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
The electrocatalytic oxidation of ethylene glycol(EG)into high-value chemicals like glycolic acid(GA)is a crucial step for upcycling waste plastics.However,catalyst deactivation and low selectivity pose significant ch...The electrocatalytic oxidation of ethylene glycol(EG)into high-value chemicals like glycolic acid(GA)is a crucial step for upcycling waste plastics.However,catalyst deactivation and low selectivity pose significant challenges.This work presents the low-coordination PtBi nanosheets(LC-PtBi NSs),featuring a unique amorphous-crystalline heterostructure with a low coordination number of 2.3-2.5.They can exhibit exceptional mass activity(8.3 A mg_(Pt)^(-1))and stability(maintaining 88.7%of initial activity after running for 3600 s)of the EG oxidation reaction(EGOR).They also achieve over 90%apparent selectivity for EG-to-GA conversion at low potentials(<0.7 V vs.RHE)and even more than 100-h continuous electrolysis.Density fu nctional theory(DFT)calculations reveal that the low-coordination PtBi heterogeneous interface is responsible for the high coverage of OH_(ad) species and weakened adsorption of carbonaceous intermediates on LC-PtBi NSs,thereby promoting the direct oxidation of C_(2) intermediates to GA.This work demonstrates a strategy of doping-mediated catalytic interface regulation and electron density rearrangement,offering insights for designing efficient Pt-based electrocatalysts toward selective oxidation of small molecules.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
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
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
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
Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combinin...Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.展开更多
To explore the material basis and mechanisms of the anti-inflammatory effects of Hibiscus mutabilis L..The active ingredients and potential targets of Hibiscus mutabilis L.were obtained through the literature review a...To explore the material basis and mechanisms of the anti-inflammatory effects of Hibiscus mutabilis L..The active ingredients and potential targets of Hibiscus mutabilis L.were obtained through the literature review and SwissADME platform.Genes related to the inflammation were collected using Genecards and OMIM databases,and the intersection genes were submitted on STRING and DAVID websites.Then,the protein interaction network(PPI),gene ontology(GO)and pathway(KEGG)were analyzed.Cytoscape 3.7.2 software was used to construct the“Hibiscus mutabilis L.-active ingredient-target-inflammation”network diagram,and AutoDockTools-1.5.6 software was used for the molecular docking verification.The antiinflammatory effect of Hibiscus mutabilis L.active ingredient was verified by the RAW264.7 inflammatory cell model.The results showed that 11 active components and 94 potential targets,1029 inflammatory targets and 24 intersection targets were obtained from Hibiscus mutabilis L..The key anti-inflammatory active ingredients of Hibiscus mutabilis L.are quercetin,apigenin and luteolin.Its action pathway is mainly related to NF-κB,cancer pathway and TNF signaling pathway.Cell experiments showed that total flavonoids of Hibiscus mutabilis L.could effectively inhibit the expression of tumor necrosis factor(TNF-α),interleukin 8(IL-8)and epidermal growth factor receptor(EGFR)in LPS-induced RAW 264.7 inflammatory cells.It also downregulates the phosphorylation of human nuclear factor ĸB inhibitory protein α(IĸBα)and NF-κB p65 subunit protein(p65).Overall,the anti-inflammatory effect of Hibiscus mutabilis L.is related to many active components,many signal pathways and targets,which provides a theoretical basis for its further development and application.展开更多
Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)t...Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.展开更多
Networked predictive control(NPC) has gained significant attention in recent years for its ability to effectively and actively address communication constraints in networked control systems(NCSs),such as network-induc...Networked predictive control(NPC) has gained significant attention in recent years for its ability to effectively and actively address communication constraints in networked control systems(NCSs),such as network-induced delays,packet dropouts,and packet disorders.Despite significant advancements,the increasing complexity and dynamism of network environments,along with the growing complexity of systems,pose new challenges for NPC.These challenges include difficulties in system modeling,cyber attacks,component faults,limited network bandwidth,and the necessity for distributed collaboration.This survey aims to provide a comprehensive review of NPC strategies.It begins with a summary of the primary challenges faced by NCSs,followed by an introduction to the control structure and core concepts of NPC.The survey then discusses several typical NPC schemes and examines their extensions in the areas of secure control,fault-tolerant control,distributed coordinated control,and event-triggered control.Moreover,it reviews notable works that have implemented these schemes.Finally,the survey concludes by exploring typical applications of NPC schemes and highlighting several challenging issues that could guide future research efforts.展开更多
