Virtual network embedding problem which is NP-hard is a key issue for implementing software-defined network which is brought about by network virtualization.Compared with other studies which focus on designing heurist...Virtual network embedding problem which is NP-hard is a key issue for implementing software-defined network which is brought about by network virtualization.Compared with other studies which focus on designing heuristic algorithms to reduce the hardness of the NP-hard problem we propose a robust VNE algorithm based on component connectivity in large-scale network.We distinguish the different components and embed VN requests onto them respectively.And k-core is applied to identify different VN topologies so that the VN request can be embedded onto its corresponding component.On the other hand,load balancing is also considered in this paper.It could avoid blocked or bottlenecked area of substrate network.Simulation experiments show that compared with other algorithms in large-scale network,acceptance ratio,average revenue and robustness can be obviously improved by our algorithm and average cost can be reduced.It also shows the relationship between the component connectivity including giant component and small components and the performance metrics.展开更多
With an increasing urgent demand for fast recovery routing mechanisms in large-scale networks,minimizing network disruption caused by network failure has become critical.However,a large number of relevant studies have...With an increasing urgent demand for fast recovery routing mechanisms in large-scale networks,minimizing network disruption caused by network failure has become critical.However,a large number of relevant studies have shown that network failures occur on the Internet inevitably and frequently.The current routing protocols deployed on the Internet adopt the reconvergence mechanism to cope with network failures.During the reconvergence process,the packets may be lost because of inconsistent routing information,which reduces the network’s availability greatly and affects the Internet service provider’s(ISP’s)service quality and reputation seriously.Therefore,improving network availability has become an urgent problem.As such,the Internet Engineering Task Force suggests the use of downstream path criterion(DC)to address all single-link failure scenarios.However,existing methods for implementing DC schemes are time consuming,require a large amount of router CPU resources,and may deteriorate router capability.Thus,the computation overhead introduced by existing DC schemes is significant,especially in large-scale networks.Therefore,this study proposes an efficient intra-domain routing protection algorithm(ERPA)in large-scale networks.Theoretical analysis indicates that the time complexity of ERPA is less than that of constructing a shortest path tree.Experimental results show that ERPA can reduce the computation overhead significantly compared with the existing algorithms while offering the same network availability as DC.展开更多
A reduction in network energy consumption and the establishment of green networks have become key scientific problems in academic and industrial research.Existing energy efficiency schemes are based on a known traffic...A reduction in network energy consumption and the establishment of green networks have become key scientific problems in academic and industrial research.Existing energy efficiency schemes are based on a known traffic matrix,and acquiring a real-time traffic matrix in current complex networks is difficult.Therefore,this research investigates how to reduce network energy consumption without a real-time traffic matrix.In particular,this paper proposes an intra-domain energy-efficient routing scheme based on multipath routing.It analyzes the relationship between routing availability and energy-efficient routing and integrates the two mechanisms to satisfy the requirements of availability and energy efficiency.The main research focus is as follows:(1)A link criticality model is evaluated to quantitatively measure the importance of links in a network.(2)On the basis of the link criticality model,this paper analyzes an energy-efficient routing technology based on multipath routing to achieve the goals of availability and energy efficiency simultaneously.(3)An energy-efficient routing algorithm based on multipath routing in large-scale networks is proposed.(4)The proposed method does not require a real-time traffic matrix in the network and is thus easy to apply in practice.(5)The proposed algorithm is verified in several network topologies.Experimental results show that the algorithm can not only reduce network energy consumption but can also ensure routing availability.展开更多
RECENT advances in sensing,communication and computing have open the door to the deployment of largescale networks of sensors and actuators that allow fine-grain monitoring and control of a multitude of physical proce...RECENT advances in sensing,communication and computing have open the door to the deployment of largescale networks of sensors and actuators that allow fine-grain monitoring and control of a multitude of physical processes and infrastructures.The appellation used by field experts for these paradigms is Cyber-Physical Systems(CPS)because the dynamics among computers,networking media/resources and physical systems interact in a way that multi-disciplinary technologies(embedded systems,computers,communications and controls)are required to accomplish prescribed missions.Moreover,they are expected to play a significant role in the design and development of future engineering applications such as smart grids,transportation systems,nuclear plants and smart factories.展开更多
In the graph signal processing(GSP)framework,distributed algorithms are highly desirable in processing signals defined on large-scale networks.However,in most existing distributed algorithms,all nodes homogeneously pe...In the graph signal processing(GSP)framework,distributed algorithms are highly desirable in processing signals defined on large-scale networks.However,in most existing distributed algorithms,all nodes homogeneously perform the local computation,which calls for heavy computational and communication costs.Moreover,in many real-world networks,such as those with straggling nodes,the homogeneous manner may result in serious delay or even failure.To this end,we propose active network decomposition algorithms to select non-straggling nodes(normal nodes)that perform the main computation and communication across the network.To accommodate the decomposition in different kinds of networks,two different approaches are developed,one is centralized decomposition that leverages the adjacency of the network and the other is distributed decomposition that employs the indicator message transmission between neighboring nodes,which constitutes the main contribution of this paper.By incorporating the active decomposition scheme,a distributed Newton method is employed to solve the least squares problem in GSP,where the Hessian inverse is approximately evaluated by patching a series of inverses of local Hessian matrices each of which is governed by one normal node.The proposed algorithm inherits the fast convergence of the second-order algorithms while maintains low computational and communication cost.Numerical examples demonstrate the effectiveness of the proposed algorithm.展开更多
