This paper aims to introduce the novel concept of neutrosophic crisp soft set(NCSS),including various types of neutrosophic crisp soft sets(NCSSs)and their fundamental operations.We define NCS-mapping and its inverse ...This paper aims to introduce the novel concept of neutrosophic crisp soft set(NCSS),including various types of neutrosophic crisp soft sets(NCSSs)and their fundamental operations.We define NCS-mapping and its inverse NCS-mapping between two NCS-classes.We develop a robust mathematical modeling with the help of NCS-mapping to analyze the emerging trends in social networking systems(SNSs)for our various generations.We investigate the advantages,disadvantages,and natural aspects of SNSs for five generations.With the changing of the generations,it is analyzed that emerging trends and the benefits of SNSs are increasing day by day.The suggested modeling with NCS-mapping is applicable in solving various decision-making problems.展开更多
A number of mobile Online Social Networking （OSN） services have appeared in the market in recent times. While most mobile systems benefit greatly from cloud services, centralized servers and communications infrastru...A number of mobile Online Social Networking （OSN） services have appeared in the market in recent times. While most mobile systems benefit greatly from cloud services, centralized servers and communications infrastructure is not always available. Nor are location-based services offered to mobile devices without GPS. To take advantage of cloud and to address these problems, a Wi-Fi based multihop networking system called MoNet is proposed. On top of MONET we propose a privacy-aware geosocial networking service called WiFace. Where there is no infrastructure, a distributed content sharing protocol significantly shortens the relay path, reduces conflicts, and improves data availability. Furthermore, a security mechanism is developed to protect privacy. Comprehensive experiments performed on MoNet show that the system is more than sufficient to support social networking and even audio and video applications.展开更多
The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are ...The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.展开更多
The demands on conventional communication networks are increasing rapidly because of the exponential expansion of connected multimedia content.In light of the data-centric aspect of contemporary communication,the info...The demands on conventional communication networks are increasing rapidly because of the exponential expansion of connected multimedia content.In light of the data-centric aspect of contemporary communication,the information-centric network(ICN)paradigm offers hope for a solution by emphasizing content retrieval by name instead of location.If 5G networks are to meet the expected data demand surge from expanded connectivity and Internet of Things(IoT)devices,then effective caching solutions will be required tomaximize network throughput andminimize the use of resources.Hence,an ICN-based Cooperative Caching(ICN-CoC)technique has been used to select a cache by considering cache position,content attractiveness,and rate prediction.The findings show that utilizing our suggested approach improves caching regarding the Cache Hit Ratio(CHR)of 84.3%,Average Hop Minimization Ratio(AHMR)of 89.5%,and Mean Access Latency(MAL)of 0.4 s.Within a framework,it suggests improved caching strategies to handle the difficulty of effectively controlling data consumption in 5G networks.These improvements aim to make the network run more smoothly by enhancing content delivery,decreasing latency,and relieving congestion.By improving 5G communication systems’capacity tomanage the demands faced by modern data-centric applications,the research ultimately aids in advancement.展开更多
For multi-vehicle networks,Cooperative Positioning(CP)technique has become a promising way to enhance vehicle positioning accuracy.Especially,the CP performance could be further improved by introducing Sensor-Rich Veh...For multi-vehicle networks,Cooperative Positioning(CP)technique has become a promising way to enhance vehicle positioning accuracy.Especially,the CP performance could be further improved by introducing Sensor-Rich Vehicles(SRVs)into CP networks,which is called SRV-aided CP.However,the CP system may split into several sub-clusters that cannot be connected with each other in dense urban environments,in which the sub-clusters with few SRVs will suffer from degradation of CP performance.Since Unmanned Aerial Vehicles(UAVs)have been widely used to aid vehicular communications,we intend to utilize UAVs to assist sub-clusters in CP.In this paper,a UAV-aided CP network is constructed to fully utilize information from SRVs.First,the inter-node connection structure among the UAV and vehicles is designed to share available information from SRVs.After that,the clustering optimization strategy is proposed,in which the UAV cooperates with the high-precision sub-cluster to obtain available information from SRVs,and then broadcasts this positioning-related information to other low-precision sub-clusters.Finally,the Locally-Centralized Factor Graph Optimization(LC-FGO)algorithm is designed to fuse positioning information from cooperators.Simulation results indicate that the positioning accuracy of the CP system could be improved by fully utilizing positioning-related information from SRVs.展开更多