Identifying the community structure of complex networks is crucial to extracting insights and understanding network properties.Although several community detection methods have been proposed,many are unsuitable for so...Identifying the community structure of complex networks is crucial to extracting insights and understanding network properties.Although several community detection methods have been proposed,many are unsuitable for social networks due to significant limitations.Specifically,most approaches depend mainly on user-user structural links while overlooking service-centric,semantic,and multi-attribute drivers of community formation,and they also lack flexible filtering mechanisms for large-scale,service-oriented settings.Our proposed approach,called community discovery-based service(CDBS),leverages user profiles and their interactions with consulted web services.The method introduces a novel similarity measure,global similarity interaction profile(GSIP),which goes beyond typical similarity measures by unifying user and service profiles for all attributes types into a coherent representation,thereby clarifying its novelty and contribution.It applies multiple filtering criteria related to user attributes,accessed services,and interaction patterns.Experimental comparisons against Louvain,Hierarchical Agglomerative Clustering,Label Propagation and Infomap show that CDBS reveals the higher performance as it achieves 0.74 modularity,0.13 conductance,0.77 coverage,and significantly fast response time of 9.8 s,even with 10,000 users and 400 services.Moreover,community discoverybased service consistently detects a larger number of communities with distinct topics of interest,underscoring its capacity to generate detailed and efficient structures in complex networks.These results confirm both the efficiency and effectiveness of the proposed method.Beyond controlled evaluation,communities discovery based service is applicable to targeted recommendations,group-oriented marketing,access control,and service personalization,where communities are shaped not only by user links but also by service engagement.展开更多
With the growing complexity and decentralization of network systems,the attack surface has expanded,which has led to greater concerns over network threats.In this context,artificial intelligence(AI)-based network intr...With the growing complexity and decentralization of network systems,the attack surface has expanded,which has led to greater concerns over network threats.In this context,artificial intelligence(AI)-based network intrusion detection systems(NIDS)have been extensively studied,and recent efforts have shifted toward integrating distributed learning to enable intelligent and scalable detection mechanisms.However,most existing works focus on individual distributed learning frameworks,and there is a lack of systematic evaluations that compare different algorithms under consistent conditions.In this paper,we present a comprehensive evaluation of representative distributed learning frameworks—Federated Learning(FL),Split Learning(SL),hybrid collaborative learning(SFL),and fully distributed learning—in the context of AI-driven NIDS.Using recent benchmark intrusion detection datasets,a unified model backbone,and controlled distributed scenarios,we assess these frameworks across multiple criteria,including detection performance,communication cost,computational efficiency,and convergence behavior.Our findings highlight distinct trade-offs among the distributed learning frameworks,demonstrating that the optimal choice depends strongly on systemconstraints such as bandwidth availability,node resources,and data distribution.This work provides the first holistic analysis of distributed learning approaches for AI-driven NIDS and offers practical guidelines for designing secure and efficient intrusion detection systems in decentralized environments.展开更多
With network attack technology continuing to develop,traditional anomaly traffic detection methods that rely on feature engineering are increasingly insufficient in efficiency and accuracy.Graph Neural Network(GNN),a ...With network attack technology continuing to develop,traditional anomaly traffic detection methods that rely on feature engineering are increasingly insufficient in efficiency and accuracy.Graph Neural Network(GNN),a promising Deep Learning(DL)approach,has proven to be highly effective in identifying intricate patterns in graph⁃structured data and has already found wide applications in the field of network security.In this paper,we propose a hybrid Graph Convolutional Network(GCN)⁃GraphSAGE model for Anomaly Traffic Detection,namely HGS⁃ATD,which aims to improve the accuracy of anomaly traffic detection by leveraging edge feature learning to better capture the relationships between network entities.We validate the HGS⁃ATD model on four publicly available datasets,including NF⁃UNSW⁃NB15⁃v2.The experimental results show that the enhanced hybrid model is 5.71%to 10.25%higher than the baseline model in terms of accuracy,and the F1⁃score is 5.53%to 11.63%higher than the baseline model,proving that the model can effectively distinguish normal traffic from attack traffic and accurately classify various types of attacks.展开更多
