Reinforced concrete(RC)columns are often subjected to off-central explosion due to the uncertainty of blast locations.However,few studies have focused on the dynamic response of RC columns under offcentral explosions....Reinforced concrete(RC)columns are often subjected to off-central explosion due to the uncertainty of blast locations.However,few studies have focused on the dynamic response of RC columns under offcentral explosions.A field blast experiment was conducted under close-in explosion with varying detonation offset distances(0 m,0.5 m,and 1 m),the overpressure load and dynamic responses of the full-scale RC columns were measured.Compared with the centrally detonated condition,a relative offset distance of 1.67 decreases the maximum and residual deflections of the RC column by 16.8%and 21.4%,respectively,while increasing the maximum and residual support rotations by 24.7%and 17.8%.Based on the experimental results,a theoretical model was proposed that considers the detonation location and charge mass,boundary conditions,axial compression ratio and material properties.The theoretical model exhibited good agreement with the experimental results,with prediction errors below 10%for both maximum and residual deflection.The effects of parameters were analyzed,and it indicated that an increase in offset distance results in decreased maximum and residual deflections but an increased support angle,thereby exacerbating damage.Higher axial load ratio,span-depth ratio,and longitudinal reinforcement ratio reduce both deflections and support angle.Additionally,a rapid method to predict the maximum and residual deflection of RC columns under off-central blast loading was also proposed based on the Generalized Regression Neural Network(GRNN).Eleven features which related to the RC column properties and the blast characteristics were used in the training process of GRNN,and accurate predictions were achieved with prediction errors within 20%.This study fills the gap in predicting the dynamic response of RC columns under off-central explosion,providing valuable references for blast-resistant design.展开更多
Conductive elastomers combining micromechanical sensitivity,lightweight adaptability,and environmental sustainability are critically needed for advanced flexible electronics requiring precise responsiveness and long-t...Conductive elastomers combining micromechanical sensitivity,lightweight adaptability,and environmental sustainability are critically needed for advanced flexible electronics requiring precise responsiveness and long-term wearability;however,the integration of these properties remains a significant challenge.Here,we present a biomass-derived conductive elastomer featuring a rationally engineered dynamic crosslinked network integrated with a tunable microporous architecture.This structural design imparts pronounced micromechanical sensitivity,an ultralow density(~0.25 g cm^(−3)),and superior mechanical compliance for adaptive deformation.Moreover,the unique micro-spring effect derived from the porous architecture ensures exceptional stretchability(>500%elongation at break)and superior resilience,delivering immediate and stable electrical response under both subtle(<1%)and large(>200%)mechanical stimuli.Intrinsic dynamic interactions endow the elastomer with efficient room temperature self-healing and complete recyclability without compromising performance.First-principles simulations clarify the mechanisms behind micropore formation and the resulting functionality.Beyond its facile and mild fabrication process,this work establishes a scalable route toward high-performance,sustainable conductive elastomers tailored for next-generation soft electronics.展开更多
The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats.Intrusion detection systems are cr...The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats.Intrusion detection systems are crucial to network security,playing a pivotal role in safeguarding networks from potential threats.However,in the context of an evolving landscape of sophisticated and elusive attacks,existing intrusion detection methodologies often overlook critical aspects such as changes in network topology over time and interactions between hosts.To address these issues,this paper proposes a real-time network intrusion detection method based on graph neural networks.The proposedmethod leverages the advantages of graph neural networks and employs a straightforward graph construction method to represent network traffic as dynamic graph-structured data.Additionally,a graph convolution operation with a multi-head attention mechanism is utilized to enhance the model’s ability to capture the intricate relationships within the graph structure comprehensively.Furthermore,it uses an integrated graph neural network to address dynamic graphs’structural and topological changes at different time points and the challenges of edge embedding in intrusion detection data.The edge classification problem is effectively transformed into node classification by employing a line graph data representation,which facilitates fine-grained intrusion detection tasks on dynamic graph node feature representations.The efficacy of the proposed method is evaluated using two commonly used intrusion detection datasets,UNSW-NB15 and NF-ToN-IoT-v2,and results are compared with previous studies in this field.The experimental results demonstrate that our proposed method achieves 99.3%and 99.96%accuracy on the two datasets,respectively,and outperforms the benchmark model in several evaluation metrics.展开更多
The real-time path optimization for heterogeneous vehicle fleets in large-scale road networks presents significant challenges due to conflicting traffic demands and imbalanced resource allocation.While existing vehicl...The real-time path optimization for heterogeneous vehicle fleets in large-scale road networks presents significant challenges due to conflicting traffic demands and imbalanced resource allocation.While existing vehicleto-infrastructure coordination frameworks partially address congestion mitigation,they often neglect priority-aware optimization and exhibit algorithmic bias toward dominant vehicle classes—critical limitations in mixed-priority scenarios involving emergency vehicles.To bridge this gap,this study proposes a preference game-theoretic coordination framework with adaptive strategy transfer protocol,explicitly balancing system-wide efficiency(measured by network throughput)with priority vehicle rights protection(quantified via time-sensitive utility functions).The approach innovatively combines(1)a multi-vehicle dynamic routing model with quantifiable preference weights,and(2)a distributed Nash equilibrium solver updated using replicator sub-dynamic models.The framework was evaluated on an urban road network containing 25 intersections with mixed priority ratios(10%–30%of vehicles with priority access demand),and the framework showed consistent benefits on four benchmarks(Social routing algorithm,Shortest path algorithm,The comprehensive path optimisation model,The emergency vehicle timing collaborative evolution path optimization method)showed consistent benefits.Results showthat across different traffic demand configurations,the proposed method reduces the average vehicle traveling time by at least 365 s,increases the road network throughput by 48.61%,and effectively balances the road loads.This approach successfully meets the diverse traffic demands of various vehicle types while optimizing road resource allocations.The proposed coordination paradigm advances theoretical foundations for fairness-aware traffic optimization while offering implementable strategies for next-generation cooperative vehicle-road systems,particularly in smart city deployments requiring mixed-priority mobility guarantees.展开更多
