Making plans is a good idea,but every one's schedule looks differe nt.You may have to talk about your plans before you're able to make some.It could sound like this:You ask,"Do you have plans this Friday ...Making plans is a good idea,but every one's schedule looks differe nt.You may have to talk about your plans before you're able to make some.It could sound like this:You ask,"Do you have plans this Friday night?"If the person already has plans,they may say,"I do.But I'm free on Saturday."If that day doesn't work for you,you can say,"I'm not available that day.How about Sunday after no on?"After you figure out the day and time,mark it on your calendar.展开更多
Climate change is increasingly affecting all aspects of protected areas management from changes of species ranges to visitor experiences.Due to these impacts,there is a need for managers to take more robust approaches...Climate change is increasingly affecting all aspects of protected areas management from changes of species ranges to visitor experiences.Due to these impacts,there is a need for managers to take more robust approaches to con-sidering the implications of climate change on the overall application and efficacy of protected areas management direction,including the achievement of the goals and objectives contained within management plans.Through a systematic and comprehensive content analysis approach,this study assesses the current extent to which climate change is considered in Canadian protected area management plans.Specifically,we evaluated 63 terrestrial protected area management plans against a set of climate robustness principles.Our content analysis revealed that climate change is currently not effectively factored into Canadian protected area management plans with an average climate robustness score of 18%.Climate robustness score was not found to be correlated with protected area size,International Union for the Conservation of Nature(IUCN)management classification,or jurisdictional authority.Certain climate robustness principles received higher scores across the management plans than oth-ers.For example,the principles of‘diverse knowledge sources’and‘addresses climate change’scored relatively highly whereas‘climate change vulnerability’and‘ecosystem integrity’received the lowest scores.The lack of integration of ecological integrity considerations in management plans was a particularly noteworthy deficiency considering that this guiding principle is the primary legislative objective of many national and sub-national protected areas in Canada.From this assessment,climate change needs to be more effectively and consistently integrated into protected area management plan development and coordinated across associated planning pro-cesses.We discuss the ways in which this can be achieved,for example,by integrating scenario planning into organizational management plan development processes.展开更多
In this paper,we investigate the optimal investment strategy for a target-return pension plan under a Wishart model using stochastic optimal control methods.We consider the fact that mortality and interest rates are a...In this paper,we investigate the optimal investment strategy for a target-return pension plan under a Wishart model using stochastic optimal control methods.We consider the fact that mortality and interest rates are affected by a combination of systematic and idiosyncratic factors,which are modelled by a Wishart model with positive values.On this basis,we analyse the correlation between stochastic mortality and stochastic wage rates for plan members,as well as wage movements and financial market volatility.It is shown that financial market parameters and wage rates have a significant impact on the optimal investment strategy.We provide the optimal investment strategy and the optimal yield adjustment strategy for target-return pensions.This study provides a valuable reference for policy formulation and implementation of target-return pension plans in China.展开更多
基于国际比较的地方空间规划的范围和工具研究An International Comparison of the Scope and Instruments of Local Spatial Planning源自:The TownPlanning Review,2024,95(2):197-217作者:MaciejJNowak,StefanieDühr,Sergio Pe&...基于国际比较的地方空间规划的范围和工具研究An International Comparison of the Scope and Instruments of Local Spatial Planning源自:The TownPlanning Review,2024,95(2):197-217作者:MaciejJNowak,StefanieDühr,Sergio Peña,et al.推荐:申明锐,南京大学建筑与城市规划学院。shenmingr@nju.edu.cn.展开更多
Objective： Conventional external beam irradiation techniques for nasopharyngeal carcinoma have limitations, and improving external beam irradiation techniques is needed to enhance the curative rate. This study was de...Objective： Conventional external beam irradiation techniques for nasopharyngeal carcinoma have limitations, and improving external beam irradiation techniques is needed to enhance the curative rate. This study was designed to cvaluate the difference in dose distribution of three dimensional conformal radiotherapy （3D CRT） and conventional treatment plan in early untreated nasopharyngeal carcinoma using a three dimensional treatment planning system. Methods： Twenty-two patients with early untreated nasopharyngeal carcinoma were selected. Conventional and 3D CRT plans were made for each of them and compared with respect to target volume coverage （V95）,normal tissue sparing （D50, D33 and D5, etc）, normal tissue complication probability （NTCP）. Results： The average volumetric dose comparison indicated that the V95 of PTVnx70 were 98.22% and 99.99% （P=0.06）, and PTVnd60, 98.41% and 99.63% （P=1.00）, PTV,x60, 98.44% and 99.98% （P=0.03）, PTVnx50, 98.85% and 99.63% （P=0.02） in conventional and 3DCRT treatment plans respectively. With respect to normal tissue sparing, the average D50 of unilateral parotid glands were 51.91 Gy and 64.30 Gy （P=0.00） respectively, and the unilateral temporomandibular joints, 49.98 Gy and 64.47 Gy （P=0.00）, the Dlcc of spinal cords, 44.98 Gy and 48.09 Gy （P=0.00） in 3D CRT and conventional plans. Conclusion： Though only a little bit better dose coverage of target volume in subclinical lesion region was reached in 3D CRT plans, it spared more normal tissues e.g. parotid glands and temporomandibular joints etc and decreased their NTCP while it got the similar dose distribution in target volumes as conventional plans did for these early nasopharyngeal carcinoma cases.展开更多
Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT^(*)),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narr...Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT^(*)),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narrow passages,slow convergence,and high computational costs.To address these challenges,this paper proposes a novel hybrid global path planning algorithm integrating Gaussian sampling and quadtree optimization(RRT^(*)-GSQ).This methodology aims to enhance path planning by synergistically combining a Gaussian mixture sampling strategy to improve node generation in critical regions,an adaptive step-size and direction optimization mechanism for enhanced obstacle avoidance,a Quadtree-AABB collision detection framework to lower computational complexity,and a dynamic iteration control strategy for more efficient convergence.In obstacle-free and obstructed scenarios,compared with the conventional RRT^(*),the proposed algorithm reduced the number of node evaluations by 67.57%and 62.72%,and decreased the search time by 79.72%and 78.52%,respectively.In path tracking tests,the proposed algorithm achieved substantial reductions in RMSE of the final path compared to the conventional RRT^(*).Specifically,the lateral RMSE was reduced by 41.5%in obstacle-free environments and 59.3%in obstructed environments,while the longitudinal RMSE was reduced by 57.2%and 58.5%,respectively.Furthermore,the maximum absolute errors in both lateral and longitudinal directions were constrained within 0.75 m.Field validation experiments in an operational orchard confirmed the algorithm's practical effectiveness,showing reductions in the mean tracking error of 47.6%(obstacle-free)and 58.3%(with obstructed),alongside a 5.1%and 7.2%shortening of the path length compared to the baseline method.The proposed algorithm effectively enhances path planning efficiency and navigation accuracy for robots,presenting a superior solution for high-precision autonomous navigation of agricultural robots in orchard environments and holding significant value for engineering applications.展开更多
