The year 2025 marked the 80th anniversary of the victory in the World Anti-Fascist War as well as the 10th anniversary of the Lancang-Mekong Cooperation (LMC) mechanism.Over the past decade, the six LMC countries have...The year 2025 marked the 80th anniversary of the victory in the World Anti-Fascist War as well as the 10th anniversary of the Lancang-Mekong Cooperation (LMC) mechanism.Over the past decade, the six LMC countries have maintained good-neighborly friendship, focused on development-oriented and pragmatic cooperation, and promoted inclusive, peoplecentered outcomes.展开更多
On October 28,2025,China and ASEAN signed the China-ASEAN Free Trade Area(CAFTA)3.0 Upgrade Protocol in Kuala Lumpur,Malaysia,in the presence of Chinese Premier Li Qiang,Malaysian Prime Minister Anwar Ibrahim,and lead...On October 28,2025,China and ASEAN signed the China-ASEAN Free Trade Area(CAFTA)3.0 Upgrade Protocol in Kuala Lumpur,Malaysia,in the presence of Chinese Premier Li Qiang,Malaysian Prime Minister Anwar Ibrahim,and leaders of other ASEAN member states.A close look at the protocol shows that the upgraded agreement marks substantive advances on several fronts.展开更多
China and Southeast Asia are entering a new phase of economic cooperation with the China-ASEAN Free Trade Area(CAFTA)3.0 Upgrade Protocol,in which the green economy has emerged as a central pillar of regional integrat...China and Southeast Asia are entering a new phase of economic cooperation with the China-ASEAN Free Trade Area(CAFTA)3.0 Upgrade Protocol,in which the green economy has emerged as a central pillar of regional integration.Negotiations were substantially concluded in October 2024,but the protocol wasn’t officially signed until October 28,2025,in Kuala Lumpur.The agreement moves beyond traditional tariff reductions to expand cooperation into nine major areas and introduces five new pillars:the digital economy,green economy,supply chain connectivity,competition and consumer protection,and support for micro,small,and medium-sized enterprises(MSMEs).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
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
Future mobile networks in the sixth generation(6G)are poised for a paradigm shift from conventional communication services toward comprehensive information services,driving the evolution of radio access network(RAN)ar...Future mobile networks in the sixth generation(6G)are poised for a paradigm shift from conventional communication services toward comprehensive information services,driving the evolution of radio access network(RAN)architectures toward enhanced cooperation,intelligence,and service orientation.Building upon the concept of centralized,collaborative,cloud,and clean RAN(C-RAN),this article proposes a novel cooperative,intelligent,and service-based RAN(CIS-RAN)architecture.Focusing on cooperation,CIS-RAN extends the traditional cooperative communication paradigm by further integrating cooperative sensing and cooperative artificial intelligence(AI).To improve both performance and effectiveness across diverse application scenarios,CIS-RAN enhances network cooperation throughout the entire process of acquisition,transmission,and processing,thereby enabling efficient information acquisition,diverse cooperative interactions,and intelligent fusion decision-making.Key technologies are discussed,with network cooperative multiple-input multiple-output(MIMO)examined as a case study,demonstrating superior performance over traditional architectures,as demonstrated by numerical results.Future research directions are outlined,emphasizing the continued exploration and advancement of the CIS-RAN architecture,particularly in enhancing network cooperation.展开更多
President Xi Jinping Chooses Southeast Asia for First Overseas Visit of 2025 From April 14 to 18,2025,Chinese President Xi Jinping conducted state visits to Vietnam,Malaysia,and Cambodia.China and Vietnam are socialis...President Xi Jinping Chooses Southeast Asia for First Overseas Visit of 2025 From April 14 to 18,2025,Chinese President Xi Jinping conducted state visits to Vietnam,Malaysia,and Cambodia.China and Vietnam are socialist neighbors linked by mountains and rivers,and the China-Vietnam community with a shared future carries strategic significance.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
