利他与合作:生物有机体的生存策略和进化路径

利他与合作是生物有机界普遍存在的现象．进一步研究表明,这类现象与经典的自然选择学说并不相悖,而是对其的进一步发展．利他与合作不过是生物有机体另一种不同的生存策略;利他与合作的进化也只是生物有机体为适应环境而形成的另一条不同的进化路径．     

伴性基因的选择原理

本文根据基因平衡定律和伴性基因的遗传特点,从理论上讨论了伴性基因的选择原理。主要对显性和隐性伴性基因的完全和部分选择问题进行了分析和讨论,并推导出了各种选择方式下基因频率的计算公式。     

浅谈精英流动及其选择机制

介绍了精英、精英的流动和精英的选择,阐述了社会主义国家的精英思想,分析了关于完善精英流动与选择机制的几点意见。     

稀疏对数回归及其在基因选择中的应用

针对微阵列分类中涌现出的小样本、超高维问题,提出了一种同时进行基因选择和分类的稀疏对数回归学习机.结合部分自适应弹性网络惩罚与对数似然损失函数,提出了稀疏对数回归学习机,证明了该学习机能激励自适应群体基因选择效应.     

影响植物分枝的一些基因及其分子机制

了解植物分枝机理不仅具有理论意义,对于农业、林业、果树、蔬菜和花卉等生产实践更具有重要意义.由于分子生物学的发展,人们有机会获得转基因植物和突变体,利用这些材料从不同角度对植物分枝机理进行深入研究.影响植物分枝的分子机制是多种多样的,对激素感知、激素代谢、激素运输等方面的影响都可以改变植物的分枝状况.目前,分枝的分子机制研究已取得较大进展.     

基因选择的快速Fisher优化模型

基因选择是基因芯片数据分析中的一个重要问题。基因选择的主要困难在于基因数远远大于实验样本数。在Fisher优化模型的基础上,提出了快速Fisher优化模型,从而使得算法的计算规模主要依赖于样本数而不是特征数,大大提高了计算速度。在公共数据中的实验表明该方法速度快,选择的基因对分类结果是有效的。     

论国有企业经营者的选择机制

企业家是企业的脊梁，如何保证最有经营管理能力的人成为企业经营者，如何建立起一套有效的国有企业经营者选择机制，是国有企业改革中的一个难点问题。目前，我国企业经营者的选择机制主要是行政指派选择机制，这种机制具有很多弊端。我们必须建立和完善符合社会主义市场经济体制要求的市场化经营者选择机制。     

基于置换检验的两步基因特征选择算法

针对目前大规模基因数据集中存在大量的噪声和冗余基因这一问题,提出了一种基于置换检验的两步基因特征选择算法。该算法首先采用方差分析过滤噪声基因,然后采用相关系数过滤冗余基因,最后结合置换检验的方法,可以高效、自主地处理大规模基因数据集。采用PAM(prediction analysis for microarrays)分类器,在RSCTC 2010 Discovery Challenge提供的12个竞赛数据集作基因特征选择与分类实验,实验结果表明,提出的算法能够选择高分辨、低冗余的基因子集,与目前其他基因特征选择算法相比,可以提高分类器性能。     

公共选择理论对我国行政决策机制改革的启示

公共选择理论把政府当作“经济人”,公共选择的参与者所能够做到的是尽量完善决策过程,加强对决策者的监督,加大约束力度。为此,我国在改革与完善行政决策机制过程中要加强制度上法律上、对决策过程的监督,尤其是增强民众的参与能力与监督力。     

Kin selection and eusociality

Arising from M. A. Nowak, C. E. Tarnita & E. O. Wilson 466, 1057-1062 (2010); Nowak et al. reply. Hamilton described a selective process in which individuals affect kin (kin selection), developed a novel modelling strategy for it (inclusive fitness), and derived a rule to describe it (Hamilton's rule). Nowak et al. assert that inclusive fitness is not the best modelling strategy, and also that its production has been "meagre". The former may be debated by theoreticians, but the latter is simply incorrect. There is abundant evidence to demonstrate that inclusive fitness, kin selection and Hamilton's rule have been extraordinarily productive for understanding the evolution of sociality.     