In dynamic and uncertain reconnaissance missions,effective task assignment and path planning for multiple unmanned aerial vehicles(UAVs)present significant challenges.A stochastic multi-UAV reconnaissance scheduling p...In dynamic and uncertain reconnaissance missions,effective task assignment and path planning for multiple unmanned aerial vehicles(UAVs)present significant challenges.A stochastic multi-UAV reconnaissance scheduling problem is formulated as a combinatorial optimization task with nonlinear objectives and coupled constraints.To solve the non-deterministic polynomial(NP)-hard problem efficiently,a novel learning-enhanced pigeon-inspired optimization(L-PIO)algorithm is proposed.The algorithm integrates a Q-learning mechanism to dynamically regulate control parameters,enabling adaptive exploration–exploitation trade-offs across different optimization phases.Additionally,geometric abstraction techniques are employed to approximate complex reconnaissance regions using maximum inscribed rectangles and spiral path models,allowing for precise cost modeling of UAV paths.The formal objective function is developed to minimize global flight distance and completion time while maximizing reconnaissance priority and task coverage.A series of simulation experiments are conducted under three scenarios:static task allocation,dynamic task emergence,and UAV failure recovery.Comparative analysis with several updated algorithms demonstrates that L-PIO exhibits superior robustness,adaptability,and computational efficiency.The results verify the algorithm's effectiveness in addressing dynamic reconnaissance task planning in real-time multi-UAV applications.展开更多
The energy transition inspired by carbon neutrality targets and the increasing threat of extreme events raise multi-objective development requirements for power systems.This paper proposes a multi-objective resource a...The energy transition inspired by carbon neutrality targets and the increasing threat of extreme events raise multi-objective development requirements for power systems.This paper proposes a multi-objective resource allocation model to determine the type,number and location of flexible resources to increase the values of resilience,carbon reduction and renewable energy consumption.To evaluate the values of resilience,a restoration model for transmission systems is established that considers the coordination of fossil-fuel generators,energy storage systems(ESSs)and renewable energy generators in building restoration paths.The collaborative power-carbon-tradable green certificate(TGC)market model is then applied to evaluate the resource values in terms of carbon reduction and renewable energy consumption.Finally,the model is formulated as a mixed-integer linear programming(MILP)with a nonconvex feasible domain,and the normalized normal constraint(NNC)method is applied to obtain approximate Pareto frontiers for decision makers.Case studies validate the effectiveness of the proposed model in improving multi-factor values and analyze the impact of resource regulation capacity on values of restoration and carbon reduction.展开更多
China’s economy demonstrated remarkable resilience in 2025,with its GDP exceeding RMB 140 trillion for the first time and achieving a five percent year-on-year growth.This performance marked the successful conclusion...China’s economy demonstrated remarkable resilience in 2025,with its GDP exceeding RMB 140 trillion for the first time and achieving a five percent year-on-year growth.This performance marked the successful conclusion of the 14th Five-Year Plan(2021-2025),during which the country’s economy achieved“four consecutive leaps,”expanding from RMB 110 trillion to 120 trillion,then to 130 trillion,and finally to 140 trillion.展开更多
Promoting urban-rural integration and facilitating the bidirectional flow of urban and rural elements are core spatial objectives in the new era of China.The urban-rural fringe represents the region with the most inte...Promoting urban-rural integration and facilitating the bidirectional flow of urban and rural elements are core spatial objectives in the new era of China.The urban-rural fringe represents the region with the most intense interaction between urban and rural areas,serving as a key zone for breaking down barriers and promoting urban-rural integration.Based on a systematic review of representative case studies and scholarly literature,this paper synthesizes the evolving research perspectives on the urban-rural fringe,with particular attention to how data-driven approaches that integrate official statistics,remote sensing imagery,points of interest,and mobile phone signaling data have advanced the characterization of fringe features,refined identification methods,and revealed emerging developmental trends through spatial clustering and machine learning classification.It proposes an integrated analytical framework encompassing administrative boundaries,economic metabolism,social activities,material infrastructure,and the ecological environment.The paper further examines the characteristics and emerging development trends of urban-rural fringe areas and advances a set of strategic directions to support urban-rural integration and more efficient resource allocation.These include expanding analytical dimensions,enhancing data integration,refining identification criteria,elucidating mechanisms of internal and external interactions,and strengthening interdisciplinary collaboration.Collectively,these efforts offer actionable insights for optimizing public service delivery,directing infrastructure investment in transportation and utilities,delineating ecological conservation boundaries,and implementing place-based socioeconomic revitalization strategies in the urban-rural fringe regions.展开更多
Rapidly-exploring Random Tree(RRT)and its variants have become foundational in path-planning research,yet in complex three-dimensional off-road environments their uniform blind sampling and limited safety guarantees l...Rapidly-exploring Random Tree(RRT)and its variants have become foundational in path-planning research,yet in complex three-dimensional off-road environments their uniform blind sampling and limited safety guarantees lead to slow convergence and force an unfavorable trade-off between path quality and traversal safety.To address these challenges,we introduce HS-APF-RRT*,a novel algorithm that fuses layered sampling,an enhanced Artificial Potential Field(APF),and a dynamic neighborhood-expansion mechanism.First,the workspace is hierarchically partitioned into macro,meso,and micro sampling layers,progressively biasing random samples toward safer,lower-energy regions.Second,we augment the traditional APF by incorporating a slope-dependent repulsive term,enabling stronger avoidance of steep obstacles.Third,a dynamic expansion strategy adaptively switches between 8 and 16 connected neighborhoods based on local obstacle density,striking an effective balance between search efficiency and collision-avoidance precision.In simulated off-road scenarios,HS-APF-RRT*is benchmarked against RRT*,GoalBiased RRT*,and APF-RRT*,and demonstrates significantly faster convergence,lower path-energy consumption,and enhanced safety margins.展开更多
