Image compression consists of two main parts: encoding and decoding. One of the important problems of the fractal theory is the long encoding implementation time, which hindered the acceptance of fractal image compres...Image compression consists of two main parts: encoding and decoding. One of the important problems of the fractal theory is the long encoding implementation time, which hindered the acceptance of fractal image compression as a practical method. The long encoding time results from the need to perform a large number of domain-range matches, the total encoding time is the product of the number of matches and the time to perform each match. In order to improve encoding speed, a hybrid method combining features extraction and self-organization network has been provided, which is based on the feature extraction approach the comparison pixels by pixels between the feature of range blocks and domains blocks. The efficiency of the new method was been proved by examples.展开更多
To enhance the clustering ability of self-organization network, this paper introduces a quantum inspired self-organization clustering algorithm. First, the clustering samples and the weight values in the competitive l...To enhance the clustering ability of self-organization network, this paper introduces a quantum inspired self-organization clustering algorithm. First, the clustering samples and the weight values in the competitive layer are mapped to the qubits on the Bloch sphere, and then, the winning node is obtained by computing the spherical distance between sample and weight value. Finally, the weight values of the winning nodes and its neighborhood are updated by rotating them to the sample on the Bloch sphere until the convergence. The clustering results of IRIS sample show that the proposed approach is obviously superior to the classical self-organization network and the K-mean clustering algorithm.展开更多
The self-organization mapping （SOM） neural network algorithm is a new method used to identify the ecosystem service zones at regional extent. According to the ecosystem assessment framework of Millennium Ecosystem A...The self-organization mapping （SOM） neural network algorithm is a new method used to identify the ecosystem service zones at regional extent. According to the ecosystem assessment framework of Millennium Ecosystem Assessment （ MA）, this paper develops an indicator system and conducts a spatial cluster analysis at the 1km by I km grid pixel scale with the SOM neural network algorithm to sort the core ecosystem services over the vertical and horizontal dimensions. A case study was carried out in Xilingol League. The ecosystem services in Xilingol League could be divided to six different ecological zones. The SOM neural network algorithm was capable of identifying the similarities among the input data automatically. The research provides both spatially and temporally valuable information targeted sustainable ecosystem management for decision-makers.展开更多
Cognitive radio(CR) is regarded as a promising technology for providing a high spectral efficiency to mobile users by using heterogeneous wireless network architectures and dynamic spectrum access techniques.However,c...Cognitive radio(CR) is regarded as a promising technology for providing a high spectral efficiency to mobile users by using heterogeneous wireless network architectures and dynamic spectrum access techniques.However,cognitive radio networks(CRNs)may also impose some challenges due to the ever increasing complexity of network architecture,the increasing complexity with configuration and management of large-scale networks,fluctuating nature of the available spectrum,diverse Quality-of-Service(QoS)requirements of various applications,and the intensifying difficulties of centralized control,etc.Spectrum management functions with self-organization features can be used to address these challenges and realize this new network paradigm.In this paper,fundamentals of CR,including spectrum sensing,spectrum management,spectrum mobility and spectrum sharing,have been surveyed,with their paradigms of self-organization being emphasized.Variant aspects of selforganization paradigms in CRNs,including critical functionalities of Media Access Control(MAC)- and network-layer operations,are surveyed and compared.Furthermore,new directions and open problems in CRNs are also identified in this survey.展开更多
In networked control systems(NCS),the control performance depends on not only the control algorithm but also the communication protocol stack.The performance degradation introduced by the heterogeneous and dynamic com...In networked control systems(NCS),the control performance depends on not only the control algorithm but also the communication protocol stack.The performance degradation introduced by the heterogeneous and dynamic communication environment has intensified the need for the reconfigurable protocol stack.In this paper,a novel architecture for the reconfigurable protocol stack is proposed,which is a unified specification of the protocol components and service interfaces supporting both static and dynamic reconfiguration for existing industrial communication standards.Within the architecture,a triple-level self-organization structure is designed to manage the dynamic reconfiguration procedure based on information exchanges inside and outside the protocol stack.Especially,the protocol stack can be self-adaptive to various environment and system requirements through the reconfiguration of working mode,routing and scheduling table.Finally,the study on the protocol of dynamic address management is conducted for the system of controller area network(CAN).The results show the efficiency of our self-organizing architecture for the implementation of a reconfigurable protocol stack.展开更多
