Mobile Edge Computing(MEC)is a promising solution to tackle the upcoming computing tsunami in 5G/6G era by effectively utilizing the idle resource at the mobile edge.In this work,a multi⁃hop D2D⁃enabled MEC scenario w...Mobile Edge Computing(MEC)is a promising solution to tackle the upcoming computing tsunami in 5G/6G era by effectively utilizing the idle resource at the mobile edge.In this work,a multi⁃hop D2D⁃enabled MEC scenario was studied,where mobile devices at network edge connect and share resources with each other via multi⁃hop D2D.The research focus was on the micro⁃task scheduling problem in the multi⁃hop D2D⁃enabled MEC system,where each task was divided into multiple sequential micro⁃tasks,such as data downloading micro⁃task,data processing micro⁃task,and data uploading micro⁃task,according to their functionalities as well as resource requirements.A joint Task Failure Probability and Energy Consumption Minimization(TFP⁃ECM)problem was proposed,aiming at minimizing the task failure probability and the energy consumption jointly.To solve the problem,several linearization methods were proposed to relax the constraints and convert the original problem into an integer linear programming(ILP).Simulation results show that the proposed solution outperformed the existing solutions(with indivisible tasks or without resource sharing)in terms of both total cost and task failure probability.展开更多
In the era of the Internet of Things,distributed computing alleviates the problem of insufficient terminal computing power by integrating idle resources of heterogeneous devices.However,the imbalance between task exec...In the era of the Internet of Things,distributed computing alleviates the problem of insufficient terminal computing power by integrating idle resources of heterogeneous devices.However,the imbalance between task execution delay and node energy consumption,and the scheduling and adaptation challenges brought about by device heterogeneity,urgently need to be addressed.To tackle this problem,this paper constructs a multi-objective real-time task scheduling model that considers task real-time performance,execution delay,system energy consumption,and node interests.The model aims to minimize the delay upper bound and total energy consumption while maximizing system satisfaction.A real-time task scheduling algorithm based on bilateral matching game is proposed.By designing a bidirectional preference mechanism between tasks and computing nodes,combined with a multi-round stable matching strategy,accurate matching between tasks and nodes is achieved.Simulation results show that compared with the baseline scheme,the proposed algorithm significantly reduces the total execution cost,effectively balances the task execution delay and the energy consumption of compute nodes,and takes into account the interests of each network compute node.展开更多
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
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
Micro/nanoplastics(M/NPs)have become pervasive environmental pollutants,posing significant risks to human health through various exposure routes,including ingestion,inhalation,and direct contact.This review systematic...Micro/nanoplastics(M/NPs)have become pervasive environmental pollutants,posing significant risks to human health through various exposure routes,including ingestion,inhalation,and direct contact.This review systematically examined the potential impacts of M/NPs on ocular health,focusing on exposure pathways,toxicological mechanisms,and resultant damage to the eye.Ocular exposure to M/NPs can occur via direct contact and oral ingestion,with the latter potentially leading to the penetration of particles through ocular biological barriers into ocular tissues.The review highlighted that M/NPs can induce adverse effects on the ocular surface,elevate intraocular pressure,and cause abnormalities in the vitreous and retina.Mechanistically,oxidative stress and inflammation are central to M/NP-induced ocular damage,with smaller particles often exhibiting greater toxicity.Overall,this review underscored the potential risks of M/NPs to ocular health and emphasized the need for further research to elucidate exposure mechanisms,toxicological pathways,and mitigation strategies.展开更多
Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.Howev...Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.展开更多
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
