Geothermal energy,a kind of clean and environmentally friendly energy source,is an important object of future natural resource development and utilization,among which,hot dry rock is one of the important deep geotherm...Geothermal energy,a kind of clean and environmentally friendly energy source,is an important object of future natural resource development and utilization,among which,hot dry rock is one of the important deep geothermal resources.In the current multi-objective optimization of heat extraction performance,reservoir production models are less considered and the effects of different optimization ideas are not compared comprehensively.To improve the heat extraction efficiency and prolong the exploitation life of geothermal reservoirs,this paper determines the appropriate operating parameters of geothermal system(injection temperature,injection rate,production pressure and injection-production well spacing)based on the coupled thermal-hydraulic-mechanical model of hot dry rock exploitation in the Gonghe area of Qinghai and three heat extraction optimization methods.In addition,the heat extraction performances of different schemes are comparatively evaluated.And the following research results are obtained.First,the sensitivity analysis of injection and production parameters shows that power generation and recovery factor are in a reverse relation with injection-production pressure difference,which is the direct reason for the adoption of multiobjective optimization.Second,the optimization scheme prepared on the basis of parametric study indicates that the shortest life of a geothermal reservoir is 10 years,the injection-production pressure difference is up to 67 MPa,there is a significant thermal breakthrough phenomenon and the reservoir safety faces challenges.Third,by virtue of multi-objective optimization and decision making integration,the optimal operation parameter combination of hot dry rock system is determined,the life of geothermal reservoirs can exceed 20 years and balanced optimization is achieved.In conclusion,the idea of multi-objective optimization is feasible and applicable to geothermal energy exploitation and this method provides a reference for the efficient geothermal energy development and utilization and is helpful to the realization of“double carbon”goal in China.展开更多
This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balanc...This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balance.By integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization process.The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective algorithms.Furthermore,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy trains.The research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.展开更多
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
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
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
Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may r...Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.展开更多
Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address t...Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address the critical challenges in building energy management.The proposed phase-adaptive radiative(PAR)coating is a multilayer nanostructure consisting of TiO/VO_(2)2/TiO/Ag_(2) and polydimethylsiloxane(PDMS).For different VO_(2) phases,visible transmittance T_(vis)>0.6 and emissivity difference in the atmospheric window Δε_(AW)=0.422 can be achieved,which means the PAR window can transfer interior heat to the outside through thermal radiation for cooling or minimize thermal emission for insulation,while ensuring the transmission of visible light for natural daylighting.Compared to normal glass,the PAR window has an average temperature drop of 14.8℃.The year-round energy-saving calculation for four different cities in China indicates that the PAR window can save 22%-32% of the annual cooling and heating energy consumption by seamlessly transitioning between two phases of VO_(2)modes.The multi-objective optimization of the phase-adaptive radiative smart window provides a potential strategy for energy saving.展开更多
Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrain...Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.展开更多
Objective:To analyze the improvement effect of early postoperative rehabilitation training on balance ability and quality of life in elderly patients with hip fracture.Methods:A total of 50 elderly patients with hip f...Objective:To analyze the improvement effect of early postoperative rehabilitation training on balance ability and quality of life in elderly patients with hip fracture.Methods:A total of 50 elderly patients with hip fracture admitted to our hospital from January 2023 to January 2024 were selected and divided into the observation group(25 cases)and the control group(25 cases)by random number table method.The control group received routine nursing,while the observation group received early rehabilitation training on the basis of routine nursing.The balance ability(Berg Balance Scale,BBS)and quality of life(SF-36)of the two groups were compared.Results:The BBS scores of the observation group at all postoperative time points were significantly higher than those of the control group(p<0.05),and the quality-of-life scores of the observation group were also significantly higher than those of the control group(p<0.05).Conclusion:Early postoperative rehabilitation training for elderly patients with hip fracture can improve their balance ability,enhance their quality of life,and reduce the incidence of postoperative complications,which is worthy of clinical promotion.展开更多