Social interaction with peer pressure is widely studied in social network analysis.Game theory can be utilized to model dynamic social interaction,and one class of game network models assumes that people’s decision p...Social interaction with peer pressure is widely studied in social network analysis.Game theory can be utilized to model dynamic social interaction,and one class of game network models assumes that people’s decision payoff functions hinge on individual covariates and the choices of their friends.However,peer pressure would be misidentified and induce a non-negligible bias when incomplete covariates are involved in the game model.For this reason,we develop a generalized constant peer effects model based on homogeneity structure in dynamic social networks.The new model can effectively avoid bias through homogeneity pursuit and can be applied to a wider range of scenarios.To estimate peer pressure in the model,we first present two algorithms based on the initialize expand merge method and the polynomial-time twostage method to estimate homogeneity parameters.Then we apply the nested pseudo-likelihood method and obtain consistent estimators of peer pressure.Simulation evaluations show that our proposed methodology can achieve desirable and effective results in terms of the community misclassification rate and parameter estimation error.We also illustrate the advantages of our model in the empirical analysis when compared with a benchmark model.展开更多
This article briefly reviews the topic of complex network synchronization,with its graph-theoretic criterion,showing that the homogeneous and symmetrical network structures are essential for optimal synchronization.Fu...This article briefly reviews the topic of complex network synchronization,with its graph-theoretic criterion,showing that the homogeneous and symmetrical network structures are essential for optimal synchronization.Furthermore,it briefly reviews the notion of higher-order network topologies and shows their promising potential in application to evaluating the optimality of network synchronizability.展开更多
Recent advances in statistical physics highlight the significant potential of machine learning for phase transition recognition.This study introduces a deep learning framework based on graph neural network to investig...Recent advances in statistical physics highlight the significant potential of machine learning for phase transition recognition.This study introduces a deep learning framework based on graph neural network to investigate non-equilibrium phase transitions,specifically focusing on the directed percolation process.By converting lattices with varying dimensions and connectivity schemes into graph structures and embedding the temporal evolution of the percolation process into node features,our approach enables unified analysis across diverse systems.The framework utilizes a multi-layer graph attention mechanism combined with global pooling to autonomously extract critical features from local dynamics to global phase transition signatures.The model successfully predicts percolation thresholds without relying on lattice geometry,demonstrating its robustness and versatility.Our approach not only offers new insights into phase transition studies but also provides a powerful tool for analyzing complex dynamical systems across various domains.展开更多
The rapid growth of low-Earth-orbit satellites has injected new vitality into future service provisioning.However,given the inherent volatility of network traffic,ensuring differentiated quality of service in highly d...The rapid growth of low-Earth-orbit satellites has injected new vitality into future service provisioning.However,given the inherent volatility of network traffic,ensuring differentiated quality of service in highly dynamic networks remains a significant challenge.In this paper,we propose an online learning-based resource scheduling scheme for satellite-terrestrial integrated networks(STINs)aimed at providing on-demand services with minimal resource utilization.Specifically,we focus on:①accurately characterizing the STIN channel,②predicting resource demand with uncertainty guarantees,and③implementing mixed timescale resource scheduling.For the STIN channel,we adopt the 3rd Generation Partnership Project channel and antenna models for non-terrestrial networks.We employ a one-dimensional convolution and attention-assisted long short-term memory architecture for average demand prediction,while introducing conformal prediction to mitigate uncertainties arising from burst traffic.Additionally,we develop a dual-timescale optimization framework that includes resource reservation on a larger timescale and resource adjustment on a smaller timescale.We also designed an online resource scheduling algorithm based on online convex optimization to guarantee long-term performance with limited knowledge of time-varying network information.Based on the Network Simulator 3 implementation of the STIN channel under our high-fidelity satellite Internet simulation platform,numerical results using a real-world dataset demonstrate the accuracy and efficiency of the prediction algorithms and online resource scheduling scheme.展开更多
Delay aware routing is now widely used to provide efficient network transmission. However, for newly developing or developed mobile communication networks(MCN), only limited delay data can be obtained. In such a netwo...Delay aware routing is now widely used to provide efficient network transmission. However, for newly developing or developed mobile communication networks(MCN), only limited delay data can be obtained. In such a network, the delay is with epistemic uncertainty, which makes the traditional routing scheme based on deterministic theory or probability theory not applicable. Motivated by this problem, the MCN with epistemic uncertainty is first summarized as a dynamic uncertain network based on uncertainty theory, which is widely applied to model epistemic uncertainties. Then by modeling the uncertain end-toend delay, a new delay bounded routing scheme is proposed to find the path with the maximum belief degree that satisfies the delay threshold for the dynamic uncertain network. Finally, a lowEarth-orbit satellite communication network(LEO-SCN) is used as a case to verify the effectiveness of our routing scheme. It is first modeled as a dynamic uncertain network, and then the delay bounded paths with the maximum belief degree are computed and compared under different delay thresholds.展开更多
In the complex environment of Wireless Sensor Networks(WSNs),various malicious attacks have emerged,among which internal attacks pose particularly severe security risks.These attacks seriously threaten network stabili...In the complex environment of Wireless Sensor Networks(WSNs),various malicious attacks have emerged,among which internal attacks pose particularly severe security risks.These attacks seriously threaten network stability,data transmission reliability,and overall performance.To effectively address this issue and significantly improve intrusion detection speed,accuracy,and resistance to malicious attacks,this research designs a Three-level Intrusion Detection Model based on Dynamic Trust Evaluation(TIDM-DTE).This study conducts a detailed analysis of how different attack types impact node trust and establishes node models for data trust,communication trust,and energy consumption trust by focusing on characteristics such as continuous packet loss and energy consumption changes.By dynamically predicting node trust values using the grey Markov model,the model accurately and sensitively reflects changes in node trust levels during attacks.Additionally,DBSCAN(Density-Based Spatial Clustering of Applications with Noise)data noise monitoring technology is employed to quickly identify attacked nodes,while a trust recovery mechanism restores the trust of temporarily faulty nodes to reduce False Alarm Rate.Simulation results demonstrate that TIDM-DTE achieves high detection rates,fast detection speed,and low False Alarm Rate when identifying various network attacks,including selective forwarding attacks,Sybil attacks,switch attacks,and black hole attacks.TIDM-DTE significantly enhances network security,ensures secure and reliable data transmission,moderately improves network energy efficiency,reduces unnecessary energy consumption,and provides strong support for the stable operation of WSNs.Meanwhile,the research findings offer new ideas and methods for WSN security protection,possessing important theoretical significance and practical application value.展开更多
