The electromagnetic wave absorption of silicon carbide nanowires is improved by their uniform and diverse cross-structures.This study introduces a sustainable and high value-added method for synthesizing silicon carbi...The electromagnetic wave absorption of silicon carbide nanowires is improved by their uniform and diverse cross-structures.This study introduces a sustainable and high value-added method for synthesizing silicon carbide nanowires using lignite and waste silicon powder as raw materials through carbothermal reduction.The staggered structure of nanowires promotes the creation of interfacial polarization,impedance matching,and multiple loss mechanisms,leading to enhanced electromagnetic absorption performance.The silicon carbide nanowires demonstrate outstanding electromagnetic absorption capabilities with the minimum reflection loss of-48.09 d B at10.08 GHz and an effective absorption bandwidth(the reflection loss less than-10 d B)ranging from 8.54 to 16.68 GHz with a thickness of 2.17 mm.This research presents an innovative approach for utilizing solid waste in an environmentally friendly manner to produce broadband silicon carbide composite absorbers.展开更多
Value-added evaluation focuses on individual student growth by tracking changes in academic performance,skills,literacy,etc.,at different time points.It weakens horizontal comparisons and emphasizes vertical progress ...Value-added evaluation focuses on individual student growth by tracking changes in academic performance,skills,literacy,etc.,at different time points.It weakens horizontal comparisons and emphasizes vertical progress to more fairly reflect educational effectiveness.This evaluation method is particularly suitable for vocational education,effectively motivating students’learning enthusiasm and enhancing their self-confidence.Foreign research is represented by the Tennessee Value-Added Assessment System(TVAAS),widely used in evaluating school quality and teacher performance.Domestic research currently focuses on the theoretical construction,model establishment,optimization,and practical application of value-added evaluation,still facing significant challenges in data collection comprehensiveness and model adaptability.Aiming at current issues,this study focuses on exploring the application of artificial intelligence large models in student value-added evaluation from an evidence-based perspective,committed to constructing an innovative evidence-based value-added evaluation system.It aims to achieve precise assessment of students’learning effect“net value-added”through multi-source data collection,intelligent analysis,and personalized feedback.The system integrates outcome evaluation,process evaluation,value-added evaluation,and comprehensive evaluation to form a“four-in-one”dynamic evaluation framework,considering students’starting points,process performance,and final achievements.In the future,value-added evaluation needs to further expand the assessment of non-academic dimensions(such as professional literacy and social-emotional skills)and explore the application of non-linear models to promote the deepening and innovation of educational evaluation reform.展开更多
International trade research has long sought to investigate how manufacturers can upgrade within global value chains and escape the“low-end trap”.This paper examines how collaborative innovation can facilitate this ...International trade research has long sought to investigate how manufacturers can upgrade within global value chains and escape the“low-end trap”.This paper examines how collaborative innovation can facilitate this ascent,using an undirected weighted network of joint patent applications and firm-level data.By analyzing the network’s structural characteristics and its evolution,we explore the mechanisms through which collaboration drives the rise of manufacturing enterprises within global value chains.Our findings show that:(1)China’s rapidly expanding collaborative innovation network features a distinct“core-periphery”structure,with leading firms,universities,and government research institutions at its center.(2)By strengthening market power and enabling firms to take on more advanced production,collaborative innovation contributes to a higher domestic value-added rate in exports.(3)Heterogeneity analysis reveals that the impact of collaborative innovation on moving up the value chain is particularly evident for firms with strong production and technology absorption capabilities,those positioned lower in the value chain,and those facing fewer trade barriers.展开更多
Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods...Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods.The research adopts the method of combining theoretical analysis and practical application,and designs the evidence-based value-added evaluation framework,which includes the core elements of a multi-source heterogeneous data acquisition and processing system,a value-added evaluation agent based on a large model,and an evaluation implementation and application mechanism.Through empirical research verification,the evaluation system has remarkable effects in improving learning participation,promoting ability development,and supporting teaching decision-making,and provides a theoretical reference and practical path for educational evaluation reform in the new era.The research shows that the evidence-based value-added evaluation system based on data-driven can reflect students’actual progress more fairly and objectively by accurately measuring the difference in starting point and development range of students,and provide strong support for the realization of high-quality education development.展开更多
