Dear Editor,This letter proposes a reinforcement learning-based predictive learning algorithm for unknown continuous-time nonlinear systems with observation loss.Firstly,we construct a temporal nonzero-sum game over p...Dear Editor,This letter proposes a reinforcement learning-based predictive learning algorithm for unknown continuous-time nonlinear systems with observation loss.Firstly,we construct a temporal nonzero-sum game over predictive control input sequences,deriving multiple optimal predictive control input sequences from its solution.展开更多
This paper investigates the consensus tracking control problem for high order nonlinear multi-agent systems subject to non-affine faults,partial measurable states,uncertain control coefficients,and unknown external di...This paper investigates the consensus tracking control problem for high order nonlinear multi-agent systems subject to non-affine faults,partial measurable states,uncertain control coefficients,and unknown external disturbances.Under the directed topology conditions,an observer-based finite-time control strategy based on adaptive backstepping and is proposed,in which a neural network-based state observer is employed to approximate the unmeasurable system state variables.To address the complexity explosion problem associated with the backstepping method,a finite-time command filter is incorporated,with error compensation signals designed to mitigate the filter-induced errors.Additionally,the Butterworth low-pass filter is introduced to avoid the algebraic ring problem in the design of the controller.The finite-time stability of the closed-loop system is rigorously analyzed with the finite-time Lyapunov stability criterion,validating that all closed-loop signals of the system remain bounded within a finite time.Finally,the effectiveness of the proposed control strategy is verified through a simulation example.展开更多
The technological advancement of the vehicular Internet ofThings(IoT)has revolutionized Intelligent Transportation Systems(ITS)into next-generation ITS.The connectivity of IoT nodes enables improved data availability ...The technological advancement of the vehicular Internet ofThings(IoT)has revolutionized Intelligent Transportation Systems(ITS)into next-generation ITS.The connectivity of IoT nodes enables improved data availability and facilitates automatic control in the ITS environment.The exponential increase in IoT nodes has significantly increased the demand for an energy-efficient,mobility-aware,and secure system for distributed intelligence.This article presents a mobility-aware Deep Reinforcement Learning based Federated Learning(DRL-FL)approach to design an energy-efficient and threat-resilient ITS.In this approach,a Policy Proximal Optimization(PPO)-based DRL agent is first employed for adaptive client selection.Second,an autoencoder-based anomaly detectionmodule is considered for malicious node detection.Results reveal that the proposed framework achieved an 8%higher accuracy increase,and 15%lower energy consumption.Themodel also demonstrates greater resilience under adversarial conditions compared to the state of the art in federated learning.The adaptability of the proposed approach makes it a compelling choice for next-generation vehicular networks.展开更多
Enhancing the resilience of critical infrastructure(CI)systems has become a focal point of national and inter-national policies.However,the formulation of resilience enhancement strategies often requires component-(i....Enhancing the resilience of critical infrastructure(CI)systems has become a focal point of national and inter-national policies.However,the formulation of resilience enhancement strategies often requires component-(i.e.asset-)level prioritization,which entails many complexities.Acknowledging the complex and interdependent nature of infrastructure systems,this paper aims to aid researchers,practitioners and policy-makers by pre-senting a review of the relative literature and current state-of-the-art,and by identifying future research op-portunities to improve the applicability and operationalizability of CI component identification and prioritization methods.Theoretical and practical applications are reviewed for definitions,analysis and modelling approaches regarding the resilience of interdependent infrastructure systems.A detailed review of infrastructure criticality definitions,component criticality assessment and prioritization frameworks,from scientific,policy and other documents,is presented.A discussion on social justice and equity dimensions therein is included,which have the potential to greatly influence decisions and should always be incorporated in infrastructure planning and in-vestment discussions.The findings of this review are discussed in terms of applicability and operationalizability.Key recommendations for future research include:(i)developing quantification frameworks for CI component criticality based on formal definitions and multiple criteria,(ii)incorporating the entire resilience cycle of CI in component prioritization,(iii)accounting for the socio-technical nature of CI systems by integrating social di-mensions and their wider operating environment and(iv)developing comprehensive model validation,cali-bration and uncertainty analysis frameworks.展开更多
This paper is dedicated to solving the problem of adaptive fuzzy fault-tolerant tracking control for a class of time-varying high-order uncertain nonlinear systems.The motivation comes from how to construct a compact ...This paper is dedicated to solving the problem of adaptive fuzzy fault-tolerant tracking control for a class of time-varying high-order uncertain nonlinear systems.The motivation comes from how to construct a compact set large enough in which the approximation of any unknown continuous function by a fuzzy logic system(FLS)is effective while compensating sensor/actuator faults and external disturbances.The difficulty is to verify the boundedness of closed-loop signals on the constructed compact set and to reduce the number of the variables of the fuzzy membership functions as many as possible.By a new lemma,linear/nonlinear terms are introduced in adaptive laws to dominate unknown residual terms.With adding a power integrator method,a unified fault-tolerant controller is designed to drive the tracking error to converge to a small compact set of the origin within a fixed time,regardless of whether the system suffers from faults and disturbances.Superior to the existing results,in the presence of time-varying factors the scheme of this paper clarifies the logical relationship between the compactness of the approximation and the boundedness of the state variables.Finally,the application of control strategy is demonstrated by numerical/practical examples.展开更多