Research on wide area ad hoc networks is of great significance due to its application prospect in long-range networks such as aeronautical and maritime networks,etc.The design of MAC protocols is one of the most impor...Research on wide area ad hoc networks is of great significance due to its application prospect in long-range networks such as aeronautical and maritime networks,etc.The design of MAC protocols is one of the most important parts impacting the whole network performance.In this paper,we propose a dis-tributed TDMA-based MAC protocol called Dynamic Self Organizing TDMA(DSO-TDMA)for wide area ad hoc networks.DSO-TDMA includes three main features:(1)In a distributed way,nodes in the network select transmitting slots according to the congestion situation of the local air interface.(2)In a selforganization way,nodes dynamically adjust the resource occupancy ratio according to the queue length of neighbouring nodes within two-hop range.(3)In a piggyback way,the control information is transmitted together with the payload to reduce the overhead.We design the whole mechanisms,implement them in NS-3 and evaluate the performance of DSO-TDMA compared with another dynamic TDMA MAC protocol,EHR-TDMA.Results show that the end-to-end throughput of DSO-TDMA is at most 51.4%higher than that of EHR-TDMA,and the average access delay of DSO-TDMA is at most 66.05%lower than that of EHR-TDMA.展开更多
Aqueous Zn-metal batteries(AZMBs)performance is hampered by freezing water at low temperatures,which hampers their multi-scenario application.Hydrogen bonds(HBs)play a pivotal role in water freezing,and proton transpo...Aqueous Zn-metal batteries(AZMBs)performance is hampered by freezing water at low temperatures,which hampers their multi-scenario application.Hydrogen bonds(HBs)play a pivotal role in water freezing,and proton transport is indispensable for the establishment of HBs.Here,the accelerated proton transport modulates the dynamic hydrogen bonding network of a Zn(BF4)2/EMIMBF4impregnated polyacrylamide/poly(vinyl alcohol)/xanthan gum dual network eutectic gel electrolyte(PPX-ILZSE)for lowtemperature AZMBs.The PPX-ILZSE forms more HBs,shorter HBs lifetimes,higher tetrahedral entropy,and faster desolvation processes,as demonstrated by experimental and theoretical calculations.This enhanced dynamic proton transport promotes rapid cycling of HBs formation-failure,and for polyaniline cathode(PANI)abundant redox sites of proton,confers excellent low temperature electrochemical performance to the Zn//PANI full cell.Specific capacities for 1000 and 5000 cycles at 1 and 5 A g^(-1)were149.8 and 128.4 m A h g^(-1)at room temperature,respectively.Furthermore,specific capacities of 131.1 mA hg^(-1)(92.4%capacity retention)and 0.0066%capacity decay per lap were achieved for 3000and 3500 laps at-30 and 40℃,respectively,at 0.5 A g^(-1).Furthermore,in-situ protective layer of ZnOHF nano-arrays on the Zn anode surface to eliminate dendrite growth and accelerate Zn-ions adsorption and charge transfer.展开更多
Reliable and efficient communication is essential for Unmanned Aerial Vehicle(UAV)networks,especially in dynamic and resource-constrained environments such as disaster management,surveillance,and environmental monitor...Reliable and efficient communication is essential for Unmanned Aerial Vehicle(UAV)networks,especially in dynamic and resource-constrained environments such as disaster management,surveillance,and environmental monitoring.Frequent topology changes,high mobility,and limited energy availability pose significant challenges to maintaining stable and high-performance routing.Traditional routing protocols,such as Ad hoc On-Demand Distance Vector(AODV),Load-Balanced Optimized Predictive Ad hoc Routing(LB-OPAR),and Destination-Sequenced Distance Vector(DSDV),often experience performance degradation under such conditions.To address these limitations,this study evaluates the effectiveness of Dynamic Adaptive Routing(DAR),a protocol designed to adapt routing decisions in real time based on network dynamics and resource constraints.The research utilizes the Network Simulator 3(NS-3)platform to conduct controlled simulations,measuring key performance indicators such as latency,Packet Delivery Ratio(PDR),energy consumption,and throughput.Comparative analysis reveals that DAR consistently outperforms conventional protocols,achieving a 20%-30% reduction in latency,a 25% decrease in energy consumption,and marked improvements in throughput and PDR.These results highlight DAR’s ability to maintain high communication reliability while optimizing resource usage in challenging operational scenarios.By providing empirical evidence of DAR’s advantages in highly dynamic UAV network environments,this study contributes to advancing adaptive routing strategies.The findings not only validate DAR’s robustness and scalability but also lay the groundwork for integrating artificial intelligence-driven decision-making and real-world UAV deployment.Future work will explore cross-layer optimization,multi-UAV coordination,and experimental validation in field trials,aiming to further enhance communication resilience and energy efficiency in next-generation aerial networks.展开更多
With the rapid growth of connected devices,traditional edge-cloud systems are under overload pressure.Using mobile edge computing(MEC)to assist unmanned aerial vehicles(UAVs)as low altitude platform stations(LAPS)for ...With the rapid growth of connected devices,traditional edge-cloud systems are under overload pressure.Using mobile edge computing(MEC)to assist unmanned aerial vehicles(UAVs)as low altitude platform stations(LAPS)for communication and computation to build air-ground integrated networks(AGINs)offers a promising solution for seamless network coverage of remote internet of things(IoT)devices in the future.To address the performance demands of future mobile devices(MDs),we proposed an MEC-assisted AGIN system.The goal is to minimize the long-term computational overhead of MDs by jointly optimizing transmission power,flight trajecto-ries,resource allocation,and offloading ratios,while utilizing non-orthogonal multiple access(NOMA)to improve device connectivity of large-scale MDs and spectral efficiency.We first designed an adaptive clustering scheme based on K-Means to cluster MDs and established commu-nication links,improving efficiency and load balancing.Then,considering system dynamics,we introduced a partial computation offloading algorithm based on multi-agent deep deterministic pol-icy gradient(MADDPG),modeling the multi-UAV computation offloading problem as a Markov decision process(MDP).This algorithm optimizes resource allocation through centralized training and distributed execution,reducing computational overhead.Simulation results show that the pro-posed algorithm not only converges stably but also outperforms other benchmark algorithms in han-dling complex scenarios with multiple devices.展开更多
Due to self-occlusion and high degree of freedom,estimating 3D hand pose from a single RGB image is a great challenging problem.Graph convolutional networks(GCNs)use graphs to describe the physical connection relation...Due to self-occlusion and high degree of freedom,estimating 3D hand pose from a single RGB image is a great challenging problem.Graph convolutional networks(GCNs)use graphs to describe the physical connection relationships between hand joints and improve the accuracy of 3D hand pose regression.However,GCNs cannot effectively describe the relationships between non-adjacent hand joints.Recently,hypergraph convolutional networks(HGCNs)have received much attention as they can describe multi-dimensional relationships between nodes through hyperedges;therefore,this paper proposes a framework for 3D hand pose estimation based on HGCN,which can better extract correlated relationships between adjacent and non-adjacent hand joints.To overcome the shortcomings of predefined hypergraph structures,a kind of dynamic hypergraph convolutional network is proposed,in which hyperedges are constructed dynamically based on hand joint feature similarity.To better explore the local semantic relationships between nodes,a kind of semantic dynamic hypergraph convolution is proposed.The proposed method is evaluated on publicly available benchmark datasets.Qualitative and quantitative experimental results both show that the proposed HGCN and improved methods for 3D hand pose estimation are better than GCN,and achieve state-of-the-art performance compared with existing methods.展开更多
