In order to resolve the coordination and optimization of the power network planning effectively, on the basis of introducing the concept of power intelligence center (PIC), the key factor power flow, line investment a...In order to resolve the coordination and optimization of the power network planning effectively, on the basis of introducing the concept of power intelligence center (PIC), the key factor power flow, line investment and load that impact generation sector, transmission sector and dispatching center in PIC were analyzed and a multi-objective coordination optimal model for new power intelligence center (NPIC) was established. To ensure the reliability and coordination of power grid and reduce investment cost, two aspects were optimized. The evolutionary algorithm was introduced to solve optimal power flow problem and the fitness function was improved to ensure the minimum cost of power generation. The gray particle swarm optimization (GPSO) algorithm was used to forecast load accurately, which can ensure the network with high reliability. On this basis, the multi-objective coordination optimal model which was more practical and in line with the need of the electricity market was proposed, then the coordination model was effectively solved through the improved particle swarm optimization algorithm, and the corresponding algorithm was obtained. The optimization of IEEE30 node system shows that the evolutionary algorithm can effectively solve the problem of optimal power flow. The average load forecasting of GPSO is 26.97 MW, which has an error of 0.34 MW compared with the actual load. The algorithm has higher forecasting accuracy. The multi-objective coordination optimal model for NPIC can effectively process the coordination and optimization problem of power network.展开更多
The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of...The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of energy systems.To enhance the consumption capacity of green power,the green power system consumption optimization scheduling model(GPS-COSM)is proposed,which comprehensively integrates green power system,electric boiler,combined heat and power unit,thermal energy storage,and electrical energy storage.The optimization objectives are to minimize operating cost,minimize carbon emission,and maximize the consumption of wind and solar curtailment.The multi-objective particle swarm optimization algorithm is employed to solve the model,and a fuzzy membership function is introduced to evaluate the satisfaction level of the Pareto optimal solution set,thereby selecting the optimal compromise solution to achieve a dynamic balance among economic efficiency,environmental friendliness,and energy utilization efficiency.Three typical operating modes are designed for comparative analysis.The results demonstrate that the mode involving the coordinated operation of electric boiler,thermal energy storage,and electrical energy storage performs the best in terms of economic efficiency,environmental friendliness,and renewable energy utilization efficiency,achieving the wind and solar curtailment consumption rate of 99.58%.The application of electric boiler significantly enhances the direct accommodation capacity of the green power system.Thermal energy storage optimizes intertemporal regulation,while electrical energy storage strengthens the system’s dynamic regulation capability.The coordinated optimization of multiple devices significantly reduces reliance on fossil fuels.展开更多
In the parallel steering coordination control strategy for path tracking,it is difficult to match the current driver steering model using the fixed parameters with the actual driver,and the designed steering coordinat...In the parallel steering coordination control strategy for path tracking,it is difficult to match the current driver steering model using the fixed parameters with the actual driver,and the designed steering coordination control strategy under a single objective and simple conditions is difficult to adapt to the multi-dimensional state variables’input.In this paper,we propose a deep reinforcement learning algorithm-based multi-objective parallel human-machine steering coordination strategy for path tracking considering driver misoperation and external disturbance.Firstly,the driver steering mathematical model is constructed based on the driver preview characteristics and steering delay response,and the driver characteristic parameters are fitted after collecting the actual driver driving data.Secondly,considering that the vehicle is susceptible to the influence of external disturbances during the driving process,the Tube MPC(Tube Model Predictive Control)based path tracking steering controller is designed based on the vehicle system dynamics error model.After verifying that the driver steering model meets the driver steering operation characteristics,DQN(Deep Q-network),DDPG(Deep Deterministic Policy Gradient)and TD3(Twin Delayed Deep Deterministic Policy Gradient)deep reinforcement learning algorithms are utilized to design a multi-objective parallel steering coordination strategy which satisfies