In-situ enlargement of super-large-span tunnels can intensify excavation-induced unloading in the surrounding rock,increasing deformation demand and failure risk during construction.This study combines laboratory mode...In-situ enlargement of super-large-span tunnels can intensify excavation-induced unloading in the surrounding rock,increasing deformation demand and failure risk during construction.This study combines laboratory model tests with FLAC3D simulations to evaluate the stabilizing role of prestressed anchor cables and to establish an energy-balance framework for support optimization.Comparative model tests of existing and enlarged tunnel sections,with and without anchors,show that reinforcement increases load-carrying capacity,reduces displacement,and confines damage to more localized zones.The numerical simulations reproduce displacement fields,shear-strain localization,and plastic-zone evolution with good agreement against the experimental observations.The energy framework is implemented in the in-situ simulations by quantifying unloading-related energy release in the rock mass and reinforcement work contributed by the anchors,and by introducing an energy release–reinforcement ratio as a stability indicator.Parametric analyses indicate that anchor length,spacing,and prestress influence stability in a nonlinear manner,with diminishing returns once reinforcement extends beyond the mechanically dominant deformation zone.An efficient parameter window is identified that improves deformation and yielding control while avoiding unnecessary reinforcement.The results provide an energy-consistent and design-oriented basis for prestressed anchorage selection in large-span tunnel expansion.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
In the complex architecture of global value-chain(GVC)trade,firms’technological content increasingly reflects external knowledge flows.This study examines how inter-regional technological complementarity shapes firms...In the complex architecture of global value-chain(GVC)trade,firms’technological content increasingly reflects external knowledge flows.This study examines how inter-regional technological complementarity shapes firms’GVC advancement,measured by the domestic value-added rate(DVAR)in exports.Using integrated Chinese microdata(2000-2014),we find this complementarity significantly boosts export DVAR,explaining about one-quarter of its observed growth.Two mechanisms drive this effect:increased use of domestic intermediates and gains in firm productivity.The benefits are especially large for firms with lower human capital and for those in accessible,innovation-peripheral regions,helping narrow productivity gaps across firms and space.Affected firms also exhibit broader export scopes,higher product quality,more diversified destinations,and greater markups-firm-level evidence of GVC upgrading.These findings highlight how external technological linkages drive upgrading and underscore the importance of fostering inter-regional synergies for balanced development.展开更多
Estimating the properties of foam-conditioned clay soils is important for both conditioning and recycling goals in earth pressure balance(EPB)shield tunneling.In this study,the vacuum dewatering behaviors of foam-cond...Estimating the properties of foam-conditioned clay soils is important for both conditioning and recycling goals in earth pressure balance(EPB)shield tunneling.In this study,the vacuum dewatering behaviors of foam-conditioned clay soils were investigated,with their potential use as an alternative means to assess foam optimization being examined.A series of laboratory and fieldtests was conducted,including vacuum dewatering tests that considered the effects of filtrationtime and pressure,vane shear tests,and improved cone pullout tests under different gravimetric water content(w)and foam injection ratio(FIR)conditions.It was found that the filtrate loss(FL),which characterizes dewaterability,was increased by extended vacuum filtrationtime and elevated pressure.While increases in w and FIR enhanced FL,reductions were observed in the undrained shear strength(cu),tangential adhesion stress(Fs),and normal adhesion stress(Fn).Furthermore,a linear decrease in FL with increasing mechanical indices(cu,Fs,and Fn)was demonstrated by both laboratory and fielddata fittingresults,regardless of w,FIR,and dewatering conditions.This study provides novel insights into the understanding of vacuum dewatering mechanisms in foam-conditioned clay soils,while a simple approach is proposed for evaluating foam conditioning effectiveness in EPB shield tunneling applications.展开更多