Guided by molecular networking,nine novel curvularin derivatives(1-9)and 16 known analogs(10-25)were isolated from the hydrothermal vent sediment fungus Penicillium sp.HL-50.Notably,compounds 5-7 represented a hybrid ...Guided by molecular networking,nine novel curvularin derivatives(1-9)and 16 known analogs(10-25)were isolated from the hydrothermal vent sediment fungus Penicillium sp.HL-50.Notably,compounds 5-7 represented a hybrid of curvularin and purine.The structures and absolute configurations of compounds 1-9 were elucidated via nuclear magnetic resonance(NMR)spectroscopy,X-ray diffraction,electronic circular dichroism(ECD)calculations,^(13)C NMR calculation,modified Mosher's method,and chemical derivatization.Investigation of anti-inflammatory activities revealed that compounds 7-9,11,12,14,15,and 18 exhibited significant suppressive effects against lipopolysaccharide(LPS)-induced nitric oxide(NO)production in murine macrophage RAW264.7 cells,with IC_(50)values ranging from 0.44 to 4.40μmol·L^(-1).Furthermore,these bioactive compounds were found to suppress the expression of inflammation-related proteins,including inducible NO synthase(i NOS),cyclooxygenase-2(COX-2),NLR family pyrin domain-containing protein 3(NLRP3),and nuclear factor kappa-B(NF-κB).Additional studies demonstrated that the novel compound 7 possessed potent antiinflammatory activity by inhibiting the transcription of inflammation-related genes,downregulating the expression of inflammation-related proteins,and inhibiting the release of inflammatory cytokines,indicating its potential application in the treatment of inflammatory diseases.展开更多
Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectiv...Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectively deal with nonlinearities,constraints,and noises in the system,optimize the performance metric,and present an upper bound on the stable output of the system.展开更多
The rise of large-scale artificial intelligence(AI)models,such as ChatGPT,Deep-Seek,and autonomous vehicle systems,has significantly advanced the boundaries of AI,enabling highly complex tasks in natural language proc...The rise of large-scale artificial intelligence(AI)models,such as ChatGPT,Deep-Seek,and autonomous vehicle systems,has significantly advanced the boundaries of AI,enabling highly complex tasks in natural language processing,image recognition,and real-time decisionmaking.However,these models demand immense computational power and are often centralized,relying on cloud-based architectures with inherent limitations in latency,privacy,and energy efficiency.To address these challenges and bring AI closer to real-world applications,such as wearable health monitoring,robotics,and immersive virtual environments,innovative hardware solutions are urgently needed.This work introduces a near-sensor edge computing(NSEC)system,built on a bilayer AlN/Si waveguide platform,to provide real-time,energy-efficient AI capabilities at the edge.Leveraging the electro-optic properties of AlN microring resonators for photonic feature extraction,coupled with Si-based thermo-optic Mach-Zehnder interferometers for neural network computations,the system represents a transformative approach to AI hardware design.Demonstrated through multimodal gesture and gait analysis,the NSEC system achieves high classification accuracies of 96.77%for gestures and 98.31%for gaits,ultra-low latency(<10 ns),and minimal energy consumption(<0.34 pJ).This groundbreaking system bridges the gap between AI models and real-world applications,enabling efficient,privacy-preserving AI solutions for healthcare,robotics,and next-generation human-machine interfaces,marking a pivotal advancement in edge computing and AI deployment.展开更多
This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method...This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method is employed to achieve secure control by estimating the system's state in real time.Secondly,by combining a memory-based adaptive eventtriggered mechanism with neural networks,the paper aims to approximate the nonlinear terms in the networked system and efficiently conserve system resources.Finally,based on a two-degree-of-freedom model of a vehicle affected by crosswinds,this paper constructs a multi-unmanned ground vehicle(Multi-UGV)system to validate the effectiveness of the proposed method.Simulation results show that the proposed control strategy can effectively handle external disturbances such as crosswinds in practical applications,ensuring the stability and reliable operation of the Multi-UGV system.展开更多
Real-time prediction and precise control of sinter quality are pivotal for energy saving,cost reduction,quality improvement and efficiency enhancement in the ironmaking process.To advance,the accuracy and comprehensiv...Real-time prediction and precise control of sinter quality are pivotal for energy saving,cost reduction,quality improvement and efficiency enhancement in the ironmaking process.To advance,the accuracy and comprehensiveness of sinter quality prediction,an intelligent flare monitoring system for sintering machine tails that combines hybrid neural networks integrating convolutional neural network with long short-term memory(CNN-LSTM)networks was proposed.The system utilized a high-temperature thermal imager for image acquisition at the sintering machine tail and employed a zone-triggered method to accurately capture dynamic feature images under challenging conditions of high-temperature,high dust,and occlusion.The feature images were then segmented through a triple-iteration multi-thresholding approach based on the maximum between-class variance method to minimize detail loss during the segmentation process.Leveraging the advantages of CNN and LSTM networks in capturing temporal and spatial information,a comprehensive model for sinter quality prediction was constructed,with inputs including the proportion of combustion layer,porosity rate,temperature distribution,and image features obtained from the convolutional neural network,and outputs comprising quality indicators such as underburning index,uniformity index,and FeO content of the sinter.The accuracy is notably increased,achieving a 95.8%hit rate within an error margin of±1.0.After the system is applied,the average qualified rate of FeO content increases from 87.24%to 89.99%,representing an improvement of 2.75%.The average monthly solid fuel consumption is reduced from 49.75 to 46.44 kg/t,leading to a 6.65%reduction and underscoring significant energy saving and cost reduction effects.展开更多
