用变分法分析地基-箱形基础-上部结构相互作用

利用空间子结构法凝聚上部结构的刚度与荷载,形成一个超级单元,同时,采用有限分层地基模型,并计及基础自身刚度的影响,建立起基于能量变分原理的地基-箱形基础-上部结构相互作用的数学模型.由于该模型合理的体现了三者协同工作的机制,从而可以对问题进行较为全面的分析.     

合理利用计算机辅助教学 提高旅游地理教学质量

计算机辅助教学与传统教学方法相比具有许多优越性，它是直观教学、视觉教学和听觉教学发展的结果，制作高质量的教学课件，合理使用计算机辅助教学，是提高旅游地理课教学质量的关键。     

谈计算机专业学生的数学基础

计算机技术本质上是一种数学技术,我国计算机专业学生的数学基础相对薄弱,夯实数学基础、提高数学能力是培养计算机专业高素质研究型人才的有效途径。     

基于小波的图像隐藏方法在信息安全中的应用

随着信息时代的到来,互联网和多媒体技术迅猛发展,数字多媒体信息在网上传播越来越便捷,同时也带来了信息安全的隐患问题。小波变换具有良好的时频特性且小波的多分辨分析与人眼视觉特性是一致的。在小波域对图像进行分解,可以根据HVS特性更好地隐藏信息,解决信息安全的问题。     

Interaction Dynamics in AEC Global Teamwork

Architecture, engineering, and construction (AEC) global project teamwork is communication intensive and relies heavily on synchronous and asynchronous information and collaboration technologies (ICT). We explore in this paper how an asynchronous ICT, called ThinkTank, reshaped the work practice of design-construction global teams, and how the interaction with this ICT reshaped the purpose and benefits of its use. ThinkTank is a web-based asynchronous collaboration and discussion forum. We introduce the influence diffusion model (IDM) that formalizes the process of identifying the influence of people, messages, and terms mathematically. Discovering who the influence leaders in project teams are can be beneficial and critical from a corporate management perspective, since they can guide or motivate the team towards successful actions and outcomes. We present the ThinkTank-IDM integrated system and its validation with a testbed of 53 AEC global team project archived in ThinkTank over 8 years.     

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。     

A NEW DESCRIPA NEW DESCRIPTOR OF AMINO ACIDS BASED ON THE THREEDIMENSIONAL VECTOR OF ATOMIC INTERACTION FIELD

QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP (QSAR) IS A VERY IMPORTANT TOPIC FOR MANY SCIENTIFIC FIELDS AND CAN BE TERMED AS QUANTITATIVE SEQUENCE ACTIVITY MODELS(QSAM)[1] WHEN REFERRED TO THE RESEARCH ON RELATION- SHIPS BETWEEN STRUCTURES AND ACTIVITIES OF BIOLOGICAL MOLECULES, SUCH AS PROTEINS, AND NUCLEIC ACIDS. HOW- EVER, IT IS VERY DIFFICULT TO ESTABLISH A QSAM MODEL,…     

谈公安院校计算机基础教学的改革问题

分析了公安院校当前在计算机基础课程教学中存在的问题，并针对这些问题提出一些改进的措施和建议，以适应当前公安教育的需要。     

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.