Micro silicon(mSi)is a promising anode candidate for all-solid-state batteries due to its high specific capacity,low side reactions,and high tap density.However,silicon suffers from its poor electronic and ionic condu...Micro silicon(mSi)is a promising anode candidate for all-solid-state batteries due to its high specific capacity,low side reactions,and high tap density.However,silicon suffers from its poor electronic and ionic conductivity,which is particularly severe on a micro scale and in solid-state systems,leading to increased polarization and inferior electrochemical performance.Doping can broaden the transmission pathways and reduce the diffusion energy barrier for electrons and lithium ions.However,achieving effective,uniform doping in mSi is challenging due to its longer diffusion paths and higher energy barriers.Therefore,current doping research is primarily limited to nanosilicon.In this study,we successfully used a Joule-heating activated staged thermal treatment to achieve full-depth doping of germanium(Ge)in the mSi substrate.The Joule-heating process activated the mSi substrate,resulting in abundant vacancy defects that reduced the diffusion barrier of Ge into the silicon lattice and facilitated full-depth Ge doping.Surprisingly,the resulting Si-Ge anode exhibited significantly enhanced electrical conductivity(70 times).Meanwhile,the improved Li-ion conductivity in mSi and the reduced Young’s modulus enhance the electrode reaction kinetics and integrity after cycling.Ge-doped silicon anodes demonstrate excellent electrochemical performance when applied in sulfide solid-state half-cells and full-cells.This work provides substantial insights into the rational structural design of mSi alloyed anode materials,paving the way for the development of high-performance solid-state Li-ion batteries.展开更多
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
Extreme cold weather seriously harms human thermoregulatory system,necessitating high-performance insulating garments to maintain body temperature.However,as the core insulating layer,advanced fibrous materials always...Extreme cold weather seriously harms human thermoregulatory system,necessitating high-performance insulating garments to maintain body temperature.However,as the core insulating layer,advanced fibrous materials always struggle to balance mechanical properties and thermal insulation,resulting in their inability to meet the demands for both washing resistance and personal protection.Herein,inspired by the natural spring-like structures of cucumber tendrils,a superelastic and washable micro/nanofibrous sponge(MNFS)based on biomimetic helical fibers is directly prepared utilizing multiple-jet electrospinning technology for high-performance thermal insulation.By regulating the conductivity of polyvinylidene fluoride solution,multiple-jet ejection and multiple-stage whipping of jets are achieved,and further control of phase separation rates enables the rapid solidification of jets to form spring-like helical fibers,which are directly entangled to assemble MNFS.The resulting MNFS exhibits superelasticity that can withstand large tensile strain(200%),1000 cyclic tensile or compression deformations,and retain good resilience even in liquid nitrogen(-196℃).Furthermore,the MNFS shows efficient thermal insulation with low thermal conductivity(24.85 mW m^(-1)K^(-1)),close to the value of dry air,and remains structural stability even after cyclic washing.This work offers new possibilities for advanced fibrous sponges in transportation,environmental,and energy applications.展开更多
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
The deployment of flexible zinc-ion batteries is impeded by dendrite growth from random anode defects.Conventional defect-elimination strategies often compromise flexibility and fail to achieve uniform interfaces.We p...The deployment of flexible zinc-ion batteries is impeded by dendrite growth from random anode defects.Conventional defect-elimination strategies often compromise flexibility and fail to achieve uniform interfaces.We propose a paradigm shift:reconfiguring random defects into engineered,monodisperse artificial micro-curves to homogenize electric fields and guide aligned zinc(Zn)deposition.Using moisture-assisted flash heating,we transform zincophilic silver(Ag)coatings on carbon fibers into uniformly dispersed micro-curved particles(Ag Particles@CC),creating identical nucleation sites with optimal zinc ion(Zn^(2+))adsorption energetics.Theoretical simulations confirm these structures eliminate localized field concentrations,enabling homogeneous plating/stripping.This design demonstrates remarkable performance,with ultrastable 1500 cycles at 10 mA cm^(-2)(98.6%avg.Coulombic efficiency)and symmetric cell operation>650 h(57.7 mV hysteresis).Crucially,interparticle discontinuities preserve intrinsic flexibility,enabling flexible pouch cells(Ag Particles@CC-Zn//NaV_(3)O_(8)·1,5H_(2)O)to successfully power wearable devices such as smartwatches and smartphones.This work establishes defect reconfiguration via artificial micro-curvature engineering as a universal strategy toward dendritesuppressed,flexible energy storage.展开更多
基金National Natural Science Foundation of China(Grant Nos.61831008,61525103 and 61972113)the Guangdong Science and Tech⁃nology Planning Project(Grant No.2018B030322004)the Basic Research Project of Shenzhen Science and Technology Program(Grant No.JCYJ20190806112215116).