In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic h...In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services.展开更多
Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression...Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression is crucial for deploying deep neural network(DNN)models on resource-constrained embedded devices.展开更多
Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relati...Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relationships among nodes.This paper proposes a novel graph coupling convolutional model that introduces an adaptive weighting mechanism to assign distinct importance to neighboring nodes based on their similarity to the central node.Unlike traditional methods,the proposed coupling strategy enhances the interpretability of node interactions while maintaining competitive classification performance.The model operates in the spatial domain,utilizing adjacency list structures for efficient convolution and addressing the limitations of weight sharing through a coupling-based similarity computation.Extensive experiments are conducted on five graph-structured datasets,including Cora,Citeseer,PubMed,Reddit,and BlogCatalog,as well as a custom topology dataset constructed from the Open University Learning Analytics Dataset(OULAD)educational platform.Results demonstrate that the proposed model achieves good classification accuracy,while significantly reducing training time through direct second-order neighbor fusion and data preprocessing.Moreover,analysis of neighborhood order reveals that considering third-order neighbors offers limited accuracy gains but introduces considerable computational overhead,confirming the efficiency of first-and second-order convolution in practical applications.Overall,the proposed graph coupling model offers a lightweight,interpretable,and effective framework for multi-label node classification in complex networks.展开更多
The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-gener...The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.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supported by the National Natural Science Foundation of China(NSFC,No.22172121)the Fundamental Research Funds for the Central Universities(No.ZYN2025267)Southwest Minzu University。
文摘The electrocatalytic oxidation of ethylene glycol(EG)into high-value chemicals like glycolic acid(GA)is a crucial step for upcycling waste plastics.However,catalyst deactivation and low selectivity pose significant challenges.This work presents the low-coordination PtBi nanosheets(LC-PtBi NSs),featuring a unique amorphous-crystalline heterostructure with a low coordination number of 2.3-2.5.They can exhibit exceptional mass activity(8.3 A mg_(Pt)^(-1))and stability(maintaining 88.7%of initial activity after running for 3600 s)of the EG oxidation reaction(EGOR).They also achieve over 90%apparent selectivity for EG-to-GA conversion at low potentials(<0.7 V vs.RHE)and even more than 100-h continuous electrolysis.Density fu nctional theory(DFT)calculations reveal that the low-coordination PtBi heterogeneous interface is responsible for the high coverage of OH_(ad) species and weakened adsorption of carbonaceous intermediates on LC-PtBi NSs,thereby promoting the direct oxidation of C_(2) intermediates to GA.This work demonstrates a strategy of doping-mediated catalytic interface regulation and electron density rearrangement,offering insights for designing efficient Pt-based electrocatalysts toward selective oxidation of small molecules.
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Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.
文摘To explore the material basis and mechanisms of the anti-inflammatory effects of Hibiscus mutabilis L..The active ingredients and potential targets of Hibiscus mutabilis L.were obtained through the literature review and SwissADME platform.Genes related to the inflammation were collected using Genecards and OMIM databases,and the intersection genes were submitted on STRING and DAVID websites.Then,the protein interaction network(PPI),gene ontology(GO)and pathway(KEGG)were analyzed.Cytoscape 3.7.2 software was used to construct the“Hibiscus mutabilis L.-active ingredient-target-inflammation”network diagram,and AutoDockTools-1.5.6 software was used for the molecular docking verification.The antiinflammatory effect of Hibiscus mutabilis L.active ingredient was verified by the RAW264.7 inflammatory cell model.The results showed that 11 active components and 94 potential targets,1029 inflammatory targets and 24 intersection targets were obtained from Hibiscus mutabilis L..The key anti-inflammatory active ingredients of Hibiscus mutabilis L.are quercetin,apigenin and luteolin.Its action pathway is mainly related to NF-κB,cancer pathway and TNF signaling pathway.Cell experiments showed that total flavonoids of Hibiscus mutabilis L.could effectively inhibit the expression of tumor necrosis factor(TNF-α),interleukin 8(IL-8)and epidermal growth factor receptor(EGFR)in LPS-induced RAW 264.7 inflammatory cells.It also downregulates the phosphorylation of human nuclear factor ĸB inhibitory protein α(IĸBα)and NF-κB p65 subunit protein(p65).Overall,the anti-inflammatory effect of Hibiscus mutabilis L.is related to many active components,many signal pathways and targets,which provides a theoretical basis for its further development and application.