This paper proposes a fault-tolerant control scheme for Euler-Lagrange systems that ensures the tracking error decays to a pre-specified accuracy level within a prescribed time period,despite unknown actuation charact...This paper proposes a fault-tolerant control scheme for Euler-Lagrange systems that ensures the tracking error decays to a pre-specified accuracy level within a prescribed time period,despite unknown actuation characteristics and potential fading powering faults.By performing deliberately designed coordinate transformations on the tracking error,the complex and demanding problem of“reaching specified precision within a given time”is transformed into a bounded control problem,facilitating the development of the control scheme.To enhance practicality,the design incorporates smooth function fitting and dynamic surface control techniques.Additionally,the proposed control algorithm is robust to faults,effectively handling a combination of fading powering faults and additive actuator faults without requiring additional human intervention.Numerical simulations on a two-link robotic manipulator verify the effectiveness of the proposed control algorithm.展开更多
Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from sei...Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from seismic networks,satellite observations,and geospatial repositories,creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making.Data warehousing technologies provide a robust foundation for this purpose;however,existing earthquake-oriented data warehouses remain limited,often relying on simplified schemas,domain-specific analytics,or cataloguing efforts.This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity.The framework integrates spatial and temporal dimensions in a unified schema and introduces a novel array-based approach for managing many-to-many relationships between facts and dimensions without intermediate bridge tables.A comparative evaluation against a conventional bridge-table schema demonstrates that the array-based design improves fact-centric query performance,while the bridge-table schema remains advantageous for dimension-centric queries.To reconcile these trade-offs,a hybrid schema is proposed that retains both representations,ensuring balanced efficiency across heterogeneous workloads.The proposed framework demonstrates how spatio-temporal data warehousing can address schema complexity,improve query performance,and support multidimensional visualization.In doing so,it provides a foundation for integrating seismic analysis into broader big data-driven intelligent decision systems for disaster resilience,risk mitigation,and emergency management.展开更多
In this paper,a unified terminal sliding mode(UTSM)control method is proposed for second-order nonlinear systems with uncertainties and disturbances.It is seen that the newly defined terminal sliding surface is integr...In this paper,a unified terminal sliding mode(UTSM)control method is proposed for second-order nonlinear systems with uncertainties and disturbances.It is seen that the newly defined terminal sliding surface is integrated with both conventional and fast terminal sliding mode and exhibits design advantages such as a variable exponent,adjustable sliding mode parameters,and chattering-alleviation effect.The inherent dynamic properties of the closed-loop systems with the UTSM control are discussed in detail via the phase plane and Lyapunov stability theory.Both numerical simulations and experimental results show the flexible sliding manifold design,strong robustness against uncertain dynamics,and effective attenuation of chattering phenomenon.展开更多
Radiative cooling systems(RCSs)possess the distinctive capability to dissipate heat energy via solar and thermal radiation,making them suitable for thermal regulation and energy conservation applications,essential for...Radiative cooling systems(RCSs)possess the distinctive capability to dissipate heat energy via solar and thermal radiation,making them suitable for thermal regulation and energy conservation applications,essential for mitigating the energy crisis.A comprehensive review connecting the advancements in engineered radiative cooling systems(ERCSs),encompassing material and structural design as well as thermal and energy-related applications,is currently absent.Herein,this review begins with a concise summary of the essential concepts of ERCSs,followed by an introduction to engineered materials and structures,containing nature-inspired designs,chromatic materials,meta-structural configurations,and multilayered constructions.It subsequently encapsulates the primary applications,including thermal-regulating textiles and energy-saving devices.Next,it highlights the challenges of ERCSs,including maximized thermoregulatory effects,environmental adaptability,scalability and sustainability,and interdisciplinary integration.It seeks to offer direction for forthcoming fundamental research and industrial advancement of radiative cooling systems in real-world applications.展开更多
