1.Introduction Data inference(DInf)is a data security threat in which critical information is inferred from low-sensitivity data.Once regarded as an advanced professional threat limited to intelligence analysts,DInf h...1.Introduction Data inference(DInf)is a data security threat in which critical information is inferred from low-sensitivity data.Once regarded as an advanced professional threat limited to intelligence analysts,DInf has become a widespread risk in the artificial intelligence(AI)era.展开更多
Metro systems are important transport infrastructures in megacities,and their long-term operational safety is challenged by frequent external disturbances,such as environmental extremes and human construction activ-it...Metro systems are important transport infrastructures in megacities,and their long-term operational safety is challenged by frequent external disturbances,such as environmental extremes and human construction activ-ities.A metro system must be resilient to resist,adapt to,and recover its performance when such disruptions occur.Current studies on metro system resilience often lack a comprehensive view from a complex system perspective,leading to a plethora of choices for methods of analysis and indicators applied to different metro systems and external disturbances.Therefore,the present paper aims to provide a comprehensive review of the topics and works revolving around the resilience of metro systems.It first clarifies the concept of metro system resilience based on classical definitions from a technical perspective.Metro resilience encompasses both the structural and operational aspects of metro systems,including their damage mechanisms,analysis methods and indicators of resilience.Methods for enhancing metro system resilience across structural,operational and monitoring dimensions are explored.Finally,future research directions are discussed,emphasizing the impor-tance of considering the"system of systems"formed by interdependent infrastructure,refining uncertainty analysis,and investigating the opportunities arising from the application of artificial intelligence for improving metro system resilience against external disturbances.展开更多
基金supported by the National Key Research and Development Program of China(2022YFB2703503)the National Natural Science Foundation of China(62293501,62525210,and 62293502)the China Scholarship Council(202306280318).
文摘1.Introduction Data inference(DInf)is a data security threat in which critical information is inferred from low-sensitivity data.Once regarded as an advanced professional threat limited to intelligence analysts,DInf has become a widespread risk in the artificial intelligence(AI)era.
基金supported by National Key R&D Program of China 2024YFC3808804National Natural Science Foundation of China NO.52478411+1 种基金China Scholarship Council No.202506260003Jinan Rail Transit Group Co.,Ltd(ggjn-sd-gd2022008fw).
文摘Metro systems are important transport infrastructures in megacities,and their long-term operational safety is challenged by frequent external disturbances,such as environmental extremes and human construction activ-ities.A metro system must be resilient to resist,adapt to,and recover its performance when such disruptions occur.Current studies on metro system resilience often lack a comprehensive view from a complex system perspective,leading to a plethora of choices for methods of analysis and indicators applied to different metro systems and external disturbances.Therefore,the present paper aims to provide a comprehensive review of the topics and works revolving around the resilience of metro systems.It first clarifies the concept of metro system resilience based on classical definitions from a technical perspective.Metro resilience encompasses both the structural and operational aspects of metro systems,including their damage mechanisms,analysis methods and indicators of resilience.Methods for enhancing metro system resilience across structural,operational and monitoring dimensions are explored.Finally,future research directions are discussed,emphasizing the impor-tance of considering the"system of systems"formed by interdependent infrastructure,refining uncertainty analysis,and investigating the opportunities arising from the application of artificial intelligence for improving metro system resilience against external disturbances.