Carbonate gas reservoirs are often characterized by strong heterogeneity,complex inter-well connectivity,extensive edge or bottom water,and unbalanced production,challenges that are also common in many heterogeneous g...Carbonate gas reservoirs are often characterized by strong heterogeneity,complex inter-well connectivity,extensive edge or bottom water,and unbalanced production,challenges that are also common in many heterogeneous gas reservoirs with intricate storage and flow behavior.To address these issues within a unified,data-driven framework,this study develops a multi-block material balance model that accounts for inter-block flow and aquifer influx,and is applicable to a wide range of reservoir types.The model incorporates inter-well and well-group conductive connectivity together with pseudo–steady-state aquifer support.The governing equations are solved using a Newton–Raphson scheme,while particle swarm optimization is employed to estimate formation pressures,inter-well connectivity,and effective aquifer volumes.An unbalanced exploitation factor,UEF,is introduced to quantify production imbalance and to guide development optimization.Validation using a synthetic reservoir model demonstrates that the approach accurately reproduces pressure evolution,crossflow behavior,and water influx.Application to a representative case(the Longwangmiao)field further confirms its robustness under highly heterogeneous conditions,achieving a 12.9%reduction in UEF through optimized production allocation.展开更多
Maintaining population diversity is an important task in the multimodal multi-objective optimization.Although the zoning search(ZS)can improve the diversity in the decision space,assigning the same computational costs...Maintaining population diversity is an important task in the multimodal multi-objective optimization.Although the zoning search(ZS)can improve the diversity in the decision space,assigning the same computational costs to each search subspace may be wasteful when computational resources are limited,especially on imbalanced problems.To alleviate the above-mentioned issue,a zoning search with adaptive resource allocating(ZS-ARA)method is proposed in the current study.In the proposed ZS-ARA,the entire search space is divided into many subspaces to preserve the diversity in the decision space and to reduce the problem complexity.Moreover,the computational resources can be automatically allocated among all the subspaces.The ZS-ARA is compared with seven algorithms on two different types of multimodal multi-objective problems(MMOPs),namely,balanced and imbalanced MMOPs.The results indicate that,similarly to the ZS,the ZS-ARA achieves high performance with the balanced MMOPs.Also,it can greatly assist a“regular”algorithm in improving its performance on the imbalanced MMOPs,and is capable of allocating the limited computational resources dynamically.展开更多
Understanding the neural underpinning of human gait and balance is one of the most pertinent challenges for 21st-century translational neuroscience due to the profound impact that falls and mobility disturbances have ...Understanding the neural underpinning of human gait and balance is one of the most pertinent challenges for 21st-century translational neuroscience due to the profound impact that falls and mobility disturbances have on our aging population.Posture and gait control does not happen automatically,as previously believed,but rather requires continuous involvement of central nervous mechanisms.To effectively exert control over the body,the brain must integrate multiple streams of sensory information,including visual,vestibular,and somatosensory signals.The mechanisms which underpin the integration of these multisensory signals are the principal topic of the present work.Existing multisensory integration theories focus on how failure of cognitive processes thought to be involved in multisensory integration leads to falls in older adults.Insufficient emphasis,however,has been placed on specific contributions of individual sensory modalities to multisensory integration processes and cross-modal interactions that occur between the sensory modalities in relation to gait and balance.In the present work,we review the contributions of somatosensory,visual,and vestibular modalities,along with their multisensory intersections to gait and balance in older adults and patients with Parkinson’s disease.We also review evidence of vestibular contributions to multisensory temporal binding windows,previously shown to be highly pertinent to fall risk in older adults.Lastly,we relate multisensory vestibular mechanisms to potential neural substrates,both at the level of neurobiology(concerning positron emission tomography imaging)and at the level of electrophysiology(concerning electroencephalography).We hope that this integrative review,drawing influence across multiple subdisciplines of neuroscience,paves the way for novel research directions and therapeutic neuromodulatory approaches,to improve the lives of older adults and patients with neurodegenerative diseases.展开更多
The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition...The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.展开更多
