Dual-comb spectroscopy(DCS)is one of the most promising technologies for ultra-long open-path multiplegreenhouse gas detection.Ultra-long open-path DCS has the potential to realize horizontal open-path links over hund...Dual-comb spectroscopy(DCS)is one of the most promising technologies for ultra-long open-path multiplegreenhouse gas detection.Ultra-long open-path DCS has the potential to realize horizontal open-path links over hundredsof kilometers and vertical open-path links between satellites and the ground base.Under these extreme detection conditions,identifying an appropriate wavelength band that ensures both technical feasibility and a reasonable absorbance fortarget components is critical but currently lacks studies.In this work,we simulate transmission spectra under different detectionconfigurations to identify optimal wavelength bands for carbon dioxide(CO_(2))and methane(CH_(4))measurement.The simulation results show that the 1540 nm Watt-level high-power frequency combs developed are suitable for CO_(2)measurement in both horizontal and vertical ultra-long detection configurations.The results also suggest that developinghigh-power fiber amplifiers for 1630 nm and 1636 nm will facilitate CH_(4)measurement in horizontal and vertical ultra-longdetection configurations,respectively.The amplification at 1636 nm will be a future research focus,as it is expected to enablesimultaneous measurements of CH_(4),CO_(2),and water vapor in the vertical detection configuration.展开更多
Shenzhen,a major city in southern China,has experienced rapid advancements in Unmanned Aerial Vehicle(UAV)technology,resulting in extensive logistics networks with thousands of daily flights.However,frequent disruptio...Shenzhen,a major city in southern China,has experienced rapid advancements in Unmanned Aerial Vehicle(UAV)technology,resulting in extensive logistics networks with thousands of daily flights.However,frequent disruptions due to its subtropical monsoon climate,including typhoons and gusty winds,present ongoing challenges.Despite the growing focus on operational costs and third-party risks,research on low-altitude urban wind fields remains scarce.This study addresses this gap by integrating wind field analysis into UAV path planning,introducing key innovations to the classical model.First,UAV wind resistance and turbulence constraints are analyzed,mapping high-wind-speed and turbulence-prone zones in the airspace.Second,wind dynamics are incorporated into path planning by considering airspeed and groundspeed variation,optimizing waypoint selection and flight speed adjustments to improve overall energy efficiency.Additionally,a wind-aware Theta*algorithm is proposed,leveraging wind vectors to expedite search process,while Computational Fluid Dynamics(CFD)techniques are employed to calculate wind fields.A case study of Shenzhen,examining wind patterns over the past decade,demonstrates a 6.23%improvement in groundspeed and a 7.69%reduction in energy consumption compared to wind-agnostic models.This framework advances UAV logistics by enhancing route safety and energy efficiency,contributing to more cost-effective operations.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
Discussions on Chinese modernization are offering African countries both conceptual inspiration and practical references as they explore their own sustainable development paths.
