Dear Editor,This letter proposes a novel Nash bargaining solution-based multiobjective model predictive control(MPC)scheme to deal with the interaction force control and the path-following problem of the constrained i...Dear Editor,This letter proposes a novel Nash bargaining solution-based multiobjective model predictive control(MPC)scheme to deal with the interaction force control and the path-following problem of the constrained interactive robot.Considering the elastic interaction force model,a mechanical trade-off always exists between the interaction force and position,which means that neither force nor path following can satisfy their desired demands completely.Based on this consideration,two irreconcilable control specifications,the force object function and the position track object function,are proposed,and a new multi-objective MPC scheme is then designed.展开更多
In this study,FeCr_(x)MnAlCu(x=0,0.5,1.0,1.5,2.0)high-entropy alloys were fabricated using vacuum arc melting,and the corrosion behavior of these alloys in 3.5wt%NaCl solution at room temperature was investigated by e...In this study,FeCr_(x)MnAlCu(x=0,0.5,1.0,1.5,2.0)high-entropy alloys were fabricated using vacuum arc melting,and the corrosion behavior of these alloys in 3.5wt%NaCl solution at room temperature was investigated by electrochemical dynamic potential polarization curves and immersion experiments.The microstructure results show that the high-entropy alloy with x=0 has a body-centered cubic phase structure,whereas the high-entropy alloys with x=0.5–2.0 have a mixed face-centered cubic+body-centered cubic dual-phase structure.The corrosion results show that the corrosion resistance of the high-entropy alloy is increased with the increase in Cr content.Among them,the high-entropy alloy with x=2.0 exhibits the optimal corrosion resistance:the highest self-corrosion potential(E_(corr)=−0.354 V vs.Ag/AgCl),the smallest self-corrosion current density(I_(corr)=1.991×10^(−6)A·cm^(−2)),and the smallest corrosion rate(0.0292 mm/a).The composite passivation film of oxides and hydroxides is formed on the surface of the corroded high-entropy alloys,and the Cr_(2)O_(3)content is increased with the increase in Cr content,which effectively improves the stability and protective properties of the passivation film.展开更多
In this study,a straightforward one-step hydrothermal method was successfully utilized to synthesize the solid solution Na_(0.9)Mg_(0.45)Ti_(3.55)O_(8)-Na_(2)Ni_(2)Ti_(6)O_(16)(NNMTO-x),where x denotes the molar perce...In this study,a straightforward one-step hydrothermal method was successfully utilized to synthesize the solid solution Na_(0.9)Mg_(0.45)Ti_(3.55)O_(8)-Na_(2)Ni_(2)Ti_(6)O_(16)(NNMTO-x),where x denotes the molar percentage of Na_(2)Ni_(2)Ti_(6)O_(16)(NNTO)within Na_(0.9)Mg_(0.45)Ti_(3.55)O_(8)(NMTO),with x values of 10,20,30,40,and 50.Both XPS(X-ray Photoelectron Spectroscopy)and EDX(Energy Dispersive X-ray Spectroscopy)analyses unequivocally validated the formation of the NNMTO-x solid solutions.It was observed that when x is below 40,the NNMTO-x solid solution retains the structural characteristics of the original NMTO.However,beyond this threshold,significant alterations in crystal morphology were noted,accompanied by a noticeable decline in photocatalytic activity.Notably,the absorption edge of NNMTO-x(x<40)exhibited a shift towards the visible-light spectrum,thereby substantially broadening the absorption range.The findings highlight that NNMTO-30 possesses the most pronounced photocatalytic activity for the reduction of CO_(2).Specifically,after a 6 h irradiation period,the production rates of CO and CH_(4)were recorded at 42.38 and 1.47μmol/g,respectively.This investigation provides pivotal insights that are instrumental in the advancement of highly efficient and stable photocatalysts tailored for CO_(2)reduction processes.展开更多
To investigate the effect of solution treatment and aging process parameters on the microstructure and mechanical properties of TB18 titanium alloy,process optimization research was conducted based on the mixed-level ...To investigate the effect of solution treatment and aging process parameters on the microstructure and mechanical properties of TB18 titanium alloy,process optimization research was conducted based on the mixed-level orthogonal experiment design of factor levels.Results show that through range analysis,the significance order of process parameters is determined as follows:solution cooling method>solution temperature>aging time>aging temperature>solution time.Considering the strength-ductility matching and engineering application requirements,the benchmark parameters are selected as solution time of 1 h,solution cooling method of air cooling(AC),aging temperature of 525℃,and aging time of 4 h.Furthermore,the effects of solution temperature in the range of 790–870℃ on the impact toughness and micro-fracture characteristics of the alloy were studied.The results reveal that the larger the area of shear lip and fibrous zone,and the smaller the area of radiation zone,the better the toughness of the alloy.With the increase in solution temperature,the length of secondary cracks on the fracture surface increases,the number of dimples increases,and the toughness is enhanced.Based on the collaborative optimization of strength and toughness,the optimal heat treatment process for TB18 alloy is determined as 870℃/1 h,AC+525℃/4 h,AC.展开更多
