Network Intrusion Detection System(NIDS)detection of minority class attacks is always a difficult task when dealing with attacks in complex network environments.To improve the detection capability of minority-class at...Network Intrusion Detection System(NIDS)detection of minority class attacks is always a difficult task when dealing with attacks in complex network environments.To improve the detection capability of minority-class attacks,this study proposes an intrusion detection method based on a two-layer structure.The first layer employs a CNN-BiLSTM model incorporating an attention mechanism to classify network traffic into normal traffic,majority class attacks,and merged minority class attacks.The second layer further segments the minority class attacks through Stacking ensemble learning.The datasets are selected from the generic network dataset CIC-IDS2017,NSL-KDD,and the industrial network dataset Mississippi Gas Pipeline dataset to enhance the generalization and practical applicability of the model.Experimental results show that the proposed model achieves an overall detection accuracy of 99%,99%,and 95%on the CIC-IDS2017,NSL-KDD,and industrial network datasets,respectively.It also significantly outperforms traditional methods in terms of detection accuracy and recall rate for minority class attacks.Compared with the single-layer deep learning model,the two-layer structure effectively reduces the false alarm rate while improving the minority-class attack detection performance.The research in this paper not only improves the adaptability of NIDS to complex network environments but also provides a new solution for minority-class attack detection in industrial network security.展开更多
An evolutionary prisoner＇s dilemma game is investigated on two-layered complex networks respectively representing interaction and learning networks in one and two dimensions. A parameter q is introduced to denote the...An evolutionary prisoner＇s dilemma game is investigated on two-layered complex networks respectively representing interaction and learning networks in one and two dimensions. A parameter q is introduced to denote the correlation degree between the two-layered networks. Using Monte Carlo simulations we studied the effects of the correlation degree on cooperative behaviour and found that the cooperator density nontrivially changes with q for different payoff parameter values depending on the detailed strategy updating and network dimension. An explanation for the obtained results is provided.展开更多
In view of the fact that news can generate derivative topics when it spreads through micro-blogs,a two-layer coupled SEIR public opinion propagation model is proposed in this paper.The model divides the process of pub...In view of the fact that news can generate derivative topics when it spreads through micro-blogs,a two-layer coupled SEIR public opinion propagation model is proposed in this paper.The model divides the process of public opinion propagation into two layers:the original topic layer and the derived topic layer.Messages are transmitted separately by the SEIR model in the two topic layers,which are independent and interactive.The influence of the topic derivation rate on the propagation trend is established by solving for the equilibrium point and propagation threshold.Further,we establish the relationship between the original topic and the derived topic by simulation.This paper uses the Baidu index to demonstrate the correctness of the model.The relationship between the derived topic and the original topic is verified by adjusting the parameters by the control variable method.The results show that the proposed model is consistent with the propagation of actual public opinion.展开更多
Soil microbial communities are key factors in maintaining ecosystem multifunctionality(EMF).However,the distribution patterns of bacterial diversity and how the different bacterial taxa and their diversity dimensions ...Soil microbial communities are key factors in maintaining ecosystem multifunctionality(EMF).However,the distribution patterns of bacterial diversity and how the different bacterial taxa and their diversity dimensions affect EMF remain largely unknown.Here,we investigated variation in three measures of diversity(alpha diversity,community composition and network complexity)among rare,intermediate,and abundant taxa across a latitudinal gradient spanning five forest plots in Yunnan Province,China and examined their contributions on EMF.We aimed to characterize the diversity distributions of bacterial groups across latitudes and to assess the differences in the mechanisms underlying their contributions to EMF.We found that multifaceted diversity(i.e.,diversity assessed by the three different metrics)of rare,intermediate,and abundant bacteria generally decreased with increasing latitude.More importantly,we found that rare bacterial taxa tended to be more diverse,but they contributed less to EMF than intermediate or abundant bacteria.Among the three dimensions of diversity we assessed,only community composition significantly affected EMF across all locations,while alpha diversity had a negative effect,and network complexity showed no significant impact.Our study further emphasizes the importance of intermediate and abundant bacterial taxa as well as community composition to EMF and provides a theoretical basis for investigating the mechanisms by which belowground microorganisms drive EMF along a latitudinal gradient.展开更多
The mutual coupling between neurons in a realistic neuronal system is much complex, and a two-layer neuronal network is designed to investigate the transition of electric activities of neurons. The Hindmarsh–Rose neu...The mutual coupling between neurons in a realistic neuronal system is much complex, and a two-layer neuronal network is designed to investigate the transition of electric activities of neurons. The Hindmarsh–Rose neuron model is used to describe the local dynamics of each neuron, and neurons in the two-layer networks are coupled in dislocated type. The coupling intensity between two-layer networks, and the coupling ratio(Pro), which defines the percentage involved in the coupling in each layer, are changed to observe the synchronization transition of collective behaviors in the two-layer networks. It is found that the two-layer networks of neurons becomes synchronized with increasing the coupling intensity and coupling ratio(Pro) beyond certain thresholds. An ordered wave in the first layer is useful to wake up the rest state in the second layer, or suppress the spatiotemporal state in the second layer under coupling by generating target wave or spiral waves. And the scheme of dislocation coupling can be used to suppress spatiotemporal chaos and excite quiescent neurons.展开更多