Q-learning is a classical reinforcement learning method with broad applicability.It can respond effectively to environmental changes and provide flexible strategies,making it suitable for solving robot path-planning p...Q-learning is a classical reinforcement learning method with broad applicability.It can respond effectively to environmental changes and provide flexible strategies,making it suitable for solving robot path-planning problems.However,Q-learning faces challenges in search and update efficiency.To address these issues,we propose an improved Q-learning(IQL)algorithm.We use an enhanced Ant Colony Optimization(ACO)algorithmto optimizeQtable initialization.We also introduce the UCH mechanism to refine the reward function and overcome the exploration dilemma.The IQL algorithm is extensively tested in three grid environments of different scales.The results validate the accuracy of themethod and demonstrate superior path-planning performance compared to traditional approaches.The algorithm reduces the number of trials required for convergence,improves learning efficiency,and enables faster adaptation to environmental changes.It also enhances stability and accuracy by reducing the standard deviation of trials to zero.On grid maps of different sizes,IQL achieves higher expected returns.Compared with the original Q-learning algorithm,IQL improves performance by 12.95%,18.28%,and 7.98% on 10*10,20*20,and 30*30 maps,respectively.The proposed algorithm has promising applications in robotics,path planning,intelligent transportation,aerospace,and game development.展开更多
Directly burning methane for energy production wastes chemical potential and exacerbates CO_(2) emissions,while catalytic conversion into high-value fuel/chemicals provides economic and environmental sustainability.Ph...Directly burning methane for energy production wastes chemical potential and exacerbates CO_(2) emissions,while catalytic conversion into high-value fuel/chemicals provides economic and environmental sustainability.Photocatalytic CH_(4) conversion has emerged as a transformative technology,enabling selective oxidation under ambient conditions to directly synthesize value-added organic compounds.This addresses the dual challenges of climate mitigation and sustainable energy conversion.This review systematically examines the development of photocata lytic CH_(4) conversion,with three key dimensions.Firstly,we elucidate fundamental reaction mechanisms governing CH_(4) activation,emphasizing critical steps such as C-H bond scission via charge transfer,intermediate stabilization,and product desorption kinetics.Subsequently,we classify emerging photocata lytic pathways(partial oxidation,coupling,and reforming)and analyze material innovations.Finally,the challenges of the current photocata lytic CH_(4)conversion system and catalyst development were discussed,and perspectives were presented.The overarching objective of this work is to provide a comprehensive roadmap for the development of solardriven CH_(4) conversion systems that are aligned with global carbon neutrality goals.展开更多
The deep integration of artificial intelligence technology and agricultural industry has pushed smart agriculture into a new stage of"AI+scenario",and put forward a transformation requirement for the talent ...The deep integration of artificial intelligence technology and agricultural industry has pushed smart agriculture into a new stage of"AI+scenario",and put forward a transformation requirement for the talent cultivation of smart agriculture major in universities from"technology application"to"intelligent innovation".In response to the problems of insufficient AI integration,lack of contextualization,and insufficient collaboration between industry and education in the traditional"technology+"practical course system,this paper takes the smart agriculture major at Yulin Normal University as an example to construct a"AI+agriculture"practical course reconstruction framework and propose a four-dimensional transformation path of"goal-content-mode-evaluation".Through the practical exploration of modular curriculum design,scenario based practical design,integration of industry and education,and intelligent evaluation reform,a practical teaching system with local application-oriented university characteristics has been formed,providing a reference example for the cultivation of smart agriculture professionals under the background of new agricultural science.展开更多
The advent of digital and intelligent era has brought profound impacts to higher education in terms of teaching models,learning resources,teacher-student roles,and instructional management.Among them,local universitie...The advent of digital and intelligent era has brought profound impacts to higher education in terms of teaching models,learning resources,teacher-student roles,and instructional management.Among them,local universities with relatively weak resources but large scale are particularly noteworthy.A questionnaire survey on the current situation of informative teaching among teachers from local universities found that their overall awareness of informative teaching is positive,but their ability structure is uneven,especially in data-driven teaching evaluation and human-machine collaborative design,where there are obvious shortcomings.Research has revealed that the development of teachers'informative teaching ability is influenced by internal factors such as intrinsic motivation and data literacy,as well as external factors such as training systems and school support.Based on this,a systematic improvement path of"concept guidance-precise empowerment-environmental support-institutional guarantee"four-in-one is proposed to provide practical reference for promoting the professional development of teachers from local universities and the digital transformation of higher education.展开更多