In December 2025,the ASEAN Centre for Energy(ACE)convened the third ASEAN Power Grid Partnership Meeting,bringing partners together for consultations on key issues.After more than two decades of planning and explorati...In December 2025,the ASEAN Centre for Energy(ACE)convened the third ASEAN Power Grid Partnership Meeting,bringing partners together for consultations on key issues.After more than two decades of planning and exploration,the ASEAN Power Grid is now entering a new phase—shifting from predominantly bilateral,one-way connections toward a multilateral,multidirectional network.展开更多
Educational cooperation,as the cornerstone and vanguard of people-to-people exchanges and practical cooperation between China and Africa,holds irreplaceable strategic significance for enhancing the quality of Africa&#...Educational cooperation,as the cornerstone and vanguard of people-to-people exchanges and practical cooperation between China and Africa,holds irreplaceable strategic significance for enhancing the quality of Africa's human capital and accelerating its structural transformation and modernization process.This paper employs“demand–supply–adaptation”as its core analytical framework and aims to systematically explore how the educational cooperation between China and Africa can serve Africa's goal of modernization more precisely and effectively.First,through reviewing and analyzing the domestic and international research literature,this paper clarifies the focus,paradigms,and shortcomings of existing research,identifying the knowledge contribution of this study.Second,utilizing detailed macro-level data,case studies,and comparative research methods,it comprehensively presents the multidimensional status,structural characteristics,and development trends of China–Africa educational cooperation in areas such as student exchanges,cooperative education,vocational and technical training,language and cultural exchange,and emerging digital education.This paper also deeply analyzes the pressing and specific demands placed on the education system by Africa's modernization development across key dimensions like economic diversification,industrialization,agricultural modernization,social governance upgrading,and digital transformation.Third,based on the cooperative principle of“Africa's needs and China's strengths,”this paper innovatively proposes systematic countermeasures and suggestions for constructing new,multi-level,high-quality,sustainable,and future-oriented pathways for China–Africa educational cooperation:(a)promoting the strategic focus of cooperation to extend from“hard infrastructure”support to empowering“soft infrastructure”;(b)deepening the integration of industry and education and school–enterprise collaboration to precisely align with Africa's industrial development needs;vigorously developing digital education and jointly building a smart education ecosystem to help Africa bridge the digital divide;(c)improving an evidence-based,third-party evaluation system for cooperative effectiveness and a full-process quality assurance system;and(d)promoting the collaborative participation of multiple actors including governments,schools,enterprises,think tanks,and social organizations to build a new cooperative pattern of coconstruction,co-governance,and shared benefits.展开更多
In response to the Ministry of Education’s requirements for building distinctive model software schools,Software College of Northeastern University and Shenzhen Kingdom Technology Co.,Ltd.jointly developed the specia...In response to the Ministry of Education’s requirements for building distinctive model software schools,Software College of Northeastern University and Shenzhen Kingdom Technology Co.,Ltd.jointly developed the specialized course“Application and Practice of RPA Technology in FinTech”.Addressing pain points in financial digital transformation,the course integrates robotic process automation(RPA)principles,financial domain knowledge,and RPA platform practice into a“technology-scenario-capability”trinity teaching system.Through 64 credit hours of integrated theory and practice,it covers RPA fundamentals,financial applications,RPA operations(including core skills like Web/desktop automation),and AI integration,cultivating students’ability to design and implement automated financial workflows.It innovatively features a RPA simulation platform,30+financial case studies,and modular task resources,creating a“teacher-machine-student”interactive model.Practice demonstrates the course effectively enhances students’integration of technical application and business acumen,providing a scalable paradigm for cultivating interdisciplinary FinTech talent.展开更多
Experts and officials shared their insights on poverty reduction cooperation and sustainable development during the 2025 International Seminar on Global Poverty Reduction Partnerships.