An image-reconstruction approach for optical tomography is presented,in which a two-layered BP neural network is used to distinguish the tumor location.The inverse problem is solved as optimization problem by Femlab s...An image-reconstruction approach for optical tomography is presented,in which a two-layered BP neural network is used to distinguish the tumor location.The inverse problem is solved as optimization problem by Femlab software and Levenberg–Marquardt algorithm.The concept of the average optical coefficient is proposed in this paper,which is helpful to understand the distribution of the scattering photon from tumor.The reconstructive¯µs by the trained network is reasonable for showing the changes of photon number transporting inside tumor tissue.It realized the fast reconstruction of tissue optical properties and provided optical OT with a new method.展开更多
Multilayer network is a frontier direction of network science research. In this paper, the cluster ring network is extended to a two-layer network model, and the inner structures of the cluster blocks are random, smal...Multilayer network is a frontier direction of network science research. In this paper, the cluster ring network is extended to a two-layer network model, and the inner structures of the cluster blocks are random, small world or scale-free. We study the influence of network scale, the interlayer linking weight and interlayer linking fraction on synchronizability. It is found that the synchronizability of the two-layer cluster ring network decreases with the increase of network size. There is an optimum value of the interlayer linking weight in the two-layer cluster ring network, which makes the synchronizability of the network reach the optimum. When the interlayer linking weight and the interlayer linking fraction are very small, the change of them will affect the synchronizability.展开更多
Accurately identifying key nodes is essential for evaluating network robustness and controlling information propagation in complex network analysis. However, current research methods face limitations in applicability ...Accurately identifying key nodes is essential for evaluating network robustness and controlling information propagation in complex network analysis. However, current research methods face limitations in applicability and accuracy. To address these challenges, this study introduces the K-GCN model, which integrates neighborhood k-shell distribution analysis with Graph Convolutional Network(GCN) technology to enhance key node identification in complex networks. The K-GCN model first leverages neighborhood k-shell distributions to calculate entropy values for each node, effectively quantifying node importance within the network. These entropy values are then used as key features within the GCN, which subsequently formulates intelligent strategies to maximize network connectivity disruption by removing a minimal set of nodes, thereby impacting the overall network architecture. Through iterative interactions with the environment, the GCN continuously refines its strategies, achieving precise identification of key nodes in the network. Unlike traditional methods, the K-GCN model not only captures local node features but also integrates the network structure and complex interrelations between neighboring nodes, significantly improving the accuracy and efficiency of key node identification.Experimental validation in multiple real-world network scenarios demonstrates that the K-GCN model outperforms existing methods.展开更多
This paper study the finite time internal synchronization and the external synchronization(hybrid synchronization)for duplex heterogeneous complex networks by time-varying intermittent control.There few study hybrid s...This paper study the finite time internal synchronization and the external synchronization(hybrid synchronization)for duplex heterogeneous complex networks by time-varying intermittent control.There few study hybrid synchronization of heterogeneous duplex complex networks.Therefore,we study the finite time hybrid synchronization of heterogeneous duplex networks,which employs the time-varying intermittent control to drive the duplex heterogeneous complex networks to achieve hybrid synchronization in finite time.To be specific,the switch frequency of the controllers can be changed with time by devise Lyapunov function and boundary function,the internal synchronization and external synchronization are achieved simultaneously in finite time.Finally,numerical examples are presented to illustrate the validness of theoretical results.展开更多
This paper investigates modified fixed-time synchronization(FxTS)of complex networks(CNs)with time-varying delays based on continuous and discontinuous controllers.First,for the sake of making the settling time(ST)of ...This paper investigates modified fixed-time synchronization(FxTS)of complex networks(CNs)with time-varying delays based on continuous and discontinuous controllers.First,for the sake of making the settling time(ST)of FxTS is independent of the initial values and parameters of the CNs,a modified fixed-time(FxT)stability theorem is proposed,where the ST is determined by an arbitrary positive number given in advance.Then,continuous controller and discontinuous controller are designed to realize the modified FxTS target of CNs.In addition,based on the designed controllers,CNs can achieve synchronization at any given time,or even earlier.And control strategies effectively solve the problem of ST related to the parameters of CNs.Finally,an appropriate simulation example is conducted to examine the effectiveness of the designed control strategies.展开更多
Using complex network methods,we construct undirected and directed heatwave networks to systematically analyze heatwave events over China from 1961 to 2023,exploring their spatiotemporal evolution patterns in differen...Using complex network methods,we construct undirected and directed heatwave networks to systematically analyze heatwave events over China from 1961 to 2023,exploring their spatiotemporal evolution patterns in different regions.The findings reveal a significant increase in heatwaves since the 2000s,with the average occurrence rising from approximately 3 to 5 times,and their duration increasing from 15 to around 30 days,nearly doubling.An increasing trend of“early onset and late withdrawal”of heatwaves has become more pronounced each year.In particular,eastern regions experience heatwaves that typically start earlier and tend to persist into September,exhibiting greater interannual variability compared to western areas.The middle and lower reaches of the Yangtze River and Xinjiang are identified as high-frequency heatwave areas.Complex network analysis reveals the dynamics of heatwave propagation,with degree centrality and synchronization distance indicating that the middle and lower reaches of the Yangtze River,Northeast China,and Xinjiang are key nodes in heatwave spread.Additionally,network divergence analysis shows that Xinjiang acts as a“source”area for heatwaves,exporting heat to surrounding regions,while the central region functions as a major“sink,”receiving more heatwave events.Further analysis from 1994 to 2023 indicates that heatwave events exhibit stronger network centrality and more complex synchronization patterns.These results suggest that complex networks provide a refined framework for depicting the spatiotemporal dynamics of heatwave propagation,offering new avenues for studying their occurrence and development patterns.展开更多
