Pub Date : 2020-01-01DOI: 10.2991/jracr.k.200120.001
Chongfu Huang, Gordon H. Huang
{"title":"Introduction for Volume 9, Issue 4","authors":"Chongfu Huang, Gordon H. Huang","doi":"10.2991/jracr.k.200120.001","DOIUrl":"https://doi.org/10.2991/jracr.k.200120.001","url":null,"abstract":"","PeriodicalId":31887,"journal":{"name":"Journal of Risk Analysis and Crisis Response JRACR","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84714777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.2991/jracr.k.200415.003
Chongfu Huang, Gordon H. Huang
{"title":"Introduction for Volume 10, Issue 1","authors":"Chongfu Huang, Gordon H. Huang","doi":"10.2991/jracr.k.200415.003","DOIUrl":"https://doi.org/10.2991/jracr.k.200415.003","url":null,"abstract":"","PeriodicalId":31887,"journal":{"name":"Journal of Risk Analysis and Crisis Response JRACR","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74079864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.2991/jracr.k.200720.001
Chongfu Huang, Gordon H. Huang
{"title":"Introduction for Volume 10, Issue 2","authors":"Chongfu Huang, Gordon H. Huang","doi":"10.2991/jracr.k.200720.001","DOIUrl":"https://doi.org/10.2991/jracr.k.200720.001","url":null,"abstract":"","PeriodicalId":31887,"journal":{"name":"Journal of Risk Analysis and Crisis Response JRACR","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76285336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.2991/jracr.k.200117.005
亚男 Yanan胡Hu, 兴华 Xinghua李Li
Due to the long and narrow regional characteristics of Inner Mongolia, there are obvious climatic differences between the east and the west, and various meteorological factors have different influences on the development and utilization of solar energy. The abundance and stability index of solar energy resources are relatively high in the central and western region of Inner Mongolia, but there are more sandstorms, high temperature and strong wind. The eastern region is greatly affected by cloud cover, relative humidity and snowfall, which weakens the solar radiation. Through the comprehensive evaluation of the solar energy resources and meteorological elements, it is found that from the Alashan League to the west of Xilin Gol League, and the most of the Chifeng City are low-risk areas, which are relatively suitable for the development and utilization of rooftop solar energy. The central and northern areas of hulunbuir are high-risk areas, which is not suitable for that. Based on the meteorological industry standard “Solar Energy Resource Assessment Method”, we calculate the solar energy resources abundance and stability index, combined with the annual surface meteorological data of 119 meteorological stations in Inner Mongolia from 1988 to 2018. The distribution characteristics of solar energy resources and meteorological elements in Inner Mongolia is analyzed and the suitability and meteorological risks of rooftop solar resources development and utilization are evaluated.
{"title":"Meteorological Risk Assessment of Rooftop Solar Resource Development in Inner Mongolia","authors":"亚男 Yanan胡Hu, 兴华 Xinghua李Li","doi":"10.2991/jracr.k.200117.005","DOIUrl":"https://doi.org/10.2991/jracr.k.200117.005","url":null,"abstract":"Due to the long and narrow regional characteristics of Inner Mongolia, there are obvious climatic differences between the east and the west, and various meteorological factors have different influences on the development and utilization of solar energy. The abundance and stability index of solar energy resources are relatively high in the central and western region of Inner Mongolia, but there are more sandstorms, high temperature and strong wind. The eastern region is greatly affected by cloud cover, relative humidity and snowfall, which weakens the solar radiation. Through the comprehensive evaluation of the solar energy resources and meteorological elements, it is found that from the Alashan League to the west of Xilin Gol League, and the most of the Chifeng City are low-risk areas, which are relatively suitable for the development and utilization of rooftop solar energy. The central and northern areas of hulunbuir are high-risk areas, which is not suitable for that. Based on the meteorological industry standard “Solar Energy Resource Assessment Method”, we calculate the solar energy resources abundance and stability index, combined with the annual surface meteorological data of 119 meteorological stations in Inner Mongolia from 1988 to 2018. The distribution characteristics of solar energy resources and meteorological elements in Inner Mongolia is analyzed and the suitability and meteorological risks of rooftop solar resources development and utilization are evaluated.","PeriodicalId":31887,"journal":{"name":"Journal of Risk Analysis and Crisis Response JRACR","volume":"43 1","pages":"177-184"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81006352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.2991/jracr.k.200117.002
Jian He, Hongmei Zhang
Ecological agriculture is developed using modern scientific and technological achievements and modern management methods, drawing on the effective experience of traditional agriculture, using ecological principles and economic principles. Ecological agriculture is a modernization that can obtain higher economic, ecological and social benefits of agriculture. Eco-agriculture requires that chemical fertilizers and chemical pesticides are not used, and that the use of farm manure increases soil fertility, uses biological methods to control crop diseases and insect pests, and protects the living environment.
