{"id":20102,"date":"2020-06-21T21:15:09","date_gmt":"2020-06-21T13:15:09","guid":{"rendered":"https:\/\/swarma.org\/?p=20102"},"modified":"2020-06-21T21:15:09","modified_gmt":"2020-06-21T13:15:09","slug":"%e6%96%b0%e5%9e%8b%e5%86%a0%e7%8a%b6%e7%97%85%e6%af%92%e8%82%ba%e7%82%8e%e5%9c%a8%e4%b8%8d%e5%90%8c%e7%a4%be%e5%8c%ba%e4%bc%a0%e6%92%ad%e7%9a%84-sir-%e6%a8%a1%e5%9e%8b%e5%81%87%e8%ae%be-%e7%bd%91","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=20102","title":{"rendered":"\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u5728\u4e0d\u540c\u793e\u533a\u4f20\u64ad\u7684 SIR \u6a21\u578b\u5047\u8bbe | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901230\u7bc7"},"content":{"rendered":"
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      \u51cf\u5c11  SARS-COV-2\u548c\u5176\u4ed6\u75c5\u6bd2\u5bf9 spike \u86cb\u767d\u6e17\u900f\u7684\u6709\u6548\u9014\u5f84: \u901a\u8fc7\u8868\u9762\u7c92\u5b50\u9759\u7535\u8377\u534f\u5546<\/span><\/h2>\n<\/li>\n
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      \u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u5728<\/strong><\/span><\/p>\n

      \u4e0d\u540c\u793e\u533a\u4f20\u64ad\u7684 SIR \u6a21\u578b\u5047\u8bbe<\/strong><\/span><\/strong><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


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      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

      A SIR model assumption for the spread of COVID-19 in different communities<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/h2>\n

      http:\/\/arxiv.org\/abs\/2006.10651<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/span><\/p>\n

      Ian Cooper,Argha Mondal,Chris G. Antonopoulos<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>In this paper, we study the effectiveness of the modelling approach on the pandemic due to the spreading of the novel COVID-19 disease and develop a susceptible-infected-removed (SIR) model that provides a theoretical framework to investigate its spread within a community. Here, the model is based upon the well-known susceptible-infected-removed (SIR) model with the difference that a total population is not defined or kept constant per se and the number of susceptible individuals does not decline monotonically. To the contrary, as we show herein, it can be increased in surge periods! In particular, we investigate the time evolution of different populations and monitor diverse significant parameters for the spread of the disease in various communities, represented by countries and the state of Texas in the USA. The SIR model can provide us with insights and predictions of the spread of the virus in communities that the recorded data alone cannot. Our work shows the importance of modelling the spread of COVID-19 by the SIR model that we propose here, as it can help to assess the impact of the disease by offering valuable predictions. Our analysis takes into account data from January to June, 2020, the period that contains the data before and during the implementation of strict and control measures. We propose predictions on various parameters related to the spread of COVID-19 and on the number of susceptible, infected and removed populations until September 2020. By comparing the recorded data with the data from our modelling approaches, we deduce that the spread of COVID-19 can be under control in all communities considered, if proper restrictions and strong policies are implemented to control the infection rates early from the spread of the disease.<\/span><\/p>\n

      \u6458\u8981\uff1a<\/span><\/strong>\u5728\u8fd9\u7bc7\u8bba\u6587\u4e2d\uff0c\u6211\u4eec\u7814\u7a76\u4e86\u7531\u4e8e\u65b0\u578b\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u7684\u4f20\u64ad\u800c\u5f15\u8d77\u7684\u5927\u6d41\u884c\u7684\u6a21\u578b\u65b9\u6cd5\u7684\u6709\u6548\u6027\uff0c\u5e76\u53d1\u5c55\u4e86\u4e00\u4e2a\u6613\u611f-\u611f\u67d3-\u79fb\u9664(SIR)\u6a21\u578b\uff0c\u8be5\u6a21\u578b\u63d0\u4f9b\u4e86\u4e00\u4e2a\u7406\u8bba\u6846\u67b6\u6765\u8c03\u67e5\u5b83\u5728\u793e\u533a\u5185\u7684\u4f20\u64ad\u3002\u8be5\u6a21\u578b\u57fa\u4e8e\u5df2\u77e5\u7684\u6613\u611f-\u611f\u67d3-\u79fb\u9664(SIR)\u6a21\u578b\uff0c\u5176\u5dee\u5f02\u5728\u4e8e\u603b\u79cd\u7fa4\u4e0d\u786e\u5b9a\u6216\u81ea\u8eab\u4fdd\u6301\u4e0d\u53d8\uff0c\u6613\u611f\u4e2a\u4f53\u7684\u6570\u91cf\u4e0d\u5355\u8c03\u4e0b\u964d\u3002\u76f8\u53cd\uff0c\u6b63\u5982\u6211\u4eec\u5728\u8fd9\u91cc\u6240\u5c55\u793a\u7684\uff0c\u5b83\u53ef\u4ee5\u5728\u9ad8\u5cf0\u671f\u589e\u52a0\uff01\u7279\u522b\u662f\uff0c\u6211\u4eec\u8c03\u67e5\u4e0d\u540c\u4eba\u53e3\u7684\u65f6\u95f4\u6f14\u53d8\u548c\u76d1\u6d4b\u4e0d\u540c\u7684\u91cd\u8981\u53c2\u6570\u7684\u75be\u75c5\u5728\u5404\u4e2a\u793e\u533a\u7684\u4f20\u64ad\uff0c\u4ee3\u8868\u56fd\u5bb6\u548c\u5fb7\u514b\u8428\u65af\u5dde\u5728\u7f8e\u56fd\u3002Sir \u6a21\u578b\u53ef\u4ee5\u4e3a\u6211\u4eec\u63d0\u4f9b\u5173\u4e8e\u75c5\u6bd2\u5728\u793e\u533a\u4f20\u64ad\u7684\u6d1e\u5bdf\u529b\u548c\u9884\u6d4b\u529b\uff0c\u800c\u4ec5\u4ec5\u8bb0\u5f55\u6570\u636e\u662f\u65e0\u6cd5\u505a\u5230\u7684\u3002\u6211\u4eec\u7684\u5de5\u4f5c\u8868\u660e\u4e86\u6211\u4eec\u5728\u8fd9\u91cc\u63d0\u51fa\u7684 SIR \u6a21\u578b\u6a21\u62df\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u4f20\u64ad\u7684\u91cd\u8981\u6027\uff0c\u56e0\u4e3a\u5b83\u53ef\u4ee5\u901a\u8fc7\u63d0\u4f9b\u6709\u4ef7\u503c\u7684\u9884\u6d4b\u6765\u5e2e\u52a9\u8bc4\u4f30\u75be\u75c5\u7684\u5f71\u54cd\u3002\u6211\u4eec\u7684\u5206\u6790\u8003\u8651\u4e862020\u5e741\u6708\u81f36\u6708\u7684\u6570\u636e\uff0c\u8fd9\u4e00\u65f6\u671f\u5305\u542b\u4e86\u5728\u5b9e\u65bd\u4e25\u683c\u63a7\u5236\u63aa\u65bd\u4e4b\u524d\u548c\u671f\u95f4\u7684\u6570\u636e\u3002\u6211\u4eec\u5efa\u8bae\u9884\u6d4b\u4e0e\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u4f20\u64ad\u76f8\u5173\u7684\u5404\u79cd\u53c2\u6570\uff0c\u4ee5\u53ca\u52302020\u5e749\u6708\u4e3a\u6b62\u7684\u6613\u611f\u3001\u611f\u67d3\u548c\u79fb\u9664\u79cd\u7fa4\u7684\u6570\u91cf\u3002\u901a\u8fc7\u6bd4\u8f83\u8bb0\u5f55\u7684\u6570\u636e\u548c\u6211\u4eec\u7684\u6a21\u578b\u65b9\u6cd5\u5f97\u51fa\u7684\u6570\u636e\uff0c\u6211\u4eec\u63a8\u65ad\uff0c\u5982\u679c\u5b9e\u65bd\u9002\u5f53\u7684\u9650\u5236\u548c\u5f3a\u6709\u529b\u7684\u653f\u7b56\uff0c\u4ece\u75be\u75c5\u4f20\u64ad\u7684\u65e9\u671f\u63a7\u5236\u611f\u67d3\u7387\uff0c\u90a3\u4e48\u5728\u6240\u6709\u8003\u8651\u5230\u7684\u793e\u533a\u4e2d\uff0c\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u7684\u4f20\u64ad\u53ef\u4ee5\u5f97\u5230\u63a7\u5236<\/span><\/p>\n


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      \u4f20\u67d3\u75c5\u5728\u968f\u673a\u56fe\u4e0a\u7684\u8513\u5ef6: <\/strong><\/span><\/p>\n

      \u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u7684\u903e\u6e17\u578b\u6a21\u578b<\/strong><\/span><\/strong><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


      <\/p>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

      Spreading of infections on random graphs: A percolation-type model for COVID-19<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/p>\n

      https:\/\/arxiv.org\/abs\/2006.10490<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/span><\/p>\n

      Fabrizio Croccolo,H. Eduardo Roman<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>We introduce an epidemic spreading model on a network using concepts from percolation theory. The model is motivated by discussing the standard SIR model, with extensions to describe effects of lockdowns within a population. The underlying ideas and behavior of the lattice model, implemented using the same lockdown scheme as for the SIR scheme, are discussed in detail and illustrated with extensive simulations. A comparison between both models is presented for the case of COVID-19 data from the USA. Both fits to the empirical data are very good, but some differences emerge between the two approaches which indicate the usefulness of having an alternative approach to the widespread SIR model.<\/span><\/p>\n

      \u6458\u8981\uff1a<\/span><\/strong>\u5229\u7528\u903e\u6e17\u7406\u8bba\u4e2d\u7684\u6982\u5ff5\uff0c\u5728\u7f51\u7edc\u4e0a\u5efa\u7acb\u4e86\u4e00\u4e2a\u4f20\u67d3\u75c5\u4f20\u64ad\u6a21\u578b\u3002\u8be5\u6a21\u578b\u7684\u52a8\u673a\u662f\u8ba8\u8bba\u6807\u51c6\u7684 SIR \u6a21\u578b\uff0c\u5e76\u6269\u5c55\u63cf\u8ff0\u5c01\u9501\u5728\u7fa4\u4f53\u4e2d\u7684\u5f71\u54cd\u3002\u8be6\u7ec6\u8ba8\u8bba\u4e86\u683c\u6a21\u578b\u7684\u57fa\u672c\u601d\u60f3\u548c\u884c\u4e3a\uff0c\u4f7f\u7528\u4e0e SIR \u65b9\u6848\u76f8\u540c\u7684\u5c01\u9501\u65b9\u6848\u5b9e\u73b0\uff0c\u5e76\u7528\u5927\u91cf\u7684\u4eff\u771f\u8fdb\u884c\u4e86\u8bf4\u660e\u3002\u672c\u6587\u4ee5\u7f8e\u56fd\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6570\u636e\u4e3a\u4f8b\uff0c\u5bf9\u4e24\u79cd\u6a21\u578b\u8fdb\u884c\u4e86\u6bd4\u8f83\u3002\u8fd9\u4e24\u79cd\u65b9\u6cd5\u90fd\u975e\u5e38\u7b26\u5408\u7ecf\u9a8c\u6570\u636e\uff0c\u4f46\u4e24\u79cd\u65b9\u6cd5\u4e4b\u95f4\u51fa\u73b0\u4e86\u4e00\u4e9b\u5dee\u5f02\uff0c\u8fd9\u8868\u660e\u5bf9\u5e7f\u6cdb\u4f7f\u7528\u7684 SIR \u6a21\u578b\u91c7\u7528\u66ff\u4ee3\u65b9\u6cd5\u662f\u6709\u7528\u7684<\/span><\/p>\n


      <\/span><\/p>\n

      <\/h2>\n
      \n
      \n
      \n


      <\/p>\n

      \n
      \n
      <\/p>\n

      \u5f31\u7684\u53ef\u79ef\u6027\u7834\u574f\uff1a<\/strong><\/span><\/p>\n

      \u76f8\u5e72\u6269\u6563\u4e2d\u5177\u6709\u53ef\u79ef\u6027\u7279\u5f81\u7684\u6df7\u6c8c<\/strong><\/span><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

      Weak integrability breaking: chaos with integrability signature in coherent diffusion<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/p>\n

      https:\/\/arxiv.org\/abs\/2006.09793<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/span><\/p>\n

      Marko Znidaric<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>We study how perturbations affect dynamics of an integrable many-body quantum system. Looking at spin transport in the Heisenberg chain with impurities, we find that in the thermodynamic limit transport gets diffusive already at an infinitesimal perturbation. Small extensive perturbations therefore cause an immediate transition from integrability to chaos. Nevertheless, there is a remnant of integrability encoded in the dependence of diffusion constant on the impurity density. At small densities it is proportional to the square root of the density, instead of to the density as would follow from Matthiessen’s rule. Results also highlight nontrivial interacting scattering on a single impurity.<\/span><\/p>\n

      \u6458\u8981\uff1a<\/span><\/strong>\u7814\u7a76\u4e86\u5fae\u6270\u5bf9\u53ef\u79ef\u591a\u4f53\u7cfb\u7edf\u52a8\u529b\u5b66\u884c\u4e3a\u7684\u5f71\u54cd\u3002\u89c2\u5bdf\u5e26\u6709\u6742\u8d28\u7684\u6d77\u68ee\u5821\u94fe\u4e2d\u7684\u81ea\u65cb\u8f93\u8fd0\uff0c\u6211\u4eec\u53d1\u73b0\u5728\u70ed\u529b\u5b66\u6781\u9650\u4e2d\uff0c\u8f93\u8fd0\u5728\u65e0\u7a77\u5c0f\u7684\u6270\u52a8\u4e0b\u5df2\u7ecf\u6269\u6563\u4e86\u3002\u56e0\u6b64\uff0c\u5fae\u5c0f\u800c\u5e7f\u6cdb\u7684\u6270\u52a8\u4f1a\u7acb\u5373\u5f15\u8d77\u4ece\u53ef\u79ef\u5230\u6df7\u6c8c\u7684\u8fc7\u6e21\u3002\u7136\u800c\uff0c\u5728\u6269\u6563\u5e38\u6570\u5bf9\u6742\u8d28\u5bc6\u5ea6\u7684\u4f9d\u8d56\u6027\u4e2d\u5b58\u5728\u53ef\u79ef\u7f16\u7801\u7684\u6b8b\u4f59\u3002\u5728\u8f83\u5c0f\u7684\u5bc6\u5ea6\u4e0b\uff0c\u5b83\u4e0e\u5bc6\u5ea6\u7684\u5e73\u65b9\u6839\u6210\u6b63\u6bd4\uff0c\u800c\u4e0d\u662f\u50cf\u6309\u7167\u9a6c\u6cf0\u68ee\u5b9a\u5f8b\u90a3\u6837\u4e0e\u5bc6\u5ea6\u6210\u6b63\u6bd4\u3002\u7ed3\u679c\u8fd8\u7a81\u51fa\u4e86\u5355\u4e2a\u6742\u8d28\u4e0a\u7684\u975e\u5e73\u51e1\u76f8\u4e92\u4f5c\u7528\u6563\u5c04<\/span><\/p>\n


      <\/p>\n

      \n
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      <\/p>\n

      \n
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      <\/p>\n

      \u56fa\u6001\u5408\u6210\u4e2d\u524d\u4f53\u7684\u76f8\u4f3c\u6027<\/strong><\/span><\/p>\n

      \uff08\u4ece\u79d1\u5b66\u6587\u732e\u4e2d\u63d0\u53d6\uff09<\/strong><\/span><\/strong><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


      <\/p>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

      Similarity of Precursors in Solid-state Synthesis as Text-Mined from Scientific Literature<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/p>\n

      https:\/\/arxiv.org\/abs\/2006.10315<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/span><\/p>\n

      Tanjin He,Wenhao Sun,Haoyan Huo,Olga Kononova,Ziqin Rong,Vahe Tshitoyan,Tiago Botari,Gerbrand Ceder<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>Collecting and analyzing the vast amount of information available in the solid-state chemistry literature may accelerate our understanding of materials synthesis. However, one major problem is the difficulty of identifying which materials from a synthesis paragraph are precursors or are target materials. In this study, we developed a two-step Chemical Named Entity Recognition (CNER) model to identify precursors and targets, based on information from the context around material entities. Using the extracted data, we conducted a meta-analysis to study the similarities and differences between precursors in the context of solid-state synthesis. To quantify precursor similarity, we built a substitution model to calculate the viability of substituting one precursor with another while retaining the target. From a hierarchical clustering of the precursors, we demonstrate that “chemical similarity” of precursors can be extracted from text data. Quantifying the similarity of precursors helps provide a foundation for suggesting candidate reactants in a predictive synthesis model.<\/span><\/p>\n

      \u6458\u8981\uff1a<\/span><\/strong>\u6536\u96c6\u548c\u5206\u6790\u56fa\u4f53\u5316\u5b66\u6587\u732e\u4e2d\u7684\u5927\u91cf\u4fe1\u606f\u53ef\u4ee5\u52a0\u5feb\u6211\u4eec\u5bf9\u6750\u6599\u5408\u6210\u7684\u7406\u89e3\u3002\u7136\u800c\uff0c\u4e00\u4e2a\u4e3b\u8981\u95ee\u9898\u662f\u96be\u4ee5\u786e\u5b9a\u5408\u6210\u6bb5\u843d\u4e2d\u7684\u54ea\u4e9b\u6750\u6599\u662f\u524d\u4f53\u6216\u76ee\u6807\u6750\u6599\u3002\u5728\u8fd9\u9879\u7814\u7a76\u4e2d\uff0c\u6211\u4eec\u5f00\u53d1\u4e86\u4e00\u4e2a\u4e24\u6b65\u7684\u5316\u5b66\u547d\u540d\u5b9e\u4f53\u8bc6\u522b(CNER)\u6a21\u578b\u6765\u8bc6\u522b\u524d\u4f53\u548c\u76ee\u6807\uff0c\u57fa\u4e8e\u4fe1\u606f\u4ece\u5468\u56f4\u7684\u7269\u8d28\u5b9e\u4f53\u3002\u5229\u7528\u63d0\u53d6\u7684\u6570\u636e\uff0c\u6211\u4eec\u8fdb\u884c\u4e86\u5143\u5206\u6790\uff0c\u4ee5\u7814\u7a76\u5728\u56fa\u6001\u5408\u6210\u7684\u80cc\u666f\u4e0b\u524d\u4f53\u4e4b\u95f4\u7684\u5f02\u540c\u3002\u4e3a\u4e86\u91cf\u5316\u524d\u4f53\u76f8\u4f3c\u6027\uff0c\u6211\u4eec\u5efa\u7acb\u4e86\u4e00\u4e2a\u66ff\u4ee3\u6a21\u578b\u6765\u8ba1\u7b97\u4e00\u4e2a\u524d\u4f53\u4e0e\u53e6\u4e00\u4e2a\u524d\u4f53\u66ff\u4ee3\u7684\u751f\u5b58\u80fd\u529b\uff0c\u540c\u65f6\u4fdd\u7559\u76ee\u6807\u3002\u4ece\u524d\u4f53\u7684\u5c42\u6b21\u805a\u7c7b\uff0c\u6211\u4eec\u6f14\u793a\u4e86\u524d\u4f53\u7684\u201c\u5316\u5b66\u76f8\u4f3c\u6027\u201d\u53ef\u4ee5\u4ece\u6587\u672c\u6570\u636e\u4e2d\u63d0\u53d6\u3002\u91cf\u5316\u524d\u4f53\u7269\u7684\u76f8\u4f3c\u6027\u6709\u52a9\u4e8e\u5728\u9884\u6d4b\u5408\u6210\u6a21\u578b\u4e2d\u5efa\u7acb\u5019\u9009\u53cd\u5e94\u7269<\/span><\/p>\n


      <\/p>\n

      \n
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      \n
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      <\/p>\n

      \u4e00\u4e2a\u6539\u8fdb\u7684\u6d41\u884c\u75c5\u5b66\u6a21\u578b\u6765<\/strong><\/span><\/p>\n