As the enlightenment stage of students’Chinese learning,primary school Chinese education plays a key role in cultivating students’language ability,thinking development,and humanistic literacy.Value-added evaluation,...As the enlightenment stage of students’Chinese learning,primary school Chinese education plays a key role in cultivating students’language ability,thinking development,and humanistic literacy.Value-added evaluation,as an evaluation method that focuses on the changes in students’individual development and attaches importance to the learning process,has gradually attracted attention in the application of primary school Chinese education.This paper first analyzes the problems existing in the current implementation of value-added evaluation in primary school Chinese,and then explores the countermeasures to improve the effectiveness of value-added evaluation in primary school Chinese from the aspects of evaluation concept,evaluation content,evaluation method,evaluation subject,and application of evaluation results.The purpose is to provide strong support for the improvement of primary school Chinese teaching quality and the all-round development of students.展开更多
Maize value-added products play a crucial role in reducing post-harvest losses, enhancing food security, and generating income. While extensive research has focused on maize production in Cameroon, the exploration of ...Maize value-added products play a crucial role in reducing post-harvest losses, enhancing food security, and generating income. While extensive research has focused on maize production in Cameroon, the exploration of its value-added products and their profitability in the North-West Region remains underexplored. This study examined the profitability of maize value-added products in Mezam Division, with the objectives to: 1) identify various maize-based products, 2) assess the diversity of these products, 3) conduct a cost-benefit analysis of selected products, 4) examine the relationship between profitability and product diversity, and 5) identify key constraints impacting profitability. To achieve these objectives, structured questionnaires were administered to 500 small-scale maize entrepreneurs randomly selected from five subdivisions. Descriptive statistics were used to analyze objective 1 and 5, while the Shannon Diversity Index was employed to assess product diversity. Additionally, a cost-benefit analysis was conducted on four selected products namely pap, parched corn, peeled parboiled corn, and corn beer, and a correlation analysis was used to examine objective 4. In total, 13 maize value-added products were identified, with a diversity index of 4.4. The total cost of processing the four selected products per entrepreneur using 18 kg of maize per product was FCFA 83631.5 (US $132.75), while the total revenue was FCFA 121864.5 (US $193.43), resulting in an economic profit of FCFA 38,233 (US $60.69). Pap emerged as the most profitable product, with an economic profit of FCFA 27,875 (US $44.24), while corn beer was the least profitable, with an economic profit of FCFA 2133.46 (US $3.39). The correlation analysis revealed a strong negative relationship between product diversity and profitability (r = −0.91), indicating that entrepreneurs can maximize profitability by focusing on a few high-demand products like pap and parched corn. Key constraints to profitability included fluctuating market prices, high production costs, limited access to finance, and inadequate storage facilities. Despite these challenges, our findings indicate that maize value addition is profitable in Mezam Division. Entrepreneurs can leverage this data for informed decision-making and future investments. It is recommended that the government promote maize value addition and provide financial support for modern processing equipment to boost profitability and income generation.展开更多
Electrochemical synthesis of value-added chemicals represents a promising approach to address multidisciplinary demands.This technology establishes direct pathways for electricity-to-chemical conversion while signific...Electrochemical synthesis of value-added chemicals represents a promising approach to address multidisciplinary demands.This technology establishes direct pathways for electricity-to-chemical conversion while significantly reducing the carbon footprint of chemical manufacturing.It simultaneously optimizes chemical energy storage and grid management,offering sustainable solutions for renewable energy utilization and overcoming geographical constraints in energy distribution.As a critical nexus between renewable energy and green chemistry,electrochemical synthesis serves dual roles in energy transformation and chemical production,emerging as a vital component in developing carbon-neutral circular economies.Focusing on key small molecules(H_(2)O,CO_(2),N_(2),O_(2)),this comment examines fundamental scientific challenges and practical barriers in electrocatalytic conversion processes,bridging laboratory innovations with industrial-scale implementation.展开更多
