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Adaptive Enhanced Grey Wolf Optimizer for Efficient Cluster Head Selection and Network Lifetime Maximization in Wireless Sensor Networks 认领 引用
1
作者 Omar Almomani Mahran Al-Zyoud +3 位作者 Ahmad Adel Abu-Shareha Ammar Almomani Said A.Salloum Khaled Mohammad Alomari 《Computers, Materials & Continua》 SCIE EI 2026年第5期784-813,共30页
In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe ... In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe energy constraints inWSNs.This paper presents an Adaptive Enhanced GreyWolf Optimizer(AEGWO)for energy-efficient cluster head(CH)selection that mitigates the exploration–exploitation imbalance,preserves population diversity,and avoids premature convergence inherent in baseline GWO.The AEGWO combines adaptive control of the parameter of the search pressure to accelerate convergence without stagnation,a hybrid velocity-momentum update based on the dynamics of PSO,and an intelligent mutation operator to maintain the diversity of the population.The search is guided by a multi-objective fitness,which aims at maximizing the residual energy,equal distribution of CH,minimizing the intra-cluster distance,desirable proximity to sinks,and enhancing the coverage.Simulations on 100 nodes homogeneousWSN Tested the proposed AEGWO under the same conditions with LEACH,GWO,IGWO,PSO,WOA,and GA,AEGWO significantly increases stability and lifetime compared to LEACHand other tested algorithms;it has the best first,half,and last node dead,and higher residual energy and smaller communication overhead.The findings prove that AEGWO provides sustainable energy management and better lifetime extension,which makes it a robust,flexible clustering protocol of large-scaleWSNs. 展开更多
关键词 Wireless sensor networks energy efficiency cluster head selection grey wolf optimizer
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MKS-YOLO:A Multi-kernel Dynamic Selection Network for Object Detection in Remote Sensing Images 认领 引用
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作者 Shengfeng Guo Xiaowei He +3 位作者 Zhonglong Zheng Dawei Zhang Xuedong He Wenpeng Xia 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第3期1918-1931,共14页
Object detection in remote sensing images presents numerous challenges,such as significant variations in object scale and pronounced differences in aspect ratios.Current mainstream approaches typically employ large co... Object detection in remote sensing images presents numerous challenges,such as significant variations in object scale and pronounced differences in aspect ratios.Current mainstream approaches typically employ large convolutional kernels or multi-scale convolutional architectures to address these challenges.However,large convolutional kernels tend to introduce substantial background noise,while the use of kernels with varying scales may lead to feature information redundancy.To tackle these issues,we propose a robust and efficient rotation-aware object detection method-MKS-YOLO.MKS-YOLO explicitly decouples high-and low-informative features through an information-aware threshold gating mechanism,effectively suppressing background noise interference.Furthermore,to reduce noise and enhance the model’s ability to perceive objects at multiple scales,we design a Multi-Kernel Dynamic Selection Module(MKSM).This module integrates multi-scale convolutional kernels and introduces a spatial attention mechanism to achieve dynamic weighted feature fusion.In addition,we incorporate a Contextual Large Kernel Attention(CLKA)mechanism to enhance feature representation capabilities for objects with high aspect ratios.Moreover,during the feature extraction stage,we introduce an Attention-based Internal Feature Interaction module(AIFI),which further strengthens the correlations among deep semantic features.To validate the effectiveness of the proposed method,we conduct extensive experiments on two widely used remote sensing image datasets:DOTA-v1.0 and DIOR-R.Experimental results demonstrate that MKS-YOLO achieves state-of-the-art detection accuracy,with Mean Average Precision(mAP)reaching 77.71%and 82.50%,respectively,fully validating the superior performance of the proposed method. 展开更多
关键词 Object detection Detectors Remote sensing Dynamic selection network
Metal-free fluorinated carbon nitride with piezo-boosted hydrogen-bonding networks enable 100%CO selectivity in CO2 reduction 认领 引用
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作者 Xingchen He Junhui Shao +5 位作者 Najun Li Dongyun Chen Hua Li Qingfeng Xu Haozhi Wang Jianmei Lu 《Chinese Journal of Catalysis》 SCIE EI CAS CSCD 2026年第6期130-142,共13页