      \u7406\u89e3\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u5bf9\u8106\u5f31\u4e2a\u4f53<\/strong><\/span><\/p>\n

      \u7684\u4e0d\u5747\u8861\u5f71\u54cd\u4ee5\u53ca<\/strong><\/span><\/p>\n

      \u5e2e\u52a9\u4ed6\u4eec\u6446\u8131\u5c01\u9501\u6240\u9700\u7684\u65b9\u6cd5<\/strong><\/span><\/strong><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


      <\/p>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

      A Modified Epidemiological Model to Understand the Uneven Impact of COVID-19 on Vulnerable Individuals and the Approaches Required to Help them Emerge from Lockdown<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/p>\n

      http:\/\/arxiv.org\/abs\/2006.10495<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/span><\/p>\n

      Dario Ortega Anderez,Eiman Kanjo,Ganna Pogrebna,Shane Johnson,John Alan Hunt<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>COVID-19 has shown a relatively low mortality rate in young healthy individuals, with the majority of this group being asymptomatic or having mild symptoms, while the severity of the disease among individuals with underlying health conditions has caused signiffcant mortality rates worldwide. Understanding these differences in mortality amongst different sectors of society and modelling this will enable the different levels of risk and vulnerabilities to be determined to enable strategies exit the lockdown. However, epidemiological models do not account for the variability encountered in the severity of the SARS-CoV-2 disease across different population groups. To overcome this limitation, it is proposed that a modiffed SEIR model, namely SEIR-v, through which the population is separated into two groups regarding their vulnerability to SARS-CoV-2 is applied. This enables the analysis of the spread of the epidemic when different contention measures are applied to different groups in society regarding their vulnerability to the disease. A Monte Carlo simulation indicates a large number of deaths could be avoided by slightly decreasing the exposure of vulnerable groups to the disease. From this modelling a number of mechanisms can be proposed to limit the exposure of vulnerable individuals to the disease in order to reduce the mortality rate among this group. One option could be the provision of a wristband to vulnerable people and those without a contact-tracing app. By combining very dense contact tracing data from smartphone apps and wristband signals with information about infection status and symptoms, vulnerable people can be protected and kept safer. Widespread utilisation would extend the protection further beyond these high risk groups.<\/span><\/p>\n

      \u6458\u8981\uff1a<\/span><\/strong>\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u663e\u793a\uff0c\u5065\u5eb7\u5e74\u8f7b\u4eba\u7684\u6b7b\u4ea1\u7387\u76f8\u5bf9\u8f83\u4f4e\uff0c\u5176\u4e2d\u5927\u591a\u6570\u4eba\u6ca1\u6709\u75c7\u72b6\u6216\u75c7\u72b6\u8f7b\u5fae\uff0c\u800c\u5177\u6709\u6f5c\u5728\u5065\u5eb7\u72b6\u51b5\u7684\u4e2a\u4eba\u7684\u75be\u75c5\u4e25\u91cd\u7a0b\u5ea6\u5df2\u5bfc\u81f4\u5168\u7403\u6b7b\u4ea1\u7387\u663e\u8457\u4e0a\u5347\u3002\u4e86\u89e3\u793e\u4f1a\u4e0d\u540c\u90e8\u95e8\u4e4b\u95f4\u6b7b\u4ea1\u7387\u7684\u8fd9\u4e9b\u5dee\u5f02\u5e76\u5efa\u7acb\u6a21\u578b\uff0c\u5c06\u6709\u52a9\u4e8e\u786e\u5b9a\u4e0d\u540c\u7a0b\u5ea6\u7684\u98ce\u9669\u548c\u8106\u5f31\u6027\uff0c\u4ece\u800c\u4f7f\u5404\u9879\u6218\u7565\u5f97\u4ee5\u8131\u79bb\u5c01\u9501\u72b6\u6001\u3002\u7136\u800c\uff0c\u6d41\u884c\u75c5\u5b66\u6a21\u578b\u6ca1\u6709\u8003\u8651\u5230\u4e0d\u540c\u4eba\u7fa4\u7fa4\u4f53\u4e2d SARS-CoV-2\u75be\u75c5\u4e25\u91cd\u7a0b\u5ea6\u7684\u53d8\u5f02\u6027\u3002\u4e3a\u4e86\u514b\u670d\u8fd9\u4e00\u5c40\u9650\u6027\uff0c\u63d0\u51fa\u4e86\u4e00\u79cd\u6539\u8fdb\u7684 SEIR \u6a21\u578b\uff0c\u5373 SEIR-v \u6a21\u578b\uff0c\u901a\u8fc7\u8be5\u6a21\u578b\u5c06\u79cd\u7fa4\u6309\u7167\u5bf9 sars cov-2\u7684\u6613\u611f\u6027\u5206\u4e3a\u4e24\u7ec4\u3002\u8fd9\u4f7f\u6211\u4eec\u80fd\u591f\u5206\u6790\u5728\u793e\u4f1a\u4e0a\u4e0d\u540c\u7fa4\u4f53\u5bf9\u75be\u75c5\u7684\u8106\u5f31\u6027\u91c7\u53d6\u4e0d\u540c\u7684\u4e89\u8bba\u63aa\u65bd\u65f6\uff0c\u6d41\u884c\u75c5\u7684\u4f20\u64ad\u60c5\u51b5\u3002\u8499\u7279\u5361\u7f57\u6a21\u62df\u8868\u660e\uff0c\u7a0d\u5fae\u51cf\u5c11\u6613\u611f\u4eba\u7fa4\u5bf9\u75be\u75c5\u7684\u63a5\u89e6\u53ef\u4ee5\u907f\u514d\u5927\u91cf\u7684\u6b7b\u4ea1\u3002\u901a\u8fc7\u8fd9\u79cd\u5efa\u6a21\uff0c\u53ef\u4ee5\u63d0\u51fa\u4e00\u4e9b\u673a\u5236\uff0c\u9650\u5236\u6613\u53d7\u4f24\u5bb3\u7684\u4e2a\u4eba\u63a5\u89e6\u8fd9\u79cd\u75be\u75c5\uff0c\u4ee5\u964d\u4f4e\u8fd9\u4e00\u7fa4\u4f53\u7684\u6b7b\u4ea1\u7387\u3002\u5176\u4e2d\u4e00\u4e2a\u9009\u62e9\u53ef\u80fd\u662f\u5411\u5f31\u52bf\u7fa4\u4f53\u548c\u90a3\u4e9b\u6ca1\u6709\u8054\u7cfb\u4eba\u8ffd\u8e2a\u5e94\u7528\u7a0b\u5e8f\u7684\u4eba\u63d0\u4f9b\u8155\u5e26\u3002\u901a\u8fc7\u5c06\u6765\u81ea\u667a\u80fd\u624b\u673a\u5e94\u7528\u7a0b\u5e8f\u548c\u8155\u5e26\u4fe1\u53f7\u7684\u5bc6\u96c6\u63a5\u89e6\u8ffd\u8e2a\u6570\u636e\u4e0e\u611f\u67d3\u72b6\u51b5\u548c\u75c7\u72b6\u7684\u4fe1\u606f\u7ed3\u5408\u8d77\u6765\uff0c\u8106\u5f31\u7684\u4eba\u7fa4\u53ef\u4ee5\u5f97\u5230\u4fdd\u62a4\uff0c\u5e76\u4e14\u66f4\u52a0\u5b89\u5168\u3002\u5e7f\u6cdb\u7684\u5229\u7528\u5c06\u4f7f\u4fdd\u62a4\u8303\u56f4\u8fdb\u4e00\u6b65\u6269\u5927\u5230\u8fd9\u4e9b\u9ad8\u98ce\u9669\u7fa4\u4f53\u4e4b\u5916<\/span><\/p>\n


      <\/p>\n

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      <\/p>\n

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      <\/p>\n

      \u793e\u4ea4\u5a92\u4f53\u5b9e\u65f6\u653b\u51fb\u6027\u68c0\u6d4b<\/strong><\/span><\/strong><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


      <\/p>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

      Catching them red-handed: Real-time Aggression Detection on Social Media<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/p>\n

      http:\/\/arxiv.org\/abs\/2006.10104<\/span><\/p>\n

      \u4f5c<\/span><\/strong>\u8005:<\/span><\/strong><\/span><\/p>\n

      Herodotos Herodotou,Despoina Chatzakou,Nicolas Kourtellis<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/strong>The rise of online aggression on social media is evolving into a major point of concern. Several machine and deep learning approaches have been proposed recently for detecting various types of aggressive behavior. However, social media are fast paced, generating an increasing amount of content, while aggressive behavior evolves over time. In this work, we introduce the first practical, real-time framework for detecting aggression on Twitter via embracing the streaming machine learning paradigm. Our method adapts its ML classifiers in an incremental fashion as it receives new annotated examples and is able to achieve the same (or even higher) performance as batch-based ML models, with over 90% accuracy, precision, and recall. At the same time, our experimental analysis on real Twitter data reveals how our framework can easily scale to accommodate the entire Twitter Firehose (of 778 million tweets per day) with only 3 commodity machines. Finally, we show that our framework is general enough to detect other related behaviors such as sarcasm, racism, and sexism in real time.<\/span><\/p>\n

      \u6458\u8981\uff1a\u793e\u4ea4\u5a92\u4f53\u4e0a\u7f51\u7edc\u653b\u51fb\u884c\u4e3a\u7684\u589e\u52a0\u6b63\u5728\u6f14\u53d8\u6210\u4e00\u4e2a\u4e3b\u8981\u7684\u5173\u6ce8\u70b9\u3002\u4e00\u4e9b\u673a\u5668\u548c\u6df1\u5ea6\u5b66\u4e60\u7684\u65b9\u6cd5\u6700\u8fd1\u88ab\u63d0\u51fa\u6765\u68c0\u6d4b\u5404\u79cd\u7c7b\u578b\u7684\u653b\u51fb\u6027\u884c\u4e3a\u3002\u7136\u800c\uff0c\u793e\u4ea4\u5a92\u4f53\u662f\u5feb\u8282\u594f\u7684\uff0c\u4ea7\u751f\u4e86\u8d8a\u6765\u8d8a\u591a\u7684\u5185\u5bb9\uff0c\u800c\u653b\u51fb\u6027\u884c\u4e3a\u968f\u7740\u65f6\u95f4\u7684\u63a8\u79fb\u800c\u53d1\u5c55\u3002\u5728\u8fd9\u9879\u5de5\u4f5c\u4e2d\uff0c\u6211\u4eec\u4ecb\u7ecd\u4e86\u7b2c\u4e00\u4e2a\u5b9e\u7528\u7684\u3001\u5b9e\u65f6\u7684\u6846\u67b6\uff0c\u901a\u8fc7\u91c7\u7528\u6d41\u5f0f\u673a\u5668\u5b66\u4e60\u8303\u5f0f\u6765\u68c0\u6d4b Twitter \u4e0a\u7684\u653b\u51fb\u884c\u4e3a\u3002\u6211\u4eec\u7684\u65b9\u6cd5\u5728\u63a5\u6536\u65b0\u7684\u6ce8\u91ca\u793a\u4f8b\u65f6\uff0c\u4ee5\u589e\u91cf\u7684\u65b9\u5f0f\u8c03\u6574\u5176\u673a\u5668\u5b66\u4e60\u5206\u7c7b\u5668\uff0c\u5e76\u4e14\u80fd\u591f\u83b7\u5f97\u4e0e\u57fa\u4e8e\u6279\u5904\u7406\u7684\u673a\u5668\u5b66\u4e60\u6a21\u578b\u76f8\u540c(\u751a\u81f3\u66f4\u9ad8)\u7684\u6027\u80fd\uff0c\u51c6\u786e\u7387\u3001\u7cbe\u786e\u5ea6\u548c\u53ec\u56de\u7387\u8d85\u8fc790% \u3002\u4e0e\u6b64\u540c\u65f6\uff0c\u6211\u4eec\u5bf9\u771f\u5b9e Twitter \u6570\u636e\u7684\u5b9e\u9a8c\u5206\u6790\u63ed\u793a\u4e86\u6211\u4eec\u7684\u6846\u67b6\u5982\u4f55\u80fd\u591f\u8f7b\u677e\u5730\u6269\u5c55\u4ee5\u9002\u5e94\u6574\u4e2a Twitter Firehose (\u6bcf\u59297.78\u4ebf\u6761 tweet) \uff0c\u800c\u53ea\u9700\u89813\u53f0\u666e\u901a\u673a\u5668\u3002\u6700\u540e\uff0c\u6211\u4eec\u5c55\u793a\u4e86\u6211\u4eec\u7684\u6846\u67b6\u8db3\u591f\u901a\u7528\uff0c\u53ef\u4ee5\u5b9e\u65f6\u68c0\u6d4b\u5176\u4ed6\u76f8\u5173\u884c\u4e3a\uff0c\u5982\u8bbd\u523a\u3001\u79cd\u65cf\u6b67\u89c6\u548c\u6027\u522b\u6b67\u89c6<\/span><\/p>\n


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      \u5229\u7528\u60c5\u611f\u4fe1\u606f\u9884\u5148<\/strong><\/span><\/p>\n

      \u68c0\u6d4b\u5728\u7ebf\u4f1a\u8bdd\u4e2d\u7684\u6709\u6bd2\u8bc4\u8bba<\/strong><\/span><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


      <\/p>\n

      \u539f\u6587\u6807\u9898<\/span><\/strong>\uff1a<\/span><\/strong><\/h2>\n

      Using Sentiment Information for Preemptive Detection of Toxic Comments in Online Conversations<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/p>\n

      https:\/\/arxiv.org\/abs\/2006.10145<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/p>\n

      \u00c9loi Brassard-Gourdeau,Richard Khoury<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>The challenge of automatic detection of toxic comments online has been the subject of a lot of research recently, but the focus has been mostly on detecting it in individual messages after they have been posted. Some authors have tried to predict if a conversation will derail into toxicity using the features of the first few messages. In this paper, we combine that approach with previous work on toxicity detection using sentiment information, and show how the sentiments expressed in the first messages of a conversation can help predict upcoming toxicity. Our results show that adding sentiment features does help improve the accuracy of toxicity prediction, and also allow us to make important observations on the general task of preemptive toxicity detection.<\/span><\/p>\n

      \u6458\u8981\uff1a<\/span><\/strong>\u81ea\u52a8\u68c0\u6d4b\u7f51\u4e0a\u6709\u6bd2\u8bc4\u8bba\u7684\u6311\u6218\u6700\u8fd1\u5df2\u7ecf\u6210\u4e3a\u8bb8\u591a\u7814\u7a76\u7684\u4e3b\u9898\uff0c\u4f46\u662f\u91cd\u70b9\u4e3b\u8981\u662f\u5728\u4e2a\u4eba\u4fe1\u606f\u53d1\u5e03\u540e\u68c0\u6d4b\u5b83\u4eec\u3002\u4e00\u4e9b\u4f5c\u8005\u8bd5\u56fe\u901a\u8fc7\u524d\u51e0\u6761\u4fe1\u606f\u7684\u7279\u5f81\u6765\u9884\u6d4b\u4e00\u6bb5\u5bf9\u8bdd\u662f\u5426\u4f1a\u8131\u79bb\u6b63\u5e38\u8f68\u9053\u3002\u5728\u8fd9\u7bc7\u8bba\u6587\u4e2d\uff0c\u6211\u4eec\u5c06\u8fd9\u79cd\u65b9\u6cd5\u4e0e\u4e4b\u524d\u5229\u7528\u60c5\u611f\u4fe1\u606f\u8fdb\u884c\u6bd2\u6027\u68c0\u6d4b\u7684\u5de5\u4f5c\u7ed3\u5408\u8d77\u6765\uff0c\u5e76\u5c55\u793a\u4e86\u5728\u4e00\u6b21\u8c08\u8bdd\u7684\u7b2c\u4e00\u6761\u4fe1\u606f\u4e2d\u8868\u8fbe\u7684\u60c5\u611f\u5982\u4f55\u5e2e\u52a9\u9884\u6d4b\u5373\u5c06\u5230\u6765\u7684\u6bd2\u6027\u3002\u6211\u4eec\u7684\u7ed3\u679c\u8868\u660e\uff0c\u589e\u52a0\u60c5\u611f\u7279\u5f81\u786e\u5b9e\u6709\u52a9\u4e8e\u63d0\u9ad8\u6bd2\u6027\u9884\u6d4b\u7684\u51c6\u786e\u6027\uff0c\u4e5f\u5141\u8bb8\u6211\u4eec\u5bf9\u62a2\u5148\u6bd2\u6027\u68c0\u6d4b\u7684\u4e00\u822c\u4efb\u52a1\u8fdb\u884c\u91cd\u8981\u89c2\u5bdf<\/span><\/p>\n


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      \u5b50\u56fe\u795e\u7ecf\u7f51\u7edc<\/strong><\/span><\/strong><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


      <\/p>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/h2>\n

      Subgraph Neural Networks<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/p>\n

      http:\/\/arxiv.org\/abs\/2006.10538<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/p>\n

      Emily Alsentzer,Samuel G. Finlayson,Michelle M. Li,Marinka Zitnik<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>Deep learning methods for graphs achieve remarkable performance on many node-level and graph-level prediction tasks. However, despite the proliferation of the methods and their success, prevailing Graph Neural Networks (GNNs) neglect subgraphs, rendering subgraph prediction tasks challenging to tackle in many impactful applications. Further, subgraph prediction tasks present several unique challenges, because subgraphs can have non-trivial internal topology, but also carry a notion of position and external connectivity information relative to the underlying graph in which they exist. Here, we introduce SUB-GNN, a subgraph neural network to learn disentangled subgraph representations. In particular, we propose a novel subgraph routing mechanism that propagates neural messages between the subgraph’s components and randomly sampled anchor patches from the underlying graph, yielding highly accurate subgraph representations. SUB-GNN specifies three channels, each designed to capture a distinct aspect of subgraph structure, and we provide empirical evidence that the channels encode their intended properties. We design a series of new synthetic and real-world subgraph datasets. Empirical results for subgraph classification on eight datasets show that SUB-GNN achieves considerable performance gains, outperforming strong baseline methods, including node-level and graph-level GNNs, by 12.4% over the strongest baseline. SUB-GNN performs exceptionally well on challenging biomedical datasets when subgraphs have complex topology and even comprise multiple disconnected components.<\/span><\/p>\n