The information exchange among satellites is crucial for the implementation of cluster satellite cooperative missions.However,achieving fast perception,rapid networking,and highprecision time synchronization among nod...The information exchange among satellites is crucial for the implementation of cluster satellite cooperative missions.However,achieving fast perception,rapid networking,and highprecision time synchronization among nodes without the support of the Global Navigation Satellite System(GNSS)and other prior information remains a formidable challenge to real-time wireless networks design.Therefore,a self-organizing network methodology based on multi-agent negotiation is proposed,which autonomously determines the master node through collaborative negotiation and competitive elections.On this basis,a real-time network protocol design is carried out and a high-precision time synchronization method with motion compensation is proposed.Simulation results demonstrate that the proposed method enables rapid networking with the capability of selfdiscovery,self-organization,and self-healing.For a cluster of 8 satellites,the networking time and the reorganization time are less than 4 s.The time synchronization accuracy exceeds 10-10s with motion compensation,demonstrating excellent real-time performance and stability.The research presented in this paper provides a valuable reference for the design and application of spacebased self-organizing networks for satellite cluster.展开更多
We propose a model of weighted networks in which the structural evolution is coupled with weight dynamics. Based on a simple merging and regeneration process, the model gives powel-law distributions of degree, strengt...We propose a model of weighted networks in which the structural evolution is coupled with weight dynamics. Based on a simple merging and regeneration process, the model gives powel-law distributions of degree, strength and weight, as observed in many real networks. It should be emphasized that, in our model, the nontrivial degree-strength correlation can be reproduced and in agreement with empirical data. Moreover, the size-growing evolution model is also presented to meet the properties of real-world systems.展开更多
This article presents an adaptive fault-tolerant tracking control strategy for unknown affine nonlinear systems subject to actuator faults and external disturbances.To address the hyperparameter initialization challen...This article presents an adaptive fault-tolerant tracking control strategy for unknown affine nonlinear systems subject to actuator faults and external disturbances.To address the hyperparameter initialization challenges inherent in conventional neural network training,an improved self-organizing radial basis function neural network(SRBFNN)with an input-dependent variable structure is developed.Furthermore,a novel selforganizing RBFNN-based observer is introduced to estimate system states across all dimensions.Leveraging the reconstructed states,the proposed adaptive controller effectively compensates for all uncertainties,including estimation errors in the observer,ensuring accurate state tracking with reduced control effort.The uniform ultimate boundedness of all closed-loop signals and tracking errors is rigorously established via Lyapunov stability analysis.Finally,simulations on two different nonlinear systems comprehensively validate the effectiveness and superiority of the proposed control approach.展开更多
In this paper, a new mechanism for the emergence of scale-free distribution is proposed. It is more realistic than the existing mechanism. Based on our mechanism, a model responsible for the scale-free distribution wi...In this paper, a new mechanism for the emergence of scale-free distribution is proposed. It is more realistic than the existing mechanism. Based on our mechanism, a model responsible for the scale-free distribution with an exponent in a range of 3-to-5 is given. Moreover, this model could also reproduce the exponential distribution that is discovered in some real networks. Finally, the analytical result of the model is given and the simulation shows the validity of our result,展开更多
Wireless Sensor Networks for Rainfall Monitoring (RM-WSNs) is a sensor network for the large-scale regional and moving rainfall monitoring,which could be controlled deployment. Delivery delay and cross-cluster calcula...Wireless Sensor Networks for Rainfall Monitoring (RM-WSNs) is a sensor network for the large-scale regional and moving rainfall monitoring,which could be controlled deployment. Delivery delay and cross-cluster calculation leads to information inaccuracy by the existing dynamic collabo-rative self-organization algorithm in WSNs. In this letter,a Local Dynamic Cluster Self-organization algorithm (LDCS) is proposed for the large-scale regional and moving target monitoring in RM-WSNs. The algorithm utilizes the resource-rich node in WSNs as the cluster head,which processes target information obtained by sensor nodes in cluster. The cluster head shifts with the target moving in chance and re-groups a new cluster. The target information acquisition is limited in the dynamic cluster,which can reduce information across-clusters transfer delay and improve the real-time of information acquisition. The simulation results show that,LDCS can not only relieve the problem of "too frequent leader switches" in IDSQ,also make full use of the history monitoring information of target and con-tinuous monitoring of sensor nodes that failed in DCS.展开更多