The low Earth orbit(LEO)satellite networks have outstanding advantages such as wide coverage area and not being limited by geographic environment,which can provide a broader range of communication services and has bec...The low Earth orbit(LEO)satellite networks have outstanding advantages such as wide coverage area and not being limited by geographic environment,which can provide a broader range of communication services and has become an essential supplement to the terrestrial network.However,the dynamic changes and uneven distribution of satellite network traffic inevitably bring challenges to multipath routing.Even worse,the harsh space environment often leads to incomplete collection of network state data for routing decision-making,which further complicates this challenge.To address this problem,this paper proposes a state-incomplete intelligent dynamic multipath routing algorithm(SIDMRA)to maximize network efficiency even with incomplete state data as input.Specifically,we model the multipath routing problem as a markov decision process(MDP)and then combine the deep deterministic policy gradient(DDPG)and the K shortest paths(KSP)algorithm to solve the optimal multipath routing policy.We use the temporal correlation of the satellite network state to fit the incomplete state data and then use the message passing neuron network(MPNN)for data enhancement.Simulation results show that the proposed algorithm outperforms baseline algorithms regarding average end-to-end delay and packet loss rate and performs stably under certain missing rates of state data.展开更多
Modern compression and acceleration methods for exploring efficient deep neural networks render real-world applications more feasible.Existing approaches uniformly apply the same procedure to every input image,overloo...Modern compression and acceleration methods for exploring efficient deep neural networks render real-world applications more feasible.Existing approaches uniformly apply the same procedure to every input image,overlooking instancewise complexity variations.Moreover,owing to pruning or decomposition techniques,the upper bound of network representation capabilities might be permanently diminished.In this work,an input-dependent multiscale dynamic inference method(MSDI)is developed to strike a better balance between model performance and inference acceleration.Specifically,we modify the main body of a convolutional network to obtain a series of parameter-sharing subnetworks with varying levels of complexity.A side branch structure is then introduced to assign an input instance to a suitable subnetwork as its inference route,and we expect to accelerate the inference by assigning the easy input to the subnetwork with low capacity.We further propose multiscale distillation training to optimize the training of the modified subnetworks.Additionally,we compare the entropy-based and learning-based grading approaches,aiming to obtain a more suitable route assignment method.Experiments show that MSDI can accelerate most existing convolutional models,achieving up to 74.7%computation savings across diverse datasets.展开更多
In this paper,we use a direct method to study the almost periodic dynamics of an octonion-valued stochastic shunting inhibitory cellular neural network with variable delays.By using the fixed point method and inequali...In this paper,we use a direct method to study the almost periodic dynamics of an octonion-valued stochastic shunting inhibitory cellular neural network with variable delays.By using the fixed point method and inequality technique,the existence,uniqueness and stability of almost periodic solutions in the sense of distribution of the neural network under consideration are obtained.Our results are brand new.展开更多
High-quality services in today’s mobile networks require stable delivery of bandwidth-intensive network content.Multipath QUIC(MPQUIC),as a multipath protocol that extends QUIC,can utilize multiple paths to support s...High-quality services in today’s mobile networks require stable delivery of bandwidth-intensive network content.Multipath QUIC(MPQUIC),as a multipath protocol that extends QUIC,can utilize multiple paths to support stable and efficient transmission.The standard coupled congestion control algorithm in MPQUIC synchronizes these paths to manage congestion,meeting fairness requirements and improving transmission efficiency.However,current algorithms’Congestion Window(CWND)reduction approach significantly decreases CWND upon packet loss,which lowers effective throughput,regardless of the congestion origin.Furthermore,the uncoupled Slow-Start(SS)in MPQUIC leads to independent exponential CWND growth on each path,potentially causing buffer overflow.To address these issues,we propose the CC-OLIA,which incorporates Packet Loss Classifcation(PLC)and Coupled Slow-Start(CSS).The PLC distinguishes between congestion-induced and random packet losses,adjusting CWND reduction accordingly to maintain throughput.Concurrently,the CSS module coordinates CWND growth during the SS,preventing abrupt increases.Implementation on MININET shows that CC-OLIA not only maintains fair performance but also enhances transmission efficiency across diverse network conditions.展开更多
The deepwater subsea wellhead(SW)system is the foundation for the construction of oil and gas wells and the crucial channel for operation.During riser connection operation,the SW system is subjected to cyclic dynamic ...The deepwater subsea wellhead(SW)system is the foundation for the construction of oil and gas wells and the crucial channel for operation.During riser connection operation,the SW system is subjected to cyclic dynamic loads which cause fatigue damage to the SW system,and continuously accumulated fatigue damage leads to fatigue failure of the SW system,rupture,and even blowout accidents.This paper proposes a hybrid Bayesian network(HBN)-based dynamic reliability assessment approach for deepwater SW systems during their service life.In the proposed approach,the relationship between the accumulation of fatigue damage and the fatigue failure probability of the SW system is predicted,only considering normal conditions.The HBN model,which includes the accumulation of fatigue damage under normal conditions and the other factors affecting the fatigue of the SW system,is subsequently developed.When predictive and diagnostic analysis techniques are adopted,the dynamic reliability of the SW system is achieved,and the most influential factors are determined.Finally,corresponding safety control measures are proposed to improve the reliability of the SW system effectively.The results illustrate that the fatigue failure speed increases rapidly when the accumulation fatigue damage is larger than 0.45 under normal conditions and that the reliability of the SW system is larger than 94%within the design life.展开更多
基金financially supported by the National Natural Science Foundation of China(Grants No.12472399)。
文摘Reinforced concrete(RC)columns are often subjected to off-central explosion due to the uncertainty of blast locations.However,few studies have focused on the dynamic response of RC columns under offcentral explosions.A field blast experiment was conducted under close-in explosion with varying detonation offset distances(0 m,0.5 m,and 1 m),the overpressure load and dynamic responses of the full-scale RC columns were measured.Compared with the centrally detonated condition,a relative offset distance of 1.67 decreases the maximum and residual deflections of the RC column by 16.8%and 21.4%,respectively,while increasing the maximum and residual support rotations by 24.7%and 17.8%.Based on the experimental results,a theoretical model was proposed that considers the detonation location and charge mass,boundary conditions,axial compression ratio and material properties.The theoretical model exhibited good agreement with the experimental results,with prediction errors below 10%for both maximum and residual deflection.The effects of parameters were analyzed,and it indicated that an increase in offset distance results in decreased maximum and residual deflections but an increased support angle,thereby exacerbating damage.Higher axial load ratio,span-depth ratio,and longitudinal reinforcement ratio reduce both deflections and support angle.Additionally,a rapid method to predict the maximum and residual deflection of RC columns under off-central blast loading was also proposed based on the Generalized Regression Neural Network(GRNN).Eleven features which related to the RC column properties and the blast characteristics were used in the training process of GRNN,and accurate predictions were achieved with prediction errors within 20%.This study fills the gap in predicting the dynamic response of RC columns under off-central explosion,providing valuable references for blast-resistant design.