文摘Mobile Edge Computing(MEC)is a promising solution to tackle the upcoming computing tsunami in 5G/6G era by effectively utilizing the idle resource at the mobile edge.In this work,a multi⁃hop D2D⁃enabled MEC scenario was studied,where mobile devices at network edge connect and share resources with each other via multi⁃hop D2D.The research focus was on the micro⁃task scheduling problem in the multi⁃hop D2D⁃enabled MEC system,where each task was divided into multiple sequential micro⁃tasks,such as data downloading micro⁃task,data processing micro⁃task,and data uploading micro⁃task,according to their functionalities as well as resource requirements.A joint Task Failure Probability and Energy Consumption Minimization(TFP⁃ECM)problem was proposed,aiming at minimizing the task failure probability and the energy consumption jointly.To solve the problem,several linearization methods were proposed to relax the constraints and convert the original problem into an integer linear programming(ILP).Simulation results show that the proposed solution outperformed the existing solutions(with indivisible tasks or without resource sharing)in terms of both total cost and task failure probability.
基金Supported by the National Program on Key Basic Research Project(2020YFA0713600)the National Natural Science Foundation of China(62272214)。
文摘In the era of the Internet of Things,distributed computing alleviates the problem of insufficient terminal computing power by integrating idle resources of heterogeneous devices.However,the imbalance between task execution delay and node energy consumption,and the scheduling and adaptation challenges brought about by device heterogeneity,urgently need to be addressed.To tackle this problem,this paper constructs a multi-objective real-time task scheduling model that considers task real-time performance,execution delay,system energy consumption,and node interests.The model aims to minimize the delay upper bound and total energy consumption while maximizing system satisfaction.A real-time task scheduling algorithm based on bilateral matching game is proposed.By designing a bidirectional preference mechanism between tasks and computing nodes,combined with a multi-round stable matching strategy,accurate matching between tasks and nodes is achieved.Simulation results show that compared with the baseline scheme,the proposed algorithm significantly reduces the total execution cost,effectively balances the task execution delay and the energy consumption of compute nodes,and takes into account the interests of each network compute node.
文摘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.
基金supported by the National Key R&D Program of China under Grant Nos.2024YFD2400200 and 2024YFD2400204supported in part by the Science and Technology Development Program for the Two Zones under Grant No.2023LQ02004.
文摘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.
基金Supported by the Guangdong Provincial Natural Science Foundation(No.2114050001527).
文摘Micro/nanoplastics(M/NPs)have become pervasive environmental pollutants,posing significant risks to human health through various exposure routes,including ingestion,inhalation,and direct contact.This review systematically examined the potential impacts of M/NPs on ocular health,focusing on exposure pathways,toxicological mechanisms,and resultant damage to the eye.Ocular exposure to M/NPs can occur via direct contact and oral ingestion,with the latter potentially leading to the penetration of particles through ocular biological barriers into ocular tissues.The review highlighted that M/NPs can induce adverse effects on the ocular surface,elevate intraocular pressure,and cause abnormalities in the vitreous and retina.Mechanistically,oxidative stress and inflammation are central to M/NP-induced ocular damage,with smaller particles often exhibiting greater toxicity.Overall,this review underscored the potential risks of M/NPs to ocular health and emphasized the need for further research to elucidate exposure mechanisms,toxicological pathways,and mitigation strategies.
文摘Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.
文摘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.
基金financially supported by the National Key Research and Development Program(2022YFE0127400)the National Natural Science Foundation of China(52172040,52202041,and U23B2077)+1 种基金Taishan Scholar Project of Shandong Province(tsqn202211086,ts202208832,tsqnz20221118)the Fundamental Research Funds for the Central Universities(23CX06055A).
