The semi-interpenetrating network anion exchange membranes(AEMs)based on quaternized polyvinyl alcohol(QPVA)and poly(-diallyldimethylammonium chloride)(PDDA)are synthesized.The chemical cross-linking structure is form...The semi-interpenetrating network anion exchange membranes(AEMs)based on quaternized polyvinyl alcohol(QPVA)and poly(-diallyldimethylammonium chloride)(PDDA)are synthesized.The chemical cross-linking structure is formed between hydroxyl groups of QPVA and aldehyde groups of glutaraldehyde(GA),which makes PDDA more stable embed in the QPVA matrix and also improves the mechanical properties and dimensional stability of AEMs.Due to the phase separation phenomenon of AEMs swelling in water,a microporous structure may be formed in the membrane,which reduces the transmission resistance of hydroxide ions and provides a larger space for the transfer of hydroxide ions,thus improving the conductivity.The ring structure of PDDA is introduced as a cationic group to transfer hydroxide ions,and shields the nucleophilic attack of the hydroxide ions through the steric hindrance effect,which improves alkaline stability.The hydroxide conductivity of semi-interpenetrating network membrane(QPVA/PDDA0.5-GA)is 36.5 mS cm^(-1) at 60℃.And the membrane of QPVA/PDDA0.5-GA exhibits excellent mechanical property with maximum tensile strength of 19.6 MPa.After immersing into hot 3 mol L^(-1) NaOH solutions at 60℃ for 300 h,the OHconductivity remains 78%of its initial value.The semi-interpenetrating network AEMs with microporous structure exhibit good ionic conductivity,mechanical strength and alkaline durability.展开更多
Bio-based phenol-formaldehyde polymer (BioNovolac) was developed by reacting molar excess of bio-oil/phenolwith formaldehyde in acidic medium. Glycidyl 3,5-diglycidoxybenzoate (GDGB), was prepared by directglycidylati...Bio-based phenol-formaldehyde polymer (BioNovolac) was developed by reacting molar excess of bio-oil/phenolwith formaldehyde in acidic medium. Glycidyl 3,5-diglycidoxybenzoate (GDGB), was prepared by directglycidylation of α-resorcylic acid (RA), a naturally occurring phenolic monomer. GDGB was crosslinked in thepresence of BioNovolac by anionic polymerization. Fourier transform infrared spectroscopy (FTIR) confirmedthe formation of semi-interpenetrating polymer networks. The glass transition temperature and moduli of biobasedcrosslinked systems were observed to increase with increasing GDGB content. Active chain density andmass retention measured by dynamic mechanical analysis (DMA) and Soxhlet extraction, respectively, indicated ahigh crosslink density of the cured networks. Scanning electron microscopy (SEM) images depicted thehomogeneity of the bulk phase. The preparation of bio-based epoxy-novolac thermoset network resulted inreduced consumption of petroleum-based chemicals.展开更多
Low methanol permeability of proton exchange membranes (PEMs) is greatly important for direct methanol fuel cells (DMFCs). Here, sulfonated poly (ether ether ketone) (SPEEK) based semiinterpenetrating polymer networks...Low methanol permeability of proton exchange membranes (PEMs) is greatly important for direct methanol fuel cells (DMFCs). Here, sulfonated poly (ether ether ketone) (SPEEK) based semiinterpenetrating polymer networks (semi-IPNs) are successfully prepared by interpenetrating SPEEK into the in-situ synthesized crosslinking networks. The polymeric networks are formed by the covalent bonds between bromobenzyl groups of bro mo methylated poly (phenylene oxide) and amine groups of diamine linkers as well as the ionic bonds between amine species and sulfonated groups. Two linkers without and with sulfonated groups are applied to fabricate the semi-IPNs. The core properties of the membranes, like phase separation, water uptake, proton conductivity and methanol permeability, are systematically studied and compared. The DMFCs assembled by using the semi-IPN membranes display better performance than Nafion 117 in high concentration methanol solutions. The present work provides a facile way to prepare PEMs with enhanced DMFC performance.展开更多
