With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,whic...With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,which makes task assignment inefficient due to insufficient workers.In this paper,an Intelligent and Trustworthy task assignment method based on Trust and Social relations(ITTS)is proposed for scenarios with many tasks and few workers.Specifically,ITTS first makes initial assignments based on trust and social influences,thereby transforming the complex large-scale industrial task assignment of the platform into the small-scale task assignment for each worker.Then,an intelligent Q-decision mechanism based on workers'social relation is proposed,which adopts the first-exploration-then-utilization principle to allocate tasks.Only when a worker cannot cope with the assigned tasks,it initiates dynamic worker recruitment,thus effectively solving the worker shortage problem as well as the cold start issue.More importantly,we consider trust and security issues,and evaluate the trust and social circles of workers by accumulating task feedback,to provide the platform a reference for worker recruitment,thereby creating a high-quality worker pool.Finally,extensive simulations demonstrate ITTS outperforms two benchmark methods by increasing task completion rates by 56.49%-61.53%and profit by 42.34%-47.19%.展开更多
Task allocation is a key aspect of Unmanned Aerial Vehicle(UAV)swarm collaborative operations.With an continuous increase of UAVs’scale and the complexity and uncertainty of tasks,existing methods have poor performan...Task allocation is a key aspect of Unmanned Aerial Vehicle(UAV)swarm collaborative operations.With an continuous increase of UAVs’scale and the complexity and uncertainty of tasks,existing methods have poor performance in computing efficiency,robustness,and realtime allocation,and there is a lack of theoretical analysis on the convergence and optimality of the solution.This paper presents a novel intelligent framework for distributed decision-making based on the evolutionary game theory to address task allocation for a UAV swarm system in uncertain scenarios.A task allocation model is designed with the local utility of an individual and the global utility of the system.Then,the paper analytically derives a potential function in the networked evolutionary potential game and proves that the optimal solution of the task allocation problem is a pure strategy Nash equilibrium of a finite strategy game.Additionally,a PayOff-based Time-Variant Log-linear Learning Algorithm(POTVLLA)is proposed,which includes a novel learning strategy based on payoffs for an individual and a time-dependent Boltzmann parameter.The former aims to reduce the system’s computational burden and enhance the individual’s effectiveness,while the latter can ensure that the POTVLLA converges to the optimal Nash equilibrium with a probability of one.Numerical simulation results show that the approach is optimal,robust,scalable,and fast adaptable to environmental changes,even in some realistic situations where some UAVs or tasks are likely to be lost and increased,further validating the effectiveness and superiority of the proposed framework and algorithm.展开更多
As cloud computing usage grows,cloud data centers play an increasingly important role.To maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage p...As cloud computing usage grows,cloud data centers play an increasingly important role.To maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage performance effectively.The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers.The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies,categories,and gaps.A literature review was conducted,which included the analysis of 463 task allocations and 480 performance management papers.The review revealed three task allocation research topics and seven performance management methods.Task allocation research areas are resource allocation,load-Balancing,and scheduling.Performance management includes monitoring and control,power and energy management,resource utilization optimization,quality of service management,fault management,virtual machine management,and network management.The study proposes new techniques to enhance cloud computing work allocation and performance management.Short-comings in each approach can guide future research.The research’s findings on cloud data center task allocation and performance management can assist academics,practitioners,and cloud service providers in optimizing their systems for dependability,cost-effectiveness,and scalability.Innovative methodologies can steer future research to fill gaps in the literature.展开更多
As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the...As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the task offloading strategies by interacting with the entities. In actual application scenarios, users of edge computing are always changing dynamically. However, the existing task offloading strategies cannot be applied to such dynamic scenarios. To solve this problem, we propose a novel dynamic task offloading framework for distributed edge computing, leveraging the potential of meta-reinforcement learning (MRL). Our approach formulates a multi-objective optimization problem aimed at minimizing both delay and energy consumption. We model the task offloading strategy using a directed acyclic graph (DAG). Furthermore, we propose a distributed edge computing adaptive task offloading algorithm rooted in MRL. This algorithm integrates multiple Markov decision processes (MDP) with a sequence-to-sequence (seq2seq) network, enabling it to learn and adapt task offloading strategies responsively across diverse network environments. To achieve joint optimization of delay and energy consumption, we incorporate the non-dominated sorting genetic algorithm II (NSGA-II) into our framework. Simulation results demonstrate the superiority of our proposed solution, achieving a 21% reduction in time delay and a 19% decrease in energy consumption compared to alternative task offloading schemes. Moreover, our scheme exhibits remarkable adaptability, responding swiftly to changes in various network environments.展开更多
Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power sta...Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power stations).To solve the problem,we propose an energy harvesting based task scheduling and resource management framework to provide robust and low-cost edge computing services for smart grid.First,we formulate an energy consumption minimization problem with regard to task offloading,time switching,and resource allocation for mobile devices,which can be decoupled and transformed into a typical knapsack problem.Then,solutions are derived by two different algorithms.Furthermore,we deploy renewable energy and energy storage units at edge servers to tackle intermittency and instability problems.Finally,we design an energy management algorithm based on sampling average approximation for edge computing servers to derive the optimal charging/discharging strategies,number of energy storage units,and renewable energy utilization.The simulation results show the efficiency and superiority of our proposed framework.展开更多
The study evaluated the impact of digital health tools, task sharing, capacity building, and maternal health financing on Maternal, Newborn, and Child Health (MNCH) systems in Kisii, Kajiado, and Migori counties in Ke...The study evaluated the impact of digital health tools, task sharing, capacity building, and maternal health financing on Maternal, Newborn, and Child Health (MNCH) systems in Kisii, Kajiado, and Migori counties in Kenya, under the Tekeleza project1. It aimed to assess how these interventions influenced MNCH service uptake, outcomes, and referral systems, using a mixed-methods approach with data from healthcare providers, community health promoters, and mothers. Findings showed that digital tools like the LUCY App improved coordination and maternal care but highlighted the need for better app features and wider access. Task sharing in areas like sonography enhanced service delivery but required stronger stakeholder engagement. Capacity-building through training was crucial for skill development, though additional training on emerging health issues was needed. Financial interventions, such as the Linda Mama program, boosted service uptake, but reimbursement processes needed improvement. Strengthening referral systems between lower- and higher-level facilities was identified as essential for timely access to care. The study concludes that integrating digital tools, expanding task sharing, enhancing training, and reinforcing health financing and referral systems are critical for strengthening MNCH services in rural Kenya, with broader implications for similar low-resource settings.展开更多
With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions...With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.展开更多
The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability an...The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability and resource efficiency,particularly in mass customization scenarios where production schedules must accommodate dynamic and personalized demands.To address the challenges of dynamic task allocation,uncertainty,and realtime decision-making,this paper proposes Pathfinder,a deep reinforcement learning-based scheduling framework.Pathfinder models scheduling data through three key matrices:execution time(the time required for a job to complete),completion time(the actual time at which a job is finished),and efficiency(the performance of executing a single job).By leveraging neural networks,Pathfinder extracts essential features from these matrices,enabling intelligent decision-making in dynamic production environments.Unlike traditional approaches with fixed scheduling rules,Pathfinder dynamically selects from ten diverse scheduling rules,optimizing decisions based on real-time environmental conditions.To further enhance scheduling efficiency,a specialized reward function is designed to support dynamic task allocation and real-time adjustments.This function helps Pathfinder continuously refine its scheduling strategy,improving machine utilization and minimizing job completion times.Through reinforcement learning,Pathfinder adapts to evolving production demands,ensuring robust performance in real-world applications.Experimental results demonstrate that Pathfinder outperforms traditional scheduling approaches,offering improved coordination and efficiency in smart factories.By integrating deep reinforcement learning,adaptable scheduling strategies,and an innovative reward function,Pathfinder provides an effective solution to the growing challenges of multi-robot job scheduling in mass customization environments.展开更多
Blockchain technology,based on decentralized data storage and distributed consensus design,has become a promising solution to address data security risks and provide privacy protection in the Internet-of-Things(IoT)du...Blockchain technology,based on decentralized data storage and distributed consensus design,has become a promising solution to address data security risks and provide privacy protection in the Internet-of-Things(IoT)due to its tamper-proof and non-repudiation features.Although blockchain typically does not require the endorsement of third-party trust organizations,it mostly needs to perform necessary mathematical calculations to prevent malicious attacks,which results in stricter requirements for computation resources on the participating devices.By offloading the computation tasks required to support blockchain consensus to edge service nodes or the cloud,while providing data privacy protection for IoT applications,it can effectively address the limitations of computation and energy resources in IoT devices.However,how to make reasonable offloading decisions for IoT devices remains an open issue.Due to the excellent self-learning ability of Reinforcement Learning(RL),this paper proposes a RL enabled Swarm Intelligence Optimization Algorithm(RLSIOA)that aims to improve the quality of initial solutions and achieve efficient optimization of computation task offloading decisions.The algorithm considers various factors that may affect the revenue obtained by IoT devices executing consensus algorithms(e.g.,Proof-of-Work),it optimizes the proportion of sub-tasks to be offloaded and the scale of computing resources to be rented from the edge and cloud to maximize the revenue of devices.Experimental results show that RLSIOA can obtain higher-quality offloading decision-making schemes at lower latency costs compared to representative benchmark algorithms.展开更多
Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G applications.Artificial Intelligence(AI)algorithms will ...Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G applications.Artificial Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and reliability.In this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task completion.However,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource wastage.Additionally,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities problem.This paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH scenarios.Additionally,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH scenarios.The performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed approach.The simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.展开更多
Drawing upon self-determination theory,this study examines the effects of vicarious abusive supervision on third-party’s self-efficacy and task performance within organizational contexts.Data were collected via surve...Drawing upon self-determination theory,this study examines the effects of vicarious abusive supervision on third-party’s self-efficacy and task performance within organizational contexts.Data were collected via surveys from 337 employees across diverse organizations.The results indicate that vicarious abusive supervision significantly undermines both self-efficacy and task performance among employees who are indirectly exposed to such behavior but not directly targeted.Furthermore,self-efficacy serves as a mediator between vicarious abusive supervision and task performance;however,this mediating effect is attenuated for employees with a high promotion focus.These findings provide valuable theoretical and practical insights,particularly in the domain of organizational behavior,by emphasizing the critical role of promotion focus in mitigating the negative effects of vicarious abusive supervision.This research contributes to the organizational behavior literature by shifting the focus from the traditional supervisor-subordinate dynamic to a third-party perspective,thereby enriching our understanding of how vicarious abusive supervision impacts employees within organizational settings.The study underscores the importance of self-efficacy and promotion focus as key factors in unethical leadership contexts.展开更多
Due to the intense data flow in expanding Internet of Things(IoT)applications,a heavy processing cost and workload on the fog-cloud side become inevitable.One of the most critical challenges is optimal task scheduling...Due to the intense data flow in expanding Internet of Things(IoT)applications,a heavy processing cost and workload on the fog-cloud side become inevitable.One of the most critical challenges is optimal task scheduling.Since this is an NP-hard problem type,a metaheuristic approach can be a good option.This study introduces a novel enhancement to the Artificial Rabbits Optimization(ARO)algorithm by integrating Chaotic maps and Levy flight strategies(CLARO).This dual approach addresses the limitations of standard ARO in terms of population diversity and convergence speed.It is designed for task scheduling in fog-cloud environments,optimizing energy consumption,makespan,and execution time simultaneously three critical parameters often treated individually in prior works.Unlike conventional single-objective methods,the proposed approach incorporates a multi-objective fitness function that dynamically adjusts the weight of each parameter,resulting in better resource allocation and load balancing.In analysis,a real-world dataset,the Open-source Google Cloud Jobs Dataset(GoCJ_Dataset),is used for performance measurement,and analyses are performed on three considered parameters.Comparisons are applied with well-known algorithms:GWO,SCSO,PSO,WOA,and ARO to indicate the reliability of the proposed method.In this regard,performance evaluation is performed by assigning these tasks to Virtual Machines(VMs)in the resource pool.Simulations are performed on 90 base cases and 30 scenarios for each evaluation parameter.The results indicated that the proposed algorithm achieved the best makespan performance in 80% of cases,ranked first in execution time in 61%of cases,and performed best in the final parameter in 69% of cases.In addition,according to the obtained results based on the defined fitness function,the proposed method(CLARO)is 2.52%better than ARO,3.95%better than SCSO,5.06%better than GWO,8.15%better than PSO,and 9.41%better than WOA.展开更多
Porters play a crucial role in hospitals because they ensure the efficient transportation of patients,medical equipment,and vital documents.Despite its importance,there is a lack of research addressing the prediction ...Porters play a crucial role in hospitals because they ensure the efficient transportation of patients,medical equipment,and vital documents.Despite its importance,there is a lack of research addressing the prediction of completion times for porter tasks.To address this gap,we utilized real-world porter delivery data from Taiwan University Hospital,China,Yunlin Branch,Taiwan Region of China.We first identified key features that can influence the duration of porter tasks.We then employed three widely-used machine learning algorithms:decision tree,random forest,and gradient boosting.To leverage the strengths of each algorithm,we finally adopted an ensemble modeling approach that aggregates their individual predictions.Our experimental results show that the proposed ensemble model can achieve a mean absolute error of 3 min in predicting task response time and 4.42 min in task completion time.The prediction error is around 50%lower compared to using only the historical average.These results demonstrate that our method significantly improves the accuracy of porter task time prediction,supporting better resource planning and patient care.It helps ward staff streamline workflows by reducing delays,enables porter managers to allocate resources more effectively,and shortens patient waiting times,contributing to a better care experience.展开更多
This paper considers the swarm vigilance problem for multi-agent systems(MAS),where multiple agents are deployed within a rectangular region for perception-based vigilance.There are two main challenges,namely the task...This paper considers the swarm vigilance problem for multi-agent systems(MAS),where multiple agents are deployed within a rectangular region for perception-based vigilance.There are two main challenges,namely the task allocation for vigilance roles and the coverage planning of the perception ranges.Firstly,vigilance behavioral patterns and processes in animal populations within natural habitats are investigated.Inspired by these biological vigilance behaviors,an efficient vigilance task allocation model for MAS is proposed.Secondly,the subsequent optimization of task layouts can achieve efficient surveillance coverage with fewer agents,minimizing resource consumption.Thirdly,an improved particle swarm optimization(IPSO)algorithm is proposed,which incorporates fitness-driven adaptive inertia weight dynamics.According to simulation analysis and comparative studies,optimal parameter configurations for genetic algorithm(GA)and IPSO are determined.Finally,the results indicate the proposed IPSO's superior performance to both GA and standard particle swarm optimization(PSO)in vigilance task allocation optimization,with satisfying advantages in computational efficiency and solution quality.展开更多
Cybertwin-enabled 6th Generation(6G)network is envisioned to support artificial intelligence-native management to meet changing demands of 6G applications.Multi-Agent Deep Reinforcement Learning(MADRL)technologies dri...Cybertwin-enabled 6th Generation(6G)network is envisioned to support artificial intelligence-native management to meet changing demands of 6G applications.Multi-Agent Deep Reinforcement Learning(MADRL)technologies driven by Cybertwins have been proposed for adaptive task offloading strategies.However,the existence of random transmission delay between Cybertwin-driven agents and underlying networks is not considered in related works,which destroys the standard Markov property and increases the decision reaction time to reduce the task offloading strategy performance.In order to address this problem,we propose a pipelining task offloading method to lower the decision reaction time and model it as a delay-aware Markov Decision Process(MDP).Then,we design a delay-aware MADRL algorithm to minimize the weighted sum of task execution latency and energy consumption.Firstly,the state space is augmented using the lastly-received state and historical actions to rebuild the Markov property.Secondly,Gate Transformer-XL is introduced to capture historical actions'importance and maintain the consistent input dimension dynamically changed due to random transmission delays.Thirdly,a sampling method and a new loss function with the difference between the current and target state value and the difference between real state-action value and augmented state-action value are designed to obtain state transition trajectories close to the real ones.Numerical results demonstrate that the proposed methods are effective in reducing reaction time and improving the task offloading performance in the random-delay Cybertwin-enabled 6G networks.展开更多
BACKGROUND Working memory serves as a fundamental cognitive function that substantially impacts performance in various cognitive tasks.Extensive neurophysiological research has established that theta oscillations(4-8 ...BACKGROUND Working memory serves as a fundamental cognitive function that substantially impacts performance in various cognitive tasks.Extensive neurophysiological research has established that theta oscillations(4-8 Hz)play an essential role in supporting working memory operations.Theta-band transcranial alternating current stimulation(tACS)offers a potential mechanism for working memory enhancement through direct modulation of these fundamental neural oscillations.Nevertheless,current empirical evidence shows substantial variability in the observed effects of theta-tACS across studies.AIM To conduct a systematic review and meta-analysis evaluating the effects of thetatACS on working memory performance in healthy adults.METHODS A systematic literature search was performed on PubMed,EMBASE,and Web of Science up to March 10,2025.Effect sizes were computed using Hedges’g with 95%confidence intervals(CIs),with separate meta-analyses for all included studies and for distinct working memory paradigms[n-back and delayed matchto-sample(DMTS)tasks]to examine potential task-specific effects.Subgroup analyses and meta-regression were performed to evaluate the influence of key moderating variables.RESULTS The systematic review included 21 studies(67 effect sizes).Initial meta-analysis showed theta-tACS moderately improved working memory(Hedges’g=0.405,95%CI:0.212-0.598).However,this effect became nonsignificant after correcting for publication bias(trim-and-fill adjusted Hedges’g=0.082,95%CI:-0.052 to 0.217).Task-specific analyses revealed significant benefits in n-back tasks(Hedges’g=0.463,95%CI:0.193-0.733)but not in DMTS tasks(Hedges’g=0.257,95%CI:-0.186 to 0.553).Moderator analyses showed that performance in n-back tasks was influenced by stimulation frequency(P=0.001),concurrent status(P=0.014),task modality(P=0.005),and duration(P=0.013),whereas only the region of targeted stimulation(P=0.012)moderated DMTS tasks.CONCLUSION Theta-tACS enhances working memory in healthy adults,with effects modulated by the task type and protocol parameters,offering dual implications for cognitive enhancement and clinical interventions.展开更多
BACKGROUND The neural mechanisms underlying aggressive behavior in schizophrenia(SCZ)remain poorly understood.To date,no studies have reported on the event-related potential(ERP)characteristics of aggression in SCZ us...BACKGROUND The neural mechanisms underlying aggressive behavior in schizophrenia(SCZ)remain poorly understood.To date,no studies have reported on the event-related potential(ERP)characteristics of aggression in SCZ using the competitive reaction time task(CRTT).Further investigation into the ERP correlates of aggression in SCZ would provide valuable insights into the neural processes involved.AIM To explore the neural mechanism of aggressive behavior in SCZ.METHODS Participants of this study included 40 SCZ patients and 42 healthy controls(HCs).The Reactive Proactive Aggression Questionnaire was used to assess trait of aggression.The Barratt Impulsiveness Scale 11 was used to measure impulsiveness.The Positive and Negative Symptom Scale(PANSS)was used to evaluate psychopathological features and disease severity.All participants were measured with ERP while performing the CRTT.Data of behavior,ERP components(P2,N2,and P3),and feedback-related negativity(FRN)were analyzed.RESULTS Analysis of the behavioral data revealed that compared with HCs,SCZ patients exhibited higher punishment choices.Analysis of ERP components showed that compared with HCs,SCZ patients exhibited higher N2 amplitudes and P2 amplitudes during the decision phase of the CRTT;however,SCZ patients exhibited lower FRN amplitudes and lower P3 amplitudes during the outcome phase of the CRTT.The N2 amplitudes evoked by highintensity provocation were positively related to PANSS-P scores.And the P3 amplitudes evoked in the winning trials were negatively correlated with the PANSS-G scores.CONCLUSION SCZ patients exhibit abnormal ERP characteristics evoked by the CRTT,which suggests the neural correlates of aggressive behavior in SCZ.展开更多
Edge computing has transformed smart grids by lowering latency,reducing network congestion,and enabling real-time decision-making.Nevertheless,devising an optimal task-offloading strategy remains challenging,as it mus...Edge computing has transformed smart grids by lowering latency,reducing network congestion,and enabling real-time decision-making.Nevertheless,devising an optimal task-offloading strategy remains challenging,as it must jointly minimise energy consumption and response time under fluctuating workloads and volatile network conditions.We cast the offloading problem as aMarkov Decision Process(MDP)and solve it with Deep Reinforcement Learning(DRL).Specifically,we present a three-tier architecture—end devices,edge nodes,and a cloud server—and enhance Proximal Policy Optimization(PPO)to learn adaptive,energy-aware policies.A Convolutional Neural Network(CNN)extracts high-level features from system states,enabling the agent to respond continually to changing conditions.Extensive simulations show that the proposed method reduces task latency and energy consumption far more than several baseline algorithms,thereby improving overall system performance.These results demonstrate the effectiveness and robustness of the framework for real-time task offloading in dynamic smart-grid environments.展开更多
In task offloading,the movement of vehicles causes the switching of connected RSUs and servers,which may lead to task offloading failure or high service delay.In this paper,we analyze the impact of vehicle movements o...In task offloading,the movement of vehicles causes the switching of connected RSUs and servers,which may lead to task offloading failure or high service delay.In this paper,we analyze the impact of vehicle movements on task offloading and reveal that data preparation time for task execution can be minimized via forward-looking scheduling.Then,a Bi-LSTM-based model is proposed to predict the trajectories of vehicles.The service area is divided into several equal-sized grids.If the actual position of the vehicle and the predicted position by the model belong to the same grid,the prediction is considered correct,thereby reducing the difficulty of vehicle trajectory prediction.Moreover,we propose a scheduling strategy for delay optimization based on the vehicle trajectory prediction.Considering the inevitable prediction error,we take some edge servers around the predicted area as candidate execution servers and the data required for task execution are backed up to these candidate servers,thereby reducing the impact of prediction deviations on task offloading and converting the modest increase of resource overheads into delay reduction in task offloading.Simulation results show that,compared with other classical schemes,the proposed strategy has lower average task offloading delays.展开更多
Under the background of training practical compound talents in software engineering,this paper analyzes the problems existing in the current teaching of software engineering courses represented by software project man...Under the background of training practical compound talents in software engineering,this paper analyzes the problems existing in the current teaching of software engineering courses represented by software project management,puts forward the team task mechanism of software engineering courses with AI empowerment and cooperation and competition,develops a unified project management platform to support the implementation of team tasks,and proves the effectiveness of the scheme through the results obtained.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.62072475 and No.62302062in part by the Hunan Provincial Natural Science Foundation of China under Grant Number 2023JJ40081。
文摘With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,which makes task assignment inefficient due to insufficient workers.In this paper,an Intelligent and Trustworthy task assignment method based on Trust and Social relations(ITTS)is proposed for scenarios with many tasks and few workers.Specifically,ITTS first makes initial assignments based on trust and social influences,thereby transforming the complex large-scale industrial task assignment of the platform into the small-scale task assignment for each worker.Then,an intelligent Q-decision mechanism based on workers'social relation is proposed,which adopts the first-exploration-then-utilization principle to allocate tasks.Only when a worker cannot cope with the assigned tasks,it initiates dynamic worker recruitment,thus effectively solving the worker shortage problem as well as the cold start issue.More importantly,we consider trust and security issues,and evaluate the trust and social circles of workers by accumulating task feedback,to provide the platform a reference for worker recruitment,thereby creating a high-quality worker pool.Finally,extensive simulations demonstrate ITTS outperforms two benchmark methods by increasing task completion rates by 56.49%-61.53%and profit by 42.34%-47.19%.