Topological phases are governed by lattice symmetries,yet how different symmetry-breaking paths(SBPs)affect topological transitions remains insufficiently understood.Most existing studies rely on a single SBP,and addr...Topological phases are governed by lattice symmetries,yet how different symmetry-breaking paths(SBPs)affect topological transitions remains insufficiently understood.Most existing studies rely on a single SBP,and address only one bandgap,limiting independent control of multiple gaps.Here,we investigate multiple isolated Dirac points in a trefoil-knot-modified honeycomb lattice,and show that a single SBP generally inverts all relevant Dirac points simultaneously,whereas the tailored combinations of SBPs enable selective and programmable band inversion at targeted gaps.The excitation-dependent responses reveal strong modal selectivity.This capability is exploited to realize independently controllable multi-channel signal splitting,which is unattainable with a single SBP.The results enable SBPs as an effective design degree of freedom for programmable and reconfigurable topological elastic devices.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
The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduce...The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.展开更多
Excavation causes stress redistribution and affects the stress path during the shearing process of rock.The shear strength of rock varies under different stress paths,and the presence of defects reduces the shear stre...Excavation causes stress redistribution and affects the stress path during the shearing process of rock.The shear strength of rock varies under different stress paths,and the presence of defects reduces the shear strength.To further investigate this phenomenon,this study investigates the shear behaviour of rocks with different shear surface integrities under the influenceof different stress paths through laboratory tests and numerical simulations.The results indicate that the shear strength depends on the stress path and a decrease in the shear surface integrity reduces the degree of dependence.The cohesion and friction angle of the Mohr‒Coulomb criterion decrease with weakening of the shear surface integrity.For different stress paths,the direct shear strength is always greater than that of other shear stress paths.The pattern of changes in the acoustic emission count and cumulative count indirectly reflectsthe above findings.Numerical simulations further indicate that the different principal stress states and normal suppression effects during the shearing process lead to changes in the factors of crack propagation,resulting in different mechanical behaviours under various stress paths.For rocks with different integrity levels,the main reason for the different path dependences of shear strength is that the size of the area affected by shear is different.Shear failure will concentrate on the shear plane when the normal inhibition effect is greater.This study explores the mechanism of rock shear behaviour,providing a theoretical basis for establishing more accurate constitutive models and strength criteria.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
This paper develops a semi-analytical solution for pile penetration in natural soft clays using the strain path method(SPM).The stress-strain behavior of soils is characterized by the S-CLAY1S model,which can capture ...This paper develops a semi-analytical solution for pile penetration in natural soft clays using the strain path method(SPM).The stress-strain behavior of soils is characterized by the S-CLAY1S model,which can capture the anisotropic evolution and destructuring nature of soft clays.By integrating the S-CLAY1S model into the theoretical framework of the SPM,a set of ordinary differential equations is formulated with respect to the vertical coordinate of soil particles.The distribution of excess pore water pressure(EPWP)following pile installation is approximated through one-dimensional(1D)radial integration around the pile shaft.The distribution of stresses and EPWP,along with the evolution of fabric anisotropy within the soil surrounding the pile,is presented to illustrate the response of pile penetration in natural soft clays.The proposed solution is validated against existing theoretical solutions using the SPM and cavity expansion method(CEM),along with experimental data.The findings demonstrate that the SPM reveals lower radial effective stresses and EPWP at the pile shaft than that of CEM.Pile penetration alters the soil's anisotropic properties,inducing rotational hardening and affecting post-installation stress distribution.Soil destructuration eliminates bonding among particles near the pile,resulting in a complete disruption of soil structure at the pile surface,which is particularly pronounced for higher initial soil structure ratios.Minimal variation was observed in the three principal stresses and shear stress on the cone side surface as the angle increased from 18°to 60°,except for a slight reduction in EPWP.展开更多
