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
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
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
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
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
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
The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a n...The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.展开更多
This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential g...This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential growth of mobile devices and data traffic has substantially increased network congestion,particularly in urban areas and regions with limited terrestrial infrastructure.Our approach jointly optimizes unmanned aerial vehicle(UAV)trajectories and satellite-assisted offloading strategies to simultaneously maximize data throughput,minimize energy consumption,and maintain equitable resource distribution.The proposed RMAPPO framework incorporates recurrent neural networks(RNNs)to model temporal dependencies in UAV mobility patterns and utilizes a decentralized multi-agent reinforcement learning architecture to reduce communication overhead while improving system robustness.The proposed RMAPPO algorithm was evaluated through simulation experiments,with the results indicating that it significantly enhances the cumulative traffic offloading rate of nodes and reduces the energy consumption of UAVs.展开更多
Background:Wenqing Yin(WQY)is a classic prescription used to treat skin diseases like atopic dermatitis(AD)in China,and the aim of this study is to investigate the therapeutic effects and molecular mechanisms of WQY o...Background:Wenqing Yin(WQY)is a classic prescription used to treat skin diseases like atopic dermatitis(AD)in China,and the aim of this study is to investigate the therapeutic effects and molecular mechanisms of WQY on AD.Methods:The DNFB-induced mouse models of AD were established to investigate the therapeutic effects of WQY on AD.The symptoms of AD in the ears and backs of the mice were assessed,while inflammatory factors in the ear were quantified using quantitative real-time-polymerase chain reaction(qRT-PCR),and the percentages of CD4^(+)and CD8^(+)cells in the spleen were analyzed through flow cytometry.The compounds in WQY were identified using ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)analysis and the key targets and pathways of WQY to treat AD were predicted by network pharmacology.Subsequently,the key genes were tested and verified by qRT-PCR,and the potential active components and target proteins were verified by molecular docking.Results:WQY relieved the AD symptoms and histopathological injuries in the ear and back skin of mice with AD.Meanwhile,WQY significantly reduced the levels of inflammatory factors IL-6 and IL-1βin ear tissue,as well as the ratio of CD4^(+)/CD8^(+)cells in spleen.Additionally,a total of 142 compounds were identified from the water extract of WQY by UPLC-Orbitrap-MS/MS.39 key targets related to AD were screened out by network pharmacology methods.The KEGG analysis indicated that the effects of WQY were primarily mediated through pathways associated with Toll-like receptor signaling and T cell receptor signaling.Moreover,the results of qRT-PCR demonstrated that WQY significantly reduced the mRNA expressions of IL-4,IL-10,GATA3 and FOXP3,and molecular docking simulation verified that the active components of WQY had excellent binding abilities with IL-4,IL-10,GATA3 and FOXP3 proteins.Conclusion:The present study demonstrated that WQY effectively relieved AD symptoms in mice,decreased the inflammatory factors levels,regulated the balance of CD4^(+)and CD8^(+)cells,and the mechanism may be associated with the suppression of Th2 and Treg cell immune responses.展开更多
Skin diseases affect millions worldwide.Early detection is key to preventing disfigurement,lifelong disability,or death.Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and ...Skin diseases affect millions worldwide.Early detection is key to preventing disfigurement,lifelong disability,or death.Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance,and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks(CNNs).We frame skin lesion recognition as graph-based reasoning and,to ensure fair evaluation and avoid data leakage,adopt a strict lesion-level partitioning strategy.Each image is first over-segmented using SLIC(Simple Linear Iterative Clustering)to produce perceptually homogeneous superpixels.These superpixels form the nodes of a region-adjacency graph whose edges encode spatial continuity.Node attributes are 1280-dimensional embeddings extracted with a lightweight yet expressive EfficientNet-B0 backbone,providing strong representational power at modest computational cost.The resulting graphs are processed by a five-layer Graph Attention Network(GAT)that learns to weight inter-node relationships dynamically and aggregates multi-hop context before classifying lesions into seven classes with a log-softmax output.Extensive experiments on the DermaMNIST benchmark show the proposed pipeline achieves 88.35%accuracy and 98.04%AUC,outperforming contemporary CNNs,AutoML approaches,and alternative graph neural networks.An ablation study indicates EfficientNet-B0 produces superior node descriptors compared with ResNet-18 and DenseNet,and that roughly five GAT layers strike a good balance between being too shallow and over-deep while avoiding oversmoothing.The method requires no data augmentation or external metadata,making it a drop-in upgrade for clinical computer-aided diagnosis systems.展开更多
With the increasing severity of network security threats,Network Intrusion Detection(NID)has become a key technology to ensure network security.To address the problem of low detection rate of traditional intrusion det...With the increasing severity of network security threats,Network Intrusion Detection(NID)has become a key technology to ensure network security.To address the problem of low detection rate of traditional intrusion detection models,this paper proposes a Dual-Attention model for NID,which combines Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM)to design two modules:the FocusConV and the TempoNet module.The FocusConV module,which automatically adjusts and weights CNN extracted local features,focuses on local features that are more important for intrusion detection.The TempoNet module focuses on global information,identifies more important features in time steps or sequences,and filters and weights the information globally to further improve the accuracy and robustness of NID.Meanwhile,in order to solve the class imbalance problem in the dataset,the EQL v2 method is used to compute the class weights of each class and to use them in the loss computation,which optimizes the performance of the model on the class imbalance problem.Extensive experiments were conducted on the NSL-KDD,UNSW-NB15,and CIC-DDos2019 datasets,achieving average accuracy rates of 99.66%,87.47%,and 99.39%,respectively,demonstrating excellent detection accuracy and robustness.The model also improves the detection performance of minority classes in the datasets.On the UNSW-NB15 dataset,the detection rates for Analysis,Exploits,and Shellcode attacks increased by 7%,7%,and 10%,respectively,demonstrating the Dual-Attention CNN-BiLSTM model’s excellent performance in NID.展开更多
The rapid growth of distributed data-centric applications and AI workloads increases demand for low-latency,high-throughput communication,necessitating frequent and flexible updates to network routing configurations.H...The rapid growth of distributed data-centric applications and AI workloads increases demand for low-latency,high-throughput communication,necessitating frequent and flexible updates to network routing configurations.However,maintaining consistent forwarding states during these updates is challenging,particularly when rerouting multiple flows simultaneously.Existing approaches pay little attention to multi-flow update,where improper update sequences across data plane nodes may construct deadlock dependencies.Moreover,these methods typically involve excessive control-data plane interactions,incurring significant resource overhead and performance degradation.This paper presents P4LoF,an efficient loop-free update approach that enables the controller to reroute multiple flows through minimal interactions.P4LoF first utilizes a greedy-based algorithm to generate the shortest update dependency chain for the single-flow update.These chains are then dynamically merged into a dependency graph and resolved as a Shortest Common Super-sequence(SCS)problem to produce the update sequence of multi-flow update.To address deadlock dependencies in multi-flow updates,P4LoF builds a deadlock-fix forwarding model that leverages the flexible packet processing capabilities of the programmable data plane.Experimental results show that P4LoF reduces control-data plane interactions by at least 32.6%with modest overhead,while effectively guaranteeing loop-free consistency.展开更多