Human Resource(HR)operations increasingly rely on cloud-based platforms that provide hiring,payroll,employee management,and compliance services.These systems,typically built on multi-tenant microservice architectures,...Human Resource(HR)operations increasingly rely on cloud-based platforms that provide hiring,payroll,employee management,and compliance services.These systems,typically built on multi-tenant microservice architectures,offer scalability and efficiency but also expand the attack surface for adversaries.Ransomware has emerged as a leading threat in this domain,capable of halting workflows and exposing sensitive employee records.Traditional defenses such as static hardening and signature-based detection often fail to address the dynamic requirements of HR Software as a Service(SaaS),where continuous availability and privacy compliance are critical.This paper presents a Moving Target Defense(MTD)framework for HR SaaS that combines container mutation,IP hopping,and node reassignment to randomize the attack surface without pausing services.Many prior defenses for cloud or IoT rely on static hardening or signature-driven detection and do not meet HR SaaS needs such as uninterrupted sessions,privacy compliance,and live service continuity.This paper presents a MTD framework for HR SaaS that combines container mutation,IP hopping,and node reassignment to randomize the attack surface without pausing services.The framework runs on Kubernetes and uses a KL-divergence-based anomaly detector that monitors HR access logs across five modules(onboarding,employee records,leave,payroll,and exit).In simulation with realistic HR traffic,the approach reaches 96.9% average detection accuracy with AUC 0.94-0.98,cuts mean time to containment to 91.4 s,and lowers the ransomware encryption rate to 13.2%.Measured overheads for CPU,memory,and per-mutation latency remainmodest.Comparedwith priorMTDand non-MTD baselines,the design provides stronger containment without service interruption and aligns with zero-trust and compliance goals.Its modular implementation and control-plane orchestration support stepwise,enterprise-scale deployment in HR SaaS environments.展开更多
A novel aperiodically intermittent impulse control(AIIC)method is proposed to investigate the exponential synchronization in mean square(ESMS)of a class of impulsive stochastic infinite-dimensional systems with Poisso...A novel aperiodically intermittent impulse control(AIIC)method is proposed to investigate the exponential synchronization in mean square(ESMS)of a class of impulsive stochastic infinite-dimensional systems with Poisson jumps(ISIDSP).The AIIC control strategy inherits the flexibility of aperiodically intermittent control,including the variable control period,adjustable control interval length,and the discretization of impulsive control.In addition,this article introduces a novel mild Itô's formula.By leveraging semigroup theory,the contraction mapping principle,and graph theory,along with constructing the Lyapunov function,the criterion for the existence and uniqueness of a mild solution of ISIDSP is thereby established.Furthermore,the mean-square exponential synchronization problem of the above systems is resolved,and the constraints within the mild solution domain are alleviated.These criteria clarify the impact of control parameters,control intervals and network topology on ESMS.The theoretical results are subsequently applied to a class of neural networks with reaction-diffusion processes,and the validity of the results is verified using numerical simulations.展开更多
This paper explores the adaptive exponentially designated-time stabilization issue via event-triggered feedback for a kind of uncertain high-order nonlinear systems.The motivation mainly comes from the following two c...This paper explores the adaptive exponentially designated-time stabilization issue via event-triggered feedback for a kind of uncertain high-order nonlinear systems.The motivation mainly comes from the following two challenges:the undesired singularity problem arising from infinite control gains at the prescribed-time instant,the effective trade-off between the control amplitude and the triggering duration.The goal is to build an event-triggered mechanism comprising a skillful triggered rule alongside a time-dependent threshold.Utilizing the designed control strategy,the solutions'existence and the prevention of Zeno phenomenon are successfully guaranteed by using a new transformation equipped with a time-varying function and redesigning the continuous state-feedback dominance approach with an array of integral functions involving embedded sign functions.Better than existing prescribed-time methods,our approach not only ensures that state variables converge to a small compact set before a designated time and stay there henceforth,and converge to the origin exponentially,but also ensures that the controller continuously works on the whole-time horizon.Two illustrative examples are given to show the effectiveness of the devised scheme.展开更多
The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-gener...The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.展开更多
Aeolian deposits across the Yarlung Zangbo River Basin on the southern Tibetan Plateau record the landscape and atmospheric evolution of Earth's Third Pole.The complex mountain-basin system exhibits nonlinear resp...Aeolian deposits across the Yarlung Zangbo River Basin on the southern Tibetan Plateau record the landscape and atmospheric evolution of Earth's Third Pole.The complex mountain-basin system exhibits nonlinear responses to climate forcing,complicating the interpretation of its high-altitude environmental dynamics.Investigating the magnetic enhancement mechanism of aeolian deposits offers an opportunity to decipher climate signals.Our analysis of three aeolian sections from the basin indicates that magnetic minerals are predominantly low-coercivity ferrimagnetic minerals,and grain sizes fine from upper to lower reaches due to climate shifts from arid to humid.Magnetic enhancement in the upper reaches primarily originates from dust input,while dust input and pedogenesis contribute variably over time in the middle and lower reaches.Similar complex patterns occur in the Ili basin,a mountain-basin system in northwestern China.They differ from the Chinese Loess Plateau,where long-distance-transported dust is well-mixed and the pedogenic enhancement model is applied,and desert peripheries where short-distance dust is transported and the dust input model is applied.We summarize the magnetic enhancement mechanisms in various settings and offer a new framework for applying magnetic techniques in paleoclimate reconstruction within global mountain-basin systems,which highlights the need for caution in interpreting their magnetic susceptibility records.展开更多