基金supported by National Natural Science Foundation of China(No.52103044)Double First-Class Initiative University of Science and Technology of China(KY2400000037)the Young Talent Programme(GG2400007009).
文摘Conductive elastomers combining micromechanical sensitivity,lightweight adaptability,and environmental sustainability are critically needed for advanced flexible electronics requiring precise responsiveness and long-term wearability;however,the integration of these properties remains a significant challenge.Here,we present a biomass-derived conductive elastomer featuring a rationally engineered dynamic crosslinked network integrated with a tunable microporous architecture.This structural design imparts pronounced micromechanical sensitivity,an ultralow density(~0.25 g cm^(−3)),and superior mechanical compliance for adaptive deformation.Moreover,the unique micro-spring effect derived from the porous architecture ensures exceptional stretchability(>500%elongation at break)and superior resilience,delivering immediate and stable electrical response under both subtle(<1%)and large(>200%)mechanical stimuli.Intrinsic dynamic interactions endow the elastomer with efficient room temperature self-healing and complete recyclability without compromising performance.First-principles simulations clarify the mechanisms behind micropore formation and the resulting functionality.Beyond its facile and mild fabrication process,this work establishes a scalable route toward high-performance,sustainable conductive elastomers tailored for next-generation soft electronics.
文摘The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats.Intrusion detection systems are crucial to network security,playing a pivotal role in safeguarding networks from potential threats.However,in the context of an evolving landscape of sophisticated and elusive attacks,existing intrusion detection methodologies often overlook critical aspects such as changes in network topology over time and interactions between hosts.To address these issues,this paper proposes a real-time network intrusion detection method based on graph neural networks.The proposedmethod leverages the advantages of graph neural networks and employs a straightforward graph construction method to represent network traffic as dynamic graph-structured data.Additionally,a graph convolution operation with a multi-head attention mechanism is utilized to enhance the model’s ability to capture the intricate relationships within the graph structure comprehensively.Furthermore,it uses an integrated graph neural network to address dynamic graphs’structural and topological changes at different time points and the challenges of edge embedding in intrusion detection data.The edge classification problem is effectively transformed into node classification by employing a line graph data representation,which facilitates fine-grained intrusion detection tasks on dynamic graph node feature representations.The efficacy of the proposed method is evaluated using two commonly used intrusion detection datasets,UNSW-NB15 and NF-ToN-IoT-v2,and results are compared with previous studies in this field.The experimental results demonstrate that our proposed method achieves 99.3%and 99.96%accuracy on the two datasets,respectively,and outperforms the benchmark model in several evaluation metrics.
基金funded by the National Key Research and Development Program Project 2022YFB4300404.
文摘The real-time path optimization for heterogeneous vehicle fleets in large-scale road networks presents significant challenges due to conflicting traffic demands and imbalanced resource allocation.While existing vehicleto-infrastructure coordination frameworks partially address congestion mitigation,they often neglect priority-aware optimization and exhibit algorithmic bias toward dominant vehicle classes—critical limitations in mixed-priority scenarios involving emergency vehicles.To bridge this gap,this study proposes a preference game-theoretic coordination framework with adaptive strategy transfer protocol,explicitly balancing system-wide efficiency(measured by network throughput)with priority vehicle rights protection(quantified via time-sensitive utility functions).The approach innovatively combines(1)a multi-vehicle dynamic routing model with quantifiable preference weights,and(2)a distributed Nash equilibrium solver updated using replicator sub-dynamic models.The framework was evaluated on an urban road network containing 25 intersections with mixed priority ratios(10%–30%of vehicles with priority access demand),and the framework showed consistent benefits on four benchmarks(Social routing algorithm,Shortest path algorithm,The comprehensive path optimisation model,The emergency vehicle timing collaborative evolution path optimization method)showed consistent benefits.Results showthat across different traffic demand configurations,the proposed method reduces the average vehicle traveling time by at least 365 s,increases the road network throughput by 48.61%,and effectively balances the road loads.This approach successfully meets the diverse traffic demands of various vehicle types while optimizing road resource allocations.The proposed coordination paradigm advances theoretical foundations for fairness-aware traffic optimization while offering implementable strategies for next-generation cooperative vehicle-road systems,particularly in smart city deployments requiring mixed-priority mobility guarantees.
基金supported by the National Nature Science Foundation of China(71771201,72531009,71973001)the USTC Research Funds of the Double First-Class Initiative(FSSF-A-240202).
