Flood catastrophe risk assessment is imperative for the steady development of agriculture under the context of global climate change,and meanwhile,it is an urgent scientific issue need to be solved in agricultural ris...Flood catastrophe risk assessment is imperative for the steady development of agriculture under the context of global climate change,and meanwhile,it is an urgent scientific issue need to be solved in agricultural risk assessment discipline.This paper developed the methodology of flood catastrophe risk assessment,which can be shown as the standard process of crop loss calculation,Monte Carlo simulation,the generalized extreme value distribution（GEV） fitting,and risk evaluation.Data on crop loss were collected based on hectares covered by natural disasters,hectares affected by natural disasters,and hectares destroyed by natural disasters using the standard equation.Monte Carlo simulation based on appropriate distribution was used to expand sample size to overcome the insufficiency of crop loss data.Block maxima model（BMM） approach based on the extreme value theory was for modeling the generalized extreme value distribution（GEV） of flood catastrophe loss,and then flood catastrophe risk at the provincial scale in China was calculated.The Type III Extreme distribution（Weibull） has a weighted advantage of modeling flood catastrophe risk for grain production.The impact of flood catastrophe to grain production in China was significantly serious,and high or very high risk of flood catastrophe mainly concentrates on the central and eastern regions of China.Given the scenario of suffering once-in-a-century flood disaster,for majority of the major-producing provinces,the probability of 10% reduction of grain output is more than 90%.Especially,the probabilities of more than 15% decline in grain production reach up to 99.99,99.86,99.69,and 91.60% respectively in Anhui,Jilin,Liaoning,and Heilongjiang.Flood catastrophe assessment can provide multifaceted information about flood catastrophe risk that can help to guide management of flood catastrophe.展开更多
近年来,随着保险市场的发展,人身险(包括健康险、寿险和意外险)占据的份额不断上升,这些险种极端理赔的现象也一直存在。国内外人身险的实践经验表明,准确掌握保险理赔可能存在的极端风险,有利于保险公司更精确地定价保险产品。文章以20...近年来,随着保险市场的发展,人身险(包括健康险、寿险和意外险)占据的份额不断上升,这些险种极端理赔的现象也一直存在。国内外人身险的实践经验表明,准确掌握保险理赔可能存在的极端风险,有利于保险公司更精确地定价保险产品。文章以2005-2014年全国六个地区人身险的月理赔额为样本,通过对数据特征进行系统性分析,筛选出符合"尖峰厚尾"特征的理赔数据。运用区间极大值模型(Block Maxima Method,BMM)对理赔数据进行风险度量,以求得其理赔VaR(Value at Risk)。结果显示,意外险的理赔出现极端风险的可能性较大,多数地区意外险的理赔都存在极端风险;而健康险出现极端理赔的可能性相对较小,但一旦出现极端情况,其理赔VaR很有可能大于意外险。展开更多
蛋白质复合体对于研究细胞活动具有重要意义.随着新的生物实验技术的不断出现,产生了大量的蛋白质相互作用网络.通过对蛋白质相互作用网络进行聚类识别蛋白质复合体是当前研究热点.然而,目前大多数蛋白质复合体识别算法的性能不够理想....蛋白质复合体对于研究细胞活动具有重要意义.随着新的生物实验技术的不断出现,产生了大量的蛋白质相互作用网络.通过对蛋白质相互作用网络进行聚类识别蛋白质复合体是当前研究热点.然而,目前大多数蛋白质复合体识别算法的性能不够理想.为此,提出了蛋白质复合体模块度函数(PQ),并在此基础上提出了基于蛋白质复合体模块度函数的模块合并(based on protein complexes modularity function for merging modules,BMM)算法.BMM算法首先识别网络中一些稠密子图作为初始模块,然后依据PQ函数对这些初始模块进行合并,最终得到了质量较高的蛋白质复合体.将识别出的复合体分别与2种已知的蛋白质复合体数据集进行比对,结果表明BMM算法具有很好的识别性能.此外,与其他最新的识别算法相比,BMM算法的识别准确率较高.展开更多
基金jointly funded by the National Natural Science Foundation of China(41201551)the Key Technology R&D Program of China(2012BAH20B04-2)
文摘Flood catastrophe risk assessment is imperative for the steady development of agriculture under the context of global climate change,and meanwhile,it is an urgent scientific issue need to be solved in agricultural risk assessment discipline.This paper developed the methodology of flood catastrophe risk assessment,which can be shown as the standard process of crop loss calculation,Monte Carlo simulation,the generalized extreme value distribution（GEV） fitting,and risk evaluation.Data on crop loss were collected based on hectares covered by natural disasters,hectares affected by natural disasters,and hectares destroyed by natural disasters using the standard equation.Monte Carlo simulation based on appropriate distribution was used to expand sample size to overcome the insufficiency of crop loss data.Block maxima model（BMM） approach based on the extreme value theory was for modeling the generalized extreme value distribution（GEV） of flood catastrophe loss,and then flood catastrophe risk at the provincial scale in China was calculated.The Type III Extreme distribution（Weibull） has a weighted advantage of modeling flood catastrophe risk for grain production.The impact of flood catastrophe to grain production in China was significantly serious,and high or very high risk of flood catastrophe mainly concentrates on the central and eastern regions of China.Given the scenario of suffering once-in-a-century flood disaster,for majority of the major-producing provinces,the probability of 10% reduction of grain output is more than 90%.Especially,the probabilities of more than 15% decline in grain production reach up to 99.99,99.86,99.69,and 91.60% respectively in Anhui,Jilin,Liaoning,and Heilongjiang.Flood catastrophe assessment can provide multifaceted information about flood catastrophe risk that can help to guide management of flood catastrophe.
文摘近年来,随着保险市场的发展,人身险(包括健康险、寿险和意外险)占据的份额不断上升,这些险种极端理赔的现象也一直存在。国内外人身险的实践经验表明,准确掌握保险理赔可能存在的极端风险,有利于保险公司更精确地定价保险产品。文章以2005-2014年全国六个地区人身险的月理赔额为样本,通过对数据特征进行系统性分析,筛选出符合"尖峰厚尾"特征的理赔数据。运用区间极大值模型(Block Maxima Method,BMM)对理赔数据进行风险度量,以求得其理赔VaR(Value at Risk)。结果显示,意外险的理赔出现极端风险的可能性较大,多数地区意外险的理赔都存在极端风险;而健康险出现极端理赔的可能性相对较小,但一旦出现极端情况,其理赔VaR很有可能大于意外险。
文摘蛋白质复合体对于研究细胞活动具有重要意义.随着新的生物实验技术的不断出现,产生了大量的蛋白质相互作用网络.通过对蛋白质相互作用网络进行聚类识别蛋白质复合体是当前研究热点.然而,目前大多数蛋白质复合体识别算法的性能不够理想.为此,提出了蛋白质复合体模块度函数(PQ),并在此基础上提出了基于蛋白质复合体模块度函数的模块合并(based on protein complexes modularity function for merging modules,BMM)算法.BMM算法首先识别网络中一些稠密子图作为初始模块,然后依据PQ函数对这些初始模块进行合并,最终得到了质量较高的蛋白质复合体.将识别出的复合体分别与2种已知的蛋白质复合体数据集进行比对,结果表明BMM算法具有很好的识别性能.此外,与其他最新的识别算法相比,BMM算法的识别准确率较高.
