Highly transparent,durable,and flexible liquid-repellent coatings are urgently needed in the realm of transparent materials,such as car windows,optical lenses,solar panels,and flexible screen materials.However,it has ...Highly transparent,durable,and flexible liquid-repellent coatings are urgently needed in the realm of transparent materials,such as car windows,optical lenses,solar panels,and flexible screen materials.However,it has been difficult to strike a balance between the robustness and flexibility of coatings constructed by a single cross-linked network design.To overcome the conundrum,this innovative approach effectively combines two distinct cross-linked networks with unique functions,thus overcoming the challenge.Through a tightly interwoven structure comprised of added crosslinking sites,the coating achieves improved liquid repellency(WCA>100°,OSA<10°),increased durability(withstands 2,000 cycles of cotton wear),enhanced flexibility(endures 5,000 cycles of bending with a bending radius of 1 mm),and maintains high transparency(over 98%in the range of 410 nm to 760 nm).Additionally,the coating with remarkable adhesion can be applied to multiple substrates,enabling large-scale preparation and easy cycling coating,thus expanding its potential applications.The architecture of this fluoride-free dual cross-linked network not only advances liquid-repellent surfaces but also provides valuable insights for the development of eco-friendly materials in the future.展开更多
Photocatalytic H_(2) evolution from wastewater exhibits fascinating prospects in environment and energy fields.Here,we propose a novel 3D cross-linked g-C_(3)N_(4) network(SCN)assembling with 1D nanowires.This network...Photocatalytic H_(2) evolution from wastewater exhibits fascinating prospects in environment and energy fields.Here,we propose a novel 3D cross-linked g-C_(3)N_(4) network(SCN)assembling with 1D nanowires.This network structure endows SCN with abundant carbon defects,creating a defect energy level and shallow charge trapping centres,which significantly prolongs the photocarrier lifetime,suppresses their recombination and facilitates the mass transfer process during the dye photodegradation.Consequently,in photocatalytic H_(2) evolution coupled with Rhodamine B(RhB)photodegradation under visible light,the H_(2) production rate of SCN is 283μmol h^(-1)g^(-1),accompanying by 97%RhB photodegradation efficiency,much higher than UCN's 31μmol h^(-1)g^(-1)and 64%.In particular,AQY of SCN for H_(2) evolution from RhB solution reaches 23.7%at 380 nm.Furthermore,the calculated transition states demonstrate that the N1 site connected to the defect in SCN has a minimum Gibbs free energy ΔG(H^(*)),indicating that H~+undergoes an H^(+)→H^(*)→H_(2) evolution process.展开更多
Nanomaterials are extensively utilized in a multitude of sectors,but their propensity to aggregate can considerably diminish the efficacy of functional materials.A pivotal challenge in this domain is achieving a homog...Nanomaterials are extensively utilized in a multitude of sectors,but their propensity to aggregate can considerably diminish the efficacy of functional materials.A pivotal challenge in this domain is achieving a homogenous distribution of nanomaterials,which is essential for enhancing their performance while also reducing production costs.In this work,we achieve uniform and stable dispersion of various nano-materials through the confinement effect generated by the stereocomplex cross-linked network formed by the combination of poly(L-lactic)acid and poly(D-lactic)acid.The unique confinement effect of poly-lactic acid(PLA)isomers is universal and significantly enhances the dispersion of nanomaterials in both PLA solutions and films.To demonstrate the efficacy of our approach,we disperse aggregation-induced emission(AIE)molecules within PLA,which leads to the production of PLA films exhibiting improved fluorescence property.This work provides an effective solution for the preparation of nanocomposite ma-terials that are both high-performing and cost-efficient.展开更多