With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm impro...With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm improvement.To reduce the operational costs of micro-grid systems and the energy abandonment rate of renewable energy,while simultaneously enhancing user satisfaction on the demand side,this paper introduces an improvedmultiobjective Grey Wolf Optimizer based on Cauchy variation.The proposed approach incorporates a Cauchy variation strategy during the optimizer’s search phase to expand its exploration range and minimize the likelihood of becoming trapped in local optima.At the same time,adoptingmultiple energy storage methods to improve the consumption rate of renewable energy.Subsequently,under different energy balance orders,themulti-objective particle swarmalgorithm,multi-objective grey wolf optimizer,and Cauchy’s variant of the improvedmulti-objective grey wolf optimizer are used for example simulation,solving the Pareto solution set of the model and comparing.The analysis of the results reveals that,compared to the original optimizer,the improved optimizer decreases the daily cost by approximately 100 yuan,and reduces the energy abandonment rate to zero.Meanwhile,it enhances user satisfaction and ensures the stable operation of the micro-grid.展开更多
This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Op...This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Optimization(SFO)algorithm.The primary objective is to address multi-objective optimization challenges within mechanical engineering,with a specific emphasis on planetary gearbox optimization.The algorithm is equipped with the ability to dynamically select the optimal mutation operator,contingent upon an adaptive normalized population spacing parameter.The efficacy of HMODESFO has been substantiated through rigorous validation against estab-lished industry benchmarks,including a suite of Zitzler-Deb-Thiele(ZDT)and Zeb-Thiele-Laumanns-Zitzler(DTLZ)problems,where it exhibited superior performance.The outcomes underscore the algorithm’s markedly enhanced optimization capabilities relative to existing methods,particularly in tackling highly intricate multi-objective planetary gearbox optimization problems.Additionally,the performance of HMODESFO is evaluated against selected well-known mechanical engineering test problems,further accentuating its adeptness in resolving complex optimization challenges within this domain.展开更多
Background:Isotonic crystalloids are recommended as the first choice for fluid therapy in acute pan-creatitis(AP),with normal saline(NS)and lactate Ringer’s(LR)used most often.Evidence based recom-mendations on the t...Background:Isotonic crystalloids are recommended as the first choice for fluid therapy in acute pan-creatitis(AP),with normal saline(NS)and lactate Ringer’s(LR)used most often.Evidence based recom-mendations on the type of fluid are conflicting and generally come from small single-center randomized controlled trials(RCTs).We therefore conducted a systematic review and meta-analysis to compare the effect of balanced solutions(BS)versus NS on patient-centered clinical outcomes in AP.Methods:From four databases searched up to October 2024,we included only RCTs of adult patients with AP that compared the use of BS(including LR,acetate Ringer’s,etc.)with NS.The primary out-come was the disease advances from AP to moderately severe and severe AP(MSAP/SAP).Trial sequential analyses(TSA)were conducted to control for type-I and type-II errors and Grading of Recommendations Assessment,Development,and Evaluation(GRADE)was used to assess the quality of evidence.Results:Six RCTs were identified and included,involving 260 patients treated with BS and 298 patients with NS.Patients who received the BS had less MSAP/SAP[odds ratio(OR)=0.50,95%confidence in-terval(CI):0.29 to 0.85,P=0.01,I^(2)=0%;5 studies,299 patients],reduced the need of ICU admission(OR=0.60,95%CI:0.39 to 0.93,P=0.02,I^(2)=0%;5 studies,507 patients)and shorter length of hospital stay[mean difference(MD)=-0.88,95%CI:-1.48 to-0.28,P=0.004,I^(2)=0%;6 studies,558 patients;confirmed by TSA with high certainty]compared with those who received NS.The evidence for most of the clinical outcomes was rated as moderate to low due to the risk of bias,imprecision and inconsistency.Conclusions:BS,compared with NS,was associated with improved clinical outcomes in patients with AP.However,given the moderate to low quality of evidence for most of the outcomes assessed,further trials are warranted.展开更多
基金National Natural Science Foundation of China(NSFC)“Theory and Technology of Complex Seam Network in High Temperature Rock for Storage”(No.52192621)National Key Research and Development Program(No.2018YFB1501804)+2 种基金Sichuan Science and Technology Program Project“Research on the Mechanism of Enhanced Heat Transfer between Geothermal Well Completion Structure and Downhole Heat Exchanger”(No.2021Ya1.389)National Key Research and Development Program(No.2021YJ0389)“Research on the Mechanism of Fracture Damage in Dry Hot Rock Extraction”(PRP/open-2110)of the State Key Laboratory of Oil and Gas Resources and Exploration,China University of Petroleum(Beijing).