In the context of urban-rural integration development in China,the distribution of value-added income of rural land collective ownership is related to the protection of farmers rights and interests and the specific im...In the context of urban-rural integration development in China,the distribution of value-added income of rural land collective ownership is related to the protection of farmers rights and interests and the specific implementation of rural revitalization strategy.Based on the entry of rural collectively-owned construction land into the market and the compensation system for land expropriation,this paper discusses in detail the distribution of value-added income of rural land collective ownership,analyzes the current situation,existing problems and causes of the current distribution mechanism,and puts forward countermeasures and suggestions for optimizing the distribution mechanism.Through literature research and case analysis,this paper reveals the unfair phenomenon in the distribution of value-added income of rural land,and discusses the roles and responsibilities of government,collective organizations and individual farmers in the distribution of income.The results show that establishing a fair and reasonable income distribution mechanism,strengthening the construction of laws and regulations,improving farmers participation and protecting their rights and interests are the key to optimizing the distribution of rural land value-added income.In addition,it is expected that this paper will provide some theoretical basis and practical guidance for improving the distribution mechanism of value-added income of rural land collective ownership.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
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
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 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
With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper pro...With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper proposes an intelligent path planning framework based on grid map modeling.First,an improved Safe and Smooth A*(SSA*)algorithm is employed for global path planning.By incorporating obstacle expansion and cornerpoint optimization,the proposed SSA*enhances the safety and smoothness of the planned path.Then,a Partitioned Dynamic Window Approach(PDWA)is integrated for local planning,which is triggered when dynamic or sudden static obstacles appear,enabling real-time obstacle avoidance and path adjustment.A unified objective function is constructed,considering path length,safety,and smoothness comprehensively.Multiple simulation experiments are conducted on typical port grid maps.The results demonstrate that the improved SSA*significantly reduces the number of expanded nodes and computation time in static environmentswhile generating smoother and safer paths.Meanwhile,the PDWA exhibits strong real-time performance and robustness in dynamic scenarios,achieving shorter paths and lower planning times compared to other graph search algorithms.The proposedmethodmaintains stable performance across maps of different scales and various port scenarios,verifying its practicality and potential for wider application.展开更多
Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always ...Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always influenced by external factors,making the original path planning solution ineffective.In this paper,the multi-depot multi-UAV path planning problem with uncertain flight time is modeled as a robust optimization model with a budget uncertainty set.Then,the robust optimization model is transformed into a mixed integer linear programming model by the strong duality theorem,which makes the problem easy to solve.To effectively solve large-scale instances,a simulated annealing algorithm with a robust feasibility check(SA-RFC)is developed.The numerical experiment shows that the SA-RFC can find high-quality solutions within a few seconds.Moreover,the effect of the task location distribution,depot counts,and variations in robustness parameters on the robust optimization solution is analyzed by using Monte Carlo experiments.The results demonstrate that the proposed robust model can effectively reduce the risk of the UAV failing to return to the depot without significantly compromising the profit.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
A safe and reliable path planning algorithm is fundamental for unmanned surface vehicles(USVs)to perform autonomous navigation tasks.However,a single global or local planning strategy cannot fully meet the requirement...A safe and reliable path planning algorithm is fundamental for unmanned surface vehicles(USVs)to perform autonomous navigation tasks.However,a single global or local planning strategy cannot fully meet the requirements of complex maritime environments.Global planning alone cannot effectively handle dynamic obstacles,while local planning alone may fall into local optima.To address these issues,this paper proposes a multi-dynamic-obstacle avoidance path planning method that integrates an improved A^(*)algorithm with the dynamic window approach(DWA).The traditional A^(*)algorithm often generates paths that are too close to obstacle boundaries and contain excessive turning points,whereas the traditional DWA tends to skirt densely clustered obstacles,resulting in longer routes and insufficient dynamic obstacle avoidance.To overcome these limitations,improved versions of both algorithms are developed.Key points extracted from the optimized A^(*)path are used as intermediate start-destination pairs for the improved DWA,and the weights of the DWA evaluation function are adjusted to achieve effective fusion.Furthermore,a multi-dynamic-obstacle avoidance strategy is designed for complex navigation scenarios.Simulation results demonstrate that the USV can adaptively switch between dynamic obstacle avoidance and path tracking based on obstacle distribution,validating the effectiveness of the proposed method.展开更多