The design of casting gating system directly determines the solidification sequence,defect severity,and overall quality of the casting.A novel machine learning strategy was developed to design the counter pressure cas...The design of casting gating system directly determines the solidification sequence,defect severity,and overall quality of the casting.A novel machine learning strategy was developed to design the counter pressure casting gating system of a large thin-walled cabin casting.A high-quality dataset was established through orthogonal experiments combined with design criteria for the gating system.Spearman’s correlation analysis was used to select high-quality features.The gating system dimensions were predicted using a gated recurrent unit(GRU)recurrent neural network and an elastic network model.Using EasyCast and ProCAST casting software,a comparative analysis of the flow field,temperature field,and solidification field can be conducted to demonstrate the achievement of steady filling and top-down sequential solidification.Compared to the empirical formula method,this method eliminates trial-and-error iterations,reduces porosity,reduces casting defect volume from 11.23 cubic centimeters to 2.23 cubic centimeters,eliminates internal casting defects through the incorporation of an internally cooled iron,fulfilling the goal of intelligent gating system design.展开更多
The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by...The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are essential.Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns.This paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT systems.However,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss functions.To address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN hyper-parameters.The 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local minima.The proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model training.The generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded features.We evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and IoT-23.Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives.The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence trend.The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and IoT-23 datasets,with values of 0.24,1.10,and 0.09,respectively.Additionally,it attained the highest accuracy,ranging from 94%to 100%.These results suggest a promising direction for future IoT security frameworks,offering a scalable and efficient solution to safeguard against evolving cyber threats.展开更多
Projective synchronization problems of a drive system and a particular response network were investigated,where the drive system is an arbitrary system with n+1 dimensions;it may be a linear or nonlinear system,and ev...Projective synchronization problems of a drive system and a particular response network were investigated,where the drive system is an arbitrary system with n+1 dimensions;it may be a linear or nonlinear system,and even a chaotic or hyperchaotic system,the response network is complex system coupled by N nodes,and every node is showed by the approximately linear part of the drive system.Only controlling any one node of the response network by designed controller can achieve the projective synchronization.Some numerical examples were employed to verify the effectiveness and correctness of the designed controller.展开更多
In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained f...In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.展开更多
With the large-scale deployment of satellite constellations such as Starlink and the rapid advancement of technologies including artificial intelligence (AI) and non-terrestrial networks (NTNs), the integration of hig...With the large-scale deployment of satellite constellations such as Starlink and the rapid advancement of technologies including artificial intelligence (AI) and non-terrestrial networks (NTNs), the integration of high, medium, and low Earth orbit satellite networks with terrestrial networks has become a critical direction for future communication technologies. The objective is to develop a space-terrestrial integrated 6G network that ensures ubiquitous connectivity and seamless services, facilitating intelligent interconnection and collaborative symbiosis among humans, machines, and objects. This integration has become a central focus of global technological innovation.展开更多
The rise of time-sensitive applications with broad geographical scope drives the development of time-sensitive networking(TSN)from intra-domain to inter-domain to ensure overall end-to-end connectivity requirements in...The rise of time-sensitive applications with broad geographical scope drives the development of time-sensitive networking(TSN)from intra-domain to inter-domain to ensure overall end-to-end connectivity requirements in heterogeneous deployments.When multiple TSN networks interconnect over non-TSN networks,all devices in the network need to be syn-chronized by sharing a uniform time reference.How-ever,most non-TSN networks are best-effort.Path delay asymmetry and random noise accumulation can introduce unpredictable time errors during end-to-end time synchronization.These factors can degrade syn-chronization performance.Therefore,cross-domain time synchronization becomes a challenging issue for multiple TSN networks interconnected by non-TSN networks.This paper presents a cross-domain time synchronization scheme that follows the software-defined TSN(SD-TSN)paradigm.It utilizes a com-bined control plane constructed by a coordinate con-troller and a domain controller for centralized control and management of cross-domain time synchroniza-tion.The general operation flow of the cross-domain time synchronization process is designed.The mecha-nism of cross-domain time synchronization is revealed by introducing a synchronization model and an error compensation method.A TSN cross-domain proto-type testbed is constructed for verification.Results show that the scheme can achieve end-to-end high-precision time synchronization with accuracy and sta-bility.展开更多