      \u6458\u8981:<\/span><\/strong>\u56fe\u7684\u6df1\u5ea6\u5b66\u4e60\u65b9\u6cd5\u5728\u8bb8\u591a\u8282\u70b9\u7ea7\u548c\u56fe\u7ea7\u9884\u6d4b\u4efb\u52a1\u4e0a\u53d6\u5f97\u4e86\u663e\u8457\u7684\u6548\u679c\u3002\u7136\u800c\uff0c\u5c3d\u7ba1\u8fd9\u4e9b\u65b9\u6cd5\u4e0d\u65ad\u6d8c\u73b0\u5e76\u53d6\u5f97\u4e86\u6210\u529f\uff0c\u4f46\u662f\u5f53\u524d\u6d41\u884c\u7684\u56fe\u795e\u7ecf\u7f51\u7edc\u5ffd\u7565\u4e86\u5b50\u56fe\uff0c\u4f7f\u5f97\u5b50\u56fe\u9884\u6d4b\u5728\u8bb8\u591a\u6709\u5f71\u54cd\u7684\u5e94\u7528\u4e2d\u5177\u6709\u6311\u6218\u6027\u3002\u6b64\u5916\uff0c\u5b50\u56fe\u9884\u6d4b\u4efb\u52a1\u63d0\u51fa\u4e86\u51e0\u4e2a\u72ec\u7279\u7684\u6311\u6218\uff0c\u56e0\u4e3a\u5b50\u56fe\u53ef\u4ee5\u6709\u975e\u5e73\u51e1\u7684\u5185\u90e8\u62d3\u6251\u7ed3\u6784\uff0c\u4f46\u4e5f\u643a\u5e26\u4f4d\u7f6e\u548c\u76f8\u5bf9\u4e8e\u5b83\u4eec\u6240\u5728\u7684\u5e95\u5c42\u56fe\u7684\u5916\u90e8\u8fde\u63a5\u4fe1\u606f\u7684\u6982\u5ff5\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u4ecb\u7ecd\u4e86 SUB-GNN\uff0c\u4e00\u4e2a\u5b50\u56fe\u795e\u7ecf\u7f51\u7edc\u5b66\u4e60\u89e3\u7ea0\u7f20\u5b50\u56fe\u8868\u793a\u3002\u7279\u522b\u5730\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u7684\u5b50\u56fe\u8def\u7531\u673a\u5236\uff0c\u5728\u5b50\u56fe\u7684\u7ec4\u4ef6\u4e4b\u95f4\u4f20\u64ad\u795e\u7ecf\u4fe1\u606f\uff0c\u5e76\u4ece\u4e0b\u9762\u7684\u56fe\u4e2d\u968f\u673a\u91c7\u6837\u951a\u5b9a\u5757\uff0c\u4ece\u800c\u4ea7\u751f\u9ad8\u5ea6\u7cbe\u786e\u7684\u5b50\u56fe\u8868\u793a\u3002\u6307\u5b9a\u4e863\u4e2a\u901a\u9053\uff0c\u6bcf\u4e2a\u901a\u9053\u7684\u8bbe\u8ba1\u90fd\u662f\u4e3a\u4e86\u6355\u6349\u5b50\u56fe\u7ed3\u6784\u7684\u4e0d\u540c\u65b9\u9762\uff0c\u6211\u4eec\u63d0\u4f9b\u4e86\u901a\u9053\u7f16\u7801\u5176\u9884\u671f\u5c5e\u6027\u7684\u7ecf\u9a8c\u8bc1\u660e\u3002\u6211\u4eec\u8bbe\u8ba1\u4e86\u4e00\u7cfb\u5217\u65b0\u7684\u5408\u6210\u7684\u548c\u771f\u5b9e\u4e16\u754c\u7684\u5b50\u56fe\u6570\u636e\u96c6\u3002\u5bf98\u4e2a\u6570\u636e\u96c6\u5b50\u56fe\u5206\u7c7b\u7684\u5b9e\u8bc1\u7ed3\u679c\u8868\u660e\uff0cSUB-GNN \u5728\u6700\u5f3a\u57fa\u7ebf\u4e0a\u7684\u6027\u80fd\u63d0\u9ad8\u53ef\u89c2\uff0c\u4f18\u4e8e\u5f3a\u57fa\u7ebf\u65b9\u6cd5\uff0c\u5305\u62ec\u8282\u70b9\u7ea7\u548c\u56fe\u7ea7 gnn\uff0c\u63d0\u9ad8\u4e8612.4% \u3002\u5f53\u5b50\u56fe\u5177\u6709\u590d\u6742\u7684\u62d3\u6251\u7ed3\u6784\uff0c\u751a\u81f3\u5305\u542b\u591a\u4e2a\u4e0d\u8fde\u63a5\u7684\u7ec4\u4ef6\u65f6\uff0cSUB-GNN \u5728\u6311\u6218\u751f\u7269\u533b\u5b66\u6570\u636e\u96c6\u65b9\u9762\u8868\u73b0\u51fa\u8272<\/span><\/p>\n


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      \u4e00\u79cd\u57fa\u4e8e\u547d\u540d\u884c\u4e3a\u5efa\u6a21\u7684<\/strong><\/span><\/p>\n

      \u591a\u89c6\u56fe\u4e2d\u6587\u7528\u6237\u8d26\u6237\u8de8\u7f51\u7edc\u5bf9\u9f50\u65b9\u6cd5<\/strong><\/span><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


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      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/h2>\n

      A Multi-View Approach Based on Naming Behavioral Modeling for Aligning Chinese User Accounts across Multiple Networks<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/p>\n

      https:\/\/arxiv.org\/abs\/2006.10633<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/p>\n

      Junxing Zhu,Xiang Wang,Qiang Liu,Xiaoyong Li,Chengcheng Shao,Bin Zhou<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>Hundreds of millions of Chinese people have become social network users in recent years, and aligning the accounts of common Chinese users across multiple social networks is valuable to many inter-network applications, e.g., cross-network recommendation, cross-network link prediction. Many methods have explored the proper ways of utilizing account name information into aligning the common English users’ accounts. However, how to properly utilize the account name information when aligning the Chinese user accounts remains to be detailedly studied. In this paper, we firstly discuss the available naming behavioral models as well as the related features for different types of Chinese account name matchings. Secondly, we propose the framework of Multi-View Cross-Network User Alignment (MCUA) method, which uses a multi-view framework to creatively integrate different models to deal with different types of Chinese account name matchings, and can consider all of the studied features when aligning the Chinese user accounts. Finally, we conduct experiments to prove that MCUA can outperform many existing methods on aligning Chinese user accounts between Sina Weibo and Twitter. Besides, we also study the best learning models and the top-k valuable features of different types of name matchings for MCUA over our experimental data sets.<\/span><\/p>\n

      \u6458\u8981\uff1a<\/span><\/strong>\u8fd1\u5e74\u6765\uff0c\u6570\u4ee5\u4ebf\u8ba1\u7684\u4e2d\u56fd\u4eba\u5df2\u7ecf\u6210\u4e3a\u793e\u4ea4\u7f51\u7edc\u7528\u6237\uff0c\u5c06\u4e2d\u56fd\u666e\u901a\u7528\u6237\u7684\u8d26\u6237\u8de8\u8d8a\u591a\u4e2a\u793e\u4ea4\u7f51\u7edc\u5bf9\u8bb8\u591a\u8de8\u7f51\u7edc\u5e94\u7528(\u5982\u8de8\u7f51\u7edc\u63a8\u8350\u3001\u8de8\u7f51\u7edc\u94fe\u63a5\u9884\u6d4b)\u662f\u6709\u4ef7\u503c\u7684\u3002\u8bb8\u591a\u65b9\u6cd5\u5df2\u7ecf\u63a2\u7d22\u4e86\u5229\u7528\u8d26\u6237\u540d\u79f0\u4fe1\u606f\u5bf9\u9f50\u666e\u901a\u82f1\u8bed\u7528\u6237\u8d26\u6237\u7684\u6b63\u786e\u65b9\u6cd5\u3002\u7136\u800c\uff0c\u5982\u4f55\u5728\u4e2d\u6587\u7528\u6237\u8d26\u6237\u5bf9\u9f50\u65f6\u6b63\u786e\u5730\u5229\u7528\u8d26\u6237\u540d\u4fe1\u606f\uff0c\u8fd8\u6709\u5f85\u4e8e\u8fdb\u4e00\u6b65\u7814\u7a76\u3002\u672c\u6587\u9996\u5148\u8ba8\u8bba\u4e86\u4e0d\u540c\u7c7b\u578b\u7684\u4e2d\u6587\u8d26\u6237\u540d\u79f0\u5339\u914d\u7684\u547d\u540d\u884c\u4e3a\u6a21\u578b\u53ca\u5176\u76f8\u5173\u7279\u5f81\u3002\u5176\u6b21\uff0c\u63d0\u51fa\u4e86\u591a\u89c6\u89d2\u8de8\u7f51\u7edc\u7528\u6237\u5bf9\u9f50\u65b9\u6cd5\u6846\u67b6\uff0c\u8be5\u65b9\u6cd5\u5229\u7528\u591a\u89c6\u89d2\u6846\u67b6\u521b\u9020\u6027\u5730\u6574\u5408\u4e0d\u540c\u6a21\u578b\uff0c\u5904\u7406\u4e0d\u540c\u7c7b\u578b\u7684\u4e2d\u6587\u8d26\u6237\u540d\u5339\u914d\uff0c\u5e76\u5728\u5bf9\u9f50\u4e2d\u6587\u8d26\u6237\u65f6\u8003\u8651\u6240\u6709\u7814\u7a76\u7279\u5f81\u3002\u6700\u540e\uff0c\u6211\u4eec\u901a\u8fc7\u5b9e\u9a8c\u8bc1\u660e\u4e86 MCUA \u80fd\u591f\u6bd4\u73b0\u6709\u7684\u8bb8\u591a\u65b9\u6cd5\u66f4\u597d\u5730\u5728\u65b0\u6d6a\u5fae\u535a\u548c Twitter \u4e4b\u95f4\u8c03\u6574\u4e2d\u56fd\u7528\u6237\u8d26\u6237\u3002\u6b64\u5916\uff0c\u6211\u4eec\u8fd8\u5728\u5b9e\u9a8c\u6570\u636e\u96c6\u4e0a\u7814\u7a76\u4e86\u4e0d\u540c\u7c7b\u578b\u540d\u79f0\u5339\u914d\u7684\u6700\u4f73\u5b66\u4e60\u6a21\u578b\u548c\u6700\u6709\u4ef7\u503c\u7684\u7279\u5f81<\/span><\/p>\n


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      \u57fa\u4e8e QUBO \u548c\u6570\u5b57\u9000\u706b\u7684\u5e02\u573a\u56fe\u805a\u7c7b<\/strong><\/span><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


      <\/p>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/h2>\n

      Market Graph Clustering Via QUBO and Digital Annealing<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/p>\n

      http:\/\/arxiv.org\/abs\/2006.10716<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/p>\n

      Seo Hong,Pierre Miasnikof,Roy Kwon,Yuri Lawryshyn<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>Our goal is to find representative nodes of a market graph that best replicate the returns of a broader market graph (index), a common task in the financial industry. We model our reference index as a market graph and express the index tracking problem in a quadratic K-medoids form. We take advantage of a purpose built hardware architecture, the Fujitsu Digital Annealer, to circumvent the NP-hard nature of the problem and solve our formulation efficiently. In this article, we combine three separate areas of the literature, market graph models, K-medoid clustering and quadratic binary optimization modeling, to formulate the index-tracking problem as a quadratic K-medoid graph-clustering problem. Our initial results show we accurately replicate the returns of a broad market index, using only a small subset of its constituent assets. Moreover, our quadratic formulation allows us to take advantage of recent hardware advances, to overcome the NP-hard nature of the problem.<\/span><\/p>\n

      \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u7684\u76ee\u6807\u662f\u627e\u5230\u5e02\u573a\u56fe\u8868\u7684\u4ee3\u8868\u6027\u8282\u70b9\uff0c\u4ee5\u6700\u597d\u5730\u590d\u5236\u66f4\u5e7f\u6cdb\u7684\u5e02\u573a\u56fe\u8868(\u6307\u6570)\u7684\u56de\u62a5\uff0c\u8fd9\u662f\u91d1\u878d\u4e1a\u7684\u4e00\u9879\u5e38\u89c1\u4efb\u52a1\u3002\u6211\u4eec\u5c06\u6211\u4eec\u7684\u53c2\u8003\u6307\u6570\u5efa\u6a21\u4e3a\u4e00\u4e2a\u5e02\u573a\u56fe\uff0c\u5e76\u5c06\u6307\u6570\u8ddf\u8e2a\u95ee\u9898\u8868\u793a\u4e3a\u4e8c\u6b21 K-medoids \u5f62\u5f0f\u3002\u6211\u4eec\u5229\u7528\u4e13\u95e8\u5efa\u7acb\u7684\u786c\u4ef6\u67b6\u6784\uff0c\u5bcc\u58eb\u901a\u6570\u5b57\u9000\u706b\u7089\uff0c\u4ee5\u89c4\u907f\u95ee\u9898\u7684 np \u96be\u7684\u6027\u8d28\uff0c\u5e76\u89e3\u51b3\u6211\u4eec\u7684\u516c\u5f0f\u6709\u6548\u3002\u672c\u6587\u7ed3\u5408\u5e02\u573a\u56fe\u6a21\u578b\u3001 k- \u4e2d\u5fc3\u70b9\u805a\u7c7b\u548c\u4e8c\u6b21\u4e8c\u8fdb\u5236\u4f18\u5316\u5efa\u6a21\u4e09\u4e2a\u72ec\u7acb\u7684\u9886\u57df\uff0c\u5c06\u6307\u6570\u8ddf\u8e2a\u95ee\u9898\u8f6c\u5316\u4e3a\u4e8c\u6b21 k- \u4e2d\u5fc3\u70b9\u56fe\u805a\u7c7b\u95ee\u9898\u3002\u6211\u4eec\u7684\u521d\u6b65\u7ed3\u679c\u8868\u660e\uff0c\u6211\u4eec\u51c6\u786e\u5730\u590d\u5236\u4e86\u4e00\u4e2a\u5e7f\u6cdb\u7684\u5e02\u573a\u6307\u6570\u7684\u6536\u76ca\uff0c\u53ea\u4f7f\u7528\u5176\u7ec4\u6210\u8d44\u4ea7\u7684\u4e00\u4e2a\u5c0f\u5b50\u96c6\u3002\u6b64\u5916\uff0c\u6211\u4eec\u7684\u4e8c\u6b21\u516c\u5f0f\u5141\u8bb8\u6211\u4eec\u5229\u7528\u6700\u65b0\u7684\u786c\u4ef6\u8fdb\u6b65\uff0c\u6765\u514b\u670d\u95ee\u9898\u7684 np \u96be\u6027\u8d28<\/span><\/p>\n


      <\/p>\n

      \n
      \n
      \n


      <\/p>\n

      \n
      \n
      <\/p>\n

      \u793e\u4f1a\u7f51\u7edc\u7b49\u4ef7\u5173\u7cfb\u7684\u7edf\u4e00\u6846\u67b6<\/strong><\/span><\/strong><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


      <\/p>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/h2>\n

      A unified framework for equivalences in social networks<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/p>\n

      http:\/\/arxiv.org\/abs\/2006.10733<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/p>\n

      Nina Otter,Mason A. Porter<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>A key concern in network analysis is the study of social positions and roles of actors in a network. The notion of “position” refers to an equivalence class of nodes that have similar ties to other nodes, whereas a “role” is an equivalence class of compound relations that connect the same pairs of nodes. An open question in network science is whether it is possible to simultaneously perform role and positional analysis. Motivated by the principle of functoriality in category theory we propose a new method that allows to tie role and positional analysis together. We illustrate our methods on two well-studied data sets in network science.<\/span><\/p>\n

      \u6458\u8981\uff1a<\/span><\/strong>\u7f51\u7edc\u5206\u6790\u7684\u4e00\u4e2a\u5173\u952e\u95ee\u9898\u662f\u7814\u7a76\u793e\u4f1a\u5730\u4f4d\u548c\u7f51\u7edc\u4e2d\u884c\u4e3a\u8005\u7684\u89d2\u8272\u3002\u4f4d\u7f6e\u7684\u6982\u5ff5\u6307\u7684\u662f\u4e00\u4e2a\u7b49\u4ef7\u7c7b\u7684\u8282\u70b9\u6709\u76f8\u4f3c\u7684\u8054\u7cfb\u5176\u4ed6\u8282\u70b9\uff0c\u800c\u4e00\u4e2a\u201c\u89d2\u8272\u201d\u662f\u4e00\u4e2a\u590d\u5408\u5173\u7cfb\u7684\u7b49\u4ef7\u7c7b\uff0c\u8fde\u63a5\u76f8\u540c\u7684\u8282\u70b9\u5bf9\u3002\u7f51\u7edc\u79d1\u5b66\u4e2d\u4e00\u4e2a\u60ac\u800c\u672a\u51b3\u7684\u95ee\u9898\u662f\uff0c\u662f\u5426\u6709\u53ef\u80fd\u540c\u65f6\u8fdb\u884c\u89d2\u8272\u548c\u4f4d\u7f6e\u5206\u6790\u3002\u57fa\u4e8e\u8303\u7574\u7406\u8bba\u4e2d\u7684\u529f\u80fd\u6027\u539f\u5219\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u79cd\u5c06\u89d2\u8272\u548c\u4f4d\u7f6e\u5206\u6790\u7ed3\u5408\u8d77\u6765\u7684\u65b0\u65b9\u6cd5\u3002\u6211\u4eec\u5728\u7f51\u7edc\u79d1\u5b66\u7684\u4e24\u4e2a\u5145\u5206\u7814\u7a76\u7684\u6570\u636e\u96c6\u4e0a\u6f14\u793a\u4e86\u6211\u4eec\u7684\u65b9\u6cd5<\/span><\/p>\n

      <\/span>
      <\/h2>\n
      \n
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      \n
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      \u793e\u4f1a\u8ddd\u79bb\u5e72\u9884\u7684\u5065\u5eb7\u548c\u7ecf\u6d4e\u6548\u5e94: <\/strong><\/span><\/p>\n

      \u4e00\u4e2a\u4e2a\u4f53\u4e3a\u672c\u6a21\u578b\u7684<\/strong><\/span><\/p>\n

      \u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6d41\u884c\u75c5\u6a21\u62df<\/strong><\/span><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


      <\/h2>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/h2>\n

      COVID-ABS: An Agent-Based Model of COVID-19 Epidemic to Simulate Health and Economic Effects of Social Distancing Interventions<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/p>\n

      http:\/\/arxiv.org\/abs\/2006.10532<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/p>\n

      Petr\u00f4nio C. L. Silva,Paulo V. C. Batista,H\u00e9lder S. Lima,Marcos A. Alves,Frederico G. Guimar\u00e3es,Rodrigo C. P. Silva<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>The COVID-19 pandemic due to the SARS-CoV-2 coronavirus have directly impacted the public health and economy. To overcome this problem, the countries have adopted different policies for controlling the spread of the virus. This paper proposes the COVID-ABS, a new SEIR agent-based model that aims to simulate the pandemic dynamics using a society of agents emulating people, business and government. Seven different scenarios varying epidemiological and economical effects of social distance interventions were performed, which are: (1) do nothing, (2) lockdown, (3) conditional lockdown, (4) vertical isolation, (5) partial isolation, (6) use of face masks, and (7) use of face masks together with 50% of adhesion to social isolation. In the impossibility of implementing scenarios with lockdown which present the lowest number of deaths and highest impact on the economy, scenarios combining the use of face masks and partial isolation can be the more realistic for implementation in terms of social cooperation. The model can be easily extended to new societies by varying the parameters as well as allows the creating of a multitude of other scenarios.<\/span><\/p>\n

      \u6458\u8981\uff1a<\/span><\/strong>\u7531 SARS-CoV-2\u51a0\u72b6\u75c5\u6bd2\u5f15\u8d77\u7684\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u5927\u6d41\u884c\u5df2\u7ecf\u76f4\u63a5\u5f71\u54cd\u4e86\u516c\u5171\u536b\u751f\u548c\u7ecf\u6d4e\u3002\u4e3a\u4e86\u514b\u670d\u8fd9\u4e2a\u95ee\u9898\uff0c\u5404\u56fd\u91c7\u53d6\u4e86\u4e0d\u540c\u7684\u653f\u7b56\u6765\u63a7\u5236\u75c5\u6bd2\u7684\u4f20\u64ad\u3002\u8fd9\u7bc7\u8bba\u6587\u63d0\u51fa\u4e86 COVID-ABS\uff0c\u4e00\u4e2a\u65b0\u7684 SEIR \u4e2a\u4f53\u4e3a\u672c\u6a21\u578b\uff0c\u65e8\u5728\u6a21\u62df\u5927\u6d41\u884c\u7684\u52a8\u6001\u4f7f\u7528\u4e00\u4e2a\u4ee3\u7406\u4eba\u6a21\u4eff\u4eba\uff0c\u5546\u4e1a\u548c\u653f\u5e9c\u7684\u793e\u4f1a\u3002\u6211\u4eec\u5206\u522b\u57287\u4e2a\u4e0d\u540c\u7684\u573a\u666f\u4e2d\u8fdb\u884c\u4e86\u793e\u4f1a\u8ddd\u79bb\u5e72\u9884\uff0c\u8fd9\u4e9b\u573a\u666f\u5206\u522b\u662f: (1)\u65e0\u6240\u4f5c\u4e3a\uff0c(2)\u5c01\u95ed\uff0c(3)\u6709\u6761\u4ef6\u5c01\u95ed\uff0c(4)\u5782\u76f4\u9694\u79bb\uff0c(5)\u90e8\u5206\u9694\u79bb\uff0c(6)\u4f7f\u7528\u53e3\u7f69\uff0c(7)\u4f7f\u7528\u53e3\u7f69\uff0c\u540c\u65f6\u5bf9\u793e\u4f1a\u9694\u79bb\u670950% \u7684\u7c98\u9644\u3002\u7531\u4e8e\u4e0d\u53ef\u80fd\u6267\u884c\u4e00\u7ea7\u9632\u8303\u7981\u95ed\u7684\u60c5\u51b5\uff0c\u8fd9\u79cd\u60c5\u51b5\u9020\u6210\u7684\u6b7b\u4ea1\u4eba\u6570\u6700\u5c11\uff0c\u5bf9\u7ecf\u6d4e\u7684\u5f71\u54cd\u6700\u5927\uff0c\u56e0\u6b64\uff0c\u5c31\u793e\u4f1a\u5408\u4f5c\u800c\u8a00\uff0c\u5c06\u4f7f\u7528\u9762\u7f69\u548c\u90e8\u5206\u9694\u79bb\u7ed3\u5408\u8d77\u6765\u7684\u60c5\u51b5\u53ef\u80fd\u66f4\u4e3a\u73b0\u5b9e\u3002\u8fd9\u4e2a\u6a21\u578b\u53ef\u4ee5\u901a\u8fc7\u6539\u53d8\u53c2\u6570\u4ee5\u53ca\u521b\u5efa\u5927\u91cf\u7684\u5176\u4ed6\u573a\u666f\uff0c\u5f88\u5bb9\u6613\u5730\u6269\u5c55\u5230\u65b0\u7684\u793e\u4f1a<\/span><\/p>\n


