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。     

哈贝马斯社会批判理论根基的语言学转向

生活世界理论在哈贝马斯社会批判理论中居于核心地位，但他对生活世界的理解并不是前后一致的。他早期基本上是在综合胡塞尔和许茨生活世界理论基础上而形成自己关于生活世界的理解，而他后期的社会批判理论则奠基在语言学转向后的生活世界理论基础之上。由于这一语言学转向，与其早期社会批判理论相比，他后期的社会批判理论在形式上更趋合理，在内容上更深刻，在批判的同时还形成了自己关于未来理想社会的建构理论。     

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？     

加强意识形态工作 巩固意识形态安全

在国内外综合因素的影响下,我国主流意识形态的指导地位存在安全隐患,因此加强意识形态工作,确保意识形态安全是摆在我们面前的一项重大而艰巨的任务。     

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.     

Effects of self evaluation on late ERP components

High-density event-related potentials (ERPs) were measured when participants were required to evaluate oneself, significant others (father and mother) and the most familiar public figures (Lei Feng and Jiang Qing) based on trait adjectives. There were three referential conditions and a non-referential control condition including self-evaluation (SE), significant other-evaluation (SOE), general other-evaluation (GOE), and semantic positivity-evaluation (SPE). The behavioral data showed that RTs to GOE condition were longer than the other three conditions. Scalp ERP analysis revealed that (1) SPE condition elicited a more negative N2 than SE condition, which was associated with inhibition of responses; (2) SE condition elicited a more positive P300 than SPE condition, which was an index of attention to self-relevant stimuli; (3) there was no difference on the amplitude and latency of P300 between SE and SOE conditions, which reflected that the concept of “self” included intimate others (e.g., father and mother) in Chinese culture.     

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.