Safeguarding modern networks from cyber intrusions has become increasingly challenging as attackers continually refine their evasion tactics.Although numerousmachine-learning-based intrusion detection systems(IDS)have...Safeguarding modern networks from cyber intrusions has become increasingly challenging as attackers continually refine their evasion tactics.Although numerousmachine-learning-based intrusion detection systems(IDS)have been developed,their effectiveness is often constrained by high dimensionality and redundant features that degrade both accuracy and efficiency.This study introduces a hybrid feature-selection framework that integrates the exploration capability of Prairie Dog Optimization(PDO)with the exploitation behavior of Ant Colony Optimization(ACO).The proposed PDO–ACO algorithm identifies a concise yet discriminative subset of features from the NSLKDD dataset and evaluates them using a Support Vector Machine(SVM)classifier.Experimental analyses reveal that the PDO–ACO model achieves superior detection accuracy of 98%while significantly lowering false alarms and computational overhead.Further validation on the CEC2017 benchmark suite confirms the robustness and adaptability of the hybrid model across diverse optimization landscapes,positioning PDO–ACO as an efficient and scalable approach for intelligent intrusion detection.展开更多
WITH the rapid development of technologies such as Artificial Intelligence(AI),edge computing,and cloud intelligence,the medical field is undergoing a fundamental transformation[1].These technologies significantly enh...WITH the rapid development of technologies such as Artificial Intelligence(AI),edge computing,and cloud intelligence,the medical field is undergoing a fundamental transformation[1].These technologies significantly enhance the medical system's capability to process complex data and also improve the real-time response rate to patient needs.In this wave of technological innovation,parallel intelligence,along with Artificial systems,Computational experiments,and Parallel execution(ACP)approach[2]will play a crucial role.Through parallel interactions between virtual and real systems,this approach optimizes the functionality of medical devices and instruments,enhancing the accuracy of diagnoses and treatments while enabling the autonomous evolution and adaptive adjustment of medical systems.展开更多
The passing of Professor Wolter“Wolt”Fabrycky,an outstanding member and great leader,is a big loss to our international systems engineering professional community.“Wolt was a legend in the systems engineering commu...The passing of Professor Wolter“Wolt”Fabrycky,an outstanding member and great leader,is a big loss to our international systems engineering professional community.“Wolt was a legend in the systems engineering community with his teaching,advising,and dissemination of knowledge through the books he authored.”,as stated by Professor Eileen Aken,a former student of Wolt and the head of the Virginia Tech’s Grado Department of Industrial and Systems Engineeirng where Wolt had served and led for 30 years and retired as John L.Lawrence Professor emeritus.展开更多
Dear Editor,This letter presents a joint probabilistic scheduling and resource allocation method(PSRA) for 5G-based wireless networked control systems(WNCSs). As a control-aware optimization method, PSRA minimizes the...Dear Editor,This letter presents a joint probabilistic scheduling and resource allocation method(PSRA) for 5G-based wireless networked control systems(WNCSs). As a control-aware optimization method, PSRA minimizes the linear quadratic Gaussian(LQG) control cost of WNCSs by optimizing the activation probability of subsystems, the number of uplink repetitions, and the durations of uplink and downlink phases. Simulation results show that PSRA achieves smaller LQG control costs than existing works.展开更多
Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present so...Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present some sufficient conditions for the exponential stability of a particular category of switched systems.展开更多
A significant number and range of challenges besetting sustainability can be traced to the actions and inter actions of multiple autonomous agents(people mostly)and the entities they create(e.g.,institutions,policies,...A significant number and range of challenges besetting sustainability can be traced to the actions and inter actions of multiple autonomous agents(people mostly)and the entities they create(e.g.,institutions,policies,social network)in the corresponding social-environmental systems(SES).To address these challenges,we need to understand decisions made and actions taken by agents,the outcomes of their actions,including the feedbacks on the corresponding agents and environment.The science of complex adaptive systems-complex adaptive sys tems(CAS)science-has a significant potential to handle such challenges.We address the advantages of CAS science for sustainability by identifying the key elements and challenges in sustainability science,the generic features of CAS,and the key advances and challenges in modeling CAS.Artificial intelligence and data science combined with agent-based modeling promise to improve understanding of agents’behaviors,detect SES struc tures,and formulate SES mechanisms.展开更多
基金supported by the National Natural Science Foundation of China(62433014,62373287,62573324,62333005,62273255)in part by the International Exchange Program for Graduate Students of Tongji University(4360143306)+3 种基金in part by the Fundamental Research Funds for Central Universities(22120230311)supported by DeutscheForschungsgemeinschaft(DFG,German Research Foundation)under Germany’s Excellence Strategy(EXC 2075390740016,468094890)support by the Stuttgart Center for Simulation Science(SimTech)the International Max Planck Research School for Intelligent Systems(IMPRS-IS)for supporting Y.Xie。
文摘Dear Editor,This letter proposes a reinforcement learning-based predictive learning algorithm for unknown continuous-time nonlinear systems with observation loss.Firstly,we construct a temporal nonzero-sum game over predictive control input sequences,deriving multiple optimal predictive control input sequences from its solution.