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      \u9002\u5e94\u6027\u4ea4\u901a\u653f\u7b56\u5bf9\u57ce\u5e02\u73af\u5883\u4e2d<\/strong><\/span><\/p>\n

      \u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u4f20\u64ad\u7684\u5f71\u54cd:<\/strong><\/span><\/p>\n

      \u97e9\u56fd\u9996\u5c14\u7684\u5e72\u9884\u5206\u6790<\/strong><\/span><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

      <\/h2>\n


      <\/h2>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

      The effect of adaptive mobility policy to the spread of COVID-19 in urban environment: intervention analysis of Seoul, South Korea<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/p>\n

      https:\/\/arxiv.org\/abs\/2006.10526<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/span><\/p>\n

      Yoonjin Yoon,Soohwan Oh,Jungwoo Cho,Yuyol Shin,Seyun Kim,Namwoo Kim,Haechan Cho<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>Although severe mobility restrictions are recognized as the key enabler to contain COVID-19, there has been few scientific studies to validate such approach, especially in urban context. This study analyzes mobility pattern changes in Seoul, South Korea that adopted adaptive approach toward mobility. Intervention analyses reveal that major mobility reduction did occur two weeks before the city’s case peak. Such voluntary adjustments exhibit strong preference shift toward private mode from public transit. Large reductions occurred in non-essential and high-contact activities of shopping and dining, while work and Starbucks trips were less affected. The collective evaluation reveal that major changes in epidemiology, mobility and policy occurred simultaneously, with no lagging nor leading contributors. Our study demonstrates that collective understanding the mutual aspects among mobility, epidemiology and policy is essential. Incremental and flexible mobility restriction is not only possible but necessary, especially for a pandemic of extensive spatial and temporal scales.<\/span><\/p>\n

      \u6458\u8981\uff1a<\/span><\/strong>\u867d\u7136\u4e25\u683c\u7684\u673a\u52a8\u6027\u9650\u5236\u88ab\u8ba4\u4e3a\u662f\u904f\u5236\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u7684\u5173\u952e\u56e0\u7d20\uff0c\u4f46\u662f\u5f88\u5c11\u6709\u79d1\u5b66\u7814\u7a76\u6765\u9a8c\u8bc1\u8fd9\u79cd\u65b9\u6cd5\uff0c\u7279\u522b\u662f\u5728\u57ce\u5e02\u73af\u5883\u4e2d\u3002\u672c\u7814\u7a76\u5206\u6790\u4e86\u97e9\u56fd\u9996\u5c14\u91c7\u7528\u9002\u5e94\u6027\u65b9\u6cd5\u7684\u79fb\u52a8\u6a21\u5f0f\u7684\u53d8\u5316\u3002\u5e72\u9884\u5206\u6790\u663e\u793a\uff0c\u4e3b\u8981\u6d41\u52a8\u6027\u4e0b\u964d\u786e\u5b9e\u53d1\u751f\u5728\u57ce\u5e02\u75c5\u4f8b\u9ad8\u5cf0\u524d\u4e24\u5468\u3002\u8fd9\u79cd\u81ea\u613f\u7684\u8c03\u6574\u663e\u793a\u51fa\u4ece\u516c\u5171\u4ea4\u901a\u5411\u79c1\u4eba\u4ea4\u901a\u65b9\u5f0f\u7684\u5f3a\u70c8\u504f\u597d\u8f6c\u53d8\u3002\u975e\u5fc5\u8981\u548c\u9ad8\u63a5\u89e6\u6027\u7684\u8d2d\u7269\u548c\u7528\u9910\u6d3b\u52a8\u5927\u5e45\u51cf\u5c11\uff0c\u800c\u5de5\u4f5c\u548c\u661f\u5df4\u514b\u65c5\u884c\u53d7\u5230\u7684\u5f71\u54cd\u8f83\u5c0f\u3002\u96c6\u4f53\u8bc4\u4f30\u663e\u793a\uff0c\u6d41\u884c\u75c5\u5b66\u3001\u6d41\u52a8\u6027\u548c\u653f\u7b56\u7684\u4e3b\u8981\u53d8\u5316\u540c\u65f6\u53d1\u751f\uff0c\u6ca1\u6709\u843d\u540e\u6216\u4e3b\u8981\u7684\u8d21\u732e\u8005\u3002\u6211\u4eec\u7684\u7814\u7a76\u8868\u660e\uff0c\u96c6\u4f53\u7406\u89e3\u6d41\u52a8\u6027\u3001\u6d41\u884c\u75c5\u5b66\u548c\u653f\u7b56\u4e4b\u95f4\u7684\u76f8\u4e92\u5f71\u54cd\u662f\u5fc5\u4e0d\u53ef\u5c11\u7684\u3002\u589e\u91cf\u548c\u7075\u6d3b\u7684\u6d41\u52a8\u6027\u9650\u5236\u4e0d\u4ec5\u662f\u53ef\u80fd\u7684\uff0c\u800c\u4e14\u662f\u5fc5\u8981\u7684\uff0c\u7279\u522b\u662f\u5bf9\u4e8e\u5e7f\u6cdb\u7684\u7a7a\u95f4\u548c\u65f6\u95f4\u5c3a\u5ea6\u7684\u6d41\u884c<\/span><\/p>\n


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      \u5728\u5370\u5ea6\uff0c\u7ea2\u706f\u533a\u5ef6\u957f\u5173\u95ed<\/strong><\/span><\/p>\n

      \u5bf9\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u4f20\u64ad\u7684\u5f71\u54cd<\/strong><\/span><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


      <\/p>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

      The effect of extended closure of red-light areas on COVID-19 transmission in India<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/p>\n

      http:\/\/arxiv.org\/abs\/2006.10488<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/span><\/p>\n

      Abhishek Pandey,Sudhakar V. Nuti,Pratha Sah,Chad R. Wells,Alison P. Galvani,Jeffrey P. Townsend<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>The novel coronavirus disease (COVID-19) pandemic has resulted in over 200,000 cases in India. Thus far, India has implemented lockdown measures to curb disease transmission. However, commercial sex work in red-light areas (RLAs) has potential to lead to COVID-19 resurgence after lockdown. We developed a model of COVID-19 transmission in RLAs, evaluating the impact of extended RLA closure compared with RLA reopening on cases, hospitalizations, and mortality rates within the RLAs of five major Indian cities, within the cities, and across India. Closure lowered transmission at all scales. More than 90% of cumulative cases and deaths among RLA residents of Kolkata, Pune, and Nagpur could be averted by the time the epidemic would peak under a re-opening scenario. Across India, extended closure of RLAs would benefit the population at large, delaying the peak of COVID-19 cases by 8 to 23 days, and avert 32% to 60.2% of cumulative cases and 43% to 67.6% of cumulative deaths at the peak of the epidemic. Extended closure of RLAs until better prevention and treatment strategies are developed would benefit public health in India.<\/span><\/p>\n

      \u6458\u8981\uff1a<\/span><\/strong>\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u75c5(\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e)\u5728\u5370\u5ea6\u7684\u6d41\u884c\u5df2\u5bfc\u81f4\u8d85\u8fc720\u4e07\u75c5\u4f8b\u3002\u5230\u76ee\u524d\u4e3a\u6b62\uff0c\u5370\u5ea6\u5df2\u7ecf\u5b9e\u65bd\u4e86\u5c01\u9501\u63aa\u65bd\u6765\u904f\u5236\u75be\u75c5\u7684\u4f20\u64ad\u3002\u7136\u800c\uff0c\u7ea2\u706f\u533a\u7684\u5546\u4e1a\u6027\u5de5\u4f5c\u6709\u53ef\u80fd\u5bfc\u81f4\u7981\u95ed\u4e4b\u540e\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u7684\u6b7b\u7070\u590d\u71c3\u3002\u6211\u4eec\u5f00\u53d1\u4e86\u4e00\u4e2a RLA \u4e2d\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u4f20\u64ad\u7684\u6a21\u578b\uff0c\u4e0e RLA \u91cd\u65b0\u5f00\u653e\u76f8\u6bd4\uff0c\u8bc4\u4f30\u4e86\u5ef6\u957f RLA \u5173\u95ed\u5bf9\u75c5\u4f8b\u3001\u4f4f\u9662\u548c\u6b7b\u4ea1\u7387\u7684\u5f71\u54cd\u3002\u5173\u95ed\u964d\u4f4e\u4e86\u4f20\u8f93\u5728\u6240\u6709\u89c4\u6a21\u3002\u5728\u91cd\u65b0\u5f00\u653e\u7684\u60c5\u51b5\u4e0b\uff0c\u5f53\u75ab\u60c5\u8fbe\u5230\u9ad8\u5cf0\u65f6\uff0c\u52a0\u5c14\u5404\u7b54\u3001\u6d66\u90a3\u548c\u90a3\u683c\u6d66\u5c14\u7684 RLA \u5c45\u6c11\u4e2d\u8d85\u8fc790% \u7684\u7d2f\u79ef\u75c5\u4f8b\u548c\u6b7b\u4ea1\u53ef\u4ee5\u907f\u514d\u3002\u5728\u6574\u4e2a\u5370\u5ea6\uff0c\u5ef6\u957f\u5927\u89c4\u6a21\u6740\u4f24\u6027\u6b66\u5668\u7684\u5173\u95ed\u671f\u5c06\u6709\u5229\u4e8e\u5e7f\u5927\u4eba\u53e3\uff0c\u5c06\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u75c5\u4f8b\u7684\u9ad8\u5cf0\u671f\u63a8\u8fdf8\u81f323\u5929\uff0c\u5e76\u907f\u514d32% \u81f360.2% \u7684\u7d2f\u79ef\u75c5\u4f8b\u548c43% \u81f367.6% \u7684\u7d2f\u79ef\u6b7b\u4ea1\u5728\u75ab\u60c5\u9ad8\u5cf0\u671f\u3002\u5728\u5236\u5b9a\u66f4\u597d\u7684\u9884\u9632\u548c\u6cbb\u7597\u6218\u7565\u4e4b\u524d\uff0c\u5ef6\u957f\u5927\u89c4\u6a21\u6740\u4f24\u6027\u6b66\u5668\u7684\u5173\u95ed\u671f\uff0c\u5c06\u6709\u5229\u4e8e\u5370\u5ea6\u7684\u516c\u5171\u536b\u751f<\/span><\/p>\n


      <\/span><\/p>\n

      <\/h2>\n
      \n
      \n
      \n


      <\/p>\n

      \n
      \n
      <\/p>\n

      \u7cfb\u7edf\u6027\u98ce\u9669\u7684\u7f51\u7edc\u654f\u611f\u6027<\/strong><\/span><\/strong><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


      <\/p>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

      Network Sensitivity of Systemic Risk<\/span><\/h2>\n

      \u5730\u5740\uff1a
      <\/span><\/strong><\/p>\n

      http:\/\/arxiv.org\/abs\/1805.04325<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/span><\/p>\n

      Amanah Ramadiah,Domenico Di Gangi,D. Ruggiero Lo Sardo,Valentina Macchiati,Tuan Pham Minh,Francesco Pinotti,Mateusz Wilinski,Paolo Barucca,Giulio Cimini<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>A growing body of studies on systemic risk in financial markets has emphasized the key importance of taking into consideration the complex interconnections among financial institutions. Much effort has been put in modeling the contagion dynamics of financial shocks, and to assess the resilience of specific financial markets – either using real network data, reconstruction techniques or simple toy networks. Here we address the more general problem of how shock propagation dynamics depends on the topological details of the underlying network. To this end we consider different realistic network topologies, all consistent with balance sheets information obtained from real data on financial institutions. In particular, we consider networks of varying density and with different block structures, and diversify as well in the details of the shock propagation dynamics. We confirm that the systemic risk properties of a financial network are extremely sensitive to its network features. Our results can aid in the design of regulatory policies to improve the robustness of financial markets.<\/span><\/p>\n

      \u6458\u8981\uff1a<\/span><\/strong>\u8d8a\u6765\u8d8a\u591a\u7684\u5173\u4e8e\u91d1\u878d\u5e02\u573a\u7cfb\u7edf\u6027\u98ce\u9669\u7684\u7814\u7a76\u5f3a\u8c03\u4e86\u8003\u8651\u91d1\u878d\u673a\u6784\u4e4b\u95f4\u590d\u6742\u7684\u76f8\u4e92\u8054\u7cfb\u7684\u5173\u952e\u91cd\u8981\u6027\u3002\u91d1\u7ba1\u5c40\u81f4\u529b\u5229\u7528\u771f\u5b9e\u7f51\u7edc\u6570\u636e\u3001\u91cd\u7ec4\u6280\u672f\u6216\u7b80\u5355\u7684\u73a9\u5177\u7f51\u7edc\u6a21\u62df\u91d1\u878d\u9707\u8361\u7684\u4f20\u67d3\u52a8\u6001\uff0c\u4ee5\u53ca\u8bc4\u4f30\u7279\u5b9a\u91d1\u878d\u5e02\u573a\u7684\u5f39\u6027\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u5904\u7406\u66f4\u4e00\u822c\u7684\u95ee\u9898\u5982\u4f55\u51b2\u51fb\u4f20\u64ad\u52a8\u529b\u5b66\u53d6\u51b3\u4e8e\u62d3\u6251\u7ec6\u8282\u7684\u57fa\u7840\u7f51\u7edc\u3002\u4e3a\u6b64\uff0c\u6211\u4eec\u8003\u8651\u4e86\u4e0d\u540c\u7684\u73b0\u5b9e\u7f51\u7edc\u62d3\u6251\u7ed3\u6784\uff0c\u5b83\u4eec\u90fd\u4e0e\u4ece\u91d1\u878d\u673a\u6784\u771f\u5b9e\u6570\u636e\u4e2d\u83b7\u5f97\u7684\u8d44\u4ea7\u8d1f\u503a\u8868\u4fe1\u606f\u4e00\u81f4\u3002\u7279\u522b\u5730\uff0c\u6211\u4eec\u8003\u8651\u4e86\u4e0d\u540c\u5bc6\u5ea6\u548c\u4e0d\u540c\u5757\u72b6\u7ed3\u6784\u7684\u7f51\u7edc\uff0c\u4ee5\u53ca\u591a\u6837\u5316\u7684\u51b2\u51fb\u6ce2\u4f20\u64ad\u52a8\u529b\u5b66\u7684\u7ec6\u8282\u3002\u6211\u4eec\u786e\u8ba4\uff0c\u91d1\u878d\u7f51\u7edc\u7684\u7cfb\u7edf\u6027\u98ce\u9669\u7279\u6027\u5bf9\u5176\u7f51\u7edc\u7279\u6027\u6781\u4e3a\u654f\u611f\u3002\u6211\u4eec\u7684\u7814\u7a76\u7ed3\u679c\u53ef\u4ee5\u5e2e\u52a9\u8bbe\u8ba1\u76d1\u7ba1\u653f\u7b56\uff0c\u4ee5\u63d0\u9ad8\u91d1\u878d\u5e02\u573a\u7684\u7a33\u5065\u6027<\/span><\/p>\n


      <\/p>\n

      \n
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      <\/p>\n

      \n
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      <\/p>\n

      \u75c5\u6bd2\u4f20\u64ad\u5206\u6790: <\/strong><\/span><\/p>\n

      \u968f\u673a\u6d41\u884c\u75c5\u5b66\u6a21\u578b\u7684\u8f6c\u79fb\u6a21\u578b\u8868<\/strong><\/span><\/strong><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


      <\/p>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

      Analysis of Virus Propagation: A Transition Model Representation of Stochastic Epidemiological Models<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/p>\n

      http:\/\/arxiv.org\/abs\/2006.10265<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/span><\/p>\n

      Christian Gourieroux,Joann Jasiak<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>T<\/span>he growing literature on the propagation of COVID-19 relies on various dynamic SIR-type models (Susceptible-Infected-Recovered) which yield model-dependent results. For transparency and ease of comparing the results, we introduce a common representation of the SIR-type stochastic epidemiological models. This representation is a discrete time transition model, which allows us to classify the epidemiological models with respect to the number of states (compartments) and their interpretation. Additionally, the transition model eliminates several limitations of the deterministic continuous time epidemiological models which are pointed out in the paper. We also show that all SIR-type models have a nonlinear (pseudo) state space representation and are easily estimable from an extended Kalman filter.<\/span><\/p>\n

      \u6458\u8981\uff1a<\/span><\/strong>\u8d8a\u6765\u8d8a\u591a\u7684\u5173\u4e8e\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u4f20\u64ad\u7684\u6587\u732e\u4f9d\u8d56\u4e8e\u5404\u79cd\u52a8\u6001 sir- \u7c7b\u578b\u7684\u6a21\u578b(\u6613\u611f-\u611f\u67d3-\u6062\u590d) \uff0c\u8fd9\u4e9b\u6a21\u578b\u4ea7\u751f\u4e86\u4f9d\u8d56\u4e8e\u6a21\u578b\u7684\u7ed3\u679c\u3002\u4e3a\u4e86\u900f\u660e\u548c\u65b9\u4fbf\u6bd4\u8f83\u7ed3\u679c\uff0c\u6211\u4eec\u4ecb\u7ecd\u4e86 sir- \u578b\u968f\u673a\u6d41\u884c\u75c5\u5b66\u6a21\u578b\u7684\u4e00\u4e2a\u5171\u540c\u8868\u793a\u3002\u8fd9\u79cd\u8868\u793a\u662f\u4e00\u4e2a\u79bb\u6563\u65f6\u95f4\u8f6c\u79fb\u6a21\u578b\uff0c\u5b83\u5141\u8bb8\u6211\u4eec\u6839\u636e\u72b6\u6001(\u90e8\u5206)\u7684\u6570\u76ee\u53ca\u5176\u89e3\u91ca\u5bf9\u6d41\u884c\u75c5\u5b66\u6a21\u578b\u8fdb\u884c\u5206\u7c7b\u3002\u6b64\u5916\uff0c\u8f6c\u79fb\u6a21\u578b\u6d88\u9664\u4e86\u672c\u6587\u6307\u51fa\u7684\u786e\u5b9a\u6027\u8fde\u7eed\u65f6\u95f4\u6d41\u884c\u75c5\u5b66\u6a21\u578b\u7684\u51e0\u4e2a\u5c40\u9650\u6027\u3002\u6211\u4eec\u8fd8\u8bc1\u660e\u4e86\u6240\u6709 sir- \u578b\u6a21\u578b\u90fd\u5177\u6709\u975e\u7ebf\u6027(\u4f2a)\u72b6\u6001\u7a7a\u95f4\uff0c\u5e76\u4e14\u5f88\u5bb9\u6613\u4ece\u6269\u5c55\u5361\u5c14\u66fc\u6ee4\u6ce2\u5668\u4e2d\u4f30\u8ba1\u51fa\u6765<\/span><\/p>\n