In this paper,we consider a Schr¨odinger-Poisson system with sublinear nonlinearity.The growth of nonlinearity depends on potential function and a bounded function.We first obtain the existence of nontrivial solu...In this paper,we consider a Schr¨odinger-Poisson system with sublinear nonlinearity.The growth of nonlinearity depends on potential function and a bounded function.We first obtain the existence of nontrivial solution sequence with negative energy for the system via a variant Clark’s theorem.Then we get the asymptotical property of the solution sequence by L∞norm.展开更多
Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based...Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based car-following(CF)framework employing the Deep Deterministic Policy Gradient(DDPG)algorithm,which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning.Utilizing real-world driving data from the highD dataset,the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios.The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control(MPC-ACC)controller.Results show that theDRLmodel significantly enhances safety,achieving zero collisions and a higher average time-to-collision(TTC)of 8.45 s,compared to 5.67 s for MPC and 6.12 s for human drivers.For efficiency,the model demonstrates 89.2% headway compliance and maintains speed tracking errors below 1.2 m/s in 90% of cases.In terms of energy optimization,the proposed approach reduces fuel consumption by 5.4% relative to MPC.Additionally,it enhances passenger comfort by lowering jerk values by 65%,achieving 0.12 m/s3 vs.0.34 m/s3 for human drivers.A multi-objective reward function is integrated to ensure stable policy convergence while simultaneously balancing the four key performance metrics.Moreover,the findings underscore the potential of DRL in advancing autonomous vehicle control,offering a robust and sustainable solution for safer,more efficient,and more comfortable transportation systems.展开更多
This study investigates the dimensionless quasi-geostrophic potential vorticity(QG-PV)equation with external sources.Employing the Gardner-Morikawa transformation and weakly nonlinear perturbation expansion,we derive ...This study investigates the dimensionless quasi-geostrophic potential vorticity(QG-PV)equation with external sources.Employing the Gardner-Morikawa transformation and weakly nonlinear perturbation expansion,we derive the nonlinear Boussinesq equation with external sources.We demonstrate the existence of explicit zero-order and first-order Wronskian solutions for the model equation whenα_4=0.Furthermore,using a modified Jacobi elliptic function method,we obtain soliton-like solutions for bothα_4=0 andα_4≠0.Analysis of these solutions reveals that the generalizedβ-plane approximation and shear flow are significant factors in inducing nonlinear Rossby waves,and that external sources play a crucial role in influencing Rossby wave behavior.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
Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structu...Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:https://github.com/jinhao-ok1/Topo-for-DCTWS.git.展开更多
In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and red...In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and reduces population diversity.To address these challenges,we propose a novel algorithm named Constraint IntensityDriven Evolutionary Multitasking(CIDEMT),which employs a two-stage,tri-task framework to dynamically integrates problem structure and knowledge transfer.In the first stage,three cooperative tasks are designed to explore the Constrained Pareto Front(CPF),the Unconstrained Pareto Front(UPF),and theε-relaxed constraint boundary,respectively.A CPF-UPF relationship classifier is employed to construct a problem-type-aware evolutionary strategy pool.At the end of the first stage,each task selects strategies from this strategy pool based on the specific type of problem,thereby guiding the subsequent evolutionary process.In the second stage,while each task continues to evolve,aτ-driven knowledge transfer mechanism is introduced to selectively incorporate effective solutions across tasks.enhancing the convergence and feasibility of the main task.Extensive experiments conducted on 32 benchmark problems from three test suites(LIRCMOP,DASCMOP,and DOC)demonstrate that CIDEMT achieves the best Inverted Generational Distance(IGD)values on 24 problems and the best Hypervolume values(HV)on 22 problems.Furthermore,CIDEMT significantly outperforms six state-of-the-art constrained multi-objective evolutionary algorithms(CMOEAs).These results confirm CIDEMT’s superiority in promoting convergence,diversity,and robustness in solving complex CMOPs.展开更多
Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may r...Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.展开更多
Federated learning is a distributed framework that trains a centralised model using data from multiple clients without transferring that data to a central server.Despite rapid progress,federated learning still faces s...Federated learning is a distributed framework that trains a centralised model using data from multiple clients without transferring that data to a central server.Despite rapid progress,federated learning still faces several unsolved challenges.Specifically,communication costs and system heterogeneity,such as nonidentical data distribution,hinder federated learning's progress.Several approaches have recently emerged for federated learning involving heterogeneous clients with varying computational capabilities(namely,heterogeneous federated learning).However,heterogeneous federated learning faces two key challenges:optimising model size and determining client selection ratios.Moreover,efficiently aggregating local models from clients with diverse capabilities is crucial for addressing system heterogeneity and communication efficiency.This paper proposes an evolutionary multiobjective optimisation framework for heterogeneous federated learning(MOHFL)to address these issues.Our approach elegantly formulates and solves a biobjective optimisation problem that minimises communication cost and model error rate.The decision variables in this framework comprise model sizes and client selection ratios for each Q client cluster,yielding a total of 2×Q optimisation parameters to be tuned.We develop a partition-based strategy for MOHFL that segregates clients into clusters based on their communication and computation capabilities.Additionally,we implement an adaptive model sizing mechanism that dynamically assigns appropriate subnetwork architectures to clients based on their computational constraints.We also propose a unified aggregation framework to combine models of varying sizes from heterogeneous clients effectively.Extensive experiments on multiple datasets demonstrate the effectiveness and superiority of our proposed method compared to existing approaches.展开更多
Let A be a 3×3 singular or diagonalizable matrix,all solutions to the Yang-Baxter-like matrix equation have been determined.However,finding all solutions for full rank,non-diagonalizable matrices remains challeng...Let A be a 3×3 singular or diagonalizable matrix,all solutions to the Yang-Baxter-like matrix equation have been determined.However,finding all solutions for full rank,non-diagonalizable matrices remains challenging.By utilizing classification techniques,we establish all solutions of the Yang-Baxter-like matrix equation in this paper when the coefficient matrix A is similar to non-diagonalizable matrix diag(λ,J_(2)(λ))withλ̸=0.More specifically,we divide the non-diagonal elements of the solution into 10 different cases.By discussing each situation,we establish all solutions of the Yang-Baxter-like matrix equation.The results of this work enrich the existing ones.展开更多
Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrain...Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.展开更多
Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To addres...Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To address these challenges,this paper proposes a multi-objective trajectory optimization framework.The system dynamics capture both nonlinear rigid-flexible coupling and antenna deformation through a reduced-order formulation.To enhance discretization efficiency,a predictive-terminal hp-adaptive pseudospectral method is employed,assigning collocation density based on task-phase characteristics:finer resolution is applied to dynamic segments requiring higher accuracy,especially near the terminal phase.This enables efficient transcription of the continuous-time problem into a Nonlinear Programming Problem(NLP).The resulting NLP is then solved using a multi-objective optimization strategy based on the nondominated sorting genetic algorithm II,which explores trade-offs among antenna pointing accuracy,energy consumption,and structural vibration.Numerical results demonstrate that the proposed method achieves a reduction of approximately 14.0% in control energy and 41.8%in peak actuation compared to a GPOPS-II baseline,while significantly enhancing vibration suppression.The resulting Pareto front reveals structured trade-offs and clustered solutions,offering robust and diverse options for precision,low-disturbance mission planning.展开更多
