A social computing method for energy safety

Information and communication technologies enable the transformation of traditional energy systems into cyber-physical energy systems(CPESs),but such systems have also become popular targets of cyberattacks.Currently,available methods for evaluating the impacts of cyberattacks suffer from limited resilience,efficacy,and practical value.To mitigate their potentially disastrous consequences,this study suggests a two-stage,discrepancy-based optimization approach that considers both preparatory actions and response measures,integrating concepts from social computing.The proposed Kullback-Leibler divergence-based,distributionally robust optimization(KDR)method has a hierarchical,two-stage objective function that incorporates the operating costs of both system infrastructures(e.g.,energy resources,reserve capacity)and real-time response measures(e.g.,load shedding,demand-side management,electric vehicle charging station management).By incorporating social computing principles,the optimization framework can also capture the social behavior and interactions of energy consumers in response to cyberattacks.The preparatory stage entails day-ahead operational decisions,leveraging insights from social computing to model and predict the behaviors of individuals and communities affected by potential cyberattacks.The mitigation stage generates responses designed to contain the consequences of the attack by directing and optimizing energy use from the demand side,taking into account the social context and preferences of energy consumers,to ensure resilient,economically efficient CPES operations.Our method can determine optimal schemes in both stages,accounting for the social dimensions of the problem.An original disaster mitigation model uses an abstract formulation to develop a risk-neutral model that characterizes cyberattacks through KDR,incorporating social computing techniques to enhance the understanding and response to cyber threats.This approach can mitigate the impacts more effectively than several existing methods,even with limited data availability.To extend this risk-neutral model,we incorporate conditional value at risk as an essential risk measure,capturing the uncertainty and diverse impact scenarios arising from social computing factors.The empirical results affirm that the KDR method,which is enriched with social computing considerations,produces resilient,economically efficient solutions for managing the impacts of cyberattacks on a CPES.By integrating social computing principles into the optimization framework,it becomes possible to better anticipate and address the social and behavioral aspects associated with cyberattacks on CPESs,ultimately improving the overall resilience and effectiveness of the system’s response measures.

AI for science: Predicting infectious diseases

The global health landscape has been persistently challenged by the emergence and re-emergence of infectious diseases.Traditional epidemiological models,rooted in the early 2oth century,have provided foundational in-sights into disease dynamics.However,the intricate web of modern global interactions and the exponential growth of available data demand more advanced predictive tools.This is where AI for Science(AI4S)comes into play,offering a transformative approach by integrating artificial intelligence(Al)into infectious disease pre-diction.This paper elucidates the pivotal role of AI4s in enhancing and,in some instances,superseding tradi-tional epidemiological methodologies.By harnessing AI's capabilities,AI4S facilitates real-time monitoring,sophisticated data integration,and predictive modeling with enhanced precision.The comparative analysis highlights the stark contrast between conventional models and the innovative strategies enabled by AI4S.In essence,Al4S represents a paradigm shift in infectious disease research.It addresses the limitations of traditional models and paves the way for a more proactive and informed response to future outbreaks.As we navigate the complexities of global health challenges,Al4S stands as a beacon,signifying the next phase of evolution in disease prediction,characterized by increased accuracy,adaptability,and efficiency.