The Global Governance Initiative(GGI)is another important public good that China has provided for the international community,following the Global Development Initiative,the Global Security Initiative,and the Global C...The Global Governance Initiative(GGI)is another important public good that China has provided for the international community,following the Global Development Initiative,the Global Security Initiative,and the Global Civilization Initiative.As a sub-field of global governance,global climate governance has hitherto progressed slowly,exhibiting growing deficits in discourse,institutions,and actions.The governance paradigm is in desperate need of revamping,especially under the guidance of an innovative initiative.At the conceptual level,the GGI has gone beyond the Western governance discourse by outlining the institutional starting point,foundations of legitimacy,action orientations,ultimate goals,and practical support for good global climate governance through its five core concepts.At the practical level,the spillover effects of China's achievements in climate governance and the collective rise of the Global South have created favorable conditions,domestically and internationally,for the GGI to lead the way forward toward good global climate governance.However,the fact that the international political and economic order remains unjust and inequitable indicates that this will inevitably be a long-term process replete with rivalries in the pursuit of breakthroughs.展开更多
In recent years, the overall strength of Western developed countries, led by the United States, has declined relatively, resulting in a trend of decentralization. This has posed severe challenges to the traditional gl...In recent years, the overall strength of Western developed countries, led by the United States, has declined relatively, resulting in a trend of decentralization. This has posed severe challenges to the traditional global governance system with regard to trade, security, and multilateral cooperation. However, this trend also provides a historical opportunity to transform the global governance system into one that is more inclusive and equitable. This can be seen in the reconstruction of value priorities, the adjustment of governance focus, the reform of governance mechanisms, and the promotion of new mechanisms. Only by adhering to openness and inclusiveness and balancing rights and responsibilities can we build a future governance order that conforms to the common interests of all humanity.展开更多
On September 1,2025,Chinese President Xi Jinping solemnly proposed the Global Governance Initiative(GGI)at the“Shanghai Cooperation Organization Plus”meeting,answering a question posed by the times:what kind of glob...On September 1,2025,Chinese President Xi Jinping solemnly proposed the Global Governance Initiative(GGI)at the“Shanghai Cooperation Organization Plus”meeting,answering a question posed by the times:what kind of global governance system to build and how to reform and improve global governance.The GGI is committed to sovereign equality,the international rule of law,multilateralism,the people-centered approach,and real results,offering China's wisdom and solutions for building a more just and equitable global governance system.On the journey ahead,China will hold high the banner of a community with a shared future for humanity and advance the GGI through multiple measures,effectively strengthening and improving global governance.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
In the evolving global economy,China’s Outward Foreign Direct Investment(OFDI)holds increasing significance,specially within the context of the Belt and Road Initiative(BRI).Despite earlier studies have acknowledged ...In the evolving global economy,China’s Outward Foreign Direct Investment(OFDI)holds increasing significance,specially within the context of the Belt and Road Initiative(BRI).Despite earlier studies have acknowledged the role of governance quality in at-tracting Chinese OFDI,their explanatory capacity regarding human development has not received sufficient attention.This study ad-dresses this gap,investigating the relationship between Chinese OFDI and human development,emphasizing the governance quality’s moderating role.Using data from 122 BRI countries from 2004 to 2021,we employed the Panel Corrected Standard Error model through Prais-Winsten regression,complimented by robustness check via the Spatial Durbin Model.Our analysis revealed a significant positive relationship between Chinese OFDI and human development in BRI countries,indicating that Chinese investments act as cata-lysts for improved well-being.More importantly,governance quality plays a vital role in shaping this relationship,revealing that effect-ive governance enhanced the human development outcomes of OFDI.Post-hoc analysis of governance indicators,both as a composite index and individual dimensions,supported this finding.Regional heterogeneity analysis further showed that Chinese OFDI has stronger effects on Human Development Index(HDI)in Asia and Africa compared to Europe,Latin America,and Oceania.However,the moder-ating effect of governance quality seemed more prominent in Oceania and Latin America,indicating its role in fostering the benefits of Chinese investments in these regions.These findings hold implications for academia and policy,emphasizing the need for transparent,accountable,and effective governance to drive sustainable and inclusive human development within the BRI framework.展开更多
Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address t...Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address the critical challenges in building energy management.The proposed phase-adaptive radiative(PAR)coating is a multilayer nanostructure consisting of TiO/VO_(2)2/TiO/Ag_(2) and polydimethylsiloxane(PDMS).For different VO_(2) phases,visible transmittance T_(vis)>0.6 and emissivity difference in the atmospheric window Δε_(AW)=0.422 can be achieved,which means the PAR window can transfer interior heat to the outside through thermal radiation for cooling or minimize thermal emission for insulation,while ensuring the transmission of visible light for natural daylighting.Compared to normal glass,the PAR window has an average temperature drop of 14.8℃.The year-round energy-saving calculation for four different cities in China indicates that the PAR window can save 22%-32% of the annual cooling and heating energy consumption by seamlessly transitioning between two phases of VO_(2)modes.The multi-objective optimization of the phase-adaptive radiative smart window provides a potential strategy for energy saving.展开更多
Governance debates gained strong momentum in Africa in early December 2025 as the China-Kenya Readers Forum on Xi Jinping:The Governance of China convened in Nairobi on 1 December 2025,followed by a promotional event ...Governance debates gained strong momentum in Africa in early December 2025 as the China-Kenya Readers Forum on Xi Jinping:The Governance of China convened in Nairobi on 1 December 2025,followed by a promotional event for the English edition of the book’s fifth volume on 3 December 2025 in Johannesburg,South Africa.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
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
As we enter the year 2026,the international system continues to receive shocks from Donald Trump,arguably the 21st century’s most disruptive US leader.His return to the White House in 2025 coincided with the 80th ann...As we enter the year 2026,the international system continues to receive shocks from Donald Trump,arguably the 21st century’s most disruptive US leader.His return to the White House in 2025 coincided with the 80th anniversary of the defeat of fascism and the establishment of the United Nations,the multilateral platform that carried the world’s hope for peace,restraint and collective security.The coincidence was unsettling.A world order designed to limit power now confronts a moment in which power is exercised openly,transactionally and unapologetically.展开更多
Two international conferences in November 2025 jointly outlined a profound transformation of climate governance.The Committee on Trade and Environment(CTE)of the World Trade Organization(WTO)held a conference in Genev...Two international conferences in November 2025 jointly outlined a profound transformation of climate governance.The Committee on Trade and Environment(CTE)of the World Trade Organization(WTO)held a conference in Geneva,Switzerland,on November 4,where the topic of cooperation on trade-related carbon standards aroused heated discussions.The Leaders'Summit of the 30th Conference of the Parties(COP)to the UN Framework Convention on Climate Change(UNFCCC)was held in Belém,Brazil,on November 7.At the meeting,the Open Coalition on Compliance Carbon Markets was officially launched with the initial membership of 11 economies including Brazil,China,and the EU.As the world's first transnational alliance on compliant carbon markets,the coalition aims to coordinate carbon pricing mechanisms,emission trading systems and related policies in various countries,and realize the interconnection of global compliance carbon market networks.展开更多
The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition...The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.展开更多
With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm impro...With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm improvement.To reduce the operational costs of micro-grid systems and the energy abandonment rate of renewable energy,while simultaneously enhancing user satisfaction on the demand side,this paper introduces an improvedmultiobjective Grey Wolf Optimizer based on Cauchy variation.The proposed approach incorporates a Cauchy variation strategy during the optimizer’s search phase to expand its exploration range and minimize the likelihood of becoming trapped in local optima.At the same time,adoptingmultiple energy storage methods to improve the consumption rate of renewable energy.Subsequently,under different energy balance orders,themulti-objective particle swarmalgorithm,multi-objective grey wolf optimizer,and Cauchy’s variant of the improvedmulti-objective grey wolf optimizer are used for example simulation,solving the Pareto solution set of the model and comparing.The analysis of the results reveals that,compared to the original optimizer,the improved optimizer decreases the daily cost by approximately 100 yuan,and reduces the energy abandonment rate to zero.Meanwhile,it enhances user satisfaction and ensures the stable operation of the micro-grid.展开更多
