供应链绩效评价指标体系与评价方法研究

供应链管理(SCM)与现行企业模式有较大区别,其绩效评价指标体系的建立与评价方法也就有其特殊性,目前这方面的研究还略显不足。本文在综合分析现有的SCM评价指标体系的基础上,初步建立了一套适合我国SCM绩效评价的指标体系。同时,给出了SCM绩效评价的多级动态模糊综合评价方法。     

人力资源个体价值计量方法——当期实现价值系数法

由于人力资源自身具有复杂性、不确定性的特点,使得人力资源价值难以计量。而准确的计量人力资源价值,尤其是人力资源的个体价值是人力资源会计能否实施的关键因素。针对这个问题,本文在分析前人成果的基础上提出了人力资源当期实现价值系数法计量模型。并通过实例验证了计量模型的实用性。     

基于医疗质量的公立医院绩效体系构建研究

目的：运用德尔菲法构建一套科学、可操作的公立医院绩效评价体系。方法：采用文献法研究国内外绩效评价理论,结合问卷结果形成初始量表。通过德尔菲法对指标进行两轮筛选及优化。结果：初步形成由“社会效益”、“质量安全”、“运行效益与效率”为基本结构的指标体系,包括3个一级指标、13个二级指标和72个三级指标。结论：该指标体系重点关注医院公益性及内涵质量,对公立医院绩效评价具有一定参考意义。     

核设施运营单位的核应急能力评估指标体系构建

核设施运营单位的核应急能力评价是核设施运营单位风险评估及核应急管理的重要内容,构建核设施运营单位的核应急能力评价体系可以提升核设施运营单位的核应急响应能力和管理能力。在对某4个核设施运营单位的20位专家进行两轮咨询的基础上,结合已有国内外研究成果,并采用临界值法对评价指标进行筛选,建立了一个包含3项一级指标,13项二级指标,38项三级指标的核设施运营单位核应急能力评价指标体系。指标体系综合考虑了影响核设施运营单位的核应急能力的主要因素,涵盖了核设施运营单位的核应急工作的各个方面,且各指标之间相对独立,操作方便,实施起来简单易行,可协助发现核设施营运单位在应急响应能力方面存在的问题,对核设施运营单位的核应急能力建设起到积极的促进作用。     

学校本位教学视导模式的实证研究——以兰州市E中学为例

本研究目的在于通过对兰州市E中学施行的学校本位教学视导模式的深入观察,全方位了解学校本位教学视导模式的具体运作方式。以兰州市一所薄弱学校为研究场域,通过课堂观察的方式,全面了解学校为提高办学质量所开展的“学案分组教学法”,及进行学校本位化视导模式的具体运作状态。在兰州市E中学开展学校本位教学视导模式的过程中,学校校长与某大学教育学院课程与教学领域的教授专家参与视导过程。主要采取校长主导,专家参与的合作视导方式,对兰州市E中学初中二年级屈老师数学课进行为期一周的教学观察与记录。实施以学校为本位的合作视导方式,目的在于发现兰州市E中学进行“学案分组教学法”中的具体教学问题,通过大学与中学的合作,开展学校本位视导模式,有效促进教师的专业发展与增强教学有效性,提高学校办学质量。     

大学物理教学中的课堂提问

大学物理课堂上很少作演示实验,课容量大,内容抽象,根据这一特点,大学物理课堂上的提问就有其自身的特点。课堂提问在整个教学环节中有着极其重要的作用,提问的方式方法不同,产生的效果往往会发生根本的变化。因此,课堂提问应该注意提问的对象、提问的范围、提问的多样性、提问的针对性等等问题。     

语义成分分析法与大学英语词汇教学

在大学英语教学的过程中,教师会发现,学生虽然学了很多新的词汇,但是很难做到正确地运用它们,本文作者提出利用语义学中语义成分分析法来改革英语词汇教学的方法。通过采用该方法,可以达到使学生准确理解和正确运用所学词汇的目的。     

Statistical inference for a relaxation index of stochastic dominance under density ratio model

Stochastic dominance is usually used to rank random variables by comparing their distributions, so it is widely applied in economics and finance. In actual applications, complete stochastic dominance is too demanding to meet, so relaxation indexes of stochastic dominance have attracted more attention. The π index, the biggest gap between two distributions, can be a measure of the degree of deviation from complete dominance. The traditional estimation method is to use the empirical distribution functions to estimate it. Considering the populations under comparison are generally of the same nature, we can link the populations through density ratio model under certain condition. Based on this model, we propose a new estimator and establish its statistical inference theory. Simulation results show that the proposed estimator substantially improves estimation efficiency and power of the tests and coverage probabilities satisfactorily match the confidence levels of the tests, which show the superiority of the proposed estimator. Finally we apply our method to a real example of the Chinese household incomes.     

Improving logistic regression on the imbalanced data by a novel penalized log-likelihood function

Logistic regression is estimated by maximizing the log-likelihood objective function formulated under the assumption of maximizing the overall accuracy. That does not apply to the imbalanced data. The resulting models tend to be biased towards the majority class (i.e. non-event), which can bring great loss in practice. One strategy for mitigating such bias is to penalize the misclassification costs of observations differently in the log-likelihood function. Existing solutions require either hard hyperparameter estimating or high computational complexity. We propose a novel penalized log-likelihood function by including penalty weights as decision variables for observations in the minority class (i.e. event) and learning them from data along with model coefficients. In the experiments, the proposed logistic regression model is compared with the existing ones on the statistics of area under receiver operating characteristics (ROC) curve from 10 public datasets and 16 simulated datasets, as well as the training time. A detailed analysis is conducted on an imbalanced credit dataset to examine the estimated probability distributions, additional performance measurements (i.e. type I error and type II error) and model coefficients. The results demonstrate that both the discrimination ability and computation efficiency of logistic regression models are improved using the proposed log-likelihood function as the learning objective.     

Regression modelling of interval censored data based on the adaptive ridge procedure

A new method for the analysis of time to ankylosis complication on a dataset of replanted teeth is proposed. In this context of left-censored, interval-censored and right-censored data, a Cox model with piecewise constant baseline hazard is introduced. Estimation is carried out with the expectation maximisation (EM) algorithm by treating the true event times as unobserved variables. This estimation procedure is shown to produce a block diagonal Hessian matrix of the baseline parameters. Taking advantage of this interesting feature in the EM algorithm, a L0 penalised likelihood method is implemented in order to automatically determine the number and locations of the cuts of the baseline hazard. This procedure allows to detect specific areas of time where patients are at greater risks for ankylosis. The method can be directly extended to the inclusion of exact observations and to a cure fraction. Theoretical results are obtained which allow to derive statistical inference of the model parameters from asymptotic likelihood theory. Through simulation studies, the penalisation technique is shown to provide a good fit of the baseline hazard and precise estimations of the resulting regression parameters.