ZnO thin-film transistors(TFTs)with channel layers fabricated by spin-coating are demonstrated.A nano ZnO colloidal aqueous solution with zinc nitrate dissolved in it was first deposited on the ATO/ITO/glass substrate...ZnO thin-film transistors(TFTs)with channel layers fabricated by spin-coating are demonstrated.A nano ZnO colloidal aqueous solution with zinc nitrate dissolved in it was first deposited on the ATO/ITO/glass substrate by spin-coating process.The thin-film transistor with well-controlled and densely packed ZnO crystalline layer was obtained by thermal annealing the system of colloidal solution film coated ATO/ITO/glass substrate.By optimizing the fabrication conditions,the fabricated thin-film transistors exhibited superior field-effect properties,which were stable,highly transparent,n-channel and enhancement-mode with a channel mobility as large as 3.02 cm^(2)·V^(-1).s^(-1).Our method of fabricating ZnO thin-film transistors was simple,high efficiency,and feasible for the batch production with low cost.展开更多
This paper is concerned with an initial boundary value problem for the planar magnetohydrodynamic compressible flow with temperature dependent heat conductivity in a half-line.In particular,the transverse magnetic fie...This paper is concerned with an initial boundary value problem for the planar magnetohydrodynamic compressible flow with temperature dependent heat conductivity in a half-line.In particular,the transverse magnetic field is assumed to satisfy the Neumann boundary condition,which was first investigated by Kazhikhov in 1987.We establish the global existence of the unique strong solutions to the MHD equations without any smallness conditions on the initial data.More precisely,our result can be regarded as a natural generalization of Kazhikov’s result for applying the constant heat-conductivity in bounded domains to the degenerate case in unbounded domains.展开更多
Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method f...Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers.展开更多
In this article,we show the existence,uniqueness and stability of bounded solutions to the following quasilinear problems with mean curvature operator(φ'(x′(t)))′=f(t,x),t≥t_(0),lim_(t→∞)x(t)=ψ_(0),lim_(t→...In this article,we show the existence,uniqueness and stability of bounded solutions to the following quasilinear problems with mean curvature operator(φ'(x′(t)))′=f(t,x),t≥t_(0),lim_(t→∞)x(t)=ψ_(0),lim_(t→∞)x′(t)e^(t)=0,where t_(0) and ψ_(0) are real constants,φ(s)=s/√1−s^(2),s∈R with s∈(−1,1),f:[t_(0),∞)×R→R satisfies the Lipschitz or Osgood-type conditions.展开更多
基金supported by the National Natural Science Foundation of China(62303095)the Natural Science Foundation of Sichuan Province(2023NSFSC0872).
文摘Dear Editor,This letter proposes a novel Nash bargaining solution-based multiobjective model predictive control(MPC)scheme to deal with the interaction force control and the path-following problem of the constrained interactive robot.Considering the elastic interaction force model,a mechanical trade-off always exists between the interaction force and position,which means that neither force nor path following can satisfy their desired demands completely.Based on this consideration,two irreconcilable control specifications,the force object function and the position track object function,are proposed,and a new multi-objective MPC scheme is then designed.
基金Gansu Provincial Science and Technology Major Special Program(24ZDWA008)Fourth Batch of Top Leading Talents Fund Projects in Gansu Province(ZZ2023G50100013)。
文摘In this study,FeCr_(x)MnAlCu(x=0,0.5,1.0,1.5,2.0)high-entropy alloys were fabricated using vacuum arc melting,and the corrosion behavior of these alloys in 3.5wt%NaCl solution at room temperature was investigated by electrochemical dynamic potential polarization curves and immersion experiments.The microstructure results show that the high-entropy alloy with x=0 has a body-centered cubic phase structure,whereas the high-entropy alloys with x=0.5–2.0 have a mixed face-centered cubic+body-centered cubic dual-phase structure.The corrosion results show that the corrosion resistance of the high-entropy alloy is increased with the increase in Cr content.Among them,the high-entropy alloy with x=2.0 exhibits the optimal corrosion resistance:the highest self-corrosion potential(E_(corr)=−0.354 V vs.Ag/AgCl),the smallest self-corrosion current density(I_(corr)=1.991×10^(−6)A·cm^(−2)),and the smallest corrosion rate(0.0292 mm/a).The composite passivation film of oxides and hydroxides is formed on the surface of the corroded high-entropy alloys,and the Cr_(2)O_(3)content is increased with the increase in Cr content,which effectively improves the stability and protective properties of the passivation film.
基金Supported by the Doctoral Research Start-up Project of Yuncheng University(YQ-2023067)Project of Shanxi Natural Science Foundation(202303021211189)+1 种基金Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Provinces(20220036)Shanxi ProvinceIntelligent Optoelectronic Sensing Application Technology Innovation Center and Shanxi Province Optoelectronic Information Science and TechnologyLaboratory,Yuncheng University.