Understanding how aging influences the thermal hazards of lithium-ion batteries(LIBs)is critical for enhancing their safety across a wide range of applications.This study systematically investigates the thermal runawa...Understanding how aging influences the thermal hazards of lithium-ion batteries(LIBs)is critical for enhancing their safety across a wide range of applications.This study systematically investigates the thermal runaway(TR)behavior of LIBs,with particular emphasis on combined-pathway aging,evaluated in terms of normalized usable capacity(U_(E)).Key thermal safety parameters,i.e.,TR triggering temperature,mass loss,and heat generation under diverse aging conditions,are quantified.To enable a fair comparison,thermal hazards are evaluated based on equivalent usable capacity,revealing that aged cells exhibit lower TR triggering temperatures and higher heat generation than fresh cells under thermal abuse with elevated thermal risks.Mechanistic analysis identifies lithium plating,solid electrolyte interphase(SEI)formation,and lithium depletion,particularly under high-temperature charging,as the dominant contributors to increased hazard.Using an aging-stressor matrix,a trade-off between high-C-rateinduced thermal instability and high-temperature-induced thermal stability is discovered and quantified,underscoring the strong dependence of thermal hazards on specific aging pathways.This work advances the fundamental understanding of aging-induced safety risks in LIBs and offers practical guidance for the development of safer battery systems,optimized charging protocols,and improved battery management strategies across applications in electric vehicles,consumer electronics,and grid-scale energy storage.展开更多
Based on the"three new"characteristics of new quality productivity,focusing on the systematic reshaping requirements of digital rural construction,a four-dimensional analysis framework of"subject-tool-o...Based on the"three new"characteristics of new quality productivity,focusing on the systematic reshaping requirements of digital rural construction,a four-dimensional analysis framework of"subject-tool-object-ecology"is constructed.Research has found that there are four major practical challenges in the process of empowering new quality productivity:a shortage of new types of workers and a mismatch in their skills among labor subjects;the labor tools are manifested as a mismatch between digital infrastructure and rural scenes;the labor objects are facing inertia obstacles in the transformation of industrial data;the external ecology lacks a systematic environment for element collaboration and effective governance.In response to the above difficulties,this paper proposes a systematic breakthrough path:by cultivating digital talents rooted in rural areas,creating truly useful digital tools,designing low threshold industrial transformation paths,and building a governance ecology of co construction and sharing,the coordinated efforts of technology,talent,industry,and environment are promoted,aiming to promote the rooting of new quality productivity in rural soil,and ultimately promoting the emergence of a new landscape of digital rural development with strong endogenous power.展开更多
With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy...With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.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
基金supported by the Fundamental Research Funds for the Central Universities(WK3420000020)the National Natural Science Foundation of China(42125402 and 42188101).
文摘Dual-comb spectroscopy(DCS)is one of the most promising technologies for ultra-long open-path multiplegreenhouse gas detection.Ultra-long open-path DCS has the potential to realize horizontal open-path links over hundredsof kilometers and vertical open-path links between satellites and the ground base.Under these extreme detection conditions,identifying an appropriate wavelength band that ensures both technical feasibility and a reasonable absorbance fortarget components is critical but currently lacks studies.In this work,we simulate transmission spectra under different detectionconfigurations to identify optimal wavelength bands for carbon dioxide(CO_(2))and methane(CH_(4))measurement.The simulation results show that the 1540 nm Watt-level high-power frequency combs developed are suitable for CO_(2)measurement in both horizontal and vertical ultra-long detection configurations.The results also suggest that developinghigh-power fiber amplifiers for 1630 nm and 1636 nm will facilitate CH_(4)measurement in horizontal and vertical ultra-longdetection configurations,respectively.The amplification at 1636 nm will be a future research focus,as it is expected to enablesimultaneous measurements of CH_(4),CO_(2),and water vapor in the vertical detection configuration.
基金supported by the National Natural Science Foundation of China(No.U2433214)。
文摘Shenzhen,a major city in southern China,has experienced rapid advancements in Unmanned Aerial Vehicle(UAV)technology,resulting in extensive logistics networks with thousands of daily flights.However,frequent disruptions due to its subtropical monsoon climate,including typhoons and gusty winds,present ongoing challenges.Despite the growing focus on operational costs and third-party risks,research on low-altitude urban wind fields remains scarce.This study addresses this gap by integrating wind field analysis into UAV path planning,introducing key innovations to the classical model.First,UAV wind resistance and turbulence constraints are analyzed,mapping high-wind-speed and turbulence-prone zones in the airspace.Second,wind dynamics are incorporated into path planning by considering airspeed and groundspeed variation,optimizing waypoint selection and flight speed adjustments to improve overall energy efficiency.Additionally,a wind-aware Theta*algorithm is proposed,leveraging wind vectors to expedite search process,while Computational Fluid Dynamics(CFD)techniques are employed to calculate wind fields.A case study of Shenzhen,examining wind patterns over the past decade,demonstrates a 6.23%improvement in groundspeed and a 7.69%reduction in energy consumption compared to wind-agnostic models.This framework advances UAV logistics by enhancing route safety and energy efficiency,contributing to more cost-effective operations.
