This study explored the therapeutic targets and molecular mechanisms of Huangqi Guizhi Decoction (HGD) in alleviatingpulmonary embolism (PE) by employing network pharmacology and molecular docking techniques. Firstly,...This study explored the therapeutic targets and molecular mechanisms of Huangqi Guizhi Decoction (HGD) in alleviatingpulmonary embolism (PE) by employing network pharmacology and molecular docking techniques. Firstly, the effective activecomponents of the Chinese herbs in HGD were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database(TCMSP), and their potential therapeutic targets were predicted using the Swiss Target Prediction platform. Subsequently, PErelatedtarget genes were obtained from the Online Mendelian Inheritance in Man (OMIM) database and GeneCards database.Then, the Wei Sheng Xin tool was used to generate a Venn diagram for identifying the common targets between the herb-relatedtargets and PE-related targets. After screening these common targets, a “drug-component-target network” and a protein-proteininteraction (PPI) network were constructed. Furthermore, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia ofGenes and Genomes (KEGG) enrichment analysis were conducted on the intersecting targets, and molecular docking verificationwas performed using AutoDockTools and PyMol software. Finally, 20 active components were screened from Astragali Radix, 7from Cinnamomi Ramulus, 13 from Paeoniae Radix Alba, 5 from Zingiberis Rhizoma Recens, and 29 from Jujubae Fructus, witha total of 983 therapeutic targets. Among these targets, 134 were associated with PE, and protein kinase B1 (AKT1), mitogenactivatedprotein kinase 1 (MAPK1), and transformation-related protein 53 (TP53) served as the core targets. The results of GOand KEGG enrichment analyses indicated that the alleviation of PE by HGD is mainly related to pathways including immuneresponse, regulation of gene expression, atherosclerosis, and tumorigenesis. Molecular docking results showed that the keyactive components in HGD could bind to the core targets spontaneously and stably. This study revealed that HGD may alleviatesymptoms in PE patients by regulating signaling pathways, modulating platelet function to exert anticoagulant effects, andregulating the expression of anti-inflammatory genes, which provided a direction for subsequent experimental research.展开更多
Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the u...Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the use of virtual reality(VR)technology.VR has been demonstrated to be an effective treatment for pain associated with medical procedures,as well as for chronic pain conditions for which no effective treatment has been established.The precise mechanism by which the diversion from reality facilitated by VR contributes to the diminution of pain and anxiety has yet to be elucidated.However,the provision of positive images through VR-based visual stimulation may enhance the functionality of brain networks.The salience network is diminished,while the default mode network is enhanced.Additionally,the medial prefrontal cortex may establish a stronger connection with the default mode network,which could result in a reduction of pain and anxiety.Further research into the potential of VR technology to alleviate pain could lead to a reduction in the number of individuals who overdose on painkillers and contribute to positive change in the medical field.展开更多
In the sixth generation mobile communication(6G) system,Non-Terrestrial Networks(NTN),as a supplement to terrestrial network,can meet the requirements of wide area intelligent connection and global ubiquitous seamless...In the sixth generation mobile communication(6G) system,Non-Terrestrial Networks(NTN),as a supplement to terrestrial network,can meet the requirements of wide area intelligent connection and global ubiquitous seamless access,establish intelligent connection for wide area objects,and provide intelligent services.Due to issues such as massive access,doppler shift,and limited spectrum resources in NTN,research on resource management is crucial for optimizing NTN performance.In this paper,a comprehensive survey of multi-pattern heterogeneous NTN resource management is provided.Firstly,the key technologies involved in NTN resource management is summarized.Secondly,NTN resource management is discussed from network pattern and resource pattern.The network pattern focuses on the application of different optimization methods to different network dimension communication resource management,and the resource type pattern focuses on the research and application of multi-domain resource management such as computation,cache,communication and sensing.Finally,future research directions and challenges of 6G NTN resource management are discussed.展开更多
