With the increasing integration of emerging source-load types such as distributed photovoltaics,electric vehicles,and energy storage into distribution networks,the operational characteristics of these networks have ev...With the increasing integration of emerging source-load types such as distributed photovoltaics,electric vehicles,and energy storage into distribution networks,the operational characteristics of these networks have evolved from traditional single-load centers to complex multi-source,multi-load systems.This transition not only increases the difficulty of effectively classifying distribution networks due to their heightened complexity but also renders traditional energy management approaches-primarily focused on economic objectives-insufficient to meet the growing demands for flexible scheduling and dynamic response.To address these challenges,this paper proposes an adaptive multi-objective energy management strategy that accounts for the distinct operational requirements of distribution networks with a high penetration of new-type source-loads.The goal is to establish a comprehensive energy management framework that optimally balances energy efficiency,carbon reduction,and economic performance in modern distribution networks.To enhance classification accuracy,the strategy constructs amulti-dimensional scenario classification model that integrates environmental and climatic factors by analyzing the operational characteristics of new-type distribution networks and incorporating expert knowledge.An improved split-coupling K-means preclustering algorithm is employed to classify distribution networks effectively.Based on the classification results,fuzzy logic control is then utilized to dynamically optimize the weighting of each objective,allowing for an adaptive adjustment of priorities to achieve a flexible and responsivemulti-objective energy management strategy.The effectiveness of the proposed approach is validated through practical case studies.Simulation results indicate that the proposed method improves classification accuracy by 18.18%compared to traditional classification methods and enhances energy savings and carbon reduction by 4.34%and 20.94%,respectively,compared to the fixed-weight strategy.展开更多
Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based...Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based car-following(CF)framework employing the Deep Deterministic Policy Gradient(DDPG)algorithm,which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning.Utilizing real-world driving data from the highD dataset,the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios.The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control(MPC-ACC)controller.Results show that theDRLmodel significantly enhances safety,achieving zero collisions and a higher average time-to-collision(TTC)of 8.45 s,compared to 5.67 s for MPC and 6.12 s for human drivers.For efficiency,the model demonstrates 89.2% headway compliance and maintains speed tracking errors below 1.2 m/s in 90% of cases.In terms of energy optimization,the proposed approach reduces fuel consumption by 5.4% relative to MPC.Additionally,it enhances passenger comfort by lowering jerk values by 65%,achieving 0.12 m/s3 vs.0.34 m/s3 for human drivers.A multi-objective reward function is integrated to ensure stable policy convergence while simultaneously balancing the four key performance metrics.Moreover,the findings underscore the potential of DRL in advancing autonomous vehicle control,offering a robust and sustainable solution for safer,more efficient,and more comfortable transportation systems.展开更多
Objective:To investigate the application effect of intelligent empowerment standardized airway management process in patients receiving mechanical ventilation.Methods:A retrospective analysis was conducted on the clin...Objective:To investigate the application effect of intelligent empowerment standardized airway management process in patients receiving mechanical ventilation.Methods:A retrospective analysis was conducted on the clinical data of 79 EICU inpatients who underwent tracheal intubation and mechanical ventilation treatment at our hospital from January 2023 to May 2025.The patients were divided into a control group(conventional airway management process,n=40)and a study group(intelligent empowerment standardized airway management process,n=39)based on the intervention protocols they received.Oral health scores,dental plaque index,oral odor,serum inflammatory markers[C-reactive protein(CRP),procalcitonin(PCT)],clinical pulmonary infection score(CPIS),as well as the incidence of ventilator-associated pneumonia(VAP),duration of mechanical ventilation,and length of stay in the EICU were assessed before and after treatment.Results:The baseline values of all indicators were consistent between the two groups before intervention(p>0.05).After corresponding interventions,both groups showed significant improvements in Beck oral health scores,dental plaque index,and oral odor,with more pronounced improvements observed in the study group(p<0.05).After the intervention,the research group showed a significant decrease in serum CRP and PCT levels,as well as CPIS scores(p<0.05).In contrast,the control group experienced an increase in these three indicators to a certain extent(p<0.05).Moreover,the incidence of ventilator-associated pneumonia(VAP),duration of mechanical ventilation,and length of stay in the EICU were all lower in the research group compared to the control group,while the nurse’s compliance rate with the protocol was higher in the research group(p<0.05).Conclusion:The standardized airway management protocol empowered by intelligent technology can significantly improve nursing compliance,benefit oral health status,reduce the risk of pulmonary infection and systemic inflammation levels,and promote rapid patient recovery,demonstrating considerable potential for widespread adoption.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
