基于Cosserat理论的微观结构对非均质材料有效性能的影响分析

为更有效地研究具有周期性微观结构的非均质材料平面应变问题,用基于Cosserat理论的渐进均匀化方法得到微观结构对非均质材料有效性能的影响情况．计算结果表明,单胞内夹杂体的形状对有效杨氏弹性模量、有效泊松比、有效Cosserat弹性常数和有效材料特征长度有影响,并且随着夹杂体与单胞体积比的增大而影响明显．     

基于异构体系结构的路桥收费管理系统的实现

为了实现在收费所内、外和收费站3个地点的机动车辆资料管理和收费系统收费,应用异构软件体系结构,将大型软件系统按功能的可扩展性分层。完成最基本的公共操作,融合层次式和正交式两种异构软件体系结构,使得系统结构清晰,具有可扩展性,易维护,显著提高了编程效率。     

分布式异构教学资源管理系统核心技术与实现

分布式异构教学资源管理系统针对目前教学资源应用过程中资源分散、开放性差的特点,以目录集中、资源分布为原则,应用最短路径优先算法原理、全相联映射策略等技术,建立完备的资源服务体系,打破孤岛模式,实现区域教学资源的广泛高速共享。     

异构数据集成方案的优化设计与实现

针对现有数据集成方案在处理特定问题时系统查询性能不理想的情况,以Mediator／Wrap-per集成模式为基础,结合数据仓库思想,提出1种异构数据集成的优化方案．该方案基于XML和元数据设计查询构造、解析搜索及缓存等模块,优化系统的查询性能．将该方案应用于石油储层评价系统的数据集成中能明显缩短查询的平均耗用时间,提高查询效率。     

XML数据交换技术的应用与研究

针对目前存在越来越多的异构数据交换问题,本文首先介绍了基于XML在不同平台／系统之间的数据交换方面的发展趋势,分析了各种不同异构数据交换技术,最后以书费结算系统为例探讨基于XML在不同平台／系统之间异构数据交换的具体实现。     

Pre-Activation of CO2 at Cobalt Phthalocyanine-Mg(OH)2 Interface for Enhanced Turnover Rate

Cobalt phthalocyanine (CoPc) anchored on heterogeneous scaffold has drawn great attention as promising electrocatalyst for carbon dioxide reduction reaction (CO₂RR), but the molecule/substrate interaction is still pending for clarification and optimization to maximize the reaction kinetics. Herein, a CO₂RR catalyst is fabricated by affixing CoPc onto the Mg(OH)₂ substrate primed with conductive carbon, demonstrating an ultra-low overpotential of 0.31 ± 0.03 V at 100 mA cm⁻² and high faradaic efficiency of >95% at a wide current density range for CO production, as well as a heavy-duty operation at 100 mA cm⁻² for more than 50 h in a membrane electrode assembly. Mechanistic investigations employing in situ Raman and attenuated total reflection surface-enhanced infrared absorption spectroscopy unravel that Mg(OH)₂ plays a pivotal role to enhance the CO₂RR kinetics by facilitating the first-step electron transfer to form anionic *CO₂⁻ intermediates. DFT calculations further elucidate that introducing Lewis acid sites help to polarize CO₂ molecules absorbed at the metal centers of CoPc and consequently lower the activation barrier. This work signifies the tailoring of catalyst-support interface at molecular level for enhancing the turnover rate of CO₂RR.     

Creating Atomic Ordering in Electrocatalysis

Catalysis always proceeds in a chaotic fashion. Therefore, identifying the working principles of heterogeneous catalysts is a challenging task. Creating atomic order in heterogeneous catalysts simplifies this task and also offers new opportunities for rationally designing active sites to manipulate catalytic performance. The recent rapid advances in heterogeneous electrocatalysis have led to exciting progress in the construction of atomically ordered materials. Here, the latest progress in electrocatalysts with the periodic atomic arrangement, including intermetallic compounds with long-range order and metal atom-array catalysts with short-range order is summarized. The synthesis principles and the intriguing physical and chemical properties of these electrocatalysts are discussed. Furthermore, the compelling prospects of atomically ordered catalysts in the frontier of catalyst research are outlined.     

面向异构DHT存储的数据与位置解耦算法

分布式哈希表(distributed hash table, DHT)由于其高效的数据寻址方式而被广泛应用于分布式存储.传统DHT必须将数据存放在指定节点中才能实现高效的数据分布式寻址,极大地限制了DHT技术的应用范围.例如,在异构存储网络中,节点的存储空间、带宽、稳定性等均有较大差异,结合数据特征和节点性能差异选择合适的数据存放节点可以很大程度上提高数据的访问效率,而传统DHT数据和存储位置紧耦合的特征导致其难以应用于异构的存储网络中.针对此问题,提出了vRoute算法以实现DHT中数据标识与其存储位置的解耦.通过构建基于Bloom Filter的分布式数据索引, vRoute算法可以在不降低数据寻址效率的基础上允许数据存储在网络中的任意节点.通过扩展Kademlia算法实现了vRoute,并从理论上证明了vRoute算法的有有效性.最后,模拟实验表明vRoute以较低的存储、网络开销实现了和传统的DHT算法接近的数据寻址效率.     

异质信息网络高阶层次化嵌入学习与推荐预测

异质信息网络是一种异质数据表示形式,如何融合异质数据复杂语义信息,是推荐系统面临的挑战之一.利用弱关系具有的丰富语义和信息传递能力,构建一种面向推荐系统的异质信息网络高阶嵌入学习框架,主要包括:初始化信息嵌入、高阶信息嵌入聚合与推荐预测3个模块.初始化信息嵌入模块首先采用基于弱关系的异质信息网络最佳信任路径筛选算法,有效地避免在全关系异质信息网络中,采样固定数量邻居造成的信息损失,其次利用新定义的基于多头图注意力的多任务共享特征重要性度量因子,筛选出节点的语义信息,并结合交互结构,有效地表征网络节点;高阶信息嵌入聚合模块通过融入弱关系及网络嵌入对知识良好的表征能力,实现高阶信息表达,并利用异质信息网络的层级传播机制,将被采样节点的特征聚合到待预测节点;推荐预测模块利用高阶信息的影响力推荐方法,实现了推荐任务.该框架具有嵌入节点类型丰富、融合共享属性和隐式交互信息等特点.最后,实验验证UI-HEHo学习框架可有效地改善评级预测的准确性,以及推荐生成的针对性、新颖性和多样性,尤其是在数据稀疏的应用场景中,具有良好的推荐效果.     

Heterogeneous clustering via adversarial deep Bayesian generative model

This paper aims to study the deep clustering problem with heterogeneous features and unknown cluster number. To address this issue, a novel deep Bayesian clustering framework is proposed. In particular, a heterogeneous feature metric is first constructed to measure the similarity between different types of features. Then, a feature metric-restricted hierarchical sample generation process is established, in which sample with heterogeneous features is clustered by generating it from a similarity constraint hidden space. When estimating the model parameters and posterior probability, the corresponding variational inference algorithm is derived and implemented. To verify our model capability, we demonstrate our model on the synthetic dataset and show the superiority of the proposed method on some real datasets. Our source code is released on the website: Github.com/yexlwh/Heterogeneousclustering.