Self-organization theory informs an analysis on the evolution of labor self-organizations (LSOs), but lacks technical analysis on the evolution of their organizational structures. Fortunately, complex network technolo...Self-organization theory informs an analysis on the evolution of labor self-organizations (LSOs), but lacks technical analysis on the evolution of their organizational structures. Fortunately, complex network technology offers a new approach to analyzing these structures. Built on an extension of the Barabási-Albert (BA) model, we can simulate the evolution of LSOs by analyzing indicators including the clustering coefficient, degree distribution (DD) and average path length (APL) of workers, thereby demonstrating the evolving patterns of LSOs. Accordingly, governmental mechanism designs based on such patterns may not only stimulate energy growth and functional realization of LSOs, but also reduce the social percussions of abrupt evolutions. A comparative analysis of the evolutionary trajectories of LSOs in China and the U.S. finds that the U.S. government’s mechanism designs for protecting capitalism not only prevented the effective gathering of workers, but also prolonged the history of industrial conflicts. Such mechanism designs also led to the early dispersion and decline of LSOs and hindered the evolution of the working class. In contrast, the Chinese government established a socialist system that allowed workers to become the underlying force of socialist productivity. This design reduced labor strife while accelerating the evolution of workers towards higher stages.展开更多
Molecular motors play an important role in the organization of cytoskeletal filament networks. These nanometer-sized natural molecular machines opened up a new frontier of nano-technology. This article describes biomo...Molecular motors play an important role in the organization of cytoskeletal filament networks. These nanometer-sized natural molecular machines opened up a new frontier of nano-technology. This article describes biomolecular nano-machines, their internal structures, and dynamical interactions between molecular motors and their molecular tracks which reorganize a network of long protein filaments, particularly during cell division to form cytoskeleton of daughter cells. Towards the end, the article also takes up some still-to-be resolved matters and prospects for future developments in this exciting multidisciplinary area of science.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
The existence of absorption and reflection of light underwater leads to problems such as color distortion and blue-green bias in underwater images.In this study,a depthwise separable convolution-based generative adver...The existence of absorption and reflection of light underwater leads to problems such as color distortion and blue-green bias in underwater images.In this study,a depthwise separable convolution-based generative adversarial network(GAN)algorithm was proposed.Taking GAN as the basic framework,it combined a depthwise separable convolution module,attention mechanism,and reconstructed convolution module to realize the enhancement of underwater degraded images.Multi-scale features were captured by the depthwise separable convolution module,and the attention mechanism was utilized to enhance attention to important features.The reconstructed convolution module further extracts and fuses local and global features.Experimental results showed that the algorithm performs well in improving the color bias and blurring of underwater images,with PSNR reaching 27.835,SSIM reaching 0.883,UIQM reaching 3.205,and UCIQE reaching 0.713.The enhanced image outperforms the comparison algorithm in both subjective and objective metrics.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
Networked predictive control(NPC) has gained significant attention in recent years for its ability to effectively and actively address communication constraints in networked control systems(NCSs),such as network-induc...Networked predictive control(NPC) has gained significant attention in recent years for its ability to effectively and actively address communication constraints in networked control systems(NCSs),such as network-induced delays,packet dropouts,and packet disorders.Despite significant advancements,the increasing complexity and dynamism of network environments,along with the growing complexity of systems,pose new challenges for NPC.These challenges include difficulties in system modeling,cyber attacks,component faults,limited network bandwidth,and the necessity for distributed collaboration.This survey aims to provide a comprehensive review of NPC strategies.It begins with a summary of the primary challenges faced by NCSs,followed by an introduction to the control structure and core concepts of NPC.The survey then discusses several typical NPC schemes and examines their extensions in the areas of secure control,fault-tolerant control,distributed coordinated control,and event-triggered control.Moreover,it reviews notable works that have implemented these schemes.Finally,the survey concludes by exploring typical applications of NPC schemes and highlighting several challenging issues that could guide future research efforts.展开更多
A multi-stimuli-responsive hydrogel,P(VI-co-MAAC-NE),was successfully constructed by covalently integrating the aggregation-induced emission(AIE)moiety(Z)-N-(4-(1-cyano-2-(4-(diethylamino)phenyl)vinyl)-phenyl)methacry...A multi-stimuli-responsive hydrogel,P(VI-co-MAAC-NE),was successfully constructed by covalently integrating the aggregation-induced emission(AIE)moiety(Z)-N-(4-(1-cyano-2-(4-(diethylamino)phenyl)vinyl)-phenyl)methacrylamide(NE)into a dynamic hydrogen-bonding network composed of 1-vinylimidazole(VI)and methacrylic acid(MAAC)groups.The dense hydrogen-bonding network not only provides enhanced mechanical robustness,but also significantly enhances the AIE effect of NE by restricting its molecular motion.Under various external stimuli,the hydrogen bonds within the hydrogel network undergo reversible dissociation and reformation,thus enabling synergistic modulation of the hydrogel’s mechanical properties and luminescence behavior.Specifically,organic solvents disrupt the hydrogen-bonding network