基金National Natural Science Foundation of China(32301712)Natural Science Foundation of Jiangsu Province(BK20230548,BK20250876)+2 种基金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.
文摘Discussions on Chinese modernization are offering African countries both conceptual inspiration and practical references as they explore their own sustainable development paths.
基金Financial supports from the National Natural Science Foundation of China(GrantNo.52374123&51974144)Project of Liaoning Provincial Department of Education(GrantNo.LJKZ0340)Liaoning Revitalization Talents Program(Grant No.XLYC2211085)are greatly acknowledged.
文摘Q-learning is a classical reinforcement learning method with broad applicability.It can respond effectively to environmental changes and provide flexible strategies,making it suitable for solving robot path-planning problems.However,Q-learning faces challenges in search and update efficiency.To address these issues,we propose an improved Q-learning(IQL)algorithm.We use an enhanced Ant Colony Optimization(ACO)algorithmto optimizeQtable initialization.We also introduce the UCH mechanism to refine the reward function and overcome the exploration dilemma.The IQL algorithm is extensively tested in three grid environments of different scales.The results validate the accuracy of themethod and demonstrate superior path-planning performance compared to traditional approaches.The algorithm reduces the number of trials required for convergence,improves learning efficiency,and enables faster adaptation to environmental changes.It also enhances stability and accuracy by reducing the standard deviation of trials to zero.On grid maps of different sizes,IQL achieves higher expected returns.Compared with the original Q-learning algorithm,IQL improves performance by 12.95%,18.28%,and 7.98% on 10*10,20*20,and 30*30 maps,respectively.The proposed algorithm has promising applications in robotics,path planning,intelligent transportation,aerospace,and game development.
基金supported by the National Natural Science Foundation of China(No.52500142)the National Postdoctoral Program for Innovative Talents under Grant Number BX20250334the China Postdoctoral Science Foundation(Certificate Number:2025M771285)。
文摘Directly burning methane for energy production wastes chemical potential and exacerbates CO_(2) emissions,while catalytic conversion into high-value fuel/chemicals provides economic and environmental sustainability.Photocatalytic CH_(4) conversion has emerged as a transformative technology,enabling selective oxidation under ambient conditions to directly synthesize value-added organic compounds.This addresses the dual challenges of climate mitigation and sustainable energy conversion.This review systematically examines the development of photocata lytic CH_(4) conversion,with three key dimensions.Firstly,we elucidate fundamental reaction mechanisms governing CH_(4) activation,emphasizing critical steps such as C-H bond scission via charge transfer,intermediate stabilization,and product desorption kinetics.Subsequently,we classify emerging photocata lytic pathways(partial oxidation,coupling,and reforming)and analyze material innovations.Finally,the challenges of the current photocata lytic CH_(4)conversion system and catalyst development were discussed,and perspectives were presented.The overarching objective of this work is to provide a comprehensive roadmap for the development of solardriven CH_(4) conversion systems that are aligned with global carbon neutrality goals.
基金Supported by the Autonomous Region-level Research and Practice Projects for New Engineering,New Medicine,New Agriculture,and New Humanities of Guangxi Department of Education(XNK202409)the Undergraduate Teaching Reform Project of Guangxi Higher Education(2024JGB332+1 种基金2024JGA304)the Guangxi Degree and Graduate Education Reform Project(JGY2025382).