{"title":"Prospects of Guizhou Province’s Ecological Agriculture Benefit Evaluation Index System","authors":"Jian He, Hongmei Zhang","doi":"10.2991/jracr.k.200117.002","DOIUrl":"https://doi.org/10.2991/jracr.k.200117.002","url":null,"abstract":"Ecological agriculture is developed using modern scientific and technological achievements and modern management methods, drawing on the effective experience of traditional agriculture, using ecological principles and economic principles. Ecological agriculture is a modernization that can obtain higher economic, ecological and social benefits of agriculture. Eco-agriculture requires that chemical fertilizers and chemical pesticides are not used, and that the use of farm manure increases soil fertility, uses biological methods to control crop diseases and insect pests, and protects the living environment.","PeriodicalId":31887,"journal":{"name":"Journal of Risk Analysis and Crisis Response JRACR","volume":"166 1","pages":"185-193"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76455456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.2991/dramclr-19.2019.40
L. Pang, Mu Zhang
In order to promote the coordinated development of big data industry and financial agglomeration, according to the characteristics of big data industry and financial agglomeration in Guizhou at this stage, the paper first understands the relevant literature of big data industry and financial agglomeration. Then, using the original data of nine cities in Guizhou from 2013 to 2017, we measure the development level of big data industry and financial agglomeration level of nine cities in Guizhou by using the hesitant fuzzy linguistic TOPSIS method and hesitant fuzzy linguistic PROMETHEE method respectively. Finally, the coupling coordination degree of big data industry and financial agglomeration in nine cities of Guizhou is calculated by using the coupling coordination model, and relevant policy recommendations are put forward. The empirical results show that Guiyang city is moderately coordinated development of big data industry and financial agglomeration, Zunyi city is reluctantly coordinated development, Liupanshui, Bijie and Southwest Guizhou are slightly maladjusted recessions, and the remaining cities are moderately maladjusted recessions. During the sample period, the degree of coupling and coordination between big data industry and financial agglomeration in Guizhou has been continuously improved, but the phenomenon of regional imbalance is more obvious. Keywords—Big Data Industry; Financial Agglomeration; Coupling and Coordination; Hesitant Fuzzy Linguistic TOPSIS Method; Hesitant Fuzzy Linguistic PROMETHEE Method 摘要—为了促进大数据产业与金融集聚协调发展,针对现 阶段贵州大数据产业和金融集聚的特点,文章首先了解了大 数据产业与金融集聚相关文献;然后,选取 2013-2017 年贵 州九个地州市的指标原始数据,分别运用犹豫模糊语言 TOPSIS 法和犹豫模糊语言 PROMETHEE 方法对贵州九个 地州市的大数据产业发展水平和金融集聚水平进行测度;最 后运用耦合协调模型计算贵州九个地州市的大数据产业与金 融集聚的耦合协调度,并提出相关政策建议。