文摘Geothermal energy,a kind of clean and environmentally friendly energy source,is an important object of future natural resource development and utilization,among which,hot dry rock is one of the important deep geothermal resources.In the current multi-objective optimization of heat extraction performance,reservoir production models are less considered and the effects of different optimization ideas are not compared comprehensively.To improve the heat extraction efficiency and prolong the exploitation life of geothermal reservoirs,this paper determines the appropriate operating parameters of geothermal system(injection temperature,injection rate,production pressure and injection-production well spacing)based on the coupled thermal-hydraulic-mechanical model of hot dry rock exploitation in the Gonghe area of Qinghai and three heat extraction optimization methods.In addition,the heat extraction performances of different schemes are comparatively evaluated.And the following research results are obtained.First,the sensitivity analysis of injection and production parameters shows that power generation and recovery factor are in a reverse relation with injection-production pressure difference,which is the direct reason for the adoption of multiobjective optimization.Second,the optimization scheme prepared on the basis of parametric study indicates that the shortest life of a geothermal reservoir is 10 years,the injection-production pressure difference is up to 67 MPa,there is a significant thermal breakthrough phenomenon and the reservoir safety faces challenges.Third,by virtue of multi-objective optimization and decision making integration,the optimal operation parameter combination of hot dry rock system is determined,the life of geothermal reservoirs can exceed 20 years and balanced optimization is achieved.In conclusion,the idea of multi-objective optimization is feasible and applicable to geothermal energy exploitation and this method provides a reference for the efficient geothermal energy development and utilization and is helpful to the realization of“double carbon”goal in China.
基金supported by the National Natural Science Foundation of China(Project No.5217232152102391)+2 种基金Sichuan Province Science and Technology Innovation Talent Project(2024JDRC0020)China Shenhua Energy Company Limited Technology Project(GJNY-22-7/2300-K1220053)Key science and technology projects in the transportation industry of the Ministry of Transport(2022-ZD7-132).
文摘This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balance.By integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization process.The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective algorithms.Furthermore,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy trains.The research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.
文摘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 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 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.
文摘Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.
基金supported by the Fundamental Research Funds for the Provincial Universities (Grant No.2024-KYYWF-0141)the National Natural Science Foundation of China (Grant Nos.52406076,52227813)+1 种基金the National Key Research and Development Program of China (Grant No.2022YFE0133900)the China Postdoctoral Science Foundation (Grant No.2023M740905)。
文摘Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address the critical challenges in building energy management.The proposed phase-adaptive radiative(PAR)coating is a multilayer nanostructure consisting of TiO/VO_(2)2/TiO/Ag_(2) and polydimethylsiloxane(PDMS).For different VO_(2) phases,visible transmittance T_(vis)>0.6 and emissivity difference in the atmospheric window Δε_(AW)=0.422 can be achieved,which means the PAR window can transfer interior heat to the outside through thermal radiation for cooling or minimize thermal emission for insulation,while ensuring the transmission of visible light for natural daylighting.Compared to normal glass,the PAR window has an average temperature drop of 14.8℃.The year-round energy-saving calculation for four different cities in China indicates that the PAR window can save 22%-32% of the annual cooling and heating energy consumption by seamlessly transitioning between two phases of VO_(2)modes.The multi-objective optimization of the phase-adaptive radiative smart window provides a potential strategy for energy saving.
基金supported by Key Science and Technology Program of Henan Province,China(Grant Nos.242102210147,242102210027)Fujian Province Young and Middle aged Teacher Education Research Project(Science and Technology Category)(No.JZ240101)(Corresponding author:Dong Yuan).
文摘Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.
基金Chongqing Education Science Planning Project.Project Name:Research on Talent Training of Community Rehabilitation Major in Higher Vocational Colleges Based on OBE Concept(Project No.:K23ZG3420222)。
文摘Objective:To analyze the improvement effect of early postoperative rehabilitation training on balance ability and quality of life in elderly patients with hip fracture.Methods:A total of 50 elderly patients with hip fracture admitted to our hospital from January 2023 to January 2024 were selected and divided into the observation group(25 cases)and the control group(25 cases)by random number table method.The control group received routine nursing,while the observation group received early rehabilitation training on the basis of routine nursing.The balance ability(Berg Balance Scale,BBS)and quality of life(SF-36)of the two groups were compared.Results:The BBS scores of the observation group at all postoperative time points were significantly higher than those of the control group(p<0.05),and the quality-of-life scores of the observation group were also significantly higher than those of the control group(p<0.05).Conclusion:Early postoperative rehabilitation training for elderly patients with hip fracture can improve their balance ability,enhance their quality of life,and reduce the incidence of postoperative complications,which is worthy of clinical promotion.
基金funded by the National Key R&D Program of China,China(No.2024YFF0507903)the National Key Research and Development Program of China(Grant No.2024YFF0507904)the National Natural Science Foundation of China,China(Grant No.52379114).These supports are gratefully acknowledged.