基金supported in part by the Beijing Natural Science Foundation under Grant 4252050in part by the National Science Fund for Distinguished Young Scholars under Grant 62425304in part by the Basic Science Center Programs of NSFC under Grant 62088101.
文摘This paper investigates the consensus tracking control problem for high order nonlinear multi-agent systems subject to non-affine faults,partial measurable states,uncertain control coefficients,and unknown external disturbances.Under the directed topology conditions,an observer-based finite-time control strategy based on adaptive backstepping and is proposed,in which a neural network-based state observer is employed to approximate the unmeasurable system state variables.To address the complexity explosion problem associated with the backstepping method,a finite-time command filter is incorporated,with error compensation signals designed to mitigate the filter-induced errors.Additionally,the Butterworth low-pass filter is introduced to avoid the algebraic ring problem in the design of the controller.The finite-time stability of the closed-loop system is rigorously analyzed with the finite-time Lyapunov stability criterion,validating that all closed-loop signals of the system remain bounded within a finite time.Finally,the effectiveness of the proposed control strategy is verified through a simulation example.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project No.PNURSP2025R510Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The technological advancement of the vehicular Internet ofThings(IoT)has revolutionized Intelligent Transportation Systems(ITS)into next-generation ITS.The connectivity of IoT nodes enables improved data availability and facilitates automatic control in the ITS environment.The exponential increase in IoT nodes has significantly increased the demand for an energy-efficient,mobility-aware,and secure system for distributed intelligence.This article presents a mobility-aware Deep Reinforcement Learning based Federated Learning(DRL-FL)approach to design an energy-efficient and threat-resilient ITS.In this approach,a Policy Proximal Optimization(PPO)-based DRL agent is first employed for adaptive client selection.Second,an autoencoder-based anomaly detectionmodule is considered for malicious node detection.Results reveal that the proposed framework achieved an 8%higher accuracy increase,and 15%lower energy consumption.Themodel also demonstrates greater resilience under adversarial conditions compared to the state of the art in federated learning.The adaptability of the proposed approach makes it a compelling choice for next-generation vehicular networks.
基金supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No.101037424.
文摘Enhancing the resilience of critical infrastructure(CI)systems has become a focal point of national and inter-national policies.However,the formulation of resilience enhancement strategies often requires component-(i.e.asset-)level prioritization,which entails many complexities.Acknowledging the complex and interdependent nature of infrastructure systems,this paper aims to aid researchers,practitioners and policy-makers by pre-senting a review of the relative literature and current state-of-the-art,and by identifying future research op-portunities to improve the applicability and operationalizability of CI component identification and prioritization methods.Theoretical and practical applications are reviewed for definitions,analysis and modelling approaches regarding the resilience of interdependent infrastructure systems.A detailed review of infrastructure criticality definitions,component criticality assessment and prioritization frameworks,from scientific,policy and other documents,is presented.A discussion on social justice and equity dimensions therein is included,which have the potential to greatly influence decisions and should always be incorporated in infrastructure planning and in-vestment discussions.The findings of this review are discussed in terms of applicability and operationalizability.Key recommendations for future research include:(i)developing quantification frameworks for CI component criticality based on formal definitions and multiple criteria,(ii)incorporating the entire resilience cycle of CI in component prioritization,(iii)accounting for the socio-technical nature of CI systems by integrating social di-mensions and their wider operating environment and(iv)developing comprehensive model validation,cali-bration and uncertainty analysis frameworks.
基金supported by National Natural Science Foundation of China[grant number 62173208]Taishan Scholar Project of Shandong Province of China[grant number tsqn202103061]。
文摘This paper is dedicated to solving the problem of adaptive fuzzy fault-tolerant tracking control for a class of time-varying high-order uncertain nonlinear systems.The motivation comes from how to construct a compact set large enough in which the approximation of any unknown continuous function by a fuzzy logic system(FLS)is effective while compensating sensor/actuator faults and external disturbances.The difficulty is to verify the boundedness of closed-loop signals on the constructed compact set and to reduce the number of the variables of the fuzzy membership functions as many as possible.By a new lemma,linear/nonlinear terms are introduced in adaptive laws to dominate unknown residual terms.With adding a power integrator method,a unified fault-tolerant controller is designed to drive the tracking error to converge to a small compact set of the origin within a fixed time,regardless of whether the system suffers from faults and disturbances.Superior to the existing results,in the presence of time-varying factors the scheme of this paper clarifies the logical relationship between the compactness of the approximation and the boundedness of the state variables.Finally,the application of control strategy is demonstrated by numerical/practical examples.
