核动力装置模拟器就地系统中双机通讯的实现

利用微机软仿真实现模拟器就地系统中仪表的动态实时显示和开关的实时控制，可以节省的大量外部设备，提高系统可扩充性。本文介绍了该技术实现的方法，即服务器与客户机之间数据双向通讯的实现途径。经实践验证，本文的方法是正确、可行的。     

随机控制序在保险决策中的应用

通过介绍随机控制序的概念和性质 ,从而探讨了随机控制序在保险决策问题中的应用 ,得到了很有意义的结果     

股份制企业筹资最佳资金结构的确定

最佳资金结构的确定，关系到股份制企业资金成本的高低和财务风险的大小．如何通过筹资发挥财务杠杆的作用，确定最佳资金结构是股份制企业筹资决策的关键．针对这一问题，比较详细地介绍了每股利润分析法、比较资金成本法在确定最佳资金结构中的运用．     

累次极限换序的一个充分条件

本文对于二元函数的累次极限的换序给出了一致收敛这一充分条件。     

浅谈大学引领社会发展的途径

阐述了新形势下大学职能的新变化。从为社会培养高素质的人才、进行科研创新以促进社会经济的发展、利用智力优势参与政府政策的制定等方面探讨了大学引领社会发展的各种途径。     

基于GIS的生态功能区划在生态城市建设中的应用——以陕西省凤县为例

在对目前生态功能区划众多方法进行比较的基础上,将GIS技术应用到陕西省宝鸡市凤县生态功能区划中。根据研究区生态环境要素、生态环境敏感性及生态服务功能的差异,把区域划分成四个不同的生态功能区,即北部森林生态重点保护区,中部林、药、养殖、矿产限制开发区,中部粮、椒、果、城镇与贸易重点开发区,南部生物多样性保护与生态旅游区,并综合考虑区域实际情况进行深一步规划研究。     

Linex损失下Pareto分布参数的Bayes估计

该文讨论了在Linex非对称损失函数下Pareto分布参数的Bayes估计及多层Bayes估计,并证明了相应的估计是可容许的.     

反周期函数(0,P(I))三角插值

通过引进积分算子P(I),研究了反周期函数插值及反周期函数的2-周期(0,P(I))插值,得到了解存在的条件,并给出对应条件下解的显式.     

论网络环境下图书馆职能的变化

根据由于电子信息网络的发展状况,论述了网络图书馆职能的变化。     

Finding finer functions for partially characterized proteins by protein-protein interaction networks

Based on high-throughput data, numerous algorithms have been designed to find functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors, including (1) the low a-priori probability of novel proteins participating in a detailed function; (2) the huge false data present in high-throughput datasets; (3) the incomplete data coverage of functional classes; (4) the abundant but heterogeneous negative samples for training the algorithms; and (5) the lack of detailed functional knowledge for training algorithms. Here, for partially characterized proteins, we suggest an approach to finding their finer functions based on protein interaction sub-networks or gene expression patterns, defined in function-specific subspaces. The proposed approach can lessen the above-mentioned problems by properly defining the prediction range and functionally filtering the noisy data, and thus can efficiently find proteins’ novel functions. For thousands of yeast and human proteins partially characterized, it is able to reliably find their finer functions (e.g., the translational functions) with more than 90% precision. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins.