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Dynamic task assignment for multi-UAV system in pursuit-evasion game with evolving performance evaluation model 认领 引用
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作者 Xinning WU Huangchao YU +2 位作者 Junchong MA Yongbin ZHENG Xiangke WANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第4期412-430,共19页
Execution uncertainties,such as motion delay or confrontation in pursuit-evasion problems with rapidly changing states,affect the task performance of multi-Unmanned Aerial Vehicle(UAV)systems.This may lead to the fail... Execution uncertainties,such as motion delay or confrontation in pursuit-evasion problems with rapidly changing states,affect the task performance of multi-Unmanned Aerial Vehicle(UAV)systems.This may lead to the failure of the initial task assignment scheme.To address this problem,this paper takes the interception scenario as a typical case.It proposes a distributed dynamic task assignment algorithm based on an evolving task performance model to reassign UAVs to tasks in an event-triggered manner.This paper combines the underlying execution model with the interception effectiveness model to design the evolving task performance model.This model describes the UAV task performance in a finite interval by predicting and integrating the states and actions of the intercepted UAVs and the targets under execution uncertainty.The discrete task monitor triggers reassignment based on the severity of the task performance deviation.The Consensus-based Auction Algorithm(CBAA)is extended to optimize the task performance function and efficiently give the reassignment scheme.Simulation results demonstrate the feasibility and effectiveness of the proposed algorithm. 展开更多
关键词 Dynamic task assignment Multi-UAV system Task assignment Unmanned aerial vehicle(UAV) Unmanned systems
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CRISPR/Cas9 multiplex editing of four TaSK41 genes increases grain size and weight and reveals a TaSK41-TaSnRK1β1 module associated with carbohydrate metabolism in wheat 认领 引用
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作者 Peipei Zhang Binxue Kong +6 位作者 Lijian Guo Ming Chen Weidong Gao Yanyan Zhang Jianfei Wang Jingfu Ma Delong Yang 《The Crop Journal》 SCIE CSCD 2026年第3期761-774,共14页
Grain size and weight contribute to wheat(Triticum aestivum L.)yield,yet how Glycogen synthase kinase3(GSK3)/SHAGGY-like kinase signaling interfaces with carbohydrate metabolism during grain development remains poorly... Grain size and weight contribute to wheat(Triticum aestivum L.)yield,yet how Glycogen synthase kinase3(GSK3)/SHAGGY-like kinase signaling interfaces with carbohydrate metabolism during grain development remains poorly understood.In this study,we characterized four wheat TaSK41 gene copies(TaSK41-1A,-4A,-5B,and-5D),which were preferentially expressed in young spikes and developing grains,particularly in the pericarp during early development.TaSK41-5B localizes to both the cytoplasm and nucleus.Using CRISPR/Cas9-mediated multiplex genome editing,we generated two independent quadruple mutant lines(task41-cr1 and task41-cr2)in the wheat cultivar‘Fielder'with all four TaSK41 copies simultaneously disrupted.The quadruple mutants exhibited a greater number of grains per spike,increased thousand-grain weight,larger grain size,and enhanced starch accumulation.The larger grains in the mutant lines were associated with increased cell proliferation in the outer pericarp and higher levels of auxin(IAA and IBA)in developing grains.TaSK41 physically interacted with TaSnRK1β1,theβregulatory subunit of SNF1-related protein kinase 1(SnRK1)and promoted its phosphorylation in vivo,supporting that the TaSK41-TaSnRK1β1 module is associated with carbohydrate metabolism.Transcriptomic profiling revealed coordinated changes in genes related to phytohormone signaling,cell-wall remodeling,and starch/sucrose metabolism in developing grains of task41 mutants.Moreover,haplotype association analysis revealed that TaSK41-5B-HapI was significantly associated with higher thousand-grain weight across 233 hexaploid wheat accessions.These results demonstrate that TaSK41 acts as a negative regulator of wheat grain size and weight and provide genetic and haplotype resources for yield improvement. 展开更多
关键词 Wheat CRISPR/Cas9 Thousand-grain weight Haplotype TaSK41
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Fairness-Aware Task Offloading Based on Location Prediction in Collaborative Edge Networks 认领 引用
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作者 Xiaocong Wang Jiajian Li +2 位作者 Peng Zhao Hui Lian Yanjun Shi 《Computers, Materials & Continua》 SCIE EI 2026年第5期1232-1254,共23页
With the widespread deployment of assembly robots in smart manufacturing,efficiently offloading tasks and allocating resources in highly dynamic industrial environments has become a critical challenge for Mobile Edge ... With the widespread deployment of assembly robots in smart manufacturing,efficiently offloading tasks and allocating resources in highly dynamic industrial environments has become a critical challenge for Mobile Edge Computing(MEC).To address this challenge,this paper constructs a cloud-edge-end collaborative MEC system that enables assembly robots to offload complex workflow tasks via multiple paths(horizontal,vertical,and hybrid collaboration).Tomitigate uncertainties arising frommobility,the location predictionmodule is employed.This enables proactive channel-quality estimation,providing forward-looking insights for offloading decisions.Furthermore,we propose a fairness-aware joint optimization framework.Utilizing an improved Multi-Agent Deep Reinforcement Learning(MADRL)algorithm whose reward function incorporates total system cost,positional reliability,and timeout penalties,the framework aims to balance resource distribution among assembly robots while maximizing system utility.Simulation results demonstrate that the proposed framework outperforms traditional offloading strategies.By integrating predictive mobility management with fairness-aware optimization,the framework offers a robust solution for dynamic industrial MEC environments. 展开更多
关键词 Smart manufacturing MEC task offloading location prediction MADRL
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Analysis of a Mirror-based Assist-as-needed Strategy via Task-performance-based Adaptive Impedance Control 认领 引用
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作者 Qing Sun Qingfeng Li +2 位作者 Xiaolong Yang Shuai Guo Jianwei Niu 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第2期771-782,共12页
Mirror-assisted strategies are commonly used in the rehabilitation training of patients with hemiparesis in the upper limbs following a stroke.Traditional robotic mirror assistance focuses on achieving high-precision ... Mirror-assisted strategies are commonly used in the rehabilitation training of patients with hemiparesis in the upper limbs following a stroke.Traditional robotic mirror assistance focuses on achieving high-precision mirror trajectory tracking,often neglecting the issue of active movement in the affected side.This paper proposes a task performance-based adaptive impedance control,where the robot assists the affected side in an assist-as-needed manner,thereby encouraging the patient to perform active movements.To account for inter-individual variability,a method for assessing the affected side’s motor performance,based on the healthy side’s movement level,is introduced.Adaptive impedance control is then constructed based on the motor performance of the affected side,enabling the robot to provide adaptive assistance force.Eight healthy participants were recruited for experimental testing.Experimental results show that when the robot provides mirror-based assist-as-needed to the affected side,the robot’s stiffness coefficient and assistance force are positively correlated with the motor assessment coefficient of the affected side,thereby verifying the feasibility of the proposed strategy.This study offers a robotic-assisted rehabilitation strategy for stroke patients that balances active participation and individual adaptability,with the potential to enhance rehabilitation outcomes and enable precise rehabilitation interventions. 展开更多
关键词 Bimanual robot Adaptive impedance control Task performance Mirror assistance Assist-as-needed
Task Offloading and Edge Computing in IoT-Gaps, Challenges and Future Directions 认领 引用 被引量：1
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作者 Hitesh Mohapatra 《Computers, Materials & Continua》 SCIE EI 2026年第6期268-296,共29页
This review examines current approaches to real-time decision-making and task optimization in Internet of Things systems through the application of machine learning models deployed at the network edge.Existing literat... This review examines current approaches to real-time decision-making and task optimization in Internet of Things systems through the application of machine learning models deployed at the network edge.Existing literature shows that edge-based distributed intelligence reduces cloud dependency.It addresses transmission latency,device energy use,and bandwidth limits.Recent optimization strategies employ dynamic task offloading mechanisms to determine optimal workload placement across local devices and edge servers without centralized coordination.Empirical findings from the literature indicate performance improvements with latency reductions of approximately 32.8%and energy efficiency gains of 27.4%compared to conventional cloud-centric models.However,critical gaps remain in current methodologies.Most studies focus on static network topologies and do not adequately address load balancing across multiple edge nodes.Security vulnerabilities during task transmission are underexplored,and privacy considerations for sensitive data remain insufficiently integrated into existing frameworks.Task caching strategies and fault tolerance mechanisms require further investigation in highly dynamic environments.The ability of existing approaches to handle large-scale deployments and complex edge-cloud collaborative scenarios has not been thoroughly validated.This review synthesizes current progress while identifying fundamental challenges that must be resolved for practical deployment in time-sensitive applications spanning smart manufacturing,autonomous systems,and healthcare monitoring.Future work should prioritize robust security integration,efficient load distribution,and scalability across heterogeneous edge infrastructures. 展开更多
关键词 Edge computing task offloading deep reinforcement learning mobile edge computing IoT performance optimization methodological rigor assessment
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An Adaptive Hybrid Edge-Cloud Collaborative Offloading Method for Large-Scale Computational Tasks of Intelligent Machine Tool:Low-Latency,Energy-Efficient,and Secure 认领 引用
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作者 Zhiwen Lin Kaien Wei +4 位作者 Yiqiao Wang Chuanhai Chen Jinyan Guo Qiang Cheng Zhifeng Liu 《Engineering》 SCIE EI CSCD 2026年第1期201-218,共18页
Intelligent machine tools operating in continuous machining environments are commonly influenced by the coupled effects of multi-component degradation and updates in machining tasks.These factors result in the generat... Intelligent machine tools operating in continuous machining environments are commonly influenced by the coupled effects of multi-component degradation and updates in machining tasks.These factors result in the generation of vast multi-source sensor data streams and numerous computational tasks with interdependent data relationships.The stringent real-time constraints and intricate dependency structures present considerable challenges to traditional single-mode computational frameworks.Furthermore,there is a growing demand for computational offloading solutions in intelligent machine tools that extend beyond merely optimizing latency.These solutions must also address energy management for sustainable manufacturing and ensure security to protect sensitive industrial data.This paper introduces an adaptive hybrid edge-cloud collaborative offloading mechanism that combines single-edge-cloud collaboration with multi-edge-cloud collaboration.This mechanism is capable of dynamically switching between collaborative modes based on the status of computational nodes,task characteristics,dependency complexity,and resource availability,ultimately facilitating low-latency,energy-efficient,and secure task processing.A novel hybrid hyper-heuristic algorithm has been developed to address largescale task allocation challenges in heterogeneous edge-cloud environments,enabling the flexible allocation of computational resources and performance optimization.Extensive experiments indicate that the proposed approach achieves average enhancements of 27.36%in task processing time and 7.89%in energy efficiency when compared to state-of-the-art techniques,all while maintaining superior security performance.Validation through case studies on a digital twin gantry five-axis machining center illustrates that the mechanism effectively coordinates task execution across multi-source concurrent data processing,complex dependency task collaboration,high-computational machine learning workloads,and continuous batch task deployment scenarios,achieving a 37.03%reduction in latency and a 25.93%optimization in energy use relative to previous generation collaboration methods.These results provide both theoretical and technical backing for sustainable and secure computational offloading in intelligent machine tools,thereby contributing to the evolution of next-generation smart manufacturing systems. 展开更多
关键词 Intelligent machine tools Edge-cloud collaboration Task offloading Resilient resources Sustainable computing
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An Enhanced Genetic Algorithm via an Innovative Elite Retention Strategy for Task Offloading in MEC Scenarios 认领 引用
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作者 Chengyu Hou Wenzao Li +3 位作者 Hanyun Li Kui Liu Zhuoning Zhao Hongping Shu 《Computers, Materials & Continua》 SCIE EI 2026年第8期2198-2218,共21页
The rapid growth of Internet of Things(IoT)and 5G technologies has led to a sharp increase in computing demands from wireless devices,making efficient task offloading a critical challenge.Key issues include reducing a... The rapid growth of Internet of Things(IoT)and 5G technologies has led to a sharp increase in computing demands from wireless devices,making efficient task offloading a critical challenge.Key issues include reducing application latency,lowering the energy consumption of terminal devices,and improving overall system performance,all of which directly affect user experience.Traditional genetic algorithms(GA),inspired by biological evolution,have been widely used in task offloading,but they often suffer from slow convergence and a tendency to fall into local optima in complex scenarios,limiting their effectiveness.To address these drawbacks,this paper proposes a task offloading strategy based on a refined elite mechanism in a GA.The algorithm introduces multi-point variation in both crossover and mutation operations to enhance population diversity,avoid local optima,and accelerate convergence.This design leverages the GA’s strength in multi-objective optimization,which outperforms other bionic heuristic algorithms that excel in single domains.Comparative experiments with GA,ant colony optimization,Deep Q-Network,Greedy algorithms,simulated annealing algorithm and particle swarm optimization,show that the proposed algorithm improves convergence speed by 35%,reduces task completion time by 6%,and optimizes energy consumption by approximately 18%. 展开更多
关键词 Task offloading genetic algorithm bandwidth constraint mobile edge computing
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Optimal Task Assignment in Holonic Multi-Agent Systems by Resolving Performative Inconsistencies 认领 引用
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作者 Awais Qasim Aniqa Iftikhar +2 位作者 Hanaa Nafea Nay Chi Moe Oo Byung-Seo Kim 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第7期993-1010,共18页
Optimal task assignment in holonic multi-agent systems has emerged as a pivotal problem in modern distributed systems.Despite substantial gains in agent coordination,many large-scale systems still suffer from poor job... Optimal task assignment in holonic multi-agent systems has emerged as a pivotal problem in modern distributed systems.Despite substantial gains in agent coordination,many large-scale systems still suffer from poor job allocation,resulting in performance bottlenecks and resource waste.Effective task assignment is critical for these systems since it influences individual agent performance and overall adaptability.A significant challenge within holonic multi-agent systems is ensuring optimal task assignment while resolving performative inconsistencies,such as role conflicts and coordination failures among agents.This research proposes a novel optimization framework to address these inconsistencies,enabling more efficient task allocation in holonic multi-agent systems.An objective function that minimizes task completion time,resource usage,and task priority while accounting for performative inconsistencies is presented.The effectiveness of the approach is demonstrated through real-world scenarios of smart transportation systems.Simulation results show that the proposed task allocation framework enables holons to achieve improved overall system performance through optimal distribution of task sets and effective resolution of performative inconsistencies. 展开更多
关键词 Holonic multi agent system task assignment optimization resource management intelligent transport system
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DRL-Based Task Scheduling and Trajectory Control for UAV-Assisted MEC Systems 认领 引用
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作者 Sai Xu Jun Liu +1 位作者 Shengyu Huang Zhi Li 《Computers, Materials & Continua》 SCIE EI 2026年第3期1349-1364,共16页
In scenarios where ground-based cloud computing infrastructure is unavailable,unmanned aerial vehicles(UAVs)act as mobile edge computing(MEC)servers to provide on-demand computation services for ground terminals.To ad... In scenarios where ground-based cloud computing infrastructure is unavailable,unmanned aerial vehicles(UAVs)act as mobile edge computing(MEC)servers to provide on-demand computation services for ground terminals.To address the challenge of jointly optimizing task scheduling and UAV trajectory under limited resources and high mobility of UAVs,this paper presents PER-MATD3,a multi-agent deep reinforcement learning algorithm with prioritized experience replay(PER)into the Centralized Training with Decentralized Execution(CTDE)framework.Specifically,PER-MATD3 enables each agent to learn a decentralized policy using only local observations during execution,while leveraging a shared replay buffer with prioritized sampling and centralized critic during training to accelerate convergence and improve sample efficiency.Simulation results show that PER-MATD3 reduces average task latency by up to 23%,improves energy efficiency by 21%,and enhances service coverage compared to state-of-the-art baselines,demonstrating its effectiveness and practicality in scenarios without terrestrial networks. 展开更多
关键词 Mobile edge computing deep reinforcement learning task offloading resource allocation trajectory control
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Aero-engine fleets scheduling optimization and task assignment under task-constraints 认领 引用
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作者 Xiangzhao XIA Xuyun FU Shisheng ZHONG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第6期355-388,共34页
The scheduling optimization and task assignment of aero-engine fleets are complex and dynamic,presenting a significant challenge in aviation engineering.This study proposes a Dual-layer Collaborative Optimization Fram... The scheduling optimization and task assignment of aero-engine fleets are complex and dynamic,presenting a significant challenge in aviation engineering.This study proposes a Dual-layer Collaborative Optimization Framework(DCOF)to address these challenges.The problem is decomposed into two interrelated sub-problems,scheduling optimization and task assignment,with distinct mathematical models formulated for each.For scheduling optimization,this study proposes an Improved Gravity Particle Swarm Optimization(IGPSO)algorithm.The algorithm enhances global search capability and convergence speed through dynamic weight adjustment and constraint processing repair strategies,effectively handling dynamic variations in engine health and remaining life.For task assignment,an Improved Branch-and-Price(IB&P)method is used.This method combines column generation with branch-and-bound strategies,while integrating heuristic rules and parallel computing techniques to efficiently find optimal solutions under multi-dimensional constraints.By clearly distinguishing between operational and maintenance tasks and considering their interdependencies,the proposed DCOF better captures real operational needs,improving fleet scheduling efficiency and reliability.Experimental validation and engineering simulations confirm the method’s effectiveness,showing advantages in repair balance,task assignment balance,and minimizing engine life waste.The approach enhances both usage efficiency and maintenance management of aero-engine fleets. 展开更多
关键词 Aero-engine fleets Improved branch-and-price Improved gravity particle swarm optimization Scheduling optimization Task assignment
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Unpacking the Role of Grammarly in Iterative Continuation Tasks to Develop L2 Grammar Learning Strategies,Grit,and Competence 认领 引用
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作者 Jianling Zhan Chuyi Zhou 《Chinese Journal of Applied Linguistics》 2026年第1期112-132,161,共21页
The iterative continuation task(ICT)requires English as a foreign language(EFL)learners to read a segment and write a continuation that aligns with the preceding segment of an English novel with successive turns,offer... The iterative continuation task(ICT)requires English as a foreign language(EFL)learners to read a segment and write a continuation that aligns with the preceding segment of an English novel with successive turns,offering exposure to diverse grammatical structures and opportunities for contextualized usage.Given the importance of integrating technology into second language(L2)writing and the critical role that grammar plays in L2 writing development,automated written corrective feedback provided by Grammarly has gained significant attention.This study investigates the impact of Grammarly on grammar learning strategies,grammar grit,and grammar competence among EFL college students engaged in ICT.This study employed a mixed-methods sequential exploratory design;56 participants were divided into an experimental group(n=28),receiving Grammarly feedback for ICT,and a control group(n=28),completing ICT without Grammarly feedback.Quantitative results revealed that both groups showed improvements in L2 grammar learning strategies,grit and competence.For the experimental group,significant differences were observed across all variables of L2 grammar learning strategies,grit,and competence between pre-and post-tests.For the control group,significant differences were only observed in the affective dimension of grammar learning strategies,Consistency of Interest(COI)of grammar grit,and grammar competence.However,the control group presented a significantly higher improvement in grammar competence.Qualitative analysis showed both positive and negative perceptions of Grammarly.The pedagogical implications of integrating Grammarly and ICT for L2 grammar development are discussed. 展开更多
关键词 grammar learning strategies grammar grit grammar competence iterative continuation tasks Grammarly
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Dynamic Reconnaissance Task Planning for Multi-UAV Based on Learning-Enhanced Pigeon-Inspired Optimization 认领 引用
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作者 Yalan Peng Haibin Duan 《Journal of Beijing Institute of Technology》 EI CAS 2026年第1期53-62,共10页
In dynamic and uncertain reconnaissance missions,effective task assignment and path planning for multiple unmanned aerial vehicles(UAVs)present significant challenges.A stochastic multi-UAV reconnaissance scheduling p... In dynamic and uncertain reconnaissance missions,effective task assignment and path planning for multiple unmanned aerial vehicles(UAVs)present significant challenges.A stochastic multi-UAV reconnaissance scheduling problem is formulated as a combinatorial optimization task with nonlinear objectives and coupled constraints.To solve the non-deterministic polynomial(NP)-hard problem efficiently,a novel learning-enhanced pigeon-inspired optimization(L-PIO)algorithm is proposed.The algorithm integrates a Q-learning mechanism to dynamically regulate control parameters,enabling adaptive exploration–exploitation trade-offs across different optimization phases.Additionally,geometric abstraction techniques are employed to approximate complex reconnaissance regions using maximum inscribed rectangles and spiral path models,allowing for precise cost modeling of UAV paths.The formal objective function is developed to minimize global flight distance and completion time while maximizing reconnaissance priority and task coverage.A series of simulation experiments are conducted under three scenarios:static task allocation,dynamic task emergence,and UAV failure recovery.Comparative analysis with several updated algorithms demonstrates that L-PIO exhibits superior robustness,adaptability,and computational efficiency.The results verify the algorithm's effectiveness in addressing dynamic reconnaissance task planning in real-time multi-UAV applications. 展开更多
关键词 unmanned aerial vehicle(UAV) pigeon-inspired optimization reinforcement learning dynamic task planning coverage path planning
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The Continuation Task and the Model-as-Feedback Writing Task in L2 Writing Development:Timing of Model Texts 认领 引用
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作者 Xiaoyan Zhang 《Chinese Journal of Applied Linguistics》 2026年第1期76-91,160,共16页
This study compares the relative efficacy of the continuation task and the model-as-feedbackwriting (MAFW) task in EFL writing development. Ninety intermediate-level Chinese EFL learnerswere randomly assigned to a con... This study compares the relative efficacy of the continuation task and the model-as-feedbackwriting (MAFW) task in EFL writing development. Ninety intermediate-level Chinese EFL learnerswere randomly assigned to a continuation group, a MAFW group, and a control group, each with30 learners. A pretest and a posttest were used to gauge L2 writing development. Results showedthat the continuation task outperformed the MAFW task not only in enhancing the overall qualityof L2 writing, but also in promoting the quality of three components of L2 writing, namely, content,organization, and language. The finding has important implications for L2 writing teaching andlearning. 展开更多
关键词 continuation task model-as-feedback writing task L2 writing development timing of model texts
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Ok Null Test with Multi-Task Gaussian Processes:Cosmic Curvature and Data Compatibility 认领 引用
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作者 Yungui Gong Qing Gao +1 位作者 Xuchen Lu Zhu Yi 《Chinese Physics Letters》 SCIE EI CAS CSCD 2026年第5期414-421,共8页
The Ok null test can not only assess whether the cosmic curvature is zero—thereby,if true,reducing degeneracies between cosmic curvature and other cosmological parameters—but also provide a model-independent chec... The Ok null test can not only assess whether the cosmic curvature is zero—thereby,if true,reducing degeneracies between cosmic curvature and other cosmological parameters—but also provide a model-independent check of compatibility between different data sets.However,traditional implementations often require absolute distance data from Type Ia supernovae(SNe Ia)or baryon acoustic oscillation(BAO)measurements,limiting their applicability because such absolute distance data are usually not accessible.The BAO Alcock-Paczynski(AP)parameter FAP is a measurement of a distance ratio,making the Dark Energy Spectroscopic Instrument(DESI)AP measurements particularly well suited for the Oknull test,as no absolute distance measurements are required.We propose a novel null test of cosmic curvature tailored to DESI BAO data that combines FAPwith ratios such as D′V/DVor D′M/DM.Crucially,this construction eliminates the need for absolute distance measurements.We further develop multi-task Gaussian processes to perform the null test.This approach can also be applied to a joint DESI BAO and SNe Ia dataset,and we find that DESI BAO and SNe Ia data are compatible.Although there is~2σ evidence of nonzero curvature at low redshift z■0.5,this result is not conclusive,largely due to the lack of observational data in the corresponding redshift range. 展开更多
关键词 baryon acoustic oscillation bao measurementslimiting multi task Gaussian processes type ia supernovae sne data setshowevertraditional data compatibility absolute distance data cosmic curvature assess whether cosmic curvature zero therebyif
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Task导向下高职数字化染整虚拟仿真实训体系构建与实践 认领 引用
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作者 郑志荣 钟铉 +2 位作者 王华清 赵新浩 肖德芳 《化纤与纺织技术》 CAS 2026年第7期237-239,共3页
立足高职院校技能型人才培养定位,对接染整企业真实岗位要求,以任务(Task)岗位分析模型为核心驱动,顺应染整产业数字化、绿色化发展趋势,借助虚拟仿真技术破解传统实训中“三高三难”问题。从平台建设、课程重组、教学方式、评价机制、... 立足高职院校技能型人才培养定位,对接染整企业真实岗位要求,以任务(Task)岗位分析模型为核心驱动,顺应染整产业数字化、绿色化发展趋势,借助虚拟仿真技术破解传统实训中“三高三难”问题。从平台建设、课程重组、教学方式、评价机制、校企合作五个层面,构建系统化的虚拟仿真实训育人体系,重组四个递进式实训模块,形成Task导向下模块化染整实训课程方案。实践结果显示,该方案突破了传统实训在时间和成本上的限制,促进了人才培养与产业岗位的有效对接,显著改善了实训教学效果和学生的岗位胜任力,为高职染整专业的数字化教学改革及产教深度融合提供了可推广的实践样板。 展开更多
关键词 Task导向 数字化染整 虚拟仿真 实训课程体系 产教融合
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Service chain caching and task offloading in two-tier UAV-assisted vehicular edge computing networks:An attention-DRL method 认领 引用
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作者 Fengyan Wu Chao Yang +2 位作者 Yanqun Tang Zhen Li Shengli Xie 《Digital Communications and Networks》 SCIE EI CSCD 2026年第4期607-617,共11页
Unmanned Aerial Vehicle(UAV)-assisted Vehicular Edge Computing Networks(VECNs)have emerged as a promising solution to enhance service quality for ground vehicle users.However,the growing demands from users and the lim... Unmanned Aerial Vehicle(UAV)-assisted Vehicular Edge Computing Networks(VECNs)have emerged as a promising solution to enhance service quality for ground vehicle users.However,the growing demands from users and the limited computing and storage resources of UAVs present significant challenges in designing an efficient edge service caching scheme to minimize latency.Moreover,the integration of service caching and task offloading complicates the support of complex tasks by a single UAV.To address these challenges,this paper proposes a novel two-tier UAV-assisted VECNs framework.In this framework,multi-rotor UAVs function as hovering nodes for computational offloading,while a fixed-wing UAV serves as a mobile auxiliary cloud platform,forming a cohesive UAV group.User tasks are structured into a task chain based on the available UAVs.We integrate a joint service chain caching and task offloading scheme that considers UAV computing and storage capacities,duplicate caching,and dynamic transmission latency.To optimize task chain completion latency,we propose an Attention-based Multi-Agent Deep Q-Network(A-MADQN)algorithm.This algorithm incorporates an attention mechanism to narrow the UAV selection space,enabling the selected UAVs to collaboratively make caching and task offloading decisions.Numerical results demonstrate that the proposed algorithm significantly enhances system processing efficiency and reduces task completion latency compared to the benchmark approaches. 展开更多
关键词 Service caching Task chain Multi-UAV Attention mechanism Multi-agent DQN
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小麦GSK激酶TaSK41的功能分析及互作蛋白的筛选 认领 引用
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作者 李灿 张喜伟 +1 位作者 朱博涛 张沛沛 《作物学报》 CAS CSCD 北大核心 2026年第3期677-687,共11页
植物糖原合成激酶3(GSK3)家族成员SK41在籽粒发育和粒重形成过程中发挥重要作用。为进一步探究TaSK41调控小麦籽粒发育的生物学功能及其潜在的分子机制,本研究分析了TaSK41在不同组织中的表达模式、亚细胞定位、过表达Ta SK41水稻的籽... 植物糖原合成激酶3(GSK3)家族成员SK41在籽粒发育和粒重形成过程中发挥重要作用。为进一步探究TaSK41调控小麦籽粒发育的生物学功能及其潜在的分子机制,本研究分析了TaSK41在不同组织中的表达模式、亚细胞定位、过表达Ta SK41水稻的籽粒表型,以及与TaSK41相互作用的蛋白。q RT-PCR分析表明, Ta SK41在各个组织中均有表达,其中在穗部、早期发育的籽粒、子房以及种皮中的表达量较高。亚细胞定位显示, TaSK41-GFP融合蛋白主要定位于细胞质和细胞核中。Ta SK41过表达转基因水稻株系的千粒重显著降低,粒长和粒宽均显著减小。通过酵母双杂交系统筛选小麦籽粒cDNA文库,共获得17个可能与TaSK41相互作用的候选蛋白。进一步对TaSK41与调控籽粒发育相关基因TaARF4和Ta BSK3的全长互作验证,结果显示, TaSK41能够与小麦生长素响应因子TaARF4的全长蛋白发生相互作用。利用荧光素酶互补系统在体内对其互作关系进行了验证,结果表明, TaSK41-nLUC与TaARF4-cLUC共转化烟草叶片,可观察到荧光信号,这表明它们在体内确实存在互作关系。本研究结果为深入解析Ta SK41调控小麦粒重形成的分子机制提供了重要理论依据。 展开更多
关键词 小麦 粒重 TaSK41 酵母双杂交 互作蛋白
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GCN and DRL based on dependent task offloading mechanism in edge computing 认领 引用
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作者 Ruiqi Tong Shaoyong Guo +2 位作者 Xuesong Qiu Feng Qi Dongxiao Yu 《Digital Communications and Networks》 SCIE EI CSCD 2026年第3期397-404,共8页
Task offloading is critical for optimizing resource allocation in edge computing systems.In practical scenarios,user applications often comprise multiple interdependent tasks,where both task dependencies and paralleli... Task offloading is critical for optimizing resource allocation in edge computing systems.In practical scenarios,user applications often comprise multiple interdependent tasks,where both task dependencies and parallelism strongly affect offloading decisions.This paper presents a novel dependent task offloading framework for multiedge server environments.The task offloading problem is formulated as a Markov Decision Process(MDP)to minimize computational delay.Task dependencies are modeled using a Directed Acyclic Graph(DAG),and a Graph Convolutional Network(GCN)encoder is employed to extract DAG features as inputs for a Deep Reinforcement Learning(DRL)model.The proposed DRL-based method applies the Proximal Policy Optimization(PPO)algorithm to simultaneously select subtasks and determine their offloading decisions.Experimental evaluations across varying numbers of subtasks confirm the effectiveness of the approach,demonstrating superior performance compared to state-of-the-art solutions. 展开更多
关键词 Task offloading Edge computing Graph neural network Deep reinforcement learning
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A Deep Reinforcement Learning-Based Pre-Allocation Mechanism for Efficient Task Offloading in Mobile Edge Computing 认领 引用
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作者 Chaobin Wang Xianghong Tang +2 位作者 Jianguang Lu Jing Yang Panliang Yuan 《Computers, Materials & Continua》 SCIE EI 2026年第7期1086-1113,共28页
Mobile Edge Computing(MEC)facilitates the rapid response and energy-efficient execution of tasks on mobile devices.However,determining whether and where to offload tasks remains a significant challenge due to the cons... Mobile Edge Computing(MEC)facilitates the rapid response and energy-efficient execution of tasks on mobile devices.However,determining whether and where to offload tasks remains a significant challenge due to the constantly changing character of workloads in MEC environments.To address this issue,this paper proposes PreAlloc-A2C—a deep reinforcement learning actor-critic-based framework that calculates allocation scores by leveraging both task features(task size,required completion time,and waiting time)and server features(queue length and historical workload).This design enables fully distributed task offloading decisions without centralized coordination.Additionally,a Long Short-Term Memory(LSTM)network is integrated to forecast impending server loads,thereby supporting adaptive scheduling.A tailored reward function is also designed to jointly optimize three key performance metrics:task delay,device energy consumption,and task drop rate.Extensive experiments are conducted to evaluate PreAlloc-A2C against five baseline algorithms:Particle Swarm Optimization(PSO),Advantage Actor-Critic(A2C),Deep Q-Network(DQN),Double Deep Q-Network(DDQN),and Dueling Deep Q-Network(Dueling DQN).The results show that PreAlloc-A2C outperforms all baselines,achieving lower latency,reduced energy consumption,and a lower task drop rate. 展开更多
关键词 Mobile edge computing task offloading deep reinforcement learning
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Optimizing the cyber-physical intelligent transportation system network using enhanced models for data routing and task scheduling 认领 引用
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作者 Srinivasa Gowda G.K Hayder M.A.Ghanimi +5 位作者 Sudhakar Sengan Kolla Bhanu Prakash Meshal Alharbi Roobaea Alroobaea Sultan Algarni Abdullah M.Baqasah 《Digital Communications and Networks》 SCIE EI CSCD 2026年第1期210-222,共13页
Advanced technologies like Cyber-Physical Systems(CPS)and the Internet of Things(IoT)have supported modernizing and automating the transportation region through the introduction of Intelligent Transportation Systems(I... Advanced technologies like Cyber-Physical Systems(CPS)and the Internet of Things(IoT)have supported modernizing and automating the transportation region through the introduction of Intelligent Transportation Systems(ITS).Integrating CPS-ITS and IoT provides real-time Vehicle-to-Infrastructure(V2I)communication,supporting better traffic management,safety,and efficiency.These technological innovations generate complex problems that need to be addressed,uniquely about data routing and Task Scheduling(TS)in ITS.Attempts to solve those problems were primarily based on traditional and experimental methods,and the solutions were not so successful due to the dynamic nature of ITS.This is where the scope of Machine learning(ML)and Swarm Intelligence(SI)has significantly impacted dealing with these challenges;in this line,this research paper presents a novel method for TS and data routing in the CPS-ITS.This paper proposes using a cutting-edge ML algorithm for data transmission from CPS-ITS.This ML has Gated Linear Unit-approximated Reinforcement Learning(GLRL).Greedy Iterative-Particle Swarm Optimization(GI-PSO)has been recommended to develop the Particle Swarm Optimization(PSO)for TS.The primary objective of this study is to enhance the security and effectiveness of ITS systems that utilize CPS-ITS.This study trained and validated the models using a network simulation dataset of 50 nodes from numerous ITS environments.The experiments demonstrate that the proposed GLRL reduces End-toEnd Delay(EED)by 12%,enhances data size use from 83.6%to 88.6%,and achieves higher bandwidth allocation,particularly in high-demand scenarios such as multimedia data streams where adherence improved to 98.15%.Furthermore,the GLRL reduced Network Congestion(NC)by 5.5%,demonstrating its efficiency in managing complex traffic conditions across several environments.The model passed simulation tests in three different environments:urban(UE),suburban(SE),and rural(RE).It met the high bandwidth requirements,made task scheduling more efficient,and increased network throughput(NT).This proved that it was robust and flexible enough for scalable ITS applications.These innovations provide robust,scalable solutions for real-time traffic management,ultimately improving safety,reducing NC,and increasing overall NT.This study can affect ITS by developing it to be more responsive,safe,and effective and by creating a perfect method to set up UE,SE,and RE. 展开更多
关键词 Cyber-physical systems Internet of things Task scheduling optimization Gated linear unit Machine learning
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