With the expanding applications of unmanned aerial vehicles(UAVs),precise flight evaluation has emerged as a critical enabler for efficient path planning,directly impacting operational performance and safety.Tradition...With the expanding applications of unmanned aerial vehicles(UAVs),precise flight evaluation has emerged as a critical enabler for efficient path planning,directly impacting operational performance and safety.Traditional path planning algorithms typically combine Dubins curves with local optimization to minimize trajectory length under 3D spatial constraints.However,these methods often overlook the correlation between pilot control quality and UAV flight dynamics,limiting their adaptability in complex scenarios.In this paper,we propose an intelligent flight evaluation model specifically designed to enhancemulti-waypoint trajectory optimization algorithms.Our model leverages a decision tree to integrate attitude parameters and trajectory matching metrics,establishing a quantitative link between pilot control quality and UAV flight states.Experimental results demonstrate that the proposed model not only accurately assesses pilot performance across diverse skill levels but also improves the optimality of generated trajectories.When integrated with our path planning algorithm,it efficiently produces optimal trajectories while strictly adhering to UAV flight constraints.This integrated framework highlights significant potential for real-time UAV training,performance assessment,and adaptive mission planning applications.展开更多
AIM:To investigate age-related differences in the irislens angle(ILA)among patients with age-related cortical cataracts and elucidate the impact of age on lens stability.METHODS:A prospective observational study was c...AIM:To investigate age-related differences in the irislens angle(ILA)among patients with age-related cortical cataracts and elucidate the impact of age on lens stability.METHODS:A prospective observational study was conducted on patients with age-related cortical cataracts scheduled for phacoemulsification surgery.Preoperative ultrasound biomicroscopy(UBM)images were collected and analyzed.Initially,patients were stratified into two age groups:<60y and≥60y,with no significant intergroup differences in sex or eye laterality.For further analysis,participants were subdivided into three age strata:<60y,60-75y,and>75y.The ILA was measured in four quadrants(superior,inferior,nasal,and temporal).Intergroup differences in ILA were compared,and correlations between age and ILA parameters were analyzed using statistical methods.RESULTS:The sample data were categorized into three groups according to age,<60y(113 patients;55.8%female),60–75y(245 patients;61.0%female),and>75y(70 patients;50.2%female).The superior quadrant ILA increased progressively with age stratification(P=0.02),and the maximum ILA difference(ΔILA)was significantly higher in patients over 75y(P<0.01).Simple linear regression analysis demonstrated a positive correlation between age and ILA in the superior(Y=7.487+0.096X,R=0.191,P<0.001)and temporal(Y=10.254+0.052X,R=0.104,P=0.032)quadrants.Additionally,the mean ILA across all quadrants(ILAmean)andΔILA were positively correlated with age(ILAmean:Y=9.721+0.055X,R=0.138,P=0.004;ΔILA:Y=3.267+0.044X,R=0.006,P<0.05).CONCLUSION:In patients with age-related cortical cataracts,ILA increases with age,particularly in the superior and temporal quadrants,suggesting that advanced age is associated with greater lens deviation and decreased lens stability.UBM imaging can effectively evaluate the status of the zonule and lens stability,providing crucial evidence for personalized surgical planning based on patients’age.展开更多
Environmental problems are intensifying due to the rapid growth of the population,industry,and urban infrastructure.This expansion has resulted in increased air and water pollution,intensified urban heat island effect...Environmental problems are intensifying due to the rapid growth of the population,industry,and urban infrastructure.This expansion has resulted in increased air and water pollution,intensified urban heat island effects,and greater runoff from parks and other green spaces.Addressing these challenges requires prioritizing green infrastructure and other sustainable urban development strategies.This study introduces a novel Integrated Decision Support System that combines Pythagorean Fuzzy Sets with the Advanced Alternative Ranking Order Method allowing for Two-Step Normalization(AAROM-TN),enhanced by a dual weighting strategy.The weighting approach integrates the Criteria Importance Through Intercriteria Correlation(CRITIC)method with the Criteria Importance through Means and Standard Deviation(CIMAS)technique.The originality of the proposed framework lies in its ability to objectively quantify criteria importance using CRITIC,incorporate decision-makers’preferences through CIMAS,and capture the uncertainty and hesitation inherent in human judgment via Pythagorean Fuzzy Sets.A case study evaluating green infrastructure alternatives in metropolitan regions demonstrates the applicability and effectiveness of the framework.A sensitivity analysis is conducted to examine how variations in criteria weights affect the rankings and to evaluate the robustness of the results.Furthermore,a comparative analysis highlights the practical and financial implications of each alternative by assessing their respective strengths and weaknesses.展开更多
Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex,real-world environments,where coordinated formation control and obstacle avoidance are essential f...Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex,real-world environments,where coordinated formation control and obstacle avoidance are essential for executing sophisticated collective tasks.This paper presents a Distributed Formation Control and Obstacle Avoidance(DFCOA)framework for multi-unmanned ground vehicles(UGV).DFCOA integrates a virtual leader structure for global guidance,an improved A^(*)path planning algorithm with an advanced cost function for efficient path planning,and a repulsive-force-based improved vector field histogram star(VFH^(*))technique for collision avoidance.The virtual leader generates a reference trajectory while enabling distributed execution;the improved A^(*)algorithm reduces planning time and number of nodes to determine the shortest path from the starting position to the goal;and the improved VFH^(*)uses 2D LiDAR data with inter-agent repulsive force to simultaneously avoid collision with obstacles and maintain safe inter-vehicle distances.The formation stability of the proposed DFCOA reaches 95.8%and 94.6%in two scenarios,with root mean square(RMS)centroid errors of 0.9516 and 1.0008 m,respectively.Velocity tracking is precise(velocity centroid error RMS of 0.2699 and 0.1700 m/s),and linear velocities closely match the desired 0.3 m/s.Safety metrics showed average collision risks of 0.7773 and 0.5143,with minimum inter-vehicle distances of 0.4702 and 0.8763 m,confirming collision-free navigation of four UGVs.DFCOA outperforms conventional methods in formation stability,path efficiency,and scalability,proving its suitability for decentralized multi-UGV applications.展开更多
Driven by the global energy transition and the urgent“dual carbon”goals,regional integrated energy system(RIES)planning is undergoing a paradigm shift from carbon reduction to negative carbon emissions.This paper pr...Driven by the global energy transition and the urgent“dual carbon”goals,regional integrated energy system(RIES)planning is undergoing a paradigm shift from carbon reduction to negative carbon emissions.This paper provides a comprehensive review of the theoretical frameworks and technical pathways for RIES planning from a carbon-centric perspective.A key contribution is the proposed Carbon-Energy-Economy(CEE)triple-dimensional governance framework,which endogenizes carbon factors into planning decisions through emission constraints,trading mechanisms,and capture technologies.We first analyze the fundamental characteristics of RIES and their critical role in achieving carbon neutrality,detailing advancements in multi-energy coupling models,energy router concepts,and standardized energy hub modeling.The paper further explores multi-energy flow analysis methods,and systematically compares the applicability and limitations of various planning algorithms,with emphasis on addressing uncertainties from renewable integration.Finally,we highlight the integration of artificial intelligence with traditional optimization methods,offering new pathways for intelligent,adaptive,and low-carbon RIES planning.This review underscores the transition towards data-physical fusion models,cooperative uncertainty optimization,multi-market planning,and innovative zero/negative-carbon technological routes.展开更多