基金supported in part by the National Natural Science Foundation of China(W2411061,624B2029)the Graduate Research and Innovation Foundation of Chongqing,China(CYS20069)+1 种基金the Fundamental Research Funds for the Central Universities(2024CDJYXTD-007)the Natural Science Foundation of Chongqing(CSTB2023NSCQ-LZX0026).
文摘This paper proposes a fault-tolerant control scheme for Euler-Lagrange systems that ensures the tracking error decays to a pre-specified accuracy level within a prescribed time period,despite unknown actuation characteristics and potential fading powering faults.By performing deliberately designed coordinate transformations on the tracking error,the complex and demanding problem of“reaching specified precision within a given time”is transformed into a bounded control problem,facilitating the development of the control scheme.To enhance practicality,the design incorporates smooth function fitting and dynamic surface control techniques.Additionally,the proposed control algorithm is robust to faults,effectively handling a combination of fading powering faults and additive actuator faults without requiring additional human intervention.Numerical simulations on a two-link robotic manipulator verify the effectiveness of the proposed control algorithm.
文摘Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from seismic networks,satellite observations,and geospatial repositories,creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making.Data warehousing technologies provide a robust foundation for this purpose;however,existing earthquake-oriented data warehouses remain limited,often relying on simplified schemas,domain-specific analytics,or cataloguing efforts.This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity.The framework integrates spatial and temporal dimensions in a unified schema and introduces a novel array-based approach for managing many-to-many relationships between facts and dimensions without intermediate bridge tables.A comparative evaluation against a conventional bridge-table schema demonstrates that the array-based design improves fact-centric query performance,while the bridge-table schema remains advantageous for dimension-centric queries.To reconcile these trade-offs,a hybrid schema is proposed that retains both representations,ensuring balanced efficiency across heterogeneous workloads.The proposed framework demonstrates how spatio-temporal data warehousing can address schema complexity,improve query performance,and support multidimensional visualization.In doing so,it provides a foundation for integrating seismic analysis into broader big data-driven intelligent decision systems for disaster resilience,risk mitigation,and emergency management.
基金supported by the National Natural Science Foundation of China(62473337,62003305)the Key Research and Development Program of Zhejiang Province(2024C03040,2022C03029)the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang Province(2023R01006)。
文摘In this paper,a unified terminal sliding mode(UTSM)control method is proposed for second-order nonlinear systems with uncertainties and disturbances.It is seen that the newly defined terminal sliding surface is integrated with both conventional and fast terminal sliding mode and exhibits design advantages such as a variable exponent,adjustable sliding mode parameters,and chattering-alleviation effect.The inherent dynamic properties of the closed-loop systems with the UTSM control are discussed in detail via the phase plane and Lyapunov stability theory.Both numerical simulations and experimental results show the flexible sliding manifold design,strong robustness against uncertain dynamics,and effective attenuation of chattering phenomenon.
基金support from the Contract Research(“Development of Breathable Fabrics with Nano-Electrospun Membrane”,CityU ref.:9231419“Research and application of antibacterial and healing-promoting smart nanofiber dressing for children’s burn wounds”,CityU ref:PJ9240111)+1 种基金the National Natural Science Foundation of China(“Study of Multi-Responsive Shape Memory Polyurethane Nanocomposites Inspired by Natural Fibers”,Grant No.51673162)Startup Grant of CityU(“Laboratory of Wearable Materials for Healthcare”,Grant No.9380116).
文摘Radiative cooling systems(RCSs)possess the distinctive capability to dissipate heat energy via solar and thermal radiation,making them suitable for thermal regulation and energy conservation applications,essential for mitigating the energy crisis.A comprehensive review connecting the advancements in engineered radiative cooling systems(ERCSs),encompassing material and structural design as well as thermal and energy-related applications,is currently absent.Herein,this review begins with a concise summary of the essential concepts of ERCSs,followed by an introduction to engineered materials and structures,containing nature-inspired designs,chromatic materials,meta-structural configurations,and multilayered constructions.It subsequently encapsulates the primary applications,including thermal-regulating textiles and energy-saving devices.Next,it highlights the challenges of ERCSs,including maximized thermoregulatory effects,environmental adaptability,scalability and sustainability,and interdisciplinary integration.It seeks to offer direction for forthcoming fundamental research and industrial advancement of radiative cooling systems in real-world applications.