实证结果表 明:贵阳市为大数据产业与金融集聚中度协调发展,遵义市 为勉强协调发展,六盘水、毕节、黔西南是轻度失调衰退, 剩余州市为中度失调衰退。样本期间内,总体上贵州大数据 产业与金融集聚耦合协调程度不断提高,但区域间不均衡现 象较为明显。 关键词—大数据产业,金融集聚,耦合协调,犹豫模糊语 言 TOPSIS 法,犹豫模糊语言 PROMETHEE 法 I. 引言 围绕实施国家大数据战略,加强大数据在金融行业 领域的深入应用,促进跨行业大数据融合创新,推动大 数据与金融行业领域的融合发展,对促进经济转型和创 新发展有重大意义。贵州正在建立大数据产业投融资体 系,鼓励金融机构为符合条件的大数据企业给予信贷支 持,围绕大数据产业开发和创新金融产品,延伸服务网 络,为贵州大数据项目融资提供支撑 [1] 。贵州建成全国 领先的大数据资源集聚地和大数据应用服务示范基地, 需要财政金融的大力支持和健全大数据产业投融资机 制,大数据产业的发展也有利于金融行业改变传统的运 行方式和运行机制,促进大数据金融服务业的发展。因 此,对大数据产业与金融集聚协调发展现状进行分析, 有利于把握大数据产业与金融行业发展差异,促进行业 大数据应用发展。 目前而言,国内外学者大多集中于对金融集聚与产 业发展之间关系的研究。国外研究中,Rajan 和 Zingales (1998)[1]通过建立理论模型研究金融发展到一定程度 形成的金融集聚与产业结构之间的关系;Carlin 和 Mayer(2003)[2]从不同类型的金融结构角度出发,研 究其对产业的影响;Audress 等(2006)[3]阐述了金融集 聚带来的溢出效应显著提高了地区间的技术创新和产业 升级;Cotugno 等(2013)[4]认为金融集聚下,产业结 构升级出现的空间溢出效用不但促进本地区的产业升 级,同时也促进了周边地区产业的发展。国内研究中, 陈峰(1991)[5]最早研究了金融发展与产业发展之间的 关系;孙晶和蒋伏心(2013) [6]借助空间滞后模型和空间 误差模型进行研究发现,金融集聚对区域产业结构升级 存在空间溢出效应;杨义武和方大春(2013)[7]运用面 板向量自回归模型研究金融集聚与产业结构变迁的互动 关系;邓向荣和刘文强(2013)[8]实证分析了金融集聚 对产业结构升级作用;郭露和丁峰(2015)[9] 运用协调 发展度评价的改进模型对长三角地区 16 个地级市的三 大产业结构与金融集聚的分布进行综合评价与比较;何 宜庆等(2015)[10]运用物理学中的三者耦合模型,将金 融要素集聚、区域产业结构和生态效率三个系统相结 合,进行了耦合协调实证研究;于斌斌(2017)[11]认为 金融集聚对经济增长的影响是通过促进产业结构升级实 Fourth Symposium on Disaster Risk Analysis and Management in Chinese Lit
{"title":"The Harmonious Development of Big Data Industry and Financial Agglomeration in Guizhou","authors":"L. Pang, Mu Zhang","doi":"10.2991/dramclr-19.2019.40","DOIUrl":"https://doi.org/10.2991/dramclr-19.2019.40","url":null,"abstract":"In order to promote the coordinated development of big data industry and financial agglomeration, according to the characteristics of big data industry and financial agglomeration in Guizhou at this stage, the paper first understands the relevant literature of big data industry and financial agglomeration. Then, using the original data of nine cities in Guizhou from 2013 to 2017, we measure the development level of big data industry and financial agglomeration level of nine cities in Guizhou by using the hesitant fuzzy linguistic TOPSIS method and hesitant fuzzy linguistic PROMETHEE method respectively. Finally, the coupling coordination degree of big data industry and financial agglomeration in nine cities of Guizhou is calculated by using the coupling coordination model, and relevant policy recommendations are put forward. The empirical results show that Guiyang city is moderately coordinated development of big data industry and financial agglomeration, Zunyi city is reluctantly coordinated development, Liupanshui, Bijie and Southwest Guizhou are slightly maladjusted recessions, and the remaining cities are moderately maladjusted recessions. During the sample period, the degree of coupling and coordination between big data industry and financial agglomeration in Guizhou has been continuously improved, but the phenomenon of regional imbalance is more obvious. Keywords—Big Data Industry; Financial Agglomeration; Coupling and Coordination; Hesitant Fuzzy Linguistic TOPSIS Method; Hesitant Fuzzy Linguistic PROMETHEE Method 摘要—为了促进大数据产业与金融集聚协调发展,针对现 阶段贵州大数据产业和金融集聚的特点,文章首先了解了大 数据产业与金融集聚相关文献;然后,选取 2013-2017 年贵 州九个地州市的指标原始数据,分别运用犹豫模糊语言 TOPSIS 法和犹豫模糊语言 PROMETHEE 方法对贵州九个 地州市的大数据产业发展水平和金融集聚水平进行测度;最 后运用耦合协调模型计算贵州九个地州市的大数据产业与金 融集聚的耦合协调度,并提出相关政策建议。