The rise of time-sensitive applications with broad geographical scope drives the development of time-sensitive networking(TSN)from intra-domain to inter-domain to ensure overall end-to-end connectivity requirements in...The rise of time-sensitive applications with broad geographical scope drives the development of time-sensitive networking(TSN)from intra-domain to inter-domain to ensure overall end-to-end connectivity requirements in heterogeneous deployments.When multiple TSN networks interconnect over non-TSN networks,all devices in the network need to be syn-chronized by sharing a uniform time reference.How-ever,most non-TSN networks are best-effort.Path delay asymmetry and random noise accumulation can introduce unpredictable time errors during end-to-end time synchronization.These factors can degrade syn-chronization performance.Therefore,cross-domain time synchronization becomes a challenging issue for multiple TSN networks interconnected by non-TSN networks.This paper presents a cross-domain time synchronization scheme that follows the software-defined TSN(SD-TSN)paradigm.It utilizes a com-bined control plane constructed by a coordinate con-troller and a domain controller for centralized control and management of cross-domain time synchroniza-tion.The general operation flow of the cross-domain time synchronization process is designed.The mecha-nism of cross-domain time synchronization is revealed by introducing a synchronization model and an error compensation method.A TSN cross-domain proto-type testbed is constructed for verification.Results show that the scheme can achieve end-to-end high-precision time synchronization with accuracy and sta-bility.展开更多
Time synchronization is a prerequisite for ensuring determinism in time-sensitive networking(TSN).While time synchronization errors cannot be overlooked,pursuing minimal time errors may incur unnecessary costs.Using c...Time synchronization is a prerequisite for ensuring determinism in time-sensitive networking(TSN).While time synchronization errors cannot be overlooked,pursuing minimal time errors may incur unnecessary costs.Using complex network theory,this study proposes a hierarchy for TSN and introduces the concept of bounded time error.A coupling model between traffic scheduling and time synchronization is established,deriving functional relationships among end-to-end delay,delay jitter,gate window,and time error.These relationships illustrate that time errors can trigger jumps in delay and delay jitter.To evaluate different time errors impact on traffic scheduling performance,an end-to-end transmission experiment scheme is designed,along with the construction of a TSN test platform implementing two representative cases.Case A is a closed TSN domain scenario with pure TSN switches emulating closed factory floor network.Case B depicts remote factory interconnection where TSN domains link via non-TSN domains composed of OpenFlow switches.Results from Case A show that delay and delay jitter on a single node are most significantly affected by time errors,up to one gating cycle.End-to-end delay jitter tends to increase with the number of hops.When the ratio of time error bound to window exceeds 10%,the number of schedulable traffic flows decreases rapidly.Case B reveals that when time error is below 1μs,the number of schedulable traffic flows begins to increase significantly,approaching full schedulability at errors below 0.6μs.展开更多
Time-Sensitive Network(TSN)with deterministic transmission capability is increasingly used in many emerging fields.It mainly guarantees the Quality of Service(QoS)of applications with strict requirements on time and s...Time-Sensitive Network(TSN)with deterministic transmission capability is increasingly used in many emerging fields.It mainly guarantees the Quality of Service(QoS)of applications with strict requirements on time and security.One of the core features of TSN is traffic scheduling with bounded low delay in the network.However,traffic scheduling schemes in TSN are usually synthesized offline and lack dynamism.To implement incremental scheduling of newly arrived traffic in TSN,we propose a Dynamic Response Incremental Scheduling(DR-IS)method for time-sensitive traffic and deploy it on a software-defined time-sensitive network architecture.Under the premise of meeting the traffic scheduling requirements,we adopt two modes,traffic shift and traffic exchange,to dynamically adjust the time slot injection position of the traffic in the original scheme,and determine the sending offset time of the new timesensitive traffic to minimize the global traffic transmission jitter.The evaluation results show that DRIS method can effectively control the large increase of traffic transmission jitter in incremental scheduling without affecting the transmission delay,thus realizing the dynamic incremental scheduling of time-sensitive traffic in TSN.展开更多
Time-sensitive networking(TSN)is an important research area for updating the infrastructure of industrial Internet of Things.As a product of the integration of the operation technology(OT)and the information technolog...Time-sensitive networking(TSN)is an important research area for updating the infrastructure of industrial Internet of Things.As a product of the integration of the operation technology(OT)and the information technology(IT),it meets the real-time and deterministic nature of industrial control and is compatible with Ethernet to support the mixed transmission of industrial control data and Ethernet data.This paper systematically summarizes and analyzes the shortcomings of the current mixed transmission technologies of the bursty flows and the periodic flows.To conquer these shortages,we propose a predictive mixed-transmission scheme of the bursty flows and the periodic flows.The core idea is to use the predictability of timetriggered transmission of TSN to further reduce bandwidth loss of the previous mixed-transmission methods.This paper formalizes the probabilistic model of the predictive mixed transmission mechanism and proves that the proposed mecha⁃nism can effectively reduce the loss of bandwidth.Finally,based on the formalized probabilistic model,we simulate the bandwidth loss of the proposed mechanism.The results demonstrate that compared with the previous mixed-transmission method,the bandwidth loss of the pro⁃posed mechanism achieves a 79.48%reduction on average.展开更多
Time-sensitive networks(TSNs)support not only traditional best-effort communications but also deterministic communications,which send each packet at a deterministic time so that the data transmissions of networked con...Time-sensitive networks(TSNs)support not only traditional best-effort communications but also deterministic communications,which send each packet at a deterministic time so that the data transmissions of networked control systems can be precisely scheduled to guarantee hard real-time constraints.No-wait scheduling is suitable for such TSNs and generates the schedules of deterministic communications with the minimal network resources so that all of the remaining resources can be used to improve the throughput of best-effort communications.However,due to inappropriate message fragmentation,the realtime performance of no-wait scheduling algorithms is reduced.Therefore,in this paper,joint algorithms of message fragmentation and no-wait scheduling are proposed.First,a specification for the joint problem based on optimization modulo theories is proposed so that off-the-shelf solvers can be used to find optimal solutions.Second,to improve the scalability of our algorithm,the worst-case delay of messages is analyzed,and then,based on the analysis,a heuristic algorithm is proposed to construct low-delay schedules.Finally,we conduct extensive test cases to evaluate our proposed algorithms.The evaluation results indicate that,compared to existing algorithms,the proposed joint algorithm improves schedulability by up to 50%.展开更多
