Large-scale electric vehicles(EVs) connected to the micro grid would cause many problems. In this paper, with the consideration of vehicle to grid(V2 G), two charging and discharging load modes of EVs were constructed...Large-scale electric vehicles(EVs) connected to the micro grid would cause many problems. In this paper, with the consideration of vehicle to grid(V2 G), two charging and discharging load modes of EVs were constructed. One was the disorderly charging and discharging mode based on travel habits, and the other was the orderly charging and discharging mode based on time-of-use(TOU) price;Monte Carlo method was used to verify the case. The scheme of the capacity optimization of photovoltaic charging station under two different charging and discharging modes with V2 G was proposed. The mathematical models of the objective function with the maximization of energy efficiency, the minimization of the investment and the operation cost of the charging system were established. The range of decision variables, constraints of the requirements of the power balance and the strategy of energy exchange were given. NSGA-Ⅱ and NSGA-SA algorithm were used to verify the cases, respectively. In both algorithms, by comparing with the simulation results of the two different modes, it shows that the orderly charging and discharging mode with V2 G is obviously better than the disorderly charging and discharging mode in the aspects of alleviating the pressure of power grid, reducing system investment and improving energy efficiency.展开更多
To address issues such as poor initial population diversity, low stability and local convergence accuracy, and easy local optima in the traditional Multi-Objective Artificial Hummingbird Algorithm (MOAHA), an Improved...To address issues such as poor initial population diversity, low stability and local convergence accuracy, and easy local optima in the traditional Multi-Objective Artificial Hummingbird Algorithm (MOAHA), an Improved MOAHA (IMOAHA) was proposed. The improvements involve Tent mapping based on random variables to initialize the population, a logarithmic decrease strategy for inertia weight to balance search capability, and the improved search operators in the territory foraging phase to enhance the ability to escape from local optima and increase convergence accuracy. The effectiveness of IMOAHA was verified through Matlab/Simulink. The results demonstrate that IMOAHA exhibits superior convergence, diversity, uniformity, and coverage of solutions across 6 test functions, outperforming 4 comparative algorithms. A Wilcoxon rank-sum test further confirmed its exceptional performance. To assess IMOAHA’s ability to solve engineering problems, an optimization model for a multi-track, multi-train urban rail traction power supply system with Supercapacitor Energy Storage Systems (SCESSs) was established, and IMOAHA was successfully applied to solving the capacity allocation problem of SCESSs, demonstrating that it is an effective tool for solving complex Multi-Objective Optimization Problems (MOOPs) in engineering domains.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
Background:The trajectory of intrinsic capacity(IC)among the older population is characterized by its diversity and is predictive of adverse health outcomes such as disability,nursing home admission,decline in quality...Background:The trajectory of intrinsic capacity(IC)among the older population is characterized by its diversity and is predictive of adverse health outcomes such as disability,nursing home admission,decline in quality of life,and mortality.Gaining an understanding of the trajectory of IC and the factors that influence it is of paramount importance for fostering healthy aging.This research is focused on exploring the trajectory of IC among older adults in China and examining the factors that influence it.Methods:This observational longitudinal cohort study leveraged data from the China Health and Retirement Longitudinal Study(CHARLS),which was conducted in the years 2011,2013,and 2015.For the purpose of this analysis,a total of 2,233 participants who were aged 60 and over were included.A Growth Mixture Model(GMM)was utilized to define trajectory categories for IC.Influential factors were ascertained based on the health ecology model,and binary logistic regression analysis was utilized to investigate the factors linked with the different trajectory categories.Results:Two distinct trajectory classes of IC were identified:Class 1,the normal-stable group,encompassed 90.4%of the elderly population,while Class 2,the declining group,made up 9.6%.Advanced age and a history of stroke were found to be significantly associated with Class 2.High scores in activities of daily living(ADL),employment status,receiving primary or junior high school education,and residence in the East or Central regions of China were significantly linked with Class 1.Conclusion:The trajectory of IC among older Chinese adults is marked by its heterogeneity.Advanced age and a history of stroke are significant risk factors for a declining IC trajectory,while higher ADL scores,being employed,receiving primary or junior high school education,and residing in the East or Central regions of China are protective factors associated with a stable IC trajectory.Healthcare institutions must closely monitor IC levels and understand these trajectory patterns to implement personalized and targeted interventions promptly to maintain IC at a healthy level and advocate for healthy aging.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