With the rapid development of information technology,the scale of the network is expanding,and the complexity is increasing day by day.The traditional network management is facing great challenges.The emergence of sof...With the rapid development of information technology,the scale of the network is expanding,and the complexity is increasing day by day.The traditional network management is facing great challenges.The emergence of software-defined network(SDN)technology has brought revolutionary changes to modern network management.This paper aims to discuss the application and prospects of SDN technology in modern network management.Firstly,the basic principle and architecture of SDN are introduced,including the separation of control plane and data plane,centralized control and open programmable interface.Then,it analyzes the advantages of SDN technology in network management,such as simplifying network configuration,improving network flexibility,optimizing network resource utilization,and realizing fast fault recovery.The application examples of SDN in data center networks and WAN optimization management are analyzed.This paper also discusses the development status and trend of SDN in enterprise networks,including the integration of technologies such as cloud computing,big data,and artificial intelligence,the construction of an intelligent and automated network management platform,the improvement of network management efficiency and quality,and the openness and interoperability of network equipment.Finally,the advantages and challenges of SDN technology are summarized,and its future development direction is provided.展开更多
To investigate the targets and mechanism of Hedysarum Multijugum Maxim(HMM)in treatment of bladder cancer(BC).Based on Traditional Chinese Medicine Systems Pharmacology(TCMSP)and gene databases,active substances and p...To investigate the targets and mechanism of Hedysarum Multijugum Maxim(HMM)in treatment of bladder cancer(BC).Based on Traditional Chinese Medicine Systems Pharmacology(TCMSP)and gene databases,active substances and potential targets of HMM were screened,and the HMM-active substances-targets-BC(HATB)regulatory network and PPI network were constructed.Hub targets were screened by Cytoscape.The main active substances and Hub targets were molecularly docked with AutoDock and visualized by PyMOL.12 Hub targets were screened.Molecular docking showed that active substances mainly acted on MAPK14,MAPK1 and CCND1.The bindings of calycosin to MAPK14,formononetin to MAPK14,and calycosin to CCND1 were stable.展开更多
The 5G-R network is on the verge of entering the construction stage.Given that the dedicated network for railways is closely linked to train operation safety,there are extremely high requirements for network security....The 5G-R network is on the verge of entering the construction stage.Given that the dedicated network for railways is closely linked to train operation safety,there are extremely high requirements for network security.As a result,there is an urgent need to conduct research on 5G-R network security.To comprehensively enhance the end-to-end security protection of the 5G-R network,this study summarized the security requirements of the GSM-R network,analyzed the security risks and requirements faced by the 5G-R network,and proposed an overall 5G-R network security architecture.The security technical schemes were detailed from various aspects:5G-R infrastructure security,terminal access security,networking security,operation and maintenance security,data security,and network boundary security.Additionally,the study proposed leveraging the 5G-R security situation awareness system to achieve a comprehensive upgrade from basic security technologies to endogenous security capabilities within the 5G-R system.展开更多
Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribut...Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribute management methods based on manual extraction face several issues,such as high costs for attribute extraction,long processing times,unstable accuracy,and poor scalability.To address these problems,this paper proposes an attribute mining technology for access control institutions based on hybrid capsule networks.This technology leverages transfer learning ideas,utilizing Bidirectional Encoder Representations from Transformers(BERT)pre-trained language models to achieve vectorization of unstructured text data resources.Furthermore,we have designed a novel end-to-end parallel hybrid network structure,where the parallel networks handle global and local information features of the text that they excel at,respectively.By employing techniques such as attention mechanisms,capsule networks,and dynamic routing,effective mining of security attributes for access control resources has been achieved.Finally,we evaluated the performance level of the proposed attribute mining method for access control institutions through experiments on the medical referral text resource dataset.The experimental results show that,compared with baseline algorithms,our method adopts a parallel network structure that can better balance global and local feature information,resulting in improved overall performance.Specifically,it achieves a comprehensive performance enhancement of 2.06%to 8.18%in the F1 score metric.Therefore,this technology can effectively provide attribute support for access control of unstructured text big data resources.展开更多