and the aggregation of the AIE moiety NE,resulting in macroscopic swelling and fluorescence quenching of the hydrogel.In strongly acidic conditions,protonation of NE molecules suppresses the intramolecular charge transfer(ICT)process,yielding a blue-shifted emission band accompanied by intense blue fluorescence;in highly alkaline environments,deprotonation of carboxyl groups induces hydrogel swelling and disperses NE aggregates,leading to pronounced fluorescence quenching.Moreover,the system exhibits thermally activated shape-memory behavior:heating above the glass transition temperature(T_(g):ca.62℃)softens the hydrogel to allow programmable reshaping,and subsequent hydrogen bond reformation at ambient conditions locks in the resultant geometries without sacrificing the hydrogel’s fluorescence performance.By capitalizing on these multi-stimuli-responsive characteristics and shape-memory behavior,the potential of hydrogel P(VI-co-MAAC-NE)for advanced information encryption and anti-counterfeiting applications is demonstrated.This work not only provides a versatile material platform for sensing and information storage,but also offers new insights into the design of intelligent soft materials integrating AIE features with dynamically regulated supramolecular network structures.展开更多
Urbanization is a significant driver of the loss of biodiversity and the disruption of ecosystems.Amphibians are especially vulnerable to the negative impact of urbanization as their life cycles and habitat requiremen...Urbanization is a significant driver of the loss of biodiversity and the disruption of ecosystems.Amphibians are especially vulnerable to the negative impact of urbanization as their life cycles and habitat requirements are complex.The present study investigated the effects of urbanization on amphibian predation networks in suburban Kunming in Yunnan,China and aimed to understand how predation network structure and stability vary with urbanization level.We constructed predation networks by analyzing the stomach contents of amphibians from 12d istinct urbanization gradients.We used the bipartite package in R to evaluate network robustness metrics such as modularity,nestedness,connectivity,and average shortest path length(ASPL).We found that urbanization level is negatively correlated with predation network connectivity(R=−0.67,Ρ=0.02),but there were no significant correlations between urbanization level and nestedness,modularity,or ASPL.Removal of the keystone species destabilized the predation networks at certain locations.The present work highlighted that maintaining prey quantity and diversity preserves predation network connectivity and stabilizes the overall network in urbanizing landscapes.It also underscored the critical role that keystone species play in sustaining network robustness.The results of this research provided insights into the ecological consequences of urbanization.They also suggested that conservation measures should protect the key species and habitats of amphibian predation networks and mitigate the negative impact of urban development on them.展开更多
In the process of programmable networks simplifying network management and increasing network flexibility through custom packet behavior,security incidents caused by human logic errors are seriously threatening their ...In the process of programmable networks simplifying network management and increasing network flexibility through custom packet behavior,security incidents caused by human logic errors are seriously threatening their safe operation,robust verificationmethods are required to ensure their correctness.As one of the formalmethods,symbolic execution offers a viable approach for verifying programmable networks by systematically exploring all possible paths within a program.However,its application in this field encounters scalability issues due to path explosion and complex constraint-solving.Therefore,in this paper,we propose NetVerifier,a scalable verification system for programmable networks.Tomitigate the path explosion issue,we developmultiple pruning strategies that strategically eliminate irrelevant execution paths while preserving verification integrity by precisely identifying the execution paths related to the verification purpose.To address the complex constraint-solving problem,we introduce an execution results reuse solution to avoid redundant computation of the same constraints.To apply these solutions intelligently,a matching algorithm is implemented to automatically select appropriate solutions based on the characteristics of the verification requirement.Moreover,Language Aided Verification(LAV),an assertion language,is designed to express verification intentions in a concise form.Experimental results on diverse open-source programs of varying scales demonstrate NetVerifier’s improvement in scalability and effectiveness in identifying potential network errors.In the best scenario,compared with ASSERT-P4,NetVerifier reduced the execution path,verification time,and memory occupation of the verification process by 99.92%,94.76%,and 65.19%,respectively.展开更多
文摘Image compression consists of two main parts: encoding and decoding. One of the important problems of the fractal theory is the long encoding implementation time, which hindered the acceptance of fractal image compression as a practical method. The long encoding time results from the need to perform a large number of domain-range matches, the total encoding time is the product of the number of matches and the time to perform each match. In order to improve encoding speed, a hybrid method combining features extraction and self-organization network has been provided, which is based on the feature extraction approach the comparison pixels by pixels between the feature of range blocks and domains blocks. The efficiency of the new method was been proved by examples.