The proton exchange membrane fuel cell(PEMFC)and the hydrogen hybrid power system are studied by the fuzzy-PID(FPID)controlmethod and the fuzzy-PID controlmethod by Artificial Bee Colony algorithm(ABCFPID),respectivel...The proton exchange membrane fuel cell(PEMFC)and the hydrogen hybrid power system are studied by the fuzzy-PID(FPID)controlmethod and the fuzzy-PID controlmethod by Artificial Bee Colony algorithm(ABCFPID),respectively.The results reveal that compared with the FPID control method,the temperature overshoot of the PEMFC stack under the ABC-FPID control method is decreased by 0.6%.Moreover,the circulating water flow rate within the full operating envelope(about 3 min)is reduced by 19.46 L,which means the ABC-FPID control method is more effective in regulating the stack temperature.Then,the ABC-FPID control method is proposed to study the hydrogen hybrid power system,and the system output power matching,operating characteristic curve of the fuel cell,state of charge(SOC)of the lithium battery,system efficiency and hydrogen demand are obtained.The results indicate that the maximum system efficiency reaches 46.3%,the average system efficiency is 33.8%,and the average hydrogen demand is 0.192 kg/s.Overall,the ABC-FPID control method can efficiently ensure the stability of the fuel cell’s output power,and actively prompt the lithium battery to fulfill the function of“peak shaving and valley filling”under variable load power conditions.展开更多
Understanding the complex interactions between human activities and ecosystem functions is a prerequisite for achieving sustainable development.Since the implementation of the“Grain for Green”Project in 1999,ecosyst...Understanding the complex interactions between human activities and ecosystem functions is a prerequisite for achieving sustainable development.Since the implementation of the“Grain for Green”Project in 1999,ecosystem functions in China’s Loess Plateau have significantly improved.However,intensified human activities have also exacerbated the pressures on the region’s fragile ecological environment.This study investigates the spatiotemporal variations in the human activity intensity index(HAI)and net ecosystem benefits(NEB)from 2000 to 2020,using expert-based assessments and an enhanced cost-benefit evaluation framework.Results indicate that HAI increased by 16.7% and 16.6% at the grid and county levels,respectively.NEB exhibited pronounced spatial heterogeneity,with a total increase of USD 36.2 trillion at the grid scale.At the county level,the average NEB rose by 75%.The degree of trade-off was higher at the grid scale than at the county scale,while the synergistic areas initially expanded and then declined at both scales.Key areas for improvement and regions of lagging development were identified as priority zones for ecological management and spatial planning at both spatial resolutions.This study offers scientific insights and practical guidance for harmonizing ecological conservation with high-quality development in ecologically vulnerable regions.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
Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrain...Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.展开更多
Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method f...Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers.展开更多
With the intensification of population aging in China,the problem of cognitive impairment in the elderly has become increasingly prominent,attracting widespread attention from all sectors of society.Geriatric cognitiv...With the intensification of population aging in China,the problem of cognitive impairment in the elderly has become increasingly prominent,attracting widespread attention from all sectors of society.Geriatric cognitive impairment is characterized by chronicity,which not only seriously threatens the health of the elderly and reduces their quality of life,but also imposes a heavy burden on families and society due to its long course.Attaching importance to and strengthening the chronic disease management of elderly cognitive impairment has profound significance for delaying disease progression,improving patients’quality of life,and reducing the burden of family care.Therefore,this paper first comprehensively understands elderly cognitive impairment by briefly elaborating on its definition and characteristics;on this basis,it focuses on exploring effective strategies for the chronic disease management of elderly cognitive impairment,hoping to provide new ideas and methods for the management of this condition and offer useful references for relevant clinical research and practice.展开更多
The environmental wind tunnel of high-speed railway trains serves as a crucial experimental facility for the research and development of high-speed railway technology.The refrigeration system within the wind tunnel is...The environmental wind tunnel of high-speed railway trains serves as a crucial experimental facility for the research and development of high-speed railway technology.The refrigeration system within the wind tunnel is an important subsystem.However,the design of the wind tunnel refrigeration system management program presents significant scientific challenges and limitations.Traditional management approaches in wind tunnel refrigeration systems suffer from prolonged decision-making times and reliance on experiential knowledge,necessitating the need for intelligent transformation.This paper aims to address these issues by exploring existing intelligent management methodologies and defining the concept of a wind tunnel intelligent laboratory along with its primary modules.Furthermore,we propose a water cooler failure prediction model based on the existing equipment model of the wind tunnel's refrigeration system.This model effectively predicts the Remaining Useful Life(RUL) of the water cooler in the case of fouling failure,contributing to enhanced efficiency,cost reduction,and safety improvements in laboratories.展开更多