      <\/p>\n

      \n
      \n
      \n


      <\/p>\n

      \n
      \n
      <\/p>\n

      \u4e13\u5bb6\u5bf9\u7269\u7406\u8bba\u6587\u7684\u6e10\u8fdb\u5f0f\u4f5c\u4e1a\u4e2d<\/strong><\/span><\/p>\n

      \u4f30\u8ba1\u5e72\u6270\u7a0b\u5ea6\u7684\u51e0\u4e2a\u6307\u6807\u7684\u8d8b\u540c\u6548\u5ea6<\/strong><\/span><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


      <\/p>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

      Convergent validity of several indicators measuring disruptiveness with milestone assignments to physics papers by experts<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/p>\n

      http:\/\/arxiv.org\/abs\/2006.10606<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/span><\/p>\n

      Lutz Bor<\/span>nmann,Alexander Tekles<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>This study focuses on a recently introduced type of indicator measuring disruptiveness in science. Disruptive research diverges from current lines of research by opening up new lines. In the current study, we included the initially proposed indicator of this new type (Wu, Wang, & Evans, 2019) and several variants with DI1: DI5, DI1n, DI5n, and DEP. Since indicators should measure what they propose to measure, we investigated the convergent validity of the indicators. We used a list of milestone papers, selected and published by editors of Physical Review Letters, and investigated whether this human (experts – based list is related to values of the several disruption indicators variants and – if so – which variants show the highest correlation with expert judgements. We used bivariate statistics, multiple regression models, and (coarsened) exact matching (CEM) to investigate the convergent validity of the indicators. The results show that the indicators correlate differently with the milestone paper assignments by the editors. It is not the initially proposed disruption index that performed best (DI1), but the variant DI5 which has been introduced by Bornmann, Devarakonda, Tekles, and Chacko (2019). In the CEM analysis of this study, the DEP variant – introduced by Bu, Waltman, and Huang (2019) – also showed favorable results.<\/span><\/p>\n

      \u6458\u8981\uff1a<\/span><\/strong>\u8fd9\u9879\u7814\u7a76\u7684\u91cd\u70b9\u662f\u6700\u8fd1\u5f15\u8fdb\u7684\u4e00\u79cd\u6307\u6807\uff0c\u8861\u91cf\u5e72\u6270\u7684\u79d1\u5b66\u3002\u7834\u574f\u6027\u7814\u7a76\u5f00\u8f9f\u4e86\u65b0\u7684\u7814\u7a76\u65b9\u5411\uff0c\u4ece\u800c\u504f\u79bb\u4e86\u5f53\u524d\u7684\u7814\u7a76\u65b9\u5411\u3002\u5728\u5f53\u524d\u7684\u7814\u7a76\u4e2d\uff0c\u6211\u4eec\u5305\u62ec\u4e86\u6700\u521d\u63d0\u51fa\u7684\u8fd9\u79cd\u65b0\u578b\u6307\u6807(Wu\uff0cWang\uff0c& Evans\uff0c2019)\u548c DI1: DI5\uff0cDI1n\uff0cDI5n\uff0c\u548c DEP \u7684\u51e0\u4e2a\u53d8\u4f53\u3002\u56e0\u4e3a\u6307\u6807\u5e94\u8be5\u8861\u91cf\u4ed6\u4eec\u63d0\u8bae\u8861\u91cf\u7684\u4e1c\u897f\uff0c\u6211\u4eec\u8c03\u67e5\u4e86\u6307\u6807\u7684\u6536\u655b\u6709\u6548\u6027\u3002\u6211\u4eec\u4f7f\u7528\u300a\u7269\u7406\u8bc4\u8bba\u5feb\u62a5\u300b\u7f16\u8f91\u9009\u51fa\u5e76\u53d1\u8868\u7684\u4e00\u4efd\u91cc\u7a0b\u7891\u8bba\u6587\u6e05\u5355\uff0c\u8c03\u67e5\u8fd9\u4efd\u57fa\u4e8e\u4e13\u5bb6\u7684\u4eba\u7c7b\u5217\u8868\u662f\u5426\u4e0e\u51e0\u4e2a\u5e72\u6270\u6307\u6807\u53d8\u91cf\u7684\u503c\u6709\u5173\uff0c\u5982\u679c\u6709\uff0c\u54ea\u4e9b\u53d8\u91cf\u4e0e\u4e13\u5bb6\u5224\u65ad\u7684\u76f8\u5173\u6027\u6700\u9ad8\u3002\u6211\u4eec\u4f7f\u7528\u53cc\u53d8\u91cf\u7edf\u8ba1\u3001\u591a\u5143\u56de\u5f52\u6a21\u578b\u548c(\u7c97\u5316)\u7cbe\u786e\u5339\u914d(CEM)\u6765\u7814\u7a76\u6307\u6807\u7684\u6536\u655b\u6709\u6548\u6027\u3002\u7ed3\u679c\u8868\u660e\uff0c\u8fd9\u4e9b\u6307\u6807\u4e0e\u7f16\u8f91\u7684\u91cc\u7a0b\u7891\u8bba\u6587\u4f5c\u4e1a\u7684\u76f8\u5173\u7a0b\u5ea6\u4e0d\u540c\u3002\u6700\u521d\u63d0\u51fa\u7684\u7834\u574f\u6027\u6307\u6570(DI1)\u5e76\u975e\u8868\u73b0\u6700\u597d\uff0c\u800c\u662f\u7531 Bornmann\u3001 Devarakonda\u3001 Tekles \u548c Chacko (2019)\u5f15\u5165\u7684\u53d8\u5f02\u4f53 DI5\u3002\u5728\u672c\u7814\u7a76\u7684 CEM \u5206\u6790\u4e2d\uff0cBu\u3001 Waltman \u548c Huang (2019)\u5f15\u5165\u7684 DEP \u53d8\u5f02\u4e5f\u663e\u793a\u4e86\u826f\u597d\u7684\u7ed3\u679c<\/span><\/p>\n


      <\/span><\/p>\n


      <\/span><\/p>\n

      \n
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      <\/p>\n

      \u79d1\u5b66\u4e0e\u7b97\u6cd5\u7684\u975e\u4e2d\u7acb\u6027:<\/strong><\/span><\/p>\n

      \u57fa\u7840\u7269\u7406\u4e0e\u793e\u4f1a\u4e4b\u95f4\u7684\u673a\u5668\u5b66\u4e60<\/strong><\/span><\/strong><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


      <\/p>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

      (Non)-neutrality of science and algorithms: Machine Learning between fundamental physics and society<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/p>\n

      https:\/\/arxiv.org\/abs\/2006.10745<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/span><\/p>\n

      Aniello Lampo,Michele Mancarella,Angelo Piga<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>The impact of Machine Learning (ML) algorithms in the age of big data and platform capitalism has not spared scientific research in academia. In this work, we will analyse the use of ML in fundamental physics and its relationship to other cases that directly affect society. We will deal with different aspects of the issue, from a bibliometric analysis of the publications, to a detailed discussion of the literature, to an overview on the productive and working context inside and outside academia. The analysis will be conducted on the basis of three key elements: the non-neutrality of science, understood as its intrinsic relationship with history and society; the non-neutrality of the algorithms, in the sense of the presence of elements that depend on the choices of the programmer, which cannot be eliminated whatever the technological progress is; the problematic nature of a paradigm shift in favour of a data-driven science (and society). The deconstruction of the presumed universality of scientific thought from the inside becomes in this perspective a necessary first step also for any social and political discussion. This is the subject of this work in the case study of ML.<\/span><\/p>\n

      \u6458\u8981\uff1a<\/span><\/strong>\u673a\u5668\u5b66\u4e60(ML)\u7b97\u6cd5\u5728\u5927\u6570\u636e\u548c\u5e73\u53f0\u8d44\u672c\u4e3b\u4e49\u65f6\u4ee3\u7684\u5f71\u54cd\u5e76\u6ca1\u6709\u653e\u8fc7\u5b66\u672f\u754c\u7684\u79d1\u5b66\u7814\u7a76\u3002\u5728\u8fd9\u9879\u5de5\u4f5c\u4e2d\uff0c\u6211\u4eec\u5c06\u5206\u6790\u673a\u5668\u5b66\u4e60\u5728\u57fa\u7840\u7269\u7406\u5b66\u4e2d\u7684\u5e94\u7528\u53ca\u5176\u4e0e\u5176\u4ed6\u76f4\u63a5\u5f71\u54cd\u793e\u4f1a\u7684\u6848\u4f8b\u7684\u5173\u7cfb\u3002\u6211\u4eec\u5c06\u8ba8\u8bba\u8fd9\u4e2a\u95ee\u9898\u7684\u4e0d\u540c\u65b9\u9762\uff0c\u4ece\u51fa\u7248\u7269\u7684\u6587\u732e\u8ba1\u91cf\u5206\u6790\uff0c\u5230\u6587\u732e\u7684\u8be6\u7ec6\u8ba8\u8bba\uff0c\u518d\u5230\u5b66\u672f\u754c\u5185\u5916\u751f\u4ea7\u548c\u5de5\u4f5c\u73af\u5883\u7684\u6982\u8ff0\u3002\u5206\u6790\u5c06\u6839\u636e\u4e09\u4e2a\u5173\u952e\u8981\u7d20\u8fdb\u884c: \u79d1\u5b66\u7684\u975e\u4e2d\u7acb\u6027\uff0c\u5373\u79d1\u5b66\u4e0e\u5386\u53f2\u548c\u793e\u4f1a\u7684\u5185\u5728\u5173\u7cfb; \u7b97\u6cd5\u7684\u975e\u4e2d\u7acb\u6027\uff0c\u5373\u4f9d\u8d56\u7a0b\u5e8f\u5458\u9009\u62e9\u7684\u8981\u7d20\u7684\u5b58\u5728\uff0c\u65e0\u8bba\u6280\u672f\u8fdb\u6b65\u662f\u4ec0\u4e48\u90fd\u65e0\u6cd5\u6d88\u9664; \u6709\u5229\u4e8e\u6570\u636e\u9a71\u52a8\u7684\u79d1\u5b66(\u548c\u793e\u4f1a)\u7684\u8303\u5f0f\u8f6c\u53d8\u7684\u95ee\u9898\u6027\u8d28\u3002\u4ece\u8fd9\u4e2a\u89d2\u5ea6\u6765\u770b\uff0c\u4ece\u5185\u90e8\u89e3\u6784\u79d1\u5b66\u601d\u60f3\u7684\u666e\u904d\u6027\u6210\u4e3a\u4efb\u4f55\u793e\u4f1a\u548c\u653f\u6cbb\u8ba8\u8bba\u7684\u5fc5\u8981\u7684\u7b2c\u4e00\u6b65\u3002\u8fd9\u5c31\u662f\u673a\u5668\u5b66\u4e60\u6848\u4f8b\u7814\u7a76\u4e2d\u8fd9\u9879\u5de5\u4f5c\u7684\u4e3b\u9898<\/span><\/p>\n


      <\/span><\/p>\n


      <\/p>\n

      \n
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      <\/p>\n

      \u901a\u8fc7 Twitter <\/strong><\/span><\/p>\n

      \u8bc4\u4f30\u7f8e\u56fd\u57ce\u5e02\u516c\u56ed\u7684\u5e78\u798f\u611f<\/strong><\/span><\/strong><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


      <\/p>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

      Gauging the happiness benefit of US urban parks through Twitter<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/h2>\n

      http:\/\/arxiv.org\/abs\/2006.10658<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/span><\/p>\n

      A. J. Schwartz,P. S. Dodds,J. P. M. O’Neil-Dunne,T. H. Ricketts,C. M. Danforth<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>The relationship between nature contact and mental well-being has received increasing attention in recent years. While a body of evidence has accumulated demonstrating a positive relationship between time in nature and mental well-being, there have been few studies comparing this relationship in different locations over long periods of time. In this study, we estimate a happiness benefit, the difference in expressed happiness between in- and out-of-park tweets, for the 25 largest cities in the US by population. People write happier words during park visits when compared with non-park user tweets collected around the same time. While the words people write are happier in parks on average and in most cities, we find considerable variation across cities. Tweets are happier in parks at all times of the day, week, and year, not just during the weekend or summer vacation. Across all cities, we find that the happiness benefit is highest in parks larger than 100 acres. Overall, our study suggests the happiness benefit associated with park visitation is on par with US holidays such as Thanksgiving and New Year’s Day.<\/span><\/p>\n

      \u6458\u8981\uff1a<\/span><\/strong>\u8fd1\u5e74\u6765\uff0c\u81ea\u7136\u63a5\u89e6\u4e0e\u5fc3\u7406\u5065\u5eb7\u7684\u5173\u7cfb\u8d8a\u6765\u8d8a\u53d7\u5230\u4eba\u4eec\u7684\u91cd\u89c6\u3002\u867d\u7136\u79ef\u7d2f\u7684\u5927\u91cf\u8bc1\u636e\u8868\u660e\uff0c\u81ea\u7136\u754c\u7684\u65f6\u95f4\u4e0e\u5fc3\u7406\u5065\u5eb7\u4e4b\u95f4\u5b58\u5728\u7740\u79ef\u6781\u7684\u5173\u7cfb\uff0c\u4f46\u5f88\u5c11\u6709\u7814\u7a76\u6bd4\u8f83\u957f\u65f6\u95f4\u5185\u4e0d\u540c\u5730\u70b9\u7684\u8fd9\u79cd\u5173\u7cfb\u3002\u5728\u8fd9\u9879\u7814\u7a76\u4e2d\uff0c\u6211\u4eec\u5bf9\u7f8e\u56fd25\u4e2a\u4eba\u53e3\u6700\u591a\u7684\u57ce\u5e02\u7684\u5e78\u798f\u611f\u8fdb\u884c\u4e86\u4f30\u8ba1\u3002\u4eba\u4eec\u5728\u6e38\u89c8\u516c\u56ed\u7684\u65f6\u5019\u5199\u51fa\u66f4\u5feb\u4e50\u7684\u8bdd\uff0c\u800c\u4e0d\u662f\u5728\u540c\u4e00\u65f6\u95f4\u6536\u96c6\u7684\u975e\u516c\u56ed\u7528\u6237\u7684\u63a8\u6587\u3002\u867d\u7136\u5728\u516c\u56ed\u548c\u5927\u591a\u6570\u57ce\u5e02\uff0c\u4eba\u4eec\u5199\u7684\u5355\u8bcd\u5e73\u5747\u6765\u8bf4\u66f4\u5feb\u4e50\uff0c\u4f46\u6211\u4eec\u53d1\u73b0\u57ce\u5e02\u4e4b\u95f4\u7684\u5dee\u5f02\u76f8\u5f53\u5927\u3002\u4e0d\u4ec5\u4ec5\u662f\u5728\u5468\u672b\u6216\u8005\u6691\u5047\uff0c\u5728\u516c\u56ed\u7684\u4efb\u4f55\u65f6\u5019\uff0c\u63a8\u7279\u5728\u4e00\u5929\u3001\u4e00\u5468\u6216\u8005\u4e00\u5e74\u4e2d\u7684\u4efb\u4f55\u65f6\u5019\u90fd\u66f4\u5feb\u4e50\u3002\u5728\u6240\u6709\u57ce\u5e02\u4e2d\uff0c\u6211\u4eec\u53d1\u73b0\u9762\u79ef\u5927\u4e8e100\u82f1\u4ea9\u7684\u516c\u56ed\u5e78\u798f\u611f\u6700\u9ad8\u3002\u603b\u7684\u6765\u8bf4\uff0c\u6211\u4eec\u7684\u7814\u7a76\u8868\u660e\uff0c\u516c\u56ed\u6e38\u89c8\u5e26\u6765\u7684\u5e78\u798f\u611f\u4e0e\u7f8e\u56fd\u7684\u8282\u65e5\u5982\u611f\u6069\u8282\u548c\u5143\u65e6\u662f\u4e00\u6837\u7684<\/span><\/p>\n


      <\/p>\n


      <\/p>\n

      \n
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      \u6a21\u62df\u586b\u6599\u548c\u88c2\u5316<\/strong><\/span><\/strong><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


      <\/p>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

      Simulated packing and cracking<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/h2>\n

      http:\/\/arxiv.org\/abs\/2006.10665<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/span><\/p>\n

      Jeffrey S. Buzas,Gregory S. Warrington<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>We introduce simulated packing and cracking as a technique for evaluating partisan-gerrymandering measures. We apply it to historical congressional and legislative elections to evaluate four measures: partisan bias, declination, efficiency gap, and mean-median difference. While the efficiency gap recognizes simulated packing and cracking in a completely predictable manner (a fact that follows immediately from the efficiency gap’s definition) and the declination does a very good job of recording simulated packing and cracking, we conclude that both of the other two measures record it poorly. This deficiency is especially notable given the frequent use of such measures in outlier analyses.<\/span><\/p>\n

      \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u4ecb\u7ecd\u4e86\u6a21\u62df\u5305\u88c5\u548c\u88c2\u89e3\u4f5c\u4e3a\u4e00\u79cd\u6280\u672f\u8bc4\u4f30\u515a\u6d3e-\u91cd\u5212\u9009\u533a\u7684\u63aa\u65bd\u3002\u6211\u4eec\u5c06\u5176\u5e94\u7528\u4e8e\u5386\u53f2\u4e0a\u7684\u56fd\u4f1a\u548c\u7acb\u6cd5\u9009\u4e3e\uff0c\u4ee5\u8bc4\u4f30\u56db\u4e2a\u6307\u6807: \u515a\u6d3e\u504f\u89c1\u3001\u8870\u9000\u3001\u6548\u7387\u5dee\u8ddd\u548c\u5e73\u5747-\u4e2d\u4f4d\u6570\u5dee\u5f02\u3002\u867d\u7136\u6548\u7387\u5dee\u5f02\u4ee5\u4e00\u79cd\u5b8c\u5168\u53ef\u9884\u6d4b\u7684\u65b9\u5f0f\u8bc6\u522b\u6a21\u62df\u586b\u5145\u548c\u7834\u88c2(\u8fd9\u4e00\u4e8b\u5b9e\u7d27\u63a5\u7740\u6548\u7387\u5dee\u5f02\u7684\u5b9a\u4e49) \uff0c\u800c\u4e14\u8870\u51cf\u5728\u8bb0\u5f55\u6a21\u62df\u586b\u5145\u548c\u7834\u88c2\u65b9\u9762\u505a\u4e86\u975e\u5e38\u597d\u7684\u5de5\u4f5c\uff0c\u6211\u4eec\u5f97\u51fa\u7ed3\u8bba\uff0c\u5176\u4ed6\u4e24\u79cd\u6d4b\u91cf\u65b9\u6cd5\u90fd\u8bb0\u5f55\u5f97\u5f88\u5dee\u3002\u8fd9\u4e00\u7f3a\u9677\u7279\u522b\u503c\u5f97\u6ce8\u610f\uff0c\u56e0\u4e3a\u5728\u79bb\u7fa4\u503c\u5206\u6790\u4e2d\u7ecf\u5e38\u4f7f\u7528\u8fd9\u79cd\u63aa\u65bd<\/span><\/p>\n


      <\/p>\n


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      \u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6d41\u611f\u5927\u6d41\u884c\u671f\u95f4<\/strong><\/span><\/p>\n

      \u5ba4\u5185\u5c42\u9762\u975e\u836f\u7269\u5e72\u9884\u5efa\u6a21: <\/strong><\/span><\/p>\n

      \u57fa\u4e8e\u884c\u4eba\u52a8\u529b\u5b66\u7684\u5fae\u89c2\u6a21\u62df\u65b9\u6cd5<\/strong><\/span><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


      <\/h2>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

      Modeling indoor-level non-pharmaceutical interventions during the COVID-19 pandemic: a pedestrian dynamics-based microscopic simulation approach<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/h2>\n

      http:\/\/arxiv.org\/abs\/2006.10666<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/span><\/p>\n