The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditio...The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditional approaches like network compression,quantization,and lightweight design often sacrifice accuracy or feature representation robustness.This article introduces the Fast Multi-scale Channel Shuffling Network(FMCSNet),a novel lightweight detection model optimized for mobile devices.FMCSNet integrates a fully convolutional Multilayer Perceptron(MLP)module,offering global perception without significantly increasing parameters,effectively bridging the gap between CNNs and Vision Transformers.FMCSNet achieves a delicate balance between computation and accuracy mainly by two key modules:the ShiftMLP module,including a shift operation and an MLP module,and a Partial group Convolutional(PGConv)module,reducing computation while enhancing information exchange between channels.With a computational complexity of 1.4G FLOPs and 1.3M parameters,FMCSNet outperforms CNN-based and DWConv-based ShuffleNetv2 by 1%and 4.5%mAP on the Pascal VOC 2007 dataset,respectively.Additionally,FMCSNet achieves a mAP of 30.0(0.5:0.95 IoU threshold)with only 2.5G FLOPs and 2.0M parameters.It achieves 32 FPS on low-performance i5-series CPUs,meeting real-time detection requirements.The versatility of the PGConv module’s adaptability across scenarios further highlights FMCSNet as a promising solution for real-time mobile object detection.展开更多
Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may r...Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.展开更多
With the rapid development of the Artificial Intelligence of Things(AIoT),convolutional neural networks(CNNs)have demonstrated potential and remarkable performance in AIoT applications due to their excellent performan...With the rapid development of the Artificial Intelligence of Things(AIoT),convolutional neural networks(CNNs)have demonstrated potential and remarkable performance in AIoT applications due to their excellent performance in various inference tasks.However,the users have concerns about privacy leakage for the use of AI and the performance and efficiency of computing on resource-constrained IoT edge devices.Therefore,this paper proposes an efficient privacy-preserving CNN framework(i.e.,EPPA)based on the Fully Homomorphic Encryption(FHE)scheme for AIoT application scenarios.In the plaintext domain,we verify schemes with different activation structures to determine the actual activation functions applicable to the corresponding ciphertext domain.Within the encryption domain,we integrate batch normalization(BN)into the convolutional layers to simplify the computation process.For nonlinear activation functions,we use composite polynomials for approximate calculation.Regarding the noise accumulation caused by homomorphic multiplication operations,we realize the refreshment of ciphertext noise through minimal“decryption-encryption”interactions,instead of adopting bootstrapping operations.Additionally,in practical implementation,we convert three-dimensional convolution into two-dimensional convolution to reduce the amount of computation in the encryption domain.Finally,we conduct extensive experiments on four IoT datasets,different CNN architectures,and two platforms with different resource configurations to evaluate the performance of EPPA in detail.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 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 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 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.
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.
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 Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1A6A1A10044950).
文摘The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.
文摘This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential growth of mobile devices and data traffic has substantially increased network congestion,particularly in urban areas and regions with limited terrestrial infrastructure.Our approach jointly optimizes unmanned aerial vehicle(UAV)trajectories and satellite-assisted offloading strategies to simultaneously maximize data throughput,minimize energy consumption,and maintain equitable resource distribution.The proposed RMAPPO framework incorporates recurrent neural networks(RNNs)to model temporal dependencies in UAV mobility patterns and utilizes a decentralized multi-agent reinforcement learning architecture to reduce communication overhead while improving system robustness.The proposed RMAPPO algorithm was evaluated through simulation experiments,with the results indicating that it significantly enhances the cumulative traffic offloading rate of nodes and reduces the energy consumption of UAVs.
基金supported by grants from the National Natural Science Foundation of China(82004252)the Project of Administration of Traditional Chinese Medicine of Guangdong Province(202405112017596500)the Basic and Applied Basic Research Foundation of Guangzhou Municipal Science and Technology Bureau(202102020533).