Objective:To systematically sort out the application forms and effects of digital health intervention technologies in oral health management,and provide references for the digital development of stomatology.Methods:By...Objective:To systematically sort out the application forms and effects of digital health intervention technologies in oral health management,and provide references for the digital development of stomatology.Methods:By reviewing relevant domestic and foreign studies and clinical practices,this paper summarizes and analyzes the main application forms of digital health interventions,including digital health education,intelligent detection equipment,telemedicine platforms,oral health big data platforms,and school-hospital collaborative screening robots.Results:Studies have shown that digital health interventions can effectively improve the public’s oral health knowledge level,optimize personal health behaviors,enhance clinical diagnosis efficiency,reduce overall medical costs,and promote the innovation and upgrading of oral health management models.Conclusion:Digital health intervention represents an inevitable trend in the future development of stomatology.In the future,it is still necessary to improve data security and privacy protection,technology adaptability and popularity,as well as relevant policies and norms,to give full play to its potential value.展开更多
Cellulose frameworks have emerged as promising materials for light management due to their exceptional light-scattering capabilities and sustainable nature.Conventional biomass-derived cellulose frameworks face a fund...Cellulose frameworks have emerged as promising materials for light management due to their exceptional light-scattering capabilities and sustainable nature.Conventional biomass-derived cellulose frameworks face a fundamental trade-off between haze and transparency,coupled with impractical thicknesses(≥1 mm).Inspired by squid’s skin-peeling mechanism,this work develops a peroxyformic acid(HCOOOH)-enabled precision peeling strategy to isolate intact 10-μm-thick bamboo green(BG)frameworks—100×thinner than wood-based counterparts while achieving an unprecedented optical performance(88%haze with 80%transparency).This performance surpasses delignified biomass(transparency<40%at 1 mm)and matches engineered cellulose composites,yet requires no energy-intensive nanofibrillation.The preserved native cellulose I crystalline structure(64.76%crystallinity)and wax-coated uniaxial fibril alignment(Hermans factor:0.23)contribute to high mechanical strength(903 MPa modulus)and broadband light scattering.As a light-management layer in polycrystalline silicon solar cells,the BG framework boosts photoelectric conversion efficiency by 0.41%absolute(18.74%→19.15%),outperforming synthetic anti-reflective coatings.The work establishes a scalable,waste-to-wealth route for optical-grade cellulose materials in next-generation optoelectronics.展开更多
Perovskite solar cells(PSCs)have emerged as promising photovoltaic technologies owing to their remarkable power conversion efficiency(PCE).However,heat accumulation under continuous illumination remains a critical bot...Perovskite solar cells(PSCs)have emerged as promising photovoltaic technologies owing to their remarkable power conversion efficiency(PCE).However,heat accumulation under continuous illumination remains a critical bottleneck,severely affecting device stability and long-term operational performance.Herein,we present a multifunctional strategy by incorporating highly thermally conductive Ti_(3)C_(2)T_(X) MXene nanosheets into the perovskite layer to simultaneously enhance thermal management and optoelectronic properties.The Ti_(3)C_(2)T_(X) nanosheets,embedded at perovskite grain boundaries,construct efficient thermal conduction pathways,significantly improving the thermal conductivity and diffusivity of the film.This leads to a notable reduction in the device’s steady-state operating temperature from 42.96 to 39.97 under 100 mW cm^(−2) illumination,thereby alleviating heat-induced performance degradation.Beyond thermal regulation,Ti_(3)C_(2)T_(X),with high conductivity and negatively charged surface terminations,also serves as an effective defect passivation agent,reducing trap-assisted recombination,while simultaneously facilitating charge extraction and transport by optimizing interfacial energy alignment.As a result,the Ti_(3)C_(2)T_(X)-modified PSC achieve a champion PCE of 25.13%and exhibit outstanding thermal stability,retaining 80%of the initial PCE after 500 h of thermal aging at 85 and 30±5%relative humidity.(In contrast,control PSC retain only 58%after 200 h.)Moreover,under continuous maximum power point tracking in N2 atmosphere,Ti_(3)C_(2)T_(X)-modified PSC retained 70%of the initial PCE after 500 h,whereas the control PSC drop sharply to 20%.These findings highlight the synergistic role of Ti_(3)C_(2)T_(X) in thermal management and optoelectronic performance,paving the way for the development of high-efficiency and heat-resistant perovskite photovoltaics.展开更多
As an emerging thermal management strategy,dynamic radiative cooling(DRC)technology enables dynamic modulation of spectral radiation properties under varying environmental conditions through the directional design of ...As an emerging thermal management strategy,dynamic radiative cooling(DRC)technology enables dynamic modulation of spectral radiation properties under varying environmental conditions through the directional design of material spectral characteristics.However,a comprehensive review of the basic physical mechanisms of radiative heat transfer in DRC materials and various design principles involved in dynamic radiative thermal regulation is still lacking.This review systematically summarizes recent advances in this field,spanning from fundamental physical principles to intrinsic molecular and electronic mechanisms,and further to representative material systems and multi-band regulation strategies,highlighting the interdisciplinary research achievements and technological innovations.This work outlines the core mechanisms governing the regulation of different spectral bands during radiative heat transfer processes.Then,the main categories of DRC materials are systematically reviewed,including actively responsive structures,passively responsive structures,and multi-stimuli-responsive materials.Furthermore,the challenges faced by current DRC technology and future development trends are summarized and discussed,providing valuable reference and guidance for further research in this field.Although DRC technologies still face significant challenges in material stability,manufacturing processes,and system integration,the continuous advances in related areas and multifunctional materials are expected to broaden the application prospects of DRC in the future.展开更多