A major challenge of network virtualization is the virtual network resource allocation problem that deals with efficient mapping of virtual nodes and virtual links onto the substrate network resources. However, the ex...A major challenge of network virtualization is the virtual network resource allocation problem that deals with efficient mapping of virtual nodes and virtual links onto the substrate network resources. However, the existing algorithms are almost concentrated on the randomly small-scale network topology, which is not suitable for practical large-scale network environments, because more time is spent on traversing SN and VN, resulting in VN requests congestion. To address this problem, virtual network mapping algorithm is proposed for large-scale network based on small-world characteristic of complex network and network coordinate system. Compared our algorithm with algorithm D-ViNE, experimental results show that our algorithm improves the overall performance.展开更多
Many science and engineering applications involve solvinga linear least-squares system formed from some field measurements. In the distributed cyber-physical systems(CPS),each sensor node used for measurement often on...Many science and engineering applications involve solvinga linear least-squares system formed from some field measurements. In the distributed cyber-physical systems(CPS),each sensor node used for measurement often only knowspartial independent rows of the least-squares system. To solve the least-squares all the measurements must be gathered at a centralized location and then perform the computa-tion. Such data collection and computation are inefficient because of bandwidth and time constraints and sometimes areinfeasible because of data privacy concerns. Iterative methods are natural candidates for solving the aforementionedproblem and there are many studies regarding this. However,most of the proposed solutions are related to centralized/parallel computations while only a few have the potential to beapplied in distributed networks. Thus distributed computations are strongly preferred or demanded in many of the realworld applications, e.g. smart-grid, target tracking, etc. Thispaper surveys the representative iterative methods for distributed least-squares in networks.展开更多
In this paper, we conduct research on the large-scale network intrusion mode based on the principal component analysis and dropquality sampling. With the growing of network security issues, invasion detection becomes ...In this paper, we conduct research on the large-scale network intrusion mode based on the principal component analysis and dropquality sampling. With the growing of network security issues, invasion detection becomes the study hotspot. There are two main types of thatinvasion detection technology, the fi rst is that misuse detection and the anomaly detection. Misuse detection can more accurately detect attacks,but high non-response rates, anomaly detection could detect the unknown attacks, but higher rate of false positives. Network invasion detectionproblem is summed up in the network data fl ow of discriminant problem, namely the judgment of network data fl ow is normal or malicious andin this sense here invasion detection problem can be understood as a pattern recognition problem. Our research integrates the PCA and samplingtechnique to propose the new idea on the IDS that is innovative and will promote the development of the corresponding techniques.展开更多
Among various architectures of polymers,end-group-free rings have attracted growing interests due to their distinct physicochemical performances over the linear counterparts which are exemplified by reduced hydrodynam...Among various architectures of polymers,end-group-free rings have attracted growing interests due to their distinct physicochemical performances over the linear counterparts which are exemplified by reduced hydrodynamic size and slower degradation.It is key to develop facile methods to large-scale synthesis of polymer rings with tunable compositions and microstructures.Recent progresses in large-scale synthesis of polymer rings against single-chain dynamic nanoparticles,and the example applications in synchronous enhancing toughness and strength of polymer nanocomposites are summarized.Once there is the breakthrough in rational design and effective large-scale synthesis of polymer rings and their functional derivatives,a family of cyclic functional hybrids would be available,thus providing a new paradigm in developing polymer science and engineering.展开更多
This study develops an event-triggered control strategy utilizing the fully actuated system approach for nonlinear interconnected large-scale systems containing actuator failures.First,to reduce the complexity of the ...This study develops an event-triggered control strategy utilizing the fully actuated system approach for nonlinear interconnected large-scale systems containing actuator failures.First,to reduce the complexity of the design process,we transform the studied system into the form of a fully actuated system through a state transformation.Then,to address the unknown nonlinear functions and actuator fault parameters,we employ neural networks and adaptive estimation techniques,respectively.Moreover,to reduce the control cost and improve the control efficiency,we introduce event-triggered inputs into the control strategy.It is proved by the Lyapunov stability analysis that all signals of the closed-loop system are bounded and the output of system eventually converge to a bounded region.The efficacy of the control approach is ultimately demonstrated via the simulation of an actual machine feeding system.展开更多
Large-scale complex systems are integral to the functioning of various organizations within the national economy.Despite their significance,the lengthy construction cycles and the involvement of multiple entities ofte...Large-scale complex systems are integral to the functioning of various organizations within the national economy.Despite their significance,the lengthy construction cycles and the involvement of multiple entities often result in the deprioritization of standardized management practices,as they do not yield immediate benefits.The implementation of such systems typically encompasses the integrated phases of "development,construction,utiliz ation,and operation and maintenance".To enhance the overall delivery quality of these systems,it is imperative to dismantle the management barriers among these phases and adopt a holistic approach to standardized management.This paper takes a specific system project as a research object to identify common challenges,and proposes improvement strategies in the implementation of standar dized management.Empirical results indicate a substantial reduction in the system s full-lifecycle costs.展开更多