文摘Background:Wenqing Yin(WQY)is a classic prescription used to treat skin diseases like atopic dermatitis(AD)in China,and the aim of this study is to investigate the therapeutic effects and molecular mechanisms of WQY on AD.Methods:The DNFB-induced mouse models of AD were established to investigate the therapeutic effects of WQY on AD.The symptoms of AD in the ears and backs of the mice were assessed,while inflammatory factors in the ear were quantified using quantitative real-time-polymerase chain reaction(qRT-PCR),and the percentages of CD4^(+)and CD8^(+)cells in the spleen were analyzed through flow cytometry.The compounds in WQY were identified using ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)analysis and the key targets and pathways of WQY to treat AD were predicted by network pharmacology.Subsequently,the key genes were tested and verified by qRT-PCR,and the potential active components and target proteins were verified by molecular docking.Results:WQY relieved the AD symptoms and histopathological injuries in the ear and back skin of mice with AD.Meanwhile,WQY significantly reduced the levels of inflammatory factors IL-6 and IL-1βin ear tissue,as well as the ratio of CD4^(+)/CD8^(+)cells in spleen.Additionally,a total of 142 compounds were identified from the water extract of WQY by UPLC-Orbitrap-MS/MS.39 key targets related to AD were screened out by network pharmacology methods.The KEGG analysis indicated that the effects of WQY were primarily mediated through pathways associated with Toll-like receptor signaling and T cell receptor signaling.Moreover,the results of qRT-PCR demonstrated that WQY significantly reduced the mRNA expressions of IL-4,IL-10,GATA3 and FOXP3,and molecular docking simulation verified that the active components of WQY had excellent binding abilities with IL-4,IL-10,GATA3 and FOXP3 proteins.Conclusion:The present study demonstrated that WQY effectively relieved AD symptoms in mice,decreased the inflammatory factors levels,regulated the balance of CD4^(+)and CD8^(+)cells,and the mechanism may be associated with the suppression of Th2 and Treg cell immune responses.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01296).
文摘Skin diseases affect millions worldwide.Early detection is key to preventing disfigurement,lifelong disability,or death.Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance,and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks(CNNs).We frame skin lesion recognition as graph-based reasoning and,to ensure fair evaluation and avoid data leakage,adopt a strict lesion-level partitioning strategy.Each image is first over-segmented using SLIC(Simple Linear Iterative Clustering)to produce perceptually homogeneous superpixels.These superpixels form the nodes of a region-adjacency graph whose edges encode spatial continuity.Node attributes are 1280-dimensional embeddings extracted with a lightweight yet expressive EfficientNet-B0 backbone,providing strong representational power at modest computational cost.The resulting graphs are processed by a five-layer Graph Attention Network(GAT)that learns to weight inter-node relationships dynamically and aggregates multi-hop context before classifying lesions into seven classes with a log-softmax output.Extensive experiments on the DermaMNIST benchmark show the proposed pipeline achieves 88.35%accuracy and 98.04%AUC,outperforming contemporary CNNs,AutoML approaches,and alternative graph neural networks.An ablation study indicates EfficientNet-B0 produces superior node descriptors compared with ResNet-18 and DenseNet,and that roughly five GAT layers strike a good balance between being too shallow and over-deep while avoiding oversmoothing.The method requires no data augmentation or external metadata,making it a drop-in upgrade for clinical computer-aided diagnosis systems.
基金supported by the High-Level Talent Foundation of Jinling Institute of Technology(grant number.JIT-B-202413).
文摘With the increasing severity of network security threats,Network Intrusion Detection(NID)has become a key technology to ensure network security.To address the problem of low detection rate of traditional intrusion detection models,this paper proposes a Dual-Attention model for NID,which combines Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM)to design two modules:the FocusConV and the TempoNet module.The FocusConV module,which automatically adjusts and weights CNN extracted local features,focuses on local features that are more important for intrusion detection.The TempoNet module focuses on global information,identifies more important features in time steps or sequences,and filters and weights the information globally to further improve the accuracy and robustness of NID.Meanwhile,in order to solve the class imbalance problem in the dataset,the EQL v2 method is used to compute the class weights of each class and to use them in the loss computation,which optimizes the performance of the model on the class imbalance problem.Extensive experiments were conducted on the NSL-KDD,UNSW-NB15,and CIC-DDos2019 datasets,achieving average accuracy rates of 99.66%,87.47%,and 99.39%,respectively,demonstrating excellent detection accuracy and robustness.The model also improves the detection performance of minority classes in the datasets.On the UNSW-NB15 dataset,the detection rates for Analysis,Exploits,and Shellcode attacks increased by 7%,7%,and 10%,respectively,demonstrating the Dual-Attention CNN-BiLSTM model’s excellent performance in NID.
基金supported by the National Key Research and Development Program of China under Grant 2022YFB2901501in part by the Science and Technology Innovation leading Talents Subsidy Project of Central Plains under Grant 244200510038.