文摘Social interaction with peer pressure is widely studied in social network analysis.Game theory can be utilized to model dynamic social interaction,and one class of game network models assumes that people’s decision payoff functions hinge on individual covariates and the choices of their friends.However,peer pressure would be misidentified and induce a non-negligible bias when incomplete covariates are involved in the game model.For this reason,we develop a generalized constant peer effects model based on homogeneity structure in dynamic social networks.The new model can effectively avoid bias through homogeneity pursuit and can be applied to a wider range of scenarios.To estimate peer pressure in the model,we first present two algorithms based on the initialize expand merge method and the polynomial-time twostage method to estimate homogeneity parameters.Then we apply the nested pseudo-likelihood method and obtain consistent estimators of peer pressure.Simulation evaluations show that our proposed methodology can achieve desirable and effective results in terms of the community misclassification rate and parameter estimation error.We also illustrate the advantages of our model in the empirical analysis when compared with a benchmark model.
基金Hong Kong Research Grants Council under the GRF(9043664).
文摘This article briefly reviews the topic of complex network synchronization,with its graph-theoretic criterion,showing that the homogeneous and symmetrical network structures are essential for optimal synchronization.Furthermore,it briefly reviews the notion of higher-order network topologies and shows their promising potential in application to evaluating the optimality of network synchronizability.
基金supported by the Fund from the Science and Technology Department of Henan Province,China(Grant Nos.222102210233 and 232102210064)the National Natural Science Foundation of China(Grant Nos.62373169 and 72474086)+5 种基金the Young and Midcareer Academic Leader of Jiangsu Province,China(Grant No.Qinglan Project in 2024)the National Statistical Science Research Project(Grant No.2022LZ03)Shaanxi Provincial Soft Science Project(Grant No.2022KRM111)Shaanxi Provincial Social Science Foundation(Grant No.2022R016)the Special Project for Philosophical and Social Sciences Research in Shaanxi Province,China(Grant No.2024QN018)the Fund from the Henan Office of Philosophy and Social Science(Grant No.2023CJJ112).
文摘Recent advances in statistical physics highlight the significant potential of machine learning for phase transition recognition.This study introduces a deep learning framework based on graph neural network to investigate non-equilibrium phase transitions,specifically focusing on the directed percolation process.By converting lattices with varying dimensions and connectivity schemes into graph structures and embedding the temporal evolution of the percolation process into node features,our approach enables unified analysis across diverse systems.The framework utilizes a multi-layer graph attention mechanism combined with global pooling to autonomously extract critical features from local dynamics to global phase transition signatures.The model successfully predicts percolation thresholds without relying on lattice geometry,demonstrating its robustness and versatility.Our approach not only offers new insights into phase transition studies but also provides a powerful tool for analyzing complex dynamical systems across various domains.
基金supported in part by the Major Program of the National Natural Science Foundation of China(62495021 and 62495020).
文摘The rapid growth of low-Earth-orbit satellites has injected new vitality into future service provisioning.However,given the inherent volatility of network traffic,ensuring differentiated quality of service in highly dynamic networks remains a significant challenge.In this paper,we propose an online learning-based resource scheduling scheme for satellite-terrestrial integrated networks(STINs)aimed at providing on-demand services with minimal resource utilization.Specifically,we focus on:①accurately characterizing the STIN channel,②predicting resource demand with uncertainty guarantees,and③implementing mixed timescale resource scheduling.For the STIN channel,we adopt the 3rd Generation Partnership Project channel and antenna models for non-terrestrial networks.We employ a one-dimensional convolution and attention-assisted long short-term memory architecture for average demand prediction,while introducing conformal prediction to mitigate uncertainties arising from burst traffic.Additionally,we develop a dual-timescale optimization framework that includes resource reservation on a larger timescale and resource adjustment on a smaller timescale.We also designed an online resource scheduling algorithm based on online convex optimization to guarantee long-term performance with limited knowledge of time-varying network information.Based on the Network Simulator 3 implementation of the STIN channel under our high-fidelity satellite Internet simulation platform,numerical results using a real-world dataset demonstrate the accuracy and efficiency of the prediction algorithms and online resource scheduling scheme.
基金National Natural Science Foundation of China (61773044,62073009)National key Laboratory of Science and Technology on Reliability and Environmental Engineering(WDZC2019601A301)。
文摘Delay aware routing is now widely used to provide efficient network transmission. However, for newly developing or developed mobile communication networks(MCN), only limited delay data can be obtained. In such a network, the delay is with epistemic uncertainty, which makes the traditional routing scheme based on deterministic theory or probability theory not applicable. Motivated by this problem, the MCN with epistemic uncertainty is first summarized as a dynamic uncertain network based on uncertainty theory, which is widely applied to model epistemic uncertainties. Then by modeling the uncertain end-toend delay, a new delay bounded routing scheme is proposed to find the path with the maximum belief degree that satisfies the delay threshold for the dynamic uncertain network. Finally, a lowEarth-orbit satellite communication network(LEO-SCN) is used as a case to verify the effectiveness of our routing scheme. It is first modeled as a dynamic uncertain network, and then the delay bounded paths with the maximum belief degree are computed and compared under different delay thresholds.
基金supported by Gansu Provincial Higher Education Teachers’Innovation Fund under Grant 2025A-124Key Research Project of Gansu University of Political Science and Law under Grant No.GZF2022XZD08Soft Science Special Project of Gansu Basic Research Plan under Grant No.22JR11RA106.
文摘In the complex environment of Wireless Sensor Networks(WSNs),various malicious attacks have emerged,among which internal attacks pose particularly severe security risks.These attacks seriously threaten network stability,data transmission reliability,and overall performance.To effectively address this issue and significantly improve intrusion detection speed,accuracy,and resistance to malicious attacks,this research designs a Three-level Intrusion Detection Model based on Dynamic Trust Evaluation(TIDM-DTE).This study conducts a detailed analysis of how different attack types impact node trust and establishes node models for data trust,communication trust,and energy consumption trust by focusing on characteristics such as continuous packet loss and energy consumption changes.By dynamically predicting node trust values using the grey Markov model,the model accurately and sensitively reflects changes in node trust levels during attacks.Additionally,DBSCAN(Density-Based Spatial Clustering of Applications with Noise)data noise monitoring technology is employed to quickly identify attacked nodes,while a trust recovery mechanism restores the trust of temporarily faulty nodes to reduce False Alarm Rate.Simulation results demonstrate that TIDM-DTE achieves high detection rates,fast detection speed,and low False Alarm Rate when identifying various network attacks,including selective forwarding attacks,Sybil attacks,switch attacks,and black hole attacks.TIDM-DTE significantly enhances network security,ensures secure and reliable data transmission,moderately improves network energy efficiency,reduces unnecessary energy consumption,and provides strong support for the stable operation of WSNs.Meanwhile,the research findings offer new ideas and methods for WSN security protection,possessing important theoretical significance and practical application value.