文摘In this study,a straightforward one-step hydrothermal method was successfully utilized to synthesize the solid solution Na_(0.9)Mg_(0.45)Ti_(3.55)O_(8)-Na_(2)Ni_(2)Ti_(6)O_(16)(NNMTO-x),where x denotes the molar percentage of Na_(2)Ni_(2)Ti_(6)O_(16)(NNTO)within Na_(0.9)Mg_(0.45)Ti_(3.55)O_(8)(NMTO),with x values of 10,20,30,40,and 50.Both XPS(X-ray Photoelectron Spectroscopy)and EDX(Energy Dispersive X-ray Spectroscopy)analyses unequivocally validated the formation of the NNMTO-x solid solutions.It was observed that when x is below 40,the NNMTO-x solid solution retains the structural characteristics of the original NMTO.However,beyond this threshold,significant alterations in crystal morphology were noted,accompanied by a noticeable decline in photocatalytic activity.Notably,the absorption edge of NNMTO-x(x<40)exhibited a shift towards the visible-light spectrum,thereby substantially broadening the absorption range.The findings highlight that NNMTO-30 possesses the most pronounced photocatalytic activity for the reduction of CO_(2).Specifically,after a 6 h irradiation period,the production rates of CO and CH_(4)were recorded at 42.38 and 1.47μmol/g,respectively.This investigation provides pivotal insights that are instrumental in the advancement of highly efficient and stable photocatalysts tailored for CO_(2)reduction processes.
基金Key Program of National Natural Science Foundation of China(52431001)。
文摘To investigate the effect of solution treatment and aging process parameters on the microstructure and mechanical properties of TB18 titanium alloy,process optimization research was conducted based on the mixed-level orthogonal experiment design of factor levels.Results show that through range analysis,the significance order of process parameters is determined as follows:solution cooling method>solution temperature>aging time>aging temperature>solution time.Considering the strength-ductility matching and engineering application requirements,the benchmark parameters are selected as solution time of 1 h,solution cooling method of air cooling(AC),aging temperature of 525℃,and aging time of 4 h.Furthermore,the effects of solution temperature in the range of 790–870℃ on the impact toughness and micro-fracture characteristics of the alloy were studied.The results reveal that the larger the area of shear lip and fibrous zone,and the smaller the area of radiation zone,the better the toughness of the alloy.With the increase in solution temperature,the length of secondary cracks on the fracture surface increases,the number of dimples increases,and the toughness is enhanced.Based on the collaborative optimization of strength and toughness,the optimal heat treatment process for TB18 alloy is determined as 870℃/1 h,AC+525℃/4 h,AC.
基金Supported by the National Natural Science Foundation of China(12226412)Natural Science Foundation of Jiangsu Province(BK20221339)。
文摘In this paper,we consider a Schr¨odinger-Poisson system with sublinear nonlinearity.The growth of nonlinearity depends on potential function and a bounded function.We first obtain the existence of nontrivial solution sequence with negative energy for the system via a variant Clark’s theorem.Then we get the asymptotical property of the solution sequence by L∞norm.
基金the Hebei Province Science and Technology Plan Project(19221909D)rincess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R308),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based car-following(CF)framework employing the Deep Deterministic Policy Gradient(DDPG)algorithm,which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning.Utilizing real-world driving data from the highD dataset,the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios.The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control(MPC-ACC)controller.Results show that theDRLmodel significantly enhances safety,achieving zero collisions and a higher average time-to-collision(TTC)of 8.45 s,compared to 5.67 s for MPC and 6.12 s for human drivers.For efficiency,the model demonstrates 89.2% headway compliance and maintains speed tracking errors below 1.2 m/s in 90% of cases.In terms of energy optimization,the proposed approach reduces fuel consumption by 5.4% relative to MPC.Additionally,it enhances passenger comfort by lowering jerk values by 65%,achieving 0.12 m/s3 vs.0.34 m/s3 for human drivers.A multi-objective reward function is integrated to ensure stable policy convergence while simultaneously balancing the four key performance metrics.Moreover,the findings underscore the potential of DRL in advancing autonomous vehicle control,offering a robust and sustainable solution for safer,more efficient,and more comfortable transportation systems.
