浅论信息化社会图书馆的服务功能

本文着眼于信息化社会对图书馆工作的影响和要求 ,阐述了图书馆由“借阅服务”到“信息服务”的服务功能的演变、信息服务的特点和信息服务对图书馆员的要求。对做好新时期的图书馆工作具有较强的理论价值和现实指导意义     

如何改进英语写作

写作是反映学生综合运用语言能力的表达性技能，同时也是英语教学中的薄弱环节。本从加强英语基本功训练，培养写作兴趣，正确辅导学生练笔等方面探讨如何提高英语写作的能力。     

试论张洁初期作品中的叙述语言

从叙事学的角度，对张洁步入文坛初期作品的叙述语言，进行了全面系统的分析，探讨了其作品叙述语言的特色和规律。指出了张洁叙述语言的秀逸清丽、揭示心灵、对比观照三方面的特点，着重分析了张洁在作品中所运用的叙述方式和手法。     

韶关体育旅游的现状及发展对策

采用献、调查和访谈等方法对韶关体育旅游的现状进行调查，找出其存在的主要问题，提出发展韶关体育旅游的对策，可为有关职能部门的决策提供依据。     

基于计算机和课堂的英语教学模式的教师角色研究

本文按照《大学英语课程教学要求》，简要分析了基于计算机和课堂的英语教学模式，提出了在这种模式下大学英语教师的角色定位，以更好的发挥学生的主体作用和教师的主导作用。     

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.     

基于LPC2104的μC/OS-II引导装载系统的设计与实现

在嵌入式系统设计中,针对嵌入式处理器和操作系统的引导装载系统代码的设计是一个重点.根据LPC2104的硬件结构,给出在基于μC/OS-II操作系统的嵌入式应用中,BootLoader程序的设计流程,编写了启动代码,整个方案有较大的灵活性和实用性.     

上环的若干性质在其对偶中的推广

在Brzezinski T(2002)A-上环的基础上介绍了C-环的基本概念,C是一个余代数.分析了C-环及其模范畴的结构,并构造了C-环的例子.研究了C-环中的两个主要函子:忘却函子F:MA→MC和导出函子-□CA:MC→MA.并且讨论了两个函子可分的条件以及这两个函子的伴随情况.其次结合伽罗华理论将上环的一些主要性质推广到C-环,进而得到了C-环中的一些有意义的结论.     

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.