Independent cascade(IC)models,by simulating how one node can activate another,are important tools for studying the dynamics of information spreading in complex networks.However,traditional algorithms for the IC model ...Independent cascade(IC)models,by simulating how one node can activate another,are important tools for studying the dynamics of information spreading in complex networks.However,traditional algorithms for the IC model implementation face significant efficiency bottlenecks when dealing with large-scale networks and multi-round simulations.To settle this problem,this study introduces a GPU-based parallel independent cascade(GPIC)algorithm,featuring an optimized representation of the network data structure and parallel task scheduling strategies.Specifically,for this GPIC algorithm,we propose a network data structure tailored for GPU processing,thereby enhancing the computational efficiency and the scalability of the IC model.In addition,we design a parallel framework that utilizes the full potential of GPU's parallel processing capabilities,thereby augmenting the computational efficiency.The results from our simulation experiments demonstrate that GPIC not only preserves accuracy but also significantly boosts efficiency,achieving a speedup factor of 129 when compared to the baseline IC method.Our experiments also reveal that when using GPIC for the independent cascade simulation,100-200 simulation rounds are sufficient for higher-cost studies,while high precision studies benefit from 500 rounds to ensure reliable results,providing empirical guidance for applying this new algorithm to practical research.展开更多
Numerous studies have examined the impact ofwater quality degradation on bacterial community structure,yet insights into its effects on the bacterial ecological networks remain scarce.In this study,we investigated the...Numerous studies have examined the impact ofwater quality degradation on bacterial community structure,yet insights into its effects on the bacterial ecological networks remain scarce.In this study,we investigated the diversity,composition,assembly patterns,ecological networks,and environmental determinants of bacterial communities across 20 ponds to understand the impact of water quality degradation.Our findings revealed that water quality degradation significantly reduces the α-diversity of bacterial communities in water samples,while sediment samples remain unaffected.Additionally,water quality deterioration increases the complexity of bacterial networks in water samples but reduces it in sediment samples.These shifts in bacterial communities were primarily governed by deterministic processes,with heterogeneous selection being particularly influential.Through redundancy analysis(RDA),multiple regression on matrices(MRM),and Mantel tests,we identified dissolved oxygen(DO),ammonium nitrogen(NH_(4)^(+)-N),and C/N ratio as key factors affecting the composition and network complexity of bacterial communities in both water and sediment.Overall,this study contributes a novel perspective on the effect ofwater quality deterioration on microbial ecosystems and provides valuable insights for improving ecological evaluations and biomonitoring practices related to water quality management.展开更多
This paper investigates the problem of cluster synchronization of master-slave complex net-works with time-varying delay via linear and adaptive feedback pinning controls.We need not non-delayed and delayed coupling m...This paper investigates the problem of cluster synchronization of master-slave complex net-works with time-varying delay via linear and adaptive feedback pinning controls.We need not non-delayed and delayed coupling matrices to be symmetric or irreducible.We have the advantages of using adaptive control method to reduce control gain and pinning control technology to reduce cost.By con-structing Lyapunov function,some sufficient synchronization criteria are established.Finally,numerical examples are employed to illustrate the effectiveness of the proposed approach.展开更多
This paper investigates a new SEIQR(susceptible–exposed–infected–quarantined–recovered) epidemic model with quarantine mechanism on heterogeneous complex networks. Firstly, the nonlinear SEIQR epidemic spreading d...This paper investigates a new SEIQR(susceptible–exposed–infected–quarantined–recovered) epidemic model with quarantine mechanism on heterogeneous complex networks. Firstly, the nonlinear SEIQR epidemic spreading dynamic differential coupling model is proposed. Then, by using mean-field theory and the next-generation matrix method, the equilibriums and basic reproduction number are derived. Theoretical results indicate that the basic reproduction number significantly relies on model parameters and topology of the underlying networks. In addition, the globally asymptotic stability of equilibrium and the permanence of the disease are proved in detail by the Routh–Hurwitz criterion, Lyapunov method and La Salle's invariance principle. Furthermore, we find that the quarantine mechanism, that is the quarantine rate(γ1, γ2), has a significant effect on epidemic spreading through sensitivity analysis of basic reproduction number and model parameters. Meanwhile, the optimal control model of quarantined rate and analysis method are proposed, which can optimize the government control strategies and reduce the number of infected individual. Finally, numerical simulations are given to verify the correctness of theoretical results and a practice application is proposed to predict and control the spreading of COVID-19.展开更多
Epidemic spreading has been studied for a long time and is currently focused on the spreading of multiple pathogens,especially in multiplex networks. However, little attention has been paid to the case where the mutua...Epidemic spreading has been studied for a long time and is currently focused on the spreading of multiple pathogens,especially in multiplex networks. However, little attention has been paid to the case where the mutual influence between different pathogens comes from a fraction of epidemic propagators, such as bisexual people in two separated groups of heterosexual and homosexual people. We here study this topic by presenting a network model of two layers connected by impulsive links, in contrast to the persistent links in each layer. We let each layer have a distinct pathogen and their interactive infection is implemented by a fraction of propagators jumping between the corresponding pairs of nodes in the two layers. By this model we show that（i） the propagators take the key role to transmit pathogens from one layer to the other,which significantly influences the stabilized epidemics;（ii） the epidemic thresholds will be changed by the propagators;and（iii） a reverse-feeding effect can be expected when the infective rate is smaller than its threshold of isolated spreading.A theoretical analysis is presented to explain the numerical results.展开更多