To achieve smart and personalized medicine, the development of hydrogel dressings with sensing properties and biotherapeutic properties that can act as a sensor to monitor of human health in real-time while speeding u...To achieve smart and personalized medicine, the development of hydrogel dressings with sensing properties and biotherapeutic properties that can act as a sensor to monitor of human health in real-time while speeding up wound healing face great challenge. In the present study, a biocompatible dual-network composite hydrogel(DNCGel) sensor was obtained via a simple process. The dual network hydrogel is constructed by the interpenetration of a flexible network formed of poly(vinyl alcohol)(PVA) physical cross-linked by repeated freeze-thawing and a rigid network of iron-chelated xanthan gum(XG) impregnated with Fe^(3+) interpenetration. The pure PVA/XG hydrogels were chelated with ferric ions by immersion to improve the gel strength(compressive modulus and tensile modulus can reach up to 0.62 MPa and0.079 MPa, respectively), conductivity(conductivity values ranging from 9 × 10^(-4) S/cm to 1 × 10^(-3)S/cm)and bacterial inhibition properties(up to 98.56%). Subsequently, the effects of the ratio of PVA and XG and the immersion time of Fe^(3+) on the hydrogels were investigated, and DNGel3 was given the most priority on a comprehensive consideration. It was demonstrated that the DNCGel exhibit good biocompatibility in vitro, effectively facilitate wound healing in vivo(up to 97.8% healing rate) under electrical stimulation, and monitors human movement in real time. This work provides a novel avenue to explore multifunctional intelligent hydrogels that hold great promise in biomedical fields such as smart wound dressings and flexible wearable sensors.展开更多
The self-healing solid polymer electrolytes(SHSPEs)can spontaneously eliminate mechanical damages or micro-cracks generated during the assembly or operation of lithium-ion batteries(LIBs),significantly improving cycli...The self-healing solid polymer electrolytes(SHSPEs)can spontaneously eliminate mechanical damages or micro-cracks generated during the assembly or operation of lithium-ion batteries(LIBs),significantly improving cycling performance and extending service life of LIBs.Here,we report a novel cross-linked network SHSPE(PDDP)containing hydrogen bonds and dynamic disulfide bonds with excellent self-healing properties and nonflammability.The combination of hydrogen bonding between urea groups and the metathesis reaction of dynamic disulfide bonds endows PDDP with rapid self-healing capacity at 28°C without external stimulation.Furthermore,the addition of 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide(EMIMTFSI)improves the ionic conductivity(1.13×10^(−4)S cm^(−1)at 28°C)and non-flammability of PDDP.The assembled Li/PDDP/LiFePO_(4)cell exhibits excellent cycling performance with a discharge capacity of 137 mA h g^(−1)after 300 cycles at 0.2 C.More importantly,the self-healed PDDP can recover almost the same ionic conductivity and cycling performance as the original PDDP.展开更多
Gel polymer electrolytes(GPEs)effectively combine the advantages of high ionic conductivity and re-duce the risk of leakage associated with liquid.In this study,a chemically cross-linked gel polymer electrolyte was pr...Gel polymer electrolytes(GPEs)effectively combine the advantages of high ionic conductivity and re-duce the risk of leakage associated with liquid.In this study,a chemically cross-linked gel polymer electrolyte was prepared by in-situ polymerization using polymethyl methacrylate(PMMA)as a matrix and neopentyl glycol diacrylate(NPGDA)as cross-linking agent.The cross-linked structure of the GPE was preliminarily investigated,as well as the influence of the degree of cross-linking on its phys-ical properties.The GPE exhibited a superior conductivity of 1.391 mS cm^(-1) at 25℃.Herein,the Li|GPE|LiNi_(0.8) Co_(0.1) Mn_(0.1) O_(2) cell has an excellent capacity retention rate of 80.7%after 150 cycles at 0.5 C in addition to a high discharge specific capacity of 203 mAh g^(-1).The structure of the cathode ma-terial is shielded from the production of byproducts during the charging and discharging of lithium-ion batteries by the cross-linked PMMA GPE.展开更多