the multi-dimensional state variables’input of the vehicle.Finally,the tracking accuracy,lateral safety,human-machine conflict and driver steering load evaluation index are designed in different driver operation states and different road environments,and the performance of the parallel steering coordination control strategies with different deep reinforcement learning algorithms and fuzzy algorithms are compared by simulations and hardware in the loop experiments.The results show that the parallel steering collaborative strategy based on a deep reinforcement learning algorithm can more effectively assist the driver in tracking the target path under lateral wind interference and driver misoperation,and the TD3-based coordination control strategy has better overall performance.展开更多
Under solvothermal conditions,1,4‑naphthalenedicarboxylic acid(H_(2)ndc)and 9,9′‑dihexyl‑2,7‑di(pyridin‑4‑yl)fluorene(hfdp)reacted with Co^(2+)ions and Cd^(2+)ions to form two coordination polymers,[Co(hfdp)(ndc)(H2O...Under solvothermal conditions,1,4‑naphthalenedicarboxylic acid(H_(2)ndc)and 9,9′‑dihexyl‑2,7‑di(pyridin‑4‑yl)fluorene(hfdp)reacted with Co^(2+)ions and Cd^(2+)ions to form two coordination polymers,[Co(hfdp)(ndc)(H2O)]·DMA}n(1)and{[Cd(hfdp)(ndc)(H_(2)O)]·DMA}_(n)(2),respectively(DMA=N,N‑dimethylacetamide).Single‑crystal X‑ray diffraction analyses showed that both complexes 1 and 2 contain similar structures.Topological analysis indicates that complexes 1 and 2 have a{44·62}planar structure.In addition,both complexes reveal good thermal stability and fluorescence sensing performance.They exhibited good sensitivity and selectivity towards 2,4,6‑trinitrophenol(TNP)by fluorescent quenching.The limits of detection of 1 and 2 for TNP were 0.107 and 0.327μmol·L^(-1),respectively.CCDC:2475515,1;2475516,2.展开更多
Four distinct coordination polymers(CPs)were successfully synthesized by altering solvent types and adjusting ligand concentrations,and their crystal structures were investigated.[Co(L)(FDCA)(H_(2)O)_(2)]·0.5H_(2...Four distinct coordination polymers(CPs)were successfully synthesized by altering solvent types and adjusting ligand concentrations,and their crystal structures were investigated.[Co(L)(FDCA)(H_(2)O)_(2)]·0.5H_(2)O(1)was synthesized as a 2D structure using Coas the metal source,methanol‑water(4∶6,V/V)as the solvent,and specific concentrations of 2,5‑furandicarboxylic acid(H_(2)FDCA)and 1,3,5‑triimidazole benzene(L).Adjusting to pure water and lowering the concentration of L yielded the 1D chain structure of[Co(HL)2(H_(2)O)_(2)](FDCA)_(2)·6H_(2)O(2).Using Cu(Ⅱ)as the metal source,methanol/water(9∶1,V/V)as the solvent,and specific concentrations of L and H2FDCA,the 1D chain structure of[Cu(L)(FDCA)(H_(2)O)]·2H_(2)O(3)was synthesized.Upon increasing the concentrations of L and H2FDCA,and switching the solvent to pure water,the 1D chain structure of[Cu(HL)_(2)(H_(2)O)_(2)](FDCA)_(2)·6H_(2)O(4)was obtained.This shows that changing the solvent and ligand concentrations can affect the structural changes of CPs.In addition,the solid‑state photoluminescence of CPs 1‑4 at room temperature was studied,and their morphological changes were observed via scanning electron microscopy.Density functional theory calculations revealed that the negative charge concentrates on the O and N atoms of the ligand,facilitating ligand‑metal ion coordination.CCDC:2403934,1;2403935,2;2403936,3;2403938,4.展开更多
The recycling of neptunium(Np)from nuclear wastes is crucial for the sustainable development of nuclear energy,yet it is still a challenging task owing to the complexity of Np chemistry.Precise control of oxidation st...The recycling of neptunium(Np)from nuclear wastes is crucial for the sustainable development of nuclear energy,yet it is still a challenging task owing to the complexity of Np chemistry.Precise control of oxidation state is highly desirable for the effective recovery of Np.In this study,we report an innovative strategy for Np recovery through in-situ coordination and reduction of Np(Ⅴ)in a biphasic extraction system.By leveraging the synergistic effects of coordination by a P=O donating ligand(trialkyl phosphine oxide,TRPO)and reduction by hydroquinone(HQ)in the organic phase,efficient Np(Ⅴ)-to-Np(Ⅳ)conversion and high distribution ratio(D)of Np were achieved in a single extraction contact.The reduction mechanism of Np was elucidated through spectroscopic and theoretical analyses.This work enriches the redox chemistry of Np and provides a novel pathway for Np recovery in advanced nuclear fuel cycles.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
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
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