文摘The deep integration of artificial intelligence technology and agricultural industry has pushed smart agriculture into a new stage of"AI+scenario",and put forward a transformation requirement for the talent cultivation of smart agriculture major in universities from"technology application"to"intelligent innovation".In response to the problems of insufficient AI integration,lack of contextualization,and insufficient collaboration between industry and education in the traditional"technology+"practical course system,this paper takes the smart agriculture major at Yulin Normal University as an example to construct a"AI+agriculture"practical course reconstruction framework and propose a four-dimensional transformation path of"goal-content-mode-evaluation".Through the practical exploration of modular curriculum design,scenario based practical design,integration of industry and education,and intelligent evaluation reform,a practical teaching system with local application-oriented university characteristics has been formed,providing a reference example for the cultivation of smart agriculture professionals under the background of new agricultural science.
基金Supported by the Yangtze University School-level Teaching Research Project(JY2024059,JY2021038,JY2022009).
文摘The advent of digital and intelligent era has brought profound impacts to higher education in terms of teaching models,learning resources,teacher-student roles,and instructional management.Among them,local universities with relatively weak resources but large scale are particularly noteworthy.A questionnaire survey on the current situation of informative teaching among teachers from local universities found that their overall awareness of informative teaching is positive,but their ability structure is uneven,especially in data-driven teaching evaluation and human-machine collaborative design,where there are obvious shortcomings.Research has revealed that the development of teachers'informative teaching ability is influenced by internal factors such as intrinsic motivation and data literacy,as well as external factors such as training systems and school support.Based on this,a systematic improvement path of"concept guidance-precise empowerment-environmental support-institutional guarantee"four-in-one is proposed to provide practical reference for promoting the professional development of teachers from local universities and the digital transformation of higher education.
基金Project supported by the National Natural Science Foundation of China(Nos.12232015 and12572106)the National Key R&D Program of China(Nos.2024YFB3408700,2024YFB3408701,2024YFB3408703)the Natural Science Foundation of Shaanxi Province of China(No.2023-JC-YB-073)。
文摘Topological phases are governed by lattice symmetries,yet how different symmetry-breaking paths(SBPs)affect topological transitions remains insufficiently understood.Most existing studies rely on a single SBP,and address only one bandgap,limiting independent control of multiple gaps.Here,we investigate multiple isolated Dirac points in a trefoil-knot-modified honeycomb lattice,and show that a single SBP generally inverts all relevant Dirac points simultaneously,whereas the tailored combinations of SBPs enable selective and programmable band inversion at targeted gaps.The excitation-dependent responses reveal strong modal selectivity.This capability is exploited to realize independently controllable multi-channel signal splitting,which is unattainable with a single SBP.The results enable SBPs as an effective design degree of freedom for programmable and reconfigurable topological elastic devices.
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supported by Ho Chi Minh City Open University,Vietnam under grant number E2024.02.1CD and Suan Sunandha Rajabhat University,Thailand.
文摘The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.
基金support from the Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(Grant No.KYCX24_2822)the Graduate Innovation Program of China University of Mining and Technology(Grant No.2024WLKXJ205)the National Natural Science Foundation of China(Grant No.52474157).
文摘Excavation causes stress redistribution and affects the stress path during the shearing process of rock.The shear strength of rock varies under different stress paths,and the presence of defects reduces the shear strength.To further investigate this phenomenon,this study investigates the shear behaviour of rocks with different shear surface integrities under the influenceof different stress paths through laboratory tests and numerical simulations.The results indicate that the shear strength depends on the stress path and a decrease in the shear surface integrity reduces the degree of dependence.The cohesion and friction angle of the Mohr‒Coulomb criterion decrease with weakening of the shear surface integrity.For different stress paths,the direct shear strength is always greater than that of other shear stress paths.The pattern of changes in the acoustic emission count and cumulative count indirectly reflectsthe above findings.Numerical simulations further indicate that the different principal stress states and normal suppression effects during the shearing process lead to changes in the factors of crack propagation,resulting in different mechanical behaviours under various stress paths.For rocks with different integrity levels,the main reason for the different path dependences of shear strength is that the size of the area affected by shear is different.Shear failure will concentrate on the shear plane when the normal inhibition effect is greater.This study explores the mechanism of rock shear behaviour,providing a theoretical basis for establishing more accurate constitutive models and strength criteria.