文摘Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.
基金supported by the National Natural Science Foundation of China(62173002,62403235,62403010,52301408,62173255)the Beijing Natural Science Foundation(L241015,4222045)+2 种基金the Yuxiu Innovation Project of NCUT(2024NCUTYXCX111)the China Postdoctoral Science Foundation(2025T180466)the Beijing Postdoctoral Research Foundation(2025-ZZ-70)。
文摘Networked predictive control(NPC) has gained significant attention in recent years for its ability to effectively and actively address communication constraints in networked control systems(NCSs),such as network-induced delays,packet dropouts,and packet disorders.Despite significant advancements,the increasing complexity and dynamism of network environments,along with the growing complexity of systems,pose new challenges for NPC.These challenges include difficulties in system modeling,cyber attacks,component faults,limited network bandwidth,and the necessity for distributed collaboration.This survey aims to provide a comprehensive review of NPC strategies.It begins with a summary of the primary challenges faced by NCSs,followed by an introduction to the control structure and core concepts of NPC.The survey then discusses several typical NPC schemes and examines their extensions in the areas of secure control,fault-tolerant control,distributed coordinated control,and event-triggered control.Moreover,it reviews notable works that have implemented these schemes.Finally,the survey concludes by exploring typical applications of NPC schemes and highlighting several challenging issues that could guide future research efforts.
文摘Identifying the community structure of complex networks is crucial to extracting insights and understanding network properties.Although several community detection methods have been proposed,many are unsuitable for social networks due to significant limitations.Specifically,most approaches depend mainly on user-user structural links while overlooking service-centric,semantic,and multi-attribute drivers of community formation,and they also lack flexible filtering mechanisms for large-scale,service-oriented settings.Our proposed approach,called community discovery-based service(CDBS),leverages user profiles and their interactions with consulted web services.The method introduces a novel similarity measure,global similarity interaction profile(GSIP),which goes beyond typical similarity measures by unifying user and service profiles for all attributes types into a coherent representation,thereby clarifying its novelty and contribution.It applies multiple filtering criteria related to user attributes,accessed services,and interaction patterns.Experimental comparisons against Louvain,Hierarchical Agglomerative Clustering,Label Propagation and Infomap show that CDBS reveals the higher performance as it achieves 0.74 modularity,0.13 conductance,0.77 coverage,and significantly fast response time of 9.8 s,even with 10,000 users and 400 services.Moreover,community discoverybased service consistently detects a larger number of communities with distinct topics of interest,underscoring its capacity to generate detailed and efficient structures in complex networks.These results confirm both the efficiency and effectiveness of the proposed method.Beyond controlled evaluation,communities discovery based service is applicable to targeted recommendations,group-oriented marketing,access control,and service personalization,where communities are shaped not only by user links but also by service engagement.
基金supported by the Research year project of the KongjuNational University in 2025 and the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2024-00444170,Research and International Collaboration on Trust Model-Based Intelligent Incident Response Technologies in 6G Open Network Environment).
