In the paper, we illustrate the importance of the concept of mobile network computer from a technological perspective. Because of the usefulness of mobile network computers, with the growth of the Internet of things, ...In the paper, we illustrate the importance of the concept of mobile network computer from a technological perspective. Because of the usefulness of mobile network computers, with the growth of the Internet of things, mobile network computers may include not only TV box audio-visual equipment, wireless household appliances, and mobile communication equipment, but may also include devices such as intelligent foot rings, smart watches, smart glasses, smart shoes and smart coats. Considering the different types of networks, e.g. IP multimedia Subsystem(IMS), we explain why some network elements are inaccurate and misleading from a technological perspective. We aim to popularize the concept of mobile network computers for its accuracy and importance, which better define modern mobile terminals and reflects the nature of multiple mobile terminals based on the structure of their integrated computers and the capabilities of processing multimedia. In the computer and Internet age, network computers and mobile network computers are the main terminals of fixed and mobile networks, respectively. Therefore, based on the concept of mobile network computers, we discuss the future of information society.展开更多
Based on the implementation of NNSPC (Neural NetWork Synchronous Parallel Computer) developed by NJU, this paper discusses two schemes for implementing artificial neural network computer withdistributed memories: One ...Based on the implementation of NNSPC (Neural NetWork Synchronous Parallel Computer) developed by NJU, this paper discusses two schemes for implementing artificial neural network computer withdistributed memories: One is Switch Network Structure; the other is Ring Topology Structure. This papergives a comparison betWeen the two schemes and the principles of scheme selection.展开更多
With the rapid development of wearable electronic skin technology, flexible strain sensors have shown great application prospects in the fields of human motion and physiological signal detection, medical diagnostics, ...With the rapid development of wearable electronic skin technology, flexible strain sensors have shown great application prospects in the fields of human motion and physiological signal detection, medical diagnostics, and human-computer interaction owing to their outstanding sensing performance. This paper reports a strain sensor with synergistic conductive network, consisting of stable carbon nanotube dispersion (CNT) layer and brittle MXene layer by dip-coating and electrostatic self-assembly method, and breathable three-dimensional (3D) flexible substrate of thermoplastic polyurethane (TPU) fibrous membrane prepared through electrospinning technology. The MXene/CNT@PDA-TPU (MC@p-TPU) flexible strain sensor had excellent air permeability, wide operating range (0–450 %), high sensitivity (Gauge Factor, GFmax = 8089.7), ultra-low detection limit (0.05 %), rapid response and recovery times (40 ms/60 ms), and excellent cycle stability and durability (10,000 cycles). Given its superior strain sensing capabilities, this sensor can be applied in physiological signals detection, human motion pattern recognition, and driving exoskeleton robots. In addition, MC@p-TPU fibrous membrane also exhibited excellent photothermal conversion performance and can be used as a wearable photo-heater, which has far-reaching application potential in the photothermal therapy of human joint diseases.展开更多
Active distribution network(ADN)planning is crucial for achieving a cost-effective transition to modern power systems,yet it poses significant challenges as the system scale increases.The advent of quantum computing o...Active distribution network(ADN)planning is crucial for achieving a cost-effective transition to modern power systems,yet it poses significant challenges as the system scale increases.The advent of quantum computing offers a transformative approach to solve ADN planning.To fully leverage the potential of quantum computing,this paper proposes a photonic quantum acceleration algorithm.First,a quantum-accelerated framework for ADN planning is proposed on the basis of coherent photonic quantum computers.The ADN planning model is then formulated and decomposed into discrete master problems and continuous subproblems to facilitate the quantum optimization process.The photonic quantum-embedded adaptive alternating direction method of multipliers(PQA-ADMM)algorithm is subsequently proposed to equivalently map the discrete master problem onto a quantum-interpretable model,enabling its deployment on a photonic quantum computer.Finally,a comparative analysis with various solvers,including Gurobi,demonstrates that the proposed PQA-ADMM algorithm achieves significant speedup on the modified IEEE 33-node and IEEE 123-node systems,highlighting its effectiveness.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
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
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
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
In modern ZnO varistors,traditional aging mechanisms based on increased power consumption are no longer relevant due to reduced power consumption during DC aging.Prolonged exposure to both AC and DC voltages results i...In modern ZnO varistors,traditional aging mechanisms based on increased power consumption are no longer relevant due to reduced power consumption during DC aging.Prolonged exposure to both AC and DC voltages results in increased leakage current,decreased breakdown voltage,and lower nonlinearity,ultimately compromising their protective performance.To investigate the evolution in electrical properties during DC aging,this work developed a finite element model based on Voronoi networks and conducted accelerated aging tests on commercial varistors.Throughout the aging process,current-voltage characteristics and Schottky barrier parameters were measured and analyzed.The results indicate that when subjected to constant voltage,current flows through regions with larger grain sizes,forming discharge channels.As aging progresses,the current focus increases on these channels,leading to a decline in the varistor’s overall performance.Furthermore,analysis of the Schottky barrier parameters shows that the changes in electrical performance during aging are non-monotonic.These findings offer theoretical support for understanding the aging mechanisms and condition assessment of modern stable ZnO varistors.展开更多
