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
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
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
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
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
The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous c...The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks.Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay.In this network,the task processed at fog nodes reduces transmission delay.Still,it increases energy consumption,while routing tasks to the cloud server saves energy at the cost of higher communication delay.Moreover,the order in which offloaded tasks are executed affects the system’s efficiency.For instance,executing lower-priority tasks before higher-priority jobs can disturb the reliability and stability of the system.Therefore,an efficient strategy of optimal computation offloading and task scheduling is required for operational efficacy.In this paper,we introduced a multi-objective and enhanced version of Cheeta Optimizer(CO),namely(MoECO),to jointly optimize the computation offloading and task scheduling in cloud-fog networks to minimize two competing objectives,i.e.,energy consumption and communication delay.MoECO first assigns tasks to the optimal computational nodes and then the allocated tasks are scheduled for processing based on the task priority.The mathematical modelling of CO needs improvement in computation time and convergence speed.Therefore,MoECO is proposed to increase the search capability of agents by controlling the search strategy based on a leader’s location.The adaptive step length operator is adjusted to diversify the solution and thus improves the exploration phase,i.e.,global search strategy.Consequently,this prevents the algorithm from getting trapped in the local optimal solution.Moreover,the interaction factor during the exploitation phase is also adjusted based on the location of the prey instead of the adjacent Cheetah.This increases the exploitation capability of agents,i.e.,local search capability.Furthermore,MoECO employs a multi-objective Pareto-optimal front to simultaneously minimize designated objectives.Comprehensive simulations in MATLAB demonstrate that the proposed algorithm obtains multiple solutions via a Pareto-optimal front and achieves an efficient trade-off between optimization objectives compared to baseline methods.展开更多
Strategically coupling nanoparticle hybrids and internal thermosensitive molecular switches establishes an innovative paradigm for constructing micro/nanoscale-reconfigurable robots,facilitating energyefficient CO_(2)...Strategically coupling nanoparticle hybrids and internal thermosensitive molecular switches establishes an innovative paradigm for constructing micro/nanoscale-reconfigurable robots,facilitating energyefficient CO_(2) management in life-support systems of confined space.Here,a micro/nano-reconfigurable robot is constructed from the CO_(2) molecular hunters,temperature-sensitive molecular switch,solar photothermal conversion,and magnetically-driven function engines.The molecular hunters within the molecular extension state can capture 6.19 mmol g^(−1) of CO_(2) to form carbamic acid and ammonium bicarbonate.Interestingly,the molecular switch of the robot activates a molecular curling state that facilitates CO_(2) release through nano-reconfiguration,which is mediated by the temperature-sensitive curling of Pluronic F127 molecular chains during the photothermal desorption.Nano-reconfiguration of robot alters the amino microenvironment,including increasing surface electrostatic potential of the amino group and decreasing overall lowest unoccupied molecular orbital energy level.This weakened the nucleophilic attack ability of the amino group toward the adsorption product derivatives,thereby inhibiting the side reactions that generate hard-to-decompose urea structures,achieving the lowest regeneration temperature of 55℃ reported to date.The engine of the robot possesses non-contact magnetically-driven micro-reconfiguration capability to achieve efficient photothermal regeneration while avoiding local overheating.Notably,the robot successfully prolonged the survival time of mice in the sealed container by up to 54.61%,effectively addressing the issue of carbon suffocation in confined spaces.This work significantly enhances life-support systems for deep-space exploration,while stimulating innovations in sustainable carbon management technologies for terrestrial extreme environments.展开更多