基金co-supported by the National Natural Science Foundation of China(Nos.71971115 and 62173305)the Postgraduate Research and Practice Innovation Program of Jiangsu Province,China(No.KYCX22_0366).
文摘Task allocation is a key aspect of Unmanned Aerial Vehicle(UAV)swarm collaborative operations.With an continuous increase of UAVs’scale and the complexity and uncertainty of tasks,existing methods have poor performance in computing efficiency,robustness,and realtime allocation,and there is a lack of theoretical analysis on the convergence and optimality of the solution.This paper presents a novel intelligent framework for distributed decision-making based on the evolutionary game theory to address task allocation for a UAV swarm system in uncertain scenarios.A task allocation model is designed with the local utility of an individual and the global utility of the system.Then,the paper analytically derives a potential function in the networked evolutionary potential game and proves that the optimal solution of the task allocation problem is a pure strategy Nash equilibrium of a finite strategy game.Additionally,a PayOff-based Time-Variant Log-linear Learning Algorithm(POTVLLA)is proposed,which includes a novel learning strategy based on payoffs for an individual and a time-dependent Boltzmann parameter.The former aims to reduce the system’s computational burden and enhance the individual’s effectiveness,while the latter can ensure that the POTVLLA converges to the optimal Nash equilibrium with a probability of one.Numerical simulation results show that the approach is optimal,robust,scalable,and fast adaptable to environmental changes,even in some realistic situations where some UAVs or tasks are likely to be lost and increased,further validating the effectiveness and superiority of the proposed framework and algorithm.
基金supported by the Ministerio Espanol de Ciencia e Innovación under Project Number PID2020-115570GB-C22,MCIN/AEI/10.13039/501100011033by the Cátedra de Empresa Tecnología para las Personas(UGR-Fujitsu).
文摘As cloud computing usage grows,cloud data centers play an increasingly important role.To maximize resource utilization,ensure service quality,and enhance system performance,it is crucial to allocate tasks and manage performance effectively.The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers.The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies,categories,and gaps.A literature review was conducted,which included the analysis of 463 task allocations and 480 performance management papers.The review revealed three task allocation research topics and seven performance management methods.Task allocation research areas are resource allocation,load-Balancing,and scheduling.Performance management includes monitoring and control,power and energy management,resource utilization optimization,quality of service management,fault management,virtual machine management,and network management.The study proposes new techniques to enhance cloud computing work allocation and performance management.Short-comings in each approach can guide future research.The research’s findings on cloud data center task allocation and performance management can assist academics,practitioners,and cloud service providers in optimizing their systems for dependability,cost-effectiveness,and scalability.Innovative methodologies can steer future research to fill gaps in the literature.
基金funded by the Fundamental Research Funds for the Central Universities(J2023-024,J2023-027).
文摘As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the task offloading strategies by interacting with the entities. In actual application scenarios, users of edge computing are always changing dynamically. However, the existing task offloading strategies cannot be applied to such dynamic scenarios. To solve this problem, we propose a novel dynamic task offloading framework for distributed edge computing, leveraging the potential of meta-reinforcement learning (MRL). Our approach formulates a multi-objective optimization problem aimed at minimizing both delay and energy consumption. We model the task offloading strategy using a directed acyclic graph (DAG). Furthermore, we propose a distributed edge computing adaptive task offloading algorithm rooted in MRL. This algorithm integrates multiple Markov decision processes (MDP) with a sequence-to-sequence (seq2seq) network, enabling it to learn and adapt task offloading strategies responsively across diverse network environments. To achieve joint optimization of delay and energy consumption, we incorporate the non-dominated sorting genetic algorithm II (NSGA-II) into our framework. Simulation results demonstrate the superiority of our proposed solution, achieving a 21% reduction in time delay and a 19% decrease in energy consumption compared to alternative task offloading schemes. Moreover, our scheme exhibits remarkable adaptability, responding swiftly to changes in various network environments.
基金supported in part by the National Natural Science Foundation of China under Grant No.61473066in part by the Natural Science Foundation of Hebei Province under Grant No.F2021501020+2 种基金in part by the S&T Program of Qinhuangdao under Grant No.202401A195in part by the Science Research Project of Hebei Education Department under Grant No.QN2025008in part by the Innovation Capability Improvement Plan Project of Hebei Province under Grant No.22567637H
文摘Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power stations).To solve the problem,we propose an energy harvesting based task scheduling and resource management framework to provide robust and low-cost edge computing services for smart grid.First,we formulate an energy consumption minimization problem with regard to task offloading,time switching,and resource allocation for mobile devices,which can be decoupled and transformed into a typical knapsack problem.Then,solutions are derived by two different algorithms.Furthermore,we deploy renewable energy and energy storage units at edge servers to tackle intermittency and instability problems.Finally,we design an energy management algorithm based on sampling average approximation for edge computing servers to derive the optimal charging/discharging strategies,number of energy storage units,and renewable energy utilization.The simulation results show the efficiency and superiority of our proposed framework.