Deterministic transmission plays a vital role in industrial networks.The time-sensitive network(TSN)protocol family offers a promising paradigm for transmitting time-critical data.To achieve low latency and high Quali...Deterministic transmission plays a vital role in industrial networks.The time-sensitive network(TSN)protocol family offers a promising paradigm for transmitting time-critical data.To achieve low latency and high Quality of Service(QoS)in TSN,appropriate data flow scheduling is needed under the given network topology and data flow requirements to fully utilize the potential of TSN.Both time-triggered flows and sporadic flows can carry high-priority data and need to be considered jointly to eliminate the effects of each other.To this end,in this work,we investigate the challenging mixed-flow scheduling problem and propose a novel diffusion-based algorithm,DiffTSN,to solve the joint routing and scheduling problem of mixed flows.We transform the sporadic flows into probabilistic flows and design certain mechanisms to fit the nature of these probabilistic flows.For routing,we transform the problem into a diffusion policy and constraint denoising process with a value guide to achieve a better routing policy.For scheduling,we adopt a first-valid-time-slot algorithm to determine the start transmission time of the flows.We train and evaluate DiffTSN in our TSN simulator.Experiments show that DiffTSN outperforms state-of-the-art algorithms in various metrics.展开更多
In the upcoming sixth-generation(6G)era,the demand for constructing a wide-area time-sensitive Internet of Things(IoT)continues to increase.As conventional cellular technologies are difficult to directly use for wide-...In the upcoming sixth-generation(6G)era,the demand for constructing a wide-area time-sensitive Internet of Things(IoT)continues to increase.As conventional cellular technologies are difficult to directly use for wide-area time-sensitive IoT,it is beneficial to use non-terrestrial infrastructures,including satellites and unmanned aerial vehicles(UAVs).Thus,we can build a non-terrestrial network(NTN)using a cell-free architecture.Driven by the time-sensitive requirements and uneven distribution of IoT devices,the NTN must be empowered using mobile edge computing(MEC)while providing oasisoriented on-demand coverage for devices.Nevertheless,communication and MEC systems are coupled with each other under the influence of a complex propagation environment in the MEC-empowered NTN,which makes it difficult to coordinate the resources.In this study,we propose a process-oriented framework to design communication and MEC systems in a time-division manner.In this framework,large-scale channel state information(CSI)is used to characterize the complex propagation environment at an affordable cost,where a nonconvex latency minimization problem is formulated.Subsequently,the approximated problem is provided,and it can be decomposed into sub-problems.These sub-problems are then solved iteratively.The simulation results demonstrated the superiority of the proposed process-oriented scheme over other algorithms,implied that the payload deployments of UAVs should be appropriately predesigned to improve the efficiency of using resources,and confirmed that it is advantageous to integrate NTN with MEC for wide-area time-sensitive IoT.展开更多
Accurate time synchronization is fundamental to the correct and efficient operation of Wireless Sensor Networks(WSNs),especially in security-critical,time-sensitive applications.However,most existing protocols degrade...Accurate time synchronization is fundamental to the correct and efficient operation of Wireless Sensor Networks(WSNs),especially in security-critical,time-sensitive applications.However,most existing protocols degrade substantially under malicious interference.We introduce iSTSP,an Intelligent and Secure Time Synchronization Protocol that implements a four-stage defense pipeline to ensure robust,precise synchronization even in hostile environments:(1)trust preprocessing that filters node participation using behavioral trust scoring;(2)anomaly isolation employing a lightweight autoencoder to detect and excise malicious nodes in real time;(3)reliability-weighted consensus that prioritizes high-trust nodes during time aggregation;and(4)convergence-optimized synchronization that dynamically adjusts parameters using theoretical stability bounds.We provide rigorous convergence analysis including a closed-form expression for convergence time,and validate the protocol through both simulations and realworld experiments on a controlled 16-node testbed.Under Sybil attacks with five malicious nodes within this testbed,iSTSP maintains synchronization error increases under 12%and achieves a rapid convergence.Compared to state-ofthe-art protocols like TPSN,SE-FTSP,and MMAR-CTS,iSTSP offers 60%faster detection,broader threat coverage,and more than 7 times lower synchronization error,with a modest 9.3%energy overhead over 8 h.We argue this is an acceptable trade-off for mission-critical deployments requiring guaranteed security.These findings demonstrate iSTSP’s potential as a reliable solution for secure WSN synchronization and motivate future work on large-scale IoT deployments and integration with energy-efficient communication protocols.展开更多
The automatic stealth task of military time-sensitive targets plays a crucial role in maintaining national military security and mastering battlefield dynamics in military applications.We propose a novel Military Time...The automatic stealth task of military time-sensitive targets plays a crucial role in maintaining national military security and mastering battlefield dynamics in military applications.We propose a novel Military Time-sensitive Targets Stealth Network via Real-time Mask Generation(MTTSNet).According to our knowledge,this is the first technology to automatically remove military targets in real-time from videos.The critical steps of MTTSNet are as follows:First,we designed a real-time mask generation network based on the encoder-decoder framework,combined with the domain expansion structure,to effectively extract mask images.Specifically,the ASPP structure in the encoder could achieve advanced semantic feature fusion.The decoder stacked high-dimensional information with low-dimensional information to obtain an effective mask layer.Subsequently,the domain expansion module guided the adaptive expansion of mask images.Second,a context adversarial generation network based on gated convolution was constructed to achieve background restoration of mask positions in the original image.In addition,our method worked in an end-to-end manner.A particular semantic segmentation dataset for military time-sensitive targets has been constructed,called the Military Time-sensitive Target Masking Dataset(MTMD).The MTMD dataset experiment successfully demonstrated that this method could create a mask that completely occludes the target and that the target could be hidden in real time using this mask.We demonstrated the concealment performance of our proposed method by comparing it to a number of well-known and highly optimized baselines.展开更多
Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combinin...Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.展开更多