Arid and semi-arid ecosystems are prone to extensive fires due to specific climatic conditions,sparse vegetation cover,and high density of fine fuels.Understanding the flammability characteristics of land covers is es...Arid and semi-arid ecosystems are prone to extensive fires due to specific climatic conditions,sparse vegetation cover,and high density of fine fuels.Understanding the flammability characteristics of land covers is essential for fire management and designing land restoration programs in arid and semi-arid ecosystems.This study provided a new approach to evaluate the flammability of shrublands and woodlands using flammability indices(FIs)including time to ignition(TI),duration of combustion(DC),and flame height(FH)of plant species and their relative frequencies in the Dalfard Basin of southeastern Iran.The results showed that there was a significant difference in FIs between land covers.Shrublands had higher flammability potential compared with woodlands.Plant moisture content had a negative relationship with TI(P<0.010)and no significant relationship with DC and FH(P>0.050).Artemisia spp.,Astragalus gossypinus Fischer,Amygdalus scoparia Spach,and Cymbopogon jwarancusa(Jones)Schult.had the highest FI.Tree species such as Rhazya stricta Decne.,and Pistacia atlantica Desf.showed greater resistance to fire.Using principal component analysis,the relationship between species and FIs was examined,and TI of wet fuel was the most important FI in relation to species.Structural equation model showed that life form(P<0.001)was the most important flammability driver.Precipitation(P<0.010)and legume species(P<0.010)were significantly related to the flammability in arid land.This study emphasizes the importance of managing high-risk species and using resistant species in vegetation restoration and shows that combining species FIs with their abundance is an effective tool for assessing fire risk and fuel management at the plant community scale.展开更多
In recent years,with the accelerating aging process of the population,China has entered an aging society,and the number of elderly patients with chronic diseases has been increasing.The traditional medical and elderly...In recent years,with the accelerating aging process of the population,China has entered an aging society,and the number of elderly patients with chronic diseases has been increasing.The traditional medical and elderly care service models can no longer fully meet their needs.The integrated medical and elderly care model has emerged as the times require.It organically combines medical resources with elderly care resources to provide comprehensive and continuous health management services for the elderly,becoming an important approach to solving the problems of chronic disease management among the elderly.In this regard,this paper first elaborates on the role of integrated medical and elderly care in the management of chronic diseases among the elderly,and then puts forward application strategies of integrated medical and elderly care in the management of chronic diseases among the elderly,in order to provide certain reference for relevant researchers.展开更多
As a major source of freshwater in Central Asia,Tajikistan is endowed with abundant glaciers and water resources.However,the country faces multiple challenges,including accelerated glacier retreat,complex inter-govern...As a major source of freshwater in Central Asia,Tajikistan is endowed with abundant glaciers and water resources.However,the country faces multiple challenges,including accelerated glacier retreat,complex inter-government water resource management,and inefficient water use.Existing research has predominantly focused on individual hydrological processes,such as glacier retreat,snow cover change,or transboundary water issues,but it has yet to fully capture the overall complexity of water system.Tajikistan’s water system functions as an integrated whole from mountain runoff to downstream supply,but a comprehensive study of its water resource has yet to be conducted.To address this research gap,this study systematically examined the status,challenges,and sustainable management strategies of Tajikistan’s water resources based on a literature review,remote sensing data analysis,and case studies.Despite Tajikistan’s relative abundance of water resources,global warming is accelerating glacier melting and altering the hydrological cycles,which have resulted in unstable runoff patterns and heightened risks of extreme events.In Tajikistan,outdated infrastructure and poor management are primary causes of low water-use efficiency in the agricultural sector,which accounts for 85.00%of the total water withdrawals.At the governance level,Tajikistan faces challenges in balancing the water-energy-food nexus and transboundary water resource issues.To address these issues,this study proposes core paths for Tajikistan to achieve sustainable water resource management,such as accelerating technological innovation,promoting water-saving agricultural technologies,improving water resource utilization efficiency,and establishing a community participation-based comprehensive management framework.Additionally,strengthening cross-border cooperation and improving real-time monitoring systems have been identified as critical steps to advance sustainable water resource utilization and evidence-based decision-making in Tajikistan and across Central Asia.展开更多
基金supported by the Science and Technology Project of the Headquarters of the State Grid Corporation(project code:5400-202323233A-1-1-ZN).