Solid polymer electrolytes(SPEs)have become increasingly important in advanced lithium-ion batteries(LIBs)due to their improved safety and mechanical properties compared to organic liquid electrolytes.Cross-linked pol...Solid polymer electrolytes(SPEs)have become increasingly important in advanced lithium-ion batteries(LIBs)due to their improved safety and mechanical properties compared to organic liquid electrolytes.Cross-linked polymers have the potential to further improve the mechanical property without trading off Li-ion conductivity.In this study,focusing on a recently developed cross-linked SPE,i.e.,the one based on poly(vinylene carbonate)-poly(ethylene oxide)cross-linked network(PVCN),we used solid-state nuclear magnetic resonance(NMR)techniques to investigate the fundamental interaction between the chain segments and Li ions,as well as the lithium-ion motion.By utilizing homonuclear/heteronuclear correlation,CP(cross-polarization)kinetics,and spin-lattice relaxation experiments,etc.,we revealed the structural characteristics and their relations to lithium-ion mobilities.It is found that the network formation prevents poly(ethylene oxide)chains from crystallization,which could create sufficient space for segmental tumbling and Li-ion co nductio n.As such,the mechanical property is greatly improved with even higher Li-ion mobilities compared to the poly(vinylene carbonate)or poly(ethylene oxide)based SPE analogues.展开更多
Efficient polymeric room-temperature phosphorescence(PRTP)with excellent processability and flexibility is highly desirable but still faces formidable challenge.Herein,a general strategy is developed for efficient PRT...Efficient polymeric room-temperature phosphorescence(PRTP)with excellent processability and flexibility is highly desirable but still faces formidable challenge.Herein,a general strategy is developed for efficient PRTP through photo-polymerization of phosphor monomers and N-isopropylacrylamide(NIPAM)spontaneously without a crosslinker.Remarkably ultralong lifetime of 3.54 s with afterglow duration time of 25 s and decent phosphorescent quantum efficiency of 13%are achieved.This efficient PRTP has been demonstrated to be derived from the synergistic effect of the covalent and hydrogen bonds networks formed through photo-polymerization of NIPAM.The electron paramagnetic resonance(EPR)spectra confirmed that methyl radicals are generated under the irradiation of ultraviolet light and promote the formation of covalent cross-linking networks.This strategy has also been proved to be generalizable to several other phosphor monomers.Interestingly,the polymer films display ultrahigh temperature resistance with long afterglows even at 140℃ and unexampled ultralong lifetime of 2.45 s in aqueous solutions.This work provides a simple and feasible avenue to obtain efficient PRTP.展开更多
Physical cross-linking by hydrogen-bonds (H-bonds), providing a good combination of application properties of thermosets and processability of thermoplastics, is a potential strategy to resolve the recycling problem o...Physical cross-linking by hydrogen-bonds (H-bonds), providing a good combination of application properties of thermosets and processability of thermoplastics, is a potential strategy to resolve the recycling problem of traditional chemically cross-linked polyethylene. However, ureidopyrimidone (UPy), the most widely used H-bonding motif, is unfavorable for large-scale industrial application due to its poor thermal stability. In this work, H-bonds cross-linked polyethylene was successfully prepared by reactive melt blending maleic anhydride grafted polyethylene (PE-g-MAH) with 3-amino-1,2,4-triazole (ATA) to form amide triazole ring-carboxylic acid units. Triazole ring can easily generate multiple H-bonds with carboxylic acid and amide. More importantly, these units are more thermal stable than UPy due to the absence of unstable urea group of UPy. The introduction of H-bonds cross-linking leads to an obvious improvement in mechanical properties and creep resistance and a good maintain in thermal properties and recyclability. Furthermore, the reinforcement effect monotonically improves with increasing the density of H-bonds. The obtained good properties are mainly attributed to largely enhanced interchain interactions induced by H-bonds cross-linking and intrinsic reversibility of H-bonds. This work develops a novel way for the simple fabrication of H-bonds cross-linked PE with high performance through reactive melt blending.展开更多