To address low learning efficiency and inadequate path safety in spraying robot navigation within complex obstacle-rich environments—with dense,dynamic,unpredictable obstacles challenging conventional methods—this p...To address low learning efficiency and inadequate path safety in spraying robot navigation within complex obstacle-rich environments—with dense,dynamic,unpredictable obstacles challenging conventional methods—this paper proposes a hybrid algorithm integrating Q-learning and improved A*-Artificial Potential Field(A-APF).Centered on theQ-learning framework,the algorithmleverages safety-oriented guidance generated byA-APF and employs a dynamic coordination mechanism that adaptively balances exploration and exploitation.The proposed system comprises four core modules:(1)an environment modeling module that constructs grid-based obstacle maps;(2)an A-APF module that combines heuristic search from A*algorithm with repulsive force strategies from APF to generate guidance;(3)a Q-learning module that learns optimal state-action values(Q-values)through spraying robot-environment interaction and a reward function emphasizing path optimality and safety;and(4)a dynamic optimization module that ensures adaptive cooperation between Q-learning and A-APF through exploration rate control and environment-aware constraints.Simulation results demonstrate that the proposed method significantly enhances path safety in complex underground mining environments.Quantitative results indicate that,compared to the traditional Q-learning algorithm,the proposed method shortens training time by 42.95% and achieves a reduction in training failures from 78 to just 3.Compared to the static fusion algorithm,it further reduces both training time(by 10.78%)and training failures(by 50%),thereby improving overall training efficiency.展开更多
Planning in lexical-prior-free environments presents a fundamental challenge for evaluating whether large language models(LLMs)possess genuine structural reasoning capabilities beyond lexical memorization.When predica...Planning in lexical-prior-free environments presents a fundamental challenge for evaluating whether large language models(LLMs)possess genuine structural reasoning capabilities beyond lexical memorization.When predicates and action names are replaced with semantically irrelevant random symbols while preserving logical structures,existing direct generation approaches exhibit severe performance degradation.This paper proposes a symbol-agnostic closed-loop planning pipeline that enables models to construct executable plans through systematic validation and iterative refinement.The system implements a complete generate-verify-repair cycle through six core processing components:semantic comprehension extracts structural constraints,language planner generates text plans,symbol translator performs structure-preserving mapping,consistency checker conducts static screening,Stanford Research Institute Problem Solver(STRIPS)simulator executes step-by-step validation,and VAL(Validator)provides semantic verification.A repair controller orchestrates four targeted strategies addressing typical failure patterns including first-step precondition errors andmid-segment statemaintenance issues.Comprehensive evaluation on PlanBench Mystery Blocksworld demonstrates substantial improvements over baseline approaches across both language models and reasoning models.Ablation studies confirm that each architectural component contributes non-redundantly to overall effectiveness,with targeted repair providing the largest impact,followed by deep constraint extraction and stepwise validation,demonstrating that superior performance emerges from synergistic integration of these mechanisms rather than any single dominant factor.Analysis reveals distinct failure patterns betweenmodel types—languagemodels struggle with local precondition satisfaction while reasoning models face global goal achievement challenges—yet the validation-driven mechanism successfully addresses these diverse weaknesses.A particularly noteworthy finding is the convergence of final success rates across models with varying intrinsic capabilities,suggesting that systematic validation and repair mechanisms play a more decisive role than raw model capacity in lexical-prior-free scenarios.This work establishes a rigorous evaluation framework incorporating statistical significance testing and mechanistic failure analysis,providingmethodological contributions for fair assessment and practical insights into building reliable planning systems under extreme constraint conditions.展开更多
This paper introduces a novel nature-inspired metaheuristic algorithm called the Gekko japonicus algorithm.The algo-rithm draws inspiration mainly from the predation strategies and survival behaviors of the Gekko japo...This paper introduces a novel nature-inspired metaheuristic algorithm called the Gekko japonicus algorithm.The algo-rithm draws inspiration mainly from the predation strategies and survival behaviors of the Gekko japonicus.The math-ematical model is developed by simulating various biological behaviors of the Gekko japonicus,such as hybrid loco-motion patterns,directional olfactory guidance,implicit group advantage tendencies,and the tail autotomy mechanism.By integrating multi-stage mutual constraints and dynamically adjusting parameters,GJA maintains an optimal balance between global exploration and local exploitation,thereby effectively solving complex optimization problems.To assess the performance of GJA,comparative analyses were performed against fourteen state-of-the-art metaheuristic algorithms using the CEC2017 and CEC2022 benchmark test sets.Additionally,a Friedman test was performed on the experimen-tal results to assess the statistical significance of differences between various algorithms.And GJA was evaluated using multiple qualitative indicators,further confirming its superiority in exploration and exploitation.Finally,GJA was utilized to solve four engineering optimization problems and further implemented in robotic path planning to verify its practical applicability.Experimental results indicate that,compared to other high-performance algorithms,GJA demonstrates excep-tional performance as a powerful optimization algorithm in complex optimization problems.We make the code publicly available at:https://github.com/zhy1109/Gekko-japonicusalgorithm.展开更多
On February 5, the Dialogue on Building a China-Laos Community with a Shared Future, themed “Focusing on the ‘Action Plan,’ Creating a Better Future Together,” took place in Vientiane, Laos.The opening ceremony fe...On February 5, the Dialogue on Building a China-Laos Community with a Shared Future, themed “Focusing on the ‘Action Plan,’ Creating a Better Future Together,” took place in Vientiane, Laos.The opening ceremony featured speeches by several distinguished vips including Fang Hong, Chinese ambassador to Laos, Daosavanh Kheugmixai, vice president of the Lao National Academy of Politics and Public Administration, Yu Yunquan, vice president of the China International Communications Group (CICG),Sakhon Phommalat, deputy head of the Propaganda and Training Board of the Lao People’s Revolutionary Party (LPRP) Central Committee, Sun Jisheng, vice president of China Foreign Affairs University (CFAU),and Vadthuninyom Duangmala,president of the Diplomatic Academy of Laos.展开更多
文摘Making plans is a good idea,but every one's schedule looks differe nt.You may have to talk about your plans before you're able to make some.It could sound like this:You ask,"Do you have plans this Friday night?"If the person already has plans,they may say,"I do.But I'm free on Saturday."If that day doesn't work for you,you can say,"I'm not available that day.How about Sunday after no on?"After you figure out the day and time,mark it on your calendar.