实证结果表 明:贵阳市为大数据产业与金融集聚中度协调发展,遵义市 为勉强协调发展,六盘水、毕节、黔西南是轻度失调衰退, 剩余州市为中度失调衰退。样本期间内,总体上贵州大数据 产业与金融集聚耦合协调程度不断提高,但区域间不均衡现 象较为明显。 关键词—大数据产业,金融集聚,耦合协调,犹豫模糊语 言 TOPSIS 法,犹豫模糊语言 PROMETHEE 法 I. 引言 围绕实施国家大数据战略,加强大数据在金融行业 领域的深入应用,促进跨行业大数据融合创新,推动大 数据与金融行业领域的融合发展,对促进经济转型和创 新发展有重大意义。贵州正在建立大数据产业投融资体 系,鼓励金融机构为符合条件的大数据企业给予信贷支 持,围绕大数据产业开发和创新金融产品,延伸服务网 络,为贵州大数据项目融资提供支撑 [1] 。贵州建成全国 领先的大数据资源集聚地和大数据应用服务示范基地, 需要财政金融的大力支持和健全大数据产业投融资机 制,大数据产业的发展也有利于金融行业改变传统的运 行方式和运行机制,促进大数据金融服务业的发展。因 此,对大数据产业与金融集聚协调发展现状进行分析, 有利于把握大数据产业与金融行业发展差异,促进行业 大数据应用发展。 目前而言,国内外学者大多集中于对金融集聚与产 业发展之间关系的研究。国外研究中,Rajan 和 Zingales (1998)[1]通过建立理论模型研究金融发展到一定程度 形成的金融集聚与产业结构之间的关系;Carlin 和 Mayer(2003)[2]从不同类型的金融结构角度出发,研 究其对产业的影响;Audress 等(2006)[3]阐述了金融集 聚带来的溢出效应显著提高了地区间的技术创新和产业 升级;Cotugno 等(2013)[4]认为金融集聚下,产业结 构升级出现的空间溢出效用不但促进本地区的产业升 级,同时也促进了周边地区产业的发展。国内研究中, 陈峰(1991)[5]最早研究了金融发展与产业发展之间的 关系;孙晶和蒋伏心(2013) [6]借助空间滞后模型和空间 误差模型进行研究发现,金融集聚对区域产业结构升级 存在空间溢出效应;杨义武和方大春(2013)[7]运用面 板向量自回归模型研究金融集聚与产业结构变迁的互动 关系;邓向荣和刘文强(2013)[8]实证分析了金融集聚 对产业结构升级作用;郭露和丁峰(2015)[9] 运用协调 发展度评价的改进模型对长三角地区 16 个地级市的三 大产业结构与金融集聚的分布进行综合评价与比较;何 宜庆等(2015)[10]运用物理学中的三者耦合模型,将金 融要素集聚、区域产业结构和生态效率三个系统相结 合,进行了耦合协调实证研究;于斌斌(2017)[11]认为 金融集聚对经济增长的影响是通过促进产业结构升级实 Fourth Symposium on Disaster Risk Analysis and Management in Chinese Lit","PeriodicalId":31887,"journal":{"name":"Journal of Risk Analysis and Crisis Response JRACR","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80291783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.2991/jracr.k.200117.004
Jun Shi, L. Cui, Zhongping Shen
Meteorological disaster is one of the natural disasters that cause the most casualties and losses in the world [1–3]. In recent years, due to global warming and the intensification of human activities, meteorological disasters (such as floods, droughts, hurricanes, extreme heat or cold, snowstorms, etc.) in many countries have become more frequent [4–7]. These disasters pose a serious threat to people’s life and property security, agricultural production, water resources and ecosystems [8–11]. China is one of the countries in the world with frequent meteorological disasters, various kinds of disasters and severe losses from disasters. The losses caused by meteorological disasters account for about 70% of the total losses of all kinds of natural disasters in China [1]. Wu et al. [12] showed that meteorological disasters accounted for 55% of the deaths and 87% of the direct economic losses caused by natural hazards in China during 1994–2013. Hence, the variations and trends of meteorological disasters are of great significance to people’s life and property, national economic construction and ecological security in China [13–15].