Pioneering initiatives in Yunnan Province are leveraging intangible cultural heritage to foster practical skills and create flexible employment,opening a sustainable path to prosperity and rural revitalization for peo...Pioneering initiatives in Yunnan Province are leveraging intangible cultural heritage to foster practical skills and create flexible employment,opening a sustainable path to prosperity and rural revitalization for people with disabilities.ACROSS China,provinces and municipalities have adopted a range of initiatives,based on local conditions to facilitate employment for people with disabilities.In southwest China’s Yunnan Province,home to many ethnic minorities,efforts have focused on integrating employment for disabled people with rural revitalization.展开更多
Most existing path planning approaches rely on discrete expansions or localized heuristics that can lead to extended re-planning,inefficient detours,and limited adaptability to complex obstacle distributions.These iss...Most existing path planning approaches rely on discrete expansions or localized heuristics that can lead to extended re-planning,inefficient detours,and limited adaptability to complex obstacle distributions.These issues are particularly pronounced when navigating cluttered or large-scale environments that demand both global coverage and smooth trajectory generation.To address these challenges,this paper proposes a Wave Water Simulator(WWS)algorithm,leveraging a physically motivated wave equation to achieve inherently smooth,globally consistent path planning.In WWS,wavefront expansions naturally identify safe corridors while seamlessly avoiding local minima,and selective corridor focusing reduces computational overhead in large or dense maps.Comprehensive simulations and real-world validations-encompassing both indoor and outdoor scenarios-demonstrate that WWS reduces path length by 2%-13%compared to conventional methods,while preserving gentle curvature and robust obstacle clearance.Furthermore,WWS requires minimal parameter tuning across diverse domains,underscoring its broad applicability to warehouse robotics,field operations,and autonomous service vehicles.These findings confirm that the proposed wave-based framework not only bridges the gap between local heuristics and global coverage but also sets a promising direction for future extensions toward dynamic obstacle scenarios and multi-agent coordination.展开更多
基金supported by the National Natural Science Foundation of China(No.52436008)the Inner Mongolia Science and Technology Projects,China(Nos.JMRHZX20210003 and 2023YFCY0009)+3 种基金the Huaneng Group Co Ltd.,China(No.HNKJ23-H50)the National Natural Science Foundation of China(No.22408044)the China Postdoctoral Science Foundation(No.2024M761877)the National Key R&D Program of China(No.SQ2024YFD2200039)。
文摘The electromagnetic wave absorption of silicon carbide nanowires is improved by their uniform and diverse cross-structures.This study introduces a sustainable and high value-added method for synthesizing silicon carbide nanowires using lignite and waste silicon powder as raw materials through carbothermal reduction.The staggered structure of nanowires promotes the creation of interfacial polarization,impedance matching,and multiple loss mechanisms,leading to enhanced electromagnetic absorption performance.The silicon carbide nanowires demonstrate outstanding electromagnetic absorption capabilities with the minimum reflection loss of-48.09 d B at10.08 GHz and an effective absorption bandwidth(the reflection loss less than-10 d B)ranging from 8.54 to 16.68 GHz with a thickness of 2.17 mm.This research presents an innovative approach for utilizing solid waste in an environmentally friendly manner to produce broadband silicon carbide composite absorbers.
基金Artificial Intelligence Education Research Project of Shandong Provincial Audio-Visual Education Center“Exploration of the Application of Large-scale AI Models in Student Value-added Evaluation from an Evidence-based Perspective”(SDDJ202501035)。
文摘Value-added evaluation focuses on individual student growth by tracking changes in academic performance,skills,literacy,etc.,at different time points.It weakens horizontal comparisons and emphasizes vertical progress to more fairly reflect educational effectiveness.This evaluation method is particularly suitable for vocational education,effectively motivating students’learning enthusiasm and enhancing their self-confidence.Foreign research is represented by the Tennessee Value-Added Assessment System(TVAAS),widely used in evaluating school quality and teacher performance.Domestic research currently focuses on the theoretical construction,model establishment,optimization,and practical application of value-added evaluation,still facing significant challenges in data collection comprehensiveness and model adaptability.Aiming at current issues,this study focuses on exploring the application of artificial intelligence large models in student value-added evaluation from an evidence-based perspective,committed to constructing an innovative evidence-based value-added evaluation system.It aims to achieve precise assessment of students’learning effect“net value-added”through multi-source data collection,intelligent analysis,and personalized feedback.The system integrates outcome evaluation,process evaluation,value-added evaluation,and comprehensive evaluation to form a“four-in-one”dynamic evaluation framework,considering students’starting points,process performance,and final achievements.In the future,value-added evaluation needs to further expand the assessment of non-academic dimensions(such as professional literacy and social-emotional skills)and explore the application of non-linear models to promote the deepening and innovation of educational evaluation reform.
