优化高校教师知识产权激励制度

阐述了高校教师知识产权激励制度的内容和功效,并在分析现行高校教师知识产权激励乏力的基础上,对优化高校教师知识产权激励制度提出建议.     

铝厂污水零排放研究

本文从水质方面分析了西南某铝厂污水零排放的可行性。为了实现污水零排放,对该铝厂东片区、氧化铝厂、热电厂三大污水处理系统进行改进。从现场试验监测及统计分析结果表明,改进后的处理系统处理的水质满足再生水水质指标要求,该铝厂污水零排放的实现创造了极大的经济和社会效益。     

中国传统服饰美学思想的特征

探讨中国服饰美学思想所涉及的“天人合一”、社会伦理道德以及等级方面的内容．运甩史料考证、逻辑推理及中外比较相结合的论证方法，表明中国传统服饰美学思想是具有鲜明的民族个性特征的，得出中国传统服饰美学思想所涵盖的社会化、自然化和多样化特征．     

Homology Priority Task Scheduling in μC/OS-Ⅱ Real-Time Kernel

μC/OS-Ⅱ is an open source real-time kernel adopting priority preemptive schedule strategy. Aiming at the problem of μC/OS-Ⅱ failing to support homology priority tasks scheduling, an approach for solution is proposed. The basic idea is adding round-robin scheduling strategy in its original scheduler in order to schedule homology priority tasks through time slice roundrobin. Implementation approach is given in detail. Firstly, the Task Control Block （TCB） is extended. And then, a new priority index table is created, in which each index pointer points to a set of homology priority tasks. Eventually, on the basis of reconstructing μC/OS-Ⅱ real-time kernel, task scheduling module is rewritten. Otherwise, schedulability of homology task supported by modified kernel had been analyzed, and deadline formula of created homology tasks is given. By theoretical analysis and experiment verification, the modified kernel can support homology priority tasks scheduling, meanwhile, it also remains preemptive property of original μC/OS-Ⅱ.     

学生管理中的沟通策略——浅谈单亲家庭子女的学校教育

本文在对单亲家庭子女的性格作客观分析的基础上,探讨了学校教育管理中如何引导单亲家庭子女健康成长的问题。主要提出了学生管理中的五大沟通策略。     

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.     

文学的存在方式现象学

文学的存在方式是指文学赖以存在的整体结构。通过对文学创作活动(事件)、文学接受活动(事件)的现象学描述,建构了文学的存在方式———文学存在的四维整体结构和文学存在之主体间的交流结构,后两者共同组建着文学的存在整体。     

谈公安院校计算机基础教学的改革问题

分析了公安院校当前在计算机基础课程教学中存在的问题，并针对这些问题提出一些改进的措施和建议，以适应当前公安教育的需要。     

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.