Prussian blue analogs(PBAs)are considered one of the excellent cathode materials for sodium-ion batteries due to their low cost and high theoretical specific capacity,especially sodium-rich iron-based PBAs(FeHCF)can p...Prussian blue analogs(PBAs)are considered one of the excellent cathode materials for sodium-ion batteries due to their low cost and high theoretical specific capacity,especially sodium-rich iron-based PBAs(FeHCF)can provide higher energy density.FeHCF has a poor charge/discharge platform stability at high voltages(FeC_(6)moiety),which is mainly affected by its coordination environment.In this research,Cu^(+)(six-coordinated),which is close to the ionic radius of Fe^(2+),was used for substitution,the FeC_(6)vacancies of FeHCF were reduced,and the coordination environment was optimized.The low Cu^(+)-substituted FeHCF(Cu^(+)0.625)has an optimal electrochemical performance at 8.5 mA/g with a reversible specific capacity of 142 mA h/g and FeC_(6)moiety contribution of more than 68 mA h/g,which is superior to that of unmodified and other Cu^(2+)-substituted FeHCFs.In situ tests demonstrate the reversible structural stability of the Cu^(+)0.625,supporting the stability of their high-voltage platform capacity.This Cu^(+)substitution strategy further enriches the approach to optimize the coordination environment of sodium-rich FeHCF.展开更多
Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination syst...Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination system for Connected and Automated Vehicles(CAVs)at single-lane intersections,particularly in the context of left-hand side driving on roads.The goal is to facilitate smooth right turns for certain vehicles without creating bottlenecks.We consider that all approaching vehicles share relevant information through vehicular communications.The Intersection Coordination Unit(ICU)processes this information and communicates the optimal crossing or turning times to the vehicles.The primary objective of this coordination is to minimize overall traffic delays,which also helps improve the fuel consumption of vehicles.By considering information from upcoming vehicles at the intersection,the coordination system solves an optimization problem to determine the best timing for executing right turns,ultimately minimizing the total delay for all vehicles.The proposed coordination system is evaluated at a typical urban intersection,and its performance is compared to traditional traffic systems.Numerical simulation results indicate that the proposed coordination system significantly enhances the average traffic speed and fuel consumption compared to the traditional traffic system in various scenarios.展开更多
Acceptorless dehydrogenative coupling of pyridinemethanol with ketones is one of the most reliable methodologies to access functionalized 1,8-naphthyridine derivatives.However,it is challenging to develop environmenta...Acceptorless dehydrogenative coupling of pyridinemethanol with ketones is one of the most reliable methodologies to access functionalized 1,8-naphthyridine derivatives.However,it is challenging to develop environmentally friendly catalytic systems,especially in constructing efficient and recyclable catalysts under water or solvent-free conditions.Here,we designed two novel coordination polymers Cd-CPs and Fe-CPs to investigate their catalytic performance in water.Gratifyingly,it was observed that Cd-CPs as a multifunctional catalyst was successfully applied to establish a universal pathway for direct fabrication of 1,8-naphthyridine derivatives under water conditions,while it was effective for the synthesis of1,3,5-triazines through acceptorless dehydrogenative coupling strategies.The features of broad substrate,high atom efficiency,and good catalyst reusability highlight the feasibility of this transformation.In additional,we demonstrated the spindle-like structures Fe-P,derived from the Fe-CPs via phosphorylation,which can be used as an efficient electrocatalyst for oxygen evolution reaction with good stability.This work provides two highly efficient non-noble metal catalysts for functionalized 1,8-naphthyridine derivatives production and oxygen evolution reaction,and opens a new avenue to further fabricate diverse metal catalysts with high catalytic performance in water.展开更多
The emergence of precision electronic devices and wearable electronic products urgently requires high-performance multifunctional electromagnetic wave(EMW)absorbers to meet the applicability and versatility in various...The emergence of precision electronic devices and wearable electronic products urgently requires high-performance multifunctional electromagnetic wave(EMW)absorbers to meet the applicability and versatility in various applications.Herein,a dual-network(DN)gel was successfully prepared using acrylamide and sodium lignosulphonate as the basic units by simple chemical cross-linking and physical cross-linking methods.Specifically,the hydrogel forms two types of cross-linking networks through metal coordination and hydrogen bonding.Benefiting from the combined effects of dipole polarization and conductivity loss,the gel achieves an effective absorption bandwidth(EAB)of 6.74 GHz at a thickness of only 1.89 mm,demonstrating excellent EMW absorption performance.In addition,this unique structural configuration endows the EMW absorber with multifunctional features,such as remarkable tensile strength,good environmental compatibility,ultraviolet(UV)resistance,and excellent adhesion.Integrating multiple functional features into the EMW gels displays a broad application prospect in a variety of application scenarios.This research reveals the significance of DN structure design in the electromagnetic wave absorption(EWA)performance of gel-based materials,providing a substantial foundation for the multifunctional design of gel-based absorbers.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