Lithium-metal anodes(LMAs)have been recognized as the ultimate anodes for next-generation batteries with high energy density,but stringent assembly-environment conditions derived from the poor moisture stability drama...Lithium-metal anodes(LMAs)have been recognized as the ultimate anodes for next-generation batteries with high energy density,but stringent assembly-environment conditions derived from the poor moisture stability dramatically hinder the transformation of LMAs from laboratory to industry.Herein,an in situ formed cross-linked polymer layer on LMAs is designed and constructed by a facile thiol-acrylate click chemistry reaction between poly(ethylene glycol)diacrylate(PEGDA)and the crosslinker containing multi thiol groups under UV irradiation.Owing to the hydrophobic nature of the layer,the treated LMAs demonstrate remarkable humid stability for more than 3 h in ambient air(70%relative humidity).The coating humid-resistant protective layer also possesses a dual-functional characterization as solid polymer electrolytes by introducing lithium bis(trifluoromethanesulfonyl)imide in the system in advance.The intimate contact between the polymer layer and LMAs reduces interfacial resistance in the assembled Li/LiFePO_(4)or Li/LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2)full cell effectively,and endows the cell with an outstanding cycle performance.展开更多
The retrospective study by Edwar et al reinforces the role of therapeutic penetrating keratoplasty(PK)as a vital intervention in severe,treatment-resistant infectious keratitis.In advanced cases—often complicated by ...The retrospective study by Edwar et al reinforces the role of therapeutic penetrating keratoplasty(PK)as a vital intervention in severe,treatment-resistant infectious keratitis.In advanced cases—often complicated by trauma,delayed presentation,and corneal perforation—PK restores globe integrity and provides limited visual recovery.However,its application is constrained by graft-related complications and donor shortages,particularly in low-resource settings.These limitations highlight the need for earlier,globe-sparing strategies to prevent progression and reduce surgical demand.Photoactivated chromophore for infectious keratitis-corneal collagen cross-linking(PACK-CXL)has emerged as a promising adjunct or alternative.With both antimicrobial and tissue-stabilizing effects,PACK-CXL may control infection and preserve corneal structure in earlier stages.A layered treatment framework that incorporates PACK-CXL as an initial intervention and reserves PK for refractory cases may help improve clinical outcomes.Further studies are needed to define their best use in practice.展开更多
Based on the characteristics of laser-induced surface ignition,energetic photosensitive films show promising potential to meet the ignition requirements of various energetic materials(EMs).In this study,DATNBI/ferric ...Based on the characteristics of laser-induced surface ignition,energetic photosensitive films show promising potential to meet the ignition requirements of various energetic materials(EMs).In this study,DATNBI/ferric alginate(DI/FeA),DI/cobalt alginate(DI/CoA),and DI/nickel alginate(DI/Ni A)films are fabricated by employing sodium alginate(SA)with a three-dimensional network structure as the film matrix,via ionic cross-linking of SA with Fe^(3+),Co^(2+),and Ni^(2+)ions.The study demonstrates that the ionic cross-linking enhances the hydrophobic performance of the films,with the water contact angle increasing from 82.1° to 123.5°.Concurrently,the films'near-infrared(NIR)light absorption improved.Furthermore,transition metal ions facilitate accelerated electron transfer,thereby catalyzing the thermal decomposition of DATNBI.Under 1064 nm laser irradiation,the DI/Fe A film exhibits exceptional combustion performance,with an ignition delay time as low as 76 ms.It successfully acts as an NIR laser ignition medium to initiate the self-sustained combustion of CL-20.This study demonstrates the synergistic realization of enhanced hydrophobicity,improved photosensitivity,and promoted catalytic decomposition through microstructural design of the material,providing new insights for the design of additive-free EMs in laser ignition applications.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
基金financially supported by the National Natu-ral Science Foundation of China(Nos.22375047,22378068,and 22075046)the Natural Science Foundation of Fujian Province(No.2022J01568)+2 种基金the National Key Research and Development Program of China(Nos.2022YFB3804905 and 2022YFB3804900)China Postdoctoral Science Foundation(No.2023M743437)start-up funding from Wenzhou Institute,University of Chinese Academy of Sciences(No.WIUCASQD2019002).
