浅议图书馆读者服务创新

在网络时代,图书馆服务必须在内部结构、管理模式、服务模式等方面进行一系列变革与创新,才能满足读者日益增长的借阅需求,提供高质量的图书馆服务。     

PBNA： An Improved Probabilistic Biological Network Alignment Method

Biological network alignment is an important research topic in the field of bioinformatics. Nowadays almost every existing alignment method is designed to solve the deterministic biological network alignment problem.However, it is worth noting that interactions in biological networks, like many other processes in the biological realm,are probabilistic events. Therefore, more accurate and better results can be obtained if biological networks are characterized by probabilistic graphs. This probabilistic information, however, increases difficulties in analyzing networks and only few methods can handle the probabilistic information. Therefore, in this paper, an improved Probabilistic Biological Network Alignment（PBNA） is proposed. Based on Iso Rank, PBNA is able to use the probabilistic information. Furthermore, PBNA takes advantages of Contributor and Probability Generating Function（PGF） to improve the accuracy of node similarity value and reduce the computational complexity of random variables in similarity matrix. Experimental results on dataset of the Protein-Protein Interaction（PPI） networks provided by Todor demonstrate that PBNA can produce some alignment results that ignored by the deterministic methods, and produce more biologically meaningful alignment results than Iso Rank does in most of the cases based on the Gene Ontology Consistency（GOC） measure. Compared with Prob method, which is designed exactly to solve the probabilistic alignment problem, PBNA can obtain more biologically meaningful mappings in less time.     

海堤渗压神经网络分布模型的建立及规律分析

为有效揭示海堤渗压的分布特征,掌握其在临海工作环境下的特殊规律,在利用神经网络建模优点的同时,采用多测点渗压监控信息,并将测点坐标因素加入到输入层,综合前期潮位因子、积分型降雨因子、时效因子,形成海堤渗压神经网络安全监控分布模型结构,以实测信息进行建模训练计算;在获得合理训练结果基础上,根据输入层因子补充插入坐标样本,获得不同位置的渗压模型值及渗压分布曲线.文中以广义回归神经网络为例,结合浦东海堤实测资料,以实例说明以上述方法在神经网络不提供显式的情况下,建立可获得分布曲线的监控模型,并以此对海堤渗压分布规律特色加以分析.     

并联式混合动力发动机神经网络法转矩预测与闭环控制

利用实际发动机的标定数据搭建了GT-Suite及Matlab/Simulink联合仿真模型，建立了基于进气和发动机状态参数的预测转矩反馈协同控制模块。对比了ANN法和现有发动机的标定脉谱插值预测（MAP）法2种方法下发动机稳态及瞬态转矩变化、升降挡等工况预测的结果误差，结果表明：稳态工况下MAP法较为可靠，低、中、高3种发动机转速下转矩预测波动小，误差比ANN法低1.31%、1.09%和1.52%；实际瞬态转矩跃变及阶跃工况下，ANN法较MAP法误差低5.62%和1.32%，升降挡工况下低1.93%和0.84%。     

网络环境下以能力培养为导向的经管类实验教学方法改革研究

网络技术的广泛运用丰富了高校经管类实验教学的手段,使实验教学突破了时空局限,大大拓展了人才培养的空间,但同时也给传统的实验教学带来很多负面的影响。在分析当前我国高校经管类实验教学存在问题的基础上,指出我国高校应以能力培养为导向,充分利用实验教学平台网络化的条件,对实验教学的内容、实验过程、实验评价等方面进行优化设计,形成以能力为导向的实验教学模式。     

对合法有序语境下的网络监督思考

网络监督是以网民为主体,以互联网为平台,通过网络技术和各种网络形式对公共权力进行监督的方式。它需要加强网络系统的建设,建立部门齐全的法律相互协作,并协调网络政治参与和网络有序发展的关系,只有这样,才能提高网民的网络监督意识与能力。     

对加强高校网络廉政舆情处置的思考

网络廉政舆情是把双刃剑,有利有弊,对网络廉政舆情及时搜集、科学研判、处置得当,将有利于推进高校的纪检监察工作。当前存在诸多高校负面网络廉政舆情,高校纪检监察部门在处置负面网络廉政舆情方面存在诸多误区,要有效处置负面网络廉政舆情,高校纪检监察部门应确立正确的原则,构建相应的机制。     

网络透明与信任建设初探

随着信息时代的到来、网络技术的广泛应用,人们对于国家安全的认识得到了深化和扩展,网络安全已成为国家安全的重要内容,而网络透明与信任同样引起了国际社会的高度重视。阐述了网络透明与信任建设的政治、历史背景,介绍了网络空间TCBMs的目标和任务、面临的挑战、发展与建设内容,以期对我国网络安全的发展提供借鉴。     

MOEPGA: A novel method to detect protein complexes in yeast protein–protein interaction networks based on MultiObjective Evolutionary Programming Genetic Algorithm

The identification of protein complexes in protein–protein interaction (PPI) networks has greatly advanced our understanding of biological organisms. Existing computational methods to detect protein complexes are usually based on specific network topological properties of PPI networks. However, due to the inherent complexity of the network structures, the identification of protein complexes may not be fully addressed by using single network topological property. In this study, we propose a novel MultiObjective Evolutionary Programming Genetic Algorithm (MOEPGA) which integrates multiple network topological features to detect biologically meaningful protein complexes. Our approach first systematically analyzes the multiobjective problem in terms of identifying protein complexes from PPI networks, and then constructs the objective function of the iterative algorithm based on three common topological properties of protein complexes from the benchmark dataset, finally we describe our algorithm, which mainly consists of three steps, population initialization, subgraph mutation and subgraph selection operation. To show the utility of our method, we compared MOEPGA with several state-of-the-art algorithms on two yeast PPI datasets. The experiment results demonstrate that the proposed method can not only find more protein complexes but also achieve higher accuracy in terms of fscore. Moreover, our approach can cover a certain number of proteins in the input PPI network in terms of the normalized clustering score. Taken together, our method can serve as a powerful framework to detect protein complexes in yeast PPI networks, thereby facilitating the identification of the underlying biological functions.