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
The precise tuning of magnetic nanoparticle size and magnetic domains,thereby shaping magnetic properties.However,the dynamic evolution mechanisms of magnetic domain configurations in relation to electromagnetic(EM)at...The precise tuning of magnetic nanoparticle size and magnetic domains,thereby shaping magnetic properties.However,the dynamic evolution mechanisms of magnetic domain configurations in relation to electromagnetic(EM)attenuation behavior remain poorly understood.To address this gap,a thermodynamically controlled periodic coordination strategy is proposed to achieve precise modulation of magnetic nanoparticle spacing.This approach unveils the evolution of magnetic domain configurations,progressing from individual to coupled and ultimately to crosslinked domain configurations.A unique magnetic coupling phenomenon surpasses the Snoek limit in low-frequency range,which is observed through micromagnetic simulation.The crosslinked magnetic configuration achieves effective low-frequency EM wave absorption at 3.68 GHz,encompassing nearly the entire C-band.This exceptional magnetic interaction significantly enhances radar camouflage and thermal insulation properties.Additionally,a robust gradient metamaterial design extends coverage across the full band(2–40 GHz),effectively mitigating the impact of EM pollution on human health and environment.This comprehensive study elucidates the evolution mechanisms of magnetic domain configurations,addresses gaps in dynamic magnetic modulation,and provides novel insights for the development of high-performance,low-frequency EM wave absorption materials.展开更多
In this paper,distributed power flow controller(DPFC)constraints are analyzed.The energy balance relationship between fundamental wave and third harmonic in series and shunt-side converter is deduced.A proportional in...In this paper,distributed power flow controller(DPFC)constraints are analyzed.The energy balance relationship between fundamental wave and third harmonic in series and shunt-side converter is deduced.A proportional integral(PI)controller of the DPFC is constructed.The PI controller uses the voltage amplitude and phase angle injected into the system in the series side,along with the modulation ratio of the three-phase converter on the shunt side as the control variables.A multiobjective coordinated control equation is proposed,which factors the constraints of the energy balance between series and shunt side,device capacity limit,safe operation limit,fundamental component,as well as third harmonic component of the injection voltage at the series side.The equation minimizes the variance between the actual value of the control target and its given value to ensure that the DC capacitor voltage,both in the series and shunt side,is stable at target value.Simulations are conducted to verify correctness and effectiveness of the proposed control method.展开更多
基金Project (70671039) supported by the National Natural Science Foundation of China
文摘In order to resolve the coordination and optimization of the power network planning effectively, on the basis of introducing the concept of power intelligence center (PIC), the key factor power flow, line investment and load that impact generation sector, transmission sector and dispatching center in PIC were analyzed and a multi-objective coordination optimal model for new power intelligence center (NPIC) was established. To ensure the reliability and coordination of power grid and reduce investment cost, two aspects were optimized. The evolutionary algorithm was introduced to solve optimal power flow problem and the fitness function was improved to ensure the minimum cost of power generation. The gray particle swarm optimization (GPSO) algorithm was used to forecast load accurately, which can ensure the network with high reliability. On this basis, the multi-objective coordination optimal model which was more practical and in line with the need of the electricity market was proposed, then the coordination model was effectively solved through the improved particle swarm optimization algorithm, and the corresponding algorithm was obtained. The optimization of IEEE30 node system shows that the evolutionary algorithm can effectively solve the problem of optimal power flow. The average load forecasting of GPSO is 26.97 MW, which has an error of 0.34 MW compared with the actual load. The algorithm has higher forecasting accuracy. The multi-objective coordination optimal model for NPIC can effectively process the coordination and optimization problem of power network.
基金funded by the National Key Research and Development Program of China(2024YFE0106800)Natural Science Foundation of Shandong Province(ZR2021ME199).