Summer rainfall in the Yangtze River basin(YRB)is favored by two key factors in the lower troposphere:the tropical anticyclonic anomaly over the western North Pacific and the extratropical northeasterly anomalies to t...Summer rainfall in the Yangtze River basin(YRB)is favored by two key factors in the lower troposphere:the tropical anticyclonic anomaly over the western North Pacific and the extratropical northeasterly anomalies to the north of the YRB.This study,however,found that approximately 46%of heavy rainfall events in the YRB occur when only one factor appears and the other is opposite signed.Accordingly,these heavy rainfall events can be categorized into two types:the extratropical northeasterly anomalies but tropical cyclonic anomaly(first unconventional type),and the tropical anticyclonic anomaly but extratropical southwesterly anomalies(second unconventional type).Anomalous water vapor convergence and upward motion exists for both types,but through different mechanisms.For the first type,the moisture convergence and upward motion are induced by a cyclonic anomaly over the YRB,which appears in the mid and lower troposphere and originates from the upstream region.For the second type,a mid-tropospheric cyclonic anomaly over Lake Baikal extends southward and results in southwesterly anomalies over the YRB,in conjunction with the tropical anticyclonic anomaly.The southwesterly anomalies transport water vapor to the YRB and lead to upward motion through warm advection.This study emphasizes the role of mid-tropospheric circulations in inducing heavy rainfall in the YRB.展开更多
The dynamic behaviors of a large-scale ring neural network with a triangular coupling structure are investigated.The characteristic equation of the high-dimensional system using Coate’s flow graph method is calculate...The dynamic behaviors of a large-scale ring neural network with a triangular coupling structure are investigated.The characteristic equation of the high-dimensional system using Coate’s flow graph method is calculated.Time delay is selected as the bifurcation parameter,and sufficient conditions for stability and Hopf bifurcation are derived.It is found that the connection coefficient and time delay play a crucial role in the dynamic behaviors of the model.Furthermore,a phase diagram of multiple equilibrium points with one saddle point and two stable nodes is presented.Finally,the effectiveness of the theory is verified through simulation results.展开更多
The complex transcriptional regulatory relationships among genes influence gene expression levels and play a crucial role in determining cellular phenotypes.In this study,we propose a novel,distributed,large-scale tra...The complex transcriptional regulatory relationships among genes influence gene expression levels and play a crucial role in determining cellular phenotypes.In this study,we propose a novel,distributed,large-scale transcriptional regulatory neural network model(DLTRNM),which integrates prior knowledge into the recon-struction of pre-trained machine learning models,followed by fine-tuning.Using Saccharomyces cerevisiae as a case study,the curated transcriptional regulatory relationships are used to define the interactions between transcription factors(TFs)and their target genes(TGs).Subsequently,DLTRNM is pre-trained on pan-transcriptomic data and fine-tuned with time-series data,enabling it to accurately predict regulatory correla-tions.Additionally,DLTRNM can help identify potential key TFs,thereby simplifying the complex and interre-lated transcriptional regulatory networks(TRNs).It can also complement previously reported transcriptional regulatory subnetworks.DLTRNM provides a powerful tool for studying transcriptional regulation with reduced computational demands and enhanced interpretability.Thus,this study marks a significant advancement in systems biology for understanding the complex transcriptional regulation within cells.展开更多
It has been argued that the human brain,as an information-processing machine,operates near a phase transition point in a non-equilibrium state,where it violates detailed balance leading to entropy production.Thus,the ...It has been argued that the human brain,as an information-processing machine,operates near a phase transition point in a non-equilibrium state,where it violates detailed balance leading to entropy production.Thus,the assessment of irreversibility in brain networks can provide valuable insights into their non-equilibrium properties.In this study,we utilized an open-source whole-brain functional magnetic resonance imaging(fMRI)dataset from both resting and task states to evaluate the irreversibility of large-scale human brain networks.Our analysis revealed that the brain networks exhibited significant irreversibility,violating detailed balance,and generating entropy.Notably,both physical and cognitive tasks increased the extent of this violation compared to the resting state.Regardless of the state(rest or task),interactions between pairs of brain regions were the primary contributors to this irreversibility.Moreover,we observed that as global synchrony increased within brain networks,so did irreversibility.The first derivative of irreversibility with respect to synchronization peaked near the phase transition point,characterized by the moderate mean synchronization and maximized synchronization entropy of blood oxygenation level-dependent(BOLD)signals.These findings deepen our understanding of the non-equilibrium dynamics of large-scale brain networks,particularly in relation to their phase transition behaviors,and may have potential clinical applications for brain disorders.展开更多
As the first stage of the quantum Internet,quantum key distribution(QKD)networks hold the promise of providing long-term security for diverse users.Most existing QKD networks have been constructed based on independent...As the first stage of the quantum Internet,quantum key distribution(QKD)networks hold the promise of providing long-term security for diverse users.Most existing QKD networks have been constructed based on independent QKD protocols,and they commonly rely on the deployment of single-protocol trusted relay chains for long reach.Driven by the evolution of QKD protocols,large-scale QKD networking is expected to migrate from a single-protocol to a multi-protocol paradigm,during which some useful evolutionary elements for the later stages of the quantum Internet may be incorporated.In this work,we delve into a pivotal technique for large-scale QKD networking,namely,multi-protocol relay chaining.A multi-protocol relay chain is established by connecting a set of trusted/untrusted relays relying on multiple QKD protocols between a pair of QKD nodes.The structures of diverse multi-protocol relay chains are described,based on which the associated model is formulated and the policies are defined for the deployment of multi-protocol relay chains.Furthermore,we propose three multi-protocol relay chaining heuristics.Numerical simulations indicate that the designed heuristics can effectively reduce the number of trusted relays deployed and enhance the average security level versus the commonly used single-protocol trusted relay chaining methods on backbone network topologies.展开更多