Biochar, known as “black gold”, has garnered wide attention in various applications. However, the potential release of toxic organic compounds has raised environmental concerns, thereby limiting its safe and sustain...Biochar, known as “black gold”, has garnered wide attention in various applications. However, the potential release of toxic organic compounds has raised environmental concerns, thereby limiting its safe and sustainable application. Herein, we propose a distillation strategy to simultaneously detoxify biochar and enhance its redox functionality. Multi-factor correlation analysis identified 30 min as the optimal distillation time, which significantly increased the biochar's Brunauer-Emmett-Teller(BET) surface area(by 143%), improved hydrophilicity(with contact angle decreased by 3.8%), and effectively reduced the dissolved organic carbon(DOC) content of the biochar. Regarding the effect of distillation solvent, both water and acetic acid significantly enhanced the electron exchange capacity(EEC) of the biochar, with lactic acid exhibiting the best performance in improving the electron donating capacity(EDC). Meanwhile, distillation with acetic acid achieved optimal detoxification by effectively removing toxic organic compounds such as naphthalene, amines, and aromatic hydrocarbons. Further validation confirmed the good generalizability of this method to biochars derived from various feedstocks. Techno-economic analysis showed a 98.7% reduction in water consumption and 22.9%-62.5% cost savings compared to traditional washing methods. This work highlights distillation as an efficient, eco-friendly, and cost-effective method to enhance biochar safety and redox functionality, thereby advancing its sustainable applications.展开更多
Background:Intrinsic capacity reflects the overall health status of older adults and decline in intrinsic abilities can lead to adverse health outcomes.However,empirical studies examining the association between digit...Background:Intrinsic capacity reflects the overall health status of older adults and decline in intrinsic abilities can lead to adverse health outcomes.However,empirical studies examining the association between digital health literacy,health-promoting lifestyles and intrinsic capacity are scarce.Methods:A cross-sectional study was conducted.Using convenience sampling method,371 older adults were recruited from communities.Multidimensional intrinsic capacity,digital health literacy,health promoting lifestyle and sociodemographic information were measured.Results:The intrinsic capacity of older adults scored 3.75±1.10.The prevalences of declined cognitive capacity,psychological capacity,sensory capacity,vitality,and locomotor capacity were 13.7%,24.3%,19.1%,14.8%,53.1%,respectively.The multiple regression analysis revealed that age(β=−0.253),only living with children and/or grandchildren(β=0.249),current working status(β=−0.132),number of chronic diseases(β=−0.149),frequency of Internet usage(β=0.193),the domain of ability to acquire and evaluate digital health information(β=0.197)in digital health literacy,and the domain of nutrition(β=0.171)in health-promoting lifestyle were the significant factors influencing intrinsic capacity,explaining 27.1%of the variance.Conclusion:Digital health literacy and health-promoting lifestyle have a significant impact on intrinsic capacity.Enhancing digital health literacy and advocating a health-promoting lifestyle are critical to promoting intrinsic capacity for community-dwelling older adults.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
Purpose-This study investigates the impact of flagship trains on high-speed railway capacity utilization and develops a brand value-oriented optimization framework that balances service quality enhancement with operat...Purpose-This study investigates the impact of flagship trains on high-speed railway capacity utilization and develops a brand value-oriented optimization framework that balances service quality enhancement with operational efficiency.Design/methodology/approach-A mathematical optimization model based on integer programming is developed,incorporating flagship train constraints into capacity optimization.Case studies compare scenarios with and without flagship train considerations using the Beijing-Shanghai High-Speed Railway data across 20 experimental groups.Findings-Operating flagship trains with hourly departure constraints results in an average decrease of 0.9 trains and an 8.4%reduction in capacity utilization rate.When scheduling 2 flagship trains within a 2-h timeframe,capacity utilization decreases from 86.43%to 83.73%,quantifying the trade-off between brand positioning and operational capacity.Originality/value-This research provides the first quantitative framework for brand value-oriented railway capacity optimization,establishing clear definitions for flagship trains and mathematical foundations for evaluating service quality versus efficiency trade-offs.The findings offer practical decision support for railway operators balancing competitive positioning with capacity maximization.展开更多
China Standardization:Can you please briefly introduce ISO,its international standards as well as your scope of work?Rachel Miller Prada:ISO is an independent non-governmental organization dedicated to developing inte...China Standardization:Can you please briefly introduce ISO,its international standards as well as your scope of work?Rachel Miller Prada:ISO is an independent non-governmental organization dedicated to developing international standards.Currently,ISO has 175 member bodies,representing 175 countries that participate in its standard development work.We have a portfolio of over 24,000 international standards,with around 100 new standards issued or existing ones revised every month.The ultimate goal of our standardization work is to support the achievement of the United Nations Sustainable Development Goals(SDGs).Every standard we develop and every task I undertake in my role contributes to these global objectives.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