In order to reveal the multi-target pharmacological mechanism of Zedoary turmeric oil combined with docetaxel in the treatment of breast cancer,we used network pharmacology and molecular docking.The targets of docetax...In order to reveal the multi-target pharmacological mechanism of Zedoary turmeric oil combined with docetaxel in the treatment of breast cancer,we used network pharmacology and molecular docking.The targets of docetaxel were retrieved from the Swiss Target Prediction database.The active components of Curcuma Zedoary turmeric were screened using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP),with criteria set as oral bioavailability OB≥30%and drug-likeness DL≥0.1.Potential targets of these components were subsequently predicted.Breast cancer-related targets were retrieved from the OMIM and GeneCards databases.The Venny tool was used to identify 177 overlapping targets between docetaxel,Zedoary turmeric oil,and breast cancer,followed by protein-protein interaction(PPI)analysis.Gene Ontology(GO)functional enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analyses were performed using the Metascape database.A drug-breast cancer-KEGG pathway target network was constructed using Cytoscape 3.8.0.Molecular docking was employed to verify the binding ability between drugs and core targets.Results showed that the combined treatment may exert anti-breast cancer effects through key targets such as MAPK1,PIK3CA,and HSP90AA1,primarily implicating the biological process of protein phosphorylation and engaging the PI3K-Akt signaling pathway.This study successfully predicted the key targets and enriched pathways of Zedoary turmeric oil combined with docetaxel for breast cancer treatment,providing new insights for further research and development.展开更多
In this editorial,we comment on the article by Micucci et al published in the recent issue.We focus on the heterogenous nature of gastric cancer(GC)and the potential benefits of integrating traditional Chinese medicin...In this editorial,we comment on the article by Micucci et al published in the recent issue.We focus on the heterogenous nature of gastric cancer(GC)and the potential benefits of integrating traditional Chinese medicine(TCM)with the modern technology of network pharmacology(NP)and omics sequencing.GC is a heterogenous disease,as it incorporates several biochemical pathways that contribute to pathogenesis.TCM acknowledges the multifactorial,heterogenous nature of disease and utilizes an integrative approach to medicine.NP,a modern philosophy within drug development,integrates traditional knowledge of nutraceuticals and modern technologies to address the complex interactions of pathways within the body.Omics technologies,which is at the core of precision medicine,has allowed for this newfound principle of drug development.Metabolic pathways are better distinguished,leading to more targeted drug development.However,the use of omics technology needs to be employed to better characterize the subtypes of GC.This will allow TCM’s use of nutraceuticals in the application of NP to better target metabolic pathways that may aid in the prevention of GC as well as enhance treatment.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
Ocean geoscience is a highly integrated and interdisciplinary field that plays a critical role in understanding the interaction between Earth’s lithosphere,hydrosphere,atmosphere,biosphere,and anthroposphere.Recent y...Ocean geoscience is a highly integrated and interdisciplinary field that plays a critical role in understanding the interaction between Earth’s lithosphere,hydrosphere,atmosphere,biosphere,and anthroposphere.Recent years have seen tremendous progress in global ocean research,driven by rapid advancements in deep-sea manned and unmanned submersibles,ocean drilling,seafloor observatories,big data assimilation,and supercomputing simulations.Representative examples of breakthroughs are highlighted in this work:(1)Probing sub-seafloor processes.A 10,000-meter ocean-bottom seismometer array has achieved high-resolution imaging of the deepest ocean on the Earth-the Challenger Deep of the Mariana Trench,revealing the role of key tectonic and hydrological processes within the subduction zone.The first sub-ice seafloor seismic and magnetotelluric experiments were successfully conducted at the Arctic Gakkel Ridge,providing significant insights into the dynamics of ultraslow seafloor spreading.(2)Exploration of seafloor resources.Near-seafloor investigations employing underwater robotics and multi-sensor systems have been carried out in areas of hydrothermal vents and cold seeps at global locations,including the Southwest Indian Ridge.These efforts have combined geophysical,oceanographic,chemical,and biological observations with extensive seafloor sampling.(3)Interdisciplinary research of complex catastrophic events.High-resolution simulations integrating ocean observations with supercomputing modeling have made it possible to fully model earthquake-induced seafloor deformation,tsunami propagation,and ocean basin-scale transport of the Fukushima Power Plant-derived radionuclides associated with the 2011 Tohoku earthquake.Among the world’s three major oceans,the Indian Ocean is still relatively underexplored.Major scientific challenges include elucidating crust-mantle interaction,air-sea dynamic coupling,large-scale marine hazards,and responses of ecosystems to major environmental changes,all of which require interdisciplinary collaboration.Future efforts should focus on developing intelligent unmanned observation platform systems,big data and digital twins,and AI-driven hazard modeling.Meanwhile,higher educational reforms should emphasize fostering a new generation of students and young scientists with a solid background and strong critical analysis skills to accelerate technological innovation.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
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
基金supported by Research Project on Traditional Chinese Medicine in Heilongjiang Province in 2025(Research on the pharmacological substance basis of Huangqi Guizhi decoction in improving acute pulmonary embolism and lung injury based on the theory of“Diaphoresis and expanding meridian”No.ZHY2025-043).