The Internet of Things(IoT)ecosystem faces growing security challenges because it is projected to have 76.88 billion devices by 2025 and $1.4 trillion market value by 2027,operating in distributed networks with resour...The Internet of Things(IoT)ecosystem faces growing security challenges because it is projected to have 76.88 billion devices by 2025 and $1.4 trillion market value by 2027,operating in distributed networks with resource limitations and diverse system architectures.The current conventional intrusion detection systems(IDS)face scalability problems and trust-related issues,but blockchain-based solutions face limitations because of their low transaction throughput(Bitcoin:7 TPS(Transactions Per Second),Ethereum:15-30 TPS)and high latency.The research introduces MBID(Multi-Tier Blockchain Intrusion Detection)as a groundbreaking Multi-Tier Blockchain Intrusion Detection System with AI-Enhanced Detection,which solves the problems in huge IoT networks.The MBID system uses a four-tier architecture that includes device,edge,fog,and cloud layers with blockchain implementations and Physics-Informed Neural Networks(PINNs)for edge-based anomaly detection and a dual consensus mechanism that uses Honesty-based Distributed Proof-of-Authority(HDPoA)and Delegated Proof of Stake(DPoS).The system achieves scalability and efficiency through the combination of dynamic sharding and Interplanetary File System(IPFS)integration.Experimental evaluations demonstrate exceptional performance,achieving a detection accuracy of 99.84%,an ultra-low false positive rate of 0.01% with a False Negative Rate of 0.15%,and a near-instantaneous edge detection latency of 0.40 ms.The system demonstrated an aggregate throughput of 214.57 TPS in a 3-shard configuration,providing a clear,evidence-based path for horizontally scaling to support overmillions of devices with exceeding throughput.The proposed architecture represents a significant advancement in blockchain-based security for IoT networks,effectively balancing the trade-offs between scalability,security,and decentralization.展开更多
Reconfigurable linear optical networks based on Mach-Zehnder interferometer(MZI)offer significant potential in optical information processing,particularly in emerging photonic quantum computing systems.However,device ...Reconfigurable linear optical networks based on Mach-Zehnder interferometer(MZI)offer significant potential in optical information processing,particularly in emerging photonic quantum computing systems.However,device losses and calibration errors accumulate as network complexity grows,posing challenges in performing precise mapping of matrix operations.Existing architectures,such as Diamond and Bokun,introduce MZI redundancy into Reck and Clements architectures to improve reliability,which increases complexity and differential path losses that limit scalability.We propose a compact topology architecture that achieves 100%fidelity by employing a symmetrical MZI to decouple optical loss from power ratio and introducing extra MZIs to enforce uniform loss distributions.This multi-level optimization enables direct monitoring pathways while supporting precise calibration,and it approaches theoretical fidelity in practical deployments with direct implications for scalable and fault-tolerant photonic computing systems.展开更多
With the purpose of making calculation more efficient in practical hydraulic simulations, an improved algorithm was proposed and was applied in the practical water distribution field. This methodology was developed by...With the purpose of making calculation more efficient in practical hydraulic simulations, an improved algorithm was proposed and was applied in the practical water distribution field. This methodology was developed by expanding the traditional loop-equation theory through utilization of the advantages of the graph theory in efficiency. The utilization of the spanning tree technique from graph theory makes the proposed algorithm efficient in calculation and simple to use for computer coding. The algorithms for topological generation and practical implementations are presented in detail in this paper. Through the application to a practical urban system, the consumption of the CPU time and computation memory were decreased while the accuracy was greatly enhanced compared with the present existing methods.展开更多
In this paper,the quadratic stability of continuous-time uncertain large-scale networked systems is considered.The systems are composed of many subsystems distributed in different locations in space and interconnected...In this paper,the quadratic stability of continuous-time uncertain large-scale networked systems is considered.The systems are composed of many subsystems distributed in different locations in space and interconnected according to a certain network topology.A computationally efficient sufficient condition for the quadratic stability of large-scale networked systems is established,utilising effectively the block diagonal structure of the system parameter matrix and the sparseness of the subsystem connection matrix(SCM).Furthermore,a stability condition is presented,which is entirely contingent on the parameters of each subsystem.The simulation results show that the obtained conditions have obvious advantages in analysing the quadratic stability of large-scale networked systems.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant No.61471055
文摘Virtual network embedding problem which is NP-hard is a key issue for implementing software-defined network which is brought about by network virtualization.Compared with other studies which focus on designing heuristic algorithms to reduce the hardness of the NP-hard problem we propose a robust VNE algorithm based on component connectivity in large-scale network.We distinguish the different components and embed VN requests onto them respectively.And k-core is applied to identify different VN topologies so that the VN request can be embedded onto its corresponding component.On the other hand,load balancing is also considered in this paper.It could avoid blocked or bottlenecked area of substrate network.Simulation experiments show that compared with other algorithms in large-scale network,acceptance ratio,average revenue and robustness can be obviously improved by our algorithm and average cost can be reduced.It also shows the relationship between the component connectivity including giant component and small components and the performance metrics.