文摘The study evaluated the impact of digital health tools, task sharing, capacity building, and maternal health financing on Maternal, Newborn, and Child Health (MNCH) systems in Kisii, Kajiado, and Migori counties in Kenya, under the Tekeleza project1. It aimed to assess how these interventions influenced MNCH service uptake, outcomes, and referral systems, using a mixed-methods approach with data from healthcare providers, community health promoters, and mothers. Findings showed that digital tools like the LUCY App improved coordination and maternal care but highlighted the need for better app features and wider access. Task sharing in areas like sonography enhanced service delivery but required stronger stakeholder engagement. Capacity-building through training was crucial for skill development, though additional training on emerging health issues was needed. Financial interventions, such as the Linda Mama program, boosted service uptake, but reimbursement processes needed improvement. Strengthening referral systems between lower- and higher-level facilities was identified as essential for timely access to care. The study concludes that integrating digital tools, expanding task sharing, enhancing training, and reinforcing health financing and referral systems are critical for strengthening MNCH services in rural Kenya, with broader implications for similar low-resource settings.
文摘With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.
基金supported by National Natural Science Foundation of China under Grant No.62372110Fujian Provincial Natural Science of Foundation under Grants 2023J02008,2024H0009.
文摘The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability and resource efficiency,particularly in mass customization scenarios where production schedules must accommodate dynamic and personalized demands.To address the challenges of dynamic task allocation,uncertainty,and realtime decision-making,this paper proposes Pathfinder,a deep reinforcement learning-based scheduling framework.Pathfinder models scheduling data through three key matrices:execution time(the time required for a job to complete),completion time(the actual time at which a job is finished),and efficiency(the performance of executing a single job).By leveraging neural networks,Pathfinder extracts essential features from these matrices,enabling intelligent decision-making in dynamic production environments.Unlike traditional approaches with fixed scheduling rules,Pathfinder dynamically selects from ten diverse scheduling rules,optimizing decisions based on real-time environmental conditions.To further enhance scheduling efficiency,a specialized reward function is designed to support dynamic task allocation and real-time adjustments.This function helps Pathfinder continuously refine its scheduling strategy,improving machine utilization and minimizing job completion times.Through reinforcement learning,Pathfinder adapts to evolving production demands,ensuring robust performance in real-world applications.Experimental results demonstrate that Pathfinder outperforms traditional scheduling approaches,offering improved coordination and efficiency in smart factories.By integrating deep reinforcement learning,adaptable scheduling strategies,and an innovative reward function,Pathfinder provides an effective solution to the growing challenges of multi-robot job scheduling in mass customization environments.
基金supported by the Project of Science and Technology Research Program of Chongqing Education Commission of China(No.KJZD-K202401105)High-Quality Development Action Plan for Graduate Education at Chongqing University of Technology(No.gzljg2023308,No.gzljd2024204)+1 种基金the Graduate Innovation Program of Chongqing University of Technology(No.gzlcx20233197)Yunnan Provincial Key R&D Program(202203AA080006).
文摘Blockchain technology,based on decentralized data storage and distributed consensus design,has become a promising solution to address data security risks and provide privacy protection in the Internet-of-Things(IoT)due to its tamper-proof and non-repudiation features.Although blockchain typically does not require the endorsement of third-party trust organizations,it mostly needs to perform necessary mathematical calculations to prevent malicious attacks,which results in stricter requirements for computation resources on the participating devices.By offloading the computation tasks required to support blockchain consensus to edge service nodes or the cloud,while providing data privacy protection for IoT applications,it can effectively address the limitations of computation and energy resources in IoT devices.However,how to make reasonable offloading decisions for IoT devices remains an open issue.Due to the excellent self-learning ability of Reinforcement Learning(RL),this paper proposes a RL enabled Swarm Intelligence Optimization Algorithm(RLSIOA)that aims to improve the quality of initial solutions and achieve efficient optimization of computation task offloading decisions.The algorithm considers various factors that may affect the revenue obtained by IoT devices executing consensus algorithms(e.g.,Proof-of-Work),it optimizes the proportion of sub-tasks to be offloaded and the scale of computing resources to be rented from the edge and cloud to maximize the revenue of devices.Experimental results show that RLSIOA can obtain higher-quality offloading decision-making schemes at lower latency costs compared to representative benchmark algorithms.
基金supported and funded by theDeanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-RP23082).
文摘Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G applications.Artificial Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and reliability.In this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task completion.However,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource wastage.Additionally,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities problem.This paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH scenarios.Additionally,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH scenarios.The performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed approach.The simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.
文摘Drawing upon self-determination theory,this study examines the effects of vicarious abusive supervision on third-party’s self-efficacy and task performance within organizational contexts.Data were collected via surveys from 337 employees across diverse organizations.The results indicate that vicarious abusive supervision significantly undermines both self-efficacy and task performance among employees who are indirectly exposed to such behavior but not directly targeted.Furthermore,self-efficacy serves as a mediator between vicarious abusive supervision and task performance;however,this mediating effect is attenuated for employees with a high promotion focus.These findings provide valuable theoretical and practical insights,particularly in the domain of organizational behavior,by emphasizing the critical role of promotion focus in mitigating the negative effects of vicarious abusive supervision.This research contributes to the organizational behavior literature by shifting the focus from the traditional supervisor-subordinate dynamic to a third-party perspective,thereby enriching our understanding of how vicarious abusive supervision impacts employees within organizational settings.The study underscores the importance of self-efficacy and promotion focus as key factors in unethical leadership contexts.
基金the Deanship of Postgraduate Studies and Scientific Research at Majmaah University for funding this research work through the project number(R-2025-1567).