Herein, the effect of fluoropolymer binders on the properties of polymer-bonded explosives(PBXs) was comprehensively investigated. To this end, fluorinated semi-interpenetrating polymer networks(semiIPNs) were prepare...Herein, the effect of fluoropolymer binders on the properties of polymer-bonded explosives(PBXs) was comprehensively investigated. To this end, fluorinated semi-interpenetrating polymer networks(semiIPNs) were prepared using different catalyst amounts(denoted as F23-CLF-30-D). The involved curing and phase separation processes were monitored using Fourier-transform infrared spectroscopy, differential scanning calorimetry, a haze meter and a rheometer. Curing rate constant and activation energy were calculated using a theoretical model and numerical method, respectively. Results revealed that owing to its co-continuous micro-phase separation structure, the F23-CLF-30-D3 semi-IPN exhibited considerably higher tensile strength and elongation at break than pure fluororubber F2314 and the F23-CLF-30-D0 semi-IPN because the phase separation and curing rates matched in the initial stage of curing.An arc Brazilian test revealed that F23-CLF-30-D-based composites used as mock materials for PBXs exhibited excellent mechanical performance and storage stability. Thus, the matched curing and phase separation rates play a crucial role during the fabrication of high-performance semi-IPNs;these factors can be feasibly controlled using an appropriate catalyst amount.展开更多
Anion-exchange membranes(AEMs)with high conductivity and stability are essential components of hydrogen related water electrolysis and fuel cell applications.During the past decades,polynorbornene(PNB)-based AEMs have...Anion-exchange membranes(AEMs)with high conductivity and stability are essential components of hydrogen related water electrolysis and fuel cell applications.During the past decades,polynorbornene(PNB)-based AEMs have shown excellent performance due to their saturated all-carbon-based backbones and diverse strategies to prepare cross-linked membranes.However,nearly all previously reported PNB-based AEMs rely on the alkyl-substituted norbornene monomers,whose low-yielding synthesis leads to high-cost of the AEMs.In addition,the crosslinked PNB-based AEMs usually suffered from mechanical brittleness.Herein,we propose a novel semi-interpenetrating polymer network(s-IPN)strategy to simultaneously enhance mechanical modulus and ionic conductivity,while using commercial 5-vinyl-2-norbornene(VNB)as the single norbornene derivatives to prepare high-performance AEMs.A diallylphenol quaternary ammonium salt was used for photo-induced crosslinking with poly-VNB and various dithiols to produce AEMs with s-IPN structures.The resultant membranes have excellent hydroxide conductivities and alkaline stability in 1 mol/L KOH at 80℃,and are successfully applied in alkaline anion-exchange membrane water electrolyzers to stably operateforover150h.展开更多
An anion exchange membrane(AEM)is generally expected to possess high ion exchange capacity(IEC),low water uptake(WU),and high mechanical strength when applied to electrodialysis desalination.Among different types of A...An anion exchange membrane(AEM)is generally expected to possess high ion exchange capacity(IEC),low water uptake(WU),and high mechanical strength when applied to electrodialysis desalination.Among different types of AEMs,semi-interpenetrating polymer networks(SIPNs)have been suggested for their structural superiorities,i.e.,the tunable local density of ion exchange groups for IEC and the restrained leaching of hygroscopic groups by insolubility for WU.Unfortunately,the conventional SIPN AEMs still struggle to balances IEC,WU,and mechanical strength simultaneously,due to the lack of the compact crosslinking region.In this work,we proposed a novel SIPN structure of polyvinylidene difluoride/polyvinylimidazole/1,6-dibromohexane(PVDF/PVIm/DBH).On the one hand,DBH with two cationic groups of imidazole groups are introduced to enhance the ion conductivity,which is different from the conventional monofunctional modifier with only one cationic group.On the other hand,DBH has the ability to bridge with PVIm,where the mechanical strength of the resulting AEM is increased by the increase of crosslinking degree.Results show that a low WU of 38.1%to 62.6%,high IEC of 2.12—2.22 mmol·g^(-1),and excellent tensile strength of 3.54—12.35 MPa for PVDF/PVIm/DBH membrane are achieved.This work opens a new avenue for achieving the high-quality AEMs.展开更多
The heterogeneous free-radical polymerization of methyl methylacrylate (MMA) and divinylbenzene (DVB) as cross-linker within supercritical carbon dioxide-swollen silicon rubber (SR) has been studied as an approach to ...The heterogeneous free-radical polymerization of methyl methylacrylate (MMA) and divinylbenzene (DVB) as cross-linker within supercritical carbon dioxide-swollen silicon rubber (SR) has been studied as an approach to preparing semi-interpenetrating polymer network (semi-IPN) of SR and poly(methyl methylacrylate) (PMMA). The SR/PMMA semi-IPNs were characterized by scanning electron microscopy (SEM) and dynamic mechanical analyzer (DMA).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
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
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
Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of...Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.展开更多
Medical image segmentation is of critical importance in the domain of contemporary medical imaging.However,U-Net and its variants exhibit limitations in capturing complex nonlinear patterns and global contextual infor...Medical image segmentation is of critical importance in the domain of contemporary medical imaging.However,U-Net and its variants exhibit limitations in capturing complex nonlinear patterns and global contextual information.Although the subsequent U-KAN model enhances nonlinear representation capabilities,it still faces challenges such as gradient vanishing during deep network training and spatial detail loss during feature downsampling,resulting in insufficient segmentation accuracy for edge structures and minute lesions.To address these challenges,this paper proposes the RE-UKAN model,which innovatively improves upon U-KAN.Firstly,a residual network is introduced into the encoder to effectively mitigate gradient vanishing through cross-layer identity mappings,thus enhancing modelling capabilities for complex pathological structures.Secondly,Efficient Local Attention(ELA)is integrated to suppress spatial detail loss during downsampling,thereby improving the perception of edge structures and minute lesions.Experimental results on four public datasets demonstrate that RE-UKAN outperforms existing medical image segmentation methods across multiple evaluation metrics,with particularly outstanding performance on the TN-SCUI 2020 dataset,achieving IoU of 88.18%and Dice of 93.57%.Compared to the baseline model,it achieves improvements of 3.05%and 1.72%,respectively.These results fully demonstrate RE-UKAN’s superior detail retention capability and boundary recognition accuracy in complex medical image segmentation tasks,providing a reliable solution for clinical precision segmentation.展开更多
In modern ZnO varistors,traditional aging mechanisms based on increased power consumption are no longer relevant due to reduced power consumption during DC aging.Prolonged exposure to both AC and DC voltages results i...In modern ZnO varistors,traditional aging mechanisms based on increased power consumption are no longer relevant due to reduced power consumption during DC aging.Prolonged exposure to both AC and DC voltages results in increased leakage current,decreased breakdown voltage,and lower nonlinearity,ultimately compromising their protective performance.To investigate the evolution in electrical properties during DC aging,this work developed a finite element model based on Voronoi networks and conducted accelerated aging tests on commercial varistors.Throughout the aging process,current-voltage characteristics and Schottky barrier parameters were measured and analyzed.The results indicate that when subjected to constant voltage,current flows through regions with larger grain sizes,forming discharge channels.As aging progresses,the current focus increases on these channels,leading to a decline in the varistor’s overall performance.Furthermore,analysis of the Schottky barrier parameters shows that the changes in electrical performance during aging are non-monotonic.These findings offer theoretical support for understanding the aging mechanisms and condition assessment of modern stable ZnO varistors.展开更多
With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Further...With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Furthermore,given the open environment and a multitude of devices,enhancing the security of ICPS is an urgent concern.To address these issues,this paper proposes a novel trusted virtual network embedding(T-VNE)approach for ICPS based combining blockchain and edge computing technologies.Additionally,the proposed algorithm leverages a deep reinforcement learning(DRL)model to optimize decision-making processes.It employs the policygradient-based agent to compute candidate embedding nodes and utilizes a breadth-first search(BFS)algorithm to determine the optimal embedding paths.Finally,through simulation experiments,the efficacy of the proposed method was validated,demonstrating outstanding performance in terms of security,revenue generation,and virtual network request(VNR)acceptance rate.展开更多
基金The authors gratefully acknowledge the financial support of this work by Natural Science Foundation of China(grant no.s 51673030,51603017 and 51803011)Jilin Provincial Science&Technology Department(grant no.s 20200801011GH,20180101209JC,20160520138JH,20160519020JH)+1 种基金Jilin Province Development and Reform Commission(Grant nos:2019C042-5)ChangBai Mountain Scholars Program of Jilin Province.