基金the National Natural Science Foundation of China(No.61702315)the Key R&D program(international science and technology cooperation project)of Shanxi Province China(No.201903D421003)the National Key Research and Development Program of China(No.2018YFB1800401).
文摘With an increasing urgent demand for fast recovery routing mechanisms in large-scale networks,minimizing network disruption caused by network failure has become critical.However,a large number of relevant studies have shown that network failures occur on the Internet inevitably and frequently.The current routing protocols deployed on the Internet adopt the reconvergence mechanism to cope with network failures.During the reconvergence process,the packets may be lost because of inconsistent routing information,which reduces the network’s availability greatly and affects the Internet service provider’s(ISP’s)service quality and reputation seriously.Therefore,improving network availability has become an urgent problem.As such,the Internet Engineering Task Force suggests the use of downstream path criterion(DC)to address all single-link failure scenarios.However,existing methods for implementing DC schemes are time consuming,require a large amount of router CPU resources,and may deteriorate router capability.Thus,the computation overhead introduced by existing DC schemes is significant,especially in large-scale networks.Therefore,this study proposes an efficient intra-domain routing protection algorithm(ERPA)in large-scale networks.Theoretical analysis indicates that the time complexity of ERPA is less than that of constructing a shortest path tree.Experimental results show that ERPA can reduce the computation overhead significantly compared with the existing algorithms while offering the same network availability as DC.
基金supported by the Program of Hainan Association for Science and Technology Plans to Youth R&D Innovation(QCXM201910)the National Natural Science Foundation of China(Nos.61702315,61802092)+1 种基金the Applied Basic Research Plan of Shanxi Province(No.2201901D211168)the Key R&D Program(International Science and Technology Cooperation Project)of Shanxi Province China(No.201903D421003).
文摘A reduction in network energy consumption and the establishment of green networks have become key scientific problems in academic and industrial research.Existing energy efficiency schemes are based on a known traffic matrix,and acquiring a real-time traffic matrix in current complex networks is difficult.Therefore,this research investigates how to reduce network energy consumption without a real-time traffic matrix.In particular,this paper proposes an intra-domain energy-efficient routing scheme based on multipath routing.It analyzes the relationship between routing availability and energy-efficient routing and integrates the two mechanisms to satisfy the requirements of availability and energy efficiency.The main research focus is as follows:(1)A link criticality model is evaluated to quantitatively measure the importance of links in a network.(2)On the basis of the link criticality model,this paper analyzes an energy-efficient routing technology based on multipath routing to achieve the goals of availability and energy efficiency simultaneously.(3)An energy-efficient routing algorithm based on multipath routing in large-scale networks is proposed.(4)The proposed method does not require a real-time traffic matrix in the network and is thus easy to apply in practice.(5)The proposed algorithm is verified in several network topologies.Experimental results show that the algorithm can not only reduce network energy consumption but can also ensure routing availability.
文摘RECENT advances in sensing,communication and computing have open the door to the deployment of largescale networks of sensors and actuators that allow fine-grain monitoring and control of a multitude of physical processes and infrastructures.The appellation used by field experts for these paradigms is Cyber-Physical Systems(CPS)because the dynamics among computers,networking media/resources and physical systems interact in a way that multi-disciplinary technologies(embedded systems,computers,communications and controls)are required to accomplish prescribed missions.Moreover,they are expected to play a significant role in the design and development of future engineering applications such as smart grids,transportation systems,nuclear plants and smart factories.
基金supported by National Natural Science Foundation of China(Grant No.61761011)Natural Science Foundation of Guangxi(Grant No.2020GXNSFBA297078).
文摘In the graph signal processing(GSP)framework,distributed algorithms are highly desirable in processing signals defined on large-scale networks.However,in most existing distributed algorithms,all nodes homogeneously perform the local computation,which calls for heavy computational and communication costs.Moreover,in many real-world networks,such as those with straggling nodes,the homogeneous manner may result in serious delay or even failure.To this end,we propose active network decomposition algorithms to select non-straggling nodes(normal nodes)that perform the main computation and communication across the network.To accommodate the decomposition in different kinds of networks,two different approaches are developed,one is centralized decomposition that leverages the adjacency of the network and the other is distributed decomposition that employs the indicator message transmission between neighboring nodes,which constitutes the main contribution of this paper.By incorporating the active decomposition scheme,a distributed Newton method is employed to solve the least squares problem in GSP,where the Hessian inverse is approximately evaluated by patching a series of inverses of local Hessian matrices each of which is governed by one normal node.The proposed algorithm inherits the fast convergence of the second-order algorithms while maintains low computational and communication cost.Numerical examples demonstrate the effectiveness of the proposed algorithm.