We study the target inactivation and recovery in two-layer networks. Five kinds of strategies are chosen to attack the two-layer networks and to recover the activity of the networks by increasing the inter-layer coupl...We study the target inactivation and recovery in two-layer networks. Five kinds of strategies are chosen to attack the two-layer networks and to recover the activity of the networks by increasing the inter-layer coupling strength. The results show that we can easily control the dying state effectively by a randomly attacked situation. We then investigate the recovery activity of the networks by increasing the inter-layer coupled strength. The optimal values of the inter-layer coupled strengths are found, which could provide a more effective range to recovery activity of complex networks. As the multilayer systems composed of active and inactive elements raise important and interesting problems, our results on the target inactivation and recovery in two-layer networks would be extended to different studies.展开更多
Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite a...Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite advancements in intelligent and digital technologies,assembly process design still heavily relies on manual knowledge reuse,and inefficiencies and inconsistent quality in process documentation are caused.To address the aforementioned issues,this paper proposes a knowledge push method of complex product assembly process design based on distillation model-based dynamically enhanced graph and Bayesian network.First,an initial knowledge graph is constructed using a BERT-BiLSTM-CRF model trained with integrated human expertise and a fine-tuned large language model.Then,a confidence-based dynamic weighted fusion strategy is employed to achieve dynamic incremental construction of the knowledge graph with low resource consumption.Subsequently,a Bayesian network model is constructed based on the relationships between assembly components,assembly features,and operations.Bayesian network reasoning is used to push assembly process knowledge under different design requirements.Finally,the feasibility of the Bayesian network construction method and the effectiveness of Bayesian network reasoning are verified through a specific example,significantly improving the utilization of assembly process knowledge and the efficiency of assembly process design.展开更多
In Information Centric Networking(ICN)where content is the object of exchange,in-network caching is a unique functional feature with the ability to handle data storage and distribution in remote sensing satellite netw...In Information Centric Networking(ICN)where content is the object of exchange,in-network caching is a unique functional feature with the ability to handle data storage and distribution in remote sensing satellite networks.Setting up cache space at any node enables users to access data nearby,thus relieving the processing pressure on the servers.However,the existing caching strategies still suffer from the lack of global planning of cache contents and low utilization of cache resources due to the lack of fine-grained division of cache contents.To address the issues mentioned,a cooperative caching strategy(CSTL)for remote sensing satellite networks based on a two-layer caching model is proposed.The two-layer caching model is constructed by setting up separate cache spaces in the satellite network and the ground station.Probabilistic caching of popular contents in the region at the ground station to reduce the access delay of users.A content classification method based on hierarchical division is proposed in the satellite network,and differential probabilistic caching is employed for different levels of content.The cached content is also dynamically adjusted by analyzing the subsequent changes in the popularity of the cached content.In the two-layer caching model,ground stations and satellite networks collaboratively cache to achieve global planning of cache contents,rationalize the utilization of cache resources,and reduce the propagation delay of remote sensing data.Simulation results show that the CSTL strategy not only has a high cache hit ratio compared with other caching strategies but also effectively reduces user request delay and server load,which satisfies the timeliness requirement of remote sensing data transmission.展开更多
Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian detection.However,small objects are difficult to detect accurately because they contain les...Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian detection.However,small objects are difficult to detect accurately because they contain less information.Many current methods,particularly those based on Feature Pyramid Network(FPN),address this challenge by leveraging multi-scale feature fusion.However,existing FPN-based methods often suffer from inadequate feature fusion due to varying resolutions across different layers,leading to suboptimal small object detection.To address this problem,we propose the Two-layerAttention Feature Pyramid Network(TA-FPN),featuring two key modules:the Two-layer Attention Module(TAM)and the Small Object Detail Enhancement Module(SODEM).TAM uses the attention module to make the network more focused on the semantic information of the object and fuse it to the lower layer,so that each layer contains similar semantic information,to alleviate the problem of small object information being submerged due to semantic gaps between different layers.At the same time,SODEM is introduced to strengthen the local features of the object,suppress background noise,enhance the information details of the small object,and fuse the enhanced features to other feature layers to ensure that each layer is rich in small object information,to improve small object detection accuracy.Our extensive experiments on challenging datasets such as Microsoft Common Objects inContext(MSCOCO)and Pattern Analysis Statistical Modelling and Computational Learning,Visual Object Classes(PASCAL VOC)demonstrate the validity of the proposedmethod.Experimental results show a significant improvement in small object detection accuracy compared to state-of-theart detectors.展开更多
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)—Innovative Human Resource Development for Local Intellectualization program grant funded by the Korea government(MSIT)(IITP-2025-RS-2022-00156334)in part by Liaoning Province Nature Fund Project(2024-BSLH-214).