To achieve the goals of sustainable development of the energy system and the construction of a lowcarbon society,this study proposes a multi-energy storage collaborative optimization strategy for industrial park that ...To achieve the goals of sustainable development of the energy system and the construction of a lowcarbon society,this study proposes a multi-energy storage collaborative optimization strategy for industrial park that integrates the laddered carbon trading mechanism with demand response.Firstly,a dual dimensional DR model is constructed based on the characteristics of load elasticity.The alternativeDRenables flexible substitution of energy loads through complementary conversion of electricity/heat/cold multi-energy sources,while the price DR relies on timeof-use electricity price signals to guide load spatiotemporal migration;Secondly,the LCT mechanism is introduced to achieve optimal carbon emission costs through a tiered carbon quota allocation mechanism.On this basis,an optimization decision model is established with the core objective of maximizing the annual net profit of the park.The objective function takes into account energy sales revenue,generator unit costs,and investment and operation costs of multiple types of energy storage facilities.Themodel constraint system covers three key dimensions:dynamic operation constraints of power generation units,including unit output limits,ramping capability,and minimum start-stop time;the physical boundary of an electric/hot/cold multi-energy storage system involves energy storage capacity and charge/discharge efficiency;The multi-energy network coupling balance equation ensures that the energy conversion and transmission process satisfies the law of conservation of energy.Using CPLEX mathematical programming solver for simulation verification,construct an energy storage capacity configuration decision process that includes LCT-DR synergistic effect.The research results show that compared with the traditional single energy storage configuration mode,this strategy effectively enhances the economic feasibility and engineering practicality of industrial park operation by coordinating demand side resource scheduling and finely controlling carbon costs,while maintaining stable system operation.Its methodological framework provides a technical path that combines theoretical rigor and practical operability for the low-carbon transformation of regional integrated energy systems.展开更多
Objective:To explore the application effect of combined exercise intervention based on the hospital-community-family model on intrinsic capacity in elderly patients with diabetes mellitus complicated by chronic kidney...Objective:To explore the application effect of combined exercise intervention based on the hospital-community-family model on intrinsic capacity in elderly patients with diabetes mellitus complicated by chronic kidney disease.Methods:Using convenience sampling,100 elderly patients with diabetes mellitus complicated by chronic kidney disease who received treatment in the endocrinology department of a tertiary A-level hospital from May 2024 to May 2025 were selected as the study subjects.They were randomly divided into an experimental group(50 cases)and a control group(50 cases)using a random number table method.The control group received routine health education and telephone follow-up,while the experimental group,in addition to the control group’s interventions,underwent combined exercise intervention based on the hospital-community-family model.Remote medical guidance was utilized to monitor and study the application effect of exercise intervention on intrinsic capacity in elderly patients with diabetes mellitus complicated by chronic kidney disease.Fasting blood glucose,2-hour postprandial blood glucose,glomerular filtration rate,6-minute walk distance,and scores in five dimensions of intrinsic capacity(exercise,cognition,psychology,vitality,and sensation)were measured before the intervention,at 4 weeks of intervention,and at 12 weeks of intervention for both groups.Results:Before the exercise intervention,there were no statistically significant differences(p>0.05)between the two groups in terms of fasting blood glucose,2-hour postprandial blood glucose,glomerular filtration rate,6-minute walk distance,and scores across five dimensions of intrinsic capacity:mobility,cognition,psychology,vitality,and sensation.After 12 weeks of intervention,the experimental group demonstrated significantly higher scores than the control group in glomerular filtration rate,6-minute walk distance,and the dimensions of mobility,cognition,and vitality within intrinsic capacity,with all differences being statistically significant(p<0.05).Conversely,the experimental group showed significantly lower scores than the control group in fasting blood glucose,2-hour postprandial blood glucose,and the psychological dimension of intrinsic capacity,with these differences also being statistically significant(p<0.05).Conclusion:Continuous nursing care utilizing telemedicine based on a hospital-community-family model combined with exercise intervention can effectively enhance exercise tolerance and intrinsic capacity in elderly patients with diabetes mellitus complicated by chronic kidney disease,thereby improving their quality of life.The effectiveness of the intervention is positively correlated with the duration of the intervention.展开更多
Root-inspired anchorage systems in the field of bio-inspired geotechnics are renowned for enhancing the pullout capacity of traditional geotechnical anchorage systems by simulating the morphology and architecture of p...Root-inspired anchorage systems in the field of bio-inspired geotechnics are renowned for enhancing the pullout capacity of traditional geotechnical anchorage systems by simulating the morphology and architecture of plant root systems.However,limited studies have explored their practical applications,particularly in improving slope stability.To fill this gap,this study investigates the reinforcement effect of root-inspired anchors on slope stabilization using transparent soil modeling and 3D-printed anchors,and examines the impact of anchor branching patterns(i.e.branching numbers,branching angle,and branching nodes)on slope bearing capacity,shear band evolution,and temporal and spatial variation of slope deformation.The results show that peak slope bearing capacity increases with branching numbers and branching angles,correlating with the envelope area of the curved shear band.Upper anchors result in step-like deflections in the shear band near the trailing edge,while lower anchors convert the upward concave shear band into an upward convex one,thus increasing the slope bearing capacity.Slope deformation is minimized with intermediate branching parameters,such as a branching number of 4 and a branching angle of 45°.The anchor reinforcement mechanisms,i.e.anchor rod shear resistance,interface friction,anchor pullout capacity,and plate tightening effects,are comprehensively discussed,and the installation effects resulting from compromise slope modeling are identified as the contributors.These findings shed light on the failure process of root-inspired anchors reinforced slopes and provide a preliminary reference for potential applications,especially for the tradeoff between anchor branching,slope deformation,and slope stability.展开更多