文摘The semi-interpenetrating network anion exchange membranes(AEMs)based on quaternized polyvinyl alcohol(QPVA)and poly(-diallyldimethylammonium chloride)(PDDA)are synthesized.The chemical cross-linking structure is formed between hydroxyl groups of QPVA and aldehyde groups of glutaraldehyde(GA),which makes PDDA more stable embed in the QPVA matrix and also improves the mechanical properties and dimensional stability of AEMs.Due to the phase separation phenomenon of AEMs swelling in water,a microporous structure may be formed in the membrane,which reduces the transmission resistance of hydroxide ions and provides a larger space for the transfer of hydroxide ions,thus improving the conductivity.The ring structure of PDDA is introduced as a cationic group to transfer hydroxide ions,and shields the nucleophilic attack of the hydroxide ions through the steric hindrance effect,which improves alkaline stability.The hydroxide conductivity of semi-interpenetrating network membrane(QPVA/PDDA0.5-GA)is 36.5 mS cm^(-1) at 60℃.And the membrane of QPVA/PDDA0.5-GA exhibits excellent mechanical property with maximum tensile strength of 19.6 MPa.After immersing into hot 3 mol L^(-1) NaOH solutions at 60℃ for 300 h,the OHconductivity remains 78%of its initial value.The semi-interpenetrating network AEMs with microporous structure exhibit good ionic conductivity,mechanical strength and alkaline durability.
文摘Bio-based phenol-formaldehyde polymer (BioNovolac) was developed by reacting molar excess of bio-oil/phenolwith formaldehyde in acidic medium. Glycidyl 3,5-diglycidoxybenzoate (GDGB), was prepared by directglycidylation of α-resorcylic acid (RA), a naturally occurring phenolic monomer. GDGB was crosslinked in thepresence of BioNovolac by anionic polymerization. Fourier transform infrared spectroscopy (FTIR) confirmedthe formation of semi-interpenetrating polymer networks. The glass transition temperature and moduli of biobasedcrosslinked systems were observed to increase with increasing GDGB content. Active chain density andmass retention measured by dynamic mechanical analysis (DMA) and Soxhlet extraction, respectively, indicated ahigh crosslink density of the cured networks. Scanning electron microscopy (SEM) images depicted thehomogeneity of the bulk phase. The preparation of bio-based epoxy-novolac thermoset network resulted inreduced consumption of petroleum-based chemicals.
基金support of the National Natural Science Foundation of China(Nos. 21603197, 21703212,21233006 and 21473164)Natural Science Foundation of Hubei Province of China(No.2016CFB181)+1 种基金Fundamental Research Funds for the Central University, China University of Geosciences (Wuhan)(No. CUGL180403)China University of Geosciences (Wuhan) for the program of Center for Advanced Energy Research and Technologies
文摘Low methanol permeability of proton exchange membranes (PEMs) is greatly important for direct methanol fuel cells (DMFCs). Here, sulfonated poly (ether ether ketone) (SPEEK) based semiinterpenetrating polymer networks (semi-IPNs) are successfully prepared by interpenetrating SPEEK into the in-situ synthesized crosslinking networks. The polymeric networks are formed by the covalent bonds between bromobenzyl groups of bro mo methylated poly (phenylene oxide) and amine groups of diamine linkers as well as the ionic bonds between amine species and sulfonated groups. Two linkers without and with sulfonated groups are applied to fabricate the semi-IPNs. The core properties of the membranes, like phase separation, water uptake, proton conductivity and methanol permeability, are systematically studied and compared. The DMFCs assembled by using the semi-IPN membranes display better performance than Nafion 117 in high concentration methanol solutions. The present work provides a facile way to prepare PEMs with enhanced DMFC performance.