文摘To enhance the clustering ability of self-organization network, this paper introduces a quantum inspired self-organization clustering algorithm. First, the clustering samples and the weight values in the competitive layer are mapped to the qubits on the Bloch sphere, and then, the winning node is obtained by computing the spherical distance between sample and weight value. Finally, the weight values of the winning nodes and its neighborhood are updated by rotating them to the sample on the Bloch sphere until the convergence. The clustering results of IRIS sample show that the proposed approach is obviously superior to the classical self-organization network and the K-mean clustering algorithm.
基金Supported by the National Scientific Foundation of China(4080123170873118)+6 种基金the Chinese Academy of Sciences(KZCX2-YW-305-2KSCX2-YW-N-039KZCX2-YW-326-1)the Ministry of Science and Technology of China(2006DFB91912012006BAC08B032006BAC08B062008BAK47B02)~~
文摘The self-organization mapping （SOM） neural network algorithm is a new method used to identify the ecosystem service zones at regional extent. According to the ecosystem assessment framework of Millennium Ecosystem Assessment （ MA）, this paper develops an indicator system and conducts a spatial cluster analysis at the 1km by I km grid pixel scale with the SOM neural network algorithm to sort the core ecosystem services over the vertical and horizontal dimensions. A case study was carried out in Xilingol League. The ecosystem services in Xilingol League could be divided to six different ecological zones. The SOM neural network algorithm was capable of identifying the similarities among the input data automatically. The research provides both spatially and temporally valuable information targeted sustainable ecosystem management for decision-makers.
基金ACKNOWLEDGEMENT This work was supported by National Natural Science Foundation of China （No. 61172050）, Program for New Century Excellent Talents in University （NECT-12-0774）, the open research fund of National Mobile Communications Research Laboratory, Southeast University （No.2013D12）, the Foundation of Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services. The corresponding author is Dr. Zhongshan Zhang.
文摘Cognitive radio(CR) is regarded as a promising technology for providing a high spectral efficiency to mobile users by using heterogeneous wireless network architectures and dynamic spectrum access techniques.However,cognitive radio networks(CRNs)may also impose some challenges due to the ever increasing complexity of network architecture,the increasing complexity with configuration and management of large-scale networks,fluctuating nature of the available spectrum,diverse Quality-of-Service(QoS)requirements of various applications,and the intensifying difficulties of centralized control,etc.Spectrum management functions with self-organization features can be used to address these challenges and realize this new network paradigm.In this paper,fundamentals of CR,including spectrum sensing,spectrum management,spectrum mobility and spectrum sharing,have been surveyed,with their paradigms of self-organization being emphasized.Variant aspects of selforganization paradigms in CRNs,including critical functionalities of Media Access Control(MAC)- and network-layer operations,are surveyed and compared.Furthermore,new directions and open problems in CRNs are also identified in this survey.
基金supported by National Natural Science Foundation of China(No.60674081,No.60834002,No.61074145)
文摘In networked control systems(NCS),the control performance depends on not only the control algorithm but also the communication protocol stack.The performance degradation introduced by the heterogeneous and dynamic communication environment has intensified the need for the reconfigurable protocol stack.In this paper,a novel architecture for the reconfigurable protocol stack is proposed,which is a unified specification of the protocol components and service interfaces supporting both static and dynamic reconfiguration for existing industrial communication standards.Within the architecture,a triple-level self-organization structure is designed to manage the dynamic reconfiguration procedure based on information exchanges inside and outside the protocol stack.Especially,the protocol stack can be self-adaptive to various environment and system requirements through the reconfiguration of working mode,routing and scheduling table.Finally,the study on the protocol of dynamic address management is conducted for the system of controller area network(CAN).The results show the efficiency of our self-organizing architecture for the implementation of a reconfigurable protocol stack.
基金supported by the National Natural Science Foundation of China(No.62401597)the Natural Science Foundation of Hunan Province,China(No.2024JJ6469)the Scientific Research Project of National University of Defense Technology,China(No.ZK22-02)。
文摘The information exchange among satellites is crucial for the implementation of cluster satellite cooperative missions.However,achieving fast perception,rapid networking,and highprecision time synchronization among nodes without the support of the Global Navigation Satellite System(GNSS)and other prior information remains a formidable challenge to real-time wireless networks design.Therefore,a self-organizing network methodology based on multi-agent negotiation is proposed,which autonomously determines the master node through collaborative negotiation and competitive elections.On this basis,a real-time network protocol design is carried out and a high-precision time synchronization method with motion compensation is proposed.Simulation results demonstrate that the proposed method enables rapid networking with the capability of selfdiscovery,self-organization,and self-healing.For a cluster of 8 satellites,the networking time and the reorganization time are less than 4 s.The time synchronization accuracy exceeds 10-10s with motion compensation,demonstrating excellent real-time performance and stability.The research presented in this paper provides a valuable reference for the design and application of spacebased self-organizing networks for satellite cluster.