To explore the material basis and mechanisms of the anti-inflammatory effects of Hibiscus mutabilis L..The active ingredients and potential targets of Hibiscus mutabilis L.were obtained through the literature review a...To explore the material basis and mechanisms of the anti-inflammatory effects of Hibiscus mutabilis L..The active ingredients and potential targets of Hibiscus mutabilis L.were obtained through the literature review and SwissADME platform.Genes related to the inflammation were collected using Genecards and OMIM databases,and the intersection genes were submitted on STRING and DAVID websites.Then,the protein interaction network(PPI),gene ontology(GO)and pathway(KEGG)were analyzed.Cytoscape 3.7.2 software was used to construct the“Hibiscus mutabilis L.-active ingredient-target-inflammation”network diagram,and AutoDockTools-1.5.6 software was used for the molecular docking verification.The antiinflammatory effect of Hibiscus mutabilis L.active ingredient was verified by the RAW264.7 inflammatory cell model.The results showed that 11 active components and 94 potential targets,1029 inflammatory targets and 24 intersection targets were obtained from Hibiscus mutabilis L..The key anti-inflammatory active ingredients of Hibiscus mutabilis L.are quercetin,apigenin and luteolin.Its action pathway is mainly related to NF-κB,cancer pathway and TNF signaling pathway.Cell experiments showed that total flavonoids of Hibiscus mutabilis L.could effectively inhibit the expression of tumor necrosis factor(TNF-α),interleukin 8(IL-8)and epidermal growth factor receptor(EGFR)in LPS-induced RAW 264.7 inflammatory cells.It also downregulates the phosphorylation of human nuclear factor ĸB inhibitory protein α(IĸBα)and NF-κB p65 subunit protein(p65).Overall,the anti-inflammatory effect of Hibiscus mutabilis L.is related to many active components,many signal pathways and targets,which provides a theoretical basis for its further development and application.展开更多
The existence of absorption and reflection of light underwater leads to problems such as color distortion and blue-green bias in underwater images.In this study,a depthwise separable convolution-based generative adver...The existence of absorption and reflection of light underwater leads to problems such as color distortion and blue-green bias in underwater images.In this study,a depthwise separable convolution-based generative adversarial network(GAN)algorithm was proposed.Taking GAN as the basic framework,it combined a depthwise separable convolution module,attention mechanism,and reconstructed convolution module to realize the enhancement of underwater degraded images.Multi-scale features were captured by the depthwise separable convolution module,and the attention mechanism was utilized to enhance attention to important features.The reconstructed convolution module further extracts and fuses local and global features.Experimental results showed that the algorithm performs well in improving the color bias and blurring of underwater images,with PSNR reaching 27.835,SSIM reaching 0.883,UIQM reaching 3.205,and UCIQE reaching 0.713.The enhanced image outperforms the comparison algorithm in both subjective and objective metrics.展开更多
Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)t...Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.展开更多
Networked predictive control(NPC) has gained significant attention in recent years for its ability to effectively and actively address communication constraints in networked control systems(NCSs),such as network-induc...Networked predictive control(NPC) has gained significant attention in recent years for its ability to effectively and actively address communication constraints in networked control systems(NCSs),such as network-induced delays,packet dropouts,and packet disorders.Despite significant advancements,the increasing complexity and dynamism of network environments,along with the growing complexity of systems,pose new challenges for NPC.These challenges include difficulties in system modeling,cyber attacks,component faults,limited network bandwidth,and the necessity for distributed collaboration.This survey aims to provide a comprehensive review of NPC strategies.It begins with a summary of the primary challenges faced by NCSs,followed by an introduction to the control structure and core concepts of NPC.The survey then discusses several typical NPC schemes and examines their extensions in the areas of secure control,fault-tolerant control,distributed coordinated control,and event-triggered control.Moreover,it reviews notable works that have implemented these schemes.Finally,the survey concludes by exploring typical applications of NPC schemes and highlighting several challenging issues that could guide future research efforts.展开更多
A multi-stimuli-responsive hydrogel,P(VI-co-MAAC-NE),was successfully constructed by covalently integrating the aggregation-induced emission(AIE)moiety(Z)-N-(4-(1-cyano-2-(4-(diethylamino)phenyl)vinyl)-phenyl)methacry...A multi-stimuli-responsive hydrogel,P(VI-co-MAAC-NE),was successfully constructed by covalently integrating the aggregation-induced emission(AIE)moiety(Z)-N-(4-(1-cyano-2-(4-(diethylamino)phenyl)vinyl)-phenyl)methacrylamide(NE)into a dynamic hydrogen-bonding network composed of 1-vinylimidazole(VI)and methacrylic acid(MAAC)groups.The dense hydrogen-bonding network not only provides enhanced mechanical robustness,but also significantly enhances the AIE effect of NE by restricting its molecular motion.Under various external stimuli,the hydrogen bonds within the hydrogel network undergo reversible dissociation and reformation,thus enabling synergistic modulation of the hydrogel’s mechanical properties and luminescence behavior.Specifically,organic solvents disrupt the hydrogen-bonding network and the aggregation of the AIE moiety NE,resulting in macroscopic swelling and fluorescence quenching of the hydrogel.In strongly acidic conditions,protonation of NE molecules suppresses the intramolecular charge transfer(ICT)process,yielding a blue-shifted emission band accompanied by intense blue fluorescence;in highly alkaline environments,deprotonation of carboxyl groups induces hydrogel swelling and disperses NE aggregates,leading to pronounced fluorescence quenching.Moreover,the system exhibits thermally activated shape-memory behavior:heating above the glass transition temperature(T_(g):ca.62℃)softens the hydrogel to allow programmable reshaping,and subsequent hydrogen bond reformation at ambient conditions locks in the resultant geometries without sacrificing the hydrogel’s fluorescence performance.By capitalizing on these multi-stimuli-responsive characteristics and shape-memory behavior,the potential of hydrogel P(VI-co-MAAC-NE)for advanced information encryption and anti-counterfeiting applications is demonstrated.This work not only provides a versatile material platform for sensing and information storage,but also offers new insights into the design of intelligent soft materials integrating AIE features with dynamically regulated supramolecular network structures.展开更多
Urbanization is a significant driver of the loss of biodiversity and the disruption of ecosystems.Amphibians are especially vulnerable to the negative impact of urbanization as their life cycles and habitat requiremen...Urbanization is a significant driver of the loss of biodiversity and the disruption of ecosystems.Amphibians are especially vulnerable to the negative impact of urbanization as their life cycles and habitat requirements are complex.The present study investigated the effects of urbanization on amphibian predation networks in suburban Kunming in Yunnan,China and aimed to understand how predation network structure and stability vary with urbanization level.We constructed predation networks by analyzing the stomach contents of amphibians from 12d istinct urbanization gradients.We used the bipartite package in R to evaluate network robustness metrics such as modularity,nestedness,connectivity,and average shortest path length(ASPL).We found that urbanization level is negatively correlated with predation network connectivity(R=−0.67,Ρ=0.02),but there were no significant correlations between urbanization level and nestedness,modularity,or ASPL.Removal of the keystone species destabilized the predation networks at certain locations.The present work highlighted that maintaining prey quantity and diversity preserves predation network connectivity and stabilizes the overall network in urbanizing landscapes.It also underscored the critical role that keystone species play in sustaining network robustness.The results of this research provided insights into the ecological consequences of urbanization.They also suggested that conservation measures should protect the key species and habitats of amphibian predation networks and mitigate the negative impact of urban development on them.展开更多