文摘The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of energy systems.To enhance the consumption capacity of green power,the green power system consumption optimization scheduling model(GPS-COSM)is proposed,which comprehensively integrates green power system,electric boiler,combined heat and power unit,thermal energy storage,and electrical energy storage.The optimization objectives are to minimize operating cost,minimize carbon emission,and maximize the consumption of wind and solar curtailment.The multi-objective particle swarm optimization algorithm is employed to solve the model,and a fuzzy membership function is introduced to evaluate the satisfaction level of the Pareto optimal solution set,thereby selecting the optimal compromise solution to achieve a dynamic balance among economic efficiency,environmental friendliness,and energy utilization efficiency.Three typical operating modes are designed for comparative analysis.The results demonstrate that the mode involving the coordinated operation of electric boiler,thermal energy storage,and electrical energy storage performs the best in terms of economic efficiency,environmental friendliness,and renewable energy utilization efficiency,achieving the wind and solar curtailment consumption rate of 99.58%.The application of electric boiler significantly enhances the direct accommodation capacity of the green power system.Thermal energy storage optimizes intertemporal regulation,while electrical energy storage strengthens the system’s dynamic regulation capability.The coordinated optimization of multiple devices significantly reduces reliance on fossil fuels.
基金Supported by National Natural Science Foundation of China(Grant Nos.U22A20246,52372382)Hefei Municipal Natural Science Foundation(Grant No.2022008)+1 种基金the Open Fund of State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures(Grant No.KF2023-06)S&T Program of Hebei(Grant No.225676162GH).
文摘In the parallel steering coordination control strategy for path tracking,it is difficult to match the current driver steering model using the fixed parameters with the actual driver,and the designed steering coordination control strategy under a single objective and simple conditions is difficult to adapt to the multi-dimensional state variables’input.In this paper,we propose a deep reinforcement learning algorithm-based multi-objective parallel human-machine steering coordination strategy for path tracking considering driver misoperation and external disturbance.Firstly,the driver steering mathematical model is constructed based on the driver preview characteristics and steering delay response,and the driver characteristic parameters are fitted after collecting the actual driver driving data.Secondly,considering that the vehicle is susceptible to the influence of external disturbances during the driving process,the Tube MPC(Tube Model Predictive Control)based path tracking steering controller is designed based on the vehicle system dynamics error model.After verifying that the driver steering model meets the driver steering operation characteristics,DQN(Deep Q-network),DDPG(Deep Deterministic Policy Gradient)and TD3(Twin Delayed Deep Deterministic Policy Gradient)deep reinforcement learning algorithms are utilized to design a multi-objective parallel steering coordination strategy which satisfies the multi-dimensional state variables’input of the vehicle.Finally,the tracking accuracy,lateral safety,human-machine conflict and driver steering load evaluation index are designed in different driver operation states and different road environments,and the performance of the parallel steering coordination control strategies with different deep reinforcement learning algorithms and fuzzy algorithms are compared by simulations and hardware in the loop experiments.The results show that the parallel steering collaborative strategy based on a deep reinforcement learning algorithm can more effectively assist the driver in tracking the target path under lateral wind interference and driver misoperation,and the TD3-based coordination control strategy has better overall performance.
文摘Under solvothermal conditions,1,4‑naphthalenedicarboxylic acid(H_(2)ndc)and 9,9′‑dihexyl‑2,7‑di(pyridin‑4‑yl)fluorene(hfdp)reacted with Co^(2+)ions and Cd^(2+)ions to form two coordination polymers,[Co(hfdp)(ndc)(H2O)]·DMA}n(1)and{[Cd(hfdp)(ndc)(H_(2)O)]·DMA}_(n)(2),respectively(DMA=N,N‑dimethylacetamide).Single‑crystal X‑ray diffraction analyses showed that both complexes 1 and 2 contain similar structures.Topological analysis indicates that complexes 1 and 2 have a{44·62}planar structure.In addition,both complexes reveal good thermal stability and fluorescence sensing performance.They exhibited good sensitivity and selectivity towards 2,4,6‑trinitrophenol(TNP)by fluorescent quenching.The limits of detection of 1 and 2 for TNP were 0.107 and 0.327μmol·L^(-1),respectively.CCDC:2475515,1;2475516,2.
