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提出一种适用于自复位钢木混合剪力墙结构的直接基于位移的设计方法，并设计了一栋6层自复位钢木混合剪力墙结构，通过非线性时程分析对设计方法的合理性进行了验证。进一步选取50条地震动记录进行结构的非线性时程分析，研究了结构在地震作用下的损伤状态。基于云图法的易损性分析，获得了6层自复位钢木混合剪力墙结构针对不同性能目标的易损性曲线，研究了自复位系数对结构易损性的影响。研究结果表明：直接基于位移的设计方法适用于自复位钢木混合剪力墙结构的分析，且可设置结构最大层间位移角和最大残余层间位移角为性能目标。结构在大震作用下具有良好的抗倒塌能力和自复位能力。

与传统建筑相比，模块化建筑因其建设快速高效、品质卓越、施工过程安全、施工时间可预测、资源浪费少和促进环境可持续性发展等优点而受到推广。模块化建筑往往涉及到多种结构体系和建筑材料，其中，钢制构件在模块化建筑中应用最为广泛。综述了装配式模块化建筑与模块节点的最新研究进展。首先，介绍了装配式模块化建筑的分类、材料和结构形式，讨论了不同类型模块化建筑的结构性能。此外，由于模块化建筑的结构性能高度依赖于模块的节点连接，对现有模块化节点，特别是钢结构模块间和模块内部的节点形式进行了系统的梳理。最后，阐述了模块化建筑发展面临的主要挑战和未来发展前景。从国内外研究进展可见，模块化建筑充分满足了当前建筑结构工业化的需要。为促进我国下一阶段模块化建设的发展和应用提供了重要的参考。

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To address the semantic consistency problem of migrated code in the source code migration model, this paper introduced the statement-level attention mechanism based on the token-level attention mechanism, proposed a source code migration model HPGN based on the hierarchical attention mechanism, and designed state feeding mechanisms. During migration, the statement-level attention mechanism aligned the features of source code statements and migrated code statements, the token-level attention mechanism extracted tokens from the aligned code statements, and the state feeding mechanism passed the feature of adjacent migrated code statement, thus improving the semantic consistency of migrated code. Experimenting on a real project dataset, the results show that HPGN improves the overall score by 3.4 over the best comparison model while having fewer model parameters. In addition, ablation experiments validate the effectiveness of the state feeding mechanisms and HPGN hierarchical architecture. [ABSTRACT FROM AUTHOR]

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Point of interest recommendation is one of the research hotspots in location-based social networks. Firstly, this paper analyzes the references about point-of-interest recommendation research from Web of Science, then it analyzes the factors that influence point-of-interest recommendation. Furthermore, based on the analysis of traditional methods of point-of-interest recommendation, this paper mainly analyzes the methods of point-of-interest recommendation based on deep learning from two aspects: modeling of user historical check-in information and extraction of user social generated information. Finally, it looks forward to future research directions that may improve the performance of point-of-interest recommendation. [ABSTRACT FROM AUTHOR]

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Research on the supply and demand of ecosystem services is related to human well-being. The sustainability of the supply of ecosystem services is also the basis for sustainable social, economic and natural environments. Research on supply and demand can provide theoretical guidance for the rational utilization and allocation of natural resources,which helps to improve the ecological compensation mechanism and coordinate the relationship between various stakeholders. We analyze the degree of correspondence between socioeconomic system demand and natural ecosystem supply by analyzing the research results of supply and demand of ecosystem services. It also strengthens our understanding of different factors influencing ecosystem services, which has important reference value for current land spatial planning systems. We systematically summarize the research status of ecosystem services in China and abroad through literature reviews. The literature publication status of supply and demand examines the conceptual connotation of ecosystem service supply and demand, and it distinguishes between the primary connotation and relationship of potential supply, actual supply, potential demand and actual demand. We summarize and compare the research methods related to the supply and demand of ecosystem services, summarizes the practical applications of evaluating the supply and demand of ecosystem services in China and abroad, as well as the problems and limitations in the current research on the supply and demand of ecosystem services. Further, it outlines the scope for future research to provide a reference for the research and application of supply and demand of ecosystem services. [ABSTRACT FROM AUTHOR]

Due to the diversified behavioral characteristics and regular pattern of moving objects, the trajectory data generated by these objects shows obvious uneven distribution feature in time and space, which may lead to worse performance for trajectory data indexing and querying. However, the existing trajectory data indexing methods rarely consider this problem. In this paper, a temporal-spatial distribution based indexing and querying method is proposed. In the method, Geohash code is introduced and pre-partitioned spatially by utilizing the temporal-spatial similarity of the trajectory data distribution. Then, we use the pre-partitioned Geohash code, partition number and the trajectory data timestamp to compose the index structure. With this index structure, a storage model based on HBase and a query algorithm based on Geohash partition are designed respectively. The empirical study using real trajectory dataset shows that the method improves the spatiotemporal query performance of trajectory data by comparing with the Extend_HGrid and ST-hash methods, and effectively reduces the number of sub-queries during query. [ABSTRACT FROM AUTHOR]