文摘Human Resource(HR)operations increasingly rely on cloud-based platforms that provide hiring,payroll,employee management,and compliance services.These systems,typically built on multi-tenant microservice architectures,offer scalability and efficiency but also expand the attack surface for adversaries.Ransomware has emerged as a leading threat in this domain,capable of halting workflows and exposing sensitive employee records.Traditional defenses such as static hardening and signature-based detection often fail to address the dynamic requirements of HR Software as a Service(SaaS),where continuous availability and privacy compliance are critical.This paper presents a Moving Target Defense(MTD)framework for HR SaaS that combines container mutation,IP hopping,and node reassignment to randomize the attack surface without pausing services.Many prior defenses for cloud or IoT rely on static hardening or signature-driven detection and do not meet HR SaaS needs such as uninterrupted sessions,privacy compliance,and live service continuity.This paper presents a MTD framework for HR SaaS that combines container mutation,IP hopping,and node reassignment to randomize the attack surface without pausing services.The framework runs on Kubernetes and uses a KL-divergence-based anomaly detector that monitors HR access logs across five modules(onboarding,employee records,leave,payroll,and exit).In simulation with realistic HR traffic,the approach reaches 96.9% average detection accuracy with AUC 0.94-0.98,cuts mean time to containment to 91.4 s,and lowers the ransomware encryption rate to 13.2%.Measured overheads for CPU,memory,and per-mutation latency remainmodest.Comparedwith priorMTDand non-MTD baselines,the design provides stronger containment without service interruption and aligns with zero-trust and compliance goals.Its modular implementation and control-plane orchestration support stepwise,enterprise-scale deployment in HR SaaS environments.
基金supported in part by the National Natural Science Foundation of China(12471422,62573274,12371173)the Natural Science Foundation of Shandong Province of China(ZR2022LLZ003,ZR2024MF001)the Funding for Visiting Studies and Research by Teachers in Ordinary Undergraduate Colleges and Universities in Shandong Province。
文摘A novel aperiodically intermittent impulse control(AIIC)method is proposed to investigate the exponential synchronization in mean square(ESMS)of a class of impulsive stochastic infinite-dimensional systems with Poisson jumps(ISIDSP).The AIIC control strategy inherits the flexibility of aperiodically intermittent control,including the variable control period,adjustable control interval length,and the discretization of impulsive control.In addition,this article introduces a novel mild Itô's formula.By leveraging semigroup theory,the contraction mapping principle,and graph theory,along with constructing the Lyapunov function,the criterion for the existence and uniqueness of a mild solution of ISIDSP is thereby established.Furthermore,the mean-square exponential synchronization problem of the above systems is resolved,and the constraints within the mild solution domain are alleviated.These criteria clarify the impact of control parameters,control intervals and network topology on ESMS.The theoretical results are subsequently applied to a class of neural networks with reaction-diffusion processes,and the validity of the results is verified using numerical simulations.
基金supported in part by the National Natural Science Foundation of China(62173208)Taishan Scholar Project of Shandong Province of China(tsqn202103061)the National Science and Technology Council(NSTC),Taiwan,China(NSTC 113-2221-E-006-145-MY2)。
文摘This paper explores the adaptive exponentially designated-time stabilization issue via event-triggered feedback for a kind of uncertain high-order nonlinear systems.The motivation mainly comes from the following two challenges:the undesired singularity problem arising from infinite control gains at the prescribed-time instant,the effective trade-off between the control amplitude and the triggering duration.The goal is to build an event-triggered mechanism comprising a skillful triggered rule alongside a time-dependent threshold.Utilizing the designed control strategy,the solutions'existence and the prevention of Zeno phenomenon are successfully guaranteed by using a new transformation equipped with a time-varying function and redesigning the continuous state-feedback dominance approach with an array of integral functions involving embedded sign functions.Better than existing prescribed-time methods,our approach not only ensures that state variables converge to a small compact set before a designated time and stay there henceforth,and converge to the origin exponentially,but also ensures that the controller continuously works on the whole-time horizon.Two illustrative examples are given to show the effectiveness of the devised scheme.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00559546)supported by the IITP(Institute of Information&Coummunications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korea government(Ministry of Science and ICT)(IITP-2025-RS-2023-00259004).
文摘The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.