基金Sponsored by the Funds for Creative Research Groups of China(Grant No. 60821001)National Natural Science Foundation of China(Grant No.60973108 and 60902050)973 Project of China (Grant No.2007CB310703)
文摘A major challenge of network virtualization is the virtual network resource allocation problem that deals with efficient mapping of virtual nodes and virtual links onto the substrate network resources. However, the existing algorithms are almost concentrated on the randomly small-scale network topology, which is not suitable for practical large-scale network environments, because more time is spent on traversing SN and VN, resulting in VN requests congestion. To address this problem, virtual network mapping algorithm is proposed for large-scale network based on small-world characteristic of complex network and network coordinate system. Compared our algorithm with algorithm D-ViNE, experimental results show that our algorithm improves the overall performance.
基金partially supported by US NSF under Grant No.NSF-CNS-1066391and No.NSF-CNS-0914371,NSF-CPS-1135814 and NSF-CDI-1125165
文摘Many science and engineering applications involve solvinga linear least-squares system formed from some field measurements. In the distributed cyber-physical systems(CPS),each sensor node used for measurement often only knowspartial independent rows of the least-squares system. To solve the least-squares all the measurements must be gathered at a centralized location and then perform the computa-tion. Such data collection and computation are inefficient because of bandwidth and time constraints and sometimes areinfeasible because of data privacy concerns. Iterative methods are natural candidates for solving the aforementionedproblem and there are many studies regarding this. However,most of the proposed solutions are related to centralized/parallel computations while only a few have the potential to beapplied in distributed networks. Thus distributed computations are strongly preferred or demanded in many of the realworld applications, e.g. smart-grid, target tracking, etc. Thispaper surveys the representative iterative methods for distributed least-squares in networks.
文摘In this paper, we conduct research on the large-scale network intrusion mode based on the principal component analysis and dropquality sampling. With the growing of network security issues, invasion detection becomes the study hotspot. There are two main types of thatinvasion detection technology, the fi rst is that misuse detection and the anomaly detection. Misuse detection can more accurately detect attacks,but high non-response rates, anomaly detection could detect the unknown attacks, but higher rate of false positives. Network invasion detectionproblem is summed up in the network data fl ow of discriminant problem, namely the judgment of network data fl ow is normal or malicious andin this sense here invasion detection problem can be understood as a pattern recognition problem. Our research integrates the PCA and samplingtechnique to propose the new idea on the IDS that is innovative and will promote the development of the corresponding techniques.
基金Supported by the National Natural Science Foundation of China(Nos.52293472,22473096 and 22471164)。
文摘Among various architectures of polymers,end-group-free rings have attracted growing interests due to their distinct physicochemical performances over the linear counterparts which are exemplified by reduced hydrodynamic size and slower degradation.It is key to develop facile methods to large-scale synthesis of polymer rings with tunable compositions and microstructures.Recent progresses in large-scale synthesis of polymer rings against single-chain dynamic nanoparticles,and the example applications in synchronous enhancing toughness and strength of polymer nanocomposites are summarized.Once there is the breakthrough in rational design and effective large-scale synthesis of polymer rings and their functional derivatives,a family of cyclic functional hybrids would be available,thus providing a new paradigm in developing polymer science and engineering.
基金supported by the Science Center Program of National Natural Science Foundation of China under Grant 62188101the National Natural Science Foundation of China under Grant 62573265.
文摘This study develops an event-triggered control strategy utilizing the fully actuated system approach for nonlinear interconnected large-scale systems containing actuator failures.First,to reduce the complexity of the design process,we transform the studied system into the form of a fully actuated system through a state transformation.Then,to address the unknown nonlinear functions and actuator fault parameters,we employ neural networks and adaptive estimation techniques,respectively.Moreover,to reduce the control cost and improve the control efficiency,we introduce event-triggered inputs into the control strategy.It is proved by the Lyapunov stability analysis that all signals of the closed-loop system are bounded and the output of system eventually converge to a bounded region.The efficacy of the control approach is ultimately demonstrated via the simulation of an actual machine feeding system.
文摘Large-scale complex systems are integral to the functioning of various organizations within the national economy.Despite their significance,the lengthy construction cycles and the involvement of multiple entities often result in the deprioritization of standardized management practices,as they do not yield immediate benefits.The implementation of such systems typically encompasses the integrated phases of "development,construction,utiliz ation,and operation and maintenance".To enhance the overall delivery quality of these systems,it is imperative to dismantle the management barriers among these phases and adopt a holistic approach to standardized management.This paper takes a specific system project as a research object to identify common challenges,and proposes improvement strategies in the implementation of standar dized management.Empirical results indicate a substantial reduction in the system s full-lifecycle costs.
