产业集群培育与城市功能优化

产业集群对地区经济发展具有积极的作用.集群的形成是多种因素综合作用的结果,本质上依赖于城市功能的支持.城市功能是个系统概念,是要素、结构和支持体系的统一.按照系统性、个性化的原则优化城市功能,可有效培育产业集群,带动地区经济发展.     

孔子“德治”思想断论

孔子的政治思想以德治为基本特征,其德治思想源远流长,是对远古和三代以来传统治国方略的总结和发展.孔子的德治思想内涵丰富,自成体系,相当精辟,是儒家德治主义政治思想不可动摇的基础.在中国政治文明和精神文明的历史上,众多思想家丰富和完善了孔子的德治思想,形成了法治与德治并用的完整理论,并在实际的政治运作中发挥了积极的指导作用.     

清朝末年有关农业政策的实施

清末重农与传统的重本抑末不同,它是从推动工商业发展的角度支持农业发展,采取了兴办农业教育、组建农会组织、鼓励农业垦殖等一系列兴农政策措施,推动了中国传统农业近代化。但由于封建制度性障碍,清末朝庭的腐败,中国近代农业发展仍相当缓慢。     

高新技术产业集群的区域经济一体化效应分析

高新技术产业集群的固有生命力和优势是降低交易成本、利于技术创新、形成专业化协作与分工,从而降低企业风险,有效地推动产业结构的升级;经济一体化的内涵是对内实行贸易投资自由化和经济技术合作,对外构成一定壁垒的一种谋求地区和民族利益最大化的思潮或行为,强烈的利益追求、政府的积极主导和良好的制度保障是经济一体化的发展动因;区域经济一体化具有静态和动态两种主要经济效应,充分利用区域经济一体化效应可以优化产业结构,推进我国经济新一轮增长。     

论沈从文生命观的内在矛盾及其影响

沈从文的思想与创作充满着令人困惑的矛盾现象,这可以从其生命观的内在矛盾中找到根源。沈从文生命观的内在矛盾表现为生命本体观与生命价值观的内在矛盾。生命观的这种内在矛盾不仅深刻地影响到沈从文的社会政治观念与文学思想,而且在其创作中表现出来,深刻地影响到其创作格局与艺术风格的建构。通过探讨沈从文生命观的内在构成及其矛盾,可以透视其创作中的许多重要现象,把握其创作独特性的一些重要方面。     

旅游对日本经济的"波及效应"分析

从日本多年来的情况看,旅游消费促进了其旅游事业及相关企业乃至地区经济的发展 (即“波及效应”)。然而,这种作用也有其局限性。各地因旅游资源和产业结构等方面存在差异,因而旅游消费所产生的经济效果明显不同;与国际相比,日本还处在很低的水平。可见,日本旅游消费仍有潜力可挖。提高当地中间生产资料的供给率、防止旅游漏损(资金流失)和对其产业结构作相应调整等,是提高其经济波及效应的有效手段。     

试析我国农业补贴政策

文章在论述了我国农业补贴政策演变的基础上,分析了我国农业补贴政策的现状及其目前存在的问题,并提出了完善对策,即建立完善农业补贴长效机制,探索一条高效的政策执行渠道,以此来提高中国农业补贴政策的效果。     

传统民居色彩研究

针对传统民居色彩进行专项研究,试图通过一新的层面,即以民居色彩为一个整体效应来分析其形成因素和特征.依托四季色彩理论、色彩学、色彩地理学等相关学科,提出了色彩群理念,辨析了色彩群的内涵以及外延;并以不同的观察状态分析不同地域传统民居色彩群的特质.     

Back to basics: explaining sample size in outcome trials,are statisticians doing a thorough job?

Time to event outcome trials in clinical research are typically large, expensive and high‐profile affairs. Such trials are commonplace in oncology and cardiovascular therapeutic areas but are also seen in other areas such as respiratory in indications like chronic obstructive pulmonary disease. Their progress is closely monitored and results are often eagerly awaited. Once available, the top line result is often big news, at least within the therapeutic area in which it was conducted, and the data are subsequently fully scrutinized in a series of high‐profile publications. In such circumstances, the statistician has a vital role to play in the design, conduct, analysis and reporting of the trial. In particular, in drug development it is incumbent on the statistician to ensure at the outset that the sizing of the trial is fully appreciated by their medical, and other non‐statistical, drug development team colleagues and that the risk of delivering a statistically significant but clinically unpersuasive result is minimized. The statistician also has a key role in advising the team when, early in the life of an outcomes trial, a lower than anticipated event rate appears to be emerging. This paper highlights some of the important features relating to outcome trial sample sizing and makes a number of simple recommendations aimed at ensuring a better, common understanding of the interplay between sample size and power and the final result required to provide a statistically positive and clinically persuasive outcome. Copyright © 2009 John Wiley & Sons, Ltd.     

MODELING THE LIFETIME OF LONGITUDINAL ELEMENTS

In the study of the stochastic behaviour of the lifetime of an element as a function of its length, it is often observed that the failure time (or lifetime) decreases as the length increases. In probabilistic terms, such an idea can be expressed as follows. Let T be the lifetime of a specimen of length x, so the survival function, which denotes the probability that an element of length x survives till time t, will be given by S_T (t, x) = P(T > t/α(x), where α(x) is a monotonically decreasing function. In particular, it is often assumed that T has a Weibull distribution. In this paper, we propose a generalization of this Weibull model by assuming that the distribution of T is Generalized gamma (GG). Since the GG model contains the Weibull, Gamma and Lognormal models as special and limiting cases, a GG regression model is an appropriate tool for describing the size effect on the lifetime and for selecting among the embedded models. Maximum likelihood estimates are obtained for the GG regression model with α(x) = cx^b . As a special case this provide an alternative to the usual approach to estimation for the GG distribution which involves reparametrization. Related parametric inference issues are addressed and illustrated using two experimental data sets. Some discussion of censored data is also provided.