高陡横坡段桩柱式桥梁双桩基础受力分析

基于高陡横坡段桩柱式桥梁双桩基础承载特性,提出了一种改进有限杆单元计算分析方法.分析了高陡横坡段桩柱式桥梁双桩基础承载机理及受力特性,建立了双桩基础计算分析模型.其次,根据前、后桩与边坡相对位置关系,给出了后桩所受剩余下滑力与前桩所受土压力的比例关系.在传统有限杆单元分析方法基础上,结合陡坡桩受力特征,导得了考虑桩土共同作用与"P-Δ"效应的单元刚度矩阵修正方法,并在此基础上编制了适用于高陡横坡段桩柱式桥梁双桩基础的有限杆单元分析MATLAB计算程序.采用室内模型试验对本文计算方法进行验证,给出了适用于陡坡段桥梁桩基的设计流程图.研究结果表明:本文理论计算值与模型试验实测结果吻合良好,表明本文计算方法正确可行,可为同类工程设计提供参考.     

弱相互作用下原子的玻色-爱因斯坦凝聚

从理论上分析提出了弱相互作用下Bose体系的BEC理论，并由此来解释液^4He的超流性。通过深入对比理想Bose气体和弱相互作用下Bose体系发生BEC性质的不同，分析得出了凝聚体系粒子间相互作用的有无是导致二者性质不同的主要原因。     

LaMnO3不同晶位掺杂的磁电阻效应

基于研究钙钛矿型氧化物磁电阻效应的目的,采用固相反应法制备了实验所需的单相多晶样品,主要就La位二价金属离子掺杂和稀土互掺及Mn位掺杂对LaMnO3磁电阻影响的实验结果作比较研究,发现La位稀土互掺对磁电阻的影响可以用晶格效应来解释,并明确指出La位和Mn位掺杂是提高和调制钙钛矿型锰氧化物超大磁电阻效应的一种有效途径.     

非线性叠加效应研究

对非线性叠加效应进行了研究,在Toda晶格中,当两孤立子运动方向相同时,其速度波的振幅的极大值是原来两波振幅之差,两波叠加的最大振幅是原来两波振幅之和。这些结果跟线性波叠加情况是完全不同的。     

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.     

祁连山东段冲积扇的发育时代及其成因

第四纪期间祁连山东段山麓地带广泛发育了多级冲积扇,它们是研究区域构造抬升与气候变化最为直接的载体.本文主要通过ESR,IRSL以及14C等绝对测年手段对该区冲积扇的形成年代进行研究,结果表明祁连山东段北麓最高级冲积扇形成于0.85 MaBP,而毛毛山南麓的最高级冲积扇形成于0.43 MaBP.随后各地又相继发育了0.25,0.16,0.06,0.01 MaBP等多级冲洪积台地.根据主要冲积扇的形成年代及其与区域构造气候事件的对比,认为它们是构造运动与气候变化共同作用的产物.     

嫖宿幼女罪构成要件探讨

司法解释构成嫖宿幼女罪必须要具备明知要件,这种解释违背了立法本意,不利于实际司法操作,不利于对此种犯罪行为的打击.     

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.     

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,…     

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.