文摘This study explored the therapeutic targets and molecular mechanisms of Huangqi Guizhi Decoction (HGD) in alleviatingpulmonary embolism (PE) by employing network pharmacology and molecular docking techniques. Firstly, the effective activecomponents of the Chinese herbs in HGD were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database(TCMSP), and their potential therapeutic targets were predicted using the Swiss Target Prediction platform. Subsequently, PErelatedtarget genes were obtained from the Online Mendelian Inheritance in Man (OMIM) database and GeneCards database.Then, the Wei Sheng Xin tool was used to generate a Venn diagram for identifying the common targets between the herb-relatedtargets and PE-related targets. After screening these common targets, a “drug-component-target network” and a protein-proteininteraction (PPI) network were constructed. Furthermore, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia ofGenes and Genomes (KEGG) enrichment analysis were conducted on the intersecting targets, and molecular docking verificationwas performed using AutoDockTools and PyMol software. Finally, 20 active components were screened from Astragali Radix, 7from Cinnamomi Ramulus, 13 from Paeoniae Radix Alba, 5 from Zingiberis Rhizoma Recens, and 29 from Jujubae Fructus, witha total of 983 therapeutic targets. Among these targets, 134 were associated with PE, and protein kinase B1 (AKT1), mitogenactivatedprotein kinase 1 (MAPK1), and transformation-related protein 53 (TP53) served as the core targets. The results of GOand KEGG enrichment analyses indicated that the alleviation of PE by HGD is mainly related to pathways including immuneresponse, regulation of gene expression, atherosclerosis, and tumorigenesis. Molecular docking results showed that the keyactive components in HGD could bind to the core targets spontaneously and stably. This study revealed that HGD may alleviatesymptoms in PE patients by regulating signaling pathways, modulating platelet function to exert anticoagulant effects, andregulating the expression of anti-inflammatory genes, which provided a direction for subsequent experimental research.
文摘Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the use of virtual reality(VR)technology.VR has been demonstrated to be an effective treatment for pain associated with medical procedures,as well as for chronic pain conditions for which no effective treatment has been established.The precise mechanism by which the diversion from reality facilitated by VR contributes to the diminution of pain and anxiety has yet to be elucidated.However,the provision of positive images through VR-based visual stimulation may enhance the functionality of brain networks.The salience network is diminished,while the default mode network is enhanced.Additionally,the medial prefrontal cortex may establish a stronger connection with the default mode network,which could result in a reduction of pain and anxiety.Further research into the potential of VR technology to alleviate pain could lead to a reduction in the number of individuals who overdose on painkillers and contribute to positive change in the medical field.