文摘Network Intrusion Detection System(NIDS)detection of minority class attacks is always a difficult task when dealing with attacks in complex network environments.To improve the detection capability of minority-class attacks,this study proposes an intrusion detection method based on a two-layer structure.The first layer employs a CNN-BiLSTM model incorporating an attention mechanism to classify network traffic into normal traffic,majority class attacks,and merged minority class attacks.The second layer further segments the minority class attacks through Stacking ensemble learning.The datasets are selected from the generic network dataset CIC-IDS2017,NSL-KDD,and the industrial network dataset Mississippi Gas Pipeline dataset to enhance the generalization and practical applicability of the model.Experimental results show that the proposed model achieves an overall detection accuracy of 99%,99%,and 95%on the CIC-IDS2017,NSL-KDD,and industrial network datasets,respectively.It also significantly outperforms traditional methods in terms of detection accuracy and recall rate for minority class attacks.Compared with the single-layer deep learning model,the two-layer structure effectively reduces the false alarm rate while improving the minority-class attack detection performance.The research in this paper not only improves the adaptability of NIDS to complex network environments but also provides a new solution for minority-class attack detection in industrial network security.
基金supported by the National Natural Science Foundation of China (Grant No. 10775060)
文摘An evolutionary prisoner＇s dilemma game is investigated on two-layered complex networks respectively representing interaction and learning networks in one and two dimensions. A parameter q is introduced to denote the correlation degree between the two-layered networks. Using Monte Carlo simulations we studied the effects of the correlation degree on cooperative behaviour and found that the cooperator density nontrivially changes with q for different payoff parameter values depending on the detailed strategy updating and network dimension. An explanation for the obtained results is provided.
基金in part by the National Natural Science Foundation of China(No.51334003).
文摘In view of the fact that news can generate derivative topics when it spreads through micro-blogs,a two-layer coupled SEIR public opinion propagation model is proposed in this paper.The model divides the process of public opinion propagation into two layers:the original topic layer and the derived topic layer.Messages are transmitted separately by the SEIR model in the two topic layers,which are independent and interactive.The influence of the topic derivation rate on the propagation trend is established by solving for the equilibrium point and propagation threshold.Further,we establish the relationship between the original topic and the derived topic by simulation.This paper uses the Baidu index to demonstrate the correctness of the model.The relationship between the derived topic and the original topic is verified by adjusting the parameters by the control variable method.The results show that the proposed model is consistent with the propagation of actual public opinion.
基金supported by the Fundamental Research Funds of Chinese Academy of Forestry(Nos.CAFYBB2022SY037,CAFYBB2021ZA002 and CAFYBB2022QC002)the Basic Research Foundation of Yunnan Province(Grant No.202201AT070264).
文摘Soil microbial communities are key factors in maintaining ecosystem multifunctionality(EMF).However,the distribution patterns of bacterial diversity and how the different bacterial taxa and their diversity dimensions affect EMF remain largely unknown.Here,we investigated variation in three measures of diversity(alpha diversity,community composition and network complexity)among rare,intermediate,and abundant taxa across a latitudinal gradient spanning five forest plots in Yunnan Province,China and examined their contributions on EMF.We aimed to characterize the diversity distributions of bacterial groups across latitudes and to assess the differences in the mechanisms underlying their contributions to EMF.We found that multifaceted diversity(i.e.,diversity assessed by the three different metrics)of rare,intermediate,and abundant bacteria generally decreased with increasing latitude.More importantly,we found that rare bacterial taxa tended to be more diverse,but they contributed less to EMF than intermediate or abundant bacteria.Among the three dimensions of diversity we assessed,only community composition significantly affected EMF across all locations,while alpha diversity had a negative effect,and network complexity showed no significant impact.Our study further emphasizes the importance of intermediate and abundant bacterial taxa as well as community composition to EMF and provides a theoretical basis for investigating the mechanisms by which belowground microorganisms drive EMF along a latitudinal gradient.
基金Supported by the National Natural Science Foundation of China under Grant Nos.11265008,11372122,and 11365014
文摘The mutual coupling between neurons in a realistic neuronal system is much complex, and a two-layer neuronal network is designed to investigate the transition of electric activities of neurons. The Hindmarsh–Rose neuron model is used to describe the local dynamics of each neuron, and neurons in the two-layer networks are coupled in dislocated type. The coupling intensity between two-layer networks, and the coupling ratio(Pro), which defines the percentage involved in the coupling in each layer, are changed to observe the synchronization transition of collective behaviors in the two-layer networks. It is found that the two-layer networks of neurons becomes synchronized with increasing the coupling intensity and coupling ratio(Pro) beyond certain thresholds. An ordered wave in the first layer is useful to wake up the rest state in the second layer, or suppress the spatiotemporal state in the second layer under coupling by generating target wave or spiral waves. And the scheme of dislocation coupling can be used to suppress spatiotemporal chaos and excite quiescent neurons.
基金National Nature Sci-ence Foundation of China(Grant No.30671997).