基金Supported by the National 0utstanding Young Investigator Foundation of China under Grant No 70225005, the National Natural Science Foundation of China under Grant No 70471088.
文摘We propose a model of weighted networks in which the structural evolution is coupled with weight dynamics. Based on a simple merging and regeneration process, the model gives powel-law distributions of degree, strength and weight, as observed in many real networks. It should be emphasized that, in our model, the nontrivial degree-strength correlation can be reproduced and in agreement with empirical data. Moreover, the size-growing evolution model is also presented to meet the properties of real-world systems.
基金supported in part by the National Natural Science Foundation of China(62033008,62188101,62173343,62073339)the Natural Science Foundation of Shandong Province of China(ZR2024MF072,ZR2022ZD34)the Research Fund for the Taishan Scholar Project of Shandong Province of China.
文摘This article presents an adaptive fault-tolerant tracking control strategy for unknown affine nonlinear systems subject to actuator faults and external disturbances.To address the hyperparameter initialization challenges inherent in conventional neural network training,an improved self-organizing radial basis function neural network(SRBFNN)with an input-dependent variable structure is developed.Furthermore,a novel selforganizing RBFNN-based observer is introduced to estimate system states across all dimensions.Leveraging the reconstructed states,the proposed adaptive controller effectively compensates for all uncertainties,including estimation errors in the observer,ensuring accurate state tracking with reduced control effort.The uniform ultimate boundedness of all closed-loop signals and tracking errors is rigorously established via Lyapunov stability analysis.Finally,simulations on two different nonlinear systems comprehensively validate the effectiveness and superiority of the proposed control approach.
基金Project supported by the National Natural Science Foundation of China （Grant Nos 60374037 and 60574036）, the Program for New Century Excellent Talents of High Education of China（Grant No NCET 2005-290）, The Special Research Fund for the Doctoral Program of High Education of China （Grant No 20050055013）.Acknowledgments The authors would like to thank Réka Albert for useful discussion and are grateful to the anonymous referees for their valuable suggestions and comments, which have made this paper improved.
文摘In this paper, a new mechanism for the emergence of scale-free distribution is proposed. It is more realistic than the existing mechanism. Based on our mechanism, a model responsible for the scale-free distribution with an exponent in a range of 3-to-5 is given. Moreover, this model could also reproduce the exponential distribution that is discovered in some real networks. Finally, the analytical result of the model is given and the simulation shows the validity of our result,
基金Supported by the Key Projection of Science and Technology Research of Ministry of Education of China (107057)the Science & Technology Fund for Students of Hohai University (K200803)
文摘Wireless Sensor Networks for Rainfall Monitoring (RM-WSNs) is a sensor network for the large-scale regional and moving rainfall monitoring,which could be controlled deployment. Delivery delay and cross-cluster calculation leads to information inaccuracy by the existing dynamic collabo-rative self-organization algorithm in WSNs. In this letter,a Local Dynamic Cluster Self-organization algorithm (LDCS) is proposed for the large-scale regional and moving target monitoring in RM-WSNs. The algorithm utilizes the resource-rich node in WSNs as the cluster head,which processes target information obtained by sensor nodes in cluster. The cluster head shifts with the target moving in chance and re-groups a new cluster. The target information acquisition is limited in the dynamic cluster,which can reduce information across-clusters transfer delay and improve the real-time of information acquisition. The simulation results show that,LDCS can not only relieve the problem of "too frequent leader switches" in IDSQ,also make full use of the history monitoring information of target and con-tinuous monitoring of sensor nodes that failed in DCS.