基金supported in part by the National Natural Science Foundation of China under Grant 62225103,U22B2003,U2441227,and U24A20211the Beijing Natural Science Foundation under Grant L241008+3 种基金the Defense Industrial Technology Development Program JCKY2022110C010the National Key Laboratory of Wireless Communications Foundation under Grant IFN20230201the Fundamental Research Funds for the Central Universities under Grant FRFTP-22-002C2the Xiaomi Fund of Young Scholar。
文摘In the sixth generation mobile communication(6G) system,Non-Terrestrial Networks(NTN),as a supplement to terrestrial network,can meet the requirements of wide area intelligent connection and global ubiquitous seamless access,establish intelligent connection for wide area objects,and provide intelligent services.Due to issues such as massive access,doppler shift,and limited spectrum resources in NTN,research on resource management is crucial for optimizing NTN performance.In this paper,a comprehensive survey of multi-pattern heterogeneous NTN resource management is provided.Firstly,the key technologies involved in NTN resource management is summarized.Secondly,NTN resource management is discussed from network pattern and resource pattern.The network pattern focuses on the application of different optimization methods to different network dimension communication resource management,and the resource type pattern focuses on the research and application of multi-domain resource management such as computation,cache,communication and sensing.Finally,future research directions and challenges of 6G NTN resource management are discussed.
文摘With the rapid development of information technology,the scale of the network is expanding,and the complexity is increasing day by day.The traditional network management is facing great challenges.The emergence of software-defined network(SDN)technology has brought revolutionary changes to modern network management.This paper aims to discuss the application and prospects of SDN technology in modern network management.Firstly,the basic principle and architecture of SDN are introduced,including the separation of control plane and data plane,centralized control and open programmable interface.Then,it analyzes the advantages of SDN technology in network management,such as simplifying network configuration,improving network flexibility,optimizing network resource utilization,and realizing fast fault recovery.The application examples of SDN in data center networks and WAN optimization management are analyzed.This paper also discusses the development status and trend of SDN in enterprise networks,including the integration of technologies such as cloud computing,big data,and artificial intelligence,the construction of an intelligent and automated network management platform,the improvement of network management efficiency and quality,and the openness and interoperability of network equipment.Finally,the advantages and challenges of SDN technology are summarized,and its future development direction is provided.
基金2025 Open Experimental Special Fund of Beijing Institute of Technology, “Applications and Practices of R Language in Bioinformatics”。
文摘To investigate the targets and mechanism of Hedysarum Multijugum Maxim(HMM)in treatment of bladder cancer(BC).Based on Traditional Chinese Medicine Systems Pharmacology(TCMSP)and gene databases,active substances and potential targets of HMM were screened,and the HMM-active substances-targets-BC(HATB)regulatory network and PPI network were constructed.Hub targets were screened by Cytoscape.The main active substances and Hub targets were molecularly docked with AutoDock and visualized by PyMOL.12 Hub targets were screened.Molecular docking showed that active substances mainly acted on MAPK14,MAPK1 and CCND1.The bindings of calycosin to MAPK14,formononetin to MAPK14,and calycosin to CCND1 were stable.
文摘The 5G-R network is on the verge of entering the construction stage.Given that the dedicated network for railways is closely linked to train operation safety,there are extremely high requirements for network security.As a result,there is an urgent need to conduct research on 5G-R network security.To comprehensively enhance the end-to-end security protection of the 5G-R network,this study summarized the security requirements of the GSM-R network,analyzed the security risks and requirements faced by the 5G-R network,and proposed an overall 5G-R network security architecture.The security technical schemes were detailed from various aspects:5G-R infrastructure security,terminal access security,networking security,operation and maintenance security,data security,and network boundary security.Additionally,the study proposed leveraging the 5G-R security situation awareness system to achieve a comprehensive upgrade from basic security technologies to endogenous security capabilities within the 5G-R system.
基金supported by National Natural Science Foundation of China(No.62102449).
文摘Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribute management methods based on manual extraction face several issues,such as high costs for attribute extraction,long processing times,unstable accuracy,and poor scalability.To address these problems,this paper proposes an attribute mining technology for access control institutions based on hybrid capsule networks.This technology leverages transfer learning ideas,utilizing Bidirectional Encoder Representations from Transformers(BERT)pre-trained language models to achieve vectorization of unstructured text data resources.Furthermore,we have designed a novel end-to-end parallel hybrid network structure,where the parallel networks handle global and local information features of the text that they excel at,respectively.By employing techniques such as attention mechanisms,capsule networks,and dynamic routing,effective mining of security attributes for access control resources has been achieved.Finally,we evaluated the performance level of the proposed attribute mining method for access control institutions through experiments on the medical referral text resource dataset.The experimental results show that,compared with baseline algorithms,our method adopts a parallel network structure that can better balance global and local feature information,resulting in improved overall performance.Specifically,it achieves a comprehensive performance enhancement of 2.06%to 8.18%in the F1 score metric.Therefore,this technology can effectively provide attribute support for access control of unstructured text big data resources.