文摘In-situ enlargement of super-large-span tunnels can intensify excavation-induced unloading in the surrounding rock,increasing deformation demand and failure risk during construction.This study combines laboratory model tests with FLAC3D simulations to evaluate the stabilizing role of prestressed anchor cables and to establish an energy-balance framework for support optimization.Comparative model tests of existing and enlarged tunnel sections,with and without anchors,show that reinforcement increases load-carrying capacity,reduces displacement,and confines damage to more localized zones.The numerical simulations reproduce displacement fields,shear-strain localization,and plastic-zone evolution with good agreement against the experimental observations.The energy framework is implemented in the in-situ simulations by quantifying unloading-related energy release in the rock mass and reinforcement work contributed by the anchors,and by introducing an energy release–reinforcement ratio as a stability indicator.Parametric analyses indicate that anchor length,spacing,and prestress influence stability in a nonlinear manner,with diminishing returns once reinforcement extends beyond the mechanically dominant deformation zone.An efficient parameter window is identified that improves deformation and yielding control while avoiding unnecessary reinforcement.The results provide an energy-consistent and design-oriented basis for prestressed anchorage selection in large-span tunnel expansion.
基金National Natural Science Foundation of China,No.42301470,No.52270185,No.42171389Capacity Building Program of Local Colleges and Universities in Shanghai,No.21010503300。
文摘Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers.
基金supported by the following grants:National Social Science Fund of China(NSSFC)(Major Project)“Research on the Mechanism and Breakthrough Path for Achieving Key Core Technologies through the Coupling of Innovation Chains and Industrial Chains”(Grant No.22&ZD093)Key Research Institute of Humanities and Social Sciences,Ministry of Education“Research on Innovation Development Theory Based on Chinese Practice”(Grant No.23CEDRZ03).
文摘In the complex architecture of global value-chain(GVC)trade,firms’technological content increasingly reflects external knowledge flows.This study examines how inter-regional technological complementarity shapes firms’GVC advancement,measured by the domestic value-added rate(DVAR)in exports.Using integrated Chinese microdata(2000-2014),we find this complementarity significantly boosts export DVAR,explaining about one-quarter of its observed growth.Two mechanisms drive this effect:increased use of domestic intermediates and gains in firm productivity.The benefits are especially large for firms with lower human capital and for those in accessible,innovation-peripheral regions,helping narrow productivity gaps across firms and space.Affected firms also exhibit broader export scopes,higher product quality,more diversified destinations,and greater markups-firm-level evidence of GVC upgrading.These findings highlight how external technological linkages drive upgrading and underscore the importance of fostering inter-regional synergies for balanced development.
基金supported by the National Youth Top-notch Talent Support Program of China(Grant No.00389335)the National Natural Science Foundation of China(Grant No.52378392)the“Foal Eagle Program”Youth Top-notch Talent Project of Fujian Province(Grant No.00387088).
文摘Estimating the properties of foam-conditioned clay soils is important for both conditioning and recycling goals in earth pressure balance(EPB)shield tunneling.In this study,the vacuum dewatering behaviors of foam-conditioned clay soils were investigated,with their potential use as an alternative means to assess foam optimization being examined.A series of laboratory and fieldtests was conducted,including vacuum dewatering tests that considered the effects of filtrationtime and pressure,vane shear tests,and improved cone pullout tests under different gravimetric water content(w)and foam injection ratio(FIR)conditions.It was found that the filtrate loss(FL),which characterizes dewaterability,was increased by extended vacuum filtrationtime and elevated pressure.While increases in w and FIR enhanced FL,reductions were observed in the undrained shear strength(cu),tangential adhesion stress(Fs),and normal adhesion stress(Fn).Furthermore,a linear decrease in FL with increasing mechanical indices(cu,Fs,and Fn)was demonstrated by both laboratory and fielddata fittingresults,regardless of w,FIR,and dewatering conditions.This study provides novel insights into the understanding of vacuum dewatering mechanisms in foam-conditioned clay soils,while a simple approach is proposed for evaluating foam conditioning effectiveness in EPB shield tunneling applications.