基金supported by the National Social Science Fund of China(NSSFC)“Research on Collaborative Innovation and Global Value Chain Upgrading in Manufacturing”(Grant No.23CJL019)“Research on the Advantages of Ultra-Large-Scale Market and the Construction of Modern Industrial System”(Grant No.23&ZD041).
文摘International trade research has long sought to investigate how manufacturers can upgrade within global value chains and escape the“low-end trap”.This paper examines how collaborative innovation can facilitate this ascent,using an undirected weighted network of joint patent applications and firm-level data.By analyzing the network’s structural characteristics and its evolution,we explore the mechanisms through which collaboration drives the rise of manufacturing enterprises within global value chains.Our findings show that:(1)China’s rapidly expanding collaborative innovation network features a distinct“core-periphery”structure,with leading firms,universities,and government research institutions at its center.(2)By strengthening market power and enabling firms to take on more advanced production,collaborative innovation contributes to a higher domestic value-added rate in exports.(3)Heterogeneity analysis reveals that the impact of collaborative innovation on moving up the value chain is particularly evident for firms with strong production and technology absorption capabilities,those positioned lower in the value chain,and those facing fewer trade barriers.
基金This paper is the research result of“Research on Innovation of Evidence-Based Teaching Paradigm in Vocational Education under the Background of New Quality Productivity”(2024JXQ176)the Shandong Province Artificial Intelligence Education Research Project(SDDJ202501035),which explores the application of artificial intelligence big models in student value-added evaluation from an evidence-based perspective。
文摘Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods.The research adopts the method of combining theoretical analysis and practical application,and designs the evidence-based value-added evaluation framework,which includes the core elements of a multi-source heterogeneous data acquisition and processing system,a value-added evaluation agent based on a large model,and an evaluation implementation and application mechanism.Through empirical research verification,the evaluation system has remarkable effects in improving learning participation,promoting ability development,and supporting teaching decision-making,and provides a theoretical reference and practical path for educational evaluation reform in the new era.The research shows that the evidence-based value-added evaluation system based on data-driven can reflect students’actual progress more fairly and objectively by accurately measuring the difference in starting point and development range of students,and provide strong support for the realization of high-quality education development.
基金Shandong Provincial Education and Teaching Research Topic“Research on Student Value-Added Evaluation for Promoting Deep Learning”(2023JXY500)。
文摘As the enlightenment stage of students’Chinese learning,primary school Chinese education plays a key role in cultivating students’language ability,thinking development,and humanistic literacy.Value-added evaluation,as an evaluation method that focuses on the changes in students’individual development and attaches importance to the learning process,has gradually attracted attention in the application of primary school Chinese education.This paper first analyzes the problems existing in the current implementation of value-added evaluation in primary school Chinese,and then explores the countermeasures to improve the effectiveness of value-added evaluation in primary school Chinese from the aspects of evaluation concept,evaluation content,evaluation method,evaluation subject,and application of evaluation results.The purpose is to provide strong support for the improvement of primary school Chinese teaching quality and the all-round development of students.