This paper presents a space network emulation system based on a user-space network stack named Nos to solve space networks'unique architecture and routing issues and kernel stacks'inefficiency and development ...This paper presents a space network emulation system based on a user-space network stack named Nos to solve space networks'unique architecture and routing issues and kernel stacks'inefficiency and development complexity.Our low Earth orbit satellite scenario emulation verifies the dynamic routing function of the protocol stack.The proposed system uses technologies like Open vSwitch(OVS)and traffic control(TC)to emulate the space network's highly dynamic topology and time-varying link characteristics.The emulation results demonstrate the system's high reliability,and the user-space network stack reduces development complexity and debugging difficulty,providing convenience for the development of space network protocols and network functions.展开更多
In this paper,a new study concerning the usage of artificial neural networks in the control application is given.It is shown,that the data gathered during proper operation of a given control plant can be used in the l...In this paper,a new study concerning the usage of artificial neural networks in the control application is given.It is shown,that the data gathered during proper operation of a given control plant can be used in the learning process to fully embrace the control pattern.Interestingly,the instances driven by neural networks have the ability to outperform the original analytically driven scenarios.Three different control schemes,namely perfect,linear-quadratic,and generalized predictive controllers were used in the theoretical study.In addition,the nonlinear recurrent neural network-based generalized predictive controller with the radial basis function-originated predictor was obtained to exemplify the main results of the paper regarding the real-world application.展开更多
Multi-agent systems often require good interoperability in the process of completing their assigned tasks.This paper first models the static structure and dynamic behavior of multiagent systems based on layered weight...Multi-agent systems often require good interoperability in the process of completing their assigned tasks.This paper first models the static structure and dynamic behavior of multiagent systems based on layered weighted scale-free community network and susceptible-infected-recovered(SIR)model.To solve the problem of difficulty in describing the changes in the structure and collaboration mode of the system under external factors,a two-dimensional Monte Carlo method and an improved dynamic Bayesian network are used to simulate the impact of external environmental factors on multi-agent systems.A collaborative information flow path optimization algorithm for agents under environmental factors is designed based on the Dijkstra algorithm.A method for evaluating system interoperability is designed based on simulation experiments,providing reference for the construction planning and optimization of organizational application of the system.Finally,the feasibility of the method is verified through case studies.展开更多
The construction of“park cities”requires a systematic thinking to coordinate the networked development of park system and break through the limitation of emphasizing scale and grade and neglecting dynamic correlatio...The construction of“park cities”requires a systematic thinking to coordinate the networked development of park system and break through the limitation of emphasizing scale and grade and neglecting dynamic correlation in traditional planning.Taking Haidian District of Beijing as an example,the social network analysis method is introduced to construct the network model of park green spaces.Through indicators such as clustering coefficient,network density and node centrality,the characteristics of its spatial structure and hierarchical relationship are analyzed.It is found that the network integrity presents the characteristics of“highly local concentration and global fragmentation”,fragmented park green space network and missing spatial connection,isolated clusters and collaborative failure,as well as the spatial mismatch between population and resource supply and demand.Hierarchical issues include“structural imbalance and functional disorder”,disorder between network hierarchy and park level,misalignment of functional hierarchy leading to weakened network risk resistance capacity,and a relatively dense distribution of core nodes,etc.In response to the above problems,a multi-level spatial intervention strategy should be adopted to solve the overall problem of the network.Meanwhile,it is needed to clarify the positioning of a park itself and improve the hierarchical system,so as to construct a multi-level and multi-scale park green space network,contribute to the construction of a park city,and provide residents with more diverse activity venues.展开更多
基金the Deanship of Scientific Research at King Khalid University for funding this work through General Research Project under Grant No.R.G.P.2/181/44.