This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Op...This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Optimization(SFO)algorithm.The primary objective is to address multi-objective optimization challenges within mechanical engineering,with a specific emphasis on planetary gearbox optimization.The algorithm is equipped with the ability to dynamically select the optimal mutation operator,contingent upon an adaptive normalized population spacing parameter.The efficacy of HMODESFO has been substantiated through rigorous validation against estab-lished industry benchmarks,including a suite of Zitzler-Deb-Thiele(ZDT)and Zeb-Thiele-Laumanns-Zitzler(DTLZ)problems,where it exhibited superior performance.The outcomes underscore the algorithm’s markedly enhanced optimization capabilities relative to existing methods,particularly in tackling highly intricate multi-objective planetary gearbox optimization problems.Additionally,the performance of HMODESFO is evaluated against selected well-known mechanical engineering test problems,further accentuating its adeptness in resolving complex optimization challenges within this domain.展开更多
This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balanc...This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balance.By integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization process.The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective algorithms.Furthermore,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy trains.The research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.展开更多
There is a growing recognition of the critical role of security governance in advancing democratic transition in the post-conflict environment.Despite such a recognition,the security sector reform concept has overshad...There is a growing recognition of the critical role of security governance in advancing democratic transition in the post-conflict environment.Despite such a recognition,the security sector reform concept has overshadowed the importance of the overarching strategic role of security governance in transition to democracy,particularly in Africa.This paper assesses the status and challenges facing security governance and how they thwarted the efforts to furthering the democratic transition in South Sudan.The paper shows a deterioration in security,safety and security governance outcomes since the independence of South Sudan in 2011 with such a trend unlikely to be abated in the near future without strategic interventions.Some of the challenges facing security governance in South Sudan include the legacies of some historical events including the“Big Tent Policy”,absence of strategic leadership,lack of overarching policy framework,impractical and tenuous security arrangements in the 2018 peace agreement,persistent postponement of the first elections,and dysfunctional justice sector.The paper provides some strategic and operational recommendations to improve security governance and advance democratic transition in South Sudan.These recommendations include formulation of an inclusive and people-centered national security policy,rigorous judicial reform,and early political agreement on new political infrastructure if conditions for holding the first national elections are not met in 2026.展开更多
River ethics,a significant advancement inspired by Chinese President XI Jinping's ecological civilization thought,embodies the philosophical essence of river governance and represents a legacy of innovation by gen...River ethics,a significant advancement inspired by Chinese President XI Jinping's ecological civilization thought,embodies the philosophical essence of river governance and represents a legacy of innovation by generations of water resources professionals.Rooted in river ecology,it offers a framework for advancing modern water governance systems and capabilities.This paper examines eight dimensions of river ethics to provide actionable recommendations:enhancing knowledge systems on water,rivers,and lakes;addressing critical challenges in water governance to strengthen the foundational role of water authorities in ensuring water security,resource management,ecological sustainability and environmental protection;optimizing water project planning to mitigate ecological impacts;ensuring high standards in the lifecycle management of water projects;refining water diversion strategies for precise scheduling;utilizing ecosystem complexity for river and lake restoration;implementing tiered management of water-related disasters;and driving reforms to modernize water governance systems and mechanisms.展开更多
基金2024 Shaanxi Provincial Social Science Fund Annual Project titled"Research on Chinese Modernization from the Perspective of Overseas Studies on the Communist Party of China"(Project Number:2024B002)a 2024 key project of Xi'an Jiaotong University for studying and expounding Xi Jinping Thought on Culture titled"Research on the World Implications of a New Model for Human Advancement"(Project Number:SKZX2024003)funded by the"Top Young Talents Project"(TZ0275)of Shaanxi Province's second batch of"Special Support Program for High-Level Talents"(Philosophy,Social Sciences,Culture,and Arts).