文摘Research on wide area ad hoc networks is of great significance due to its application prospect in long-range networks such as aeronautical and maritime networks,etc.The design of MAC protocols is one of the most important parts impacting the whole network performance.In this paper,we propose a dis-tributed TDMA-based MAC protocol called Dynamic Self Organizing TDMA(DSO-TDMA)for wide area ad hoc networks.DSO-TDMA includes three main features:(1)In a distributed way,nodes in the network select transmitting slots according to the congestion situation of the local air interface.(2)In a selforganization way,nodes dynamically adjust the resource occupancy ratio according to the queue length of neighbouring nodes within two-hop range.(3)In a piggyback way,the control information is transmitted together with the payload to reduce the overhead.We design the whole mechanisms,implement them in NS-3 and evaluate the performance of DSO-TDMA compared with another dynamic TDMA MAC protocol,EHR-TDMA.Results show that the end-to-end throughput of DSO-TDMA is at most 51.4%higher than that of EHR-TDMA,and the average access delay of DSO-TDMA is at most 66.05%lower than that of EHR-TDMA.
基金supported by the National Natural Science Foundation of China(NSFC 52432002,52372041,and 52302087)China Postdoctoral Science Foundation(Grant No.2023 M740895)+1 种基金Heilongjiang Touyan Team Programthe Fundamental Research Funds for the Central Universities(Grant No.HIT.OCEF.2021003 and HIT.DZJJ.2025002)。
文摘Aqueous Zn-metal batteries(AZMBs)performance is hampered by freezing water at low temperatures,which hampers their multi-scenario application.Hydrogen bonds(HBs)play a pivotal role in water freezing,and proton transport is indispensable for the establishment of HBs.Here,the accelerated proton transport modulates the dynamic hydrogen bonding network of a Zn(BF4)2/EMIMBF4impregnated polyacrylamide/poly(vinyl alcohol)/xanthan gum dual network eutectic gel electrolyte(PPX-ILZSE)for lowtemperature AZMBs.The PPX-ILZSE forms more HBs,shorter HBs lifetimes,higher tetrahedral entropy,and faster desolvation processes,as demonstrated by experimental and theoretical calculations.This enhanced dynamic proton transport promotes rapid cycling of HBs formation-failure,and for polyaniline cathode(PANI)abundant redox sites of proton,confers excellent low temperature electrochemical performance to the Zn//PANI full cell.Specific capacities for 1000 and 5000 cycles at 1 and 5 A g^(-1)were149.8 and 128.4 m A h g^(-1)at room temperature,respectively.Furthermore,specific capacities of 131.1 mA hg^(-1)(92.4%capacity retention)and 0.0066%capacity decay per lap were achieved for 3000and 3500 laps at-30 and 40℃,respectively,at 0.5 A g^(-1).Furthermore,in-situ protective layer of ZnOHF nano-arrays on the Zn anode surface to eliminate dendrite growth and accelerate Zn-ions adsorption and charge transfer.
文摘Reliable and efficient communication is essential for Unmanned Aerial Vehicle(UAV)networks,especially in dynamic and resource-constrained environments such as disaster management,surveillance,and environmental monitoring.Frequent topology changes,high mobility,and limited energy availability pose significant challenges to maintaining stable and high-performance routing.Traditional routing protocols,such as Ad hoc On-Demand Distance Vector(AODV),Load-Balanced Optimized Predictive Ad hoc Routing(LB-OPAR),and Destination-Sequenced Distance Vector(DSDV),often experience performance degradation under such conditions.To address these limitations,this study evaluates the effectiveness of Dynamic Adaptive Routing(DAR),a protocol designed to adapt routing decisions in real time based on network dynamics and resource constraints.The research utilizes the Network Simulator 3(NS-3)platform to conduct controlled simulations,measuring key performance indicators such as latency,Packet Delivery Ratio(PDR),energy consumption,and throughput.Comparative analysis reveals that DAR consistently outperforms conventional protocols,achieving a 20%-30% reduction in latency,a 25% decrease in energy consumption,and marked improvements in throughput and PDR.These results highlight DAR’s ability to maintain high communication reliability while optimizing resource usage in challenging operational scenarios.By providing empirical evidence of DAR’s advantages in highly dynamic UAV network environments,this study contributes to advancing adaptive routing strategies.The findings not only validate DAR’s robustness and scalability but also lay the groundwork for integrating artificial intelligence-driven decision-making and real-world UAV deployment.Future work will explore cross-layer optimization,multi-UAV coordination,and experimental validation in field trials,aiming to further enhance communication resilience and energy efficiency in next-generation aerial networks.
基金supported by the Gansu Province Key Research and Development Plan(No.23YFGA0062)Gansu Provin-cial Innovation Fund(No.2022A-215).
文摘With the rapid growth of connected devices,traditional edge-cloud systems are under overload pressure.Using mobile edge computing(MEC)to assist unmanned aerial vehicles(UAVs)as low altitude platform stations(LAPS)for communication and computation to build air-ground integrated networks(AGINs)offers a promising solution for seamless network coverage of remote internet of things(IoT)devices in the future.To address the performance demands of future mobile devices(MDs),we proposed an MEC-assisted AGIN system.The goal is to minimize the long-term computational overhead of MDs by jointly optimizing transmission power,flight trajecto-ries,resource allocation,and offloading ratios,while utilizing non-orthogonal multiple access(NOMA)to improve device connectivity of large-scale MDs and spectral efficiency.We first designed an adaptive clustering scheme based on K-Means to cluster MDs and established commu-nication links,improving efficiency and load balancing.Then,considering system dynamics,we introduced a partial computation offloading algorithm based on multi-agent deep deterministic pol-icy gradient(MADDPG),modeling the multi-UAV computation offloading problem as a Markov decision process(MDP).This algorithm optimizes resource allocation through centralized training and distributed execution,reducing computational overhead.Simulation results show that the pro-posed algorithm not only converges stably but also outperforms other benchmark algorithms in han-dling complex scenarios with multiple devices.