In the process of programmable networks simplifying network management and increasing network flexibility through custom packet behavior,security incidents caused by human logic errors are seriously threatening their ...In the process of programmable networks simplifying network management and increasing network flexibility through custom packet behavior,security incidents caused by human logic errors are seriously threatening their safe operation,robust verificationmethods are required to ensure their correctness.As one of the formalmethods,symbolic execution offers a viable approach for verifying programmable networks by systematically exploring all possible paths within a program.However,its application in this field encounters scalability issues due to path explosion and complex constraint-solving.Therefore,in this paper,we propose NetVerifier,a scalable verification system for programmable networks.Tomitigate the path explosion issue,we developmultiple pruning strategies that strategically eliminate irrelevant execution paths while preserving verification integrity by precisely identifying the execution paths related to the verification purpose.To address the complex constraint-solving problem,we introduce an execution results reuse solution to avoid redundant computation of the same constraints.To apply these solutions intelligently,a matching algorithm is implemented to automatically select appropriate solutions based on the characteristics of the verification requirement.Moreover,Language Aided Verification(LAV),an assertion language,is designed to express verification intentions in a concise form.Experimental results on diverse open-source programs of varying scales demonstrate NetVerifier’s improvement in scalability and effectiveness in identifying potential network errors.In the best scenario,compared with ASSERT-P4,NetVerifier reduced the execution path,verification time,and memory occupation of the verification process by 99.92%,94.76%,and 65.19%,respectively.展开更多
Network pharmacology provides a transformative framework for decoding multi-target,system-level mechanisms of the foodmedicine homology(FMH)substances,overcoming the limitations of reductionist approaches by integrati...Network pharmacology provides a transformative framework for decoding multi-target,system-level mechanisms of the foodmedicine homology(FMH)substances,overcoming the limitations of reductionist approaches by integrating multi-omics data,computational modeling,and network analysis.Central to this paradigm is the“Network Targets”theory,which conceptualizes therapeutic intervention as the reconfiguration of disease-associated biological networks rather than the modulation of isolated single targets.Artificial intelligence accelerates this process by enabling high-dimensional data integration,predictive modeling of synergistic combinations,and the identification of active constituents.This review outlines the key databases and computational tools that operationalize network pharmacology in FMH research and systematically categorizes their applications,including material screening,ingredient identification,synergy analysis,quality standard establishment,safety assessment,formula optimization,functional food discovery,and personalized recommendation,supported by experimental validation across numerous FMH items.Despite the challenges in data standardization and dynamic modeling,the integration of multi-omics,dynamic networks,and centralized repositories will further advance the field.Ultimately,network pharmacology will bridge traditional FMH wisdom with contemporary mechanistic rigor,positioning FMH as the cornerstone of precision nutrition and preventive medicine.展开更多
Near-infrared image sensors are widely used in fields such as material identification,machine vision,and autonomous driving.Lead sulfide colloidal quantum dot-based infrared photodiodes can be integrated with sil⁃icon...Near-infrared image sensors are widely used in fields such as material identification,machine vision,and autonomous driving.Lead sulfide colloidal quantum dot-based infrared photodiodes can be integrated with sil⁃icon-based readout circuits in a single step.Based on this,we propose a photodiode based on an n-i-p structure,which removes the buffer layer and further simplifies the manufacturing process of quantum dot image sensors,thus reducing manufacturing costs.Additionally,for the noise complexity in quantum dot image sensors when capturing images,traditional denoising and non-uniformity methods often do not achieve optimal denoising re⁃sults.For the noise and stripe-type non-uniformity commonly encountered in infrared quantum dot detector imag⁃es,a network architecture has been developed that incorporates multiple key modules.This network combines channel attention and spatial attention mechanisms,dynamically adjusting the importance of feature maps to en⁃hance the ability to distinguish between noise and details.Meanwhile,the residual dense feature fusion module further improves the network's ability to process complex image structures through hierarchical feature extraction and fusion.Furthermore,the pyramid pooling module effectively captures information at different scales,improv⁃ing the network's multi-scale feature representation ability.Through the collaborative effect of these modules,the network can better handle various mixed noise and image non-uniformity issues.Experimental results show that it outperforms the traditional U-Net network in denoising and image correction tasks.展开更多
Identifying the community structure of complex networks is crucial to extracting insights and understanding network properties.Although several community detection methods have been proposed,many are unsuitable for so...Identifying the community structure of complex networks is crucial to extracting insights and understanding network properties.Although several community detection methods have been proposed,many are unsuitable for social networks due to significant limitations.Specifically,most approaches depend mainly on user-user structural links while overlooking service-centric,semantic,and multi-attribute drivers of community formation,and they also lack flexible filtering mechanisms for large-scale,service-oriented settings.Our proposed approach,called community discovery-based service(CDBS),leverages user profiles and their interactions with consulted web services.The method introduces a novel similarity measure,global similarity interaction profile(GSIP),which goes beyond typical similarity measures by unifying user and service profiles for all attributes types into a coherent representation,thereby clarifying its novelty and contribution.It applies multiple filtering criteria related to user attributes,accessed services,and interaction patterns.Experimental comparisons against Louvain,Hierarchical Agglomerative Clustering,Label Propagation and Infomap show that CDBS reveals the higher performance as it achieves 0.74 modularity,0.13 conductance,0.77 coverage,and significantly fast response time of 9.8 s,even with 10,000 users and 400 services.Moreover,community discoverybased service consistently detects a larger number of communities with distinct topics of interest,underscoring its capacity to generate detailed and efficient structures in complex networks.These results confirm both the efficiency and effectiveness of the proposed method.Beyond controlled evaluation,communities discovery based service is applicable to targeted recommendations,group-oriented marketing,access control,and service personalization,where communities are shaped not only by user links but also by service engagement.展开更多
基金supported in part by National Key R&D Program of China(Grant No.2022YFC3803700)in part by the National Natural Science Foundation of China(Grant No.92067102)in part by the project of Beijing Laboratory of Advanced Information Networks.