文摘This paper aims to introduce the novel concept of neutrosophic crisp soft set(NCSS),including various types of neutrosophic crisp soft sets(NCSSs)and their fundamental operations.We define NCS-mapping and its inverse NCS-mapping between two NCS-classes.We develop a robust mathematical modeling with the help of NCS-mapping to analyze the emerging trends in social networking systems(SNSs)for our various generations.We investigate the advantages,disadvantages,and natural aspects of SNSs for five generations.With the changing of the generations,it is analyzed that emerging trends and the benefits of SNSs are increasing day by day.The suggested modeling with NCS-mapping is applicable in solving various decision-making problems.
基金supported by National Natural Science Foundation of China under Grant No. 90818021, and 9071803National Natural Science Foundation of China under Grant No. 60828003+3 种基金supported by Tsinghua National Laboratory for Information Science and Technology(TNList)NSF CNS0832120National Basic Research Program of China ("973"Program) under grant No. 2010CB328100the National High Technology Research and Development Program of China ("863"Program) under grant No. 2007AA01Z180
文摘A number of mobile Online Social Networking （OSN） services have appeared in the market in recent times. While most mobile systems benefit greatly from cloud services, centralized servers and communications infrastructure is not always available. Nor are location-based services offered to mobile devices without GPS. To take advantage of cloud and to address these problems, a Wi-Fi based multihop networking system called MoNet is proposed. On top of MONET we propose a privacy-aware geosocial networking service called WiFace. Where there is no infrastructure, a distributed content sharing protocol significantly shortens the relay path, reduces conflicts, and improves data availability. Furthermore, a security mechanism is developed to protect privacy. Comprehensive experiments performed on MoNet show that the system is more than sufficient to support social networking and even audio and video applications.
基金extend their appreciation to Researcher Supporting Project Number(RSPD2023R582)King Saud University,Riyadh,Saudi Arabia.
文摘The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.
基金New Brunswick Innovation Foundation(NBIF)for the financial support of the global project.
文摘The demands on conventional communication networks are increasing rapidly because of the exponential expansion of connected multimedia content.In light of the data-centric aspect of contemporary communication,the information-centric network(ICN)paradigm offers hope for a solution by emphasizing content retrieval by name instead of location.If 5G networks are to meet the expected data demand surge from expanded connectivity and Internet of Things(IoT)devices,then effective caching solutions will be required tomaximize network throughput andminimize the use of resources.Hence,an ICN-based Cooperative Caching(ICN-CoC)technique has been used to select a cache by considering cache position,content attractiveness,and rate prediction.The findings show that utilizing our suggested approach improves caching regarding the Cache Hit Ratio(CHR)of 84.3%,Average Hop Minimization Ratio(AHMR)of 89.5%,and Mean Access Latency(MAL)of 0.4 s.Within a framework,it suggests improved caching strategies to handle the difficulty of effectively controlling data consumption in 5G networks.These improvements aim to make the network run more smoothly by enhancing content delivery,decreasing latency,and relieving congestion.By improving 5G communication systems’capacity tomanage the demands faced by modern data-centric applications,the research ultimately aids in advancement.
基金supported by the National Natural Science Foundation of China(No.62271399)the National Key Research and Development Program of China(No.2022YFB1807102)。
文摘For multi-vehicle networks,Cooperative Positioning(CP)technique has become a promising way to enhance vehicle positioning accuracy.Especially,the CP performance could be further improved by introducing Sensor-Rich Vehicles(SRVs)into CP networks,which is called SRV-aided CP.However,the CP system may split into several sub-clusters that cannot be connected with each other in dense urban environments,in which the sub-clusters with few SRVs will suffer from degradation of CP performance.Since Unmanned Aerial Vehicles(UAVs)have been widely used to aid vehicular communications,we intend to utilize UAVs to assist sub-clusters in CP.In this paper,a UAV-aided CP network is constructed to fully utilize information from SRVs.First,the inter-node connection structure among the UAV and vehicles is designed to share available information from SRVs.After that,the clustering optimization strategy is proposed,in which the UAV cooperates with the high-precision sub-cluster to obtain available information from SRVs,and then broadcasts this positioning-related information to other low-precision sub-clusters.Finally,the Locally-Centralized Factor Graph Optimization(LC-FGO)algorithm is designed to fuse positioning information from cooperators.Simulation results indicate that the positioning accuracy of the CP system could be improved by fully utilizing positioning-related information from SRVs.