文摘Four distinct coordination polymers(CPs)were successfully synthesized by altering solvent types and adjusting ligand concentrations,and their crystal structures were investigated.[Co(L)(FDCA)(H_(2)O)_(2)]·0.5H_(2)O(1)was synthesized as a 2D structure using Coas the metal source,methanol‑water(4∶6,V/V)as the solvent,and specific concentrations of 2,5‑furandicarboxylic acid(H_(2)FDCA)and 1,3,5‑triimidazole benzene(L).Adjusting to pure water and lowering the concentration of L yielded the 1D chain structure of[Co(HL)2(H_(2)O)_(2)](FDCA)_(2)·6H_(2)O(2).Using Cu(Ⅱ)as the metal source,methanol/water(9∶1,V/V)as the solvent,and specific concentrations of L and H2FDCA,the 1D chain structure of[Cu(L)(FDCA)(H_(2)O)]·2H_(2)O(3)was synthesized.Upon increasing the concentrations of L and H2FDCA,and switching the solvent to pure water,the 1D chain structure of[Cu(HL)_(2)(H_(2)O)_(2)](FDCA)_(2)·6H_(2)O(4)was obtained.This shows that changing the solvent and ligand concentrations can affect the structural changes of CPs.In addition,the solid‑state photoluminescence of CPs 1‑4 at room temperature was studied,and their morphological changes were observed via scanning electron microscopy.Density functional theory calculations revealed that the negative charge concentrates on the O and N atoms of the ligand,facilitating ligand‑metal ion coordination.CCDC:2403934,1;2403935,2;2403936,3;2403938,4.
基金the financial support from the National Natural Science Foundation of China(22325603)the financial support from the National Natural Science Foundation of China(22376116)+3 种基金the financial support from the National Natural Science Foundation of China(22076130)the Young Elite Scientists Sponsorship Program by China Association for Science and Technology(CAST)(2023QNRC001)the Fundamental Research Funds for the Central Universities(20826041D4117)the Natural Science Foundation of Sichuan(2025ZNSFSC0109)。
文摘The recycling of neptunium(Np)from nuclear wastes is crucial for the sustainable development of nuclear energy,yet it is still a challenging task owing to the complexity of Np chemistry.Precise control of oxidation state is highly desirable for the effective recovery of Np.In this study,we report an innovative strategy for Np recovery through in-situ coordination and reduction of Np(Ⅴ)in a biphasic extraction system.By leveraging the synergistic effects of coordination by a P=O donating ligand(trialkyl phosphine oxide,TRPO)and reduction by hydroquinone(HQ)in the organic phase,efficient Np(Ⅴ)-to-Np(Ⅳ)conversion and high distribution ratio(D)of Np were achieved in a single extraction contact.The reduction mechanism of Np was elucidated through spectroscopic and theoretical analyses.This work enriches the redox chemistry of Np and provides a novel pathway for Np recovery in advanced nuclear fuel cycles.
文摘Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.
基金supported by the National Natural Science Foundation of China(No.12202295)the International(Regional)Cooperation and Exchange Projects of the National Natural Science Foundation of China(No.W2421002)+2 种基金the Sichuan Science and Technology Program(No.2025ZNSFSC0845)Zhejiang Provincial Natural Science Foundation of China(No.ZCLZ24A0201)the Fundamental Research Funds for the Provincial Universities of Zhejiang(No.GK249909299001-004)。
文摘Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:https://github.com/jinhao-ok1/Topo-for-DCTWS.git.
基金supported by the National Natural Science Foundation of China under Grant No.61972040the Science and Technology Research and Development Project funded by China Railway Material Trade Group Luban Company.
文摘In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and reduces population diversity.To address these challenges,we propose a novel algorithm named Constraint IntensityDriven Evolutionary Multitasking(CIDEMT),which employs a two-stage,tri-task framework to dynamically integrates problem structure and knowledge transfer.In the first stage,three cooperative tasks are designed to explore the Constrained Pareto Front(CPF),the Unconstrained Pareto Front(UPF),and theε-relaxed constraint boundary,respectively.A CPF-UPF relationship classifier is employed to construct a problem-type-aware evolutionary strategy pool.At the end of the first stage,each task selects strategies from this strategy pool based on the specific type of problem,thereby guiding the subsequent evolutionary process.In the second stage,while each task continues to evolve,aτ-driven knowledge transfer mechanism is introduced to selectively incorporate effective solutions across tasks.enhancing the convergence and feasibility of the main task.Extensive experiments conducted on 32 benchmark problems from three test suites(LIRCMOP,DASCMOP,and DOC)demonstrate that CIDEMT achieves the best Inverted Generational Distance(IGD)values on 24 problems and the best Hypervolume values(HV)on 22 problems.Furthermore,CIDEMT significantly outperforms six state-of-the-art constrained multi-objective evolutionary algorithms(CMOEAs).These results confirm CIDEMT’s superiority in promoting convergence,diversity,and robustness in solving complex CMOPs.