基金supported by the National Natural Science Foundation of China(No.52104018,52274030)China National Petroleum Corporation(CNPC)Innovation Foundation(No.2024DQ02-0303)China National Petroleum Corporation(CNPC)14th Five-Year Plan Major Strategic Scientific and Technological Project for Prospective and Fundamental Research(2024DJ86).
文摘Carbonate gas reservoirs are often characterized by strong heterogeneity,complex inter-well connectivity,extensive edge or bottom water,and unbalanced production,challenges that are also common in many heterogeneous gas reservoirs with intricate storage and flow behavior.To address these issues within a unified,data-driven framework,this study develops a multi-block material balance model that accounts for inter-block flow and aquifer influx,and is applicable to a wide range of reservoir types.The model incorporates inter-well and well-group conductive connectivity together with pseudo–steady-state aquifer support.The governing equations are solved using a Newton–Raphson scheme,while particle swarm optimization is employed to estimate formation pressures,inter-well connectivity,and effective aquifer volumes.An unbalanced exploitation factor,UEF,is introduced to quantify production imbalance and to guide development optimization.Validation using a synthetic reservoir model demonstrates that the approach accurately reproduces pressure evolution,crossflow behavior,and water influx.Application to a representative case(the Longwangmiao)field further confirms its robustness under highly heterogeneous conditions,achieving a 12.9%reduction in UEF through optimized production allocation.
基金This work was partially supported by the Shandong Joint Fund of the National Nature Science Foundation of China(U2006228)the National Nature Science Foundation of China(61603244).
文摘Maintaining population diversity is an important task in the multimodal multi-objective optimization.Although the zoning search(ZS)can improve the diversity in the decision space,assigning the same computational costs to each search subspace may be wasteful when computational resources are limited,especially on imbalanced problems.To alleviate the above-mentioned issue,a zoning search with adaptive resource allocating(ZS-ARA)method is proposed in the current study.In the proposed ZS-ARA,the entire search space is divided into many subspaces to preserve the diversity in the decision space and to reduce the problem complexity.Moreover,the computational resources can be automatically allocated among all the subspaces.The ZS-ARA is compared with seven algorithms on two different types of multimodal multi-objective problems(MMOPs),namely,balanced and imbalanced MMOPs.The results indicate that,similarly to the ZS,the ZS-ARA achieves high performance with the balanced MMOPs.Also,it can greatly assist a“regular”algorithm in improving its performance on the imbalanced MMOPs,and is capable of allocating the limited computational resources dynamically.
文摘Understanding the neural underpinning of human gait and balance is one of the most pertinent challenges for 21st-century translational neuroscience due to the profound impact that falls and mobility disturbances have on our aging population.Posture and gait control does not happen automatically,as previously believed,but rather requires continuous involvement of central nervous mechanisms.To effectively exert control over the body,the brain must integrate multiple streams of sensory information,including visual,vestibular,and somatosensory signals.The mechanisms which underpin the integration of these multisensory signals are the principal topic of the present work.Existing multisensory integration theories focus on how failure of cognitive processes thought to be involved in multisensory integration leads to falls in older adults.Insufficient emphasis,however,has been placed on specific contributions of individual sensory modalities to multisensory integration processes and cross-modal interactions that occur between the sensory modalities in relation to gait and balance.In the present work,we review the contributions of somatosensory,visual,and vestibular modalities,along with their multisensory intersections to gait and balance in older adults and patients with Parkinson’s disease.We also review evidence of vestibular contributions to multisensory temporal binding windows,previously shown to be highly pertinent to fall risk in older adults.Lastly,we relate multisensory vestibular mechanisms to potential neural substrates,both at the level of neurobiology(concerning positron emission tomography imaging)and at the level of electrophysiology(concerning electroencephalography).We hope that this integrative review,drawing influence across multiple subdisciplines of neuroscience,paves the way for novel research directions and therapeutic neuromodulatory approaches,to improve the lives of older adults and patients with neurodegenerative diseases.
基金supported by National Natural Science Foundations of China(nos.12271326,62102304,61806120,61502290,61672334,61673251)China Postdoctoral Science Foundation(no.2015M582606)+2 种基金Industrial Research Project of Science and Technology in Shaanxi Province(nos.2015GY016,2017JQ6063)Fundamental Research Fund for the Central Universities(no.GK202003071)Natural Science Basic Research Plan in Shaanxi Province of China(no.2022JM-354).