文摘With the increasing integration of emerging source-load types such as distributed photovoltaics,electric vehicles,and energy storage into distribution networks,the operational characteristics of these networks have evolved from traditional single-load centers to complex multi-source,multi-load systems.This transition not only increases the difficulty of effectively classifying distribution networks due to their heightened complexity but also renders traditional energy management approaches-primarily focused on economic objectives-insufficient to meet the growing demands for flexible scheduling and dynamic response.To address these challenges,this paper proposes an adaptive multi-objective energy management strategy that accounts for the distinct operational requirements of distribution networks with a high penetration of new-type source-loads.The goal is to establish a comprehensive energy management framework that optimally balances energy efficiency,carbon reduction,and economic performance in modern distribution networks.To enhance classification accuracy,the strategy constructs amulti-dimensional scenario classification model that integrates environmental and climatic factors by analyzing the operational characteristics of new-type distribution networks and incorporating expert knowledge.An improved split-coupling K-means preclustering algorithm is employed to classify distribution networks effectively.Based on the classification results,fuzzy logic control is then utilized to dynamically optimize the weighting of each objective,allowing for an adaptive adjustment of priorities to achieve a flexible and responsivemulti-objective energy management strategy.The effectiveness of the proposed approach is validated through practical case studies.Simulation results indicate that the proposed method improves classification accuracy by 18.18%compared to traditional classification methods and enhances energy savings and carbon reduction by 4.34%and 20.94%,respectively,compared to the fixed-weight strategy.
基金the Hebei Province Science and Technology Plan Project(19221909D)rincess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R308),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based car-following(CF)framework employing the Deep Deterministic Policy Gradient(DDPG)algorithm,which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning.Utilizing real-world driving data from the highD dataset,the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios.The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control(MPC-ACC)controller.Results show that theDRLmodel significantly enhances safety,achieving zero collisions and a higher average time-to-collision(TTC)of 8.45 s,compared to 5.67 s for MPC and 6.12 s for human drivers.For efficiency,the model demonstrates 89.2% headway compliance and maintains speed tracking errors below 1.2 m/s in 90% of cases.In terms of energy optimization,the proposed approach reduces fuel consumption by 5.4% relative to MPC.Additionally,it enhances passenger comfort by lowering jerk values by 65%,achieving 0.12 m/s3 vs.0.34 m/s3 for human drivers.A multi-objective reward function is integrated to ensure stable policy convergence while simultaneously balancing the four key performance metrics.Moreover,the findings underscore the potential of DRL in advancing autonomous vehicle control,offering a robust and sustainable solution for safer,more efficient,and more comfortable transportation systems.
文摘Objective:To investigate the application effect of intelligent empowerment standardized airway management process in patients receiving mechanical ventilation.Methods:A retrospective analysis was conducted on the clinical data of 79 EICU inpatients who underwent tracheal intubation and mechanical ventilation treatment at our hospital from January 2023 to May 2025.The patients were divided into a control group(conventional airway management process,n=40)and a study group(intelligent empowerment standardized airway management process,n=39)based on the intervention protocols they received.Oral health scores,dental plaque index,oral odor,serum inflammatory markers[C-reactive protein(CRP),procalcitonin(PCT)],clinical pulmonary infection score(CPIS),as well as the incidence of ventilator-associated pneumonia(VAP),duration of mechanical ventilation,and length of stay in the EICU were assessed before and after treatment.Results:The baseline values of all indicators were consistent between the two groups before intervention(p>0.05).After corresponding interventions,both groups showed significant improvements in Beck oral health scores,dental plaque index,and oral odor,with more pronounced improvements observed in the study group(p<0.05).After the intervention,the research group showed a significant decrease in serum CRP and PCT levels,as well as CPIS scores(p<0.05).In contrast,the control group experienced an increase in these three indicators to a certain extent(p<0.05).Moreover,the incidence of ventilator-associated pneumonia(VAP),duration of mechanical ventilation,and length of stay in the EICU were all lower in the research group compared to the control group,while the nurse’s compliance rate with the protocol was higher in the research group(p<0.05).Conclusion:The standardized airway management protocol empowered by intelligent technology can significantly improve nursing compliance,benefit oral health status,reduce the risk of pulmonary infection and systemic inflammation levels,and promote rapid patient recovery,demonstrating considerable potential for widespread adoption.
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supported by the Natural Science Foundation of Jiangsu Province(BK20231445)Aeronautical Science Foundation of China(20230028052001).
