A spanning heuristic for the unordered and ordered matching identification problem

The matching identification problem (MIP) is a combinatoric search problem related to the fields of learning from examples, boolean functions, and knowledge acquisition. The MIP involves identifying a single “goal” item from a large set of items. Because there is commonly a cost associated with evaluating each guess, the goal item should be identified in as few guesses as possible. As in most search problems, the items have a similar structure, which allows an evaluation of each guessed item. In other words, each guessed item elicits partial information about the goal item, i.e. how similar the guess is to the goal. With this information the goal is more quickly identified.The unordered MIP has been studied by Mehrez and Steinberg (ORSA J. Comput. 7 (1995) 211) in which they proposed two different types of algorithms. The purpose of the present paper is to suggest an improved Spanning Heuristic algorithm. Its improvement increases as the problem size increases. Further results and comparisons are derived for the unordered and ordered cases.This research shows that when the search space is very large, it is better to inquire from items that are known not to be the goal (they have been ruled out by previous guesses), for the purpose of acquiring more information about the goal. As the search space is narrowed, it is better to guess items that have not been ruled out.     

Task partitioning in new product development teams: A knowledge and learning perspective

R&D alliances and outsourcing elements of the new product development process are now commonplace practices among many firms. However, little previous work has examined how these organizational choices influence project knowledge and learning. Based on a comparison of three new product development projects in the software industry, this paper examines how task partitioning in the project influences learning and knowledge development within the firm. The paper suggests that internal development projects encourage synthetic learning and development of architectural and tacit knowledge; in contrast, outsourcing and joint ventures encourage analytic learning and development of component and explicit knowledge.     

智慧导向型知识管理的一种分析框架

本文在剖析知识、智慧的概念及其内在关系的基础上,从知识和智慧有机结合角度提出了一种分层次的智慧导向型知识管理分析框架。该框架反映了知识与智慧之间相互补充、相互促进的内在统一性,并分别将知识管理和组织智慧看作手段和导向,给出了知识和智慧两个层面的集成管理策略,体现了一种全新的系统知识管理思想。其目的是使组织更加有效地实施知识管理战略,更好地发挥知识的效能,从而使组织适应环境的变化,达到与环境长期和谐共存。最后指出了本文观点对组织实施知识管理的影响和借鉴作用。     

资本结构治理效应:中国上市公司的实证研究

资本结构影响公司治理的结果最终会体现在公司绩效的变化上。本文通过对1992-2004年间我国上市公司资本结构影响公司绩效的实证分析发现:资产主营业务净利润率、经营费用率和资产利用率三个指标比股票价值能更好地解释资本结构对公司绩效的影响;国家股比例对公司绩效的负向影响越来越不显著;法人股比例和负债融资比例对公司绩效有不显著的正向影响等。这些结论表明,完善资本结构是完善我国上市公司治理结构从而提高公司绩效的重要途径之一。     

现代信用风险度量模型的实证比较与适用性分析

本文通过实证比较分析发现,现代信用风险度量模型对银行贷款的违约率、贷款损失和损失率的预测结果的差异性较大;但信用监测模型和信用风险附加法所预测的经济资本配置比例不仅符合巴塞尔协议对银行贷款经济资本的要求,也略大于实际应该配置的比例,实证表明了它们对度量我国商业银行贷款组合的信用风险具有较好的适用性.此外,本文也充分验证了借款人信用等级的不同,银行贷款经济资本配置的比例会有显著性的差异.     

中国国民财富总量、结构及其演进模式研究——基于“自上而下-自下而上”测度范式

“财富-福利等式”表明国民财富与社会福利具有理论等价关系,是评估发展可持续性的最优指示器。我国国民财富研究相对滞后,总量测度与结构分析都亟待深化。本文基于“自上而下-自下而上”范式,依据国情优化关键参数的设定,改进了我国国民财富与分类资本估算,并剖析了省际国民财富结构以识别其演进模式。研究发现:由于资源禀赋与发展阶段有别,省际国民财富结构差异显著;随着经济发展水平提升,国民财富结构先由自然资本主导转向物质资本与人力资本共同主导,进而转变为人力资本主导,最终迈入社会资本主导。各地区应结合自身禀赋与发展阶段做好“投资组合管理”,以此来提升国民财富并优化其结构从而改善经济发展的持续性。     

学前教育师范生TPACK现状调查及教学改革建议

在“互联网+”及多元文化知识不断融合的教育背景下，信息技术在教学中被提升到一个新的高度。为了解学前教育师范生群体对信息技术知识在教学活动中的应用程度，笔者采用自编的《学前教育师范生TPACK》量表对220名在校学前教育师范生进行调查。统计分析发现：1）学前教育师范生的总体TPACK知识掌握较好，但技术知识仍然较为薄弱；2）学前教育师范生TPACK知识存在显著的年级差异，但性别差异不显著；3）信息技术爱好和训练会造成学前教育师范生TPACK知识的差异。应采取的对策：1）注重培养学前教育师范生的信息技术知识和能力；2）大力推进第二课堂，促进TPK和TCK的发展；3）进行产学研合作，提升学前教育师范生的TPACK水平。     

首都第二机场建设与航空人才需求调研

首都第二机场的建设已成定局,由此而引起的大量人才需求是各层次学校所关注的焦点。北京经济管理职业学院是否能抓住这一得天独厚的机遇,将是未来几年对北京经济管理职业学院的考验,因此根据近几年来对首都二机场建设的资料的收集和调研,了解了机场建设对航空人才的需求,为学院的发展提供了建设性的参考性意见。     

Social network and content analysis of the North American Carbon Program as a scientific community of practice

The North American Carbon Program (NACP) was formed to further the scientific understanding of sources, sinks, and stocks of carbon in Earth's environment. Carbon cycle science integrates multidisciplinary research, providing decision-support information for managing climate and carbon-related change across multiple sectors of society. This investigation uses the conceptual framework of communities of practice (CoP) to explore the role that the NACP has played in connecting researchers into a carbon cycle knowledge network, and in enabling them to conduct physical science that includes ideas from social science. A CoP describes the communities formed when people consistently engage in shared communication and activities toward a common passion or learning goal. We apply the CoP model by using keyword analysis of abstracts from scientific publications to analyze the research outputs of the NACP in terms of its knowledge domain. We also construct a co-authorship network from the publications of core NACP members, describe the structure and social pathways within the community. Results of the content analysis indicate that the NACP community of practice has substantially expanded its research on human and social impacts on the carbon cycle, contributing to a better understanding of how human and physical processes interact with one another. Results of the co-authorship social network analysis demonstrate that the NACP has formed a tightly connected community with many social pathways through which knowledge may flow, and that it has also expanded its network of institutions involved in carbon cycle research over the past seven years.