Restoration of infrastructure networks(INs)following large disruptions has received much attention lately due to examples of massive localized attacks.Within this challenge are two complex but critical problems:repair...Restoration of infrastructure networks(INs)following large disruptions has received much attention lately due to examples of massive localized attacks.Within this challenge are two complex but critical problems:repair route identification and optimizing the sequence of the repair actions for resilience improvement.Existing approaches have not,however,given due consideration to globally optimal enhancement in resilience,especially with multiple repair crews that have uneven capacities.To address this gap,this paper focuses on a resilience opti-mization(RO)strategy for coordinating multiple crews.The objective is to determine the optimal routes for each crew and the best sequence of repairs for damaged nodes and links.Given the two-layered decision-making required—coordinating between multiple crews and opti-mizing each crew's actions—this study develops a deep reinforcement learning(DRL)framework.The framework leverages an actor-critic neural network that processes IN damage data and guides Monte Carlo tree search(MCTS)to identify optimal repair routes and actions for each crew.A case study based on the 228-node power grid,simulated using Python,demonstrates that the proposed DRL approach effectively supports restoration decision-making.展开更多
The International Textile Manufac-turers Federation(ITMF),has releasedthe results of its Global Textile IndustrySurvey(GTIS)for March 2025.Thissurvey,conducted regularly across allkey regions and segments of the texti...The International Textile Manufac-turers Federation(ITMF),has releasedthe results of its Global Textile IndustrySurvey(GTIS)for March 2025.Thissurvey,conducted regularly across allkey regions and segments of the textilevalue chain,revealed a complex pictureof a deteriorated business situation,cau-tious op timism,regional divergence,andongoing structural challenges.展开更多
Highlights ZmMYC2 promoter contains favorable haplotypes selected during domestication,enhancing its expression level in modern maize.ZmMYC2 may balance the trade-off between growth and defense via jasmonate and auxin...Highlights ZmMYC2 promoter contains favorable haplotypes selected during domestication,enhancing its expression level in modern maize.ZmMYC2 may balance the trade-off between growth and defense via jasmonate and auxin signaling pathways.ZmMYC2 regulates drought-response genes(CER2 and TIP3c)to optimize drought stress resilience.展开更多
The Cyber-Physical Power System(CPPS)is one of the most critical infrastructure systems in a country because a stable and secure power supply is a key foundation for national and social development.In recent years,res...The Cyber-Physical Power System(CPPS)is one of the most critical infrastructure systems in a country because a stable and secure power supply is a key foundation for national and social development.In recent years,resilience has become a major topic in preventing and mitigating the risks caused by large-scale blackouts of CPPSs.Accordingly,the concept and significance of CPPS resilience are at first explained from the engineering perspective in this study.Then,a review of representative quantitative assessment measures of CPPS resilience applied in the existing literature is provided.On the basis of these assessment measures,the optimization methods of CPPS resilience are reviewed from three perspectives,which are mainly focused on the current research,namely,optimizing the recovery sequence of components,identifying and protecting critical nodes,and enhancing the coupling patterns between physical and cyber networks.The recent advances in modeling methods for cascading failures within the CPPS,which is the theoretical foundation for the resilience assessment and optimization research of CPPSs,are also presented.Lastly,the challenges and future research directions for resilience optimizing of CPPSs are discussed.展开更多
A combat system-of-systems (CSoS) is a network of independent entities that interact to provide overall operational capabilities.Enhancing the resilience of CSoS is garnering increasing attention due to its practical ...A combat system-of-systems (CSoS) is a network of independent entities that interact to provide overall operational capabilities.Enhancing the resilience of CSoS is garnering increasing attention due to its practical value in optimizing network architectures,improving network security and refining operational planning.Accordingly,we present a unified framework called CSoS space-time resilience enhancement (CSoS-STRE) to enhance the resilience of CSoS.Specifically,we develop a spatial combat network model and a space-time resilience optimization model that captures the complex spatial relationships between entities and reformulates the resilience enhancement problem as a linear optimization model with spatial features.Moreover,we extend the model to include obstacles.Next,a resilience-oriented recovery optimization method based on the improved non-dominated sorting genetic algorithm II (R-INSGA) is proposed to determine the optimal recovery sequence for the damaged entities.This method incorporates spatial features while providing the optimal travel paths for multiple recovery teams.Finally,the feasibility,effectiveness,and superiority of the CSoS-STRE are demonstrated through a case study,providing valuable insights for guiding recovery and developing more resilient CSoS.展开更多
文摘Restoration of infrastructure networks(INs)following large disruptions has received much attention lately due to examples of massive localized attacks.Within this challenge are two complex but critical problems:repair route identification and optimizing the sequence of the repair actions for resilience improvement.Existing approaches have not,however,given due consideration to globally optimal enhancement in resilience,especially with multiple repair crews that have uneven capacities.To address this gap,this paper focuses on a resilience opti-mization(RO)strategy for coordinating multiple crews.The objective is to determine the optimal routes for each crew and the best sequence of repairs for damaged nodes and links.Given the two-layered decision-making required—coordinating between multiple crews and opti-mizing each crew's actions—this study develops a deep reinforcement learning(DRL)framework.The framework leverages an actor-critic neural network that processes IN damage data and guides Monte Carlo tree search(MCTS)to identify optimal repair routes and actions for each crew.A case study based on the 228-node power grid,simulated using Python,demonstrates that the proposed DRL approach effectively supports restoration decision-making.