Micro-sized anatase TiO_(2) displays inferior capacity as cathode material for magnesium ion batteries because of the higher diffusion energy barrier of Mg^(2+)in anatase TiO_(2) lattice.Herein,we report that nanosize...Micro-sized anatase TiO_(2) displays inferior capacity as cathode material for magnesium ion batteries because of the higher diffusion energy barrier of Mg^(2+)in anatase TiO_(2) lattice.Herein,we report that nanosized anatase TiO_(2) exposed(001)facet doubles the capacity compared to the micro-sized sample ascribed to the interfacial Mg^(2+)ion storage.First-principles calculations reveal that the diffusion energy barrier of Mg^(2+)on the(001)facet is significantly lower than those in the bulk phase and on(100)facet,and the adsorption energy of Mg^(2+)on the(001)facet is also considerably lower than that on(100)facet,which guarantees superior interfacial Mg^(2+)storage of(001)facet.Moreover,anatase TiO_(2) exposed(001)facet displays a significantly higher capacity of 312.9 mAh g^(−1) in Mg-Li dual-salt electrolyte compared to 234.3 mAh g^(−1) in Li salt electrolyte.The adsorption energies of Mg^(2+)on(001)facet are much lower than the adsorption energies of Li+on(001)facet,implying that the Mg^(2+)ion interfacial storage is more favorable.These results highlight that controlling the crystal facet of the nanocrystals effectively enhances the interfacial storage of multivalent ions.This work offers valuable guidance for the rational design of high-capacity storage systems.展开更多
In this paper,we propose and analyze two second-order accurate finite difference schemes for the one-dimensional heat equation with concentrated capacity on a computa-tional domain=[a,b].We first transform the target ...In this paper,we propose and analyze two second-order accurate finite difference schemes for the one-dimensional heat equation with concentrated capacity on a computa-tional domain=[a,b].We first transform the target equation into the standard heat equation on the domain excluding the singular point equipped with an inner interface matching(IIM)condition on the singular point x=ξ∈(a,b),then adopt Taylor’s ex-pansion to approximate the IIM condition at the singular point and apply second-order finite difference method to approximate the standard heat equation at the nonsingular points.This discrete procedure allows us to choose different grid sizes to partition the two sub-domains[a,ξ]and[ξ,b],which ensures that x=ξ is a grid point,and hence the pro-posed schemes can be generalized to the heat equation with more than one concentrated capacities.We prove that the two proposed schemes are uniquely solvable.And through in-depth analysis of the local truncation errors,we rigorously prove that the two schemes are second-order accurate both in temporal and spatial directions in the maximum norm without any constraint on the grid ratio.Numerical experiments are carried out to verify our theoretical conclusions.展开更多
This paper presents the design optimization of composite submersible cylindrical pressure hull subjected to 3 MPa hydrostatic pressure.The design optimization study is conducted for cross-ply layups[0_(s)/90_(t)/0_(u)...This paper presents the design optimization of composite submersible cylindrical pressure hull subjected to 3 MPa hydrostatic pressure.The design optimization study is conducted for cross-ply layups[0_(s)/90_(t)/0_(u)],[0_(s)/90_(t)/0_(u)]s,[0_(s)/90_(t)]s and[90_(s)/0_(t)]s considering three uni-directional composites,i.e.Carbon/Epoxy,Glass/Epoxy,and Boron/Epoxy.The optimization study is performed by coupling a Multi-Objective Genetic Algorithm(MOGA)and Analytical Analysis.Minimizing the buoyancy factor and maximizing the buckling load factor are considered as the objectives of the optimization study.The objectives of the optimization are achieved under constraints on the Tsai-Wu,Tsai-Hill and Maximum Stress composite failure criteria and on buckling load factor.To verify the optimization approach,optimization of one particular layup configuration is also conducted in ANSYS with the same objectives and constraints.展开更多
基金Project(3502Z20179026)supported by Xiamen Science and Technology Project,China。
文摘Large-scale electric vehicles(EVs) connected to the micro grid would cause many problems. In this paper, with the consideration of vehicle to grid(V2 G), two charging and discharging load modes of EVs were constructed. One was the disorderly charging and discharging mode based on travel habits, and the other was the orderly charging and discharging mode based on time-of-use(TOU) price;Monte Carlo method was used to verify the case. The scheme of the capacity optimization of photovoltaic charging station under two different charging and discharging modes with V2 G was proposed. The mathematical models of the objective function with the maximization of energy efficiency, the minimization of the investment and the operation cost of the charging system were established. The range of decision variables, constraints of the requirements of the power balance and the strategy of energy exchange were given. NSGA-Ⅱ and NSGA-SA algorithm were used to verify the cases, respectively. In both algorithms, by comparing with the simulation results of the two different modes, it shows that the orderly charging and discharging mode with V2 G is obviously better than the disorderly charging and discharging mode in the aspects of alleviating the pressure of power grid, reducing system investment and improving energy efficiency.