Mine filling materials urgently need to improve mechanical properties and achieve low-carbon transformation.This study explores the mechanism of the synergistic effect of optimizing aggregate fractal grading and intro...Mine filling materials urgently need to improve mechanical properties and achieve low-carbon transformation.This study explores the mechanism of the synergistic effect of optimizing aggregate fractal grading and introducing CO_(2)nanobubble technology to improve the performance of cement-fly ash-based backfill materials(CFB).The properties including fluidity,setting time,uniaxial compressive strength,elastic modulus,porosity,microstructure and CO_(2)storage performance were systematically studied through methods such as fluidity evaluation,time test,uniaxial compression test,mercury intrusion porosimetry(MIP),scanning electron microscopy-energy dispersive spectroscopy analysis(SEM-EDS),and thermogravimetric-differential thermogravimetric analysis(TG-DTG).The experimental results show that the density and strength of the material are significantly improved under the synergistic effect of fractal dimension and CO_(2)nanobubbles.When the fractal dimension reaches 2.65,the mass ratio of coarse and fine aggregates reaches the optimal balance,and the structural density is greatly improved at the same time.At this time,the uniaxial compressive strength and elastic modulus reach their peak values,with increases of up to 13.46%and 27.47%,respectively.CO_(2)nanobubbles enhance the material properties by promoting hydration reaction and carbonization.At the microscopic level,CO_(2)nanobubble water promotes the formation of C-S-H(hydrated calcium silicate),C-A-S-H(hydrated calcium aluminium silicate)gel and CaCO_(3),which is the main way to enhance the performance.Thermogravimetric studies have shown that when the fractal dimension is 2.65,the dehydration of hydration products and the decarbonization process of CaCO_(3)are most obvious,and CO_(2)nanobubble water promotes the carbonization reaction,making it surpass the natural state.The CO_(2)sequestration quality of cement-fly ash-based materials treated with CO_(2)nanobubble water at different fractal dimensions increased by 12.4wt%to 99.8wt%.The results not only provide scientific insights for the design and implementation of low-carbon filling materials,but also provide a solid theoretical basis for strengthening green mining practices and promoting sustainable resource utilization.展开更多
In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task schedul...In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption.展开更多
Modern power systems increasingly depend on interconnected microgrids to enhance reliability and renewable energy utilization.However,the high penetration of intermittent renewable sources often causes frequency devia...Modern power systems increasingly depend on interconnected microgrids to enhance reliability and renewable energy utilization.However,the high penetration of intermittent renewable sources often causes frequency deviations,voltage fluctuations,and poor reactive power coordination,posing serious challenges to grid stability.Conventional Interconnection FlowControllers(IFCs)primarily regulate active power flowand fail to effectively handle dynamic frequency variations or reactive power sharing in multi-microgrid networks.To overcome these limitations,this study proposes an enhanced Interconnection Flow Controller(e-IFC)that integrates frequency response balancing and an Interconnection Reactive Power Flow Controller(IRFC)within a unified adaptive control structure.The proposed e-IFC is implemented and analyzed in DIgSILENT PowerFactory to evaluate its performance under various grid disturbances,including frequency drops,load changes,and reactive power fluctuations.Simulation results reveal that the e-IFC achieves 27.4% higher active power sharing accuracy,19.6% lower reactive power deviation,and 18.2% improved frequency stability compared to the conventional IFC.The adaptive controller ensures seamless transitions between grid-connected and islanded modes and maintains stable operation even under communication delays and data noise.Overall,the proposed e-IFCsignificantly enhances active-reactive power coordination and dynamic stability in renewable-integrated multi-microgrid systems.Future research will focus on coupling the e-IFC with tertiary-level optimization frameworks and conducting hardware-in-the-loop validation to enable its application in large-scale smart microgrid environments.展开更多
An automated approach is proposed for a microassembly task, which is to insert a 10 μm diameter glass tube into a 12 μm diameter hole in a silicon substrate, and bond them together with ultraviolet （UV） curable ad...An automated approach is proposed for a microassembly task, which is to insert a 10 μm diameter glass tube into a 12 μm diameter hole in a silicon substrate, and bond them together with ultraviolet （UV） curable adhesive. Two three-degree-of-freedom micromanipulators axe used to move the glass tube and the dispensing needle, respectively. Visual feedback is provided by an optical microscope. The angle of the microscope axis is precisely calibrated using an autofocus strategy. Robust image segmentation method and feature extraction algorithm are developed to obtain the features of the hole, the glass tube and the dispensing needle. Visual servo control is employed to achieve accurate aligning for the tube and the hole. Automated adhesive dispensing is used to bond the glass tube and the silicon substrate together after the insertion. On-line monitoring ensures that the diameter of the adhesive spot is within a desired range. Experimental results demonstrate the effectiveness of the proposed strategy.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 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.
