Forecasting bus passenger flows by using a clustering-based support vector regression approach

As a significant component of the intelligent transportation system, forecasting bus passenger flows plays a key role in resource allocation, network planning, and frequency setting. However, it remains challenging to recognize high fluctuations, nonlinearity, and periodicity of bus passenger flows due to varied destinations and departure times. For this reason, a novel forecasting model named as affinity propagation-based support vector regression (AP-SVR) is proposed based on clustering and nonlinear simulation. For the addressed approach, a clustering algorithm is first used to generate clustering-based intervals. A support vector regression (SVR) is then exploited to forecast the passenger flow for each cluster, with the use of particle swarm optimization (PSO) for obtaining the optimized parameters. Finally, the prediction results of the SVR are rearranged by chronological order rearrangement. The proposed model is tested using real bus passenger data from a bus line over four months. Experimental results demonstrate that the proposed model performs better than other peer models in terms of absolute percentage error and mean absolute percentage error. It is recommended that the deterministic clustering technique with stable cluster results (AP) can improve the forecasting performance significantly.info:eu-repo/semantics/publishedVersio

Global and partitioned reconstructions of undirected complex networks

It is a significant challenge to predict the network topology from a small amount of dynamical observations. Different from the usual framework of the node-based reconstruction, two optimization approaches (i.e., the global and partitioned reconstructions) are proposed to infer the structure of undirected networks from dynamics. These approaches are applied to evolutionary games occurring on both homogeneous and heterogeneous networks via compressed sensing, which can more efficiently achieve higher reconstruction accuracy with relatively small amounts of data. Our approaches provide different perspectives on effectively reconstructing complex networks.Comment: 6 pages, 2 figures, 1 table; revised version; added numerical results of the PR in Table 1 and expanded Section 4; added 7 reference

根据医学特点，开展临床研究生思想政治教育

Along with the reform of medical system, medical education in China is also undergoing great changes. Due to the special characteristics of medical education, it differs from other educational characteristics. It carries with the characteristics of clinical practice on the basis distributed learning, physical and mental development along with ages, enrollment expansion and medical requirement, and standardization training for resident doctors. So, ideological and political education of clinical graduates showed many new characteristics. First, medical ethics education is the basic step, combined with the related disciplines of medical humanity connotation. Second, flexible and diversified form of ideological and political education on the basis of medical work is necessary. Third, establish a system of ideological and political education for clinical graduates, to build up new education concept, and to develop ideological and political education activities for clinical graduates in depth.随着医疗体制改革的推进，医学教育也在不断摸索中前进，因为医学教育的特殊性，使它具备不同于其它学科的教育特点。临床实践为主分散式学习，年龄与身心发展的新特点，扩招和医学实现的发展需要，与住院医师规范化培训统一等等特点，使现代临床医学研究生的思想政治教育呈现许多新的特点。根据临床医学研究生的各种特殊性开展思想政治教育，首先在内容上应以医德医风教育为主，结合医学人文相关学科的思想内涵进行。其次在形式上要结合医疗工作实际开展灵活多样、形式新颖的思想政治教育。最后要建立一个适应临床研究生思想政治教育的体系，形成教育模式，建立全新的教育理念，全面进行临床医学研究生思想政治教育活动

Genes and (Common) Pathways Underlying Drug Addiction

Drug addiction is a serious worldwide problem with strong genetic and environmental influences. Different technologies have revealed a variety of genes and pathways underlying addiction; however, each individual technology can be biased and incomplete. We integrated 2,343 items of evidence from peer-reviewed publications between 1976 and 2006 linking genes and chromosome regions to addiction by single-gene strategies, microrray, proteomics, or genetic studies. We identified 1,500 human addiction-related genes and developed KARG (http://karg.cbi.pku.edu.cn), the first molecular database for addiction-related genes with extensive annotations and a friendly Web interface. We then performed a meta-analysis of 396 genes that were supported by two or more independent items of evidence to identify 18 molecular pathways that were statistically significantly enriched, covering both upstream signaling events and downstream effects. Five molecular pathways significantly enriched for all four different types of addictive drugs were identified as common pathways which may underlie shared rewarding and addictive actions, including two new ones, GnRH signaling pathway and gap junction. We connected the common pathways into a hypothetical common molecular network for addiction. We observed that fast and slow positive feedback loops were interlinked through CAMKII, which may provide clues to explain some of the irreversible features of addiction