文摘The proton exchange membrane fuel cell(PEMFC)and the hydrogen hybrid power system are studied by the fuzzy-PID(FPID)controlmethod and the fuzzy-PID controlmethod by Artificial Bee Colony algorithm(ABCFPID),respectively.The results reveal that compared with the FPID control method,the temperature overshoot of the PEMFC stack under the ABC-FPID control method is decreased by 0.6%.Moreover,the circulating water flow rate within the full operating envelope(about 3 min)is reduced by 19.46 L,which means the ABC-FPID control method is more effective in regulating the stack temperature.Then,the ABC-FPID control method is proposed to study the hydrogen hybrid power system,and the system output power matching,operating characteristic curve of the fuel cell,state of charge(SOC)of the lithium battery,system efficiency and hydrogen demand are obtained.The results indicate that the maximum system efficiency reaches 46.3%,the average system efficiency is 33.8%,and the average hydrogen demand is 0.192 kg/s.Overall,the ABC-FPID control method can efficiently ensure the stability of the fuel cell’s output power,and actively prompt the lithium battery to fulfill the function of“peak shaving and valley filling”under variable load power conditions.
基金National Natural Science Foundation of China(Grant No.U2243225)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB40000000)+2 种基金the Natural Science Basic Research Program of Shaanxi(Grant No.Z2024-ZYFS-0065)the Funding of Top Young talents of Ten Thousand talents Plan in China(2021)the Fundamental Research Funds for the Central Universities(Grants No.2452023071 and 2023HHZX002).
文摘Understanding the complex interactions between human activities and ecosystem functions is a prerequisite for achieving sustainable development.Since the implementation of the“Grain for Green”Project in 1999,ecosystem functions in China’s Loess Plateau have significantly improved.However,intensified human activities have also exacerbated the pressures on the region’s fragile ecological environment.This study investigates the spatiotemporal variations in the human activity intensity index(HAI)and net ecosystem benefits(NEB)from 2000 to 2020,using expert-based assessments and an enhanced cost-benefit evaluation framework.Results indicate that HAI increased by 16.7% and 16.6% at the grid and county levels,respectively.NEB exhibited pronounced spatial heterogeneity,with a total increase of USD 36.2 trillion at the grid scale.At the county level,the average NEB rose by 75%.The degree of trade-off was higher at the grid scale than at the county scale,while the synergistic areas initially expanded and then declined at both scales.Key areas for improvement and regions of lagging development were identified as priority zones for ecological management and spatial planning at both spatial resolutions.This study offers scientific insights and practical guidance for harmonizing ecological conservation with high-quality development in ecologically vulnerable regions.
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 Key Science and Technology Program of Henan Province,China(Grant Nos.242102210147,242102210027)Fujian Province Young and Middle aged Teacher Education Research Project(Science and Technology Category)(No.JZ240101)(Corresponding author:Dong Yuan).
文摘Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.
基金National Natural Science Foundation of China,No.42301470,No.52270185,No.42171389Capacity Building Program of Local Colleges and Universities in Shanghai,No.21010503300。
文摘Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers.
文摘With the intensification of population aging in China,the problem of cognitive impairment in the elderly has become increasingly prominent,attracting widespread attention from all sectors of society.Geriatric cognitive impairment is characterized by chronicity,which not only seriously threatens the health of the elderly and reduces their quality of life,but also imposes a heavy burden on families and society due to its long course.Attaching importance to and strengthening the chronic disease management of elderly cognitive impairment has profound significance for delaying disease progression,improving patients’quality of life,and reducing the burden of family care.Therefore,this paper first comprehensively understands elderly cognitive impairment by briefly elaborating on its definition and characteristics;on this basis,it focuses on exploring effective strategies for the chronic disease management of elderly cognitive impairment,hoping to provide new ideas and methods for the management of this condition and offer useful references for relevant clinical research and practice.
文摘The environmental wind tunnel of high-speed railway trains serves as a crucial experimental facility for the research and development of high-speed railway technology.The refrigeration system within the wind tunnel is an important subsystem.However,the design of the wind tunnel refrigeration system management program presents significant scientific challenges and limitations.Traditional management approaches in wind tunnel refrigeration systems suffer from prolonged decision-making times and reliance on experiential knowledge,necessitating the need for intelligent transformation.This paper aims to address these issues by exploring existing intelligent management methodologies and defining the concept of a wind tunnel intelligent laboratory along with its primary modules.Furthermore,we propose a water cooler failure prediction model based on the existing equipment model of the wind tunnel's refrigeration system.This model effectively predicts the Remaining Useful Life(RUL) of the water cooler in the case of fouling failure,contributing to enhanced efficiency,cost reduction,and safety improvements in laboratories.
