广东工业技术创新能力的指标评估及提升途径

在竞争日益激烈的今天,技术创新能力在产业区域竞争中具有越来越重要的地位。文章从技术创新队伍、技术创新经费投入、技术创新成果和高新技术产业化四个方面的指标对广东工业技术创新能力进行了分析和综合评价,并提出了进一步提升广东工业技术创新能力,增强产业竞争力的途径。     

工程图学创新能力的培养

在科学技术迅猛发展的今天,创新尤为重要。教育是培养创新精神和创新人才的重要摇篮,为此我们必须更新教育观念、摒弃旧的传统教学模式,开展创新教育,培养学生的创造性思维能力、获胜知识的能力,及进行个性化教育,使学生具有更强的自信心、独立的独特的思维方式,使其探求问题有更多的兴趣和毅力。积极开展创新活动,使学生在创新活动中拓宽自己的知识面,在实践中脚踏实地提高自己的各种能力,打破对创新的神秘感,树立创新信念。     

论新文学思潮与新文学流派的动态关系

新文学思潮与新文学流派之间因果莫辨、源流纠缠的动态关系 ,充分证明了近百年来新文学思潮与新文学流派本身非线性、非对称的生存状态。所谓非线性与非对称 ,其实质就是不稳定性和不成熟性 ,它们直接制约着新文学近百年发展进程 ,使之在历时态和共时态上始终呈现出某种程度的不良生存状态和发展趋势。     

基于知识链的管理

论述了知识链的有关概念和特点,通过分析以往的知识链模型,提出了改进的知识链模型;对知识链管理的机制进行了分析;从控制论的角度,对知识链内部知识成长的机制进行了分析,并指出知识链内部知识转移的影响因素,为知识管理的发展提供了一定的理论依据。     

汉代边郡军粮廪给问题探讨

粮食供给是军队后勤给养中的一项重要内容。汉代边塞地区军粮主要供给吏卒及其家属,也用于借贷和马、牛、驼、狗之食。从量制、时制、身份差别、劳动强度和年龄、性别等方面,分析了军队吏卒和吏卒家属口粮分等廪给的具体情况及其影响要素,从而揭示出汉代军粮供给体制的内在规律。正是由于汉代军粮廪给有章可循,才最大限度地提高了后勤的供应能力和军队的战斗力,保证了军队平时守土卫边和战时有效打击来犯之敌。     

试论培训后勤职工与发挥后勤德育功能

高校后勤具有很强的德育功能。培训高校后勤职工具有多方面的重要性。高校后勤工作具有繁杂性,微利服务性和部分福利性等特点。队伍和素质参差不齐、年龄老化、学历偏低、劳动技术较弱和部分职工打工仔意识强,主人翁意识弱等现状表现出了搞好后勤职工培训的必要性和紧迫性。因此,要对后勤职工搞好四个方面的培训,通过四条途径发挥其德育功能。     

Improving Convergence of the HastingsMetropolis Algorithm with an Adaptive Proposal

The HastingsMetropolis algorithm is a general MCMC method for sampling from a density known up to a constant. Geometric convergence of this algorithm has been proved under conditions relative to the instrumental (or proposal) distribution. We present an inhomogeneous HastingsMetropolis algorithm for which the proposal density approximates the target density, as the number of iterations increases. The proposal density at the n th step is a non-parametric estimate of the density of the algorithm, and uses an increasing number of i.i.d. copies of the Markov chain. The resulting algorithm converges (in n ) geometrically faster than a HastingsMetropolis algorithm with any fixed proposal distribution. The case of a strictly positive density with compact support is presented first, then an extension to more general densities is given. We conclude by proposing a practical way of implementation for the algorithm, and illustrate it over simulated examples.     

Estimating rating transition probabilites with missing data

In this article we provide a rigorous treatment of one of the central statistical issues of credit risk management. GivenK-1 rating categories, the rating of a corporate bond over a certain horizon may either stay the same or change to one of the remainingK-2 categories; in addition, it is usually the case that the rating of some bonds is withdrawn during the time interval considered in the analysis. When estimating transition probabilities, we have thus to consider aK-th category, called withdrawal, which contains (partially) missing data. We show how maximum likelihood estimation can be performed in this setup; whereas in discrete time our solution gives rigorous support to a solution often used in applications, in continuous time the maximum likelihood estimator of the transition matrix computed by means of the EM algorithm represents a significant improvement over existing methods.     

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.     

蒂布特模型启迪下的可流动公共产品供给

中国目前正处于城乡公共产品统筹供给的关键时期,研究符合中国国情的公共产品供给理论具有重要意义。蒂布特模型是关于公共产品供给机制的理论研究,其政策含义是政府要采取促进居民流动的方式来提高公共产品的配置效率。由于该模型所作假设的现实性较弱,其结论和应用有一定局限性。根据流动程度,可以把公共产品划分为可流动公共产品和不可流动公共产品两类,可以通过可流动公共产品的流动来提高公共产品的配置效率。中国目前在城乡可流动公共产品优化配置方面有较大发展空间。