Kin selection and cooperative courtship in wild turkeys

In the few species of birds in which males form display partnerships to attract females, one male secures most or all of the copulations. This leads to the question of why subordinate males help in the absence of observable reproductive benefits. Hamilton's concept of kin selection, whereby individuals can benefit indirectly by helping a relative, was a crucial breakthrough for understanding apparently altruistic systems. However in the only direct test of kin selection in coordinated display partnerships, partners were unrelated, discounting kin selection as an explanation for the evolution of cooperation. Here I show, using genetic measures of relatedness and reproductive success, that kin selection can explain the evolution of cooperative courtship in wild turkeys. Subordinate (helper) males do not themselves reproduce, but their indirect fitness as calculated by Hamilton's rule more than offsets the cost of helping. This result confirms a textbook example of kin selection that until now has been controversial and also extends recent findings of male relatedness on avian leks by quantifying the kin-selected benefits gained by non-reproducing males.     

Human behaviour: Egalitarian motive and altruistic punishment

Altruistic punishment is a behaviour in which individuals punish others at a cost to themselves in order to provide a public good. Fehr and G?chter present experimental evidence in humans indicating that negative emotions towards non-cooperators motivate punishment, which, in turn, provokes a high degree of cooperation. Using Fehr and G?chter's original data, we provide an alternative analysis of their experiment that suggests that egalitarian motives are more important than motives for punishing non-cooperative behaviour. This finding is consistent with evidence that humans may have an evolutionary incentive to punish the highest earners in order to promote equality, rather than cooperation.     

Combining rough set theory and instance selection in ontology mapping

This paper presents a novel ontology mapping approach based on rough set theory and instance selection .In this appoach the construction approach of a rough set-based inference instance base in which the instance selection （involving similarity distance, clustering set and redundancy degree） and discernibility matrix-based feature reduction are introduced respectively; and an ontology mapping approach based on multi-dimensional attribute value joint distribution is proposed. The core of this mapping aI overlapping of the inference instance space. Only valuable instances and important attributes can be selected into the ontology mapping based on the multi-dimensional attribute value joint distribution, so the sequently mapping efficiency is improved. The time complexity of the discernibility matrix-based method and the accuracy of the mapping approach are evaluated by an application example and a series of analyses and comparisons.     

ZENK基因与鸣禽鸣啭行为

雄性鸣禽的鸣转行为能诱发ZENK在相关脑区的表达,但鸣转方式（求偶性鸣转或非求偶性鸣转）的不同会导致ZENK基因活性的差异,鸟脑内ZENK基因的研究,为深入探讨动物行为与脑活动这间的相关性提供了依据。     

图论在旅游线路选择中的应用

运用“最邻近插入法”和“分枝定界法”两种方法建立寻找近似最佳旅游线路的图论模型，提出求解的算法，并以莆田市为例具体分析它们的求解过程，为旅游者设计旅游线路提供理论依据和参考。     

Machine learning in data envelopment analysis: A smart mechanism for indicator selection

Indicator selection has been a compelling problem in data envelopment analysis. With the advent of the big data era, scholars are faced with more complex indicator selection situations. The boom in machine learning presents an opportunity to address this problem. However, poor quality indicators may be selected if inappropriate methods are used in overfitting or underfitting scenarios. To date, some scholars have pioneered the use of the least absolute shrinkage and selection operator to select indicators in overfitting scenarios, but researchers have not proposed classifying the big data scenarios encountered by DEA into overfitting and underfitting scenarios, nor have they attempted to develop a complete indicator selection system for both scenarios. To fill these research gaps, this study employs machine learning methods and proposes a mean score approach based on them. Our Monte Carlo simulations show that the least absolute shrinkage and selection operator dominates in overfitting scenarios but fails to select good indicators in underfitting scenarios, while the ensemble methods are superior in underfitting scenarios, and the proposed mean approach performs well in both scenarios. Based on the strengths and limitations of the different methods, a smart indicator selection mechanism is proposed to facilitate the selection of DEA indicators.     

行为控制机制与行为管理制度的数学模型新进展

论述了行为控制研究领域近年来的主要进展，包括激励理论、契约与委托代理理论、全面控制理论等．其中，激励理论主要是通过改变回报来控制行为的：契约与委托代理理论则把行为控制关系看作是委托代理关系，侧重研究回报机制的设计与优化；而全面的行为控制理论则把行为控制的方法从单方面的回报控制扩大到资源控制、项目控制和回报控制3个方面，从而扩大了行为控制理论的研究领域。