基金funded by the National Key Research and Development Program of China(No.2022YFC2804101)the Guangdong Provincial Key R&D Program(No.2023B1111050011)+2 种基金the Guangdong Basic and Applied Basic Research Foundation(No.2023A1515010432)the Guangzhou Basic and Applied Basic Research Foundation(No.202201010305)the High-Level Talents Special Program of Zhejiang(No.2022R52036)。
文摘Guided by molecular networking,nine novel curvularin derivatives(1-9)and 16 known analogs(10-25)were isolated from the hydrothermal vent sediment fungus Penicillium sp.HL-50.Notably,compounds 5-7 represented a hybrid of curvularin and purine.The structures and absolute configurations of compounds 1-9 were elucidated via nuclear magnetic resonance(NMR)spectroscopy,X-ray diffraction,electronic circular dichroism(ECD)calculations,^(13)C NMR calculation,modified Mosher's method,and chemical derivatization.Investigation of anti-inflammatory activities revealed that compounds 7-9,11,12,14,15,and 18 exhibited significant suppressive effects against lipopolysaccharide(LPS)-induced nitric oxide(NO)production in murine macrophage RAW264.7 cells,with IC_(50)values ranging from 0.44 to 4.40μmol·L^(-1).Furthermore,these bioactive compounds were found to suppress the expression of inflammation-related proteins,including inducible NO synthase(i NOS),cyclooxygenase-2(COX-2),NLR family pyrin domain-containing protein 3(NLRP3),and nuclear factor kappa-B(NF-κB).Additional studies demonstrated that the novel compound 7 possessed potent antiinflammatory activity by inhibiting the transcription of inflammation-related genes,downregulating the expression of inflammation-related proteins,and inhibiting the release of inflammatory cytokines,indicating its potential application in the treatment of inflammatory diseases.
基金supported in part by the National Natural Science Foundation of China(62173255,62188101)Shenzhen Key Laboratory of Control Theory and Intelligent Systems(ZDSYS20220330161800001)
文摘Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectively deal with nonlinearities,constraints,and noises in the system,optimize the performance metric,and present an upper bound on the stable output of the system.
基金the National Research Foundation(NRF)Singapore mid-sized center grant(NRF-MSG-2023-0002)FrontierCRP grant(NRF-F-CRP-2024-0006)+2 种基金A*STAR Singapore MTC RIE2025 project(M24W1NS005)IAF-PP project(M23M5a0069)Ministry of Education(MOE)Singapore Tier 2 project(MOE-T2EP50220-0014).
文摘The rise of large-scale artificial intelligence(AI)models,such as ChatGPT,Deep-Seek,and autonomous vehicle systems,has significantly advanced the boundaries of AI,enabling highly complex tasks in natural language processing,image recognition,and real-time decisionmaking.However,these models demand immense computational power and are often centralized,relying on cloud-based architectures with inherent limitations in latency,privacy,and energy efficiency.To address these challenges and bring AI closer to real-world applications,such as wearable health monitoring,robotics,and immersive virtual environments,innovative hardware solutions are urgently needed.This work introduces a near-sensor edge computing(NSEC)system,built on a bilayer AlN/Si waveguide platform,to provide real-time,energy-efficient AI capabilities at the edge.Leveraging the electro-optic properties of AlN microring resonators for photonic feature extraction,coupled with Si-based thermo-optic Mach-Zehnder interferometers for neural network computations,the system represents a transformative approach to AI hardware design.Demonstrated through multimodal gesture and gait analysis,the NSEC system achieves high classification accuracies of 96.77%for gestures and 98.31%for gaits,ultra-low latency(<10 ns),and minimal energy consumption(<0.34 pJ).This groundbreaking system bridges the gap between AI models and real-world applications,enabling efficient,privacy-preserving AI solutions for healthcare,robotics,and next-generation human-machine interfaces,marking a pivotal advancement in edge computing and AI deployment.
基金The National Natural Science Foundation of China(W2431048)The Science and Technology Research Program of Chongqing Municipal Education Commission,China(KJZDK202300807)The Chongqing Natural Science Foundation,China(CSTB2024NSCQQCXMX0052).
文摘This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method is employed to achieve secure control by estimating the system's state in real time.Secondly,by combining a memory-based adaptive eventtriggered mechanism with neural networks,the paper aims to approximate the nonlinear terms in the networked system and efficiently conserve system resources.Finally,based on a two-degree-of-freedom model of a vehicle affected by crosswinds,this paper constructs a multi-unmanned ground vehicle(Multi-UGV)system to validate the effectiveness of the proposed method.Simulation results show that the proposed control strategy can effectively handle external disturbances such as crosswinds in practical applications,ensuring the stability and reliable operation of the Multi-UGV system.
基金founded by the Open Project Program of Anhui Province Key Laboratory of Metallurgical Engineering and Resources Recycling(Anhui University of Technology)(No.SKF21-06)Research Fund for Young Teachers of Anhui University of Technology in 2020(No.QZ202001).