文摘The rise of time-sensitive applications with broad geographical scope drives the development of time-sensitive networking(TSN)from intra-domain to inter-domain to ensure overall end-to-end connectivity requirements in heterogeneous deployments.When multiple TSN networks interconnect over non-TSN networks,all devices in the network need to be syn-chronized by sharing a uniform time reference.How-ever,most non-TSN networks are best-effort.Path delay asymmetry and random noise accumulation can introduce unpredictable time errors during end-to-end time synchronization.These factors can degrade syn-chronization performance.Therefore,cross-domain time synchronization becomes a challenging issue for multiple TSN networks interconnected by non-TSN networks.This paper presents a cross-domain time synchronization scheme that follows the software-defined TSN(SD-TSN)paradigm.It utilizes a com-bined control plane constructed by a coordinate con-troller and a domain controller for centralized control and management of cross-domain time synchroniza-tion.The general operation flow of the cross-domain time synchronization process is designed.The mecha-nism of cross-domain time synchronization is revealed by introducing a synchronization model and an error compensation method.A TSN cross-domain proto-type testbed is constructed for verification.Results show that the scheme can achieve end-to-end high-precision time synchronization with accuracy and sta-bility.
基金supported in part by the Science and Technology Research and Development Foundation of China Academy of Railway Sciences Corporation Limited(Grant No.2023YJ364)in part by National Key R&D Program of China(Grant No.2022YFC3803700)in part by the project of Beijing Laboratory of Advanced Information Networks.
文摘Time synchronization is a prerequisite for ensuring determinism in time-sensitive networking(TSN).While time synchronization errors cannot be overlooked,pursuing minimal time errors may incur unnecessary costs.Using complex network theory,this study proposes a hierarchy for TSN and introduces the concept of bounded time error.A coupling model between traffic scheduling and time synchronization is established,deriving functional relationships among end-to-end delay,delay jitter,gate window,and time error.These relationships illustrate that time errors can trigger jumps in delay and delay jitter.To evaluate different time errors impact on traffic scheduling performance,an end-to-end transmission experiment scheme is designed,along with the construction of a TSN test platform implementing two representative cases.Case A is a closed TSN domain scenario with pure TSN switches emulating closed factory floor network.Case B depicts remote factory interconnection where TSN domains link via non-TSN domains composed of OpenFlow switches.Results from Case A show that delay and delay jitter on a single node are most significantly affected by time errors,up to one gating cycle.End-to-end delay jitter tends to increase with the number of hops.When the ratio of time error bound to window exceeds 10%,the number of schedulable traffic flows decreases rapidly.Case B reveals that when time error is below 1μs,the number of schedulable traffic flows begins to increase significantly,approaching full schedulability at errors below 0.6μs.
基金supported by the Innovation Scientists and Technicians Troop Construction Projects of Henan Province(224000510002)。
文摘Time-Sensitive Network(TSN)with deterministic transmission capability is increasingly used in many emerging fields.It mainly guarantees the Quality of Service(QoS)of applications with strict requirements on time and security.One of the core features of TSN is traffic scheduling with bounded low delay in the network.However,traffic scheduling schemes in TSN are usually synthesized offline and lack dynamism.To implement incremental scheduling of newly arrived traffic in TSN,we propose a Dynamic Response Incremental Scheduling(DR-IS)method for time-sensitive traffic and deploy it on a software-defined time-sensitive network architecture.Under the premise of meeting the traffic scheduling requirements,we adopt two modes,traffic shift and traffic exchange,to dynamically adjust the time slot injection position of the traffic in the original scheme,and determine the sending offset time of the new timesensitive traffic to minimize the global traffic transmission jitter.The evaluation results show that DRIS method can effectively control the large increase of traffic transmission jitter in incremental scheduling without affecting the transmission delay,thus realizing the dynamic incremental scheduling of time-sensitive traffic in TSN.
基金sponsored in part by the National Key Research and Development Project under Grants Nos. 2018YFB1308601 and 2017YFE0119300the National Natural Science Foundation of China under Grant No. 62002013+1 种基金the Project funded by China Postdoctoral Science Foundation Grants Nos. 2019M660439 and 2020T130049the Industry-University-Research Cooperation Fund of ZTE Corporation.
文摘Time-sensitive networking(TSN)is an important research area for updating the infrastructure of industrial Internet of Things.As a product of the integration of the operation technology(OT)and the information technology(IT),it meets the real-time and deterministic nature of industrial control and is compatible with Ethernet to support the mixed transmission of industrial control data and Ethernet data.This paper systematically summarizes and analyzes the shortcomings of the current mixed transmission technologies of the bursty flows and the periodic flows.To conquer these shortages,we propose a predictive mixed-transmission scheme of the bursty flows and the periodic flows.The core idea is to use the predictability of timetriggered transmission of TSN to further reduce bandwidth loss of the previous mixed-transmission methods.This paper formalizes the probabilistic model of the predictive mixed transmission mechanism and proves that the proposed mecha⁃nism can effectively reduce the loss of bandwidth.Finally,based on the formalized probabilistic model,we simulate the bandwidth loss of the proposed mechanism.The results demonstrate that compared with the previous mixed-transmission method,the bandwidth loss of the pro⁃posed mechanism achieves a 79.48%reduction on average.
基金partially supported by National Key Research and Development Program of China(2018YFB1700200)National Natural Science Foundation of China(61972389,61903356,61803368,U1908212)+2 种基金Youth Innovation Promotion Association of the Chinese Academy of Sciences,National Science and Technology Major Project(2017ZX02101007-004)Liaoning Provincial Natural Science Foundation of China(2020-MS-034,2019-YQ-09)China Postdoctoral Science Foundation(2019M661156)。
文摘Time-sensitive networks(TSNs)support not only traditional best-effort communications but also deterministic communications,which send each packet at a deterministic time so that the data transmissions of networked control systems can be precisely scheduled to guarantee hard real-time constraints.No-wait scheduling is suitable for such TSNs and generates the schedules of deterministic communications with the minimal network resources so that all of the remaining resources can be used to improve the throughput of best-effort communications.However,due to inappropriate message fragmentation,the realtime performance of no-wait scheduling algorithms is reduced.Therefore,in this paper,joint algorithms of message fragmentation and no-wait scheduling are proposed.First,a specification for the joint problem based on optimization modulo theories is proposed so that off-the-shelf solvers can be used to find optimal solutions.Second,to improve the scalability of our algorithm,the worst-case delay of messages is analyzed,and then,based on the analysis,a heuristic algorithm is proposed to construct low-delay schedules.Finally,we conduct extensive test cases to evaluate our proposed algorithms.The evaluation results indicate that,compared to existing algorithms,the proposed joint algorithm improves schedulability by up to 50%.