基金supported by the National Natural Science Foundation of China(Grant No.12362027)the Scientific Research Ability of Youth Teachers of Inner Mongolia Agricultural University(Grant No.BR230110)+3 种基金Inner Mongolia National Science Fund for Excellent Young Scholars(Grant No.2025YQ033)Foundation for Basic Science Research Initiation at Inner Mongolia Agricultural University(Grant No.JC2021001)The Natural Science Foundation of Inner Mongolia Autonomous Region(2025MS01020)Supported by the Basic and Applied Basic Research Science and Technology Program Projects of Hohhot(2025-rule-basic-60)。
文摘This study investigates the dimensionless quasi-geostrophic potential vorticity(QG-PV)equation with external sources.Employing the Gardner-Morikawa transformation and weakly nonlinear perturbation expansion,we derive the nonlinear Boussinesq equation with external sources.We demonstrate the existence of explicit zero-order and first-order Wronskian solutions for the model equation whenα_4=0.Furthermore,using a modified Jacobi elliptic function method,we obtain soliton-like solutions for bothα_4=0 andα_4≠0.Analysis of these solutions reveals that the generalizedβ-plane approximation and shear flow are significant factors in inducing nonlinear Rossby waves,and that external sources play a crucial role in influencing Rossby wave behavior.
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supported by the National Natural Science Foundation of China(No.12202295)the International(Regional)Cooperation and Exchange Projects of the National Natural Science Foundation of China(No.W2421002)+2 种基金the Sichuan Science and Technology Program(No.2025ZNSFSC0845)Zhejiang Provincial Natural Science Foundation of China(No.ZCLZ24A0201)the Fundamental Research Funds for the Provincial Universities of Zhejiang(No.GK249909299001-004)。
文摘Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:https://github.com/jinhao-ok1/Topo-for-DCTWS.git.
基金supported by the National Natural Science Foundation of China under Grant No.61972040the Science and Technology Research and Development Project funded by China Railway Material Trade Group Luban Company.
文摘In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and reduces population diversity.To address these challenges,we propose a novel algorithm named Constraint IntensityDriven Evolutionary Multitasking(CIDEMT),which employs a two-stage,tri-task framework to dynamically integrates problem structure and knowledge transfer.In the first stage,three cooperative tasks are designed to explore the Constrained Pareto Front(CPF),the Unconstrained Pareto Front(UPF),and theε-relaxed constraint boundary,respectively.A CPF-UPF relationship classifier is employed to construct a problem-type-aware evolutionary strategy pool.At the end of the first stage,each task selects strategies from this strategy pool based on the specific type of problem,thereby guiding the subsequent evolutionary process.In the second stage,while each task continues to evolve,aτ-driven knowledge transfer mechanism is introduced to selectively incorporate effective solutions across tasks.enhancing the convergence and feasibility of the main task.Extensive experiments conducted on 32 benchmark problems from three test suites(LIRCMOP,DASCMOP,and DOC)demonstrate that CIDEMT achieves the best Inverted Generational Distance(IGD)values on 24 problems and the best Hypervolume values(HV)on 22 problems.Furthermore,CIDEMT significantly outperforms six state-of-the-art constrained multi-objective evolutionary algorithms(CMOEAs).These results confirm CIDEMT’s superiority in promoting convergence,diversity,and robustness in solving complex CMOPs.
文摘Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.
基金supported by the National Research Foundation of Korea grant funded by the Korea government(RS-2023-00217116)。
文摘Federated learning is a distributed framework that trains a centralised model using data from multiple clients without transferring that data to a central server.Despite rapid progress,federated learning still faces several unsolved challenges.Specifically,communication costs and system heterogeneity,such as nonidentical data distribution,hinder federated learning's progress.Several approaches have recently emerged for federated learning involving heterogeneous clients with varying computational capabilities(namely,heterogeneous federated learning).However,heterogeneous federated learning faces two key challenges:optimising model size and determining client selection ratios.Moreover,efficiently aggregating local models from clients with diverse capabilities is crucial for addressing system heterogeneity and communication efficiency.This paper proposes an evolutionary multiobjective optimisation framework for heterogeneous federated learning(MOHFL)to address these issues.Our approach elegantly formulates and solves a biobjective optimisation problem that minimises communication cost and model error rate.The decision variables in this framework comprise model sizes and client selection ratios for each Q client cluster,yielding a total of 2×Q optimisation parameters to be tuned.We develop a partition-based strategy for MOHFL that segregates clients into clusters based on their communication and computation capabilities.Additionally,we implement an adaptive model sizing mechanism that dynamically assigns appropriate subnetwork architectures to clients based on their computational constraints.We also propose a unified aggregation framework to combine models of varying sizes from heterogeneous clients effectively.Extensive experiments on multiple datasets demonstrate the effectiveness and superiority of our proposed method compared to existing approaches.