基金supported by Key Cultivation Project of Qiqihar Academy of Medical Sciences(No.2022-ZDPY-003).
文摘In order to reveal the multi-target pharmacological mechanism of Zedoary turmeric oil combined with docetaxel in the treatment of breast cancer,we used network pharmacology and molecular docking.The targets of docetaxel were retrieved from the Swiss Target Prediction database.The active components of Curcuma Zedoary turmeric were screened using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP),with criteria set as oral bioavailability OB≥30%and drug-likeness DL≥0.1.Potential targets of these components were subsequently predicted.Breast cancer-related targets were retrieved from the OMIM and GeneCards databases.The Venny tool was used to identify 177 overlapping targets between docetaxel,Zedoary turmeric oil,and breast cancer,followed by protein-protein interaction(PPI)analysis.Gene Ontology(GO)functional enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analyses were performed using the Metascape database.A drug-breast cancer-KEGG pathway target network was constructed using Cytoscape 3.8.0.Molecular docking was employed to verify the binding ability between drugs and core targets.Results showed that the combined treatment may exert anti-breast cancer effects through key targets such as MAPK1,PIK3CA,and HSP90AA1,primarily implicating the biological process of protein phosphorylation and engaging the PI3K-Akt signaling pathway.This study successfully predicted the key targets and enriched pathways of Zedoary turmeric oil combined with docetaxel for breast cancer treatment,providing new insights for further research and development.
文摘In this editorial,we comment on the article by Micucci et al published in the recent issue.We focus on the heterogenous nature of gastric cancer(GC)and the potential benefits of integrating traditional Chinese medicine(TCM)with the modern technology of network pharmacology(NP)and omics sequencing.GC is a heterogenous disease,as it incorporates several biochemical pathways that contribute to pathogenesis.TCM acknowledges the multifactorial,heterogenous nature of disease and utilizes an integrative approach to medicine.NP,a modern philosophy within drug development,integrates traditional knowledge of nutraceuticals and modern technologies to address the complex interactions of pathways within the body.Omics technologies,which is at the core of precision medicine,has allowed for this newfound principle of drug development.Metabolic pathways are better distinguished,leading to more targeted drug development.However,the use of omics technology needs to be employed to better characterize the subtypes of GC.This will allow TCM’s use of nutraceuticals in the application of NP to better target metabolic pathways that may aid in the prevention of GC as well as enhance treatment.
文摘Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.
文摘To explore the material basis and mechanisms of the anti-inflammatory effects of Hibiscus mutabilis L..The active ingredients and potential targets of Hibiscus mutabilis L.were obtained through the literature review and SwissADME platform.Genes related to the inflammation were collected using Genecards and OMIM databases,and the intersection genes were submitted on STRING and DAVID websites.Then,the protein interaction network(PPI),gene ontology(GO)and pathway(KEGG)were analyzed.Cytoscape 3.7.2 software was used to construct the“Hibiscus mutabilis L.-active ingredient-target-inflammation”network diagram,and AutoDockTools-1.5.6 software was used for the molecular docking verification.The antiinflammatory effect of Hibiscus mutabilis L.active ingredient was verified by the RAW264.7 inflammatory cell model.The results showed that 11 active components and 94 potential targets,1029 inflammatory targets and 24 intersection targets were obtained from Hibiscus mutabilis L..The key anti-inflammatory active ingredients of Hibiscus mutabilis L.are quercetin,apigenin and luteolin.Its action pathway is mainly related to NF-κB,cancer pathway and TNF signaling pathway.Cell experiments showed that total flavonoids of Hibiscus mutabilis L.could effectively inhibit the expression of tumor necrosis factor(TNF-α),interleukin 8(IL-8)and epidermal growth factor receptor(EGFR)in LPS-induced RAW 264.7 inflammatory cells.It also downregulates the phosphorylation of human nuclear factor ĸB inhibitory protein α(IĸBα)and NF-κB p65 subunit protein(p65).Overall,the anti-inflammatory effect of Hibiscus mutabilis L.is related to many active components,many signal pathways and targets,which provides a theoretical basis for its further development and application.