Type 2 diabetes mellitus(T2DM)was identified as the most prevalent form of diabetes.This study employed an integrated strategy combining network pharmacology,metabolomics,and experimental validation to elucidate the t...Type 2 diabetes mellitus(T2DM)was identified as the most prevalent form of diabetes.This study employed an integrated strategy combining network pharmacology,metabolomics,and experimental validation to elucidate the therapeutic mechanisms of red mulberry water extract(RMW)in T2DM.Systematic analysis identified six bioactive constituents,with four key components(cyanidin,quercetin,morin,andβ-carotene)demonstrating significant interactions with diabetes-related targets.Network pharmacology revealed these compounds modulate critical pathways including AMPK(P=2.3×10^(−5)),PI3K-Akt(P=1.8×10^(−4)),and PPAR signaling(P=3.1×10^(−3)).In diabetic mice,treatment significantly improved glycemic control(32.5%reduction in fasting glucose,P<0.01),lipid profiles(36.7%lower TG,P<0.05),antioxidant activity(2.1-fold increased SOD,P<0.05),and inflammation(42%reduced TNF-α,P<0.05).Metabolomic analysis further confirmed alterations in catecholamine and lipid metabolism pathways.These findings collectively demonstrate mulberry's multi-target therapeutic potential through synergistic regulation of glucose metabolism,lipid homeostasis,oxidative stress,and inflammatory responses in diabetes.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
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
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
Dear Editor,D2This letter presents a node feature similarity preserving graph convolutional framework P G.Graph neural networks(GNNs)have garnered significant attention for their efficacy in learning graph representat...Dear Editor,D2This letter presents a node feature similarity preserving graph convolutional framework P G.Graph neural networks(GNNs)have garnered significant attention for their efficacy in learning graph representations across diverse real-world applications.展开更多
In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic h...In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services.展开更多
Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression...Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression is crucial for deploying deep neural network(DNN)models on resource-constrained embedded devices.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
Next-GenerationNetworks(NGNs)demand high resilience,dynamic adaptability,and efficient resource utilization to enable ubiquitous connectivity.In this context,the Space-Air-Ground Integrated Network(SAGIN)architecture ...Next-GenerationNetworks(NGNs)demand high resilience,dynamic adaptability,and efficient resource utilization to enable ubiquitous connectivity.In this context,the Space-Air-Ground Integrated Network(SAGIN)architecture is uniquely positioned to meet these requirements.However,conventional NGN routing algorithms often fail to account for SAGIN’s intrinsic characteristics,such as its heterogeneous structure,dynamic topology,and constrained resources,leading to suboptimal performance under disruptions such as node failures or cyberattacks.To meet these demands for SAGIN,this study proposes a resilience-oriented routing optimization framework featuring dynamic weighting and multi-objective evaluation.Methodologically,we define three core routing performance metrics,quantified through a four-dimensionalmodel,encompassing robustness Rd,resilience Rr,adaptability Ra,and resource utilization efficiency Ru,and integrate them into a comprehensive evaluation metric.In simulated SAGIN environments,the proposed Multi-Indicator Weighted Resilience Evaluation Algorithm(MIW-REA)demonstrates significant improvements in resilience enhancement,recovery acceleration,and resource optimization.It maintains 82.3%service availability even with a 30%node failure rate,reduces Distributed Denial of Service(DDoS)attack recovery time by 43%,decreases bandwidth waste by 23.4%,and lowers energy consumption by 18.9%.By addressing challenges unique to the SAGIN network,this research provides a flexible real-time solution for NGN routing optimization that balances resilience,efficiency,and adaptability,advancing the field.展开更多
文摘In the paper, we illustrate the importance of the concept of mobile network computer from a technological perspective. Because of the usefulness of mobile network computers, with the growth of the Internet of things, mobile network computers may include not only TV box audio-visual equipment, wireless household appliances, and mobile communication equipment, but may also include devices such as intelligent foot rings, smart watches, smart glasses, smart shoes and smart coats. Considering the different types of networks, e.g. IP multimedia Subsystem(IMS), we explain why some network elements are inaccurate and misleading from a technological perspective. We aim to popularize the concept of mobile network computers for its accuracy and importance, which better define modern mobile terminals and reflects the nature of multiple mobile terminals based on the structure of their integrated computers and the capabilities of processing multimedia. In the computer and Internet age, network computers and mobile network computers are the main terminals of fixed and mobile networks, respectively. Therefore, based on the concept of mobile network computers, we discuss the future of information society.