基金supported by the National Natural Science Foundation of China(Grant No.42275041)the Hainan Province Science and Technology Special Fund(Grant No.SOLZSKY2025006).
文摘Summer rainfall in the Yangtze River basin(YRB)is favored by two key factors in the lower troposphere:the tropical anticyclonic anomaly over the western North Pacific and the extratropical northeasterly anomalies to the north of the YRB.This study,however,found that approximately 46%of heavy rainfall events in the YRB occur when only one factor appears and the other is opposite signed.Accordingly,these heavy rainfall events can be categorized into two types:the extratropical northeasterly anomalies but tropical cyclonic anomaly(first unconventional type),and the tropical anticyclonic anomaly but extratropical southwesterly anomalies(second unconventional type).Anomalous water vapor convergence and upward motion exists for both types,but through different mechanisms.For the first type,the moisture convergence and upward motion are induced by a cyclonic anomaly over the YRB,which appears in the mid and lower troposphere and originates from the upstream region.For the second type,a mid-tropospheric cyclonic anomaly over Lake Baikal extends southward and results in southwesterly anomalies over the YRB,in conjunction with the tropical anticyclonic anomaly.The southwesterly anomalies transport water vapor to the YRB and lead to upward motion through warm advection.This study emphasizes the role of mid-tropospheric circulations in inducing heavy rainfall in the YRB.
基金Supported by Natural Science Foundation of Shandong Province of China(Grant Nos.ZR2020MF080 and ZR2020MF065).
文摘The dynamic behaviors of a large-scale ring neural network with a triangular coupling structure are investigated.The characteristic equation of the high-dimensional system using Coate’s flow graph method is calculated.Time delay is selected as the bifurcation parameter,and sufficient conditions for stability and Hopf bifurcation are derived.It is found that the connection coefficient and time delay play a crucial role in the dynamic behaviors of the model.Furthermore,a phase diagram of multiple equilibrium points with one saddle point and two stable nodes is presented.Finally,the effectiveness of the theory is verified through simulation results.
基金supported by the National key research and development program of China(2020YFA0908300)Shanghai Municipal Science and Technology Major Project.
文摘The complex transcriptional regulatory relationships among genes influence gene expression levels and play a crucial role in determining cellular phenotypes.In this study,we propose a novel,distributed,large-scale transcriptional regulatory neural network model(DLTRNM),which integrates prior knowledge into the recon-struction of pre-trained machine learning models,followed by fine-tuning.Using Saccharomyces cerevisiae as a case study,the curated transcriptional regulatory relationships are used to define the interactions between transcription factors(TFs)and their target genes(TGs).Subsequently,DLTRNM is pre-trained on pan-transcriptomic data and fine-tuned with time-series data,enabling it to accurately predict regulatory correla-tions.Additionally,DLTRNM can help identify potential key TFs,thereby simplifying the complex and interre-lated transcriptional regulatory networks(TRNs).It can also complement previously reported transcriptional regulatory subnetworks.DLTRNM provides a powerful tool for studying transcriptional regulation with reduced computational demands and enhanced interpretability.Thus,this study marks a significant advancement in systems biology for understanding the complex transcriptional regulation within cells.
基金supported by the Fundamental Research Funds for the Central Universities(Grant Nos.lzujbky-2021-62 and lzujbky-2024-jdzx06)the National Natural Science Foundation of China(Grant No.12247101)+1 种基金the Natural Science Foundation of Gansu Province,China(Grant Nos.22JR5RA389 and 23JRRA1740)the‘111 Center’Fund(Grant No.B20063).
文摘It has been argued that the human brain,as an information-processing machine,operates near a phase transition point in a non-equilibrium state,where it violates detailed balance leading to entropy production.Thus,the assessment of irreversibility in brain networks can provide valuable insights into their non-equilibrium properties.In this study,we utilized an open-source whole-brain functional magnetic resonance imaging(fMRI)dataset from both resting and task states to evaluate the irreversibility of large-scale human brain networks.Our analysis revealed that the brain networks exhibited significant irreversibility,violating detailed balance,and generating entropy.Notably,both physical and cognitive tasks increased the extent of this violation compared to the resting state.Regardless of the state(rest or task),interactions between pairs of brain regions were the primary contributors to this irreversibility.Moreover,we observed that as global synchrony increased within brain networks,so did irreversibility.The first derivative of irreversibility with respect to synchronization peaked near the phase transition point,characterized by the moderate mean synchronization and maximized synchronization entropy of blood oxygenation level-dependent(BOLD)signals.These findings deepen our understanding of the non-equilibrium dynamics of large-scale brain networks,particularly in relation to their phase transition behaviors,and may have potential clinical applications for brain disorders.