基金by National Natural Science Foundation of China(62373142,62033014)Natural Science Foundation of Hunan Province(2025JJ70017,2022JJ50074).
文摘To address issues such as poor initial population diversity, low stability and local convergence accuracy, and easy local optima in the traditional Multi-Objective Artificial Hummingbird Algorithm (MOAHA), an Improved MOAHA (IMOAHA) was proposed. The improvements involve Tent mapping based on random variables to initialize the population, a logarithmic decrease strategy for inertia weight to balance search capability, and the improved search operators in the territory foraging phase to enhance the ability to escape from local optima and increase convergence accuracy. The effectiveness of IMOAHA was verified through Matlab/Simulink. The results demonstrate that IMOAHA exhibits superior convergence, diversity, uniformity, and coverage of solutions across 6 test functions, outperforming 4 comparative algorithms. A Wilcoxon rank-sum test further confirmed its exceptional performance. To assess IMOAHA’s ability to solve engineering problems, an optimization model for a multi-track, multi-train urban rail traction power supply system with Supercapacitor Energy Storage Systems (SCESSs) was established, and IMOAHA was successfully applied to solving the capacity allocation problem of SCESSs, demonstrating that it is an effective tool for solving complex Multi-Objective Optimization Problems (MOOPs) in engineering domains.
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.
基金China Health and Retirement Longitudinal Study(CHARLS)the 2022-2023 Nursing Research Project of Chinese Medical Association Publishing House(Grant No.CMAPH-NRD2022024)。
文摘Background:The trajectory of intrinsic capacity(IC)among the older population is characterized by its diversity and is predictive of adverse health outcomes such as disability,nursing home admission,decline in quality of life,and mortality.Gaining an understanding of the trajectory of IC and the factors that influence it is of paramount importance for fostering healthy aging.This research is focused on exploring the trajectory of IC among older adults in China and examining the factors that influence it.Methods:This observational longitudinal cohort study leveraged data from the China Health and Retirement Longitudinal Study(CHARLS),which was conducted in the years 2011,2013,and 2015.For the purpose of this analysis,a total of 2,233 participants who were aged 60 and over were included.A Growth Mixture Model(GMM)was utilized to define trajectory categories for IC.Influential factors were ascertained based on the health ecology model,and binary logistic regression analysis was utilized to investigate the factors linked with the different trajectory categories.Results:Two distinct trajectory classes of IC were identified:Class 1,the normal-stable group,encompassed 90.4%of the elderly population,while Class 2,the declining group,made up 9.6%.Advanced age and a history of stroke were found to be significantly associated with Class 2.High scores in activities of daily living(ADL),employment status,receiving primary or junior high school education,and residence in the East or Central regions of China were significantly linked with Class 1.Conclusion:The trajectory of IC among older Chinese adults is marked by its heterogeneity.Advanced age and a history of stroke are significant risk factors for a declining IC trajectory,while higher ADL scores,being employed,receiving primary or junior high school education,and residing in the East or Central regions of China are protective factors associated with a stable IC trajectory.Healthcare institutions must closely monitor IC levels and understand these trajectory patterns to implement personalized and targeted interventions promptly to maintain IC at a healthy level and advocate for healthy aging.