基金the National Key Research and Development Program of China(No.2021ZD0111902)the National Natural Science Foundation of China(Nos.62172022 and U21B2038)。
文摘Due to self-occlusion and high degree of freedom,estimating 3D hand pose from a single RGB image is a great challenging problem.Graph convolutional networks(GCNs)use graphs to describe the physical connection relationships between hand joints and improve the accuracy of 3D hand pose regression.However,GCNs cannot effectively describe the relationships between non-adjacent hand joints.Recently,hypergraph convolutional networks(HGCNs)have received much attention as they can describe multi-dimensional relationships between nodes through hyperedges;therefore,this paper proposes a framework for 3D hand pose estimation based on HGCN,which can better extract correlated relationships between adjacent and non-adjacent hand joints.To overcome the shortcomings of predefined hypergraph structures,a kind of dynamic hypergraph convolutional network is proposed,in which hyperedges are constructed dynamically based on hand joint feature similarity.To better explore the local semantic relationships between nodes,a kind of semantic dynamic hypergraph convolution is proposed.The proposed method is evaluated on publicly available benchmark datasets.Qualitative and quantitative experimental results both show that the proposed HGCN and improved methods for 3D hand pose estimation are better than GCN,and achieve state-of-the-art performance compared with existing methods.
文摘The low Earth orbit(LEO)satellite networks have outstanding advantages such as wide coverage area and not being limited by geographic environment,which can provide a broader range of communication services and has become an essential supplement to the terrestrial network.However,the dynamic changes and uneven distribution of satellite network traffic inevitably bring challenges to multipath routing.Even worse,the harsh space environment often leads to incomplete collection of network state data for routing decision-making,which further complicates this challenge.To address this problem,this paper proposes a state-incomplete intelligent dynamic multipath routing algorithm(SIDMRA)to maximize network efficiency even with incomplete state data as input.Specifically,we model the multipath routing problem as a markov decision process(MDP)and then combine the deep deterministic policy gradient(DDPG)and the K shortest paths(KSP)algorithm to solve the optimal multipath routing policy.We use the temporal correlation of the satellite network state to fit the incomplete state data and then use the message passing neuron network(MPNN)for data enhancement.Simulation results show that the proposed algorithm outperforms baseline algorithms regarding average end-to-end delay and packet loss rate and performs stably under certain missing rates of state data.
文摘Modern compression and acceleration methods for exploring efficient deep neural networks render real-world applications more feasible.Existing approaches uniformly apply the same procedure to every input image,overlooking instancewise complexity variations.Moreover,owing to pruning or decomposition techniques,the upper bound of network representation capabilities might be permanently diminished.In this work,an input-dependent multiscale dynamic inference method(MSDI)is developed to strike a better balance between model performance and inference acceleration.Specifically,we modify the main body of a convolutional network to obtain a series of parameter-sharing subnetworks with varying levels of complexity.A side branch structure is then introduced to assign an input instance to a suitable subnetwork as its inference route,and we expect to accelerate the inference by assigning the easy input to the subnetwork with low capacity.We further propose multiscale distillation training to optimize the training of the modified subnetworks.Additionally,we compare the entropy-based and learning-based grading approaches,aiming to obtain a more suitable route assignment method.Experiments show that MSDI can accelerate most existing convolutional models,achieving up to 74.7%computation savings across diverse datasets.
基金Supported by the National Natural Science Foundation of China(12261098,11861072)。
文摘In this paper,we use a direct method to study the almost periodic dynamics of an octonion-valued stochastic shunting inhibitory cellular neural network with variable delays.By using the fixed point method and inequality technique,the existence,uniqueness and stability of almost periodic solutions in the sense of distribution of the neural network under consideration are obtained.Our results are brand new.
文摘High-quality services in today’s mobile networks require stable delivery of bandwidth-intensive network content.Multipath QUIC(MPQUIC),as a multipath protocol that extends QUIC,can utilize multiple paths to support stable and efficient transmission.The standard coupled congestion control algorithm in MPQUIC synchronizes these paths to manage congestion,meeting fairness requirements and improving transmission efficiency.However,current algorithms’Congestion Window(CWND)reduction approach significantly decreases CWND upon packet loss,which lowers effective throughput,regardless of the congestion origin.Furthermore,the uncoupled Slow-Start(SS)in MPQUIC leads to independent exponential CWND growth on each path,potentially causing buffer overflow.To address these issues,we propose the CC-OLIA,which incorporates Packet Loss Classifcation(PLC)and Coupled Slow-Start(CSS).The PLC distinguishes between congestion-induced and random packet losses,adjusting CWND reduction accordingly to maintain throughput.Concurrently,the CSS module coordinates CWND growth during the SS,preventing abrupt increases.Implementation on MININET shows that CC-OLIA not only maintains fair performance but also enhances transmission efficiency across diverse network conditions.
基金financially supported by the National Natural Science Foundation of China(Grant No.52071337)the Research Initiation Funds of Zhejiang University of Science and Technology(Grant No.F701102N06)+2 种基金the High-tech Ship Research Projects Sponsored by MIIT(Grant No.CBG2N21-4-2-5)the National Key Research and Development Program of China(Grant No.2022YFC2806300)the Marine Economy Development(Six Marine Industries)Special Foundation of the Department of Natural Resources of Guangdong Province(Grant No.GDNRC[2023]50).
文摘The deepwater subsea wellhead(SW)system is the foundation for the construction of oil and gas wells and the crucial channel for operation.During riser connection operation,the SW system is subjected to cyclic dynamic loads which cause fatigue damage to the SW system,and continuously accumulated fatigue damage leads to fatigue failure of the SW system,rupture,and even blowout accidents.This paper proposes a hybrid Bayesian network(HBN)-based dynamic reliability assessment approach for deepwater SW systems during their service life.In the proposed approach,the relationship between the accumulation of fatigue damage and the fatigue failure probability of the SW system is predicted,only considering normal conditions.The HBN model,which includes the accumulation of fatigue damage under normal conditions and the other factors affecting the fatigue of the SW system,is subsequently developed.When predictive and diagnostic analysis techniques are adopted,the dynamic reliability of the SW system is achieved,and the most influential factors are determined.Finally,corresponding safety control measures are proposed to improve the reliability of the SW system effectively.The results illustrate that the fatigue failure speed increases rapidly when the accumulation fatigue damage is larger than 0.45 under normal conditions and that the reliability of the SW system is larger than 94%within the design life.