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
The China-Laos Railway(CLR),connecting two nations,has created a new paradigm for international cooperation on border management.On September 4,2025,China and Laos signed an intergovernmental agreement on"juxtapo...The China-Laos Railway(CLR),connecting two nations,has created a new paradigm for international cooperation on border management.On September 4,2025,China and Laos signed an intergovernmental agreement on"juxtaposed border control"arrangements for the CLR,the first such agreement between China and a neighboring country.展开更多
1.The importance of synergy between trade and standards In today's global economy,trade development and standards cooperation must move in tandem to make cross-border transactions more rapid,more reliable,and more...1.The importance of synergy between trade and standards In today's global economy,trade development and standards cooperation must move in tandem to make cross-border transactions more rapid,more reliable,and more inclusive.As the digital transformation accelerates,rules and standards are increasing,posing a risk of marginalizing MSMEs.Professor Kim delivered eight keynote speeches in China in 2025.This article summarizes his opinions in the speeches.He identifies a critical operational chain:Trade→Standards→Evidence→Mutual recognition.This sequence reflects the institutional pathway through which trust is established in international trade:standards define common requirements,evidence provides verifiable proof that those requirements are met,and mutual recognition enables that proof to be accepted across borders.In practice,such evidence is generated through conformity assessment processes,which convert compliance with standards into verifiable results such as test reports,certifications,or audit findings.展开更多
Promoting the integration of industry and education and deepening school-enterprise cooperation in talent cultivation and collaborative innovation are long-term goals of higher education.This paper systematically anal...Promoting the integration of industry and education and deepening school-enterprise cooperation in talent cultivation and collaborative innovation are long-term goals of higher education.This paper systematically analyzes the multiple perspectives,practical challenges,and implementation paths of in-depth school-enterprise cooperation.Based on the typical case of school-enterprise cooperation at the School of Information and Software Engineering,University of Electronic Science and Technology of China(UESTC),this paper explores the innovative practices of in-depth school-enterprise cooperation in talent cultivation,scientific research,and faculty construction.It also explores a multi-party collaborative mechanism from the perspectives of universities,enterprises,students,and the government.By policy guidance,resource integration,and benefit sharing,this mechanism achieves in-depth integration of industry and education,providing references and examples for further development of school-enterprise cooperation in the new era.展开更多
文摘The year 2025 marked the 80th anniversary of the victory in the World Anti-Fascist War as well as the 10th anniversary of the Lancang-Mekong Cooperation (LMC) mechanism.Over the past decade, the six LMC countries have maintained good-neighborly friendship, focused on development-oriented and pragmatic cooperation, and promoted inclusive, peoplecentered outcomes.
文摘On October 28,2025,China and ASEAN signed the China-ASEAN Free Trade Area(CAFTA)3.0 Upgrade Protocol in Kuala Lumpur,Malaysia,in the presence of Chinese Premier Li Qiang,Malaysian Prime Minister Anwar Ibrahim,and leaders of other ASEAN member states.A close look at the protocol shows that the upgraded agreement marks substantive advances on several fronts.
文摘China and Southeast Asia are entering a new phase of economic cooperation with the China-ASEAN Free Trade Area(CAFTA)3.0 Upgrade Protocol,in which the green economy has emerged as a central pillar of regional integration.Negotiations were substantially concluded in October 2024,but the protocol wasn’t officially signed until October 28,2025,in Kuala Lumpur.The agreement moves beyond traditional tariff reductions to expand cooperation into nine major areas and introduces five new pillars:the digital economy,green economy,supply chain connectivity,competition and consumer protection,and support for micro,small,and medium-sized enterprises(MSMEs).
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.
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National Key R&D Program of China under Grant 2022YFB2902001。
文摘Future mobile networks in the sixth generation(6G)are poised for a paradigm shift from conventional communication services toward comprehensive information services,driving the evolution of radio access network(RAN)architectures toward enhanced cooperation,intelligence,and service orientation.Building upon the concept of centralized,collaborative,cloud,and clean RAN(C-RAN),this article proposes a novel cooperative,intelligent,and service-based RAN(CIS-RAN)architecture.Focusing on cooperation,CIS-RAN extends the traditional cooperative communication paradigm by further integrating cooperative sensing and cooperative artificial intelligence(AI).To improve both performance and effectiveness across diverse application scenarios,CIS-RAN enhances network cooperation throughout the entire process of acquisition,transmission,and processing,thereby enabling efficient information acquisition,diverse cooperative interactions,and intelligent fusion decision-making.Key technologies are discussed,with network cooperative multiple-input multiple-output(MIMO)examined as a case study,demonstrating superior performance over traditional architectures,as demonstrated by numerical results.Future research directions are outlined,emphasizing the continued exploration and advancement of the CIS-RAN architecture,particularly in enhancing network cooperation.
文摘President Xi Jinping Chooses Southeast Asia for First Overseas Visit of 2025 From April 14 to 18,2025,Chinese President Xi Jinping conducted state visits to Vietnam,Malaysia,and Cambodia.China and Vietnam are socialist neighbors linked by mountains and rivers,and the China-Vietnam community with a shared future carries strategic significance.
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In December 2025,the ASEAN Centre for Energy(ACE)convened the third ASEAN Power Grid Partnership Meeting,bringing partners together for consultations on key issues.After more than two decades of planning and exploration,the ASEAN Power Grid is now entering a new phase—shifting from predominantly bilateral,one-way connections toward a multilateral,multidirectional network.