基金Supported by National Natural Science Foundation of China(Grant No.62173161).
文摘Let A be a 3×3 singular or diagonalizable matrix,all solutions to the Yang-Baxter-like matrix equation have been determined.However,finding all solutions for full rank,non-diagonalizable matrices remains challenging.By utilizing classification techniques,we establish all solutions of the Yang-Baxter-like matrix equation in this paper when the coefficient matrix A is similar to non-diagonalizable matrix diag(λ,J_(2)(λ))withλ̸=0.More specifically,we divide the non-diagonal elements of the solution into 10 different cases.By discussing each situation,we establish all solutions of the Yang-Baxter-like matrix equation.The results of this work enrich the existing ones.
基金supported by Key Science and Technology Program of Henan Province,China(Grant Nos.242102210147,242102210027)Fujian Province Young and Middle aged Teacher Education Research Project(Science and Technology Category)(No.JZ240101)(Corresponding author:Dong Yuan).
文摘Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.
基金supported by the National Natural Science Foundation of China(No.62173107).
文摘Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To address these challenges,this paper proposes a multi-objective trajectory optimization framework.The system dynamics capture both nonlinear rigid-flexible coupling and antenna deformation through a reduced-order formulation.To enhance discretization efficiency,a predictive-terminal hp-adaptive pseudospectral method is employed,assigning collocation density based on task-phase characteristics:finer resolution is applied to dynamic segments requiring higher accuracy,especially near the terminal phase.This enables efficient transcription of the continuous-time problem into a Nonlinear Programming Problem(NLP).The resulting NLP is then solved using a multi-objective optimization strategy based on the nondominated sorting genetic algorithm II,which explores trade-offs among antenna pointing accuracy,energy consumption,and structural vibration.Numerical results demonstrate that the proposed method achieves a reduction of approximately 14.0% in control energy and 41.8%in peak actuation compared to a GPOPS-II baseline,while significantly enhancing vibration suppression.The resulting Pareto front reveals structured trade-offs and clustered solutions,offering robust and diverse options for precision,low-disturbance mission planning.
文摘ZnO thin-film transistors(TFTs)with channel layers fabricated by spin-coating are demonstrated.A nano ZnO colloidal aqueous solution with zinc nitrate dissolved in it was first deposited on the ATO/ITO/glass substrate by spin-coating process.The thin-film transistor with well-controlled and densely packed ZnO crystalline layer was obtained by thermal annealing the system of colloidal solution film coated ATO/ITO/glass substrate.By optimizing the fabrication conditions,the fabricated thin-film transistors exhibited superior field-effect properties,which were stable,highly transparent,n-channel and enhancement-mode with a channel mobility as large as 3.02 cm^(2)·V^(-1).s^(-1).Our method of fabricating ZnO thin-film transistors was simple,high efficiency,and feasible for the batch production with low cost.
基金supported by the National Natural Science Foundation of China(12401279,12371219)the Academic and Technical Leaders Training Plan of Jiangxi Province(20212BCJ23027).
文摘This paper is concerned with an initial boundary value problem for the planar magnetohydrodynamic compressible flow with temperature dependent heat conductivity in a half-line.In particular,the transverse magnetic field is assumed to satisfy the Neumann boundary condition,which was first investigated by Kazhikhov in 1987.We establish the global existence of the unique strong solutions to the MHD equations without any smallness conditions on the initial data.More precisely,our result can be regarded as a natural generalization of Kazhikov’s result for applying the constant heat-conductivity in bounded domains to the degenerate case in unbounded domains.
基金National Natural Science Foundation of China,No.42301470,No.52270185,No.42171389Capacity Building Program of Local Colleges and Universities in Shanghai,No.21010503300。
文摘Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers.
基金Supported by the National Natural Science Foundation of China(Grant Nos.12361040,12061064)the National Science Foundation of Gansu Province(Grant No.22JR5RA264)State Scholarship Fund(Grant No.20230862021).
文摘In this article,we show the existence,uniqueness and stability of bounded solutions to the following quasilinear problems with mean curvature operator(φ'(x′(t)))′=f(t,x),t≥t_(0),lim_(t→∞)x(t)=ψ_(0),lim_(t→∞)x′(t)e^(t)=0,where t_(0) and ψ_(0) are real constants,φ(s)=s/√1−s^(2),s∈R with s∈(−1,1),f:[t_(0),∞)×R→R satisfies the Lipschitz or Osgood-type conditions.