基金supported by the Government of the Northwest Territories in Canada and the John McMurry Research Chair in Environmental Geography at Wilfrid Laurier University.
文摘Climate change is increasingly affecting all aspects of protected areas management from changes of species ranges to visitor experiences.Due to these impacts,there is a need for managers to take more robust approaches to con-sidering the implications of climate change on the overall application and efficacy of protected areas management direction,including the achievement of the goals and objectives contained within management plans.Through a systematic and comprehensive content analysis approach,this study assesses the current extent to which climate change is considered in Canadian protected area management plans.Specifically,we evaluated 63 terrestrial protected area management plans against a set of climate robustness principles.Our content analysis revealed that climate change is currently not effectively factored into Canadian protected area management plans with an average climate robustness score of 18%.Climate robustness score was not found to be correlated with protected area size,International Union for the Conservation of Nature(IUCN)management classification,or jurisdictional authority.Certain climate robustness principles received higher scores across the management plans than oth-ers.For example,the principles of‘diverse knowledge sources’and‘addresses climate change’scored relatively highly whereas‘climate change vulnerability’and‘ecosystem integrity’received the lowest scores.The lack of integration of ecological integrity considerations in management plans was a particularly noteworthy deficiency considering that this guiding principle is the primary legislative objective of many national and sub-national protected areas in Canada.From this assessment,climate change needs to be more effectively and consistently integrated into protected area management plan development and coordinated across associated planning pro-cesses.We discuss the ways in which this can be achieved,for example,by integrating scenario planning into organizational management plan development processes.
基金Supported by the Anhui Province Higher Education Scientific Research Project(2024AH050643)。
文摘In this paper,we investigate the optimal investment strategy for a target-return pension plan under a Wishart model using stochastic optimal control methods.We consider the fact that mortality and interest rates are affected by a combination of systematic and idiosyncratic factors,which are modelled by a Wishart model with positive values.On this basis,we analyse the correlation between stochastic mortality and stochastic wage rates for plan members,as well as wage movements and financial market volatility.It is shown that financial market parameters and wage rates have a significant impact on the optimal investment strategy.We provide the optimal investment strategy and the optimal yield adjustment strategy for target-return pensions.This study provides a valuable reference for policy formulation and implementation of target-return pension plans in China.
文摘基于国际比较的地方空间规划的范围和工具研究An International Comparison of the Scope and Instruments of Local Spatial Planning源自:The TownPlanning Review,2024,95(2):197-217作者:MaciejJNowak,StefanieDühr,Sergio Peña,et al.推荐:申明锐,南京大学建筑与城市规划学院。shenmingr@nju.edu.cn.
基金This project was supported by a grant from Guangdong Medical Research Foundation (No.Al999214).
文摘Objective： Conventional external beam irradiation techniques for nasopharyngeal carcinoma have limitations, and improving external beam irradiation techniques is needed to enhance the curative rate. This study was designed to cvaluate the difference in dose distribution of three dimensional conformal radiotherapy （3D CRT） and conventional treatment plan in early untreated nasopharyngeal carcinoma using a three dimensional treatment planning system. Methods： Twenty-two patients with early untreated nasopharyngeal carcinoma were selected. Conventional and 3D CRT plans were made for each of them and compared with respect to target volume coverage （V95）,normal tissue sparing （D50, D33 and D5, etc）, normal tissue complication probability （NTCP）. Results： The average volumetric dose comparison indicated that the V95 of PTVnx70 were 98.22% and 99.99% （P=0.06）, and PTVnd60, 98.41% and 99.63% （P=1.00）, PTV,x60, 98.44% and 99.98% （P=0.03）, PTVnx50, 98.85% and 99.63% （P=0.02） in conventional and 3DCRT treatment plans respectively. With respect to normal tissue sparing, the average D50 of unilateral parotid glands were 51.91 Gy and 64.30 Gy （P=0.00） respectively, and the unilateral temporomandibular joints, 49.98 Gy and 64.47 Gy （P=0.00）, the Dlcc of spinal cords, 44.98 Gy and 48.09 Gy （P=0.00） in 3D CRT and conventional plans. Conclusion： Though only a little bit better dose coverage of target volume in subclinical lesion region was reached in 3D CRT plans, it spared more normal tissues e.g. parotid glands and temporomandibular joints etc and decreased their NTCP while it got the similar dose distribution in target volumes as conventional plans did for these early nasopharyngeal carcinoma cases.
基金National Natural Science Foundation of China(32301712)Natural Science Foundation of Jiangsu Province(BK20230548+3 种基金BK20250876)Project of Faculty of Agricultural Equipment of Jiangsu University(NGXB20240203)A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD-2023-87)Open Funding Project of the Key Laboratory of Modern Agricultural Equipment and Technology(Jiangsu University),Ministry of Education(MAET202101)。
文摘Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT^(*)),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narrow passages,slow convergence,and high computational costs.To address these challenges,this paper proposes a novel hybrid global path planning algorithm integrating Gaussian sampling and quadtree optimization(RRT^(*)-GSQ).This methodology aims to enhance path planning by synergistically combining a Gaussian mixture sampling strategy to improve node generation in critical regions,an adaptive step-size and direction optimization mechanism for enhanced obstacle avoidance,a Quadtree-AABB collision detection framework to lower computational complexity,and a dynamic iteration control strategy for more efficient convergence.In obstacle-free and obstructed scenarios,compared with the conventional RRT^(*),the proposed algorithm reduced the number of node evaluations by 67.57%and 62.72%,and decreased the search time by 79.72%and 78.52%,respectively.In path tracking tests,the proposed algorithm achieved substantial reductions in RMSE of the final path compared to the conventional RRT^(*).Specifically,the lateral RMSE was reduced by 41.5%in obstacle-free environments and 59.3%in obstructed environments,while the longitudinal RMSE was reduced by 57.2%and 58.5%,respectively.Furthermore,the maximum absolute errors in both lateral and longitudinal directions were constrained within 0.75 m.Field validation experiments in an operational orchard confirmed the algorithm's practical effectiveness,showing reductions in the mean tracking error of 47.6%(obstacle-free)and 58.3%(with obstructed),alongside a 5.1%and 7.2%shortening of the path length compared to the baseline method.The proposed algorithm effectively enhances path planning efficiency and navigation accuracy for robots,presenting a superior solution for high-precision autonomous navigation of agricultural robots in orchard environments and holding significant value for engineering applications.