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 the National Key R&D Program of China (Grant No.2025YFE0100700)the National Natural Science Foundation of China (Grant No.52270138)the International Science and Technology Cooperation Program of Shanghai Science and Technology Innovation Action Plan (Grant No.22230712200)。
文摘Biochar, known as “black gold”, has garnered wide attention in various applications. However, the potential release of toxic organic compounds has raised environmental concerns, thereby limiting its safe and sustainable application. Herein, we propose a distillation strategy to simultaneously detoxify biochar and enhance its redox functionality. Multi-factor correlation analysis identified 30 min as the optimal distillation time, which significantly increased the biochar's Brunauer-Emmett-Teller(BET) surface area(by 143%), improved hydrophilicity(with contact angle decreased by 3.8%), and effectively reduced the dissolved organic carbon(DOC) content of the biochar. Regarding the effect of distillation solvent, both water and acetic acid significantly enhanced the electron exchange capacity(EEC) of the biochar, with lactic acid exhibiting the best performance in improving the electron donating capacity(EDC). Meanwhile, distillation with acetic acid achieved optimal detoxification by effectively removing toxic organic compounds such as naphthalene, amines, and aromatic hydrocarbons. Further validation confirmed the good generalizability of this method to biochars derived from various feedstocks. Techno-economic analysis showed a 98.7% reduction in water consumption and 22.9%-62.5% cost savings compared to traditional washing methods. This work highlights distillation as an efficient, eco-friendly, and cost-effective method to enhance biochar safety and redox functionality, thereby advancing its sustainable applications.
基金funded by the College Students’Innovation and Entrepreneurship Program(X2024110650385).
文摘Background:Intrinsic capacity reflects the overall health status of older adults and decline in intrinsic abilities can lead to adverse health outcomes.However,empirical studies examining the association between digital health literacy,health-promoting lifestyles and intrinsic capacity are scarce.Methods:A cross-sectional study was conducted.Using convenience sampling method,371 older adults were recruited from communities.Multidimensional intrinsic capacity,digital health literacy,health promoting lifestyle and sociodemographic information were measured.Results:The intrinsic capacity of older adults scored 3.75±1.10.The prevalences of declined cognitive capacity,psychological capacity,sensory capacity,vitality,and locomotor capacity were 13.7%,24.3%,19.1%,14.8%,53.1%,respectively.The multiple regression analysis revealed that age(β=−0.253),only living with children and/or grandchildren(β=0.249),current working status(β=−0.132),number of chronic diseases(β=−0.149),frequency of Internet usage(β=0.193),the domain of ability to acquire and evaluate digital health information(β=0.197)in digital health literacy,and the domain of nutrition(β=0.171)in health-promoting lifestyle were the significant factors influencing intrinsic capacity,explaining 27.1%of the variance.Conclusion:Digital health literacy and health-promoting lifestyle have a significant impact on intrinsic capacity.Enhancing digital health literacy and advocating a health-promoting lifestyle are critical to promoting intrinsic capacity for community-dwelling older adults.
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funded by the Science and Technology Research and Development Program Project of China Railway Group Co.,Ltd,grant number P2024X002the China Academy of Railway Sciences Corporation Limited,grant number 2024YJ154.
文摘Purpose-This study investigates the impact of flagship trains on high-speed railway capacity utilization and develops a brand value-oriented optimization framework that balances service quality enhancement with operational efficiency.Design/methodology/approach-A mathematical optimization model based on integer programming is developed,incorporating flagship train constraints into capacity optimization.Case studies compare scenarios with and without flagship train considerations using the Beijing-Shanghai High-Speed Railway data across 20 experimental groups.Findings-Operating flagship trains with hourly departure constraints results in an average decrease of 0.9 trains and an 8.4%reduction in capacity utilization rate.When scheduling 2 flagship trains within a 2-h timeframe,capacity utilization decreases from 86.43%to 83.73%,quantifying the trade-off between brand positioning and operational capacity.Originality/value-This research provides the first quantitative framework for brand value-oriented railway capacity optimization,establishing clear definitions for flagship trains and mathematical foundations for evaluating service quality versus efficiency trade-offs.The findings offer practical decision support for railway operators balancing competitive positioning with capacity maximization.
文摘China Standardization:Can you please briefly introduce ISO,its international standards as well as your scope of work?Rachel Miller Prada:ISO is an independent non-governmental organization dedicated to developing international standards.Currently,ISO has 175 member bodies,representing 175 countries that participate in its standard development work.We have a portfolio of over 24,000 international standards,with around 100 new standards issued or existing ones revised every month.The ultimate goal of our standardization work is to support the achievement of the United Nations Sustainable Development Goals(SDGs).Every standard we develop and every task I undertake in my role contributes to these global objectives.
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funded by Science and Technology Projects from State Grid Corporation of China,(Research on Adaptive Balance Optimization and Simulation Technology of Industrial community Energy System with High Proportion of Distributed Energy,No.:5100-202355752A-3-4-SY).