文摘Educational cooperation,as the cornerstone and vanguard of people-to-people exchanges and practical cooperation between China and Africa,holds irreplaceable strategic significance for enhancing the quality of Africa's human capital and accelerating its structural transformation and modernization process.This paper employs“demand–supply–adaptation”as its core analytical framework and aims to systematically explore how the educational cooperation between China and Africa can serve Africa's goal of modernization more precisely and effectively.First,through reviewing and analyzing the domestic and international research literature,this paper clarifies the focus,paradigms,and shortcomings of existing research,identifying the knowledge contribution of this study.Second,utilizing detailed macro-level data,case studies,and comparative research methods,it comprehensively presents the multidimensional status,structural characteristics,and development trends of China–Africa educational cooperation in areas such as student exchanges,cooperative education,vocational and technical training,language and cultural exchange,and emerging digital education.This paper also deeply analyzes the pressing and specific demands placed on the education system by Africa's modernization development across key dimensions like economic diversification,industrialization,agricultural modernization,social governance upgrading,and digital transformation.Third,based on the cooperative principle of“Africa's needs and China's strengths,”this paper innovatively proposes systematic countermeasures and suggestions for constructing new,multi-level,high-quality,sustainable,and future-oriented pathways for China–Africa educational cooperation:(a)promoting the strategic focus of cooperation to extend from“hard infrastructure”support to empowering“soft infrastructure”;(b)deepening the integration of industry and education and school–enterprise collaboration to precisely align with Africa's industrial development needs;vigorously developing digital education and jointly building a smart education ecosystem to help Africa bridge the digital divide;(c)improving an evidence-based,third-party evaluation system for cooperative effectiveness and a full-process quality assurance system;and(d)promoting the collaborative participation of multiple actors including governments,schools,enterprises,think tanks,and social organizations to build a new cooperative pattern of coconstruction,co-governance,and shared benefits.
文摘In response to the Ministry of Education’s requirements for building distinctive model software schools,Software College of Northeastern University and Shenzhen Kingdom Technology Co.,Ltd.jointly developed the specialized course“Application and Practice of RPA Technology in FinTech”.Addressing pain points in financial digital transformation,the course integrates robotic process automation(RPA)principles,financial domain knowledge,and RPA platform practice into a“technology-scenario-capability”trinity teaching system.Through 64 credit hours of integrated theory and practice,it covers RPA fundamentals,financial applications,RPA operations(including core skills like Web/desktop automation),and AI integration,cultivating students’ability to design and implement automated financial workflows.It innovatively features a RPA simulation platform,30+financial case studies,and modular task resources,creating a“teacher-machine-student”interactive model.Practice demonstrates the course effectively enhances students’integration of technical application and business acumen,providing a scalable paradigm for cultivating interdisciplinary FinTech talent.
文摘Experts and officials shared their insights on poverty reduction cooperation and sustainable development during the 2025 International Seminar on Global Poverty Reduction Partnerships.
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.
文摘The China-Laos Railway(CLR),connecting two nations,has created a new paradigm for international cooperation on border management.On September 4,2025,China and Laos signed an intergovernmental agreement on"juxtaposed border control"arrangements for the CLR,the first such agreement between China and a neighboring country.
文摘1.The importance of synergy between trade and standards In today's global economy,trade development and standards cooperation must move in tandem to make cross-border transactions more rapid,more reliable,and more inclusive.As the digital transformation accelerates,rules and standards are increasing,posing a risk of marginalizing MSMEs.Professor Kim delivered eight keynote speeches in China in 2025.This article summarizes his opinions in the speeches.He identifies a critical operational chain:Trade→Standards→Evidence→Mutual recognition.This sequence reflects the institutional pathway through which trust is established in international trade:standards define common requirements,evidence provides verifiable proof that those requirements are met,and mutual recognition enables that proof to be accepted across borders.In practice,such evidence is generated through conformity assessment processes,which convert compliance with standards into verifiable results such as test reports,certifications,or audit findings.
文摘Promoting the integration of industry and education and deepening school-enterprise cooperation in talent cultivation and collaborative innovation are long-term goals of higher education.This paper systematically analyzes the multiple perspectives,practical challenges,and implementation paths of in-depth school-enterprise cooperation.Based on the typical case of school-enterprise cooperation at the School of Information and Software Engineering,University of Electronic Science and Technology of China(UESTC),this paper explores the innovative practices of in-depth school-enterprise cooperation in talent cultivation,scientific research,and faculty construction.It also explores a multi-party collaborative mechanism from the perspectives of universities,enterprises,students,and the government.By policy guidance,resource integration,and benefit sharing,this mechanism achieves in-depth integration of industry and education,providing references and examples for further development of school-enterprise cooperation in the new era.