The efficient conversion of CO2into industrial fuels via piezocatalysis is a compelling solution to carbon emissions but often suffers from low activity and poor selectivity.While many piezocatalysts contain metals... The efficient conversion of CO2into industrial fuels via piezocatalysis is a compelling solution to carbon emissions but often suffers from low activity and poor selectivity.While many piezocatalysts contain metals,the metal-free and low-cost graphitic carbon nitride(g-C3N4)is a promising alternative.However,its modest piezoelectric response and intrinsically low surface activity are unfavorable for efficient CO2activation.Here,we demonstrate that halogen doping transforms its catalytic capability by creating highly active hybridized p-states near the Fermi level.Fluorine doping introduces F 2p orbitals that hybridize with C 2p states,forming a new,higher-energy valence band maximum.This modification simultaneously creates electronically potent sites for CO2activation and enhances the driving force for charge separation.The resulting F-C3N4converts CO2exclusively to CO with 100%selectivity and a high production rate of 201.7μmol g‒1h‒1under ultrasonic vibration without sacrificial agents.Mechanistic investigations reveal macroscopic piezoelectric polarization synergizes with a fluorine-induced local field to drive directional charge separation.Critically,reconstructed interfacial hydrogen-bond networks facilitate CO2adsorption and activation,significantly lowering the energy barrier for*COOH formation.This dynamic coupling provides a new paradigm for designing high-efficiency CO2reduction systems. 展开更多
关键词 Carbon nitride Hydrogen-bonding networks CO2reduction Piezocatalytic Selectivity
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Selective Area Growth of High-Quality In-Plane GaAs Nanowires and Nanowire Networks by Molecular Beam Epitaxy on Ge Substrates 认领 引用 被引量：1
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作者 Fengyue He Xiyu Hou +3 位作者 Xiuming Dou Yukun Yin Dong Pan Jianhua Zhao 《Chinese Physics Letters》 SCIE EI CAS CSCD 2025年第6期269-284,共16页
Anti-phase domain defects easily form in the in-plane GaAs nanowires(NWs)grown on CMOS-compatiblegroup IV substrates,which makes it difficult to obtain GaAs NWs with a designed length and also leads to asignificant li... Anti-phase domain defects easily form in the in-plane GaAs nanowires(NWs)grown on CMOS-compatiblegroup IV substrates,which makes it difficult to obtain GaAs NWs with a designed length and also leads to asignificant limitation in the growth of high-quality in-plane GaAs NW networks on such substrates.Here,wereport on the selective area growth of anti-phase domain-free in-plane GaAs NWs and NW networks on Ge(111)substrates.Detailed structural studies confirm that the GaAs NW grown using a large pattern period and GaAsNW networks grown by adding the Sb are both high-quality pure zinc-blende single crystals free of stackingfaults,twin defects,and anti-phase domain defects.Room-temperature photoluminescence measurements show asubstantial improvement in crystal quality and good consistency and uniformity of the GaAs NW networks.Ourwork provides useful insights into the controlled growth of high-quality anti-phase domain-defects-free in-planeIII-V NWs and NW networks. 展开更多
关键词 nanowire networks Ge substrates structural studies high quality plane GaAs nanowires molecular beam epitaxy gaas nws selective area growth gaas nanowires nws grown
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Joint Access Node Selection and Fast Routing Optimization for LEO Satellite Networks 认领 引用
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作者 Zhang Yifan Dong Tao +2 位作者 Liu Zhihui Di Hang Zhou Jianming 《China Communications》 SCIE EI CSCD 2026年第6期146-164,共19页
The low Earth orbit(LEO)satellite networks play an important role in the future communication networks.However,under the end-to-end(E2E)transmission background,inter-satellite routing has been widely studied,but the i... The low Earth orbit(LEO)satellite networks play an important role in the future communication networks.However,under the end-to-end(E2E)transmission background,inter-satellite routing has been widely studied,but the influence of ground-satellite links(GSL)on routing has received less attention.In this paper,a fast E2E satellite routing algorithm based on access node selection is proposed.Firstly,the delay of four path modes generated by users accessing the network from different satellites is analyzed,and the influence of delay on E2E routing performance is presented.Then,jointly considering routing delay and node load,an access node selection strategy is proposed by using the shortest E2E delay to determine the access source and destination node within satellites.Finally,an optimization domain is divided from the network topology by using the shortest delay path based on hops constraints.And a routing optimization algorithm based on Q-learning has been proposed in the optimization domain,realizing high computational speed and stable results.The simulation results show that the access node selection strategy can decrease E2E delay by up to 10 ms and enhance the performance of node load balancing.And the routing optimization algorithm can reduce the average computation time. 展开更多
关键词 access selection fast routing LEO satellite networks load balancing routing algorithm
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Prediction of Sintering Strength for Selective Laser Sintering of Polystyrene Using Artificial Neural Network 认领 引用 被引量：3
