高等职业教育呼唤创新理念

要谋求高等职业教育的永续发展,必须认真审视现阶段高等职业教育发展理念,致力于冲破传统学历教育的束缚,建立起终身教育的理念;从“唯知识”的教育质量观念向“以能力为本位”的教育理念转变;摒弃“单干”的教育模式,树立培养学生合作意识与合作能力的教育理念。高等职业教育理念的世界发展趋势符合我国情况,完善和改进高等职业教育理念,才能使我国的高等职业教育从深层次上切实地满足社会需求。     

"生态旅游"概念探微

选取国外 44个生态旅游定义作为研究对象 ,通过确认关键词、对关键词进行聚类分析 ,将定义内容归纳为 6大类 11组分 ,进而构建生态旅游概念模型。在检验模型理论上的可靠性和实践上的适应性后 ,依据模型提出自己的生态旅游定义     

基于马尔可夫链的产品市场保有量预测模型

售后服务机构为了准确地准备库存,都需要预测产品的市场保有量。基于马尔可夫链建立的状态概率矩阵模型,与抽样调查、时间序列法、回归分析法等方法相比,更能准确预测市场产品保有量。与传统产品相比较,更新换代速度快的电子产品,要预测其市场保有量,更需要利用状态概率矩阵模型。     

建筑节能激励政策的非对称博弈分析

本文探讨了建筑节能领域中政府与房地产商群体之间的非对称博弈问题,分析了该博弈所具有的模仿者动态模型、斯坦克尔伯格模型的特点,并构造了两者关于建筑节能激励政策的非对称博弈模型,最后对双方分别提出了博弈策略建议。     

客运专线运营对区域经济的影响

铁路客运专线的运营将提高干线的客货运输能力,降低社会运输总成本,带动高新产业发展,节约旅行时间,带动第三产业的发展等.本文建立了数学模型,以秦沈客运专线为例进行了定量分析,为客运专线的规划提供参考依据.     

从社会道德层面看唐宋女子再嫁问题

本文着眼于社会道德层面,通过对唐宋两代皇帝、名儒士大夫、一般平民三个阶层对女子再嫁态度的分析,指出唐宋两代各阶层对女子再嫁普遍具有认同感,不以之为非。从道德观上看,唐代贞节观念相当淡薄,各阶层皆以为再嫁理所应当,符合当时社会道德规范,故再嫁女子不为人所歧视;宋代各阶层对娶再嫁女为妻也无所顾忌,对再嫁女也没有产生偏见。     

科学发展观与国有经济

随着国有企业改革的深化,在新自由主义思潮的误导下,很多人认为国有经济存在的合理性很小,因而对巩固和发展国有经济持怀疑和否定态度。本文认为,在我国建立和谐社会,落实科学发展观,促进经济社会和人的全面、协调和可持续发展必须毫不动摇地巩固和发展国有经济。为此,国有企业的改革和调整不能偏离社会主义方向,不能只退不进,不能从竞争性领域全部退出来。     

On pooling data summaries in the absence of interactions “response-by-study”

The standard hypothesis testing procedure in meta-analysis (or multi-center clinical trials) in the absence of treatment-by-center interaction relies on approximating the null distribution of the standard test statistic by a standard normal distribution. For relatively small sample sizes, the standard procedure has been shown by various authors to have poor control of the type I error probability, leading to too many liberal decisions. In this article, two test procedures are proposed, which rely on thet—distribution as the reference distribution. A simulation study indicates that the proposed procedures attain significance levels closer to the nominal level compared with the standard procedure.     

On Block Updating in Markov Random Field Models for Disease Mapping

Gaussian Markov random field (GMRF) models are commonly used to model spatial correlation in disease mapping applications. For Bayesian inference by MCMC, so far mainly single-site updating algorithms have been considered. However, convergence and mixing properties of such algorithms can be extremely poor due to strong dependencies of parameters in the posterior distribution. In this paper, we propose various block sampling algorithms in order to improve the MCMC performance. The methodology is rather general, allows for non-standard full conditionals, and can be applied in a modular fashion in a large number of different scenarios. For illustration we consider three different applications: two formulations for spatial modelling of a single disease (with and without additional unstructured parameters respectively), and one formulation for the joint analysis of two diseases. The results indicate that the largest benefits are obtained if parameters and the corresponding hyperparameter are updated jointly in one large block. Implementation of such block algorithms is relatively easy using methods for fast sampling of Gaussian Markov random fields ( Rue, 2001 ). By comparison, Monte Carlo estimates based on single-site updating can be rather misleading, even for very long runs. Our results may have wider relevance for efficient MCMC simulation in hierarchical models with Markov random field components.     

Conditions for Non-confounding and Collapsibility without Knowledge of Completely Constructed Causal Diagrams

In this paper, we discuss several concepts in causal inference in terms of causal diagrams proposed by Pearl (1993 , 1995a , b ), and we give conditions for non-confounding, homogeneity and collapsibility for causal effects without knowledge of a completely constructed causal diagram. We first introduce the concepts of non-confounding, conditional non-confounding, uniform non-confounding, homogeneity, collapsibility and strong collapsibility for causal effects, then we present necessary and sufficient conditions for uniform non-confounding, homegeneity and collapsibilities, and finally we show sufficient conditions for non-confounding, conditional non-confounding and uniform non-confounding.