文摘Based on the implementation of NNSPC (Neural NetWork Synchronous Parallel Computer) developed by NJU, this paper discusses two schemes for implementing artificial neural network computer withdistributed memories: One is Switch Network Structure; the other is Ring Topology Structure. This papergives a comparison betWeen the two schemes and the principles of scheme selection.
基金supported by the National Natural Science Foundation of China(Nos.52373093 and 12072325)the Outstanding Youth Fund of Henan Province(No.242300421062)+1 种基金National Key R&D Program of China(No.2019YFA0706802)the 111 project(No.D18023).
文摘With the rapid development of wearable electronic skin technology, flexible strain sensors have shown great application prospects in the fields of human motion and physiological signal detection, medical diagnostics, and human-computer interaction owing to their outstanding sensing performance. This paper reports a strain sensor with synergistic conductive network, consisting of stable carbon nanotube dispersion (CNT) layer and brittle MXene layer by dip-coating and electrostatic self-assembly method, and breathable three-dimensional (3D) flexible substrate of thermoplastic polyurethane (TPU) fibrous membrane prepared through electrospinning technology. The MXene/CNT@PDA-TPU (MC@p-TPU) flexible strain sensor had excellent air permeability, wide operating range (0–450 %), high sensitivity (Gauge Factor, GFmax = 8089.7), ultra-low detection limit (0.05 %), rapid response and recovery times (40 ms/60 ms), and excellent cycle stability and durability (10,000 cycles). Given its superior strain sensing capabilities, this sensor can be applied in physiological signals detection, human motion pattern recognition, and driving exoskeleton robots. In addition, MC@p-TPU fibrous membrane also exhibited excellent photothermal conversion performance and can be used as a wearable photo-heater, which has far-reaching application potential in the photothermal therapy of human joint diseases.
基金supported in part by the National Natural Science Foundation of China under Grant 52307134the Fundamental Research Funds for the Central Universities(xzy012025022)。
文摘Active distribution network(ADN)planning is crucial for achieving a cost-effective transition to modern power systems,yet it poses significant challenges as the system scale increases.The advent of quantum computing offers a transformative approach to solve ADN planning.To fully leverage the potential of quantum computing,this paper proposes a photonic quantum acceleration algorithm.First,a quantum-accelerated framework for ADN planning is proposed on the basis of coherent photonic quantum computers.The ADN planning model is then formulated and decomposed into discrete master problems and continuous subproblems to facilitate the quantum optimization process.The photonic quantum-embedded adaptive alternating direction method of multipliers(PQA-ADMM)algorithm is subsequently proposed to equivalently map the discrete master problem onto a quantum-interpretable model,enabling its deployment on a photonic quantum computer.Finally,a comparative analysis with various solvers,including Gurobi,demonstrates that the proposed PQA-ADMM algorithm achieves significant speedup on the modified IEEE 33-node and IEEE 123-node systems,highlighting its effectiveness.
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.
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.
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In modern ZnO varistors,traditional aging mechanisms based on increased power consumption are no longer relevant due to reduced power consumption during DC aging.Prolonged exposure to both AC and DC voltages results in increased leakage current,decreased breakdown voltage,and lower nonlinearity,ultimately compromising their protective performance.To investigate the evolution in electrical properties during DC aging,this work developed a finite element model based on Voronoi networks and conducted accelerated aging tests on commercial varistors.Throughout the aging process,current-voltage characteristics and Schottky barrier parameters were measured and analyzed.The results indicate that when subjected to constant voltage,current flows through regions with larger grain sizes,forming discharge channels.As aging progresses,the current focus increases on these channels,leading to a decline in the varistor’s overall performance.Furthermore,analysis of the Schottky barrier parameters shows that the changes in electrical performance during aging are non-monotonic.These findings offer theoretical support for understanding the aging mechanisms and condition assessment of modern stable ZnO varistors.