基金supported by Wuxi HIT New Material Research Institute and China Academy of Engineering Physics。
文摘Herein, the effect of fluoropolymer binders on the properties of polymer-bonded explosives(PBXs) was comprehensively investigated. To this end, fluorinated semi-interpenetrating polymer networks(semiIPNs) were prepared using different catalyst amounts(denoted as F23-CLF-30-D). The involved curing and phase separation processes were monitored using Fourier-transform infrared spectroscopy, differential scanning calorimetry, a haze meter and a rheometer. Curing rate constant and activation energy were calculated using a theoretical model and numerical method, respectively. Results revealed that owing to its co-continuous micro-phase separation structure, the F23-CLF-30-D3 semi-IPN exhibited considerably higher tensile strength and elongation at break than pure fluororubber F2314 and the F23-CLF-30-D0 semi-IPN because the phase separation and curing rates matched in the initial stage of curing.An arc Brazilian test revealed that F23-CLF-30-D-based composites used as mock materials for PBXs exhibited excellent mechanical performance and storage stability. Thus, the matched curing and phase separation rates play a crucial role during the fabrication of high-performance semi-IPNs;these factors can be feasibly controlled using an appropriate catalyst amount.
基金financially supported by the Ministry of Science and Technology of China under the National Key R&D Program of China (No.2023YFB3811200)the National Natural Science Foundation of China (No.22075292).
文摘Anion-exchange membranes(AEMs)with high conductivity and stability are essential components of hydrogen related water electrolysis and fuel cell applications.During the past decades,polynorbornene(PNB)-based AEMs have shown excellent performance due to their saturated all-carbon-based backbones and diverse strategies to prepare cross-linked membranes.However,nearly all previously reported PNB-based AEMs rely on the alkyl-substituted norbornene monomers,whose low-yielding synthesis leads to high-cost of the AEMs.In addition,the crosslinked PNB-based AEMs usually suffered from mechanical brittleness.Herein,we propose a novel semi-interpenetrating polymer network(s-IPN)strategy to simultaneously enhance mechanical modulus and ionic conductivity,while using commercial 5-vinyl-2-norbornene(VNB)as the single norbornene derivatives to prepare high-performance AEMs.A diallylphenol quaternary ammonium salt was used for photo-induced crosslinking with poly-VNB and various dithiols to produce AEMs with s-IPN structures.The resultant membranes have excellent hydroxide conductivities and alkaline stability in 1 mol/L KOH at 80℃,and are successfully applied in alkaline anion-exchange membrane water electrolyzers to stably operateforover150h.
基金funded by National Natural Science Foundation of China(22278023,22208010)Beijing Municipal Science and Technology Planning Project(Z221100002722002)+3 种基金Bingtuan Science and Technology Program(2022DB025)Beijing Natural Science Foundation(2222015)Sinopec Group(323034)the long-term from the Ministry of Finance and the Ministry of Education of PRC。
文摘An anion exchange membrane(AEM)is generally expected to possess high ion exchange capacity(IEC),low water uptake(WU),and high mechanical strength when applied to electrodialysis desalination.Among different types of AEMs,semi-interpenetrating polymer networks(SIPNs)have been suggested for their structural superiorities,i.e.,the tunable local density of ion exchange groups for IEC and the restrained leaching of hygroscopic groups by insolubility for WU.Unfortunately,the conventional SIPN AEMs still struggle to balances IEC,WU,and mechanical strength simultaneously,due to the lack of the compact crosslinking region.In this work,we proposed a novel SIPN structure of polyvinylidene difluoride/polyvinylimidazole/1,6-dibromohexane(PVDF/PVIm/DBH).On the one hand,DBH with two cationic groups of imidazole groups are introduced to enhance the ion conductivity,which is different from the conventional monofunctional modifier with only one cationic group.On the other hand,DBH has the ability to bridge with PVIm,where the mechanical strength of the resulting AEM is increased by the increase of crosslinking degree.Results show that a low WU of 38.1%to 62.6%,high IEC of 2.12—2.22 mmol·g^(-1),and excellent tensile strength of 3.54—12.35 MPa for PVDF/PVIm/DBH membrane are achieved.This work opens a new avenue for achieving the high-quality AEMs.