文摘The Global Governance Initiative(GGI)is another important public good that China has provided for the international community,following the Global Development Initiative,the Global Security Initiative,and the Global Civilization Initiative.As a sub-field of global governance,global climate governance has hitherto progressed slowly,exhibiting growing deficits in discourse,institutions,and actions.The governance paradigm is in desperate need of revamping,especially under the guidance of an innovative initiative.At the conceptual level,the GGI has gone beyond the Western governance discourse by outlining the institutional starting point,foundations of legitimacy,action orientations,ultimate goals,and practical support for good global climate governance through its five core concepts.At the practical level,the spillover effects of China's achievements in climate governance and the collective rise of the Global South have created favorable conditions,domestically and internationally,for the GGI to lead the way forward toward good global climate governance.However,the fact that the international political and economic order remains unjust and inequitable indicates that this will inevitably be a long-term process replete with rivalries in the pursuit of breakthroughs.
文摘In recent years, the overall strength of Western developed countries, led by the United States, has declined relatively, resulting in a trend of decentralization. This has posed severe challenges to the traditional global governance system with regard to trade, security, and multilateral cooperation. However, this trend also provides a historical opportunity to transform the global governance system into one that is more inclusive and equitable. This can be seen in the reconstruction of value priorities, the adjustment of governance focus, the reform of governance mechanisms, and the promotion of new mechanisms. Only by adhering to openness and inclusiveness and balancing rights and responsibilities can we build a future governance order that conforms to the common interests of all humanity.
文摘On September 1,2025,Chinese President Xi Jinping solemnly proposed the Global Governance Initiative(GGI)at the“Shanghai Cooperation Organization Plus”meeting,answering a question posed by the times:what kind of global governance system to build and how to reform and improve global governance.The GGI is committed to sovereign equality,the international rule of law,multilateralism,the people-centered approach,and real results,offering China's wisdom and solutions for building a more just and equitable global governance system.On the journey ahead,China will hold high the banner of a community with a shared future for humanity and advance the GGI through multiple measures,effectively strengthening and improving global governance.
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Under the auspices of Fundamental Research Funds for the Central Universities,Zhongnan University of Economics and Law,China(No.2722021EG001)。
文摘In the evolving global economy,China’s Outward Foreign Direct Investment(OFDI)holds increasing significance,specially within the context of the Belt and Road Initiative(BRI).Despite earlier studies have acknowledged the role of governance quality in at-tracting Chinese OFDI,their explanatory capacity regarding human development has not received sufficient attention.This study ad-dresses this gap,investigating the relationship between Chinese OFDI and human development,emphasizing the governance quality’s moderating role.Using data from 122 BRI countries from 2004 to 2021,we employed the Panel Corrected Standard Error model through Prais-Winsten regression,complimented by robustness check via the Spatial Durbin Model.Our analysis revealed a significant positive relationship between Chinese OFDI and human development in BRI countries,indicating that Chinese investments act as cata-lysts for improved well-being.More importantly,governance quality plays a vital role in shaping this relationship,revealing that effect-ive governance enhanced the human development outcomes of OFDI.Post-hoc analysis of governance indicators,both as a composite index and individual dimensions,supported this finding.Regional heterogeneity analysis further showed that Chinese OFDI has stronger effects on Human Development Index(HDI)in Asia and Africa compared to Europe,Latin America,and Oceania.However,the moder-ating effect of governance quality seemed more prominent in Oceania and Latin America,indicating its role in fostering the benefits of Chinese investments in these regions.These findings hold implications for academia and policy,emphasizing the need for transparent,accountable,and effective governance to drive sustainable and inclusive human development within the BRI framework.