{"title":"Interannual Variation and Hazard Analysis of Meteorological Disasters in East China","authors":"Jun Shi, L. Cui, Zhongping Shen","doi":"10.2991/jracr.k.200117.004","DOIUrl":"https://doi.org/10.2991/jracr.k.200117.004","url":null,"abstract":"Meteorological disaster is one of the natural disasters that cause the most casualties and losses in the world [1–3]. In recent years, due to global warming and the intensification of human activities, meteorological disasters (such as floods, droughts, hurricanes, extreme heat or cold, snowstorms, etc.) in many countries have become more frequent [4–7]. These disasters pose a serious threat to people’s life and property security, agricultural production, water resources and ecosystems [8–11]. China is one of the countries in the world with frequent meteorological disasters, various kinds of disasters and severe losses from disasters. The losses caused by meteorological disasters account for about 70% of the total losses of all kinds of natural disasters in China [1]. Wu et al. [12] showed that meteorological disasters accounted for 55% of the deaths and 87% of the direct economic losses caused by natural hazards in China during 1994–2013. Hence, the variations and trends of meteorological disasters are of great significance to people’s life and property, national economic construction and ecological security in China [13–15].","PeriodicalId":31887,"journal":{"name":"Journal of Risk Analysis and Crisis Response JRACR","volume":"35 1","pages":"168-176"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77084580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.2991/jracr.k.191024.001
Ghaidaa Hetou
Political risk assessments are increasingly becoming a business imperatives for corporations interested in expanding their business operations into developing and emerging markets, which usually exhibit political uncertainty. Political risk associated with market entry and subsequent business operations is a slowly evolving subcategory of risk analysis, and one that has not lent itself to strict mathematical modeling and probability assessments. To counter the subjective nature of non-scientific assessments, conventional political risk analysis and country risk indexes aimed at standardizing the process within formulas to account for political and social factors, mimicking thereby a standardized approach, applicable to all countries. The persisting shortcoming in this process is that political and social factors are necessarily context specific, i.e. interconnected and part of a complex adaptive system, thereby cannot be correctly evaluated as independent static variables. The resulting indexes hence remained at an artificial interval level spanning from low to high risk, and in most cases devout of valuable and relevant content for the private sector or government agencies.