基金This work was supported by the National Natural Science Foundation of China (50173030).
文摘The heterogeneous free-radical polymerization of methyl methylacrylate (MMA) and divinylbenzene (DVB) as cross-linker within supercritical carbon dioxide-swollen silicon rubber (SR) has been studied as an approach to preparing semi-interpenetrating polymer network (semi-IPN) of SR and poly(methyl methylacrylate) (PMMA). The SR/PMMA semi-IPNs were characterized by scanning electron microscopy (SEM) and dynamic mechanical analyzer (DMA).
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.
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.
基金supported by the Chung-Ang University Research Grants in 2023.Alsothe work is supported by the ELLIIT Excellence Center at Linköping–Lund in Information Technology in Sweden.
文摘Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.
文摘Medical image segmentation is of critical importance in the domain of contemporary medical imaging.However,U-Net and its variants exhibit limitations in capturing complex nonlinear patterns and global contextual information.Although the subsequent U-KAN model enhances nonlinear representation capabilities,it still faces challenges such as gradient vanishing during deep network training and spatial detail loss during feature downsampling,resulting in insufficient segmentation accuracy for edge structures and minute lesions.To address these challenges,this paper proposes the RE-UKAN model,which innovatively improves upon U-KAN.Firstly,a residual network is introduced into the encoder to effectively mitigate gradient vanishing through cross-layer identity mappings,thus enhancing modelling capabilities for complex pathological structures.Secondly,Efficient Local Attention(ELA)is integrated to suppress spatial detail loss during downsampling,thereby improving the perception of edge structures and minute lesions.Experimental results on four public datasets demonstrate that RE-UKAN outperforms existing medical image segmentation methods across multiple evaluation metrics,with particularly outstanding performance on the TN-SCUI 2020 dataset,achieving IoU of 88.18%and Dice of 93.57%.Compared to the baseline model,it achieves improvements of 3.05%and 1.72%,respectively.These results fully demonstrate RE-UKAN’s superior detail retention capability and boundary recognition accuracy in complex medical image segmentation tasks,providing a reliable solution for clinical precision segmentation.
文摘In modern ZnO varistors,traditional aging mechanisms based on increased power consumption are no longer relevant due to reduced power consumption during DC aging.Prolonged exposure to both AC and DC voltages results in increased leakage current,decreased breakdown voltage,and lower nonlinearity,ultimately compromising their protective performance.To investigate the evolution in electrical properties during DC aging,this work developed a finite element model based on Voronoi networks and conducted accelerated aging tests on commercial varistors.Throughout the aging process,current-voltage characteristics and Schottky barrier parameters were measured and analyzed.The results indicate that when subjected to constant voltage,current flows through regions with larger grain sizes,forming discharge channels.As aging progresses,the current focus increases on these channels,leading to a decline in the varistor’s overall performance.Furthermore,analysis of the Schottky barrier parameters shows that the changes in electrical performance during aging are non-monotonic.These findings offer theoretical support for understanding the aging mechanisms and condition assessment of modern stable ZnO varistors.
基金supported by the National Natural Science Foundation of China under Grant 62471493supported by the Natural Science Foundation of Shandong Province under Grant ZR2023LZH017,ZR2024MF066。
文摘With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Furthermore,given the open environment and a multitude of devices,enhancing the security of ICPS is an urgent concern.To address these issues,this paper proposes a novel trusted virtual network embedding(T-VNE)approach for ICPS based combining blockchain and edge computing technologies.Additionally,the proposed algorithm leverages a deep reinforcement learning(DRL)model to optimize decision-making processes.It employs the policygradient-based agent to compute candidate embedding nodes and utilizes a breadth-first search(BFS)algorithm to determine the optimal embedding paths.Finally,through simulation experiments,the efficacy of the proposed method was validated,demonstrating outstanding performance in terms of security,revenue generation,and virtual network request(VNR)acceptance rate.