文摘The rapid growth of distributed data-centric applications and AI workloads increases demand for low-latency,high-throughput communication,necessitating frequent and flexible updates to network routing configurations.However,maintaining consistent forwarding states during these updates is challenging,particularly when rerouting multiple flows simultaneously.Existing approaches pay little attention to multi-flow update,where improper update sequences across data plane nodes may construct deadlock dependencies.Moreover,these methods typically involve excessive control-data plane interactions,incurring significant resource overhead and performance degradation.This paper presents P4LoF,an efficient loop-free update approach that enables the controller to reroute multiple flows through minimal interactions.P4LoF first utilizes a greedy-based algorithm to generate the shortest update dependency chain for the single-flow update.These chains are then dynamically merged into a dependency graph and resolved as a Shortest Common Super-sequence(SCS)problem to produce the update sequence of multi-flow update.To address deadlock dependencies in multi-flow updates,P4LoF builds a deadlock-fix forwarding model that leverages the flexible packet processing capabilities of the programmable data plane.Experimental results show that P4LoF reduces control-data plane interactions by at least 32.6%with modest overhead,while effectively guaranteeing loop-free consistency.
基金funded by the National Natural Science Foundation of China under Grant No.62371187the Open Program of Hunan Intelligent Rehabilitation Robot and Auxiliary Equipment Engineering Technology Research Center under Grant No.2024JS101.
文摘The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditional approaches like network compression,quantization,and lightweight design often sacrifice accuracy or feature representation robustness.This article introduces the Fast Multi-scale Channel Shuffling Network(FMCSNet),a novel lightweight detection model optimized for mobile devices.FMCSNet integrates a fully convolutional Multilayer Perceptron(MLP)module,offering global perception without significantly increasing parameters,effectively bridging the gap between CNNs and Vision Transformers.FMCSNet achieves a delicate balance between computation and accuracy mainly by two key modules:the ShiftMLP module,including a shift operation and an MLP module,and a Partial group Convolutional(PGConv)module,reducing computation while enhancing information exchange between channels.With a computational complexity of 1.4G FLOPs and 1.3M parameters,FMCSNet outperforms CNN-based and DWConv-based ShuffleNetv2 by 1%and 4.5%mAP on the Pascal VOC 2007 dataset,respectively.Additionally,FMCSNet achieves a mAP of 30.0(0.5:0.95 IoU threshold)with only 2.5G FLOPs and 2.0M parameters.It achieves 32 FPS on low-performance i5-series CPUs,meeting real-time detection requirements.The versatility of the PGConv module’s adaptability across scenarios further highlights FMCSNet as a promising solution for real-time mobile object detection.
文摘Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.
基金supported by the Natural Science Foundation of China No.62362008the Major Scientific and Technological Special Project of Guizhou Province([2024]014).
文摘With the rapid development of the Artificial Intelligence of Things(AIoT),convolutional neural networks(CNNs)have demonstrated potential and remarkable performance in AIoT applications due to their excellent performance in various inference tasks.However,the users have concerns about privacy leakage for the use of AI and the performance and efficiency of computing on resource-constrained IoT edge devices.Therefore,this paper proposes an efficient privacy-preserving CNN framework(i.e.,EPPA)based on the Fully Homomorphic Encryption(FHE)scheme for AIoT application scenarios.In the plaintext domain,we verify schemes with different activation structures to determine the actual activation functions applicable to the corresponding ciphertext domain.Within the encryption domain,we integrate batch normalization(BN)into the convolutional layers to simplify the computation process.For nonlinear activation functions,we use composite polynomials for approximate calculation.Regarding the noise accumulation caused by homomorphic multiplication operations,we realize the refreshment of ciphertext noise through minimal“decryption-encryption”interactions,instead of adopting bootstrapping operations.Additionally,in practical implementation,we convert three-dimensional convolution into two-dimensional convolution to reduce the amount of computation in the encryption domain.Finally,we conduct extensive experiments on four IoT datasets,different CNN architectures,and two platforms with different resource configurations to evaluate the performance of EPPA in detail.