文摘Type 2 diabetes mellitus(T2DM)was identified as the most prevalent form of diabetes.This study employed an integrated strategy combining network pharmacology,metabolomics,and experimental validation to elucidate the therapeutic mechanisms of red mulberry water extract(RMW)in T2DM.Systematic analysis identified six bioactive constituents,with four key components(cyanidin,quercetin,morin,andβ-carotene)demonstrating significant interactions with diabetes-related targets.Network pharmacology revealed these compounds modulate critical pathways including AMPK(P=2.3×10^(−5)),PI3K-Akt(P=1.8×10^(−4)),and PPAR signaling(P=3.1×10^(−3)).In diabetic mice,treatment significantly improved glycemic control(32.5%reduction in fasting glucose,P<0.01),lipid profiles(36.7%lower TG,P<0.05),antioxidant activity(2.1-fold increased SOD,P<0.05),and inflammation(42%reduced TNF-α,P<0.05).Metabolomic analysis further confirmed alterations in catecholamine and lipid metabolism pathways.These findings collectively demonstrate mulberry's multi-target therapeutic potential through synergistic regulation of glucose metabolism,lipid homeostasis,oxidative stress,and inflammatory responses in diabetes.
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.
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 Natural Science Foundation of China(62402399)the New Chongqing Youth Innovation Talent Project(CSTB2024NSCQ-QCXMX0035)。
文摘Dear Editor,D2This letter presents a node feature similarity preserving graph convolutional framework P G.Graph neural networks(GNNs)have garnered significant attention for their efficacy in learning graph representations across diverse real-world applications.
基金funding from the European Commission by the Ruralities project(grant agreement no.101060876).
文摘In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services.
基金supported by the Science and Technology Innovation Key R&D Program of Chongqing(CSTB2025TIAD-STX0032)National Key Research and Development Program of China(2024YFF0908200)+1 种基金the Chongqing Technology Innovation and Application Development Special Key Project(CSTB2024TIAD-KPX0018)the Southwest University Graduate Student Research Innovation(SWUB24051)。
文摘Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression is crucial for deploying deep neural network(DNN)models on resource-constrained embedded devices.
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 Beijing Natural Science Foundation under Grant 9242003partially supported by the Natural Science Foundation of Chongqing,China under Grant CSTB2023NSCQ-MSX0391+3 种基金partially supported by the National Natural Science Foundation of China under Grant 62471493partially supported by the Natural Science Foundation of Shandong Province under Grants ZR2023LZH017,ZR2024MF066supported by the Key Laboratory of Public Opinion Governance and Computational Communication under Grant YQKFYB202501The Research Project on the Development of Social Sciences in Hebei Province in 2024(No.202403150).
文摘Next-GenerationNetworks(NGNs)demand high resilience,dynamic adaptability,and efficient resource utilization to enable ubiquitous connectivity.In this context,the Space-Air-Ground Integrated Network(SAGIN)architecture is uniquely positioned to meet these requirements.However,conventional NGN routing algorithms often fail to account for SAGIN’s intrinsic characteristics,such as its heterogeneous structure,dynamic topology,and constrained resources,leading to suboptimal performance under disruptions such as node failures or cyberattacks.To meet these demands for SAGIN,this study proposes a resilience-oriented routing optimization framework featuring dynamic weighting and multi-objective evaluation.Methodologically,we define three core routing performance metrics,quantified through a four-dimensionalmodel,encompassing robustness Rd,resilience Rr,adaptability Ra,and resource utilization efficiency Ru,and integrate them into a comprehensive evaluation metric.In simulated SAGIN environments,the proposed Multi-Indicator Weighted Resilience Evaluation Algorithm(MIW-REA)demonstrates significant improvements in resilience enhancement,recovery acceleration,and resource optimization.It maintains 82.3%service availability even with a 30%node failure rate,reduces Distributed Denial of Service(DDoS)attack recovery time by 43%,decreases bandwidth waste by 23.4%,and lowers energy consumption by 18.9%.By addressing challenges unique to the SAGIN network,this research provides a flexible real-time solution for NGN routing optimization that balances resilience,efficiency,and adaptability,advancing the field.