文摘Due to the intense data flow in expanding Internet of Things(IoT)applications,a heavy processing cost and workload on the fog-cloud side become inevitable.One of the most critical challenges is optimal task scheduling.Since this is an NP-hard problem type,a metaheuristic approach can be a good option.This study introduces a novel enhancement to the Artificial Rabbits Optimization(ARO)algorithm by integrating Chaotic maps and Levy flight strategies(CLARO).This dual approach addresses the limitations of standard ARO in terms of population diversity and convergence speed.It is designed for task scheduling in fog-cloud environments,optimizing energy consumption,makespan,and execution time simultaneously three critical parameters often treated individually in prior works.Unlike conventional single-objective methods,the proposed approach incorporates a multi-objective fitness function that dynamically adjusts the weight of each parameter,resulting in better resource allocation and load balancing.In analysis,a real-world dataset,the Open-source Google Cloud Jobs Dataset(GoCJ_Dataset),is used for performance measurement,and analyses are performed on three considered parameters.Comparisons are applied with well-known algorithms:GWO,SCSO,PSO,WOA,and ARO to indicate the reliability of the proposed method.In this regard,performance evaluation is performed by assigning these tasks to Virtual Machines(VMs)in the resource pool.Simulations are performed on 90 base cases and 30 scenarios for each evaluation parameter.The results indicated that the proposed algorithm achieved the best makespan performance in 80% of cases,ranked first in execution time in 61%of cases,and performed best in the final parameter in 69% of cases.In addition,according to the obtained results based on the defined fitness function,the proposed method(CLARO)is 2.52%better than ARO,3.95%better than SCSO,5.06%better than GWO,8.15%better than PSO,and 9.41%better than WOA.
基金supported by National Taiwan University Hospital Yunlin Branch Project NTUHYL 110.C018National Science and Technology Council,Taiwan.
文摘Porters play a crucial role in hospitals because they ensure the efficient transportation of patients,medical equipment,and vital documents.Despite its importance,there is a lack of research addressing the prediction of completion times for porter tasks.To address this gap,we utilized real-world porter delivery data from Taiwan University Hospital,China,Yunlin Branch,Taiwan Region of China.We first identified key features that can influence the duration of porter tasks.We then employed three widely-used machine learning algorithms:decision tree,random forest,and gradient boosting.To leverage the strengths of each algorithm,we finally adopted an ensemble modeling approach that aggregates their individual predictions.Our experimental results show that the proposed ensemble model can achieve a mean absolute error of 3 min in predicting task response time and 4.42 min in task completion time.The prediction error is around 50%lower compared to using only the historical average.These results demonstrate that our method significantly improves the accuracy of porter task time prediction,supporting better resource planning and patient care.It helps ward staff streamline workflows by reducing delays,enables porter managers to allocate resources more effectively,and shortens patient waiting times,contributing to a better care experience.
基金The National Natural Science Foundation of China(62203015,62233001,62273351)The Beijing Natural Science Foundation(4242038)。
文摘This paper considers the swarm vigilance problem for multi-agent systems(MAS),where multiple agents are deployed within a rectangular region for perception-based vigilance.There are two main challenges,namely the task allocation for vigilance roles and the coverage planning of the perception ranges.Firstly,vigilance behavioral patterns and processes in animal populations within natural habitats are investigated.Inspired by these biological vigilance behaviors,an efficient vigilance task allocation model for MAS is proposed.Secondly,the subsequent optimization of task layouts can achieve efficient surveillance coverage with fewer agents,minimizing resource consumption.Thirdly,an improved particle swarm optimization(IPSO)algorithm is proposed,which incorporates fitness-driven adaptive inertia weight dynamics.According to simulation analysis and comparative studies,optimal parameter configurations for genetic algorithm(GA)and IPSO are determined.Finally,the results indicate the proposed IPSO's superior performance to both GA and standard particle swarm optimization(PSO)in vigilance task allocation optimization,with satisfying advantages in computational efficiency and solution quality.
基金funded by the National Key Research and Development Program of China under Grant 2019YFB1803301Beijing Natural Science Foundation (L202002)。
文摘Cybertwin-enabled 6th Generation(6G)network is envisioned to support artificial intelligence-native management to meet changing demands of 6G applications.Multi-Agent Deep Reinforcement Learning(MADRL)technologies driven by Cybertwins have been proposed for adaptive task offloading strategies.However,the existence of random transmission delay between Cybertwin-driven agents and underlying networks is not considered in related works,which destroys the standard Markov property and increases the decision reaction time to reduce the task offloading strategy performance.In order to address this problem,we propose a pipelining task offloading method to lower the decision reaction time and model it as a delay-aware Markov Decision Process(MDP).Then,we design a delay-aware MADRL algorithm to minimize the weighted sum of task execution latency and energy consumption.Firstly,the state space is augmented using the lastly-received state and historical actions to rebuild the Markov property.Secondly,Gate Transformer-XL is introduced to capture historical actions'importance and maintain the consistent input dimension dynamically changed due to random transmission delays.Thirdly,a sampling method and a new loss function with the difference between the current and target state value and the difference between real state-action value and augmented state-action value are designed to obtain state transition trajectories close to the real ones.Numerical results demonstrate that the proposed methods are effective in reducing reaction time and improving the task offloading performance in the random-delay Cybertwin-enabled 6G networks.