基金supported by the Fundamental Research Funds for the Provincial Universities (Grant No.2024-KYYWF-0141)the National Natural Science Foundation of China (Grant Nos.52406076,52227813)+1 种基金the National Key Research and Development Program of China (Grant No.2022YFE0133900)the China Postdoctoral Science Foundation (Grant No.2023M740905)。
文摘Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address the critical challenges in building energy management.The proposed phase-adaptive radiative(PAR)coating is a multilayer nanostructure consisting of TiO/VO_(2)2/TiO/Ag_(2) and polydimethylsiloxane(PDMS).For different VO_(2) phases,visible transmittance T_(vis)>0.6 and emissivity difference in the atmospheric window Δε_(AW)=0.422 can be achieved,which means the PAR window can transfer interior heat to the outside through thermal radiation for cooling or minimize thermal emission for insulation,while ensuring the transmission of visible light for natural daylighting.Compared to normal glass,the PAR window has an average temperature drop of 14.8℃.The year-round energy-saving calculation for four different cities in China indicates that the PAR window can save 22%-32% of the annual cooling and heating energy consumption by seamlessly transitioning between two phases of VO_(2)modes.The multi-objective optimization of the phase-adaptive radiative smart window provides a potential strategy for energy saving.
文摘Governance debates gained strong momentum in Africa in early December 2025 as the China-Kenya Readers Forum on Xi Jinping:The Governance of China convened in Nairobi on 1 December 2025,followed by a promotional event for the English edition of the book’s fifth volume on 3 December 2025 in Johannesburg,South Africa.
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As we enter the year 2026,the international system continues to receive shocks from Donald Trump,arguably the 21st century’s most disruptive US leader.His return to the White House in 2025 coincided with the 80th anniversary of the defeat of fascism and the establishment of the United Nations,the multilateral platform that carried the world’s hope for peace,restraint and collective security.The coincidence was unsettling.A world order designed to limit power now confronts a moment in which power is exercised openly,transactionally and unapologetically.
文摘Two international conferences in November 2025 jointly outlined a profound transformation of climate governance.The Committee on Trade and Environment(CTE)of the World Trade Organization(WTO)held a conference in Geneva,Switzerland,on November 4,where the topic of cooperation on trade-related carbon standards aroused heated discussions.The Leaders'Summit of the 30th Conference of the Parties(COP)to the UN Framework Convention on Climate Change(UNFCCC)was held in Belém,Brazil,on November 7.At the meeting,the Open Coalition on Compliance Carbon Markets was officially launched with the initial membership of 11 economies including Brazil,China,and the EU.As the world's first transnational alliance on compliant carbon markets,the coalition aims to coordinate carbon pricing mechanisms,emission trading systems and related policies in various countries,and realize the interconnection of global compliance carbon market networks.
基金supported by National Natural Science Foundations of China(nos.12271326,62102304,61806120,61502290,61672334,61673251)China Postdoctoral Science Foundation(no.2015M582606)+2 种基金Industrial Research Project of Science and Technology in Shaanxi Province(nos.2015GY016,2017JQ6063)Fundamental Research Fund for the Central Universities(no.GK202003071)Natural Science Basic Research Plan in Shaanxi Province of China(no.2022JM-354).
文摘The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.
基金supported by the Open Fund of Guangxi Key Laboratory of Building New Energy and Energy Conservation(Project Number:Guike Energy 17-J-21-3).
文摘With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm improvement.To reduce the operational costs of micro-grid systems and the energy abandonment rate of renewable energy,while simultaneously enhancing user satisfaction on the demand side,this paper introduces an improvedmultiobjective Grey Wolf Optimizer based on Cauchy variation.The proposed approach incorporates a Cauchy variation strategy during the optimizer’s search phase to expand its exploration range and minimize the likelihood of becoming trapped in local optima.At the same time,adoptingmultiple energy storage methods to improve the consumption rate of renewable energy.Subsequently,under different energy balance orders,themulti-objective particle swarmalgorithm,multi-objective grey wolf optimizer,and Cauchy’s variant of the improvedmulti-objective grey wolf optimizer are used for example simulation,solving the Pareto solution set of the model and comparing.The analysis of the results reveals that,compared to the original optimizer,the improved optimizer decreases the daily cost by approximately 100 yuan,and reduces the energy abandonment rate to zero.Meanwhile,it enhances user satisfaction and ensures the stable operation of the micro-grid.