基金a deliverable of the “Research on the Accounting of ‘Trade in Value-added’ in Chinese Services Sector and its Place in the Global Value Chain,” a project funded by the National Social Science Foundation of China(15BGJ036)“The Impacts of Economic Globalization on Entrepreneurship in China—Theoretical Research and Empirical Analysis,” a youth project funded by the National Natural Science Foundation of China(NSFC)(71603142)+3 种基金“Research on Approaches to Labor-Management Cooperation with Chinese Characteristics—A Labor Relations Evolutionary Perspective,” a Ministry of Education humanities and social sciences research youth project(16YJC790115)“Research on the Evolution of Labor Relations with Chinese Characteristics Since the 18th CPC National Congress,” a Shandong planned social sciences research project(16CZLJ05)“Research on the Evolution Mechanisms and Paths of the Marxist Labor System from a Complex Network Perspective,” a project funded by the China Postdoctoral Science Foundation(CPSF)(2017M612180)“Research on Mechanism Design of the Spatial Structure of Labor-Management Cooperation with Chinese Characteristics,” a Qingdao postdoctoral applied research project
文摘Self-organization theory informs an analysis on the evolution of labor self-organizations (LSOs), but lacks technical analysis on the evolution of their organizational structures. Fortunately, complex network technology offers a new approach to analyzing these structures. Built on an extension of the Barabási-Albert (BA) model, we can simulate the evolution of LSOs by analyzing indicators including the clustering coefficient, degree distribution (DD) and average path length (APL) of workers, thereby demonstrating the evolving patterns of LSOs. Accordingly, governmental mechanism designs based on such patterns may not only stimulate energy growth and functional realization of LSOs, but also reduce the social percussions of abrupt evolutions. A comparative analysis of the evolutionary trajectories of LSOs in China and the U.S. finds that the U.S. government’s mechanism designs for protecting capitalism not only prevented the effective gathering of workers, but also prolonged the history of industrial conflicts. Such mechanism designs also led to the early dispersion and decline of LSOs and hindered the evolution of the working class. In contrast, the Chinese government established a socialist system that allowed workers to become the underlying force of socialist productivity. This design reduced labor strife while accelerating the evolution of workers towards higher stages.
文摘Molecular motors play an important role in the organization of cytoskeletal filament networks. These nanometer-sized natural molecular machines opened up a new frontier of nano-technology. This article describes biomolecular nano-machines, their internal structures, and dynamical interactions between molecular motors and their molecular tracks which reorganize a network of long protein filaments, particularly during cell division to form cytoskeleton of daughter cells. Towards the end, the article also takes up some still-to-be resolved matters and prospects for future developments in this exciting multidisciplinary area of science.
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The existence of absorption and reflection of light underwater leads to problems such as color distortion and blue-green bias in underwater images.In this study,a depthwise separable convolution-based generative adversarial network(GAN)algorithm was proposed.Taking GAN as the basic framework,it combined a depthwise separable convolution module,attention mechanism,and reconstructed convolution module to realize the enhancement of underwater degraded images.Multi-scale features were captured by the depthwise separable convolution module,and the attention mechanism was utilized to enhance attention to important features.The reconstructed convolution module further extracts and fuses local and global features.Experimental results showed that the algorithm performs well in improving the color bias and blurring of underwater images,with PSNR reaching 27.835,SSIM reaching 0.883,UIQM reaching 3.205,and UCIQE reaching 0.713.The enhanced image outperforms the comparison algorithm in both subjective and objective metrics.
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(62173002,62403235,62403010,52301408,62173255)the Beijing Natural Science Foundation(L241015,4222045)+2 种基金the Yuxiu Innovation Project of NCUT(2024NCUTYXCX111)the China Postdoctoral Science Foundation(2025T180466)the Beijing Postdoctoral Research Foundation(2025-ZZ-70)。
文摘Networked predictive control(NPC) has gained significant attention in recent years for its ability to effectively and actively address communication constraints in networked control systems(NCSs),such as network-induced delays,packet dropouts,and packet disorders.Despite significant advancements,the increasing complexity and dynamism of network environments,along with the growing complexity of systems,pose new challenges for NPC.These challenges include difficulties in system modeling,cyber attacks,component faults,limited network bandwidth,and the necessity for distributed collaboration.This survey aims to provide a comprehensive review of NPC strategies.It begins with a summary of the primary challenges faced by NCSs,followed by an introduction to the control structure and core concepts of NPC.The survey then discusses several typical NPC schemes and examines their extensions in the areas of secure control,fault-tolerant control,distributed coordinated control,and event-triggered control.Moreover,it reviews notable works that have implemented these schemes.Finally,the survey concludes by exploring typical applications of NPC schemes and highlighting several challenging issues that could guide future research efforts.