文摘Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.
基金supported by the 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.
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 Key Talent Project of Gansu Province(2025RCXM017)the National Natural Science Foundation of China(52261040)+2 种基金the Postgraduate Innovation Star Program of Gansu Province(2025CXZX-476)the Major Science and Technology Project of Gansu Province(22ZD6GA008)the Innovation Driven Assistance Engineering Project of Gansu Association for Science and Technology(GXH20250325-5).
文摘Prussian blue analogs(PBAs)are considered one of the excellent cathode materials for sodium-ion batteries due to their low cost and high theoretical specific capacity,especially sodium-rich iron-based PBAs(FeHCF)can provide higher energy density.FeHCF has a poor charge/discharge platform stability at high voltages(FeC_(6)moiety),which is mainly affected by its coordination environment.In this research,Cu^(+)(six-coordinated),which is close to the ionic radius of Fe^(2+),was used for substitution,the FeC_(6)vacancies of FeHCF were reduced,and the coordination environment was optimized.The low Cu^(+)-substituted FeHCF(Cu^(+)0.625)has an optimal electrochemical performance at 8.5 mA/g with a reversible specific capacity of 142 mA h/g and FeC_(6)moiety contribution of more than 68 mA h/g,which is superior to that of unmodified and other Cu^(2+)-substituted FeHCFs.In situ tests demonstrate the reversible structural stability of the Cu^(+)0.625,supporting the stability of their high-voltage platform capacity.This Cu^(+)substitution strategy further enriches the approach to optimize the coordination environment of sodium-rich FeHCF.
基金supported by the Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research(C)23K03898.
文摘Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination system for Connected and Automated Vehicles(CAVs)at single-lane intersections,particularly in the context of left-hand side driving on roads.The goal is to facilitate smooth right turns for certain vehicles without creating bottlenecks.We consider that all approaching vehicles share relevant information through vehicular communications.The Intersection Coordination Unit(ICU)processes this information and communicates the optimal crossing or turning times to the vehicles.The primary objective of this coordination is to minimize overall traffic delays,which also helps improve the fuel consumption of vehicles.By considering information from upcoming vehicles at the intersection,the coordination system solves an optimization problem to determine the best timing for executing right turns,ultimately minimizing the total delay for all vehicles.The proposed coordination system is evaluated at a typical urban intersection,and its performance is compared to traditional traffic systems.Numerical simulation results indicate that the proposed coordination system significantly enhances the average traffic speed and fuel consumption compared to the traditional traffic system in various scenarios.
基金financial support of this work by the National Natural Science Foundation of China(No.21861039)Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX24_2530)the Fundamental Research Funds for the Central Universities。
文摘Acceptorless dehydrogenative coupling of pyridinemethanol with ketones is one of the most reliable methodologies to access functionalized 1,8-naphthyridine derivatives.However,it is challenging to develop environmentally friendly catalytic systems,especially in constructing efficient and recyclable catalysts under water or solvent-free conditions.Here,we designed two novel coordination polymers Cd-CPs and Fe-CPs to investigate their catalytic performance in water.Gratifyingly,it was observed that Cd-CPs as a multifunctional catalyst was successfully applied to establish a universal pathway for direct fabrication of 1,8-naphthyridine derivatives under water conditions,while it was effective for the synthesis of1,3,5-triazines through acceptorless dehydrogenative coupling strategies.The features of broad substrate,high atom efficiency,and good catalyst reusability highlight the feasibility of this transformation.In additional,we demonstrated the spindle-like structures Fe-P,derived from the Fe-CPs via phosphorylation,which can be used as an efficient electrocatalyst for oxygen evolution reaction with good stability.This work provides two highly efficient non-noble metal catalysts for functionalized 1,8-naphthyridine derivatives production and oxygen evolution reaction,and opens a new avenue to further fabricate diverse metal catalysts with high catalytic performance in water.
