拐点和一元三次方程的求根公式

用一元函数微分学理论，对一元三次实系数方程x^3 bx^2 cx d=0的求根问题作了新的探讨。     

正烯烃沸点与其分子结构之间定量关系的研究

本文根据分子结构的特点，借助图论探讨了正烯烃的沸点与分子结构的关系，提出了一个具有一定结构基础的定量关系。对４０种正烯烃的计算结果表明，沸点计算值都接近实验值，平均误差仅０．０５４％。     

NURBS曲面的算法分析及实现

讨论了ＮＵＲＢＳ曲线和节点矢量、控制点和基函数对曲面形状的影响，给出了ＮＵＲＢＳ的算法分析及其实现方案，最后，将ＮＵＲＢＳ曲面与老式曲面（如Ｂ样条、Ｂｅｚｉｅｒ曲面等）进行了比较。     

切入“历史”后的视点——论“新历史小说”的叙事角度

“新历史小说”，自生成之日起就存在着命名、指认等一系列问题上的争议，这一现象的出现，除了与其自身内涵和外延的不确定性有关之外，还与其叙事上的独特角度密不可分。有鉴于此，结合“新历史小说”出现的时代背景，通过对其结构特质的三个主要方面，即第一人称视角与“历史”同步、聚焦变换与情感演绎、民间视野及其视点应用进行阐释，就成为一道重要的逻辑线索。     

私营企业与经济发展水平相关性分析

私营企业与一个国家或地区的经济社会发展有着密切的关系。本文通过实证研究,分析了私营企业发展与经济发展的相关关系,认为私营企业的发展是经济发展的重要增长点。最后结合分析结果提出了发展私营企业进而促进经济可持续增长的一些建议。     

运用心理学 讲好网络课

研究如何将心理学认知学习论运用在教学中. 强调新知识必须在学生的认知结构中找到固定点. 在分析 <计算机网络> 课程知识点的逻辑结构的基础上, 介绍了将新知识植入学生认知结构的讲授方法. 并探讨了几种固定新知识的链条和扫除学生认知结构断裂点的途径.     

BATHTUB-TYPE CURVES IN RELIABILITY AND BEYOND

Finding optimal, or at least good, maintenance and repair policies is crucial in reliability engineering. Likewise, describing life phases of human mortality is important when determining social policy or insurance premiums. In these tasks, one searches for distributions to fit data and then makes inferences about the population(s). In the present paper, we focus on bathtub‐type distributions and provide a view of certain problems, methods and solutions, and a few challenges, that can be encountered in reliability engineering, survival analysis, demography and actuarial science.     

谈分级显性意义假说对反语中的认知参照点的解释力

本文运用Giora的分级显性意义假说分析了Langacker的认知参照点理论，并对反语中的认知参照点进行分析，得出反语中的认知参照点也是层级突显的结论，为反语的理解提供一个新的视角。     

Causal Reasoning from Longitudinal Data*

Abstract. This paper reviews some of the key statistical ideas that are encountered when trying to find empirical support to causal interpretations and conclusions, by applying statistical methods on experimental or observational longitudinal data. In such data, typically a collection of individuals are followed over time, then each one has registered a sequence of covariate measurements along with values of control variables that in the analysis are to be interpreted as causes, and finally the individual outcomes or responses are reported. Particular attention is given to the potentially important problem of confounding. We provide conditions under which, at least in principle, unconfounded estimation of the causal effects can be accomplished. Our approach for dealing with causal problems is entirely probabilistic, and we apply Bayesian ideas and techniques to deal with the corresponding statistical inference. In particular, we use the general framework of marked point processes for setting up the probability models, and consider posterior predictive distributions as providing the natural summary measures for assessing the causal effects. We also draw connections to relevant recent work in this area, notably to Judea Pearl's formulations based on graphical models and his calculus of so‐called do‐probabilities. Two examples illustrating different aspects of causal reasoning are discussed in detail.     

Shrinkage confidence intervals for the normal mean: Using a guess for greater efficiency

If the unknown mean of a univariate population is sufficiently close to the value of an initial guess then an appropriate shrinkage estimator has smaller average squared error than the sample mean. This principle has been known for some time, but it does not appear to have found extension to problems of interval estimation. The author presents valid two‐sided 95% and 99% “shrinkage” confidence intervals for the mean of a normal distribution. These intervals are narrower than the usual interval based on the Student distribution when the population mean lies in such an “effective interval.” A reduction of 20% in the mean width of the interval is possible when the population mean is sufficiently close to the value of the guess. The author also describes a modification to existing shrinkage point estimators of the general univariate mean that enables the effective interval to be enlarged.