基金supported in part by the National Natural Science Foundation of China(Grant Nos.62201276,62350001,U22B2026,and 62471248)Innovation Program for Quantum Science and Technology(Grant No.2021ZD0300701)+1 种基金the Key R&D Program(Industry Foresight and Key Core Technologies)of Jiangsu Province(Grant No.BE2022071)Natural Science Research of Jiangsu Higher Education Institutions of China(Grant No.22KJB510007)。
文摘As the first stage of the quantum Internet,quantum key distribution(QKD)networks hold the promise of providing long-term security for diverse users.Most existing QKD networks have been constructed based on independent QKD protocols,and they commonly rely on the deployment of single-protocol trusted relay chains for long reach.Driven by the evolution of QKD protocols,large-scale QKD networking is expected to migrate from a single-protocol to a multi-protocol paradigm,during which some useful evolutionary elements for the later stages of the quantum Internet may be incorporated.In this work,we delve into a pivotal technique for large-scale QKD networking,namely,multi-protocol relay chaining.A multi-protocol relay chain is established by connecting a set of trusted/untrusted relays relying on multiple QKD protocols between a pair of QKD nodes.The structures of diverse multi-protocol relay chains are described,based on which the associated model is formulated and the policies are defined for the deployment of multi-protocol relay chains.Furthermore,we propose three multi-protocol relay chaining heuristics.Numerical simulations indicate that the designed heuristics can effectively reduce the number of trusted relays deployed and enhance the average security level versus the commonly used single-protocol trusted relay chaining methods on backbone network topologies.
基金supported in part by Multimedia University under the Research Fellow Grant MMUI/250008in part by Telekom Research&Development Sdn Bhd underGrantRDTC/241149Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R140),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The Internet of Things(IoT)ecosystem faces growing security challenges because it is projected to have 76.88 billion devices by 2025 and $1.4 trillion market value by 2027,operating in distributed networks with resource limitations and diverse system architectures.The current conventional intrusion detection systems(IDS)face scalability problems and trust-related issues,but blockchain-based solutions face limitations because of their low transaction throughput(Bitcoin:7 TPS(Transactions Per Second),Ethereum:15-30 TPS)and high latency.The research introduces MBID(Multi-Tier Blockchain Intrusion Detection)as a groundbreaking Multi-Tier Blockchain Intrusion Detection System with AI-Enhanced Detection,which solves the problems in huge IoT networks.The MBID system uses a four-tier architecture that includes device,edge,fog,and cloud layers with blockchain implementations and Physics-Informed Neural Networks(PINNs)for edge-based anomaly detection and a dual consensus mechanism that uses Honesty-based Distributed Proof-of-Authority(HDPoA)and Delegated Proof of Stake(DPoS).The system achieves scalability and efficiency through the combination of dynamic sharding and Interplanetary File System(IPFS)integration.Experimental evaluations demonstrate exceptional performance,achieving a detection accuracy of 99.84%,an ultra-low false positive rate of 0.01% with a False Negative Rate of 0.15%,and a near-instantaneous edge detection latency of 0.40 ms.The system demonstrated an aggregate throughput of 214.57 TPS in a 3-shard configuration,providing a clear,evidence-based path for horizontally scaling to support overmillions of devices with exceeding throughput.The proposed architecture represents a significant advancement in blockchain-based security for IoT networks,effectively balancing the trade-offs between scalability,security,and decentralization.
基金supported by the Innovation Program for Quantum Science and Technology(Grant Nos.2021ZD0301400 and 2023ZD0301500)the National Natural Science Foundation of China(Grant Nos.62335019 and 62475291).
文摘Reconfigurable linear optical networks based on Mach-Zehnder interferometer(MZI)offer significant potential in optical information processing,particularly in emerging photonic quantum computing systems.However,device losses and calibration errors accumulate as network complexity grows,posing challenges in performing precise mapping of matrix operations.Existing architectures,such as Diamond and Bokun,introduce MZI redundancy into Reck and Clements architectures to improve reliability,which increases complexity and differential path losses that limit scalability.We propose a compact topology architecture that achieves 100%fidelity by employing a symmetrical MZI to decouple optical loss from power ratio and introducing extra MZIs to enforce uniform loss distributions.This multi-level optimization enables direct monitoring pathways while supporting precise calibration,and it approaches theoretical fidelity in practical deployments with direct implications for scalable and fault-tolerant photonic computing systems.
文摘With the purpose of making calculation more efficient in practical hydraulic simulations, an improved algorithm was proposed and was applied in the practical water distribution field. This methodology was developed by expanding the traditional loop-equation theory through utilization of the advantages of the graph theory in efficiency. The utilization of the spanning tree technique from graph theory makes the proposed algorithm efficient in calculation and simple to use for computer coding. The algorithms for topological generation and practical implementations are presented in detail in this paper. Through the application to a practical urban system, the consumption of the CPU time and computation memory were decreased while the accuracy was greatly enhanced compared with the present existing methods.
基金supported by the National Natural Science Foundation of China,No.62273189the Shandong Provincial Natural Science Foundation,No.ZR2020MF064.
文摘In this paper,the quadratic stability of continuous-time uncertain large-scale networked systems is considered.The systems are composed of many subsystems distributed in different locations in space and interconnected according to a certain network topology.A computationally efficient sufficient condition for the quadratic stability of large-scale networked systems is established,utilising effectively the block diagonal structure of the system parameter matrix and the sparseness of the subsystem connection matrix(SCM).Furthermore,a stability condition is presented,which is entirely contingent on the parameters of each subsystem.The simulation results show that the obtained conditions have obvious advantages in analysing the quadratic stability of large-scale networked systems.