文摘With the growing complexity and decentralization of network systems,the attack surface has expanded,which has led to greater concerns over network threats.In this context,artificial intelligence(AI)-based network intrusion detection systems(NIDS)have been extensively studied,and recent efforts have shifted toward integrating distributed learning to enable intelligent and scalable detection mechanisms.However,most existing works focus on individual distributed learning frameworks,and there is a lack of systematic evaluations that compare different algorithms under consistent conditions.In this paper,we present a comprehensive evaluation of representative distributed learning frameworks—Federated Learning(FL),Split Learning(SL),hybrid collaborative learning(SFL),and fully distributed learning—in the context of AI-driven NIDS.Using recent benchmark intrusion detection datasets,a unified model backbone,and controlled distributed scenarios,we assess these frameworks across multiple criteria,including detection performance,communication cost,computational efficiency,and convergence behavior.Our findings highlight distinct trade-offs among the distributed learning frameworks,demonstrating that the optimal choice depends strongly on systemconstraints such as bandwidth availability,node resources,and data distribution.This work provides the first holistic analysis of distributed learning approaches for AI-driven NIDS and offers practical guidelines for designing secure and efficient intrusion detection systems in decentralized environments.
基金National Natural Science Foundation of China(Grant No.62103434)National Science Fund for Distinguished Young Scholars(Grant No.62176263).
文摘With network attack technology continuing to develop,traditional anomaly traffic detection methods that rely on feature engineering are increasingly insufficient in efficiency and accuracy.Graph Neural Network(GNN),a promising Deep Learning(DL)approach,has proven to be highly effective in identifying intricate patterns in graph⁃structured data and has already found wide applications in the field of network security.In this paper,we propose a hybrid Graph Convolutional Network(GCN)⁃GraphSAGE model for Anomaly Traffic Detection,namely HGS⁃ATD,which aims to improve the accuracy of anomaly traffic detection by leveraging edge feature learning to better capture the relationships between network entities.We validate the HGS⁃ATD model on four publicly available datasets,including NF⁃UNSW⁃NB15⁃v2.The experimental results show that the enhanced hybrid model is 5.71%to 10.25%higher than the baseline model in terms of accuracy,and the F1⁃score is 5.53%to 11.63%higher than the baseline model,proving that the model can effectively distinguish normal traffic from attack traffic and accurately classify various types of attacks.
基金funding from the European Commission by the Ruralities project(grant agreement no.101060876).
文摘In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services.
基金supported by the Science and Technology Innovation Key R&D Program of Chongqing(CSTB2025TIAD-STX0032)National Key Research and Development Program of China(2024YFF0908200)+1 种基金the Chongqing Technology Innovation and Application Development Special Key Project(CSTB2024TIAD-KPX0018)the Southwest University Graduate Student Research Innovation(SWUB24051)。
文摘Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression is crucial for deploying deep neural network(DNN)models on resource-constrained embedded devices.
基金Support by Sichuan Science and Technology Program[2023YFSY0026,2023YFH0004]Guangzhou Huashang University[2024HSZD01,HS2023JYSZH01].
文摘Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relationships among nodes.This paper proposes a novel graph coupling convolutional model that introduces an adaptive weighting mechanism to assign distinct importance to neighboring nodes based on their similarity to the central node.Unlike traditional methods,the proposed coupling strategy enhances the interpretability of node interactions while maintaining competitive classification performance.The model operates in the spatial domain,utilizing adjacency list structures for efficient convolution and addressing the limitations of weight sharing through a coupling-based similarity computation.Extensive experiments are conducted on five graph-structured datasets,including Cora,Citeseer,PubMed,Reddit,and BlogCatalog,as well as a custom topology dataset constructed from the Open University Learning Analytics Dataset(OULAD)educational platform.Results demonstrate that the proposed model achieves good classification accuracy,while significantly reducing training time through direct second-order neighbor fusion and data preprocessing.Moreover,analysis of neighborhood order reveals that considering third-order neighbors offers limited accuracy gains but introduces considerable computational overhead,confirming the efficiency of first-and second-order convolution in practical applications.Overall,the proposed graph coupling model offers a lightweight,interpretable,and effective framework for multi-label node classification in complex networks.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00559546)supported by the IITP(Institute of Information&Coummunications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korea government(Ministry of Science and ICT)(IITP-2025-RS-2023-00259004).
文摘The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.