文摘To achieve the goals of sustainable development of the energy system and the construction of a lowcarbon society,this study proposes a multi-energy storage collaborative optimization strategy for industrial park that integrates the laddered carbon trading mechanism with demand response.Firstly,a dual dimensional DR model is constructed based on the characteristics of load elasticity.The alternativeDRenables flexible substitution of energy loads through complementary conversion of electricity/heat/cold multi-energy sources,while the price DR relies on timeof-use electricity price signals to guide load spatiotemporal migration;Secondly,the LCT mechanism is introduced to achieve optimal carbon emission costs through a tiered carbon quota allocation mechanism.On this basis,an optimization decision model is established with the core objective of maximizing the annual net profit of the park.The objective function takes into account energy sales revenue,generator unit costs,and investment and operation costs of multiple types of energy storage facilities.Themodel constraint system covers three key dimensions:dynamic operation constraints of power generation units,including unit output limits,ramping capability,and minimum start-stop time;the physical boundary of an electric/hot/cold multi-energy storage system involves energy storage capacity and charge/discharge efficiency;The multi-energy network coupling balance equation ensures that the energy conversion and transmission process satisfies the law of conservation of energy.Using CPLEX mathematical programming solver for simulation verification,construct an energy storage capacity configuration decision process that includes LCT-DR synergistic effect.The research results show that compared with the traditional single energy storage configuration mode,this strategy effectively enhances the economic feasibility and engineering practicality of industrial park operation by coordinating demand side resource scheduling and finely controlling carbon costs,while maintaining stable system operation.Its methodological framework provides a technical path that combines theoretical rigor and practical operability for the low-carbon transformation of regional integrated energy systems.
基金2024 Medical Science Research Project Plan of Hebei Province:Research on the Rehabilitation Effect of Combined Exercise Intervention Based on a Hospital-Community-Family Model for Elderly Patients with Chronic Diseases(Project No.:20240083)Youth Science and Technology Project of the Hebei Provincial Health Department:Research on the Standardization Level of Self-Management in Patients with Diabetic Foot and Related Factors Affecting Wound Healing(Project No.:20190002)。
文摘Objective:To explore the application effect of combined exercise intervention based on the hospital-community-family model on intrinsic capacity in elderly patients with diabetes mellitus complicated by chronic kidney disease.Methods:Using convenience sampling,100 elderly patients with diabetes mellitus complicated by chronic kidney disease who received treatment in the endocrinology department of a tertiary A-level hospital from May 2024 to May 2025 were selected as the study subjects.They were randomly divided into an experimental group(50 cases)and a control group(50 cases)using a random number table method.The control group received routine health education and telephone follow-up,while the experimental group,in addition to the control group’s interventions,underwent combined exercise intervention based on the hospital-community-family model.Remote medical guidance was utilized to monitor and study the application effect of exercise intervention on intrinsic capacity in elderly patients with diabetes mellitus complicated by chronic kidney disease.Fasting blood glucose,2-hour postprandial blood glucose,glomerular filtration rate,6-minute walk distance,and scores in five dimensions of intrinsic capacity(exercise,cognition,psychology,vitality,and sensation)were measured before the intervention,at 4 weeks of intervention,and at 12 weeks of intervention for both groups.Results:Before the exercise intervention,there were no statistically significant differences(p>0.05)between the two groups in terms of fasting blood glucose,2-hour postprandial blood glucose,glomerular filtration rate,6-minute walk distance,and scores across five dimensions of intrinsic capacity:mobility,cognition,psychology,vitality,and sensation.After 12 weeks of intervention,the experimental group demonstrated significantly higher scores than the control group in glomerular filtration rate,6-minute walk distance,and the dimensions of mobility,cognition,and vitality within intrinsic capacity,with all differences being statistically significant(p<0.05).Conversely,the experimental group showed significantly lower scores than the control group in fasting blood glucose,2-hour postprandial blood glucose,and the psychological dimension of intrinsic capacity,with these differences also being statistically significant(p<0.05).Conclusion:Continuous nursing care utilizing telemedicine based on a hospital-community-family model combined with exercise intervention can effectively enhance exercise tolerance and intrinsic capacity in elderly patients with diabetes mellitus complicated by chronic kidney disease,thereby improving their quality of life.The effectiveness of the intervention is positively correlated with the duration of the intervention.
基金supported by the High-end Foreign Expert Introduction Program(Grant No.G2022165004L)the Sichuan Transportation Science and Technology Project(Grant No.2018-ZL-01)China Railway 20th Bureau Science and Technology Project(Grant No.YF1900SD07B).