基金supported by the National Natural Science Foundation of China(Nos.T2121003,U24B20156)Open Fund of the National Key Laboratory of Helicopter Aeromechanics(No.2024-ZSJ-LB-02-06)。
文摘In dynamic and uncertain reconnaissance missions,effective task assignment and path planning for multiple unmanned aerial vehicles(UAVs)present significant challenges.A stochastic multi-UAV reconnaissance scheduling problem is formulated as a combinatorial optimization task with nonlinear objectives and coupled constraints.To solve the non-deterministic polynomial(NP)-hard problem efficiently,a novel learning-enhanced pigeon-inspired optimization(L-PIO)algorithm is proposed.The algorithm integrates a Q-learning mechanism to dynamically regulate control parameters,enabling adaptive exploration–exploitation trade-offs across different optimization phases.Additionally,geometric abstraction techniques are employed to approximate complex reconnaissance regions using maximum inscribed rectangles and spiral path models,allowing for precise cost modeling of UAV paths.The formal objective function is developed to minimize global flight distance and completion time while maximizing reconnaissance priority and task coverage.A series of simulation experiments are conducted under three scenarios:static task allocation,dynamic task emergence,and UAV failure recovery.Comparative analysis with several updated algorithms demonstrates that L-PIO exhibits superior robustness,adaptability,and computational efficiency.The results verify the algorithm's effectiveness in addressing dynamic reconnaissance task planning in real-time multi-UAV applications.
基金supported by the Science and Technology Project of the State Grid Corporation of China“Research on Comprehensive Value Evaluation Method of Flexible Adjusting Resources under Carbon-electricity-certificate Market Coupling Environment”(No.5108-202455038A-1-1-ZN).
文摘The energy transition inspired by carbon neutrality targets and the increasing threat of extreme events raise multi-objective development requirements for power systems.This paper proposes a multi-objective resource allocation model to determine the type,number and location of flexible resources to increase the values of resilience,carbon reduction and renewable energy consumption.To evaluate the values of resilience,a restoration model for transmission systems is established that considers the coordination of fossil-fuel generators,energy storage systems(ESSs)and renewable energy generators in building restoration paths.The collaborative power-carbon-tradable green certificate(TGC)market model is then applied to evaluate the resource values in terms of carbon reduction and renewable energy consumption.Finally,the model is formulated as a mixed-integer linear programming(MILP)with a nonconvex feasible domain,and the normalized normal constraint(NNC)method is applied to obtain approximate Pareto frontiers for decision makers.Case studies validate the effectiveness of the proposed model in improving multi-factor values and analyze the impact of resource regulation capacity on values of restoration and carbon reduction.
文摘China’s economy demonstrated remarkable resilience in 2025,with its GDP exceeding RMB 140 trillion for the first time and achieving a five percent year-on-year growth.This performance marked the successful conclusion of the 14th Five-Year Plan(2021-2025),during which the country’s economy achieved“four consecutive leaps,”expanding from RMB 110 trillion to 120 trillion,then to 130 trillion,and finally to 140 trillion.
基金Under the auspices of the Funding Project of Northeast Geological S&T Innovation Center of China Geological Survey(No.QCJJ2024-11)Natural Science Foundation of Liaoning Province(No.2025-BS-0873)+1 种基金Liaoning Provincial Joint Science and Technology Program(No.2024-MSLH-507)National Social Science Foundation of China(No.23ATJ006)。
文摘Promoting urban-rural integration and facilitating the bidirectional flow of urban and rural elements are core spatial objectives in the new era of China.The urban-rural fringe represents the region with the most intense interaction between urban and rural areas,serving as a key zone for breaking down barriers and promoting urban-rural integration.Based on a systematic review of representative case studies and scholarly literature,this paper synthesizes the evolving research perspectives on the urban-rural fringe,with particular attention to how data-driven approaches that integrate official statistics,remote sensing imagery,points of interest,and mobile phone signaling data have advanced the characterization of fringe features,refined identification methods,and revealed emerging developmental trends through spatial clustering and machine learning classification.It proposes an integrated analytical framework encompassing administrative boundaries,economic metabolism,social activities,material infrastructure,and the ecological environment.The paper further examines the characteristics and emerging development trends of urban-rural fringe areas and advances a set of strategic directions to support urban-rural integration and more efficient resource allocation.These include expanding analytical dimensions,enhancing data integration,refining identification criteria,elucidating mechanisms of internal and external interactions,and strengthening interdisciplinary collaboration.Collectively,these efforts offer actionable insights for optimizing public service delivery,directing infrastructure investment in transportation and utilities,delineating ecological conservation boundaries,and implementing place-based socioeconomic revitalization strategies in the urban-rural fringe regions.
基金supported in part by 14th Five Year National Key R&D Program Project(Project Number:2023YFB3211001)the National Natural Science Foundation of China(62273339,U24A201397).
文摘Rapidly-exploring Random Tree(RRT)and its variants have become foundational in path-planning research,yet in complex three-dimensional off-road environments their uniform blind sampling and limited safety guarantees lead to slow convergence and force an unfavorable trade-off between path quality and traversal safety.To address these challenges,we introduce HS-APF-RRT*,a novel algorithm that fuses layered sampling,an enhanced Artificial Potential Field(APF),and a dynamic neighborhood-expansion mechanism.First,the workspace is hierarchically partitioned into macro,meso,and micro sampling layers,progressively biasing random samples toward safer,lower-energy regions.Second,we augment the traditional APF by incorporating a slope-dependent repulsive term,enabling stronger avoidance of steep obstacles.Third,a dynamic expansion strategy adaptively switches between 8 and 16 connected neighborhoods based on local obstacle density,striking an effective balance between search efficiency and collision-avoidance precision.In simulated off-road scenarios,HS-APF-RRT*is benchmarked against RRT*,GoalBiased RRT*,and APF-RRT*,and demonstrates significantly faster convergence,lower path-energy consumption,and enhanced safety margins.
基金funded in part by the Fundamental Research Funds for the Central Universities under Grant NS2023052in part by the Natural Science Foundation of Jiangsu Province of China under Grants No.BK20231439 and No.BK20222012.