基金supported by the Guangdong Science and Technology Program under Grant Nos.2024B0101040007 and 2024B0101020004the Guangdong Basic and Applied Basic Research Foundation under Grant No.2023B1515120058+5 种基金the National Science Foundation of China under Grant No.62172455the Guangdong Science and Technology Department Pearl River Talent Program under Grant No.2019QN01X140the Guangzhou Basic and Applied Basic Research Program under Grant No.2024A04J6367the Fundamental Research Funds for the Central Universities of China under Grant No.24qnpy138the Program for Guangdong Introducing Innovative and Entrepreneurial Teams under Grant No.2017ZT07X355the Department of Science and Technology of Guangdong Province of China under Grant No.2022A0505050028.
文摘Deterministic transmission plays a vital role in industrial networks.The time-sensitive network(TSN)protocol family offers a promising paradigm for transmitting time-critical data.To achieve low latency and high Quality of Service(QoS)in TSN,appropriate data flow scheduling is needed under the given network topology and data flow requirements to fully utilize the potential of TSN.Both time-triggered flows and sporadic flows can carry high-priority data and need to be considered jointly to eliminate the effects of each other.To this end,in this work,we investigate the challenging mixed-flow scheduling problem and propose a novel diffusion-based algorithm,DiffTSN,to solve the joint routing and scheduling problem of mixed flows.We transform the sporadic flows into probabilistic flows and design certain mechanisms to fit the nature of these probabilistic flows.For routing,we transform the problem into a diffusion policy and constraint denoising process with a value guide to achieve a better routing policy.For scheduling,we adopt a first-valid-time-slot algorithm to determine the start transmission time of the flows.We train and evaluate DiffTSN in our TSN simulator.Experiments show that DiffTSN outperforms state-of-the-art algorithms in various metrics.
基金the National Key R&D Program of China(2018YFA0701601 and 2020YFA0711301)the National Natural Science Foundation of China(61771286,61941104,and 61922049)the Tsinghua University-China Mobile Communications Group Co.,Ltd.Joint Institute.
文摘In the upcoming sixth-generation(6G)era,the demand for constructing a wide-area time-sensitive Internet of Things(IoT)continues to increase.As conventional cellular technologies are difficult to directly use for wide-area time-sensitive IoT,it is beneficial to use non-terrestrial infrastructures,including satellites and unmanned aerial vehicles(UAVs).Thus,we can build a non-terrestrial network(NTN)using a cell-free architecture.Driven by the time-sensitive requirements and uneven distribution of IoT devices,the NTN must be empowered using mobile edge computing(MEC)while providing oasisoriented on-demand coverage for devices.Nevertheless,communication and MEC systems are coupled with each other under the influence of a complex propagation environment in the MEC-empowered NTN,which makes it difficult to coordinate the resources.In this study,we propose a process-oriented framework to design communication and MEC systems in a time-division manner.In this framework,large-scale channel state information(CSI)is used to characterize the complex propagation environment at an affordable cost,where a nonconvex latency minimization problem is formulated.Subsequently,the approximated problem is provided,and it can be decomposed into sub-problems.These sub-problems are then solved iteratively.The simulation results demonstrated the superiority of the proposed process-oriented scheme over other algorithms,implied that the payload deployments of UAVs should be appropriately predesigned to improve the efficiency of using resources,and confirmed that it is advantageous to integrate NTN with MEC for wide-area time-sensitive IoT.
基金this project under Geran Putra Inisiatif(GPI)with reference of GP-GPI/2023/976210。
文摘Accurate time synchronization is fundamental to the correct and efficient operation of Wireless Sensor Networks(WSNs),especially in security-critical,time-sensitive applications.However,most existing protocols degrade substantially under malicious interference.We introduce iSTSP,an Intelligent and Secure Time Synchronization Protocol that implements a four-stage defense pipeline to ensure robust,precise synchronization even in hostile environments:(1)trust preprocessing that filters node participation using behavioral trust scoring;(2)anomaly isolation employing a lightweight autoencoder to detect and excise malicious nodes in real time;(3)reliability-weighted consensus that prioritizes high-trust nodes during time aggregation;and(4)convergence-optimized synchronization that dynamically adjusts parameters using theoretical stability bounds.We provide rigorous convergence analysis including a closed-form expression for convergence time,and validate the protocol through both simulations and realworld experiments on a controlled 16-node testbed.Under Sybil attacks with five malicious nodes within this testbed,iSTSP maintains synchronization error increases under 12%and achieves a rapid convergence.Compared to state-ofthe-art protocols like TPSN,SE-FTSP,and MMAR-CTS,iSTSP offers 60%faster detection,broader threat coverage,and more than 7 times lower synchronization error,with a modest 9.3%energy overhead over 8 h.We argue this is an acceptable trade-off for mission-critical deployments requiring guaranteed security.These findings demonstrate iSTSP’s potential as a reliable solution for secure WSN synchronization and motivate future work on large-scale IoT deployments and integration with energy-efficient communication protocols.
基金supported in part by the National Natural Science Foundation of China(Grant No.62276274)Shaanxi Natural Science Foundation(Grant No.2023-JC-YB-528)Chinese aeronautical establishment(Grant No.201851U8012)。
文摘The automatic stealth task of military time-sensitive targets plays a crucial role in maintaining national military security and mastering battlefield dynamics in military applications.We propose a novel Military Time-sensitive Targets Stealth Network via Real-time Mask Generation(MTTSNet).According to our knowledge,this is the first technology to automatically remove military targets in real-time from videos.The critical steps of MTTSNet are as follows:First,we designed a real-time mask generation network based on the encoder-decoder framework,combined with the domain expansion structure,to effectively extract mask images.Specifically,the ASPP structure in the encoder could achieve advanced semantic feature fusion.The decoder stacked high-dimensional information with low-dimensional information to obtain an effective mask layer.Subsequently,the domain expansion module guided the adaptive expansion of mask images.Second,a context adversarial generation network based on gated convolution was constructed to achieve background restoration of mask positions in the original image.In addition,our method worked in an end-to-end manner.A particular semantic segmentation dataset for military time-sensitive targets has been constructed,called the Military Time-sensitive Target Masking Dataset(MTMD).The MTMD dataset experiment successfully demonstrated that this method could create a mask that completely occludes the target and that the target could be hidden in real time using this mask.We demonstrated the concealment performance of our proposed method by comparing it to a number of well-known and highly optimized baselines.