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 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appreciation to the Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R384)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks.Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay.In this network,the task processed at fog nodes reduces transmission delay.Still,it increases energy consumption,while routing tasks to the cloud server saves energy at the cost of higher communication delay.Moreover,the order in which offloaded tasks are executed affects the system’s efficiency.For instance,executing lower-priority tasks before higher-priority jobs can disturb the reliability and stability of the system.Therefore,an efficient strategy of optimal computation offloading and task scheduling is required for operational efficacy.In this paper,we introduced a multi-objective and enhanced version of Cheeta Optimizer(CO),namely(MoECO),to jointly optimize the computation offloading and task scheduling in cloud-fog networks to minimize two competing objectives,i.e.,energy consumption and communication delay.MoECO first assigns tasks to the optimal computational nodes and then the allocated tasks are scheduled for processing based on the task priority.The mathematical modelling of CO needs improvement in computation time and convergence speed.Therefore,MoECO is proposed to increase the search capability of agents by controlling the search strategy based on a leader’s location.The adaptive step length operator is adjusted to diversify the solution and thus improves the exploration phase,i.e.,global search strategy.Consequently,this prevents the algorithm from getting trapped in the local optimal solution.Moreover,the interaction factor during the exploitation phase is also adjusted based on the location of the prey instead of the adjacent Cheetah.This increases the exploitation capability of agents,i.e.,local search capability.Furthermore,MoECO employs a multi-objective Pareto-optimal front to simultaneously minimize designated objectives.Comprehensive simulations in MATLAB demonstrate that the proposed algorithm obtains multiple solutions via a Pareto-optimal front and achieves an efficient trade-off between optimization objectives compared to baseline methods.
基金supported by the National Natural Science Foundation of China(22168008,22378085)the Guangxi Natural Science Foundation(2024GXNSFDA010053)+1 种基金the Technology Development Project of Guangxi Bossco Environmental Protection Technology Co.,Ltd(202100039)Innovation Project of Guangxi Graduate Education(YCBZ2024065).
文摘Strategically coupling nanoparticle hybrids and internal thermosensitive molecular switches establishes an innovative paradigm for constructing micro/nanoscale-reconfigurable robots,facilitating energyefficient CO_(2) management in life-support systems of confined space.Here,a micro/nano-reconfigurable robot is constructed from the CO_(2) molecular hunters,temperature-sensitive molecular switch,solar photothermal conversion,and magnetically-driven function engines.The molecular hunters within the molecular extension state can capture 6.19 mmol g^(−1) of CO_(2) to form carbamic acid and ammonium bicarbonate.Interestingly,the molecular switch of the robot activates a molecular curling state that facilitates CO_(2) release through nano-reconfiguration,which is mediated by the temperature-sensitive curling of Pluronic F127 molecular chains during the photothermal desorption.Nano-reconfiguration of robot alters the amino microenvironment,including increasing surface electrostatic potential of the amino group and decreasing overall lowest unoccupied molecular orbital energy level.This weakened the nucleophilic attack ability of the amino group toward the adsorption product derivatives,thereby inhibiting the side reactions that generate hard-to-decompose urea structures,achieving the lowest regeneration temperature of 55℃ reported to date.The engine of the robot possesses non-contact magnetically-driven micro-reconfiguration capability to achieve efficient photothermal regeneration while avoiding local overheating.Notably,the robot successfully prolonged the survival time of mice in the sealed container by up to 54.61%,effectively addressing the issue of carbon suffocation in confined spaces.This work significantly enhances life-support systems for deep-space exploration,while stimulating innovations in sustainable carbon management technologies for terrestrial extreme environments.