文摘Highly transparent,durable,and flexible liquid-repellent coatings are urgently needed in the realm of transparent materials,such as car windows,optical lenses,solar panels,and flexible screen materials.However,it has been difficult to strike a balance between the robustness and flexibility of coatings constructed by a single cross-linked network design.To overcome the conundrum,this innovative approach effectively combines two distinct cross-linked networks with unique functions,thus overcoming the challenge.Through a tightly interwoven structure comprised of added crosslinking sites,the coating achieves improved liquid repellency(WCA>100°,OSA<10°),increased durability(withstands 2,000 cycles of cotton wear),enhanced flexibility(endures 5,000 cycles of bending with a bending radius of 1 mm),and maintains high transparency(over 98%in the range of 410 nm to 760 nm).Additionally,the coating with remarkable adhesion can be applied to multiple substrates,enabling large-scale preparation and easy cycling coating,thus expanding its potential applications.The architecture of this fluoride-free dual cross-linked network not only advances liquid-repellent surfaces but also provides valuable insights for the development of eco-friendly materials in the future.
基金supported by the National Natural Science Foundation of China(No.22202033)National College Students Innovation and Entrepreneurship Training Program(No.202310347048)Zhejiang Provincial Training Programs of Innovation and Entrepreneurship for Undergraduates(No.S202310347032)。
文摘Photocatalytic H_(2) evolution from wastewater exhibits fascinating prospects in environment and energy fields.Here,we propose a novel 3D cross-linked g-C_(3)N_(4) network(SCN)assembling with 1D nanowires.This network structure endows SCN with abundant carbon defects,creating a defect energy level and shallow charge trapping centres,which significantly prolongs the photocarrier lifetime,suppresses their recombination and facilitates the mass transfer process during the dye photodegradation.Consequently,in photocatalytic H_(2) evolution coupled with Rhodamine B(RhB)photodegradation under visible light,the H_(2) production rate of SCN is 283μmol h^(-1)g^(-1),accompanying by 97%RhB photodegradation efficiency,much higher than UCN's 31μmol h^(-1)g^(-1)and 64%.In particular,AQY of SCN for H_(2) evolution from RhB solution reaches 23.7%at 380 nm.Furthermore,the calculated transition states demonstrate that the N1 site connected to the defect in SCN has a minimum Gibbs free energy ΔG(H^(*)),indicating that H~+undergoes an H^(+)→H^(*)→H_(2) evolution process.
基金supported by the National Key R&D Program of China(No.2022YFB3804204)the National Natural Science Foundation of China(Nos.52127805,52102090,12172005,and 12325202)+1 种基金the Fundamental Research Funds for the Central Uni-versities(No.2232022D-04)the Innovation and Development Sup-port Plan for Key Industries in Southern Xinjiang(No.2022DB011).
文摘Nanomaterials are extensively utilized in a multitude of sectors,but their propensity to aggregate can considerably diminish the efficacy of functional materials.A pivotal challenge in this domain is achieving a homogenous distribution of nanomaterials,which is essential for enhancing their performance while also reducing production costs.In this work,we achieve uniform and stable dispersion of various nano-materials through the confinement effect generated by the stereocomplex cross-linked network formed by the combination of poly(L-lactic)acid and poly(D-lactic)acid.The unique confinement effect of poly-lactic acid(PLA)isomers is universal and significantly enhances the dispersion of nanomaterials in both PLA solutions and films.To demonstrate the efficacy of our approach,we disperse aggregation-induced emission(AIE)molecules within PLA,which leads to the production of PLA films exhibiting improved fluorescence property.This work provides an effective solution for the preparation of nanocomposite ma-terials that are both high-performing and cost-efficient.