基金Supported by Shanghai Municipal Health Commission’s Special Clinical Research Project for the Hygiene Industry,No.20244Y0041Youth Initiation Fund of Naval Medical University,No.2023QN028 and No.2023QN030。
文摘BACKGROUND Working memory serves as a fundamental cognitive function that substantially impacts performance in various cognitive tasks.Extensive neurophysiological research has established that theta oscillations(4-8 Hz)play an essential role in supporting working memory operations.Theta-band transcranial alternating current stimulation(tACS)offers a potential mechanism for working memory enhancement through direct modulation of these fundamental neural oscillations.Nevertheless,current empirical evidence shows substantial variability in the observed effects of theta-tACS across studies.AIM To conduct a systematic review and meta-analysis evaluating the effects of thetatACS on working memory performance in healthy adults.METHODS A systematic literature search was performed on PubMed,EMBASE,and Web of Science up to March 10,2025.Effect sizes were computed using Hedges’g with 95%confidence intervals(CIs),with separate meta-analyses for all included studies and for distinct working memory paradigms[n-back and delayed matchto-sample(DMTS)tasks]to examine potential task-specific effects.Subgroup analyses and meta-regression were performed to evaluate the influence of key moderating variables.RESULTS The systematic review included 21 studies(67 effect sizes).Initial meta-analysis showed theta-tACS moderately improved working memory(Hedges’g=0.405,95%CI:0.212-0.598).However,this effect became nonsignificant after correcting for publication bias(trim-and-fill adjusted Hedges’g=0.082,95%CI:-0.052 to 0.217).Task-specific analyses revealed significant benefits in n-back tasks(Hedges’g=0.463,95%CI:0.193-0.733)but not in DMTS tasks(Hedges’g=0.257,95%CI:-0.186 to 0.553).Moderator analyses showed that performance in n-back tasks was influenced by stimulation frequency(P=0.001),concurrent status(P=0.014),task modality(P=0.005),and duration(P=0.013),whereas only the region of targeted stimulation(P=0.012)moderated DMTS tasks.CONCLUSION Theta-tACS enhances working memory in healthy adults,with effects modulated by the task type and protocol parameters,offering dual implications for cognitive enhancement and clinical interventions.
基金Supported by Wuxi Municipal Health Commission Major Project,No.Z202107Wuxi Taihu Talent Project,No.WXTTP 2021.
文摘BACKGROUND The neural mechanisms underlying aggressive behavior in schizophrenia(SCZ)remain poorly understood.To date,no studies have reported on the event-related potential(ERP)characteristics of aggression in SCZ using the competitive reaction time task(CRTT).Further investigation into the ERP correlates of aggression in SCZ would provide valuable insights into the neural processes involved.AIM To explore the neural mechanism of aggressive behavior in SCZ.METHODS Participants of this study included 40 SCZ patients and 42 healthy controls(HCs).The Reactive Proactive Aggression Questionnaire was used to assess trait of aggression.The Barratt Impulsiveness Scale 11 was used to measure impulsiveness.The Positive and Negative Symptom Scale(PANSS)was used to evaluate psychopathological features and disease severity.All participants were measured with ERP while performing the CRTT.Data of behavior,ERP components(P2,N2,and P3),and feedback-related negativity(FRN)were analyzed.RESULTS Analysis of the behavioral data revealed that compared with HCs,SCZ patients exhibited higher punishment choices.Analysis of ERP components showed that compared with HCs,SCZ patients exhibited higher N2 amplitudes and P2 amplitudes during the decision phase of the CRTT;however,SCZ patients exhibited lower FRN amplitudes and lower P3 amplitudes during the outcome phase of the CRTT.The N2 amplitudes evoked by highintensity provocation were positively related to PANSS-P scores.And the P3 amplitudes evoked in the winning trials were negatively correlated with the PANSS-G scores.CONCLUSION SCZ patients exhibit abnormal ERP characteristics evoked by the CRTT,which suggests the neural correlates of aggressive behavior in SCZ.
基金supported by the National Natural Science Foundation of China(Grant No.62103349)the Henan Province Science and Technology Research Project(Grant No.232102210104).
文摘Edge computing has transformed smart grids by lowering latency,reducing network congestion,and enabling real-time decision-making.Nevertheless,devising an optimal task-offloading strategy remains challenging,as it must jointly minimise energy consumption and response time under fluctuating workloads and volatile network conditions.We cast the offloading problem as aMarkov Decision Process(MDP)and solve it with Deep Reinforcement Learning(DRL).Specifically,we present a three-tier architecture—end devices,edge nodes,and a cloud server—and enhance Proximal Policy Optimization(PPO)to learn adaptive,energy-aware policies.A Convolutional Neural Network(CNN)extracts high-level features from system states,enabling the agent to respond continually to changing conditions.Extensive simulations show that the proposed method reduces task latency and energy consumption far more than several baseline algorithms,thereby improving overall system performance.These results demonstrate the effectiveness and robustness of the framework for real-time task offloading in dynamic smart-grid environments.
基金supported in part by the National Science Foundation of China(Grant No.62172450)the Key R&D Plan of Hunan Province(Grant No.2022GK2008)the Nature Science Foundation of Hunan Province(Grant No.2020JJ4756)。
文摘In task offloading,the movement of vehicles causes the switching of connected RSUs and servers,which may lead to task offloading failure or high service delay.In this paper,we analyze the impact of vehicle movements on task offloading and reveal that data preparation time for task execution can be minimized via forward-looking scheduling.Then,a Bi-LSTM-based model is proposed to predict the trajectories of vehicles.The service area is divided into several equal-sized grids.If the actual position of the vehicle and the predicted position by the model belong to the same grid,the prediction is considered correct,thereby reducing the difficulty of vehicle trajectory prediction.Moreover,we propose a scheduling strategy for delay optimization based on the vehicle trajectory prediction.Considering the inevitable prediction error,we take some edge servers around the predicted area as candidate execution servers and the data required for task execution are backed up to these candidate servers,thereby reducing the impact of prediction deviations on task offloading and converting the modest increase of resource overheads into delay reduction in task offloading.Simulation results show that,compared with other classical schemes,the proposed strategy has lower average task offloading delays.
文摘Under the background of training practical compound talents in software engineering,this paper analyzes the problems existing in the current teaching of software engineering courses represented by software project management,puts forward the team task mechanism of software engineering courses with AI empowerment and cooperation and competition,develops a unified project management platform to support the implementation of team tasks,and proves the effectiveness of the scheme through the results obtained.