文摘Maize value-added products play a crucial role in reducing post-harvest losses, enhancing food security, and generating income. While extensive research has focused on maize production in Cameroon, the exploration of its value-added products and their profitability in the North-West Region remains underexplored. This study examined the profitability of maize value-added products in Mezam Division, with the objectives to: 1) identify various maize-based products, 2) assess the diversity of these products, 3) conduct a cost-benefit analysis of selected products, 4) examine the relationship between profitability and product diversity, and 5) identify key constraints impacting profitability. To achieve these objectives, structured questionnaires were administered to 500 small-scale maize entrepreneurs randomly selected from five subdivisions. Descriptive statistics were used to analyze objective 1 and 5, while the Shannon Diversity Index was employed to assess product diversity. Additionally, a cost-benefit analysis was conducted on four selected products namely pap, parched corn, peeled parboiled corn, and corn beer, and a correlation analysis was used to examine objective 4. In total, 13 maize value-added products were identified, with a diversity index of 4.4. The total cost of processing the four selected products per entrepreneur using 18 kg of maize per product was FCFA 83631.5 (US $132.75), while the total revenue was FCFA 121864.5 (US $193.43), resulting in an economic profit of FCFA 38,233 (US $60.69). Pap emerged as the most profitable product, with an economic profit of FCFA 27,875 (US $44.24), while corn beer was the least profitable, with an economic profit of FCFA 2133.46 (US $3.39). The correlation analysis revealed a strong negative relationship between product diversity and profitability (r = −0.91), indicating that entrepreneurs can maximize profitability by focusing on a few high-demand products like pap and parched corn. Key constraints to profitability included fluctuating market prices, high production costs, limited access to finance, and inadequate storage facilities. Despite these challenges, our findings indicate that maize value addition is profitable in Mezam Division. Entrepreneurs can leverage this data for informed decision-making and future investments. It is recommended that the government promote maize value addition and provide financial support for modern processing equipment to boost profitability and income generation.
文摘Electrochemical synthesis of value-added chemicals represents a promising approach to address multidisciplinary demands.This technology establishes direct pathways for electricity-to-chemical conversion while significantly reducing the carbon footprint of chemical manufacturing.It simultaneously optimizes chemical energy storage and grid management,offering sustainable solutions for renewable energy utilization and overcoming geographical constraints in energy distribution.As a critical nexus between renewable energy and green chemistry,electrochemical synthesis serves dual roles in energy transformation and chemical production,emerging as a vital component in developing carbon-neutral circular economies.Focusing on key small molecules(H_(2)O,CO_(2),N_(2),O_(2)),this comment examines fundamental scientific challenges and practical barriers in electrocatalytic conversion processes,bridging laboratory innovations with industrial-scale implementation.
文摘In the context of urban-rural integration development in China,the distribution of value-added income of rural land collective ownership is related to the protection of farmers rights and interests and the specific implementation of rural revitalization strategy.Based on the entry of rural collectively-owned construction land into the market and the compensation system for land expropriation,this paper discusses in detail the distribution of value-added income of rural land collective ownership,analyzes the current situation,existing problems and causes of the current distribution mechanism,and puts forward countermeasures and suggestions for optimizing the distribution mechanism.Through literature research and case analysis,this paper reveals the unfair phenomenon in the distribution of value-added income of rural land,and discusses the roles and responsibilities of government,collective organizations and individual farmers in the distribution of income.The results show that establishing a fair and reasonable income distribution mechanism,strengthening the construction of laws and regulations,improving farmers participation and protecting their rights and interests are the key to optimizing the distribution of rural land value-added income.In addition,it is expected that this paper will provide some theoretical basis and practical guidance for improving the distribution mechanism of value-added income of rural land collective ownership.
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 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With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper proposes an intelligent path planning framework based on grid map modeling.First,an improved Safe and Smooth A*(SSA*)algorithm is employed for global path planning.By incorporating obstacle expansion and cornerpoint optimization,the proposed SSA*enhances the safety and smoothness of the planned path.Then,a Partitioned Dynamic Window Approach(PDWA)is integrated for local planning,which is triggered when dynamic or sudden static obstacles appear,enabling real-time obstacle avoidance and path adjustment.A unified objective function is constructed,considering path length,safety,and smoothness comprehensively.Multiple simulation experiments are conducted on typical port grid maps.The results demonstrate that the improved SSA*significantly reduces the number of expanded nodes and computation time in static environmentswhile generating smoother and safer paths.Meanwhile,the PDWA exhibits strong real-time performance and robustness in dynamic scenarios,achieving shorter paths and lower planning times compared to other graph search algorithms.The proposedmethodmaintains stable performance across maps of different scales and various port scenarios,verifying its practicality and potential for wider application.