文摘Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.
文摘To explore the material basis and mechanisms of the anti-inflammatory effects of Hibiscus mutabilis L..The active ingredients and potential targets of Hibiscus mutabilis L.were obtained through the literature review and SwissADME platform.Genes related to the inflammation were collected using Genecards and OMIM databases,and the intersection genes were submitted on STRING and DAVID websites.Then,the protein interaction network(PPI),gene ontology(GO)and pathway(KEGG)were analyzed.Cytoscape 3.7.2 software was used to construct the“Hibiscus mutabilis L.-active ingredient-target-inflammation”network diagram,and AutoDockTools-1.5.6 software was used for the molecular docking verification.The antiinflammatory effect of Hibiscus mutabilis L.active ingredient was verified by the RAW264.7 inflammatory cell model.The results showed that 11 active components and 94 potential targets,1029 inflammatory targets and 24 intersection targets were obtained from Hibiscus mutabilis L..The key anti-inflammatory active ingredients of Hibiscus mutabilis L.are quercetin,apigenin and luteolin.Its action pathway is mainly related to NF-κB,cancer pathway and TNF signaling pathway.Cell experiments showed that total flavonoids of Hibiscus mutabilis L.could effectively inhibit the expression of tumor necrosis factor(TNF-α),interleukin 8(IL-8)and epidermal growth factor receptor(EGFR)in LPS-induced RAW 264.7 inflammatory cells.It also downregulates the phosphorylation of human nuclear factor ĸB inhibitory protein α(IĸBα)and NF-κB p65 subunit protein(p65).Overall,the anti-inflammatory effect of Hibiscus mutabilis L.is related to many active components,many signal pathways and targets,which provides a theoretical basis for its further development and application.
文摘The existence of absorption and reflection of light underwater leads to problems such as color distortion and blue-green bias in underwater images.In this study,a depthwise separable convolution-based generative adversarial network(GAN)algorithm was proposed.Taking GAN as the basic framework,it combined a depthwise separable convolution module,attention mechanism,and reconstructed convolution module to realize the enhancement of underwater degraded images.Multi-scale features were captured by the depthwise separable convolution module,and the attention mechanism was utilized to enhance attention to important features.The reconstructed convolution module further extracts and fuses local and global features.Experimental results showed that the algorithm performs well in improving the color bias and blurring of underwater images,with PSNR reaching 27.835,SSIM reaching 0.883,UIQM reaching 3.205,and UCIQE reaching 0.713.The enhanced image outperforms the comparison algorithm in both subjective and objective metrics.
文摘Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.
基金supported by the National Natural Science Foundation of China(62173002,62403235,62403010,52301408,62173255)the Beijing Natural Science Foundation(L241015,4222045)+2 种基金the Yuxiu Innovation Project of NCUT(2024NCUTYXCX111)the China Postdoctoral Science Foundation(2025T180466)the Beijing Postdoctoral Research Foundation(2025-ZZ-70)。
文摘Networked predictive control(NPC) has gained significant attention in recent years for its ability to effectively and actively address communication constraints in networked control systems(NCSs),such as network-induced delays,packet dropouts,and packet disorders.Despite significant advancements,the increasing complexity and dynamism of network environments,along with the growing complexity of systems,pose new challenges for NPC.These challenges include difficulties in system modeling,cyber attacks,component faults,limited network bandwidth,and the necessity for distributed collaboration.This survey aims to provide a comprehensive review of NPC strategies.It begins with a summary of the primary challenges faced by NCSs,followed by an introduction to the control structure and core concepts of NPC.The survey then discusses several typical NPC schemes and examines their extensions in the areas of secure control,fault-tolerant control,distributed coordinated control,and event-triggered control.Moreover,it reviews notable works that have implemented these schemes.Finally,the survey concludes by exploring typical applications of NPC schemes and highlighting several challenging issues that could guide future research efforts.
文摘A multi-stimuli-responsive hydrogel,P(VI-co-MAAC-NE),was successfully constructed by covalently integrating the aggregation-induced emission(AIE)moiety(Z)-N-(4-(1-cyano-2-(4-(diethylamino)phenyl)vinyl)-phenyl)methacrylamide(NE)into a dynamic hydrogen-bonding network composed of 1-vinylimidazole(VI)and methacrylic acid(MAAC)groups.The dense hydrogen-bonding network not only provides enhanced mechanical robustness,but also significantly enhances the AIE effect of NE by restricting its molecular motion.Under various external stimuli,the hydrogen bonds within the hydrogel network undergo reversible dissociation and reformation,thus enabling synergistic modulation of the hydrogel’s mechanical properties and luminescence behavior.Specifically,organic solvents disrupt the hydrogen-bonding network and the aggregation of the AIE moiety NE,resulting in macroscopic swelling and fluorescence quenching of the hydrogel.In strongly acidic conditions,protonation of NE molecules suppresses the intramolecular charge transfer(ICT)process,yielding a blue-shifted emission band accompanied by intense blue fluorescence;in highly alkaline environments,deprotonation of carboxyl groups induces hydrogel swelling and disperses NE aggregates,leading to pronounced fluorescence quenching.Moreover,the system exhibits thermally activated shape-memory behavior:heating above the glass transition temperature(T_(g):ca.62℃)softens the hydrogel to allow programmable reshaping,and subsequent hydrogen bond reformation at ambient conditions locks in the resultant geometries without sacrificing the hydrogel’s fluorescence performance.By capitalizing on these multi-stimuli-responsive characteristics and shape-memory behavior,the potential of hydrogel P(VI-co-MAAC-NE)for advanced information encryption and anti-counterfeiting applications is demonstrated.This work not only provides a versatile material platform for sensing and information storage,but also offers new insights into the design of intelligent soft materials integrating AIE features with dynamically regulated supramolecular network structures.
基金supported by Yunnan Fundamental Research Projects(202501BD070001-081).