基金supported by Physical Chemical Materials Analytical&Testing Center of Shandong University at Weihai,Natural Science Foundation of Shandong Province(No.ZR2022QD057)Open Project Fund for Hubei Key Laboratory of Oral and Maxillofacial Development and Regeneration(No.2021kqhm003)+1 种基金State Key Laboratory of Advanced Technology for Materials Synthesis and Processing(Wuhan University of Technology)the Science Fund of Shandong Laboratory of Advanced Materials and Green Manufacturing(Yantai,No.AMGM2021F02)。
文摘To achieve smart and personalized medicine, the development of hydrogel dressings with sensing properties and biotherapeutic properties that can act as a sensor to monitor of human health in real-time while speeding up wound healing face great challenge. In the present study, a biocompatible dual-network composite hydrogel(DNCGel) sensor was obtained via a simple process. The dual network hydrogel is constructed by the interpenetration of a flexible network formed of poly(vinyl alcohol)(PVA) physical cross-linked by repeated freeze-thawing and a rigid network of iron-chelated xanthan gum(XG) impregnated with Fe^(3+) interpenetration. The pure PVA/XG hydrogels were chelated with ferric ions by immersion to improve the gel strength(compressive modulus and tensile modulus can reach up to 0.62 MPa and0.079 MPa, respectively), conductivity(conductivity values ranging from 9 × 10^(-4) S/cm to 1 × 10^(-3)S/cm)and bacterial inhibition properties(up to 98.56%). Subsequently, the effects of the ratio of PVA and XG and the immersion time of Fe^(3+) on the hydrogels were investigated, and DNGel3 was given the most priority on a comprehensive consideration. It was demonstrated that the DNCGel exhibit good biocompatibility in vitro, effectively facilitate wound healing in vivo(up to 97.8% healing rate) under electrical stimulation, and monitors human movement in real time. This work provides a novel avenue to explore multifunctional intelligent hydrogels that hold great promise in biomedical fields such as smart wound dressings and flexible wearable sensors.
基金supported by R&D Program of Power Batteries with Low Temperature and High Energy,Science and Technology Bureau of Changchun(19SS013)Key Subject Construction of Physical Chemistry of Northeast Normal University+1 种基金the Fundamental Research Funds for the Central Universities(2412020FZ007,2412020FZ008)National Natural Science Foundation of China(22102020)
文摘The self-healing solid polymer electrolytes(SHSPEs)can spontaneously eliminate mechanical damages or micro-cracks generated during the assembly or operation of lithium-ion batteries(LIBs),significantly improving cycling performance and extending service life of LIBs.Here,we report a novel cross-linked network SHSPE(PDDP)containing hydrogen bonds and dynamic disulfide bonds with excellent self-healing properties and nonflammability.The combination of hydrogen bonding between urea groups and the metathesis reaction of dynamic disulfide bonds endows PDDP with rapid self-healing capacity at 28°C without external stimulation.Furthermore,the addition of 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide(EMIMTFSI)improves the ionic conductivity(1.13×10^(−4)S cm^(−1)at 28°C)and non-flammability of PDDP.The assembled Li/PDDP/LiFePO_(4)cell exhibits excellent cycling performance with a discharge capacity of 137 mA h g^(−1)after 300 cycles at 0.2 C.More importantly,the self-healed PDDP can recover almost the same ionic conductivity and cycling performance as the original PDDP.
基金supported by the National Natural Science Foundation of China(No.U22A20420)the Science and Technology Plan Project of Changzhou(No.CJ20235017)In addi-tion,the authors thank Jiangsu Development&Reform Commis-sion for their support.
文摘Gel polymer electrolytes(GPEs)effectively combine the advantages of high ionic conductivity and re-duce the risk of leakage associated with liquid.In this study,a chemically cross-linked gel polymer electrolyte was prepared by in-situ polymerization using polymethyl methacrylate(PMMA)as a matrix and neopentyl glycol diacrylate(NPGDA)as cross-linking agent.The cross-linked structure of the GPE was preliminarily investigated,as well as the influence of the degree of cross-linking on its phys-ical properties.The GPE exhibited a superior conductivity of 1.391 mS cm^(-1) at 25℃.Herein,the Li|GPE|LiNi_(0.8) Co_(0.1) Mn_(0.1) O_(2) cell has an excellent capacity retention rate of 80.7%after 150 cycles at 0.5 C in addition to a high discharge specific capacity of 203 mAh g^(-1).The structure of the cathode ma-terial is shielded from the production of byproducts during the charging and discharging of lithium-ion batteries by the cross-linked PMMA GPE.