文摘Real-time prediction and precise control of sinter quality are pivotal for energy saving,cost reduction,quality improvement and efficiency enhancement in the ironmaking process.To advance,the accuracy and comprehensiveness of sinter quality prediction,an intelligent flare monitoring system for sintering machine tails that combines hybrid neural networks integrating convolutional neural network with long short-term memory(CNN-LSTM)networks was proposed.The system utilized a high-temperature thermal imager for image acquisition at the sintering machine tail and employed a zone-triggered method to accurately capture dynamic feature images under challenging conditions of high-temperature,high dust,and occlusion.The feature images were then segmented through a triple-iteration multi-thresholding approach based on the maximum between-class variance method to minimize detail loss during the segmentation process.Leveraging the advantages of CNN and LSTM networks in capturing temporal and spatial information,a comprehensive model for sinter quality prediction was constructed,with inputs including the proportion of combustion layer,porosity rate,temperature distribution,and image features obtained from the convolutional neural network,and outputs comprising quality indicators such as underburning index,uniformity index,and FeO content of the sinter.The accuracy is notably increased,achieving a 95.8%hit rate within an error margin of±1.0.After the system is applied,the average qualified rate of FeO content increases from 87.24%to 89.99%,representing an improvement of 2.75%.The average monthly solid fuel consumption is reduced from 49.75 to 46.44 kg/t,leading to a 6.65%reduction and underscoring significant energy saving and cost reduction effects.
基金supported by the National Natural Science Foundation of China(Nos.52074246,52275390,52375394)the National Defense Basic Scientific Research Program of China(No.JCKY2020408B002)the Key R&D Program of Shanxi Province(No.202102050201011).
文摘The design of casting gating system directly determines the solidification sequence,defect severity,and overall quality of the casting.A novel machine learning strategy was developed to design the counter pressure casting gating system of a large thin-walled cabin casting.A high-quality dataset was established through orthogonal experiments combined with design criteria for the gating system.Spearman’s correlation analysis was used to select high-quality features.The gating system dimensions were predicted using a gated recurrent unit(GRU)recurrent neural network and an elastic network model.Using EasyCast and ProCAST casting software,a comparative analysis of the flow field,temperature field,and solidification field can be conducted to demonstrate the achievement of steady filling and top-down sequential solidification.Compared to the empirical formula method,this method eliminates trial-and-error iterations,reduces porosity,reduces casting defect volume from 11.23 cubic centimeters to 2.23 cubic centimeters,eliminates internal casting defects through the incorporation of an internally cooled iron,fulfilling the goal of intelligent gating system design.
基金described in this paper has been developed with in the project PRESECREL(PID2021-124502OB-C43)。
文摘The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are essential.Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns.This paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT systems.However,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss functions.To address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN hyper-parameters.The 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local minima.The proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model training.The generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded features.We evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and IoT-23.Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives.The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence trend.The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and IoT-23 datasets,with values of 0.24,1.10,and 0.09,respectively.Additionally,it attained the highest accuracy,ranging from 94%to 100%.These results suggest a promising direction for future IoT security frameworks,offering a scalable and efficient solution to safeguard against evolving cyber threats.
基金Supported by the National Natural Science Foundation of China (11161027)。
文摘Projective synchronization problems of a drive system and a particular response network were investigated,where the drive system is an arbitrary system with n+1 dimensions;it may be a linear or nonlinear system,and even a chaotic or hyperchaotic system,the response network is complex system coupled by N nodes,and every node is showed by the approximately linear part of the drive system.Only controlling any one node of the response network by designed controller can achieve the projective synchronization.Some numerical examples were employed to verify the effectiveness and correctness of the designed controller.
基金Supported by the National Natural Science Foundation of China(11971458,11471310)。
文摘In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.
文摘With the large-scale deployment of satellite constellations such as Starlink and the rapid advancement of technologies including artificial intelligence (AI) and non-terrestrial networks (NTNs), the integration of high, medium, and low Earth orbit satellite networks with terrestrial networks has become a critical direction for future communication technologies. The objective is to develop a space-terrestrial integrated 6G network that ensures ubiquitous connectivity and seamless services, facilitating intelligent interconnection and collaborative symbiosis among humans, machines, and objects. This integration has become a central focus of global technological innovation.
基金supported in part by National Key R&D Program of China(Grant No.2022YFC3803700)in part by the National Natural Science Foundation of China(Grant No.92067102)in part by the project of Beijing Laboratory of Advanced Information Networks.