文摘The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.
基金supported by the Open Fund of Guangxi Key Laboratory of Building New Energy and Energy Conservation(Project Number:Guike Energy 17-J-21-3).
文摘With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm improvement.To reduce the operational costs of micro-grid systems and the energy abandonment rate of renewable energy,while simultaneously enhancing user satisfaction on the demand side,this paper introduces an improvedmultiobjective Grey Wolf Optimizer based on Cauchy variation.The proposed approach incorporates a Cauchy variation strategy during the optimizer’s search phase to expand its exploration range and minimize the likelihood of becoming trapped in local optima.At the same time,adoptingmultiple energy storage methods to improve the consumption rate of renewable energy.Subsequently,under different energy balance orders,themulti-objective particle swarmalgorithm,multi-objective grey wolf optimizer,and Cauchy’s variant of the improvedmulti-objective grey wolf optimizer are used for example simulation,solving the Pareto solution set of the model and comparing.The analysis of the results reveals that,compared to the original optimizer,the improved optimizer decreases the daily cost by approximately 100 yuan,and reduces the energy abandonment rate to zero.Meanwhile,it enhances user satisfaction and ensures the stable operation of the micro-grid.
基金supported by the Serbian Ministry of Education and Science under Grant No.TR35006 and COST Action:CA23155—A Pan-European Network of Ocean Tribology(OTC)The research of B.Rosic and M.Rosic was supported by the Serbian Ministry of Education and Science under Grant TR35029.
文摘This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Optimization(SFO)algorithm.The primary objective is to address multi-objective optimization challenges within mechanical engineering,with a specific emphasis on planetary gearbox optimization.The algorithm is equipped with the ability to dynamically select the optimal mutation operator,contingent upon an adaptive normalized population spacing parameter.The efficacy of HMODESFO has been substantiated through rigorous validation against estab-lished industry benchmarks,including a suite of Zitzler-Deb-Thiele(ZDT)and Zeb-Thiele-Laumanns-Zitzler(DTLZ)problems,where it exhibited superior performance.The outcomes underscore the algorithm’s markedly enhanced optimization capabilities relative to existing methods,particularly in tackling highly intricate multi-objective planetary gearbox optimization problems.Additionally,the performance of HMODESFO is evaluated against selected well-known mechanical engineering test problems,further accentuating its adeptness in resolving complex optimization challenges within this domain.
文摘Background:Isotonic crystalloids are recommended as the first choice for fluid therapy in acute pan-creatitis(AP),with normal saline(NS)and lactate Ringer’s(LR)used most often.Evidence based recom-mendations on the type of fluid are conflicting and generally come from small single-center randomized controlled trials(RCTs).We therefore conducted a systematic review and meta-analysis to compare the effect of balanced solutions(BS)versus NS on patient-centered clinical outcomes in AP.Methods:From four databases searched up to October 2024,we included only RCTs of adult patients with AP that compared the use of BS(including LR,acetate Ringer’s,etc.)with NS.The primary out-come was the disease advances from AP to moderately severe and severe AP(MSAP/SAP).Trial sequential analyses(TSA)were conducted to control for type-I and type-II errors and Grading of Recommendations Assessment,Development,and Evaluation(GRADE)was used to assess the quality of evidence.Results:Six RCTs were identified and included,involving 260 patients treated with BS and 298 patients with NS.Patients who received the BS had less MSAP/SAP[odds ratio(OR)=0.50,95%confidence in-terval(CI):0.29 to 0.85,P=0.01,I^(2)=0%;5 studies,299 patients],reduced the need of ICU admission(OR=0.60,95%CI:0.39 to 0.93,P=0.02,I^(2)=0%;5 studies,507 patients)and shorter length of hospital stay[mean difference(MD)=-0.88,95%CI:-1.48 to-0.28,P=0.004,I^(2)=0%;6 studies,558 patients;confirmed by TSA with high certainty]compared with those who received NS.The evidence for most of the clinical outcomes was rated as moderate to low due to the risk of bias,imprecision and inconsistency.Conclusions:BS,compared with NS,was associated with improved clinical outcomes in patients with AP.However,given the moderate to low quality of evidence for most of the outcomes assessed,further trials are warranted.