文摘Root-inspired anchorage systems in the field of bio-inspired geotechnics are renowned for enhancing the pullout capacity of traditional geotechnical anchorage systems by simulating the morphology and architecture of plant root systems.However,limited studies have explored their practical applications,particularly in improving slope stability.To fill this gap,this study investigates the reinforcement effect of root-inspired anchors on slope stabilization using transparent soil modeling and 3D-printed anchors,and examines the impact of anchor branching patterns(i.e.branching numbers,branching angle,and branching nodes)on slope bearing capacity,shear band evolution,and temporal and spatial variation of slope deformation.The results show that peak slope bearing capacity increases with branching numbers and branching angles,correlating with the envelope area of the curved shear band.Upper anchors result in step-like deflections in the shear band near the trailing edge,while lower anchors convert the upward concave shear band into an upward convex one,thus increasing the slope bearing capacity.Slope deformation is minimized with intermediate branching parameters,such as a branching number of 4 and a branching angle of 45°.The anchor reinforcement mechanisms,i.e.anchor rod shear resistance,interface friction,anchor pullout capacity,and plate tightening effects,are comprehensively discussed,and the installation effects resulting from compromise slope modeling are identified as the contributors.These findings shed light on the failure process of root-inspired anchors reinforced slopes and provide a preliminary reference for potential applications,especially for the tradeoff between anchor branching,slope deformation,and slope stability.
基金supported by the National Key R&D Program of China(No.2023YFB3809500)the Fundamental Research Funds for the Central Universities(No.2024CDJXY003)+1 种基金the Venture&Innovation Support Program for Chongqing Overseas Returnees(cx2023087)The Chongqing Technology Innovation and Application Development Project(No.2024TIAD-KPX0003).
文摘Micro-sized anatase TiO_(2) displays inferior capacity as cathode material for magnesium ion batteries because of the higher diffusion energy barrier of Mg^(2+)in anatase TiO_(2) lattice.Herein,we report that nanosized anatase TiO_(2) exposed(001)facet doubles the capacity compared to the micro-sized sample ascribed to the interfacial Mg^(2+)ion storage.First-principles calculations reveal that the diffusion energy barrier of Mg^(2+)on the(001)facet is significantly lower than those in the bulk phase and on(100)facet,and the adsorption energy of Mg^(2+)on the(001)facet is also considerably lower than that on(100)facet,which guarantees superior interfacial Mg^(2+)storage of(001)facet.Moreover,anatase TiO_(2) exposed(001)facet displays a significantly higher capacity of 312.9 mAh g^(−1) in Mg-Li dual-salt electrolyte compared to 234.3 mAh g^(−1) in Li salt electrolyte.The adsorption energies of Mg^(2+)on(001)facet are much lower than the adsorption energies of Li+on(001)facet,implying that the Mg^(2+)ion interfacial storage is more favorable.These results highlight that controlling the crystal facet of the nanocrystals effectively enhances the interfacial storage of multivalent ions.This work offers valuable guidance for the rational design of high-capacity storage systems.
基金supported by the National Natural Science Foundation of China(Grant No.11571181)by the Natural Science Foundation of Jiangsu Province(Grant No.BK20171454).
文摘In this paper,we propose and analyze two second-order accurate finite difference schemes for the one-dimensional heat equation with concentrated capacity on a computa-tional domain=[a,b].We first transform the target equation into the standard heat equation on the domain excluding the singular point equipped with an inner interface matching(IIM)condition on the singular point x=ξ∈(a,b),then adopt Taylor’s ex-pansion to approximate the IIM condition at the singular point and apply second-order finite difference method to approximate the standard heat equation at the nonsingular points.This discrete procedure allows us to choose different grid sizes to partition the two sub-domains[a,ξ]and[ξ,b],which ensures that x=ξ is a grid point,and hence the pro-posed schemes can be generalized to the heat equation with more than one concentrated capacities.We prove that the two proposed schemes are uniquely solvable.And through in-depth analysis of the local truncation errors,we rigorously prove that the two schemes are second-order accurate both in temporal and spatial directions in the maximum norm without any constraint on the grid ratio.Numerical experiments are carried out to verify our theoretical conclusions.
基金supported by the National Natural Science Foundation of China research grant“Study on the characteristic motion and load of bubbles near a solid boundary in shear flows”(51679056)Natural Science Foundation of Heilongjiang Province of China(E2016024).
文摘This paper presents the design optimization of composite submersible cylindrical pressure hull subjected to 3 MPa hydrostatic pressure.The design optimization study is conducted for cross-ply layups[0_(s)/90_(t)/0_(u)],[0_(s)/90_(t)/0_(u)]s,[0_(s)/90_(t)]s and[90_(s)/0_(t)]s considering three uni-directional composites,i.e.Carbon/Epoxy,Glass/Epoxy,and Boron/Epoxy.The optimization study is performed by coupling a Multi-Objective Genetic Algorithm(MOGA)and Analytical Analysis.Minimizing the buoyancy factor and maximizing the buckling load factor are considered as the objectives of the optimization study.The objectives of the optimization are achieved under constraints on the Tsai-Wu,Tsai-Hill and Maximum Stress composite failure criteria and on buckling load factor.To verify the optimization approach,optimization of one particular layup configuration is also conducted in ANSYS with the same objectives and constraints.
