基于因子分析的产学研合作教育影响因素研究

本文通过构建产学研合作教育影响因素的指标体系,运用因子分析法导出其中的主要影响因素,通过多元回归分析,进一步厘清各因子的影响程度,提出促进产学研合作教育人才培养机制的对策建议。     

船舶产业投资基金发展路径研究

文章从我国目前与船舶相关的产业投资基金现状入手,通过分析,揭示了目前我国船舶产业投资基金的问题主要有:投资环境中存在的三大矛盾;未充分吸收和引导民间资本,以及对船舶企业经营管理的参与化程度不高。针对这些问题,提出了我国船舶类产业投资基金发展的步骤和策略。     

中原经济区工业与信息化自主创新对策初探

河南省工业与信息化领域如何从本省实际出发,吸取国内外成功经验,增强区域自主创新能力,是河南省科技工作和经济工作的第一要务,是全面推进中原经济区建设、加快中原崛起河南振兴的关键。通过对"十一五"时期河南省工业与信息化领域自主创新能力现状进行分析,结合国家自主创新战略,提出了"十二五"河南省工业与信息化领域提升自主创新能力应遵循的原则,并结合行业特点提出了相应行业自主创新工作对策,以期能对中原经济区建设发挥更大作用。     

Finding environmentally important industry clusters: Multiway cut approach using nonnegative matrix factorization

This paper proposes an optimal combinatorial method for finding groups of industries with relatively large CO₂ emissions through industrial relations. Using an economic input–output table, we estimated a non-symmetric matrix describing how much CO₂ is emitted in producing the commodity of industry i, which was purchased to produce commodity of industry j, to meet the final demand for a specific commodity. A symmetric strength of relations matrix describing the CO₂ emissions associated with the industrial relations was further estimated using the non-symmetric matrix. The strength of relations matrix can be viewed as a representation of the supply-chain network of the final commodity. In this study, we estimated the strength of relations matrix associated with the final demand for automobiles and applied the multiway cut approach using nonnegative matrix factorization to the matrix in order to find environmentally important industry clusters in the Japanese automobile supply chain. According to our empirical results, the optimal number of industry clusters is 19, and 4 industry clusters are playing a key role in CO₂ emission reduction.     

集群式中小企业现状与发展对策研究——以武汉城市圈为例

中小企业在一个特定的区域聚集，形成集群式企业，会极大地推动区域增长。通过对武汉城市圈的抽样调查表明，企业规模、创新能力、政府服务、资金与人才等是限制集群式中小企业进一步发展的障碍因素。建议通过构建区域合作机制，创新金融体制，推动区域创新体系建设，加强服务型政府建设，优化整合人力资源等措施以实现集群式中小企业可持续发展。     

Failure Probability Under Parameter Uncertainty

In many problems of risk analysis, failure is equivalent to the event of a random risk factor exceeding a given threshold. Failure probabilities can be controlled if a decisionmaker is able to set the threshold at an appropriate level. This abstract situation applies, for example, to environmental risks with infrastructure controls; to supply chain risks with inventory controls; and to insurance solvency risks with capital controls. However, uncertainty around the distribution of the risk factor implies that parameter error will be present and the measures taken to control failure probabilities may not be effective. We show that parameter uncertainty increases the probability (understood as expected frequency) of failures. For a large class of loss distributions, arising from increasing transformations of location‐scale families (including the log‐normal, Weibull, and Pareto distributions), the article shows that failure probabilities can be exactly calculated, as they are independent of the true (but unknown) parameters. Hence it is possible to obtain an explicit measure of the effect of parameter uncertainty on failure probability. Failure probability can be controlled in two different ways: (1) by reducing the nominal required failure probability, depending on the size of the available data set, and (2) by modifying of the distribution itself that is used to calculate the risk control. Approach (1) corresponds to a frequentist/regulatory view of probability, while approach (2) is consistent with a Bayesian/personalistic view. We furthermore show that the two approaches are consistent in achieving the required failure probability. Finally, we briefly discuss the effects of data pooling and its systemic risk implications.     

河南动漫产业发展研究

国际国内动漫产业发展迅猛,河南动漫产业发展相对滞后。制约河南动漫产业发展的主要因素是人才培养机制不健全、政府主导相对缺失、生产技术落后、市场机制没有建立,等等。应加强政府主导,建立统一的人才培养基地,推进校企合作,培养市场意识,充分利用河南历史文化资源和人力资源的优势,促进河南动漫产业的发展。     

浅谈战国时秦国的后勤经济战略

秦国能统一中国,除了政治、军事方面的原因,还有一个被史学界忽略的因素：建立了完整的经济战略体系。这套体系包括后勤基地建设、经济改革以及交通路网络。这套体系为秦统一战争提供了巨大的后勤保障。     

Machine learning for multi-criteria inventory classification applied to intermittent demand

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously.

In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems.     

试论高等教育“学、研、产”的关系

简论了教学、科研、生产三者的关系,认为三者相互依存、相互需求、相互促进、相互补充,同时指出高等教育是产学研结合组织基础中的中心主体。