入世对我国环保产业的影响及对策

通过对我国环保产业的市场绩效、整体实力及立法现状进行分析，并针对入世对我国环保产业在机遇与挑战两方面的影响进行深入探讨，提出了完善市场竞争体制以提高市场绩效、调整环保产业结构以增强整体实力、建构环保产业法律体系以维护市场运行等相应的对策。     

廊坊市“文化信息资源共享工程”建设分析与研究

在介绍廊坊市“文化信息资源共享工程”建设现状基础上,阐述了资源共享的意义,分析了资源共享存在的问题,研究了相应的解决方法。     

略论“ちょっヒ”所反映的日本文化

通过对“ちょっヒ”四种用法的考察，可以发现“ちょっヒ”的比较标准由客观存在的事物转向了主观判断，其客观性逐渐减弱，主观性逐渐加强。这些变化反映了日本人中考虑对方心理感受的文化心理。     

保障公民劳动就业权的里程碑

就业促进法这部法律属于经济法中的宏观经济调控法，把它放到宏观经济调控法的结构中看，仍有一些问题值得探讨和研究。     

范德华力：生物学家、化学家、工程师和物理学家手册

分子间的范德华力是十分微弱的，但可以说普遍的存在着，在很多体系中起着巨大的作用，在物理学、化学、生物学等学科中得到了广泛的研究。但怎样计算范德华力？如何考虑各种条件对范德华力的影响？     

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.     

我国廉租房建设中存在的问题及对策分析

廉租房作为解决城镇低收入家庭住房问题的一种主要渠道,其建设的进展情况并不乐观,在建设模式、资金供应、分配标准等方面仍存在不少问题。为此,需要加强认识、采取多元化融资方式、完善廉租房相关制度,使廉租房建设真正落实到位,从而促进我国住房保障体系乃至整个房地产市场的健康发展。     

关于新时期实验物理教学的几点思考

本文结合我校实际，对新时期实验物理教学中出现的一些问题和现象作了分析讨论。提出相应的措施和方法。     

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.