      Yao Xiao,Mofeng Yang,Zheng Zhu,Hai Yang,Lei Zhang,Sepehr Ghader<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>Mathematical modeling of epidemic spreading has been widely adopted to estimate the threats of epidemic diseases (i.e., the COVID-19 pandemic) as well as to evaluate epidemic control interventions. The indoor place is considered to be a significant epidemic spreading risk origin, but existing widely-used epidemic spreading models are usually limited for indoor places since the dynamic physical distance changes between people are ignored, and the empirical features of the essential and non-essential travel are not differentiated. In this paper, we introduce a pedestrian-based epidemic spreading model that is capable of modeling indoor transmission risks of diseases during people’s social activities. Taking advantage of the before-and-after mobility data from the University of Maryland COVID-19 Impact Analysis Platform, it’s found that people tend to spend more time in grocery stores once their travel frequencies are restricted to a low level. In other words, an increase in dwell time could balance the decrease in travel frequencies and satisfy people’s demand. Based on the pedestrian-based model and the empirical evidence, combined non-pharmaceutical interventions from different operational levels are evaluated. Numerical simulations show that restrictions on people’s travel frequency and open-hours of indoor places may not be universally effective in reducing average infection risks for each pedestrian who visit the place. Entry limitations can be a widely effective alternative, whereas the decision-maker needs to balance the decrease in risky contacts and the increase in queue length outside the place that may impede people from fulfilling their travel needs.<\/span><\/p>\n

      \u6458\u8981\uff1a<\/span><\/strong>\u6d41\u884c\u75c5\u4f20\u64ad\u7684\u6570\u5b66\u6a21\u578b\u5df2\u7ecf\u88ab\u5e7f\u6cdb\u5730\u5e94\u7528\u4e8e\u4f30\u8ba1\u6d41\u884c\u75c5\u7684\u5a01\u80c1(\u5373\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6d41\u884c\u75c5)\u4ee5\u53ca\u8bc4\u4f30\u6d41\u884c\u75c5\u63a7\u5236\u5e72\u9884\u63aa\u65bd\u3002\u5ba4\u5185\u573a\u6240\u88ab\u8ba4\u4e3a\u662f\u4e00\u4e2a\u91cd\u8981\u7684\u4f20\u67d3\u75c5\u4f20\u64ad\u98ce\u9669\u6e90\uff0c\u4f46\u73b0\u6709\u7684\u5e7f\u6cdb\u5e94\u7528\u7684\u4f20\u67d3\u75c5\u4f20\u64ad\u6a21\u578b\u5f80\u5f80\u5c40\u9650\u4e8e\u5ba4\u5185\u573a\u6240\uff0c\u5ffd\u7565\u4e86\u4eba\u4e0e\u4eba\u4e4b\u95f4\u7269\u7406\u8ddd\u79bb\u7684\u52a8\u6001\u53d8\u5316\uff0c\u6ca1\u6709\u533a\u5206\u57fa\u672c\u51fa\u884c\u548c\u975e\u57fa\u672c\u51fa\u884c\u7684\u7ecf\u9a8c\u7279\u5f81\u3002\u672c\u6587\u4ecb\u7ecd\u4e86\u4e00\u79cd\u57fa\u4e8e\u884c\u4eba\u7684\u4f20\u67d3\u75c5\u4f20\u64ad\u6a21\u578b\uff0c\u8be5\u6a21\u578b\u80fd\u591f\u6a21\u62df\u4eba\u4eec\u5728\u793e\u4f1a\u6d3b\u52a8\u4e2d\u7684\u5ba4\u5185\u4f20\u64ad\u75be\u75c5\u7684\u98ce\u9669\u3002\u5229\u7528\u9a6c\u91cc\u5170\u5927\u5b66\u5b66\u9662\u5e02\u5206\u6821\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u5f71\u54cd\u5206\u6790\u5e73\u53f0\u7684\u524d\u540e\u79fb\u52a8\u6027\u6570\u636e\uff0c\u7814\u7a76\u53d1\u73b0\uff0c\u4e00\u65e6\u51fa\u884c\u9891\u7387\u88ab\u9650\u5236\u5728\u4e00\u4e2a\u8f83\u4f4e\u7684\u6c34\u5e73\uff0c\u4eba\u4eec\u503e\u5411\u4e8e\u82b1\u66f4\u591a\u7684\u65f6\u95f4\u5728\u6742\u8d27\u5e97\u3002\u6362\u53e5\u8bdd\u8bf4\uff0c\u505c\u7559\u65f6\u95f4\u7684\u589e\u52a0\u53ef\u4ee5\u5e73\u8861\u65c5\u884c\u9891\u7387\u7684\u51cf\u5c11\u548c\u6ee1\u8db3\u4eba\u4eec\u7684\u9700\u6c42\u3002\u57fa\u4e8e\u884c\u4eba\u4e3a\u57fa\u7840\u7684\u6a21\u578b\u548c\u7ecf\u9a8c\u8bc1\u660e\uff0c\u6765\u81ea\u4e0d\u540c\u64cd\u4f5c\u5c42\u9762\u7684\u7efc\u5408\u975e\u836f\u7269\u5e72\u9884\u8fdb\u884c\u4e86\u8bc4\u4f30\u3002\u6570\u5b57\u6a21\u62df\u7ed3\u679c\u663e\u793a\uff0c\u9650\u5236\u4eba\u4eec\u7684\u51fa\u884c\u9891\u7387\u548c\u5ba4\u5185\u5730\u65b9\u7684\u5f00\u653e\u65f6\u95f4\uff0c\u53ef\u80fd\u4e0d\u4f1a\u666e\u904d\u6709\u6548\u5730\u964d\u4f4e\u6bcf\u4f4d\u524d\u5f80\u8be5\u5730\u65b9\u7684\u884c\u4eba\u7684\u5e73\u5747\u611f\u67d3\u98ce\u9669\u3002\u8fdb\u5165\u9650\u5236\u53ef\u80fd\u662f\u4e00\u4e2a\u5e7f\u6cdb\u6709\u6548\u7684\u66ff\u4ee3\u529e\u6cd5\uff0c\u800c\u51b3\u7b56\u8005\u9700\u8981\u5728\u98ce\u9669\u8054\u7cfb\u51cf\u5c11\u548c\u53ef\u80fd\u59a8\u788d\u4eba\u4eec\u6ee1\u8db3\u5176\u65c5\u884c\u9700\u6c42\u7684\u573a\u6240\u5916\u6392\u961f\u957f\u5ea6\u589e\u52a0\u4e4b\u95f4\u53d6\u5f97\u5e73\u8861<\/span><\/p>\n


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      \u672a\u6765\u7f8e\u56fd\u7535\u7f51\u7684\u5f02\u529f\u80fd\u56fe\u5f39\u6027<\/strong><\/span><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


      <\/h2>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

      Hetero-functional Graph Resilience of the Future American Electric Grid<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/h2>\n

      http:\/\/arxiv.org\/abs\/2006.10678<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/span><\/p>\n

      Dakota J. Thompson,Wester C. H. Schoonenberg,Amro M. Farid<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>As climate change takes hold in the 21st century, it places an impetus to decarbonize the American electric power system with renewable energy resources. This paper presents a structural resilience analysis of the American electric power system that incrementally incorporates architectural changes including meshed distribution lines, distributed generation, and energy storage solutions. A hetero-functional graph analysis confirms our formal graph understandings from network science in terms of cumulative degree distributions and traditional attack vulnerability measures. Additionally, The paper shows that hetero-functional graphs more precisely describe the changes in functionality associated with the addition of distributed generation and energy storage. Finally, it demonstrates that the addition of all three types of mitigation measures enhance the grid’s structural resilience; even in the presence of disruptive attacks. The paper concludes that there is no structural trade-off between grid sustainability and resilience.<\/span><\/p>\n

      \u6458\u8981\uff1a<\/span><\/strong>\u968f\u774021\u4e16\u7eaa\u6c14\u5019\u53d8\u5316\u7684\u6df1\u5165\uff0c\u53ef\u518d\u751f\u80fd\u6e90\u7684\u4f7f\u7528\u63a8\u52a8\u4e86\u7f8e\u56fd\u7535\u529b\u7cfb\u7edf\u7684\u8131\u78b3\u3002\u672c\u6587\u4ecb\u7ecd\u4e86\u7f8e\u56fd\u7535\u529b\u7cfb\u7edf\u7684\u7ed3\u6784\u5f39\u6027\u5206\u6790\uff0c\u9010\u6b65\u7eb3\u5165\u5efa\u7b51\u53d8\u5316\uff0c\u5305\u62ec\u7f51\u72b6\u914d\u7535\u7ebf\u8def\uff0c\u5206\u6563\u5f0f\u53d1\u7535\u548c\u80fd\u6e90\u50a8\u5b58\u89e3\u51b3\u65b9\u6848\u3002\u5f02\u529f\u80fd\u56fe\u5206\u6790\u4ece\u7d2f\u79ef\u5ea6\u5206\u5e03\u548c\u4f20\u7edf\u653b\u51fb\u8106\u5f31\u6027\u5ea6\u91cf\u7684\u89d2\u5ea6\u8bc1\u5b9e\u4e86\u6211\u4eec\u4ece\u7f51\u7edc\u79d1\u5b66\u4e2d\u5f97\u5230\u7684\u6b63\u5f0f\u56fe\u7406\u89e3\u3002\u6b64\u5916\uff0c\u672c\u6587\u8fd8\u8868\u660e\uff0c\u5f02\u8d28\u51fd\u6570\u56fe\u66f4\u7cbe\u786e\u5730\u63cf\u8ff0\u4e86\u4e0e\u5206\u6563\u5f0f\u53d1\u7535\u548c\u80fd\u91cf\u50a8\u5b58\u76f8\u5173\u7684\u529f\u80fd\u53d8\u5316\u3002\u6700\u540e\uff0c\u5b83\u8868\u660e\uff0c\u6240\u6709\u4e09\u79cd\u7c7b\u578b\u7684\u7f13\u89e3\u63aa\u65bd\u7684\u589e\u52a0\u52a0\u5f3a\u4e86\u7535\u7f51\u7684\u7ed3\u6784\u5f39\u6027\uff0c\u5373\u4f7f\u5728\u5b58\u5728\u7834\u574f\u6027\u653b\u51fb\u3002\u672c\u6587\u7684\u7ed3\u8bba\u662f\uff0c\u7535\u7f51\u7684\u53ef\u6301\u7eed\u6027\u548c\u590d\u539f\u529b\u4e4b\u95f4\u4e0d\u5b58\u5728\u7ed3\u6784\u6027\u7684\u6743\u8861<\/span><\/p>\n


      <\/p>\n

      \n
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      <\/p>\n

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      21\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u975e\u6d32\u7684\u51b2\u7a81:<\/strong><\/span><\/p>\n

      \u793e\u4f1a\u8ddd\u79bb\u3001\u7cae\u98df\u8106\u5f31\u6027\u548c\u798f\u5229\u53cd\u5e94<\/strong><\/span><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


      <\/h2>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

      Conflict in Africa during COVID-19: social distancing, food vulnerability and welfare response<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/h2>\n

      http:\/\/arxiv.org\/abs\/2006.10696<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/span><\/p>\n

      Roxana Guti\u00e9rrez-Romero<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>We study the effect of social distancing, food vulnerability, welfare and labour COVID-19 policy responses on riots, violence against civilians and food-related conflicts. Our analysis uses georeferenced data for 24 African countries with monthly local prices and real-time conflict data reported in the Armed Conflict Location and Event Data Project (ACLED) from January 2015 until early May 2020. Lockdowns and recent welfare policies have been implemented in light of COVID-19, but in some contexts also likely in response to ongoing conflicts. To mitigate the potential risk of endogeneity, we use instrumental variables. We exploit the exogeneity of global commodity prices, and three variables that increase the risk of COVID-19 and efficiency in response such as countries colonial heritage, male mortality rate attributed to air pollution and prevalence of diabetes in adults. We find that the probability of experiencing riots, violence against civilians, food-related conflicts and food looting has increased since lockdowns. Food vulnerability has been a contributing factor. A 10% increase in the local price index is associated with an increase of 0.7 percentage points in violence against civilians. Nonetheless, for every additional anti-poverty measure implemented in response to COVID-19 the probability of experiencing violence against civilians, riots and food-related conflicts declines by approximately 0.2 percentage points. These anti-poverty measures also reduce the number of fatalities associated with these conflicts. Overall, our findings reveal that food vulnerability has increased conflict risks, but also offer an optimistic view of the importance of the state in providing an extensive welfare safety net.<\/span><\/p>\n

      \u6458\u8981\uff1a<\/span><\/strong>\u6211\u4eec\u7814\u7a76\u4e86\u793e\u4f1a\u8ddd\u79bb\u3001\u7cae\u98df\u8106\u5f31\u6027\u3001\u798f\u5229\u548c\u52b3\u52a8\u529b \/ \u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u653f\u7b56\u5bf9\u9a9a\u4e71\u3001\u9488\u5bf9\u5e73\u6c11\u7684\u66b4\u529b\u548c\u4e0e\u7cae\u98df\u6709\u5173\u7684\u51b2\u7a81\u7684\u5f71\u54cd\u3002\u6211\u4eec\u7684\u5206\u6790\u4f7f\u7528\u4e8624\u4e2a\u975e\u6d32\u56fd\u5bb6\u7684\u5730\u7406\u53c2\u8003\u6570\u636e\uff0c\u5176\u4e2d\u5305\u62ec2015\u5e741\u6708\u81f32020\u5e745\u6708\u521d\u6b66\u88c5\u51b2\u7a81\u5730\u70b9\u548c\u4e8b\u4ef6\u6570\u636e\u9879\u76ee\u4e2d\u62a5\u544a\u7684\u6bcf\u6708\u5f53\u5730\u4ef7\u683c\u548c\u5b9e\u65f6\u51b2\u7a81\u6570\u636e\u3002\u5c01\u9501\u653f\u7b56\u548c\u6700\u8fd1\u7684\u798f\u5229\u653f\u7b56\u5df2\u7ecf\u6839\u636e\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u5b9e\u65bd\uff0c\u4f46\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\u4e5f\u53ef\u80fd\u662f\u4e3a\u4e86\u5e94\u5bf9\u6b63\u5728\u53d1\u751f\u7684\u51b2\u7a81\u3002\u4e3a\u4e86\u964d\u4f4e\u5185\u6e90\u6027\u7684\u6f5c\u5728\u98ce\u9669\uff0c\u6211\u4eec\u4f7f\u7528\u5de5\u5177\u53d8\u91cf\u3002\u6211\u4eec\u5229\u7528\u4e86\u5168\u7403\u5546\u54c1\u4ef7\u683c\u7684\u5916\u751f\u6027\uff0c\u4ee5\u53ca\u589e\u52a0\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u98ce\u9669\u548c\u6548\u7387\u76843\u4e2a\u53d8\u91cf\uff0c\u5982\u56fd\u5bb6\u6b96\u6c11\u9057\u4ea7\u3001\u7a7a\u6c14\u6c61\u67d3\u5bfc\u81f4\u7684\u7537\u6027\u6b7b\u4ea1\u7387\u548c\u6210\u5e74\u4eba\u7cd6\u5c3f\u75c5\u6d41\u884c\u7387\u3002\u6211\u4eec\u53d1\u73b0\uff0c\u81ea\u5c01\u9501\u4ee5\u6765\uff0c\u53d1\u751f\u9a9a\u4e71\u3001\u9488\u5bf9\u5e73\u6c11\u7684\u66b4\u529b\u3001\u4e0e\u7cae\u98df\u6709\u5173\u7684\u51b2\u7a81\u548c\u62a2\u52ab\u7cae\u98df\u7684\u53ef\u80fd\u6027\u6709\u6240\u589e\u52a0\u3002\u98df\u54c1\u7684\u8106\u5f31\u6027\u4e00\u76f4\u662f\u4e00\u4e2a\u5f71\u54cd\u56e0\u7d20\u3002\u5f53\u5730\u4ef7\u683c\u6307\u6570\u4e0a\u534710% \uff0c\u5bf9\u5e73\u6c11\u7684\u66b4\u529b\u884c\u4e3a\u5c31\u4f1a\u589e\u52a00.7\u4e2a\u767e\u5206\u70b9\u3002\u5c3d\u7ba1\u5982\u6b64\uff0c\u4e3a\u4e86\u5e94\u5bf9\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\uff0c\u6bcf\u589e\u52a0\u4e00\u9879\u53cd\u8d2b\u56f0\u63aa\u65bd\uff0c\u53d1\u751f\u9488\u5bf9\u5e73\u6c11\u7684\u66b4\u529b\u4e8b\u4ef6\u3001\u9a9a\u4e71\u548c\u4e0e\u7cae\u98df\u6709\u5173\u7684\u51b2\u7a81\u7684\u6982\u7387\u5c31\u4f1a\u4e0b\u964d\u5927\u7ea60.2\u4e2a\u767e\u5206\u70b9\u3002\u8fd9\u4e9b\u6276\u8d2b\u63aa\u65bd\u8fd8\u51cf\u5c11\u4e86\u4e0e\u8fd9\u4e9b\u51b2\u7a81\u6709\u5173\u7684\u6b7b\u4ea1\u4eba\u6570\u3002\u603b\u7684\u6765\u8bf4\uff0c\u6211\u4eec\u7684\u7814\u7a76\u7ed3\u679c\u8868\u660e\uff0c\u98df\u54c1\u7684\u8106\u5f31\u6027\u589e\u52a0\u4e86\u51b2\u7a81\u7684\u98ce\u9669\uff0c\u4f46\u4e5f\u4e3a\u56fd\u5bb6\u63d0\u4f9b\u5e7f\u6cdb\u7684\u798f\u5229\u5b89\u5168\u7f51\u7684\u91cd\u8981\u6027\u63d0\u4f9b\u4e86\u4e50\u89c2\u7684\u770b\u6cd5<\/span><\/p>\n


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      \u632f\u52a8\u548c\u566a\u58f0\u4f5c\u7528\u4e0b<\/strong><\/span><\/p>\n

      \u6355\u5149\u590d\u5408\u4f53\u7684\u591a\u5c3a\u5ea6\u5faa\u73af\u52a8\u529b\u5b66<\/strong><\/span><\/span><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


      <\/p>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

      Multiscale cyclic dynamics in light harvesting complex in presence of vibrations and noise<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/h2>\n

      http:\/\/arxiv.org\/abs\/1804.06260<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/span><\/p>\n

      Shmuel <\/span>Gurvitz,Gennady P. Berman,Richard T. Sayre<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>Starting from the many-body Schr”odinger equation, we derive a new type of Lindblad Master equations describing a cyclic exciton\/electron dynamics in the light harvesting complex and the reaction center. These equations resemble the Master equations for the electric current in mesoscopic systems, and they go beyond the single-exciton description by accounting for the multi-exciton states accumulated in the antenna, as well as the charge-separation, fluorescence and photo-absorption. Although these effects take place on very different timescales, their inclusion is necessary for a consistent description of the exciton dynamics. Our approach reproduces both coherent and incoherent dynamics of exciton motion along the antenna in the presence of vibrational modes and noise. We applied our results to evaluate energy (exciton) and fluorescent currents as a function of sunlight intensity.<\/span><\/p>\n