文摘Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.
基金supported by the National Natural Science Foundation of China(Grant No.92258303)the National Key Research and Development Program of China(Grant Nos.2024YFF0506704 and 2023YFF0803404).
文摘Ocean geoscience is a highly integrated and interdisciplinary field that plays a critical role in understanding the interaction between Earth’s lithosphere,hydrosphere,atmosphere,biosphere,and anthroposphere.Recent years have seen tremendous progress in global ocean research,driven by rapid advancements in deep-sea manned and unmanned submersibles,ocean drilling,seafloor observatories,big data assimilation,and supercomputing simulations.Representative examples of breakthroughs are highlighted in this work:(1)Probing sub-seafloor processes.A 10,000-meter ocean-bottom seismometer array has achieved high-resolution imaging of the deepest ocean on the Earth-the Challenger Deep of the Mariana Trench,revealing the role of key tectonic and hydrological processes within the subduction zone.The first sub-ice seafloor seismic and magnetotelluric experiments were successfully conducted at the Arctic Gakkel Ridge,providing significant insights into the dynamics of ultraslow seafloor spreading.(2)Exploration of seafloor resources.Near-seafloor investigations employing underwater robotics and multi-sensor systems have been carried out in areas of hydrothermal vents and cold seeps at global locations,including the Southwest Indian Ridge.These efforts have combined geophysical,oceanographic,chemical,and biological observations with extensive seafloor sampling.(3)Interdisciplinary research of complex catastrophic events.High-resolution simulations integrating ocean observations with supercomputing modeling have made it possible to fully model earthquake-induced seafloor deformation,tsunami propagation,and ocean basin-scale transport of the Fukushima Power Plant-derived radionuclides associated with the 2011 Tohoku earthquake.Among the world’s three major oceans,the Indian Ocean is still relatively underexplored.Major scientific challenges include elucidating crust-mantle interaction,air-sea dynamic coupling,large-scale marine hazards,and responses of ecosystems to major environmental changes,all of which require interdisciplinary collaboration.Future efforts should focus on developing intelligent unmanned observation platform systems,big data and digital twins,and AI-driven hazard modeling.Meanwhile,higher educational reforms should emphasize fostering a new generation of students and young scientists with a solid background and strong critical analysis skills to accelerate technological innovation.
文摘Identifying the community structure of complex networks is crucial to extracting insights and understanding network properties.Although several community detection methods have been proposed,many are unsuitable for social networks due to significant limitations.Specifically,most approaches depend mainly on user-user structural links while overlooking service-centric,semantic,and multi-attribute drivers of community formation,and they also lack flexible filtering mechanisms for large-scale,service-oriented settings.Our proposed approach,called community discovery-based service(CDBS),leverages user profiles and their interactions with consulted web services.The method introduces a novel similarity measure,global similarity interaction profile(GSIP),which goes beyond typical similarity measures by unifying user and service profiles for all attributes types into a coherent representation,thereby clarifying its novelty and contribution.It applies multiple filtering criteria related to user attributes,accessed services,and interaction patterns.Experimental comparisons against Louvain,Hierarchical Agglomerative Clustering,Label Propagation and Infomap show that CDBS reveals the higher performance as it achieves 0.74 modularity,0.13 conductance,0.77 coverage,and significantly fast response time of 9.8 s,even with 10,000 users and 400 services.Moreover,community discoverybased service consistently detects a larger number of communities with distinct topics of interest,underscoring its capacity to generate detailed and efficient structures in complex networks.These results confirm both the efficiency and effectiveness of the proposed method.Beyond controlled evaluation,communities discovery based service is applicable to targeted recommendations,group-oriented marketing,access control,and service personalization,where communities are shaped not only by user links but also by service engagement.
