Due to the prevalence of social network services, more and more attentions are paid to explore how information diffuses and users affect each other in these networks, which has a wide range of applications, such as vi...Due to the prevalence of social network services, more and more attentions are paid to explore how information diffuses and users affect each other in these networks, which has a wide range of applications, such as viral marketing, reposting prediction and social recommendation. Therefore, in this paper, we review the recent advances on information diffusion analysis in social networks and its applications. Specifically, we first shed light on several popular models to describe the information diffusion process in social networks, which enables three practical applications, i.e., influence evaluation, influence maximization and information source detection. Then, we discuss how to evaluate the authority and influence based on network structures. After that, current solutions to influence maximiza- tion and information source detection are discussed in detail, respectively. Finally, some possible research directions of information diffu- sion analysis are listed for further study.展开更多
In order to promote the development of the Internet of Things(IoT),there has been an increase in the coverage of the customer electric information acquisition system(CEIAS).The traditional fault location method for th...In order to promote the development of the Internet of Things(IoT),there has been an increase in the coverage of the customer electric information acquisition system(CEIAS).The traditional fault location method for the distribution network only considers the information reported by the Feeder Terminal Unit(FTU)and the fault tolerance rate is low when the information is omitted or misreported.Therefore,this study considers the influence of the distributed generations(DGs)for the distribution network.This takes the CEIAS as a redundant information source and solves the model by applying a binary particle swarm optimization algorithm(BPSO).The improved Dempster/S-hafer evidence theory(D-S evidence theory)is used for evidence fusion to achieve the fault section location for the distribution network.An example is provided to verify that the proposed method can achieve single or multiple fault locations with a higher fault tolerance.展开更多
CO_(2)emission inventory provides fundamental data for climate research and emission mitigation.Currently,most global CO_(2)emission inventories were developed with energy statistics from International Energy Agency(I...CO_(2)emission inventory provides fundamental data for climate research and emission mitigation.Currently,most global CO_(2)emission inventories were developed with energy statistics from International Energy Agency(IEA)and were available at country level with limited source categories.Here,as the first step toward a high-resolution and dynamic updated global CO_(2)emission database,we developed a data-driven approach to construct seamless and highly-resolved energy consumption data cubes for 208 countries/territories,797 sub-country administrative divisions in 29 countries,42 fuel types,and 52 sectors,with the fusion of activity data from 24 international statistics and 65 regional/local statistics.Global CO_(2)emissions from fossil fuel combustion and cement production in 1970–2021 were then estimated with highly-resolved source category(1,484 of total)and sub-country information(797 of total).Specifically,73%of global CO_(2)emissions in 2021 were estimated with sub-country information,providing considerably improved spatial resolution for global CO_(2)emission accounting.With the support of detailed information,the dynamics of global CO_(2)emissions across sectors and fuel types were presented,representing the evolution of global economy and progress of climate mitigation.Remarkable differences of sectoral contribution were found across sub-country administrative divisions within a given country,revealing the uneven distribution of energy and economic structure among different regions.Our estimates were generally consistent with existing databases at aggregated level for global total or large emitters,while large discrepancies were observed for middle and small emitters.Our database,named the Multiresolution Emission Inventory model for Climate and air pollution research(MEIC)is publicly available through http://meicmodel.org.cn with highly-resolved information and timely update,which provides an independent carbon emission accounting data source for climate research.展开更多
基金supported by National Natural Science Foundation of China(Nos.61703386,U1605251 and91546103)the Anhui Provincial Natural Science Foundation(No.1708085QF140)+1 种基金the Fundamental Research Funds for the Central Universities(No.WK2150110006)the Youth Innovation Promotion Association of Chinese Academy of Sciences(No.2014299)
文摘Due to the prevalence of social network services, more and more attentions are paid to explore how information diffuses and users affect each other in these networks, which has a wide range of applications, such as viral marketing, reposting prediction and social recommendation. Therefore, in this paper, we review the recent advances on information diffusion analysis in social networks and its applications. Specifically, we first shed light on several popular models to describe the information diffusion process in social networks, which enables three practical applications, i.e., influence evaluation, influence maximization and information source detection. Then, we discuss how to evaluate the authority and influence based on network structures. After that, current solutions to influence maximiza- tion and information source detection are discussed in detail, respectively. Finally, some possible research directions of information diffu- sion analysis are listed for further study.
基金supported by the Science and Technology Project of State Grid Shandong Electric Power Company?“Research on the Data-Driven Method for Energy Internet”?(Project No.2018A-100)。
文摘In order to promote the development of the Internet of Things(IoT),there has been an increase in the coverage of the customer electric information acquisition system(CEIAS).The traditional fault location method for the distribution network only considers the information reported by the Feeder Terminal Unit(FTU)and the fault tolerance rate is low when the information is omitted or misreported.Therefore,this study considers the influence of the distributed generations(DGs)for the distribution network.This takes the CEIAS as a redundant information source and solves the model by applying a binary particle swarm optimization algorithm(BPSO).The improved Dempster/S-hafer evidence theory(D-S evidence theory)is used for evidence fusion to achieve the fault section location for the distribution network.An example is provided to verify that the proposed method can achieve single or multiple fault locations with a higher fault tolerance.
基金supported by the National Natural Science Foundation of China(Grant No.41921005)the Major Project of High Resolution Earth Observation System(Grant No.30Y60B01-9003-22/23)+1 种基金the New Cornerstone Science Foundation through the XPLORER PRIZEthe Tsinghua University Initiative Scientific Research Program(Grant No.20223080041)。
文摘CO_(2)emission inventory provides fundamental data for climate research and emission mitigation.Currently,most global CO_(2)emission inventories were developed with energy statistics from International Energy Agency(IEA)and were available at country level with limited source categories.Here,as the first step toward a high-resolution and dynamic updated global CO_(2)emission database,we developed a data-driven approach to construct seamless and highly-resolved energy consumption data cubes for 208 countries/territories,797 sub-country administrative divisions in 29 countries,42 fuel types,and 52 sectors,with the fusion of activity data from 24 international statistics and 65 regional/local statistics.Global CO_(2)emissions from fossil fuel combustion and cement production in 1970–2021 were then estimated with highly-resolved source category(1,484 of total)and sub-country information(797 of total).Specifically,73%of global CO_(2)emissions in 2021 were estimated with sub-country information,providing considerably improved spatial resolution for global CO_(2)emission accounting.With the support of detailed information,the dynamics of global CO_(2)emissions across sectors and fuel types were presented,representing the evolution of global economy and progress of climate mitigation.Remarkable differences of sectoral contribution were found across sub-country administrative divisions within a given country,revealing the uneven distribution of energy and economic structure among different regions.Our estimates were generally consistent with existing databases at aggregated level for global total or large emitters,while large discrepancies were observed for middle and small emitters.Our database,named the Multiresolution Emission Inventory model for Climate and air pollution research(MEIC)is publicly available through http://meicmodel.org.cn with highly-resolved information and timely update,which provides an independent carbon emission accounting data source for climate research.
