气：中国美学审美域构成中的活力基元

受《周易》神秘文化影响,在中国美学看来,“气”是宇宙的原初实有,是最高的本体,“道”即混沌之“气”,宇宙万物皆由“气”所化生化合;作为宇宙万物生成的中间环节,“气”生于“道”或“太极”,是宇宙万物的共同构成质料或元素。审美创作活动中心物的感应、情景的交融离不开“气”与“气化”作用。由此,中国美学提出了“气之动物,物之感人”说：宇宙万物的生成本源为“阴阳”二气,“阴阳”二气的运行,“气”之动物,激发了万物的生长发育,自然也激发创作者的创作动机,而摇荡性情,形诸舞咏,引起审美创作。“物动心感”,引起创作者内心感动与审美域构成的原初动力是“气”。     

关系营销导向对于企业营销渠道控制行为的影响

基于营销渠道行为理论和关系营销理论,通过假设检验,本文探讨了在中国的营销渠道中关系营销导向对企业营销渠道控制行为的影响.研究结果显示,第一,关系营销导向对企业使用非强制性权力进行控制有正向的影响,对企业权力与企业使用强制性权力进行控制之间的正相关关系有负向的调节作用;第二,关系营销导向对企业通过与其渠道伙伴共同解决问题而进行控制有直接且正向的影响.此外,我们还发现企业营销渠道控制方法之间存在着交互影响.文章最后对研究结果进行了讨论,并指出了研究结果理论贡献与实际应用.     

Characterizing performance in socio-technical systems: A modeling framework in the domain of nuclear power

In modeling complex socio-technical systems, the critical nature of many interactions between human and automation are often unrecognized until the system experiences catastrophic consequences. One reason for this is that the systems are generally very robust and serious problems are rare. Another reason is the difficulty in modeling the variety of interactions in a manner compatible with analytical and/or computational models of the technological components of the system. Organizational issues and extraneous influences add to these modeling difficulties. We have developed a modeling framework that integrates certain ideas from social networks, graph theory, and simulation, all tied together using heuristics that reflect deep domain knowledge. This framework forms the basis for a computational implementation, portions of which have been implemented. Details of the model and the rationale for the model structure are described in this paper.     

产业集聚的原动力与广西产业集群的培育策略研究

产业集聚的原动力来自于产业转移的外向型拉动和内源性产业集聚的自然禀赋。广西的产业集群培育还存在一些不足之处,主要表现在产业集群的培育没有完全依托广西的资源禀赋,而在承接东部产业转移时也没有充分地与当地的资源优势相结合。广西产业集群的培育策略应该依托产业禀赋优势,并结合产业集聚的两种原动力。政府应靠市场无形之手培育产业集群。     

大数据时代的计算社会科学与学术话语体系重构

社会科学的发展经历了从分析到综合再到复杂性科学三个阶段,与之相伴,科学方法论从还原论到整体论再到复杂适应论持续演进。复杂适应系统理论把被经典科学理性所简化、排除的多样性、无序性、个体性因素重新带回科学的视野,深刻了社会科学家对研究对象的本质的认识,指明了社会科学方法体系创新和突破的方向。但由于缺乏能够满足复杂适应系统研究要求的数据条件和技术手段,使其实际取得的成果远未达到人们的期待。大数据时代带来的空前丰富的数据资源与先进的数据处理技术,为社会科学实证研究开辟出新的路径,计算社会科学已经形成大数据的获取与分析、多主体社会模拟、互联网社会科学实验三大方法体系,为社会调查、统计分析、社会实验等研究方法增添了全新的内容。复杂适应系统研究的技术实现正在成为可能,社会科学正处在一次突破性进展的前夜。在新一轮的信息技术革命浪潮中,中国已经跻身于世界先进行列,这为中国社会科学实现“弯道超车”准备了极为有利的条件。中国社会科学界应当敏锐地觉察和把握这一重大机遇,积极推动计算社会科学的发展,使之在实现国际学术话语体系重构上发挥重要作用。     

Adaptive positive and negative runs test

As a nonparametric randomness test, the positive and negative runs test is widely used in practice due to the simplicity of its procedures. The test can lose efficiency if the alternative distribution is symmetrical at 0.5. In addition, the test can only be applied to test the randomness of a sequence from the uniform distribution. In this paper, we introduce an adaptive positive and negative runs test method to maximize the power function by choosing the optimal cut point. Also, the test is extended to check the randomness of a sequence generated from any other given distributions. Furthermore, we derive the exact distribution and obtain the asymptotical critical values of the proposed test statistics. Compared with the existed test, the efficiency of the proposed adaptive positive and negative runs test is competitive through simulation study.     

Significance test of clustering under high dimensional setting with applications to cancer data

For high dimensional data, the SigClust is developed for testing the significance of clustering. The cluster index (CI) for SigClust is conducted by the ratio of the within-cluster and total sum of squares. But its empirical size is too conservative to be over controlled. By removing the cumbrous terms in the CI, an improved index (BCI) is proposed in this paper. The coefficient of variation of the BCI can be significantly reduced, implying that the new index BCI is stable. Moreover, the new significance test (NewSig) maintains the size, meanwhile, provides a greater power. Simulation experiments and two real cancer data examples are analysed for illustrating the performance of the new methodology.     

Robust regression: an inferential method for determining which independent variables are most important

Consider the usual linear regression model consisting of two or more explanatory variables. There are many methods aimed at indicating the relative importance of the explanatory variables. But in general these methods do not address a fundamental issue: when all of the explanatory variables are included in the model, how strong is the empirical evidence that the first explanatory variable is more or less important than the second explanatory variable? How strong is the empirical evidence that the first two explanatory variables are more important than the third explanatory variable? The paper suggests a robust method for dealing with these issues. The proposed technique is based on a particular version of explanatory power used in conjunction with a modification of the basic percentile method.     

Sample size determination for testing equality in frequency data under an incomplete block crossover design

When there are more than two treatments under comparison, we may consider the use of the incomplete block crossover design (IBCD) to save the number of patients needed for a parallel groups design and reduce the duration of a crossover trial. We develop an asymptotic procedure for simultaneously testing equality of two treatments versus a control treatment (or placebo) in frequency data under the IBCD with two periods. We derive a sample size calculation procedure for the desired power of detecting the given treatment effects at a nominal-level and suggest a simple ad hoc adjustment procedure to improve the accuracy of the sample size determination when the resulting minimum required number of patients is not large. We employ Monte Carlo simulation to evaluate the finite-sample performance of the proposed test, the accuracy of the sample size calculation procedure, and that with the simple ad hoc adjustment suggested here. We use the data taken as a part of a crossover trial comparing the number of exacerbations between using salbutamol or salmeterol and a placebo in asthma patients to illustrate the sample size calculation procedure.