基金financially supported by the China Scholarship Council(CSC)。
文摘Mine filling materials urgently need to improve mechanical properties and achieve low-carbon transformation.This study explores the mechanism of the synergistic effect of optimizing aggregate fractal grading and introducing CO_(2)nanobubble technology to improve the performance of cement-fly ash-based backfill materials(CFB).The properties including fluidity,setting time,uniaxial compressive strength,elastic modulus,porosity,microstructure and CO_(2)storage performance were systematically studied through methods such as fluidity evaluation,time test,uniaxial compression test,mercury intrusion porosimetry(MIP),scanning electron microscopy-energy dispersive spectroscopy analysis(SEM-EDS),and thermogravimetric-differential thermogravimetric analysis(TG-DTG).The experimental results show that the density and strength of the material are significantly improved under the synergistic effect of fractal dimension and CO_(2)nanobubbles.When the fractal dimension reaches 2.65,the mass ratio of coarse and fine aggregates reaches the optimal balance,and the structural density is greatly improved at the same time.At this time,the uniaxial compressive strength and elastic modulus reach their peak values,with increases of up to 13.46%and 27.47%,respectively.CO_(2)nanobubbles enhance the material properties by promoting hydration reaction and carbonization.At the microscopic level,CO_(2)nanobubble water promotes the formation of C-S-H(hydrated calcium silicate),C-A-S-H(hydrated calcium aluminium silicate)gel and CaCO_(3),which is the main way to enhance the performance.Thermogravimetric studies have shown that when the fractal dimension is 2.65,the dehydration of hydration products and the decarbonization process of CaCO_(3)are most obvious,and CO_(2)nanobubble water promotes the carbonization reaction,making it surpass the natural state.The CO_(2)sequestration quality of cement-fly ash-based materials treated with CO_(2)nanobubble water at different fractal dimensions increased by 12.4wt%to 99.8wt%.The results not only provide scientific insights for the design and implementation of low-carbon filling materials,but also provide a solid theoretical basis for strengthening green mining practices and promoting sustainable resource utilization.
基金supported and funded by theDeanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2503).
文摘In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption.
基金the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia,for funding this research work through the project number“NBU-FFR-2025-3623-11”.
文摘Modern power systems increasingly depend on interconnected microgrids to enhance reliability and renewable energy utilization.However,the high penetration of intermittent renewable sources often causes frequency deviations,voltage fluctuations,and poor reactive power coordination,posing serious challenges to grid stability.Conventional Interconnection FlowControllers(IFCs)primarily regulate active power flowand fail to effectively handle dynamic frequency variations or reactive power sharing in multi-microgrid networks.To overcome these limitations,this study proposes an enhanced Interconnection Flow Controller(e-IFC)that integrates frequency response balancing and an Interconnection Reactive Power Flow Controller(IRFC)within a unified adaptive control structure.The proposed e-IFC is implemented and analyzed in DIgSILENT PowerFactory to evaluate its performance under various grid disturbances,including frequency drops,load changes,and reactive power fluctuations.Simulation results reveal that the e-IFC achieves 27.4% higher active power sharing accuracy,19.6% lower reactive power deviation,and 18.2% improved frequency stability compared to the conventional IFC.The adaptive controller ensures seamless transitions between grid-connected and islanded modes and maintains stable operation even under communication delays and data noise.Overall,the proposed e-IFCsignificantly enhances active-reactive power coordination and dynamic stability in renewable-integrated multi-microgrid systems.Future research will focus on coupling the e-IFC with tertiary-level optimization frameworks and conducting hardware-in-the-loop validation to enable its application in large-scale smart microgrid environments.
基金supported by National Natural Science Foundation of China under(Nos.61227804 and 61105036)
文摘An automated approach is proposed for a microassembly task, which is to insert a 10 μm diameter glass tube into a 12 μm diameter hole in a silicon substrate, and bond them together with ultraviolet （UV） curable adhesive. Two three-degree-of-freedom micromanipulators axe used to move the glass tube and the dispensing needle, respectively. Visual feedback is provided by an optical microscope. The angle of the microscope axis is precisely calibrated using an autofocus strategy. Robust image segmentation method and feature extraction algorithm are developed to obtain the features of the hole, the glass tube and the dispensing needle. Visual servo control is employed to achieve accurate aligning for the tube and the hole. Automated adhesive dispensing is used to bond the glass tube and the silicon substrate together after the insertion. On-line monitoring ensures that the diameter of the adhesive spot is within a desired range. Experimental results demonstrate the effectiveness of the proposed strategy.