文摘Objective:To systematically sort out the application forms and effects of digital health intervention technologies in oral health management,and provide references for the digital development of stomatology.Methods:By reviewing relevant domestic and foreign studies and clinical practices,this paper summarizes and analyzes the main application forms of digital health interventions,including digital health education,intelligent detection equipment,telemedicine platforms,oral health big data platforms,and school-hospital collaborative screening robots.Results:Studies have shown that digital health interventions can effectively improve the public’s oral health knowledge level,optimize personal health behaviors,enhance clinical diagnosis efficiency,reduce overall medical costs,and promote the innovation and upgrading of oral health management models.Conclusion:Digital health intervention represents an inevitable trend in the future development of stomatology.In the future,it is still necessary to improve data security and privacy protection,technology adaptability and popularity,as well as relevant policies and norms,to give full play to its potential value.
基金supported by National Natural Science Foundation of China(32494793).
文摘Cellulose frameworks have emerged as promising materials for light management due to their exceptional light-scattering capabilities and sustainable nature.Conventional biomass-derived cellulose frameworks face a fundamental trade-off between haze and transparency,coupled with impractical thicknesses(≥1 mm).Inspired by squid’s skin-peeling mechanism,this work develops a peroxyformic acid(HCOOOH)-enabled precision peeling strategy to isolate intact 10-μm-thick bamboo green(BG)frameworks—100×thinner than wood-based counterparts while achieving an unprecedented optical performance(88%haze with 80%transparency).This performance surpasses delignified biomass(transparency<40%at 1 mm)and matches engineered cellulose composites,yet requires no energy-intensive nanofibrillation.The preserved native cellulose I crystalline structure(64.76%crystallinity)and wax-coated uniaxial fibril alignment(Hermans factor:0.23)contribute to high mechanical strength(903 MPa modulus)and broadband light scattering.As a light-management layer in polycrystalline silicon solar cells,the BG framework boosts photoelectric conversion efficiency by 0.41%absolute(18.74%→19.15%),outperforming synthetic anti-reflective coatings.The work establishes a scalable,waste-to-wealth route for optical-grade cellulose materials in next-generation optoelectronics.
基金the National Natural Science Foundation of China(Nos.62374029,22175029,62474033,and W2433038)the Young Elite Scientists Sponsorship Program by CAST(No.YESS20220550)+2 种基金the Sichuan Science and Technology Program(No.2024NSFSC0250)the Natural Science Foundation of Shenzhen Innovation Committee(JCYJ20210324135614040)the Fundamental Research Funds for the Central Universities of China(No.ZYGX2022J032).
文摘Perovskite solar cells(PSCs)have emerged as promising photovoltaic technologies owing to their remarkable power conversion efficiency(PCE).However,heat accumulation under continuous illumination remains a critical bottleneck,severely affecting device stability and long-term operational performance.Herein,we present a multifunctional strategy by incorporating highly thermally conductive Ti_(3)C_(2)T_(X) MXene nanosheets into the perovskite layer to simultaneously enhance thermal management and optoelectronic properties.The Ti_(3)C_(2)T_(X) nanosheets,embedded at perovskite grain boundaries,construct efficient thermal conduction pathways,significantly improving the thermal conductivity and diffusivity of the film.This leads to a notable reduction in the device’s steady-state operating temperature from 42.96 to 39.97 under 100 mW cm^(−2) illumination,thereby alleviating heat-induced performance degradation.Beyond thermal regulation,Ti_(3)C_(2)T_(X),with high conductivity and negatively charged surface terminations,also serves as an effective defect passivation agent,reducing trap-assisted recombination,while simultaneously facilitating charge extraction and transport by optimizing interfacial energy alignment.As a result,the Ti_(3)C_(2)T_(X)-modified PSC achieve a champion PCE of 25.13%and exhibit outstanding thermal stability,retaining 80%of the initial PCE after 500 h of thermal aging at 85 and 30±5%relative humidity.(In contrast,control PSC retain only 58%after 200 h.)Moreover,under continuous maximum power point tracking in N2 atmosphere,Ti_(3)C_(2)T_(X)-modified PSC retained 70%of the initial PCE after 500 h,whereas the control PSC drop sharply to 20%.These findings highlight the synergistic role of Ti_(3)C_(2)T_(X) in thermal management and optoelectronic performance,paving the way for the development of high-efficiency and heat-resistant perovskite photovoltaics.
基金supported by the Taishan Scholars of Shandong Province(tsqn202408151)the National Natural Science Foundation of China(Grant Nos.52476067,52306078,and 52576053).