文摘Micro silicon(mSi)is a promising anode candidate for all-solid-state batteries due to its high specific capacity,low side reactions,and high tap density.However,silicon suffers from its poor electronic and ionic conductivity,which is particularly severe on a micro scale and in solid-state systems,leading to increased polarization and inferior electrochemical performance.Doping can broaden the transmission pathways and reduce the diffusion energy barrier for electrons and lithium ions.However,achieving effective,uniform doping in mSi is challenging due to its longer diffusion paths and higher energy barriers.Therefore,current doping research is primarily limited to nanosilicon.In this study,we successfully used a Joule-heating activated staged thermal treatment to achieve full-depth doping of germanium(Ge)in the mSi substrate.The Joule-heating process activated the mSi substrate,resulting in abundant vacancy defects that reduced the diffusion barrier of Ge into the silicon lattice and facilitated full-depth Ge doping.Surprisingly,the resulting Si-Ge anode exhibited significantly enhanced electrical conductivity(70 times).Meanwhile,the improved Li-ion conductivity in mSi and the reduced Young’s modulus enhance the electrode reaction kinetics and integrity after cycling.Ge-doped silicon anodes demonstrate excellent electrochemical performance when applied in sulfide solid-state half-cells and full-cells.This work provides substantial insights into the rational structural design of mSi alloyed anode materials,paving the way for the development of high-performance solid-state Li-ion batteries.
基金supported by the National Natural Science Foundation of China under Grant No.61701100.
文摘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.
基金supported by Young Elite Scientists Sponsorship Program by China Association for Science and Technology(No.2022QNRC001)the National Natural Science Foundation of China(No.52273053)the Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission(No.21CGA41)。
文摘Extreme cold weather seriously harms human thermoregulatory system,necessitating high-performance insulating garments to maintain body temperature.However,as the core insulating layer,advanced fibrous materials always struggle to balance mechanical properties and thermal insulation,resulting in their inability to meet the demands for both washing resistance and personal protection.Herein,inspired by the natural spring-like structures of cucumber tendrils,a superelastic and washable micro/nanofibrous sponge(MNFS)based on biomimetic helical fibers is directly prepared utilizing multiple-jet electrospinning technology for high-performance thermal insulation.By regulating the conductivity of polyvinylidene fluoride solution,multiple-jet ejection and multiple-stage whipping of jets are achieved,and further control of phase separation rates enables the rapid solidification of jets to form spring-like helical fibers,which are directly entangled to assemble MNFS.The resulting MNFS exhibits superelasticity that can withstand large tensile strain(200%),1000 cyclic tensile or compression deformations,and retain good resilience even in liquid nitrogen(-196℃).Furthermore,the MNFS shows efficient thermal insulation with low thermal conductivity(24.85 mW m^(-1)K^(-1)),close to the value of dry air,and remains structural stability even after cyclic washing.This work offers new possibilities for advanced fibrous sponges in transportation,environmental,and energy applications.
基金funded by Taif University,Taif,Saudi Arabia,project number(TU-DSPP-2024-17)。
文摘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.
基金supported by the National Natural Science Foundation of China(52202218)the Fundamental Research Funds for the Central Universities(CUSF-DH-T-2023044)。
文摘The deployment of flexible zinc-ion batteries is impeded by dendrite growth from random anode defects.Conventional defect-elimination strategies often compromise flexibility and fail to achieve uniform interfaces.We propose a paradigm shift:reconfiguring random defects into engineered,monodisperse artificial micro-curves to homogenize electric fields and guide aligned zinc(Zn)deposition.Using moisture-assisted flash heating,we transform zincophilic silver(Ag)coatings on carbon fibers into uniformly dispersed micro-curved particles(Ag Particles@CC),creating identical nucleation sites with optimal zinc ion(Zn^(2+))adsorption energetics.Theoretical simulations confirm these structures eliminate localized field concentrations,enabling homogeneous plating/stripping.This design demonstrates remarkable performance,with ultrastable 1500 cycles at 10 mA cm^(-2)(98.6%avg.Coulombic efficiency)and symmetric cell operation>650 h(57.7 mV hysteresis).Crucially,interparticle discontinuities preserve intrinsic flexibility,enabling flexible pouch cells(Ag Particles@CC-Zn//NaV_(3)O_(8)·1,5H_(2)O)to successfully power wearable devices such as smartwatches and smartphones.This work establishes defect reconfiguration via artificial micro-curvature engineering as a universal strategy toward dendritesuppressed,flexible energy storage.