基金supported by the Serbian Ministry of Education and Science under Grant No.TR35006 and COST Action:CA23155—A Pan-European Network of Ocean Tribology(OTC)The research of B.Rosic and M.Rosic was supported by the Serbian Ministry of Education and Science under Grant TR35029.
文摘This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Optimization(SFO)algorithm.The primary objective is to address multi-objective optimization challenges within mechanical engineering,with a specific emphasis on planetary gearbox optimization.The algorithm is equipped with the ability to dynamically select the optimal mutation operator,contingent upon an adaptive normalized population spacing parameter.The efficacy of HMODESFO has been substantiated through rigorous validation against estab-lished industry benchmarks,including a suite of Zitzler-Deb-Thiele(ZDT)and Zeb-Thiele-Laumanns-Zitzler(DTLZ)problems,where it exhibited superior performance.The outcomes underscore the algorithm’s markedly enhanced optimization capabilities relative to existing methods,particularly in tackling highly intricate multi-objective planetary gearbox optimization problems.Additionally,the performance of HMODESFO is evaluated against selected well-known mechanical engineering test problems,further accentuating its adeptness in resolving complex optimization challenges within this domain.
基金supported by the National Natural Science Foundation of China(Project No.5217232152102391)+2 种基金Sichuan Province Science and Technology Innovation Talent Project(2024JDRC0020)China Shenhua Energy Company Limited Technology Project(GJNY-22-7/2300-K1220053)Key science and technology projects in the transportation industry of the Ministry of Transport(2022-ZD7-132).
文摘This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balance.By integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization process.The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective algorithms.Furthermore,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy trains.The research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.
文摘There is a growing recognition of the critical role of security governance in advancing democratic transition in the post-conflict environment.Despite such a recognition,the security sector reform concept has overshadowed the importance of the overarching strategic role of security governance in transition to democracy,particularly in Africa.This paper assesses the status and challenges facing security governance and how they thwarted the efforts to furthering the democratic transition in South Sudan.The paper shows a deterioration in security,safety and security governance outcomes since the independence of South Sudan in 2011 with such a trend unlikely to be abated in the near future without strategic interventions.Some of the challenges facing security governance in South Sudan include the legacies of some historical events including the“Big Tent Policy”,absence of strategic leadership,lack of overarching policy framework,impractical and tenuous security arrangements in the 2018 peace agreement,persistent postponement of the first elections,and dysfunctional justice sector.The paper provides some strategic and operational recommendations to improve security governance and advance democratic transition in South Sudan.These recommendations include formulation of an inclusive and people-centered national security policy,rigorous judicial reform,and early political agreement on new political infrastructure if conditions for holding the first national elections are not met in 2026.
基金Three Gorges Follow-up Work Fund,Grant/Award Number:WE0161A042024National Key Research Program of China,Grant/Award Number:2024YFC3210900。
文摘River ethics,a significant advancement inspired by Chinese President XI Jinping's ecological civilization thought,embodies the philosophical essence of river governance and represents a legacy of innovation by generations of water resources professionals.Rooted in river ecology,it offers a framework for advancing modern water governance systems and capabilities.This paper examines eight dimensions of river ethics to provide actionable recommendations:enhancing knowledge systems on water,rivers,and lakes;addressing critical challenges in water governance to strengthen the foundational role of water authorities in ensuring water security,resource management,ecological sustainability and environmental protection;optimizing water project planning to mitigate ecological impacts;ensuring high standards in the lifecycle management of water projects;refining water diversion strategies for precise scheduling;utilizing ecosystem complexity for river and lake restoration;implementing tiered management of water-related disasters;and driving reforms to modernize water governance systems and mechanisms.