文摘An image-reconstruction approach for optical tomography is presented,in which a two-layered BP neural network is used to distinguish the tumor location.The inverse problem is solved as optimization problem by Femlab software and Levenberg–Marquardt algorithm.The concept of the average optical coefficient is proposed in this paper,which is helpful to understand the distribution of the scattering photon from tumor.The reconstructive¯µs by the trained network is reasonable for showing the changes of photon number transporting inside tumor tissue.It realized the fast reconstruction of tissue optical properties and provided optical OT with a new method.
文摘Multilayer network is a frontier direction of network science research. In this paper, the cluster ring network is extended to a two-layer network model, and the inner structures of the cluster blocks are random, small world or scale-free. We study the influence of network scale, the interlayer linking weight and interlayer linking fraction on synchronizability. It is found that the synchronizability of the two-layer cluster ring network decreases with the increase of network size. There is an optimum value of the interlayer linking weight in the two-layer cluster ring network, which makes the synchronizability of the network reach the optimum. When the interlayer linking weight and the interlayer linking fraction are very small, the change of them will affect the synchronizability.
基金Supported by the National Natural Science Foundation of China(Grant No.12031002)。
文摘Accurately identifying key nodes is essential for evaluating network robustness and controlling information propagation in complex network analysis. However, current research methods face limitations in applicability and accuracy. To address these challenges, this study introduces the K-GCN model, which integrates neighborhood k-shell distribution analysis with Graph Convolutional Network(GCN) technology to enhance key node identification in complex networks. The K-GCN model first leverages neighborhood k-shell distributions to calculate entropy values for each node, effectively quantifying node importance within the network. These entropy values are then used as key features within the GCN, which subsequently formulates intelligent strategies to maximize network connectivity disruption by removing a minimal set of nodes, thereby impacting the overall network architecture. Through iterative interactions with the environment, the GCN continuously refines its strategies, achieving precise identification of key nodes in the network. Unlike traditional methods, the K-GCN model not only captures local node features but also integrates the network structure and complex interrelations between neighboring nodes, significantly improving the accuracy and efficiency of key node identification.Experimental validation in multiple real-world network scenarios demonstrates that the K-GCN model outperforms existing methods.
基金Project supported by Jilin Provincial Science and Technology Development Plan(Grant No.20220101137JC).
文摘This paper study the finite time internal synchronization and the external synchronization(hybrid synchronization)for duplex heterogeneous complex networks by time-varying intermittent control.There few study hybrid synchronization of heterogeneous duplex complex networks.Therefore,we study the finite time hybrid synchronization of heterogeneous duplex networks,which employs the time-varying intermittent control to drive the duplex heterogeneous complex networks to achieve hybrid synchronization in finite time.To be specific,the switch frequency of the controllers can be changed with time by devise Lyapunov function and boundary function,the internal synchronization and external synchronization are achieved simultaneously in finite time.Finally,numerical examples are presented to illustrate the validness of theoretical results.
基金Supported by the National Natural Science Foundation of China(62476082)。
文摘This paper investigates modified fixed-time synchronization(FxTS)of complex networks(CNs)with time-varying delays based on continuous and discontinuous controllers.First,for the sake of making the settling time(ST)of FxTS is independent of the initial values and parameters of the CNs,a modified fixed-time(FxT)stability theorem is proposed,where the ST is determined by an arbitrary positive number given in advance.Then,continuous controller and discontinuous controller are designed to realize the modified FxTS target of CNs.In addition,based on the designed controllers,CNs can achieve synchronization at any given time,or even earlier.And control strategies effectively solve the problem of ST related to the parameters of CNs.Finally,an appropriate simulation example is conducted to examine the effectiveness of the designed control strategies.
基金Project supported by the National Key Research and Development Program of China(Grant Nos.2022YFE0136000 and 2024YFC3013100)the Joint Meteorological Fund(Grant No.U2342211)+1 种基金the Joint Research Project for Meteorological Capacity Improvement(Grant No.22NLTSZ004)the National Meteorological Information Center(Grant No.NMICJY202301)。
文摘Using complex network methods,we construct undirected and directed heatwave networks to systematically analyze heatwave events over China from 1961 to 2023,exploring their spatiotemporal evolution patterns in different regions.The findings reveal a significant increase in heatwaves since the 2000s,with the average occurrence rising from approximately 3 to 5 times,and their duration increasing from 15 to around 30 days,nearly doubling.An increasing trend of“early onset and late withdrawal”of heatwaves has become more pronounced each year.In particular,eastern regions experience heatwaves that typically start earlier and tend to persist into September,exhibiting greater interannual variability compared to western areas.The middle and lower reaches of the Yangtze River and Xinjiang are identified as high-frequency heatwave areas.Complex network analysis reveals the dynamics of heatwave propagation,with degree centrality and synchronization distance indicating that the middle and lower reaches of the Yangtze River,Northeast China,and Xinjiang are key nodes in heatwave spread.Additionally,network divergence analysis shows that Xinjiang acts as a“source”area for heatwaves,exporting heat to surrounding regions,while the central region functions as a major“sink,”receiving more heatwave events.Further analysis from 1994 to 2023 indicates that heatwave events exhibit stronger network centrality and more complex synchronization patterns.These results suggest that complex networks provide a refined framework for depicting the spatiotemporal dynamics of heatwave propagation,offering new avenues for studying their occurrence and development patterns.