文摘The International Textile Manufac-turers Federation(ITMF),has releasedthe results of its Global Textile IndustrySurvey(GTIS)for March 2025.Thissurvey,conducted regularly across allkey regions and segments of the textilevalue chain,revealed a complex pictureof a deteriorated business situation,cau-tious op timism,regional divergence,andongoing structural challenges.
基金supported by the National Key Research and Development Program of China(2023YFD1200503 to Shuai Ma and 2021YFD1200700 to Tianyu Wang)。
文摘Highlights ZmMYC2 promoter contains favorable haplotypes selected during domestication,enhancing its expression level in modern maize.ZmMYC2 may balance the trade-off between growth and defense via jasmonate and auxin signaling pathways.ZmMYC2 regulates drought-response genes(CER2 and TIP3c)to optimize drought stress resilience.
基金This research is partially supported through the National Natural Science Foundation of China(Grant No.51537010).
文摘The Cyber-Physical Power System(CPPS)is one of the most critical infrastructure systems in a country because a stable and secure power supply is a key foundation for national and social development.In recent years,resilience has become a major topic in preventing and mitigating the risks caused by large-scale blackouts of CPPSs.Accordingly,the concept and significance of CPPS resilience are at first explained from the engineering perspective in this study.Then,a review of representative quantitative assessment measures of CPPS resilience applied in the existing literature is provided.On the basis of these assessment measures,the optimization methods of CPPS resilience are reviewed from three perspectives,which are mainly focused on the current research,namely,optimizing the recovery sequence of components,identifying and protecting critical nodes,and enhancing the coupling patterns between physical and cyber networks.The recent advances in modeling methods for cascading failures within the CPPS,which is the theoretical foundation for the resilience assessment and optimization research of CPPSs,are also presented.Lastly,the challenges and future research directions for resilience optimizing of CPPSs are discussed.
基金supported in part by the National Natural Science Foundation of China(Grant Nos.72371244,72301286,72231011,and 72431011)Postgraduate Scientific Research Innovation Project of Hunan Province,China(No.CX20240146).
文摘A combat system-of-systems (CSoS) is a network of independent entities that interact to provide overall operational capabilities.Enhancing the resilience of CSoS is garnering increasing attention due to its practical value in optimizing network architectures,improving network security and refining operational planning.Accordingly,we present a unified framework called CSoS space-time resilience enhancement (CSoS-STRE) to enhance the resilience of CSoS.Specifically,we develop a spatial combat network model and a space-time resilience optimization model that captures the complex spatial relationships between entities and reformulates the resilience enhancement problem as a linear optimization model with spatial features.Moreover,we extend the model to include obstacles.Next,a resilience-oriented recovery optimization method based on the improved non-dominated sorting genetic algorithm II (R-INSGA) is proposed to determine the optimal recovery sequence for the damaged entities.This method incorporates spatial features while providing the optimal travel paths for multiple recovery teams.Finally,the feasibility,effectiveness,and superiority of the CSoS-STRE are demonstrated through a case study,providing valuable insights for guiding recovery and developing more resilient CSoS.