基金financially supported by the National Natural Science Foundation of China(Grant No.22325405,22321002,22279153)Liaoning Revitalization Talents Program(XLYC1807207,XLYC2203134)DICP I202104。
文摘Solid polymer electrolytes(SPEs)have become increasingly important in advanced lithium-ion batteries(LIBs)due to their improved safety and mechanical properties compared to organic liquid electrolytes.Cross-linked polymers have the potential to further improve the mechanical property without trading off Li-ion conductivity.In this study,focusing on a recently developed cross-linked SPE,i.e.,the one based on poly(vinylene carbonate)-poly(ethylene oxide)cross-linked network(PVCN),we used solid-state nuclear magnetic resonance(NMR)techniques to investigate the fundamental interaction between the chain segments and Li ions,as well as the lithium-ion motion.By utilizing homonuclear/heteronuclear correlation,CP(cross-polarization)kinetics,and spin-lattice relaxation experiments,etc.,we revealed the structural characteristics and their relations to lithium-ion mobilities.It is found that the network formation prevents poly(ethylene oxide)chains from crystallization,which could create sufficient space for segmental tumbling and Li-ion co nductio n.As such,the mechanical property is greatly improved with even higher Li-ion mobilities compared to the poly(vinylene carbonate)or poly(ethylene oxide)based SPE analogues.
基金supported by the National Natural Science Foundation of China(22175149,21975215)the Natural Science Foundation of Hunan Province(2021JJ30661)the Scientific Research Foundation of Hunan Provincial Education Department(19A486)。
文摘Efficient polymeric room-temperature phosphorescence(PRTP)with excellent processability and flexibility is highly desirable but still faces formidable challenge.Herein,a general strategy is developed for efficient PRTP through photo-polymerization of phosphor monomers and N-isopropylacrylamide(NIPAM)spontaneously without a crosslinker.Remarkably ultralong lifetime of 3.54 s with afterglow duration time of 25 s and decent phosphorescent quantum efficiency of 13%are achieved.This efficient PRTP has been demonstrated to be derived from the synergistic effect of the covalent and hydrogen bonds networks formed through photo-polymerization of NIPAM.The electron paramagnetic resonance(EPR)spectra confirmed that methyl radicals are generated under the irradiation of ultraviolet light and promote the formation of covalent cross-linking networks.This strategy has also been proved to be generalizable to several other phosphor monomers.Interestingly,the polymer films display ultrahigh temperature resistance with long afterglows even at 140℃ and unexampled ultralong lifetime of 2.45 s in aqueous solutions.This work provides a simple and feasible avenue to obtain efficient PRTP.
基金financially supported by the National Natural Science Foundation of China (No. 51803130)Fundamental Research Funds for Central UniversitiesChongqing University Key Laboratory of Micro/Nano Materials Engineering and Technology (No. KFJJ2005)
文摘Physical cross-linking by hydrogen-bonds (H-bonds), providing a good combination of application properties of thermosets and processability of thermoplastics, is a potential strategy to resolve the recycling problem of traditional chemically cross-linked polyethylene. However, ureidopyrimidone (UPy), the most widely used H-bonding motif, is unfavorable for large-scale industrial application due to its poor thermal stability. In this work, H-bonds cross-linked polyethylene was successfully prepared by reactive melt blending maleic anhydride grafted polyethylene (PE-g-MAH) with 3-amino-1,2,4-triazole (ATA) to form amide triazole ring-carboxylic acid units. Triazole ring can easily generate multiple H-bonds with carboxylic acid and amide. More importantly, these units are more thermal stable than UPy due to the absence of unstable urea group of UPy. The introduction of H-bonds cross-linking leads to an obvious improvement in mechanical properties and creep resistance and a good maintain in thermal properties and recyclability. Furthermore, the reinforcement effect monotonically improves with increasing the density of H-bonds. The obtained good properties are mainly attributed to largely enhanced interchain interactions induced by H-bonds cross-linking and intrinsic reversibility of H-bonds. This work develops a novel way for the simple fabrication of H-bonds cross-linked PE with high performance through reactive melt blending.