      \u6458\u8981\uff1a<\/span><\/strong>\u4ece\u591a\u4f53\u859b\u5b9a\u8c14\u65b9\u7a0b\u51fa\u53d1\uff0c\u5bfc\u51fa\u4e86\u4e00\u7c7b\u65b0\u7684\u63cf\u8ff0\u6355\u5149\u7edc\u5408\u7269\u548c\u53cd\u5e94\u4e2d\u5fc3\u7684\u5faa\u73af\u6fc0\u5b50 \/ \u7535\u5b50\u52a8\u529b\u5b66\u7684 Lindblad \u4e3b\u65b9\u7a0b\u3002\u8fd9\u4e9b\u65b9\u7a0b\u7c7b\u4f3c\u4e8e\u4ecb\u89c2\u7cfb\u7edf\u4e2d\u7535\u6d41\u7684\u4e3b\u65b9\u7a0b\uff0c\u5b83\u4eec\u8003\u8651\u4e86\u5929\u7ebf\u4e2d\u79ef\u7d2f\u7684\u591a\u6fc0\u5b50\u6001\uff0c\u4ee5\u53ca\u7535\u8377\u5206\u79bb\u3001\u8367\u5149\u548c\u5149\u5438\u6536\uff0c\u8d85\u8d8a\u4e86\u5355\u6fc0\u5b50\u63cf\u8ff0\u3002\u867d\u7136\u8fd9\u4e9b\u6548\u5e94\u53d1\u751f\u5728\u975e\u5e38\u4e0d\u540c\u7684\u65f6\u95f4\u5c3a\u5ea6\u4e0a\uff0c\u4f46\u5b83\u4eec\u7684\u5305\u542b\u5bf9\u4e8e\u4e00\u81f4\u5730\u63cf\u8ff0\u6fc0\u5b50\u52a8\u529b\u5b66\u662f\u5fc5\u8981\u7684\u3002\u6211\u4eec\u7684\u65b9\u6cd5\u5728\u5b58\u5728\u632f\u52a8\u6a21\u5f0f\u548c\u566a\u58f0\u7684\u60c5\u51b5\u4e0b\uff0c\u6cbf\u7740\u5929\u7ebf\u4ea7\u751f\u76f8\u5e72\u548c\u975e\u76f8\u5e72\u7684\u6fc0\u5b50\u8fd0\u52a8\u52a8\u529b\u5b66\u3002\u6211\u4eec\u5e94\u7528\u6211\u4eec\u7684\u7ed3\u679c\u6765\u8bc4\u4f30\u80fd\u91cf(\u6fc0\u5b50)\u548c\u8367\u5149\u7535\u6d41\u4f5c\u4e3a\u9633\u5149\u5f3a\u5ea6\u7684\u51fd\u6570<\/span><\/p>\n


      <\/span><\/p>\n

      <\/h2>\n
      \n
      \n
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      <\/p>\n

      \n
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      <\/p>\n

      \u901a\u8fc7\u5fae\u8840\u7ba1\u5206\u53c9\u5b9e\u73b0<\/strong><\/span><\/p>\n

      \u7ec6\u80de\u8840\u6db2\u7eb3\u7c73\u7c92\u5b50\u7684\u5f02\u8d28\u5206\u914d<\/strong><\/span><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


      <\/p>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

      Heterogeneous partition of cellular blood-borne nanoparticles through microvascular bifurcations<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/h2>\n

      http:\/\/arxiv.org\/abs\/2006.10117<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/span><\/p>\n

      Zixiang L. Liu,Jonathan R. Clausen,Justin L. Wagner,Kimberly S. Butler,Dan S. Bolintineanu,Jeremy B. Lechman,Rekha R. Rao,Cyrus K. Aidun<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>Blood flowing through microvascular bifurcations has been an active research topic for many decades, while the partitioning pattern of nanoscale solutes in the blood remains relatively unexplored. Here, we demonstrate a multiscale computational framework for direct numerical simulation of the nanoparticle (NP) partitioning through physiologically-relevant vascular bifurcations in the presence of red blood cells (RBCs). The computational framework is established by embedding a newly-developed particulate suspension inflow\/outflow boundary condition into a multiscale blood flow solver. The computational framework is verified by recovering a tubular blood flow without a bifurcation and validated against the experimental measurement of an intravital bifurcation flow. The classic Zweifach-Fung (ZF) effect is shown to be well captured by the method. Moreover, we observe that NPs exhibit a ZF-like heterogeneous partition in response to the heterogeneous partition of the RBC phase. The NP partitioning prioritizes the high-flow-rate daughter branch except for extreme (large or small) suspension flow partition ratios under which the complete phase separation tends to occur. By analyzing the flow field and the particle trajectories, we show that the ZF-like heterogeneity in NP partition can be explained by the RBC-entrainment effect caused by the deviation of the flow separatrix preceded by the tank-treading of RBCs near the bifurcation junction. The recovery of homogeneity in the NP partition under extreme flow partition ratios is due to the plasma skimming of NPs in the cell-free layer. These findings, based on the multiscale computational framework, provide biophysical insights to the heterogeneous distribution of NPs in microvascular beds that are observed pathophysiologically.<\/span><\/p>\n

      \u6458\u8981\uff1a<\/span><\/strong>\u8840\u6db2\u5728\u5fae\u8840\u7ba1\u5206\u53c9\u4e2d\u7684\u6d41\u52a8\u4e00\u76f4\u662f\u8fd1\u51e0\u5341\u5e74\u6765\u7814\u7a76\u7684\u70ed\u70b9\uff0c\u800c\u7eb3\u7c73\u6eb6\u8d28\u5728\u8840\u6db2\u4e2d\u7684\u5206\u914d\u6a21\u5f0f\u5219\u76f8\u5bf9\u8f83\u5c11\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u5c55\u793a\u4e86\u4e00\u4e2a\u591a\u5c3a\u5ea6\u7684\u8ba1\u7b97\u6846\u67b6\uff0c\u76f4\u63a5\u6570\u503c\u6a21\u62df\u7684\u7eb3\u7c73\u7c92\u5b50(NP)\u5212\u5206\u901a\u8fc7\u751f\u7406\u76f8\u5173\u7684\u8840\u7ba1\u5206\u5c94\u7684\u5b58\u5728\u7ea2\u7ec6\u80de(RBCs)\u3002\u901a\u8fc7\u5728\u591a\u5c3a\u5ea6\u8840\u6d41\u6c42\u89e3\u5668\u4e2d\u5d4c\u5165\u4e00\u4e2a\u65b0\u53d1\u5c55\u7684\u60ac\u6d6e\u9897\u7c92\u6d41\u5165 \/ \u6d41\u51fa\u8fb9\u754c\u6761\u4ef6\uff0c\u5efa\u7acb\u4e86\u8ba1\u7b97\u6846\u67b6\u3002\u8ba1\u7b97\u6846\u67b6\u901a\u8fc7\u4e00\u4e2a\u6ca1\u6709\u5206\u53c9\u7684\u7ba1\u72b6\u8840\u6d41\u7684\u6062\u590d\u5f97\u5230\u4e86\u9a8c\u8bc1\uff0c\u5e76\u901a\u8fc7\u4e00\u4e2a\u6d3b\u4f53\u5185\u5206\u53c9\u8840\u6d41\u7684\u5b9e\u9a8c\u6d4b\u91cf\u5f97\u5230\u4e86\u9a8c\u8bc1\u3002\u8be5\u65b9\u6cd5\u5f88\u597d\u5730\u6355\u6349\u4e86\u7ecf\u5178\u7684 Zweifach-Fung (ZF)\u6548\u5e94\u3002\u6b64\u5916\uff0c\u6211\u4eec\u89c2\u5bdf\u5230\uff0cNPs \u8868\u73b0\u51fa\u4e00\u4e2a zf \u7c7b\u5f02\u8d28\u5206\u914d\u54cd\u5e94\u7684\u7ea2\u7ec6\u80de\u9636\u6bb5\u7684\u5f02\u8d28\u5206\u914d\u3002Np \u5206\u533a\u5c06\u9ad8\u6d41\u901f\u5b50\u5206\u652f\u6309\u4f18\u5148\u987a\u5e8f\u6392\u5217\uff0c\u9664\u4e86\u6781\u7aef(\u5927\u6216\u5c0f)\u7684\u60ac\u6d6e\u6d41\u5206\u533a\u6bd4\u4f8b\uff0c\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\u5f80\u5f80\u4f1a\u53d1\u751f\u5b8c\u5168\u7684\u76f8\u5206\u79bb\u3002\u901a\u8fc7\u5bf9\u6d41\u573a\u548c\u9897\u7c92\u8fd0\u52a8\u8f68\u8ff9\u7684\u5206\u6790\uff0c\u6211\u4eec\u53d1\u73b0\u7c7b zf \u975e\u5747\u5300\u6027\u53ef\u4ee5\u7528 rbc- \u5939\u5e26\u6548\u5e94\u6765\u89e3\u91ca\u3002\u6781\u7aef\u6d41\u52a8\u5206\u914d\u6bd4\u4e0b NP \u5206\u914d\u5747\u5300\u6027\u7684\u6062\u590d\u662f\u7531\u4e8e\u65e0\u80de\u5c42\u4e2d NPs \u7684\u7b49\u79bb\u5b50\u4f53\u63a0\u8fc7\u6240\u81f4\u3002\u8fd9\u4e9b\u53d1\u73b0\uff0c\u57fa\u4e8e\u591a\u5c3a\u5ea6\u7684\u8ba1\u7b97\u6846\u67b6\uff0c\u63d0\u4f9b\u4e86\u751f\u7269\u7269\u7406\u5b66\u7684\u89c1\u89e3\uff0c\u5728\u75c5\u7406\u751f\u7406\u5b66\u89c2\u5bdf\u5fae\u8840\u7ba1\u5e8a NPs \u7684\u4e0d\u5747\u5300\u5206\u5e03<\/span><\/p>\n


      <\/span><\/p>\n

      <\/h2>\n
      \n
      \n
      \n


      <\/p>\n

      \n
      \n
      <\/p>\n

      \u7ec4\u7ec7\u75c5\u7406\u5b66\u548c\u9ad8\u5e45\u8d85\u58f0\u4e2d<\/strong><\/span><\/p>\n

      \u5355\u6ce1\u52a8\u529b\u5b66\u7684\u5efa\u6a21\u4e0e\u9a8c\u8bc1<\/strong><\/span><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


      <\/h2>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

      Single-bubble dynamics in histotripsy and high-amplitude ultrasound: Modeling and validation<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/h2>\n

      http:\/\/arxiv.org\/abs\/2006.10171<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/span><\/p>\n

      Lauren Mancia,Mauro Rodriguez,Jonathan Sukovich,Zhen Xu,Eric Johnsen<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>A variety of approaches have been used to model the dynamics of a single, isolated bubble nucleated by a microsecond length high-amplitude ultrasound pulse (e.g., a histotripsy pulse). Until recently, the lack of single–bubble experimental radius vs. time data for bubble dynamics under a well-characterized driving pressure has limited model validation efforts. This study uses radius vs. time measurements of single, spherical histotripsy-nucleated bubbles in water [Wilson et al., Phys. Rev. E, 2019, 99, 043103] to quantitatively compare and validate a variety of bubble dynamics modeling approaches, including compressible and incompressible models as well as different thermal models. A strategy for inferring an analytic representation of histotripsy waveforms directly from experimental radius vs. time and cavitation threshold data is presented. We compare distributions of a calculated validation metric obtained for each model applied to <\/span>8<\/span><\/mjx-container>8 <\/span><\/mjx-assistive-mml><\/mjx-container>experimental data sets. There is minimal distinction (<\/span><1%<\/span><\/mjx-c><\/mjx-mn><\/mjx-math><\/mjx-container>) among the modeling approaches for compressibility and thermal effects considered in this study. These results suggest that our proposed strategy to infer the waveform, combined with simple models minimizing parametric uncertainty and computational resource demands accurately represent single-bubble dynamics in histotripsy, including at and near the maximum bubble radius. Remaining sources of parametric and model-based uncertainty are discussed.<\/span><\/p>\n

      \u6458\u8981:<\/span><\/strong>\u4eba\u4eec\u5df2\u7ecf\u4f7f\u7528\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u6a21\u62df\u5355\u4e2a\u5b64\u7acb\u6c14\u6ce1\u7684\u52a8\u529b\u5b66\uff0c\u8fd9\u4e9b\u6c14\u6ce1\u7531\u5fae\u79d2\u957f\u5ea6\u7684\u9ad8\u632f\u5e45\u8d85\u58f0\u8109\u51b2(\u4f8b\u5982\u7ec4\u7ec7\u75c5\u7406\u8109\u51b2)\u5f62\u6838\u3002\u76f4\u5230\u6700\u8fd1\uff0c\u7f3a\u4e4f\u5355\u4e2a\u6c14\u6ce1\u5b9e\u9a8c\u534a\u5f84\u4e0e\u65f6\u95f4\u6570\u636e\u7684\u6c14\u6ce1\u52a8\u529b\u5b66\u7684\u9a71\u52a8\u538b\u529b\u4e0b\uff0c\u826f\u597d\u7684\u7279\u70b9\u9650\u5236\u4e86\u6a21\u578b\u9a8c\u8bc1\u7684\u52aa\u529b\u3002\u8fd9\u9879\u7814\u7a76\u4f7f\u7528\u534a\u5f84\u4e0e\u65f6\u95f4\u7684\u6d4b\u91cf\u5355\u4e2a\uff0c\u7403\u5f62\u7ec4\u7ec7\u75c5\u7406\u6838\u5fc3\u6c14\u6ce1\u5728\u6c34\u4e2d[\u5a01\u5c14\u900a\u7b49\u4eba\u30022019,99,043103]\u5b9a\u91cf\u6bd4\u8f83\u548c\u9a8c\u8bc1\u5404\u79cd\u6c14\u6ce1\u52a8\u529b\u5b66\u6a21\u578b\u7684\u65b9\u6cd5\uff0c\u5305\u62ec\u53ef\u538b\u7f29\u548c\u4e0d\u53ef\u538b\u7f29\u6a21\u578b\u4ee5\u53ca\u4e0d\u540c\u7684\u70ed\u6a21\u578b\u3002\u63d0\u51fa\u4e86\u4e00\u79cd\u76f4\u63a5\u4ece\u5b9e\u9a8c\u534a\u5f84\u968f\u65f6\u95f4\u53d8\u5316\u548c\u7a7a\u5316\u9608\u503c\u6570\u636e\u63a8\u5bfc\u76f4\u7ec4\u7ec7\u75c5\u7406\u6ce2\u5f62\u89e3\u6790\u8868\u8fbe\u5f0f\u7684\u65b9\u6cd5\u3002\u6211\u4eec\u6bd4\u8f83\u5206\u5e03\u7684\u8ba1\u7b97\u9a8c\u8bc1\u5ea6\u91cf\u83b7\u5f97\u7684\u6bcf\u4e2a\u6a21\u578b\u9002\u7528\u4e8e<\/span><\/p>\n


      <\/p>\n

      \n
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      <\/p>\n

      \n
      \n
      <\/p>\n

      \u5305\u62ec\u6839\u830e\u751f\u957f\u5728\u5185\u7684<\/strong><\/span><\/p>\n

      \u690d\u88ab\u6a21\u5f0f\u7684\u4e00\u822c\u6a21\u578b<\/strong><\/span><\/strong><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


      <\/p>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

      A general model for vegetation patterns including rhizome growth<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/h2>\n

      http:\/\/arxiv.org\/abs\/1908.04603<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/span><\/p>\n

      Daniel Ruiz-Reyn\u00e9s,Francesca Sch\u00f6nsberg,Emilio Hern\u00e1ndez-Garc\u00eda,Dami\u00e0 Gomila<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>Vegetation patterns, a natural phenomenon observed worldwide, are typically driven by spatially distributed feedback. However, the spatial colonization mechanisms of clonal plants, driven by the growth of a rhizome, are usually not considered in prototypical models. Here we propose a general equation for the vegetation density that includes all main clonal-growth features as well as the essential ingredients leading to spatial self-organization. This generic model reproduces the phase diagram of a fully detailed model of clonal growth. The relation of each term of the model with the mechanisms of clonal growth is discussed.<\/span><\/p>\n

      \u6458\u8981\uff1a<\/span><\/strong>\u690d\u88ab\u6a21\u5f0f\u662f\u5728\u4e16\u754c\u8303\u56f4\u5185\u89c2\u5bdf\u5230\u7684\u4e00\u79cd\u81ea\u7136\u73b0\u8c61\uff0c\u901a\u5e38\u662f\u7531\u7a7a\u95f4\u5206\u5e03\u7684\u53cd\u9988\u9a71\u52a8\u7684\u3002\u7136\u800c\uff0c\u514b\u9686\u690d\u7269\u5728\u6839\u72b6\u830e\u751f\u957f\u9a71\u52a8\u4e0b\u7684\u7a7a\u95f4\u5b9a\u6b96\u673a\u5236\u5728\u539f\u578b\u6a21\u578b\u4e2d\u901a\u5e38\u4e0d\u88ab\u8003\u8651\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u4e2a\u5305\u62ec\u6240\u6709\u4e3b\u8981\u514b\u9686\u751f\u957f\u7279\u5f81\u4ee5\u53ca\u5bfc\u81f4\u7a7a\u95f4\u81ea\u6211\u7ec4\u7ec7\u7684\u57fa\u672c\u8981\u7d20\u7684\u690d\u88ab\u5bc6\u5ea6\u7684\u4e00\u822c\u65b9\u7a0b\u3002\u8fd9\u4e2a\u901a\u7528\u6a21\u578b\u518d\u73b0\u4e86\u4e00\u4e2a\u5b8c\u5168\u8be6\u7ec6\u7684\u514b\u9686\u751f\u957f\u6a21\u578b\u7684\u76f8\u56fe\u3002\u8ba8\u8bba\u4e86\u6a21\u578b\u5404\u9879\u4e0e\u514b\u9686\u751f\u957f\u673a\u5236\u7684\u5173\u7cfb<\/span><\/p>\n


      <\/p>\n

      \n
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      <\/p>\n

      \n
      \n
      <\/p>\n

      \u8fd0\u52a8\u86cb\u767d\u8d28<\/strong><\/span><\/p>\n

      \u901a\u8fc7\u5fae\u7ba1\u4ea4\u53c9\u8f6c\u8fd0\u7684\u968f\u673a\u6a21\u62df<\/strong><\/span><\/strong><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


      <\/p>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

      Stochastic modelling of collective motor protein transport through a crossing of microtubules<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/h2>\n

      http:\/\/arxiv.org\/abs\/2006.10416<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/span><\/p>\n

      Ad\u00e9la\u00efde Raguin,Norbert Kern,Andrea Parmeggiani<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>The cytoskeleton in eukaryotic cells plays several crucial roles. In terms of intracellular transport, motor proteins use the cytoskeletal filaments as a backbone along which they can actively transport biological cargos such as vesicles carrying biochemical reactants. Crossings between such filaments constitute a key element, as they may serve to alter the destination of such payload. Although motor proteins are known to display a rich behaviour at such crossings, the latter have so far only been modelled as simple branching points. Here we explore a model for a crossing between two microtubules which retains the individual tracks consisting of protofilaments, and we construct a schematic representation of the transport paths. We study collective transport exemplified by the Totally Asymmetric Simple Exclusion Process (TASEP), and provide a full analysis of the transport features and the associated phase diagram, by a generic mean-field approach which we confirm through particle-based stochastic simulations. In particular we show that transport through such a compound crossing cannot be approximated from a coarse-grained structure with a simple branching point. Instead, it gives rise to entirely new and counterintuitive features: the fundamental current-density relation for traffic flow is no longer a single-valued function, and it furthermore differs according to whether it is observed upstream or downstream from the crossing. We argue that these novel features may be directly relevant for interpreting experimental measurements.<\/span><\/p>\n