基金supported by the Research year project of the KongjuNational University in 2025 and the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2024-00444170,Research and International Collaboration on Trust Model-Based Intelligent Incident Response Technologies in 6G Open Network Environment).
文摘With the growing complexity and decentralization of network systems,the attack surface has expanded,which has led to greater concerns over network threats.In this context,artificial intelligence(AI)-based network intrusion detection systems(NIDS)have been extensively studied,and recent efforts have shifted toward integrating distributed learning to enable intelligent and scalable detection mechanisms.However,most existing works focus on individual distributed learning frameworks,and there is a lack of systematic evaluations that compare different algorithms under consistent conditions.In this paper,we present a comprehensive evaluation of representative distributed learning frameworks—Federated Learning(FL),Split Learning(SL),hybrid collaborative learning(SFL),and fully distributed learning—in the context of AI-driven NIDS.Using recent benchmark intrusion detection datasets,a unified model backbone,and controlled distributed scenarios,we assess these frameworks across multiple criteria,including detection performance,communication cost,computational efficiency,and convergence behavior.Our findings highlight distinct trade-offs among the distributed learning frameworks,demonstrating that the optimal choice depends strongly on systemconstraints such as bandwidth availability,node resources,and data distribution.This work provides the first holistic analysis of distributed learning approaches for AI-driven NIDS and offers practical guidelines for designing secure and efficient intrusion detection systems in decentralized environments.
基金National Natural Science Foundation of China(Grant No.62103434)National Science Fund for Distinguished Young Scholars(Grant No.62176263).
文摘With network attack technology continuing to develop,traditional anomaly traffic detection methods that rely on feature engineering are increasingly insufficient in efficiency and accuracy.Graph Neural Network(GNN),a promising Deep Learning(DL)approach,has proven to be highly effective in identifying intricate patterns in graph⁃structured data and has already found wide applications in the field of network security.In this paper,we propose a hybrid Graph Convolutional Network(GCN)⁃GraphSAGE model for Anomaly Traffic Detection,namely HGS⁃ATD,which aims to improve the accuracy of anomaly traffic detection by leveraging edge feature learning to better capture the relationships between network entities.We validate the HGS⁃ATD model on four publicly available datasets,including NF⁃UNSW⁃NB15⁃v2.The experimental results show that the enhanced hybrid model is 5.71%to 10.25%higher than the baseline model in terms of accuracy,and the F1⁃score is 5.53%to 11.63%higher than the baseline model,proving that the model can effectively distinguish normal traffic from attack traffic and accurately classify various types of attacks.
基金funding from the European Commission by the Ruralities project(grant agreement no.101060876).
文摘In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services.
基金Support by Sichuan Science and Technology Program[2023YFSY0026,2023YFH0004]Guangzhou Huashang University[2024HSZD01,HS2023JYSZH01].
文摘Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relationships among nodes.This paper proposes a novel graph coupling convolutional model that introduces an adaptive weighting mechanism to assign distinct importance to neighboring nodes based on their similarity to the central node.Unlike traditional methods,the proposed coupling strategy enhances the interpretability of node interactions while maintaining competitive classification performance.The model operates in the spatial domain,utilizing adjacency list structures for efficient convolution and addressing the limitations of weight sharing through a coupling-based similarity computation.Extensive experiments are conducted on five graph-structured datasets,including Cora,Citeseer,PubMed,Reddit,and BlogCatalog,as well as a custom topology dataset constructed from the Open University Learning Analytics Dataset(OULAD)educational platform.Results demonstrate that the proposed model achieves good classification accuracy,while significantly reducing training time through direct second-order neighbor fusion and data preprocessing.Moreover,analysis of neighborhood order reveals that considering third-order neighbors offers limited accuracy gains but introduces considerable computational overhead,confirming the efficiency of first-and second-order convolution in practical applications.Overall,the proposed graph coupling model offers a lightweight,interpretable,and effective framework for multi-label node classification in complex networks.
基金supported by the 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.