文摘A multi-stimuli-responsive hydrogel,P(VI-co-MAAC-NE),was successfully constructed by covalently integrating the aggregation-induced emission(AIE)moiety(Z)-N-(4-(1-cyano-2-(4-(diethylamino)phenyl)vinyl)-phenyl)methacrylamide(NE)into a dynamic hydrogen-bonding network composed of 1-vinylimidazole(VI)and methacrylic acid(MAAC)groups.The dense hydrogen-bonding network not only provides enhanced mechanical robustness,but also significantly enhances the AIE effect of NE by restricting its molecular motion.Under various external stimuli,the hydrogen bonds within the hydrogel network undergo reversible dissociation and reformation,thus enabling synergistic modulation of the hydrogel’s mechanical properties and luminescence behavior.Specifically,organic solvents disrupt the hydrogen-bonding network and the aggregation of the AIE moiety NE,resulting in macroscopic swelling and fluorescence quenching of the hydrogel.In strongly acidic conditions,protonation of NE molecules suppresses the intramolecular charge transfer(ICT)process,yielding a blue-shifted emission band accompanied by intense blue fluorescence;in highly alkaline environments,deprotonation of carboxyl groups induces hydrogel swelling and disperses NE aggregates,leading to pronounced fluorescence quenching.Moreover,the system exhibits thermally activated shape-memory behavior:heating above the glass transition temperature(T_(g):ca.62℃)softens the hydrogel to allow programmable reshaping,and subsequent hydrogen bond reformation at ambient conditions locks in the resultant geometries without sacrificing the hydrogel’s fluorescence performance.By capitalizing on these multi-stimuli-responsive characteristics and shape-memory behavior,the potential of hydrogel P(VI-co-MAAC-NE)for advanced information encryption and anti-counterfeiting applications is demonstrated.This work not only provides a versatile material platform for sensing and information storage,but also offers new insights into the design of intelligent soft materials integrating AIE features with dynamically regulated supramolecular network structures.
基金supported by Yunnan Fundamental Research Projects(202501BD070001-081).
文摘Urbanization is a significant driver of the loss of biodiversity and the disruption of ecosystems.Amphibians are especially vulnerable to the negative impact of urbanization as their life cycles and habitat requirements are complex.The present study investigated the effects of urbanization on amphibian predation networks in suburban Kunming in Yunnan,China and aimed to understand how predation network structure and stability vary with urbanization level.We constructed predation networks by analyzing the stomach contents of amphibians from 12d istinct urbanization gradients.We used the bipartite package in R to evaluate network robustness metrics such as modularity,nestedness,connectivity,and average shortest path length(ASPL).We found that urbanization level is negatively correlated with predation network connectivity(R=−0.67,Ρ=0.02),but there were no significant correlations between urbanization level and nestedness,modularity,or ASPL.Removal of the keystone species destabilized the predation networks at certain locations.The present work highlighted that maintaining prey quantity and diversity preserves predation network connectivity and stabilizes the overall network in urbanizing landscapes.It also underscored the critical role that keystone species play in sustaining network robustness.The results of this research provided insights into the ecological consequences of urbanization.They also suggested that conservation measures should protect the key species and habitats of amphibian predation networks and mitigate the negative impact of urban development on them.
基金supported by the National Key Research and Development Program of China under Grant 2023YFB2903902in part by the Science and Technology Innovation Leading Talents Subsidy Project of Central Plains under Grant 244200510038.
文摘In the process of programmable networks simplifying network management and increasing network flexibility through custom packet behavior,security incidents caused by human logic errors are seriously threatening their safe operation,robust verificationmethods are required to ensure their correctness.As one of the formalmethods,symbolic execution offers a viable approach for verifying programmable networks by systematically exploring all possible paths within a program.However,its application in this field encounters scalability issues due to path explosion and complex constraint-solving.Therefore,in this paper,we propose NetVerifier,a scalable verification system for programmable networks.Tomitigate the path explosion issue,we developmultiple pruning strategies that strategically eliminate irrelevant execution paths while preserving verification integrity by precisely identifying the execution paths related to the verification purpose.To address the complex constraint-solving problem,we introduce an execution results reuse solution to avoid redundant computation of the same constraints.To apply these solutions intelligently,a matching algorithm is implemented to automatically select appropriate solutions based on the characteristics of the verification requirement.Moreover,Language Aided Verification(LAV),an assertion language,is designed to express verification intentions in a concise form.Experimental results on diverse open-source programs of varying scales demonstrate NetVerifier’s improvement in scalability and effectiveness in identifying potential network errors.In the best scenario,compared with ASSERT-P4,NetVerifier reduced the execution path,verification time,and memory occupation of the verification process by 99.92%,94.76%,and 65.19%,respectively.