基金the Science and Technology Department of Henan Province of China(Grant No.222102240060 and 222300420541)the Education Department of Henan Province of China(Grant No.22B430023)supported by the Program for Innovative Research Team(in Science and Technology)in University of Henan Province(Grant No.23IRTSTHN009)。
文摘Lithium-metal anodes(LMAs)have been recognized as the ultimate anodes for next-generation batteries with high energy density,but stringent assembly-environment conditions derived from the poor moisture stability dramatically hinder the transformation of LMAs from laboratory to industry.Herein,an in situ formed cross-linked polymer layer on LMAs is designed and constructed by a facile thiol-acrylate click chemistry reaction between poly(ethylene glycol)diacrylate(PEGDA)and the crosslinker containing multi thiol groups under UV irradiation.Owing to the hydrophobic nature of the layer,the treated LMAs demonstrate remarkable humid stability for more than 3 h in ambient air(70%relative humidity).The coating humid-resistant protective layer also possesses a dual-functional characterization as solid polymer electrolytes by introducing lithium bis(trifluoromethanesulfonyl)imide in the system in advance.The intimate contact between the polymer layer and LMAs reduces interfacial resistance in the assembled Li/LiFePO_(4)or Li/LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2)full cell effectively,and endows the cell with an outstanding cycle performance.
文摘The retrospective study by Edwar et al reinforces the role of therapeutic penetrating keratoplasty(PK)as a vital intervention in severe,treatment-resistant infectious keratitis.In advanced cases—often complicated by trauma,delayed presentation,and corneal perforation—PK restores globe integrity and provides limited visual recovery.However,its application is constrained by graft-related complications and donor shortages,particularly in low-resource settings.These limitations highlight the need for earlier,globe-sparing strategies to prevent progression and reduce surgical demand.Photoactivated chromophore for infectious keratitis-corneal collagen cross-linking(PACK-CXL)has emerged as a promising adjunct or alternative.With both antimicrobial and tissue-stabilizing effects,PACK-CXL may control infection and preserve corneal structure in earlier stages.A layered treatment framework that incorporates PACK-CXL as an initial intervention and reserves PK for refractory cases may help improve clinical outcomes.Further studies are needed to define their best use in practice.
基金supported by Research Fund of SWUST for PhD(Grant No.22zx7175)Sichuan Science and Technology Program(Grant No.2024NSFSC1097)。
文摘Based on the characteristics of laser-induced surface ignition,energetic photosensitive films show promising potential to meet the ignition requirements of various energetic materials(EMs).In this study,DATNBI/ferric alginate(DI/FeA),DI/cobalt alginate(DI/CoA),and DI/nickel alginate(DI/Ni A)films are fabricated by employing sodium alginate(SA)with a three-dimensional network structure as the film matrix,via ionic cross-linking of SA with Fe^(3+),Co^(2+),and Ni^(2+)ions.The study demonstrates that the ionic cross-linking enhances the hydrophobic performance of the films,with the water contact angle increasing from 82.1° to 123.5°.Concurrently,the films'near-infrared(NIR)light absorption improved.Furthermore,transition metal ions facilitate accelerated electron transfer,thereby catalyzing the thermal decomposition of DATNBI.Under 1064 nm laser irradiation,the DI/Fe A film exhibits exceptional combustion performance,with an ignition delay time as low as 76 ms.It successfully acts as an NIR laser ignition medium to initiate the self-sustained combustion of CL-20.This study demonstrates the synergistic realization of enhanced hydrophobicity,improved photosensitivity,and promoted catalytic decomposition through microstructural design of the material,providing new insights for the design of additive-free EMs in laser ignition applications.
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.