基金National Natural Science Foundation of China,No.42501182The Open Foundation of Key Laboratory of Western China’s Environmental System,Ministry of Education,Lanzhou Universitythe Fundamental Research Funds for the Central Universities,No.lzujbky-2024-jdzx01。
文摘Aeolian deposits across the Yarlung Zangbo River Basin on the southern Tibetan Plateau record the landscape and atmospheric evolution of Earth's Third Pole.The complex mountain-basin system exhibits nonlinear responses to climate forcing,complicating the interpretation of its high-altitude environmental dynamics.Investigating the magnetic enhancement mechanism of aeolian deposits offers an opportunity to decipher climate signals.Our analysis of three aeolian sections from the basin indicates that magnetic minerals are predominantly low-coercivity ferrimagnetic minerals,and grain sizes fine from upper to lower reaches due to climate shifts from arid to humid.Magnetic enhancement in the upper reaches primarily originates from dust input,while dust input and pedogenesis contribute variably over time in the middle and lower reaches.Similar complex patterns occur in the Ili basin,a mountain-basin system in northwestern China.They differ from the Chinese Loess Plateau,where long-distance-transported dust is well-mixed and the pedogenic enhancement model is applied,and desert peripheries where short-distance dust is transported and the dust input model is applied.We summarize the magnetic enhancement mechanisms in various settings and offer a new framework for applying magnetic techniques in paleoclimate reconstruction within global mountain-basin systems,which highlights the need for caution in interpreting their magnetic susceptibility records.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R500)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Safeguarding modern networks from cyber intrusions has become increasingly challenging as attackers continually refine their evasion tactics.Although numerousmachine-learning-based intrusion detection systems(IDS)have been developed,their effectiveness is often constrained by high dimensionality and redundant features that degrade both accuracy and efficiency.This study introduces a hybrid feature-selection framework that integrates the exploration capability of Prairie Dog Optimization(PDO)with the exploitation behavior of Ant Colony Optimization(ACO).The proposed PDO–ACO algorithm identifies a concise yet discriminative subset of features from the NSLKDD dataset and evaluates them using a Support Vector Machine(SVM)classifier.Experimental analyses reveal that the PDO–ACO model achieves superior detection accuracy of 98%while significantly lowering false alarms and computational overhead.Further validation on the CEC2017 benchmark suite confirms the robustness and adaptability of the hybrid model across diverse optimization landscapes,positioning PDO–ACO as an efficient and scalable approach for intelligent intrusion detection.
基金supported by the Science and Technology Development Fund,Macao Special Administrative Region(SAR)(0093/2023/RIA2,0145/2023/RIA3).
文摘WITH the rapid development of technologies such as Artificial Intelligence(AI),edge computing,and cloud intelligence,the medical field is undergoing a fundamental transformation[1].These technologies significantly enhance the medical system's capability to process complex data and also improve the real-time response rate to patient needs.In this wave of technological innovation,parallel intelligence,along with Artificial systems,Computational experiments,and Parallel execution(ACP)approach[2]will play a crucial role.Through parallel interactions between virtual and real systems,this approach optimizes the functionality of medical devices and instruments,enhancing the accuracy of diagnoses and treatments while enabling the autonomous evolution and adaptive adjustment of medical systems.
文摘The passing of Professor Wolter“Wolt”Fabrycky,an outstanding member and great leader,is a big loss to our international systems engineering professional community.“Wolt was a legend in the systems engineering community with his teaching,advising,and dissemination of knowledge through the books he authored.”,as stated by Professor Eileen Aken,a former student of Wolt and the head of the Virginia Tech’s Grado Department of Industrial and Systems Engineeirng where Wolt had served and led for 30 years and retired as John L.Lawrence Professor emeritus.
基金supported by the Liaoning Revitalization Talents Program(XLYC2203148)
文摘Dear Editor,This letter presents a joint probabilistic scheduling and resource allocation method(PSRA) for 5G-based wireless networked control systems(WNCSs). As a control-aware optimization method, PSRA minimizes the linear quadratic Gaussian(LQG) control cost of WNCSs by optimizing the activation probability of subsystems, the number of uplink repetitions, and the durations of uplink and downlink phases. Simulation results show that PSRA achieves smaller LQG control costs than existing works.
基金supported in part by the National Natural Science Foundation of China(62273255,62350003,62088101)the Shanghai Science and Technology Cooperation Project(22510712000,21550760900)+1 种基金the Shanghai Municipal Science and Technology Major Project(2021SHZDZX0100)the Fundamental Research Funds for the Central Universities
文摘Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present some sufficient conditions for the exponential stability of a particular category of switched systems.
基金The National Science Foundation funded this research under the Dy-namics of Coupled Natural and Human Systems program(Grants No.DEB-1212183 and BCS-1826839)support from San Diego State University and Auburn University.
文摘A significant number and range of challenges besetting sustainability can be traced to the actions and inter actions of multiple autonomous agents(people mostly)and the entities they create(e.g.,institutions,policies,social network)in the corresponding social-environmental systems(SES).To address these challenges,we need to understand decisions made and actions taken by agents,the outcomes of their actions,including the feedbacks on the corresponding agents and environment.The science of complex adaptive systems-complex adaptive sys tems(CAS)science-has a significant potential to handle such challenges.We address the advantages of CAS science for sustainability by identifying the key elements and challenges in sustainability science,the generic features of CAS,and the key advances and challenges in modeling CAS.Artificial intelligence and data science combined with agent-based modeling promise to improve understanding of agents’behaviors,detect SES struc tures,and formulate SES mechanisms.