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support from the National Natural Science Foundation of China(Grant No.42407256)the State Key Laboratory of Hydraulics and Mountain River Engineering,China(Grant No.SKHL2113)the Sichuan Science and Technology Program(Grant No.2024YFHZ0341).
文摘This paper develops a semi-analytical solution for pile penetration in natural soft clays using the strain path method(SPM).The stress-strain behavior of soils is characterized by the S-CLAY1S model,which can capture the anisotropic evolution and destructuring nature of soft clays.By integrating the S-CLAY1S model into the theoretical framework of the SPM,a set of ordinary differential equations is formulated with respect to the vertical coordinate of soil particles.The distribution of excess pore water pressure(EPWP)following pile installation is approximated through one-dimensional(1D)radial integration around the pile shaft.The distribution of stresses and EPWP,along with the evolution of fabric anisotropy within the soil surrounding the pile,is presented to illustrate the response of pile penetration in natural soft clays.The proposed solution is validated against existing theoretical solutions using the SPM and cavity expansion method(CEM),along with experimental data.The findings demonstrate that the SPM reveals lower radial effective stresses and EPWP at the pile shaft than that of CEM.Pile penetration alters the soil's anisotropic properties,inducing rotational hardening and affecting post-installation stress distribution.Soil destructuration eliminates bonding among particles near the pile,resulting in a complete disruption of soil structure at the pile surface,which is particularly pronounced for higher initial soil structure ratios.Minimal variation was observed in the three principal stresses and shear stress on the cone side surface as the angle increased from 18°to 60°,except for a slight reduction in EPWP.
文摘Understanding how aging influences the thermal hazards of lithium-ion batteries(LIBs)is critical for enhancing their safety across a wide range of applications.This study systematically investigates the thermal runaway(TR)behavior of LIBs,with particular emphasis on combined-pathway aging,evaluated in terms of normalized usable capacity(U_(E)).Key thermal safety parameters,i.e.,TR triggering temperature,mass loss,and heat generation under diverse aging conditions,are quantified.To enable a fair comparison,thermal hazards are evaluated based on equivalent usable capacity,revealing that aged cells exhibit lower TR triggering temperatures and higher heat generation than fresh cells under thermal abuse with elevated thermal risks.Mechanistic analysis identifies lithium plating,solid electrolyte interphase(SEI)formation,and lithium depletion,particularly under high-temperature charging,as the dominant contributors to increased hazard.Using an aging-stressor matrix,a trade-off between high-C-rateinduced thermal instability and high-temperature-induced thermal stability is discovered and quantified,underscoring the strong dependence of thermal hazards on specific aging pathways.This work advances the fundamental understanding of aging-induced safety risks in LIBs and offers practical guidance for the development of safer battery systems,optimized charging protocols,and improved battery management strategies across applications in electric vehicles,consumer electronics,and grid-scale energy storage.
基金Supported by the Research Project on Social Science Development in Cangzhou City(2025271)the Research Project on Basic Research Funds for Hebei Provincial Universities in 2025(KY2025078).
文摘Based on the"three new"characteristics of new quality productivity,focusing on the systematic reshaping requirements of digital rural construction,a four-dimensional analysis framework of"subject-tool-object-ecology"is constructed.Research has found that there are four major practical challenges in the process of empowering new quality productivity:a shortage of new types of workers and a mismatch in their skills among labor subjects;the labor tools are manifested as a mismatch between digital infrastructure and rural scenes;the labor objects are facing inertia obstacles in the transformation of industrial data;the external ecology lacks a systematic environment for element collaboration and effective governance.In response to the above difficulties,this paper proposes a systematic breakthrough path:by cultivating digital talents rooted in rural areas,creating truly useful digital tools,designing low threshold industrial transformation paths,and building a governance ecology of co construction and sharing,the coordinated efforts of technology,talent,industry,and environment are promoted,aiming to promote the rooting of new quality productivity in rural soil,and ultimately promoting the emergence of a new landscape of digital rural development with strong endogenous power.
文摘With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.
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.