文摘As an emerging thermal management strategy,dynamic radiative cooling(DRC)technology enables dynamic modulation of spectral radiation properties under varying environmental conditions through the directional design of material spectral characteristics.However,a comprehensive review of the basic physical mechanisms of radiative heat transfer in DRC materials and various design principles involved in dynamic radiative thermal regulation is still lacking.This review systematically summarizes recent advances in this field,spanning from fundamental physical principles to intrinsic molecular and electronic mechanisms,and further to representative material systems and multi-band regulation strategies,highlighting the interdisciplinary research achievements and technological innovations.This work outlines the core mechanisms governing the regulation of different spectral bands during radiative heat transfer processes.Then,the main categories of DRC materials are systematically reviewed,including actively responsive structures,passively responsive structures,and multi-stimuli-responsive materials.Furthermore,the challenges faced by current DRC technology and future development trends are summarized and discussed,providing valuable reference and guidance for further research in this field.Although DRC technologies still face significant challenges in material stability,manufacturing processes,and system integration,the continuous advances in related areas and multifunctional materials are expected to broaden the application prospects of DRC in the future.
文摘Arid and semi-arid ecosystems are prone to extensive fires due to specific climatic conditions,sparse vegetation cover,and high density of fine fuels.Understanding the flammability characteristics of land covers is essential for fire management and designing land restoration programs in arid and semi-arid ecosystems.This study provided a new approach to evaluate the flammability of shrublands and woodlands using flammability indices(FIs)including time to ignition(TI),duration of combustion(DC),and flame height(FH)of plant species and their relative frequencies in the Dalfard Basin of southeastern Iran.The results showed that there was a significant difference in FIs between land covers.Shrublands had higher flammability potential compared with woodlands.Plant moisture content had a negative relationship with TI(P<0.010)and no significant relationship with DC and FH(P>0.050).Artemisia spp.,Astragalus gossypinus Fischer,Amygdalus scoparia Spach,and Cymbopogon jwarancusa(Jones)Schult.had the highest FI.Tree species such as Rhazya stricta Decne.,and Pistacia atlantica Desf.showed greater resistance to fire.Using principal component analysis,the relationship between species and FIs was examined,and TI of wet fuel was the most important FI in relation to species.Structural equation model showed that life form(P<0.001)was the most important flammability driver.Precipitation(P<0.010)and legume species(P<0.010)were significantly related to the flammability in arid land.This study emphasizes the importance of managing high-risk species and using resistant species in vegetation restoration and shows that combining species FIs with their abundance is an effective tool for assessing fire risk and fuel management at the plant community scale.
文摘In recent years,with the accelerating aging process of the population,China has entered an aging society,and the number of elderly patients with chronic diseases has been increasing.The traditional medical and elderly care service models can no longer fully meet their needs.The integrated medical and elderly care model has emerged as the times require.It organically combines medical resources with elderly care resources to provide comprehensive and continuous health management services for the elderly,becoming an important approach to solving the problems of chronic disease management among the elderly.In this regard,this paper first elaborates on the role of integrated medical and elderly care in the management of chronic diseases among the elderly,and then puts forward application strategies of integrated medical and elderly care in the management of chronic diseases among the elderly,in order to provide certain reference for relevant researchers.
基金supported by the National Natural Science Foundation of China(W2412135)the Youth Innovation Promotion Association of the Chinese Academy of Sciences.
文摘As a major source of freshwater in Central Asia,Tajikistan is endowed with abundant glaciers and water resources.However,the country faces multiple challenges,including accelerated glacier retreat,complex inter-government water resource management,and inefficient water use.Existing research has predominantly focused on individual hydrological processes,such as glacier retreat,snow cover change,or transboundary water issues,but it has yet to fully capture the overall complexity of water system.Tajikistan’s water system functions as an integrated whole from mountain runoff to downstream supply,but a comprehensive study of its water resource has yet to be conducted.To address this research gap,this study systematically examined the status,challenges,and sustainable management strategies of Tajikistan’s water resources based on a literature review,remote sensing data analysis,and case studies.Despite Tajikistan’s relative abundance of water resources,global warming is accelerating glacier melting and altering the hydrological cycles,which have resulted in unstable runoff patterns and heightened risks of extreme events.In Tajikistan,outdated infrastructure and poor management are primary causes of low water-use efficiency in the agricultural sector,which accounts for 85.00%of the total water withdrawals.At the governance level,Tajikistan faces challenges in balancing the water-energy-food nexus and transboundary water resource issues.To address these issues,this study proposes core paths for Tajikistan to achieve sustainable water resource management,such as accelerating technological innovation,promoting water-saving agricultural technologies,improving water resource utilization efficiency,and establishing a community participation-based comprehensive management framework.Additionally,strengthening cross-border cooperation and improving real-time monitoring systems have been identified as critical steps to advance sustainable water resource utilization and evidence-based decision-making in Tajikistan and across Central Asia.