基金supported by the National Natural Science Foundation of China(72571094,72271076,71871079)。
文摘Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always influenced by external factors,making the original path planning solution ineffective.In this paper,the multi-depot multi-UAV path planning problem with uncertain flight time is modeled as a robust optimization model with a budget uncertainty set.Then,the robust optimization model is transformed into a mixed integer linear programming model by the strong duality theorem,which makes the problem easy to solve.To effectively solve large-scale instances,a simulated annealing algorithm with a robust feasibility check(SA-RFC)is developed.The numerical experiment shows that the SA-RFC can find high-quality solutions within a few seconds.Moreover,the effect of the task location distribution,depot counts,and variations in robustness parameters on the robust optimization solution is analyzed by using Monte Carlo experiments.The results demonstrate that the proposed robust model can effectively reduce the risk of the UAV failing to return to the depot without significantly compromising the profit.
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supported by the National Nature Science Foundation of China(62203299,62373246,62388101)the Research Fund of State Key Laboratory of Deep-Sea Manned Vehicles(2024SKLDMV04)+1 种基金the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University(SL2023MS007)the Startup Fund for Young Faculty at SJTU(24X010502929)。
文摘A safe and reliable path planning algorithm is fundamental for unmanned surface vehicles(USVs)to perform autonomous navigation tasks.However,a single global or local planning strategy cannot fully meet the requirements of complex maritime environments.Global planning alone cannot effectively handle dynamic obstacles,while local planning alone may fall into local optima.To address these issues,this paper proposes a multi-dynamic-obstacle avoidance path planning method that integrates an improved A^(*)algorithm with the dynamic window approach(DWA).The traditional A^(*)algorithm often generates paths that are too close to obstacle boundaries and contain excessive turning points,whereas the traditional DWA tends to skirt densely clustered obstacles,resulting in longer routes and insufficient dynamic obstacle avoidance.To overcome these limitations,improved versions of both algorithms are developed.Key points extracted from the optimized A^(*)path are used as intermediate start-destination pairs for the improved DWA,and the weights of the DWA evaluation function are adjusted to achieve effective fusion.Furthermore,a multi-dynamic-obstacle avoidance strategy is designed for complex navigation scenarios.Simulation results demonstrate that the USV can adaptively switch between dynamic obstacle avoidance and path tracking based on obstacle distribution,validating the effectiveness of the proposed method.
文摘Pioneering initiatives in Yunnan Province are leveraging intangible cultural heritage to foster practical skills and create flexible employment,opening a sustainable path to prosperity and rural revitalization for people with disabilities.ACROSS China,provinces and municipalities have adopted a range of initiatives,based on local conditions to facilitate employment for people with disabilities.In southwest China’s Yunnan Province,home to many ethnic minorities,efforts have focused on integrating employment for disabled people with rural revitalization.
文摘Most existing path planning approaches rely on discrete expansions or localized heuristics that can lead to extended re-planning,inefficient detours,and limited adaptability to complex obstacle distributions.These issues are particularly pronounced when navigating cluttered or large-scale environments that demand both global coverage and smooth trajectory generation.To address these challenges,this paper proposes a Wave Water Simulator(WWS)algorithm,leveraging a physically motivated wave equation to achieve inherently smooth,globally consistent path planning.In WWS,wavefront expansions naturally identify safe corridors while seamlessly avoiding local minima,and selective corridor focusing reduces computational overhead in large or dense maps.Comprehensive simulations and real-world validations-encompassing both indoor and outdoor scenarios-demonstrate that WWS reduces path length by 2%-13%compared to conventional methods,while preserving gentle curvature and robust obstacle clearance.Furthermore,WWS requires minimal parameter tuning across diverse domains,underscoring its broad applicability to warehouse robotics,field operations,and autonomous service vehicles.These findings confirm that the proposed wave-based framework not only bridges the gap between local heuristics and global coverage but also sets a promising direction for future extensions toward dynamic obstacle scenarios and multi-agent coordination.