基金support from the National Natural Science Foundation of China(Grant No.T2293771)the STI 2030-Major Projects(Grant No.2022ZD0211400)the Sichuan Province Outstanding Young Scientists Foundation(Grant No.2023NSFSC1919)。
文摘Independent cascade(IC)models,by simulating how one node can activate another,are important tools for studying the dynamics of information spreading in complex networks.However,traditional algorithms for the IC model implementation face significant efficiency bottlenecks when dealing with large-scale networks and multi-round simulations.To settle this problem,this study introduces a GPU-based parallel independent cascade(GPIC)algorithm,featuring an optimized representation of the network data structure and parallel task scheduling strategies.Specifically,for this GPIC algorithm,we propose a network data structure tailored for GPU processing,thereby enhancing the computational efficiency and the scalability of the IC model.In addition,we design a parallel framework that utilizes the full potential of GPU's parallel processing capabilities,thereby augmenting the computational efficiency.The results from our simulation experiments demonstrate that GPIC not only preserves accuracy but also significantly boosts efficiency,achieving a speedup factor of 129 when compared to the baseline IC method.Our experiments also reveal that when using GPIC for the independent cascade simulation,100-200 simulation rounds are sufficient for higher-cost studies,while high precision studies benefit from 500 rounds to ensure reliable results,providing empirical guidance for applying this new algorithm to practical research.
基金supported by Zhejiang Provincial Natural Science Foundation of China(No.LTGS24D010004)the National Natural Science Foundation of China grant(No.42307064)+2 种基金the National Students’platform for innovation and entrepreneurship training program(No.202410346054)Hangzhou“Young science and technology talent cultivation”project(No.4305F45623004)the Fundamental Research Funds for Climbing Project from Hangzhou Normal University(No.KYQD-2023-217).
文摘Numerous studies have examined the impact ofwater quality degradation on bacterial community structure,yet insights into its effects on the bacterial ecological networks remain scarce.In this study,we investigated the diversity,composition,assembly patterns,ecological networks,and environmental determinants of bacterial communities across 20 ponds to understand the impact of water quality degradation.Our findings revealed that water quality degradation significantly reduces the α-diversity of bacterial communities in water samples,while sediment samples remain unaffected.Additionally,water quality deterioration increases the complexity of bacterial networks in water samples but reduces it in sediment samples.These shifts in bacterial communities were primarily governed by deterministic processes,with heterogeneous selection being particularly influential.Through redundancy analysis(RDA),multiple regression on matrices(MRM),and Mantel tests,we identified dissolved oxygen(DO),ammonium nitrogen(NH_(4)^(+)-N),and C/N ratio as key factors affecting the composition and network complexity of bacterial communities in both water and sediment.Overall,this study contributes a novel perspective on the effect ofwater quality deterioration on microbial ecosystems and provides valuable insights for improving ecological evaluations and biomonitoring practices related to water quality management.
文摘This paper investigates the problem of cluster synchronization of master-slave complex net-works with time-varying delay via linear and adaptive feedback pinning controls.We need not non-delayed and delayed coupling matrices to be symmetric or irreducible.We have the advantages of using adaptive control method to reduce control gain and pinning control technology to reduce cost.By con-structing Lyapunov function,some sufficient synchronization criteria are established.Finally,numerical examples are employed to illustrate the effectiveness of the proposed approach.
基金Project supported the Natural Science Foundation of Zhejiang Province, China (Grant No. LQN25F030011)the Fundamental Research Project of Hangzhou Dianzi University (Grant No. KYS065624391)+1 种基金the National Natural Science Foundation of China (Grant No. 61573148)the Science and Technology Planning Project of Guangdong Province, China (Grant No. 2019A050520001)。
文摘This paper investigates a new SEIQR(susceptible–exposed–infected–quarantined–recovered) epidemic model with quarantine mechanism on heterogeneous complex networks. Firstly, the nonlinear SEIQR epidemic spreading dynamic differential coupling model is proposed. Then, by using mean-field theory and the next-generation matrix method, the equilibriums and basic reproduction number are derived. Theoretical results indicate that the basic reproduction number significantly relies on model parameters and topology of the underlying networks. In addition, the globally asymptotic stability of equilibrium and the permanence of the disease are proved in detail by the Routh–Hurwitz criterion, Lyapunov method and La Salle's invariance principle. Furthermore, we find that the quarantine mechanism, that is the quarantine rate(γ1, γ2), has a significant effect on epidemic spreading through sensitivity analysis of basic reproduction number and model parameters. Meanwhile, the optimal control model of quarantined rate and analysis method are proposed, which can optimize the government control strategies and reduce the number of infected individual. Finally, numerical simulations are given to verify the correctness of theoretical results and a practice application is proposed to predict and control the spreading of COVID-19.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11135001,11375066,and 11405059)the National Basic Key Program of China(Grant No.2013CB834100)
文摘Epidemic spreading has been studied for a long time and is currently focused on the spreading of multiple pathogens,especially in multiplex networks. However, little attention has been paid to the case where the mutual influence between different pathogens comes from a fraction of epidemic propagators, such as bisexual people in two separated groups of heterosexual and homosexual people. We here study this topic by presenting a network model of two layers connected by impulsive links, in contrast to the persistent links in each layer. We let each layer have a distinct pathogen and their interactive infection is implemented by a fraction of propagators jumping between the corresponding pairs of nodes in the two layers. By this model we show that（i） the propagators take the key role to transmit pathogens from one layer to the other,which significantly influences the stabilized epidemics;（ii） the epidemic thresholds will be changed by the propagators;and（iii） a reverse-feeding effect can be expected when the infective rate is smaller than its threshold of isolated spreading.A theoretical analysis is presented to explain the numerical results.
