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.     

电脑刺绣机针杆及挑线机构设计

以电脑刺绣机原始针杆机构和挑线机构为研究对象,在保持原有针杆运动规律和挑线杆运动规律的前提下,运用机构创新设计理论,分析电脑刺绣机针杆机构和挑线机构的可能选型.为研制新型电脑刺绣机针杆机构和挑线机构奠定了理论基础.     

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。     

识别混凝土板内部缺陷的人工神经网络算法

以一维有缺陷混凝土板为研究对象，分别采用Leverberg-Marcluardt和径向基神经网络算法，对缺陷的深度与厚度进行识别，从而实现对混凝土板内部缺陷的三维重构，称为红外CT模拟．两类神经网络算法的识别结果表明：Leverberg-Marcluardt神经网络较径向基神经网络具有更好的收敛精度与计算效率．     

基于时延脉冲耦合神经网络的AOE-网问题求解算法

 在时延脉冲耦合神经网络(DPCNN-Delay Pulse Coupled Neural Network)的基础上,提出了mDPC-NN(modified DPCNN)模型.mDPCNN能方便地用于有向网的最大代价路径和最小代价路径的求解,并将其成功地运用其于求解AOE-网(Activity On Edge)有关问题.该方法通过快速并行计算,一次正向计算即可求解AOE-网大部分问题,与传统方法相比体现了较好的优势.     

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.