃下的APS/活化剂破胶系统及胶囊破胶剂。结果表明:在APS/活化剂破胶系统的作用下,采用有机硼作交联剂的HPG压裂液在较低温度条件下能彻底破胶水化。     

“宇宙文章”与“妖魔”：明代七子派的污名化

明代七子派的复古曾享有盛誉,“文学革命”兴起后,七子派被视为“妖魔”。陈独秀等人所倡导的“文学革命”是以七子派、桐城派作为“革命”对象,并将七子派符号化为旧文学的负面典型,断开了白话文学与七子派、桐城派的联系,试图建构白话文学的体系。对于“正统”文学,陈独秀、胡适充满焦虑,并努力将七子派打造成“摹拟”“抄袭”的典型,以此获得口诛笔伐的合法性。这种策略性的成功使原来占主导地位的“正统”文学让位于白话文学,七子派在哀怨声中消失在了新文学的大合唱里。     

双电机驱动自同步振动磨动力响应研究

以双电机驱动自同步振动磨为研究对象,对其进行简化处理,建立力学模型。通过对其施加X,y方向的简谐激 振力,建立无阻尼系统强迫振动和有阻尼系统强迫振动的数学模型,在此基础上,确定Rayleigh阻尼的常数α和β,利用 有限元ANSYS软件进行瞬态动力学分析,研究其在动载荷作用下的变形情况。研究结果表明：在工况频率16 Hz下,节 点运动轨迹更接近圆形,距理想粉碎效果的圆形轨迹较近,粉碎效果较好,进入稳定期的时间较快;在共振频率42 Hz 下,节点轨迹振幅更大,进入稳定期较慢,阻尼对振动的衰减作用较强;不同阻尼比对振动的衰减作用成正比关系。因 此,可以通过控制阻尼比等参数获得更有利的研磨效果,提高研磨效率。     

土地改革与农村权力秩序的重建--以1950年至1952年的绵阳为个案

土地改革颠覆了农村社会传统的格局和秩序,对当代中国历史发展产生了深远影响。凭借政治动员技术的巧妙运用,我党成功地将自己的意识形态传递给农民,使其形成具有明确指向的阶级意识,从而积极投身到群众运动之中;继之而起的“公审斗争”和“没收赔罚”摧毁了地主的权威,农民由此掌握了乡村话语权,成为政治活动的主体;通过干部整训,我党着力培育造就新的基层执政者阶层,希冀利用他们替代传统精英,实现乡村权力秩序的重建。     

Mixed-effects Models with Skewed Distributions for Time-varying Decay Rate in HIV Dynamics

After initiation of treatment, HIV viral load has multiphasic changes, which indicates that the viral decay rate is a time-varying process. Mixed-effects models with different time-varying decay rate functions have been proposed in literature. However, there are two unresolved critical issues: (i) it is not clear which model is more appropriate for practical use, and (ii) the model random errors are commonly assumed to follow a normal distribution, which may be unrealistic and can obscure important features of within- and among-subject variations. Because asymmetry of HIV viral load data is still noticeable even after transformation, it is important to use a more general distribution family that enables the unrealistic normal assumption to be relaxed. We developed skew-elliptical (SE) Bayesian mixed-effects models by considering the model random errors to have an SE distribution. We compared the performance among five SE models that have different time-varying decay rate functions. For each model, we also contrasted the performance under different model random error assumptions such as normal, Student-t, skew-normal, or skew-t distribution. Two AIDS clinical trial datasets were used to illustrate the proposed models and methods. The results indicate that the model with a time-varying viral decay rate that has two exponential components is preferred. Among the four distribution assumptions, the skew-t and skew-normal models provided better fitting to the data than normal or Student-t model, suggesting that it is important to assume a model with a skewed distribution in order to achieve reasonable results when the data exhibit skewness.     

Stochastically optimal bootstrap sample size for shrinkage-type statistics

In nonregular problems where the conventional \(n\) out of \(n\) bootstrap is inconsistent, the \(m\) out of \(n\) bootstrap provides a useful remedy to restore consistency. Conventionally, optimal choice of the bootstrap sample size \(m\) is taken to be the minimiser of a frequentist error measure, estimation of which has posed a major difficulty hindering practical application of the \(m\) out of \(n\) bootstrap method. Relatively little attention has been paid to a stronger, stochastic, version of the optimal bootstrap sample size, defined as the minimiser of an error measure calculated directly from the observed sample. Motivated by this stronger notion of optimality, we develop procedures for calculating the stochastically optimal value of \(m\). Our procedures are shown to work under special forms of Edgeworth-type expansions which are typically satisfied by statistics of the shrinkage type. Theoretical and empirical properties of our methods are illustrated with three examples, namely the James–Stein estimator, the ridge regression estimator and the post-model-selection regression estimator.     

Power analysis for clustered non-continuous responses in multicenter trials

Power analysis for multi-center randomized control trials is quite difficult to perform for non-continuous responses when site differences are modeled by random effects using the generalized linear mixed-effects model (GLMM). First, it is not possible to construct power functions analytically, because of the extreme complexity of the sampling distribution of parameter estimates. Second, Monte Carlo (MC) simulation, a popular option for estimating power for complex models, does not work within the current context because of a lack of methods and software packages that would provide reliable estimates for fitting such GLMMs. For example, even statistical packages from software giants like SAS do not provide reliable estimates at the time of writing. Another major limitation of MC simulation is the lengthy running time, especially for complex models such as GLMM, especially when estimating power for multiple scenarios of interest. We present a new approach to address such limitations. The proposed approach defines a marginal model to approximate the GLMM and estimates power without relying on MC simulation. The approach is illustrated with both real and simulated data, with the simulation study demonstrating good performance of the method.     

Stochastic comparisons on sample extremes of dependent and heterogenous observations

Stochastic comparison on order statistics from heterogeneous-dependent observations has been paid lots of attention recently. This paper devotes to investigating the ordering properties of order statistics from dependent observations. We derive the usual stochastic order for sample minimums and the second smallest order statistic, the dispersive order and the star order for minimums of samples having proportional hazards and Archimedean survival copulas. Similar ordering results are also obtained for maximums and the second largest order statistic of samples having proportional reversed hazards and Archimedean copulas. Several examples illustrating the main results are presented as well.