文摘With the expanding applications of unmanned aerial vehicles(UAVs),precise flight evaluation has emerged as a critical enabler for efficient path planning,directly impacting operational performance and safety.Traditional path planning algorithms typically combine Dubins curves with local optimization to minimize trajectory length under 3D spatial constraints.However,these methods often overlook the correlation between pilot control quality and UAV flight dynamics,limiting their adaptability in complex scenarios.In this paper,we propose an intelligent flight evaluation model specifically designed to enhancemulti-waypoint trajectory optimization algorithms.Our model leverages a decision tree to integrate attitude parameters and trajectory matching metrics,establishing a quantitative link between pilot control quality and UAV flight states.Experimental results demonstrate that the proposed model not only accurately assesses pilot performance across diverse skill levels but also improves the optimality of generated trajectories.When integrated with our path planning algorithm,it efficiently produces optimal trajectories while strictly adhering to UAV flight constraints.This integrated framework highlights significant potential for real-time UAV training,performance assessment,and adaptive mission planning applications.
文摘AIM:To investigate age-related differences in the irislens angle(ILA)among patients with age-related cortical cataracts and elucidate the impact of age on lens stability.METHODS:A prospective observational study was conducted on patients with age-related cortical cataracts scheduled for phacoemulsification surgery.Preoperative ultrasound biomicroscopy(UBM)images were collected and analyzed.Initially,patients were stratified into two age groups:<60y and≥60y,with no significant intergroup differences in sex or eye laterality.For further analysis,participants were subdivided into three age strata:<60y,60-75y,and>75y.The ILA was measured in four quadrants(superior,inferior,nasal,and temporal).Intergroup differences in ILA were compared,and correlations between age and ILA parameters were analyzed using statistical methods.RESULTS:The sample data were categorized into three groups according to age,<60y(113 patients;55.8%female),60–75y(245 patients;61.0%female),and>75y(70 patients;50.2%female).The superior quadrant ILA increased progressively with age stratification(P=0.02),and the maximum ILA difference(ΔILA)was significantly higher in patients over 75y(P<0.01).Simple linear regression analysis demonstrated a positive correlation between age and ILA in the superior(Y=7.487+0.096X,R=0.191,P<0.001)and temporal(Y=10.254+0.052X,R=0.104,P=0.032)quadrants.Additionally,the mean ILA across all quadrants(ILAmean)andΔILA were positively correlated with age(ILAmean:Y=9.721+0.055X,R=0.138,P=0.004;ΔILA:Y=3.267+0.044X,R=0.006,P<0.05).CONCLUSION:In patients with age-related cortical cataracts,ILA increases with age,particularly in the superior and temporal quadrants,suggesting that advanced age is associated with greater lens deviation and decreased lens stability.UBM imaging can effectively evaluate the status of the zonule and lens stability,providing crucial evidence for personalized surgical planning based on patients’age.
基金supported by the Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R259)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.Ashit Kumar Dutta would like to thank AlMaarefa University for supporting this research under project number MHIRSP2025017.
文摘Environmental problems are intensifying due to the rapid growth of the population,industry,and urban infrastructure.This expansion has resulted in increased air and water pollution,intensified urban heat island effects,and greater runoff from parks and other green spaces.Addressing these challenges requires prioritizing green infrastructure and other sustainable urban development strategies.This study introduces a novel Integrated Decision Support System that combines Pythagorean Fuzzy Sets with the Advanced Alternative Ranking Order Method allowing for Two-Step Normalization(AAROM-TN),enhanced by a dual weighting strategy.The weighting approach integrates the Criteria Importance Through Intercriteria Correlation(CRITIC)method with the Criteria Importance through Means and Standard Deviation(CIMAS)technique.The originality of the proposed framework lies in its ability to objectively quantify criteria importance using CRITIC,incorporate decision-makers’preferences through CIMAS,and capture the uncertainty and hesitation inherent in human judgment via Pythagorean Fuzzy Sets.A case study evaluating green infrastructure alternatives in metropolitan regions demonstrates the applicability and effectiveness of the framework.A sensitivity analysis is conducted to examine how variations in criteria weights affect the rankings and to evaluate the robustness of the results.Furthermore,a comparative analysis highlights the practical and financial implications of each alternative by assessing their respective strengths and weaknesses.
文摘Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex,real-world environments,where coordinated formation control and obstacle avoidance are essential for executing sophisticated collective tasks.This paper presents a Distributed Formation Control and Obstacle Avoidance(DFCOA)framework for multi-unmanned ground vehicles(UGV).DFCOA integrates a virtual leader structure for global guidance,an improved A^(*)path planning algorithm with an advanced cost function for efficient path planning,and a repulsive-force-based improved vector field histogram star(VFH^(*))technique for collision avoidance.The virtual leader generates a reference trajectory while enabling distributed execution;the improved A^(*)algorithm reduces planning time and number of nodes to determine the shortest path from the starting position to the goal;and the improved VFH^(*)uses 2D LiDAR data with inter-agent repulsive force to simultaneously avoid collision with obstacles and maintain safe inter-vehicle distances.The formation stability of the proposed DFCOA reaches 95.8%and 94.6%in two scenarios,with root mean square(RMS)centroid errors of 0.9516 and 1.0008 m,respectively.Velocity tracking is precise(velocity centroid error RMS of 0.2699 and 0.1700 m/s),and linear velocities closely match the desired 0.3 m/s.Safety metrics showed average collision risks of 0.7773 and 0.5143,with minimum inter-vehicle distances of 0.4702 and 0.8763 m,confirming collision-free navigation of four UGVs.DFCOA outperforms conventional methods in formation stability,path efficiency,and scalability,proving its suitability for decentralized multi-UGV applications.
基金supported by the Natural Science Foundation of China(Grants U2166211)Zhejiang Provincial Natural Science Foundation of China(Grants LY24E070006 and LMS25E070002).
文摘Driven by the global energy transition and the urgent“dual carbon”goals,regional integrated energy system(RIES)planning is undergoing a paradigm shift from carbon reduction to negative carbon emissions.This paper provides a comprehensive review of the theoretical frameworks and technical pathways for RIES planning from a carbon-centric perspective.A key contribution is the proposed Carbon-Energy-Economy(CEE)triple-dimensional governance framework,which endogenizes carbon factors into planning decisions through emission constraints,trading mechanisms,and capture technologies.We first analyze the fundamental characteristics of RIES and their critical role in achieving carbon neutrality,detailing advancements in multi-energy coupling models,energy router concepts,and standardized energy hub modeling.The paper further explores multi-energy flow analysis methods,and systematically compares the applicability and limitations of various planning algorithms,with emphasis on addressing uncertainties from renewable integration.Finally,we highlight the integration of artificial intelligence with traditional optimization methods,offering new pathways for intelligent,adaptive,and low-carbon RIES planning.This review underscores the transition towards data-physical fusion models,cooperative uncertainty optimization,multi-market planning,and innovative zero/negative-carbon technological routes.