      \u6458\u8981\uff1a<\/span><\/strong>\u771f\u6838\u7ec6\u80de\u4e2d\u7684\u7ec6\u80de\u9aa8\u67b6\u8d77\u7740\u51e0\u4e2a\u91cd\u8981\u7684\u4f5c\u7528\u3002\u5728\u7ec6\u80de\u5185\u8f6c\u8fd0\u65b9\u9762\uff0c\u8fd0\u52a8\u86cb\u767d\u5229\u7528\u7ec6\u80de\u9aa8\u67b6\u4e1d\u4f5c\u4e3a\u9aa8\u5e72\uff0c\u6cbf\u7740\u5b83\u4eec\u53ef\u4ee5\u79ef\u6781\u5730\u8fd0\u8f93\u751f\u7269\u7269\u8d28\uff0c\u5982\u8f7d\u6709\u751f\u5316\u53cd\u5e94\u7269\u7684\u5c0f\u6ce1\u3002\u7ec6\u4e1d\u4e4b\u95f4\u7684\u4ea4\u53c9\u662f\u4e00\u4e2a\u5173\u952e\u8981\u7d20\uff0c\u56e0\u4e3a\u5b83\u4eec\u53ef\u80fd\u6709\u52a9\u4e8e\u6539\u53d8\u8fd9\u79cd\u6709\u6548\u8f7d\u8377\u7684\u76ee\u7684\u5730\u3002\u867d\u7136\u6211\u4eec\u77e5\u9053\u8fd0\u52a8\u86cb\u767d\u5728\u8fd9\u79cd\u4ea4\u53c9\u8fc7\u7a0b\u4e2d\u8868\u73b0\u51fa\u4e30\u5bcc\u7684\u884c\u4e3a\uff0c\u4f46\u8fc4\u4eca\u4e3a\u6b62\uff0c\u8fd0\u52a8\u86cb\u767d\u4ec5\u4ec5\u88ab\u6a21\u62df\u4e3a\u7b80\u5355\u7684\u5206\u652f\u70b9\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u63a2\u7d22\u4e00\u4e2a\u6a21\u578b\u4e4b\u95f4\u7684\u4ea4\u53c9\u4e24\u4e2a\u5fae\u7ba1\u4fdd\u7559\u4e2a\u4eba\u8f68\u9053\u7ec4\u6210\u7684\u539f\u4e1d\uff0c\u6211\u4eec\u6784\u5efa\u4e00\u4e2a\u56fe\u793a\u8868\u793a\u7684\u4f20\u8f93\u8def\u5f84\u3002\u672c\u6587\u4ee5\u5b8c\u5168\u975e\u5bf9\u79f0\u7b80\u5355\u6392\u65a5\u8fc7\u7a0b(TASEP)\u4e3a\u4f8b\u7814\u7a76\u4e86\u96c6\u4f53\u8f93\u8fd0\uff0c\u5e76\u7528\u4e00\u822c\u7684\u5e73\u5747\u573a\u65b9\u6cd5\u5bf9\u8f93\u8fd0\u7279\u5f81\u548c\u76f8\u56fe\u8fdb\u884c\u4e86\u5168\u9762\u7684\u5206\u6790\uff0c\u5e76\u901a\u8fc7\u57fa\u4e8e\u7c92\u5b50\u7684\u968f\u673a\u6a21\u62df\u8fdb\u884c\u4e86\u9a8c\u8bc1\u3002\u6211\u4eec\u7279\u522b\u6307\u51fa\uff0c\u901a\u8fc7\u8fd9\u79cd\u6df7\u5408\u6742\u4ea4\u7684\u4f20\u8f93\u4e0d\u80fd\u8fd1\u4f3c\u4e8e\u7c97\u7c92\u5ea6\u7ed3\u6784\u548c\u7b80\u5355\u7684\u5206\u652f\u70b9\u3002\u76f8\u53cd\uff0c\u5b83\u4ea7\u751f\u4e86\u5168\u65b0\u7684\u8fdd\u53cd\u76f4\u89c9\u7684\u7279\u5f81: \u4ea4\u901a\u6d41\u7684\u57fa\u672c\u7535\u6d41\u5bc6\u5ea6\u5173\u7cfb\u4e0d\u518d\u662f\u4e00\u4e2a\u5355\u503c\u51fd\u6570\uff0c\u800c\u662f\u6839\u636e\u5728\u4ea4\u53c9\u53e3\u4e0a\u6e38\u6216\u4e0b\u6e38\u89c2\u5bdf\u5230\u7684\u7535\u6d41\u5bc6\u5ea6\u5173\u7cfb\u800c\u6709\u6240\u4e0d\u540c\u3002\u6211\u4eec\u8ba4\u4e3a\u8fd9\u4e9b\u65b0\u5947\u7684\u7279\u5f81\u53ef\u80fd\u4e0e\u89e3\u91ca\u5b9e\u9a8c\u6d4b\u91cf\u76f4\u63a5\u76f8\u5173<\/span><\/p>\n


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      \u51cf\u5c11SARS-COV-2\u548c\u5176\u4ed6\u75c5\u6bd2<\/strong><\/span><\/p>\n

      \u5bf9 spike \u86cb\u767d\u6e17\u900f\u7684\u6709\u6548\u9014\u5f84: <\/strong><\/span><\/p>\n

      \u901a\u8fc7\u8868\u9762\u7c92\u5b50\u9759\u7535\u8377\u534f\u5546<\/strong><\/span><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


      <\/p>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

      An effective approach to reduce the penetration potential of Sars-Cov-2 and other viruses by spike protein: Through surface particle electrostatic charge negotiation<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/h2>\n

      http:\/\/arxiv.org\/abs\/2006.10603<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/span><\/p>\n

      Kausik Rakshit,Sudip Chatterjee,Durjoy Bandyopadhyay,Somsekhar Sarkar<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>The objective of this paper is to provide a mathematical model to construct a barrier that may be useful to prevent the penetration of different viruses (Eg. SARS-COV-2) as well as charged aerosols through the concept of electrostatic charge negotiation. (Fusion for the opposite types of charges and repulsion for the similar types of charges). Reviewing the works of different authors, regarding charges, surface charge densities ({sigma}), charge mobility ({mu}) and electrostatic potentials of different aerosols under varied experimental conditions, a similar intensive study has also been carried out to investigate the electron donating and accepting (hole donating) properties of the spike proteins (S-proteins) of different RNA and DNA viruses, including SARS-COV-2. Based upon the above transport properties of electrons of different particles having different dimensions, a mathematical model has been established to find out the penetration potential of those particles under different electrostatic fields. An intensive study have been carried out to find out the generation of electrostatic charges due to the surface emission of electrons (SEE), when a conducting material like silk, nylon or wool makes a friction with the Gr IV elements like Germanium or Silicon, it creates an opposite layer of charges in the outer conducting surface and the inner semiconducting surface separated by a dielectric materials. This opposite charge barriers may be considered as Inversion layers (IL). The electrostatic charges accumulated in the layers between the Gr IV Ge is sufficient enough to either fuse or repel the charges of the spike proteins of the RNA, DNA viruses including SARS-Cov-2 (RNA virus) or the aerosols.<\/span><\/p>\n

      \u6458\u8981\uff1a<\/span><\/strong>\u672c\u6587\u7684\u76ee\u7684\u662f\u63d0\u4f9b\u4e00\u4e2a\u6570\u5b66\u6a21\u578b\u6765\u6784\u5efa\u4e00\u4e2a\u5c4f\u969c\uff0c\u8fd9\u4e2a\u5c4f\u969c\u53ef\u80fd\u6709\u52a9\u4e8e\u9632\u6b62\u4e0d\u540c\u75c5\u6bd2\u7684\u6e17\u900f\u3002Sars-cov-2)\u4ee5\u53ca\u901a\u8fc7\u9759\u7535\u7535\u8377\u534f\u5546\u6982\u5ff5\u5e26\u7535\u6c14\u6eb6\u80f6\u3002(\u805a\u53d8\u4e3a\u76f8\u53cd\u7c7b\u578b\u7684\u7535\u8377\u548c\u65a5\u529b\u4e3a\u7c7b\u4f3c\u7c7b\u578b\u7684\u7535\u8377)\u3002\u672c\u6587\u56de\u987e\u4e86\u4e0d\u540c\u4f5c\u8005\u5728\u4e0d\u540c\u5b9e\u9a8c\u6761\u4ef6\u4e0b\u5bf9\u4e0d\u540c\u6c14\u6eb6\u80f6\u7684\u7535\u8377\u3001\u8868\u9762\u7535\u8377\u5bc6\u5ea6\u3001\u7535\u8377\u8fc1\u79fb\u7387\u548c\u9759\u7535\u52bf\u7684\u7814\u7a76\u5de5\u4f5c\uff0c\u5e76\u5bf9\u4e0d\u540c RNA \u548c DNA \u75c5\u6bd2(\u5305\u62ec SARS-COV-2\u75c5\u6bd2)\u7684 s \u86cb\u767d(s \u86cb\u767d)\u7684\u7535\u5b50\u4f9b\u7ed9\u548c\u63a5\u53d7(\u7a7a\u7a74\u4f9b\u7ed9)\u7279\u6027\u8fdb\u884c\u4e86\u7c7b\u4f3c\u7684\u6df1\u5165\u7814\u7a76\u3002\u6839\u636e\u4e0d\u540c\u5c3a\u5bf8\u7c92\u5b50\u7684\u7535\u5b50\u8f93\u8fd0\u7279\u6027\uff0c\u5efa\u7acb\u4e86\u7c92\u5b50\u5728\u4e0d\u540c\u9759\u7535\u573a\u4f5c\u7528\u4e0b\u7684\u7a7f\u900f\u52bf\u7684\u6570\u5b66\u6a21\u578b\u3002\u672c\u6587\u5bf9\u4e1d\u3001\u5c3c\u9f99\u3001\u7f8a\u6bdb\u7b49\u5bfc\u7535\u6750\u6599\u4e0e\u9517\u3001\u7845\u7b49 Gr IV \u5143\u7d20\u6469\u64e6\u65f6\uff0c\u5728\u5916\u5bfc\u7535\u8868\u9762\u548c\u5185\u534a\u5bfc\u7535\u8868\u9762\u5206\u522b\u4ea7\u751f\u76f8\u53cd\u7684\u7535\u8377\u5c42\u8fdb\u884c\u4e86\u6df1\u5165\u7684\u7814\u7a76\u3002\u8fd9\u79cd\u76f8\u53cd\u7684\u7535\u8377\u52bf\u5792\u53ef\u4ee5\u770b\u4f5c\u662f\u53cd\u8f6c\u5c42\u3002Gr IV Ge \u86cb\u767d\u5c42\u95f4\u79ef\u7d2f\u7684\u9759\u7535\u8377\u8db3\u4ee5\u878d\u5408\u6216\u51fb\u9000 RNA\u3001\u5305\u62ec SARS-Cov-2(RNA \u75c5\u6bd2)\u5728\u5185\u7684 DNA \u75c5\u6bd2\u6216\u6c14\u6eb6\u80f6\u86cb\u767d\u7684\u7535\u8377<\/span><\/p>\n


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      \u55b7\u6c14\u706b\u8f66\u5728\u901a\u8bdd\u4e2d<\/strong><\/span><\/p>\n

      \u4ea7\u751f\u8fdc\u7a0b\u6e4d\u6d41\u55b7\u6c14\u5f0f\u4f20\u8f93\uff0c<\/strong><\/span><\/p>\n

      \u53ef\u80fd\u4e0e\u65e0\u75c7\u72b6\u75c5\u6bd2\u4f20\u64ad\u6709\u5173<\/strong><\/span><\/p>\n

      <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

      <\/h2>\n


      <\/h2>\n

      \u539f\u6587\u6807\u9898\uff1a<\/span><\/strong><\/span><\/h2>\n

      Puff trains in speaking produce long-range turbulent jet-like transport potentially relevant to asymptomatic spreading of viruses<\/span><\/h2>\n

      \u5730\u5740\uff1a<\/span><\/strong><\/h2>\n

      http:\/\/arxiv.org\/abs\/2006.10671<\/span><\/p>\n

      \u4f5c\u8005:<\/span><\/strong><\/span><\/p>\n

      Manouk Abkarian,Simon Mendez,Nan Xue,Fan Yang,Howard A. Stone<\/span><\/p>\n


      <\/p>\n

      Abstract\uff1a<\/span><\/strong>Droplet generation and transport during coughing and sneezing has been studied for decades to characterize disease transmission by symptomatic individuals. Nevertheless, many reports document that asymptomatic and presymptomatic individuals contribute to the spread of COVID-19, probably during conversations in social interactions. Researchers have shown that droplet emission occurs during speech, yet there are few quantitative studies of the flows that provide the transport mechanism; the relevant fluid dynamics is largely unstudied. This lack of quantitative characterization means that when virus is present there is little public health guidance for understanding risk and mitigation strategies, e.g. the “six-foot rule”. Here we analyze flows during breathing and speaking, including linguistic features, using order-of-magnitudes estimates, numerical simulations, and laboratory experiments. We show how plosive sounds like `P’ are associated with vortical structures, leading to rapid transport over half a meter in a split second. When produced individually, puffs decay over a meter, with the distance traveled in time scaling as <\/span>L<\/span><\/mi>\u223c<\/span><\/mo>t<\/span><\/mi>1<\/span><\/mn>\/<\/span><\/mo><\/mrow>4<\/span><\/mn><\/mrow><\/msup><\/mjx-assistive-mml><\/mjx-container>, and mix with the slower environmental circulation. In contrast, the transport of exhaled material over time scales longer than a few seconds, characteristic of speech, which is effectively a train of puffs, is a conical turbulent jet with a scaling law <\/span>L<\/span><\/mi>\u223c<\/span><\/mo>t<\/span><\/mi>1<\/span><\/mn>\/<\/span><\/mo><\/mrow>2<\/span><\/mn><\/mrow><\/msup><\/mjx-assistive-mml><\/mjx-container>. Typically, the exhaled air in front of a speaker extends 2 m after 30 seconds of normal speech. We believe this work will inform thinking about aerosol transport in disease transmission for humans and other animals, and yield a better understanding of linguistic aerodynamics, i.e., aerolinguistics.<\/span><\/p>\n

      \u6458\u8981:<\/span><\/strong>\u6570\u5341\u5e74\u6765\uff0c\u4eba\u4eec\u4e00\u76f4\u5728\u7814\u7a76\u54b3\u55fd\u548c\u6253\u55b7\u568f\u8fc7\u7a0b\u4e2d\u6db2\u6ef4\u7684\u4ea7\u751f\u548c\u8fd0\u8f93\uff0c\u4ee5\u8868\u5f81\u6709\u75c7\u72b6\u4e2a\u4f53\u7684\u75be\u75c5\u4f20\u64ad\u7279\u5f81\u3002\u7136\u800c\uff0c\u8bb8\u591a\u62a5\u544a\u8868\u660e\uff0c\u65e0\u75c7\u72b6\u548c\u6709\u75c7\u72b6\u7684\u4e2a\u4f53\u53ef\u80fd\u5728\u793e\u4ea4\u4e92\u52a8\u4e2d\u7684\u8c08\u8bdd\u4e2d\u4fc3\u8fdb\u4e86COVID-19\u7684\u4f20\u64ad\u3002\u7814\u7a76\u4eba\u5458\u5df2\u7ecf\u8868\u660e\uff0c\u6db2\u6ef4\u7684\u6563\u53d1\u662f\u5728\u8bed\u97f3\u8fc7\u7a0b\u4e2d\u53d1\u751f\u7684\uff0c\u4f46\u5f88\u5c11\u6709\u5b9a\u91cf\u7814\u7a76\u63d0\u4f9b\u8fd9\u79cd\u6d41\u52a8\u673a\u5236\u7684\u6d41\u52a8\u3002\u76f8\u5173\u7684\u6d41\u4f53\u52a8\u529b\u5b66\u5f88\u5927\u7a0b\u5ea6\u4e0a\u672a\u88ab\u7814\u7a76\u3002\u8fd9\u79cd\u7f3a\u4e4f\u5b9a\u91cf\u7279\u5f81\u7684\u624b\u6bb5\u610f\u5473\u7740\uff0c\u5f53\u5b58\u5728\u75c5\u6bd2\u65f6\uff0c\u51e0\u4e4e\u6ca1\u6709\u516c\u5171\u536b\u751f\u6307\u5bfc\u6765\u4e86\u89e3\u98ce\u9669\u548c\u7f13\u89e3\u7b56\u7565\uff0c\u4f8b\u5982\u201c\u516d\u5c3a\u6cd5\u5219\u201d\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u5206\u6790\u547c\u5438\u548c\u8bf4\u8bdd\u671f\u95f4\u7684\u6d41\u91cf\uff0c\u5305\u62ec\u8bed\u8a00\u7279\u5f81\uff0c\u4f7f\u7528\u91cf\u7ea7\u4f30\u8ba1\uff0c\u6570\u503c\u6a21\u62df\u548c\u5b9e\u9a8c\u5ba4\u5b9e\u9a8c\u3002\u6211\u4eec\u5c55\u793a\u4e86\u50cf\u201c P\u201d\u8fd9\u6837\u7684\u7206\u7834\u97f3\u662f\u5982\u4f55\u4e0e\u65cb\u6da1\u7ed3\u6784\u76f8\u5173\u8054\u7684\uff0c\u4ece\u800c\u5bfc\u81f4\u5728\u4e00\u79d2\u949f\u5185\u5feb\u901f\u4f20\u8f93\u8d85\u8fc7\u534a\u7c73\u3002\u5f53\u5355\u72ec\u751f\u4ea7\u65f6\uff0c\u7c89\u6251\u4f1a\u968f\u7740\u8ddd\u79bb\u7684\u63a8\u79fb\u800c\u9010\u6e10\u8870\u51cf\u4e00\u7c73t\uff0c<\/span>\u0164<\/span><\/mjx-assistive-mml><\/mjx-container>\u7f29\u653e\u4e3a <\/span>\u5927\u53f7<\/span><\/mi>\u301c<\/span><\/mo>\u0164<\/span><\/mi>1\u4e2a<\/span><\/mn>\/<\/span><\/mo><\/mrow>4<\/span><\/mn><\/mrow><\/msup><\/mjx-assistive-mml><\/mjx-container>\uff0c\u5e76\u4e0e\u8f83\u6162\u7684\u73af\u5883\u6d41\u901a\u6df7\u5408\u5728\u4e00\u8d77\u3002\u76f8\u6bd4\u4e4b\u4e0b\uff0c\u547c\u51fa\u7684\u7269\u8d28\u5728\u65f6\u95f4\u5c3a\u5ea6\u4e0a\u7684\u4f20\u8f93\u65f6\u95f4\u957f\u4e8e\u51e0\u79d2\u949f\uff0c\u800c\u8bed\u97f3\u7279\u5f81\uff08\u5b9e\u9645\u4e0a\u662f\u4e00\u8fde\u4e32\u7684\u62bd\u5438\uff09\u662f\u5177\u6709\u6bd4\u4f8b\u5b9a\u5f8b\u7684\u5706\u9525\u5f62\u6e4d\u6d41\u5c04\u6d41<\/span>\u5927\u53f7<\/span><\/mi>\u301c<\/span><\/mo>\u0164<\/span><\/mi>1\u4e2a<\/span><\/mn>\/<\/span><\/mo><\/mrow>2<\/span><\/mn><\/mrow><\/msup><\/mjx-assistive-mml><\/mjx-container>\u3002\u901a\u5e38\uff0c\u8bf4\u8bdd\u8005\u524d\u9762\u7684\u547c\u51fa\u7a7a\u6c14\u5728\u6b63\u5e38\u8bed\u97f3\u64ad\u653e30\u79d2\u540e\u4f1a\u5ef6\u4f382 m\u3002\u6211\u4eec\u76f8\u4fe1\u8fd9\u9879\u5de5\u4f5c\u5c06\u4e3a\u4eba\u4eec\u5728\u4f20\u64ad\u4eba\u7c7b\u548c\u5176\u4ed6\u52a8\u7269\u7684\u75be\u75c5\u4e2d\u8fdb\u884c\u6c14\u6eb6\u80f6\u8fd0\u8f93\u63d0\u4f9b\u53c2\u8003\uff0c\u5e76\u66f4\u597d\u5730\u7406\u89e3\u8bed\u8a00\u7a7a\u6c14\u52a8\u529b\u5b66\uff0c\u5373\u822a\u7a7a\u8bed\u8a00\u5b66\u3002<\/span>
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      \n
      \n

      \u96c6\u667a\u4ff1\u4e50\u90e8QQ\u7fa4\uff5c877391004<\/span><\/strong><\/p>\n

      \u5546\u52a1\u5408\u4f5c\u53ca\u6295\u7a3f\u8f6c\u8f7d\uff5cswarma@swarma.org
      <\/span><\/strong><\/p>\n

      \n

      \u25c6 <\/span>\u25c6 <\/span>\u25c6<\/span><\/span><\/strong><\/strong><\/h1>\n<\/section>\n

      \u641c\u7d22\u516c\u4f17\u53f7\uff1a\u96c6\u667a\u4ff1\u4e50\u90e8<\/span><\/strong><\/span><\/p>\n


      <\/p>\n

      \u52a0\u5165\u201c\u6ca1\u6709\u56f4\u5899\u7684\u7814\u7a76\u6240\u201d<\/span><\/strong><\/span><\/p>\n

      \n

      <\/p>\n<\/section>\n

      \u8ba9\u82f9\u679c\u7838\u5f97\u66f4\u731b\u70c8\u4e9b\u5427\uff01<\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section><\/div>\n","protected":false},"excerpt":{"rendered":"

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