{"title":"Re-examining Political Risk Assessments in Volatile Regions","authors":"Ghaidaa Hetou","doi":"10.2991/jracr.k.191024.001","DOIUrl":"https://doi.org/10.2991/jracr.k.191024.001","url":null,"abstract":"Political risk assessments are increasingly becoming a business imperatives for corporations interested in expanding their business operations into developing and emerging markets, which usually exhibit political uncertainty. Political risk associated with market entry and subsequent business operations is a slowly evolving subcategory of risk analysis, and one that has not lent itself to strict mathematical modeling and probability assessments. To counter the subjective nature of non-scientific assessments, conventional political risk analysis and country risk indexes aimed at standardizing the process within formulas to account for political and social factors, mimicking thereby a standardized approach, applicable to all countries. The persisting shortcoming in this process is that political and social factors are necessarily context specific, i.e. interconnected and part of a complex adaptive system, thereby cannot be correctly evaluated as independent static variables. The resulting indexes hence remained at an artificial interval level spanning from low to high risk, and in most cases devout of valuable and relevant content for the private sector or government agencies.","PeriodicalId":31887,"journal":{"name":"Journal of Risk Analysis and Crisis Response JRACR","volume":"10 22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74264558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.2991/jracr.k.191024.003
Cheng-Kuang Wu, Xingwei Hu
Emergency Response Systems (ERS) and Homeland Security Advisory Systems (HSAS) are both centralized Multiagent Systems (MASs) which delegate multiple interacting agents to resist outside attacks. However, the effectiveness of these MASs as a means to defend entire large-scale geographic regions is constrained by the available resources. The system administrator faces a density of agent deployment dilemmas, where the disposition of more agents easily leads to higher costs. These systems also lack specific measures for rational decision-making, and do not apply mathematical models to capture the interactions between attacker and defender. The administrator of a MAS should have a tool to measure the strength of the attacks and the resistance capability of the response agents. By considering the utilities of moves available to the attacker and the defender, we can find a way to build a rating system for decision making [1].
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Pub Date : 2019-11-01DOI: 10.2991/jracr.k.191024.004
Xinpu Wang, Mu Zang
Data become the basic strategic resources of the country. The Platform for Action to Promote Big Data Development and the Big Data Industry Development Plan (2016–2020) issued by the State Council and the Ministry of Industry and Information Technology in 2015 have aroused strong repercussion. “Outline” points out that encouraging financial institution to strengthen and improve financial services, increase support for big data enterprises, and continue to enhance the prosperity of the big data industry. The Development Planning of Big Data Industry (2016–2020) [1] proposes that promoting the development of big data industry is of great significance to improving government governance ability, optimizing public services for people’s livelihood, promoting economic transformation and innovation and development; studying and establishing an evaluation system for the development of big data industry is of great significance to the construction of big data resources, the degree of openness and sharing, the ability of industrial development and the level of application in China and other regions. It is of great significance to monitor, analyze and evaluate the development of the big data industry, compile and publish the development index of the big data industry, and guide and evaluate the development of the national big data.
{"title":"Evaluation on Technology Innovation Efficiency of Big Data Enterprises Based on DEA","authors":"Xinpu Wang, Mu Zang","doi":"10.2991/jracr.k.191024.004","DOIUrl":"https://doi.org/10.2991/jracr.k.191024.004","url":null,"abstract":"Data become the basic strategic resources of the country. The Platform for Action to Promote Big Data Development and the Big Data Industry Development Plan (2016–2020) issued by the State Council and the Ministry of Industry and Information Technology in 2015 have aroused strong repercussion. “Outline” points out that encouraging financial institution to strengthen and improve financial services, increase support for big data enterprises, and continue to enhance the prosperity of the big data industry. The Development Planning of Big Data Industry (2016–2020) [1] proposes that promoting the development of big data industry is of great significance to improving government governance ability, optimizing public services for people’s livelihood, promoting economic transformation and innovation and development; studying and establishing an evaluation system for the development of big data industry is of great significance to the construction of big data resources, the degree of openness and sharing, the ability of industrial development and the level of application in China and other regions. It is of great significance to monitor, analyze and evaluate the development of the big data industry, compile and publish the development index of the big data industry, and guide and evaluate the development of the national big data.","PeriodicalId":31887,"journal":{"name":"Journal of Risk Analysis and Crisis Response JRACR","volume":"263 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74947161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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