基金supported by the National Natural Science Foundation of China(Nos.52231007,51872238,52074227,and 21806129)the Fundamental Research Funds for the Central Universities(Nos.3102018zy045,3102019AX11,and 5000220455)the Natural Science Basic Research Plan in Shaanxi Province of China(Nos.2017JQ5116 and 2020JM-118).
文摘The emergence of precision electronic devices and wearable electronic products urgently requires high-performance multifunctional electromagnetic wave(EMW)absorbers to meet the applicability and versatility in various applications.Herein,a dual-network(DN)gel was successfully prepared using acrylamide and sodium lignosulphonate as the basic units by simple chemical cross-linking and physical cross-linking methods.Specifically,the hydrogel forms two types of cross-linking networks through metal coordination and hydrogen bonding.Benefiting from the combined effects of dipole polarization and conductivity loss,the gel achieves an effective absorption bandwidth(EAB)of 6.74 GHz at a thickness of only 1.89 mm,demonstrating excellent EMW absorption performance.In addition,this unique structural configuration endows the EMW absorber with multifunctional features,such as remarkable tensile strength,good environmental compatibility,ultraviolet(UV)resistance,and excellent adhesion.Integrating multiple functional features into the EMW gels displays a broad application prospect in a variety of application scenarios.This research reveals the significance of DN structure design in the electromagnetic wave absorption(EWA)performance of gel-based materials,providing a substantial foundation for the multifunctional design of gel-based absorbers.
基金National Natural Science Foundation of China,No.42301470,No.52270185,No.42171389Capacity Building Program of Local Colleges and Universities in Shanghai,No.21010503300。
文摘Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers.
基金supported by the National Natural Science Foundation of China(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.
基金supported by the National Natural Science Foundation of China(22265021,52231007,and 12327804)the Aeronautical Science Foundation of China(2020Z056056003)Jiangxi Provincial Natural Science Foundation(20232BAB212004).
文摘The precise tuning of magnetic nanoparticle size and magnetic domains,thereby shaping magnetic properties.However,the dynamic evolution mechanisms of magnetic domain configurations in relation to electromagnetic(EM)attenuation behavior remain poorly understood.To address this gap,a thermodynamically controlled periodic coordination strategy is proposed to achieve precise modulation of magnetic nanoparticle spacing.This approach unveils the evolution of magnetic domain configurations,progressing from individual to coupled and ultimately to crosslinked domain configurations.A unique magnetic coupling phenomenon surpasses the Snoek limit in low-frequency range,which is observed through micromagnetic simulation.The crosslinked magnetic configuration achieves effective low-frequency EM wave absorption at 3.68 GHz,encompassing nearly the entire C-band.This exceptional magnetic interaction significantly enhances radar camouflage and thermal insulation properties.Additionally,a robust gradient metamaterial design extends coverage across the full band(2–40 GHz),effectively mitigating the impact of EM pollution on human health and environment.This comprehensive study elucidates the evolution mechanisms of magnetic domain configurations,addresses gaps in dynamic magnetic modulation,and provides novel insights for the development of high-performance,low-frequency EM wave absorption materials.
基金This work was supported in part by the State Grid Corporation of China(Grant No.52150016000Y)in part by the State Key Laboratory of Power Grid Safety and Energy Conservation(China Electric Power Research Institute)Open Fund,the Major Projects of Technical Innovation in Hubei(Grant No.2018AAA050)the Major Projects of Technical Innovation in Hubei(Grant No.2019AAA016).
文摘In this paper,distributed power flow controller(DPFC)constraints are analyzed.The energy balance relationship between fundamental wave and third harmonic in series and shunt-side converter is deduced.A proportional integral(PI)controller of the DPFC is constructed.The PI controller uses the voltage amplitude and phase angle injected into the system in the series side,along with the modulation ratio of the three-phase converter on the shunt side as the control variables.A multiobjective coordinated control equation is proposed,which factors the constraints of the energy balance between series and shunt side,device capacity limit,safe operation limit,fundamental component,as well as third harmonic component of the injection voltage at the series side.The equation minimizes the variance between the actual value of the control target and its given value to ensure that the DC capacitor voltage,both in the series and shunt side,is stable at target value.Simulations are conducted to verify correctness and effectiveness of the proposed control method.