基金Supported by the National Basic Research Program of China under Grant Nos 2013CBA01502,2011CB921503 and 2013CB834100the National Natural Science Foundation of China under Grant Nos 11374040 and 11274051
文摘We study the target inactivation and recovery in two-layer networks. Five kinds of strategies are chosen to attack the two-layer networks and to recover the activity of the networks by increasing the inter-layer coupling strength. The results show that we can easily control the dying state effectively by a randomly attacked situation. We then investigate the recovery activity of the networks by increasing the inter-layer coupled strength. The optimal values of the inter-layer coupled strengths are found, which could provide a more effective range to recovery activity of complex networks. As the multilayer systems composed of active and inactive elements raise important and interesting problems, our results on the target inactivation and recovery in two-layer networks would be extended to different studies.
基金Supported by National Key Research and Development Program(Grant No.2024YFB3312700)National Natural Science Foundation of China(Grant No.52405541)the Changzhou Municipal Sci&Tech Program(Grant No.CJ20241131)。
文摘Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite advancements in intelligent and digital technologies,assembly process design still heavily relies on manual knowledge reuse,and inefficiencies and inconsistent quality in process documentation are caused.To address the aforementioned issues,this paper proposes a knowledge push method of complex product assembly process design based on distillation model-based dynamically enhanced graph and Bayesian network.First,an initial knowledge graph is constructed using a BERT-BiLSTM-CRF model trained with integrated human expertise and a fine-tuned large language model.Then,a confidence-based dynamic weighted fusion strategy is employed to achieve dynamic incremental construction of the knowledge graph with low resource consumption.Subsequently,a Bayesian network model is constructed based on the relationships between assembly components,assembly features,and operations.Bayesian network reasoning is used to push assembly process knowledge under different design requirements.Finally,the feasibility of the Bayesian network construction method and the effectiveness of Bayesian network reasoning are verified through a specific example,significantly improving the utilization of assembly process knowledge and the efficiency of assembly process design.
基金This research was funded by the National Natural Science Foundation of China(No.U21A20451)the Science and Technology Planning Project of Jilin Province(No.20200401105GX)the China University Industry University Research Innovation Fund(No.2021FNA01003).
文摘In Information Centric Networking(ICN)where content is the object of exchange,in-network caching is a unique functional feature with the ability to handle data storage and distribution in remote sensing satellite networks.Setting up cache space at any node enables users to access data nearby,thus relieving the processing pressure on the servers.However,the existing caching strategies still suffer from the lack of global planning of cache contents and low utilization of cache resources due to the lack of fine-grained division of cache contents.To address the issues mentioned,a cooperative caching strategy(CSTL)for remote sensing satellite networks based on a two-layer caching model is proposed.The two-layer caching model is constructed by setting up separate cache spaces in the satellite network and the ground station.Probabilistic caching of popular contents in the region at the ground station to reduce the access delay of users.A content classification method based on hierarchical division is proposed in the satellite network,and differential probabilistic caching is employed for different levels of content.The cached content is also dynamically adjusted by analyzing the subsequent changes in the popularity of the cached content.In the two-layer caching model,ground stations and satellite networks collaboratively cache to achieve global planning of cache contents,rationalize the utilization of cache resources,and reduce the propagation delay of remote sensing data.Simulation results show that the CSTL strategy not only has a high cache hit ratio compared with other caching strategies but also effectively reduces user request delay and server load,which satisfies the timeliness requirement of remote sensing data transmission.
文摘Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian detection.However,small objects are difficult to detect accurately because they contain less information.Many current methods,particularly those based on Feature Pyramid Network(FPN),address this challenge by leveraging multi-scale feature fusion.However,existing FPN-based methods often suffer from inadequate feature fusion due to varying resolutions across different layers,leading to suboptimal small object detection.To address this problem,we propose the Two-layerAttention Feature Pyramid Network(TA-FPN),featuring two key modules:the Two-layer Attention Module(TAM)and the Small Object Detail Enhancement Module(SODEM).TAM uses the attention module to make the network more focused on the semantic information of the object and fuse it to the lower layer,so that each layer contains similar semantic information,to alleviate the problem of small object information being submerged due to semantic gaps between different layers.At the same time,SODEM is introduced to strengthen the local features of the object,suppress background noise,enhance the information details of the small object,and fuse the enhanced features to other feature layers to ensure that each layer is rich in small object information,to improve small object detection accuracy.Our extensive experiments on challenging datasets such as Microsoft Common Objects inContext(MSCOCO)and Pattern Analysis Statistical Modelling and Computational Learning,Visual Object Classes(PASCAL VOC)demonstrate the validity of the proposedmethod.Experimental results show a significant improvement in small object detection accuracy compared to state-of-theart detectors.