文摘The rise of time-sensitive applications with broad geographical scope drives the development of time-sensitive networking(TSN)from intra-domain to inter-domain to ensure overall end-to-end connectivity requirements in heterogeneous deployments.When multiple TSN networks interconnect over non-TSN networks,all devices in the network need to be syn-chronized by sharing a uniform time reference.How-ever,most non-TSN networks are best-effort.Path delay asymmetry and random noise accumulation can introduce unpredictable time errors during end-to-end time synchronization.These factors can degrade syn-chronization performance.Therefore,cross-domain time synchronization becomes a challenging issue for multiple TSN networks interconnected by non-TSN networks.This paper presents a cross-domain time synchronization scheme that follows the software-defined TSN(SD-TSN)paradigm.It utilizes a com-bined control plane constructed by a coordinate con-troller and a domain controller for centralized control and management of cross-domain time synchroniza-tion.The general operation flow of the cross-domain time synchronization process is designed.The mecha-nism of cross-domain time synchronization is revealed by introducing a synchronization model and an error compensation method.A TSN cross-domain proto-type testbed is constructed for verification.Results show that the scheme can achieve end-to-end high-precision time synchronization with accuracy and sta-bility.
基金supported by the National Natural Science Foundation of China under Grant No.62131012ZTE Industry-University-Institute Cooperation Funds under Grant No.IA20230712005。
文摘This paper presents a space network emulation system based on a user-space network stack named Nos to solve space networks'unique architecture and routing issues and kernel stacks'inefficiency and development complexity.Our low Earth orbit satellite scenario emulation verifies the dynamic routing function of the protocol stack.The proposed system uses technologies like Open vSwitch(OVS)and traffic control(TC)to emulate the space network's highly dynamic topology and time-varying link characteristics.The emulation results demonstrate the system's high reliability,and the user-space network stack reduces development complexity and debugging difficulty,providing convenience for the development of space network protocols and network functions.
文摘In this paper,a new study concerning the usage of artificial neural networks in the control application is given.It is shown,that the data gathered during proper operation of a given control plant can be used in the learning process to fully embrace the control pattern.Interestingly,the instances driven by neural networks have the ability to outperform the original analytically driven scenarios.Three different control schemes,namely perfect,linear-quadratic,and generalized predictive controllers were used in the theoretical study.In addition,the nonlinear recurrent neural network-based generalized predictive controller with the radial basis function-originated predictor was obtained to exemplify the main results of the paper regarding the real-world application.
基金supported by the Key R&D Projects in Jiangsu Province(BE2021729)the Key Primary Research Project of Primary Strengthening Program(KYZYJKKCJC23001).
文摘Multi-agent systems often require good interoperability in the process of completing their assigned tasks.This paper first models the static structure and dynamic behavior of multiagent systems based on layered weighted scale-free community network and susceptible-infected-recovered(SIR)model.To solve the problem of difficulty in describing the changes in the structure and collaboration mode of the system under external factors,a two-dimensional Monte Carlo method and an improved dynamic Bayesian network are used to simulate the impact of external environmental factors on multi-agent systems.A collaborative information flow path optimization algorithm for agents under environmental factors is designed based on the Dijkstra algorithm.A method for evaluating system interoperability is designed based on simulation experiments,providing reference for the construction planning and optimization of organizational application of the system.Finally,the feasibility of the method is verified through case studies.
文摘The construction of“park cities”requires a systematic thinking to coordinate the networked development of park system and break through the limitation of emphasizing scale and grade and neglecting dynamic correlation in traditional planning.Taking Haidian District of Beijing as an example,the social network analysis method is introduced to construct the network model of park green spaces.Through indicators such as clustering coefficient,network density and node centrality,the characteristics of its spatial structure and hierarchical relationship are analyzed.It is found that the network integrity presents the characteristics of“highly local concentration and global fragmentation”,fragmented park green space network and missing spatial connection,isolated clusters and collaborative failure,as well as the spatial mismatch between population and resource supply and demand.Hierarchical issues include“structural imbalance and functional disorder”,disorder between network hierarchy and park level,misalignment of functional hierarchy leading to weakened network risk resistance capacity,and a relatively dense distribution of core nodes,etc.In response to the above problems,a multi-level spatial intervention strategy should be adopted to solve the overall problem of the network.Meanwhile,it is needed to clarify the positioning of a park itself and improve the hierarchical system,so as to construct a multi-level and multi-scale park green space network,contribute to the construction of a park city,and provide residents with more diverse activity venues.