基金supported by the National Natural Science Foundation of China(Grant No.52374156).
文摘To address low learning efficiency and inadequate path safety in spraying robot navigation within complex obstacle-rich environments—with dense,dynamic,unpredictable obstacles challenging conventional methods—this paper proposes a hybrid algorithm integrating Q-learning and improved A*-Artificial Potential Field(A-APF).Centered on theQ-learning framework,the algorithmleverages safety-oriented guidance generated byA-APF and employs a dynamic coordination mechanism that adaptively balances exploration and exploitation.The proposed system comprises four core modules:(1)an environment modeling module that constructs grid-based obstacle maps;(2)an A-APF module that combines heuristic search from A*algorithm with repulsive force strategies from APF to generate guidance;(3)a Q-learning module that learns optimal state-action values(Q-values)through spraying robot-environment interaction and a reward function emphasizing path optimality and safety;and(4)a dynamic optimization module that ensures adaptive cooperation between Q-learning and A-APF through exploration rate control and environment-aware constraints.Simulation results demonstrate that the proposed method significantly enhances path safety in complex underground mining environments.Quantitative results indicate that,compared to the traditional Q-learning algorithm,the proposed method shortens training time by 42.95% and achieves a reduction in training failures from 78 to just 3.Compared to the static fusion algorithm,it further reduces both training time(by 10.78%)and training failures(by 50%),thereby improving overall training efficiency.
基金supported by the Information,Production and Systems Research Center,Waseda University,and partly supported by the Future Robotics Organization,Waseda Universitythe Humanoid Robotics Institute,Waseda University,under the Humanoid Project+1 种基金the Waseda University Grant for Special Research Projects(grant numbers 2024C-518 and 2025E-027)was partly executed under the cooperation of organization between Kioxia Corporation andWaseda University.
文摘Planning in lexical-prior-free environments presents a fundamental challenge for evaluating whether large language models(LLMs)possess genuine structural reasoning capabilities beyond lexical memorization.When predicates and action names are replaced with semantically irrelevant random symbols while preserving logical structures,existing direct generation approaches exhibit severe performance degradation.This paper proposes a symbol-agnostic closed-loop planning pipeline that enables models to construct executable plans through systematic validation and iterative refinement.The system implements a complete generate-verify-repair cycle through six core processing components:semantic comprehension extracts structural constraints,language planner generates text plans,symbol translator performs structure-preserving mapping,consistency checker conducts static screening,Stanford Research Institute Problem Solver(STRIPS)simulator executes step-by-step validation,and VAL(Validator)provides semantic verification.A repair controller orchestrates four targeted strategies addressing typical failure patterns including first-step precondition errors andmid-segment statemaintenance issues.Comprehensive evaluation on PlanBench Mystery Blocksworld demonstrates substantial improvements over baseline approaches across both language models and reasoning models.Ablation studies confirm that each architectural component contributes non-redundantly to overall effectiveness,with targeted repair providing the largest impact,followed by deep constraint extraction and stepwise validation,demonstrating that superior performance emerges from synergistic integration of these mechanisms rather than any single dominant factor.Analysis reveals distinct failure patterns betweenmodel types—languagemodels struggle with local precondition satisfaction while reasoning models face global goal achievement challenges—yet the validation-driven mechanism successfully addresses these diverse weaknesses.A particularly noteworthy finding is the convergence of final success rates across models with varying intrinsic capabilities,suggesting that systematic validation and repair mechanisms play a more decisive role than raw model capacity in lexical-prior-free scenarios.This work establishes a rigorous evaluation framework incorporating statistical significance testing and mechanistic failure analysis,providingmethodological contributions for fair assessment and practical insights into building reliable planning systems under extreme constraint conditions.
基金CHINA POSTDOCTORAL SCIENCE FOUNDATION(Grant No.2025M771925)Young Scientists Fund(C Class)(Grant No.32501636)Special Fund of Fundamental Scientific Research Business Expense for Higher School of Central Government(Grant No.2572025JT04).
文摘This paper introduces a novel nature-inspired metaheuristic algorithm called the Gekko japonicus algorithm.The algo-rithm draws inspiration mainly from the predation strategies and survival behaviors of the Gekko japonicus.The math-ematical model is developed by simulating various biological behaviors of the Gekko japonicus,such as hybrid loco-motion patterns,directional olfactory guidance,implicit group advantage tendencies,and the tail autotomy mechanism.By integrating multi-stage mutual constraints and dynamically adjusting parameters,GJA maintains an optimal balance between global exploration and local exploitation,thereby effectively solving complex optimization problems.To assess the performance of GJA,comparative analyses were performed against fourteen state-of-the-art metaheuristic algorithms using the CEC2017 and CEC2022 benchmark test sets.Additionally,a Friedman test was performed on the experimen-tal results to assess the statistical significance of differences between various algorithms.And GJA was evaluated using multiple qualitative indicators,further confirming its superiority in exploration and exploitation.Finally,GJA was utilized to solve four engineering optimization problems and further implemented in robotic path planning to verify its practical applicability.Experimental results indicate that,compared to other high-performance algorithms,GJA demonstrates excep-tional performance as a powerful optimization algorithm in complex optimization problems.We make the code publicly available at:https://github.com/zhy1109/Gekko-japonicusalgorithm.
文摘On February 5, the Dialogue on Building a China-Laos Community with a Shared Future, themed “Focusing on the ‘Action Plan,’ Creating a Better Future Together,” took place in Vientiane, Laos.The opening ceremony featured speeches by several distinguished vips including Fang Hong, Chinese ambassador to Laos, Daosavanh Kheugmixai, vice president of the Lao National Academy of Politics and Public Administration, Yu Yunquan, vice president of the China International Communications Group (CICG),Sakhon Phommalat, deputy head of the Propaganda and Training Board of the Lao People’s Revolutionary Party (LPRP) Central Committee, Sun Jisheng, vice president of China Foreign Affairs University (CFAU),and Vadthuninyom Duangmala,president of the Diplomatic Academy of Laos.