文摘Urbanization is a significant driver of the loss of biodiversity and the disruption of ecosystems.Amphibians are especially vulnerable to the negative impact of urbanization as their life cycles and habitat requirements are complex.The present study investigated the effects of urbanization on amphibian predation networks in suburban Kunming in Yunnan,China and aimed to understand how predation network structure and stability vary with urbanization level.We constructed predation networks by analyzing the stomach contents of amphibians from 12d istinct urbanization gradients.We used the bipartite package in R to evaluate network robustness metrics such as modularity,nestedness,connectivity,and average shortest path length(ASPL).We found that urbanization level is negatively correlated with predation network connectivity(R=−0.67,Ρ=0.02),but there were no significant correlations between urbanization level and nestedness,modularity,or ASPL.Removal of the keystone species destabilized the predation networks at certain locations.The present work highlighted that maintaining prey quantity and diversity preserves predation network connectivity and stabilizes the overall network in urbanizing landscapes.It also underscored the critical role that keystone species play in sustaining network robustness.The results of this research provided insights into the ecological consequences of urbanization.They also suggested that conservation measures should protect the key species and habitats of amphibian predation networks and mitigate the negative impact of urban development on them.
基金supported by the National Key Research and Development Program of China under Grant 2023YFB2903902in part by the Science and Technology Innovation Leading Talents Subsidy Project of Central Plains under Grant 244200510038.
文摘In the process of programmable networks simplifying network management and increasing network flexibility through custom packet behavior,security incidents caused by human logic errors are seriously threatening their safe operation,robust verificationmethods are required to ensure their correctness.As one of the formalmethods,symbolic execution offers a viable approach for verifying programmable networks by systematically exploring all possible paths within a program.However,its application in this field encounters scalability issues due to path explosion and complex constraint-solving.Therefore,in this paper,we propose NetVerifier,a scalable verification system for programmable networks.Tomitigate the path explosion issue,we developmultiple pruning strategies that strategically eliminate irrelevant execution paths while preserving verification integrity by precisely identifying the execution paths related to the verification purpose.To address the complex constraint-solving problem,we introduce an execution results reuse solution to avoid redundant computation of the same constraints.To apply these solutions intelligently,a matching algorithm is implemented to automatically select appropriate solutions based on the characteristics of the verification requirement.Moreover,Language Aided Verification(LAV),an assertion language,is designed to express verification intentions in a concise form.Experimental results on diverse open-source programs of varying scales demonstrate NetVerifier’s improvement in scalability and effectiveness in identifying potential network errors.In the best scenario,compared with ASSERT-P4,NetVerifier reduced the execution path,verification time,and memory occupation of the verification process by 99.92%,94.76%,and 65.19%,respectively.
基金supported by the project of Henan-Zhongjing Pharmaceutical Big Data Repository and Large Model Algorithm Development Research(252028037).
文摘Network pharmacology provides a transformative framework for decoding multi-target,system-level mechanisms of the foodmedicine homology(FMH)substances,overcoming the limitations of reductionist approaches by integrating multi-omics data,computational modeling,and network analysis.Central to this paradigm is the“Network Targets”theory,which conceptualizes therapeutic intervention as the reconfiguration of disease-associated biological networks rather than the modulation of isolated single targets.Artificial intelligence accelerates this process by enabling high-dimensional data integration,predictive modeling of synergistic combinations,and the identification of active constituents.This review outlines the key databases and computational tools that operationalize network pharmacology in FMH research and systematically categorizes their applications,including material screening,ingredient identification,synergy analysis,quality standard establishment,safety assessment,formula optimization,functional food discovery,and personalized recommendation,supported by experimental validation across numerous FMH items.Despite the challenges in data standardization and dynamic modeling,the integration of multi-omics,dynamic networks,and centralized repositories will further advance the field.Ultimately,network pharmacology will bridge traditional FMH wisdom with contemporary mechanistic rigor,positioning FMH as the cornerstone of precision nutrition and preventive medicine.
基金Supported by the National key research and development program in the 14th five year plan 2021YFA1200700)the National Natural Science Foundation of China(62535018,62431025,62561160113)the Natural Science Foundation of Shanghai(23ZR1473400).
文摘Near-infrared image sensors are widely used in fields such as material identification,machine vision,and autonomous driving.Lead sulfide colloidal quantum dot-based infrared photodiodes can be integrated with sil⁃icon-based readout circuits in a single step.Based on this,we propose a photodiode based on an n-i-p structure,which removes the buffer layer and further simplifies the manufacturing process of quantum dot image sensors,thus reducing manufacturing costs.Additionally,for the noise complexity in quantum dot image sensors when capturing images,traditional denoising and non-uniformity methods often do not achieve optimal denoising re⁃sults.For the noise and stripe-type non-uniformity commonly encountered in infrared quantum dot detector imag⁃es,a network architecture has been developed that incorporates multiple key modules.This network combines channel attention and spatial attention mechanisms,dynamically adjusting the importance of feature maps to en⁃hance the ability to distinguish between noise and details.Meanwhile,the residual dense feature fusion module further improves the network's ability to process complex image structures through hierarchical feature extraction and fusion.Furthermore,the pyramid pooling module effectively captures information at different scales,improv⁃ing the network's multi-scale feature representation ability.Through the collaborative effect of these modules,the network can better handle various mixed noise and image non-uniformity issues.Experimental results show that it outperforms the traditional U-Net network in denoising and image correction tasks.
文摘Identifying the community structure of complex networks is crucial to extracting insights and understanding network properties.Although several community detection methods have been proposed,many are unsuitable for social networks due to significant limitations.Specifically,most approaches depend mainly on user-user structural links while overlooking service-centric,semantic,and multi-attribute drivers of community formation,and they also lack flexible filtering mechanisms for large-scale,service-oriented settings.Our proposed approach,called community discovery-based service(CDBS),leverages user profiles and their interactions with consulted web services.The method introduces a novel similarity measure,global similarity interaction profile(GSIP),which goes beyond typical similarity measures by unifying user and service profiles for all attributes types into a coherent representation,thereby clarifying its novelty and contribution.It applies multiple filtering criteria related to user attributes,accessed services,and interaction patterns.Experimental comparisons against Louvain,Hierarchical Agglomerative Clustering,Label Propagation and Infomap show that CDBS reveals the higher performance as it achieves 0.74 modularity,0.13 conductance,0.77 coverage,and significantly fast response time of 9.8 s,even with 10,000 users and 400 services.Moreover,community discoverybased service consistently detects a larger number of communities with distinct topics of interest,underscoring its capacity to generate detailed and efficient structures in complex networks.These results confirm both the efficiency and effectiveness of the proposed method.Beyond controlled evaluation,communities discovery based service is applicable to targeted recommendations,group-oriented marketing,access control,and service personalization,where communities are shaped not only by user links but also by service engagement.