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作者 王传洋 姜宁 +2 位作者 陈再良 陈瑶 董渠 《Journal of Donghua University(English Edition)》 EI CAS 2015年第5期825-830,共6页
In the present work,a study is made to investigate the effects of process parameters,namely,laser power,scanning speed,hatch spacing, layer thickness and powder temperature, on the tensile strength for selective laser... In the present work,a study is made to investigate the effects of process parameters,namely,laser power,scanning speed,hatch spacing, layer thickness and powder temperature, on the tensile strength for selective laser sintering( SLS) of polystyrene( PS). Artificial neural network( ANN) methodology is employed to develop mathematical relationships between the process parameters and the output variable of the sintering strength. Experimental data are used to train and test the network. The present neural network model is applied to predicting the experimental outcome as a function of input parameters within a specified range. Predicted sintering strength using the trained back propagation( BP) network model showed quite a good agreement with measured ones. The results showed that the networks had high processing speed,the abilities of error-correcting and self-organizing. ANN models had favorable performance and proved to be an applicable tool for predicting sintering strength SLS of PS. 展开更多
关键词 selective laser sintering(SLS) polystyrene(PS) strength artificial neural network(ANN)
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Varactor-tunable frequency selective surface with an embedded bias network 认领 引用 被引量：3
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作者 林宝勤 屈绍波 +3 位作者 童创明 周航 张衡阳 李伟 《Chinese Physics B》 SCIE EI CAS CSCD 2013年第9期395-398,共4页
A new technique for designing a varactor-tunable frequency selective surface （FSS） with an embedded bias network is proposed and experimentally verified. The proposed FSS is based on a square-ring slot FSS. The freq... A new technique for designing a varactor-tunable frequency selective surface （FSS） with an embedded bias network is proposed and experimentally verified. The proposed FSS is based on a square-ring slot FSS. The frequency tuning is achieved by inserting varactor diodes between the square mesh and each unattached square patch. The square mesh is divided into two parts for biasing the varactor diodes. Full-wave numerical simulations show that a wide tuning range can be achieved by changing the capacitances of these loaded varactors. Two homo-type samples using fixed lumped capacitors are fabricated and measured using a standard waveguide measurement setup. Excellent agreement between the measured and simulated results is demonstrated. 展开更多
关键词 frequency selective surface varactor diodes embedded bias network
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Phase selection prediction and component determination of multiple-principal amorphous alloy composites based on artificial neural network model 认领 引用
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作者 Lin WANG Pei-you LI +5 位作者 Wei ZHANG Xiao-ling FU Fang-yi WAN Yong-shan WANG Lin-sen SHU Long-quan YONG 《Transactions of Nonferrous Metals Society of China》 SCIE EI CAS CSCD 2025年第5期1543-1559,共17页
The probability of phase formation was predicted using k-nearest neighbor algorithm(KNN)and artificial neural network algorithm(ANN).Additionally,the composition ranges of Ti,Cu,Ni,and Hf in 40 unknown amorphous alloy... The probability of phase formation was predicted using k-nearest neighbor algorithm(KNN)and artificial neural network algorithm(ANN).Additionally,the composition ranges of Ti,Cu,Ni,and Hf in 40 unknown amorphous alloy composites(AACs)were predicted using ANN.The predicted alloys were then experimentally verified through X-ray diffraction(XRD)and high-resolution transmission electron microscopy(HRTEM).The prediction accuracies of the ANN for AM and IM phases are 93.12%and 85.16%,respectively,while the prediction accuracies of KNN for AM and IM phases are 93%and 84%,respectively.It is observed that when the contents of Ti,Cu,Ni,and Hf fall within the ranges of 32.7−34.5 at.%,16.4−17.3 at.%,30.9−32.7 at.%,and 17.3−18.3 at.%,respectively,it is more likely to form AACs.Based on the results of XRD and HRTEM,the Ti34Cu17Ni31.36Hf17.64and Ti36Cu18Ni29.44Hf16.56alloys are identified as good AACs,which are in closely consistent with the predicted amorphous alloy compositions. 展开更多
关键词 multiple-principal amorphous alloy composites Ti−Cu−Ni−Hf alloy phase selection artificial neural network machine learning
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AI-Integrated Feature Selection of Intrusion Detection for Both SDN and Traditional Network Architectures Using an Improved Crayfish Optimization Algorithm 认领 引用
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作者 Hui Xu Wei Huang Longtan Bai 《Computers, Materials & Continua》 SCIE EI 2025年第8期3053-3073,共21页
With the birth of Software-Defined Networking(SDN),integration of both SDN and traditional architectures becomes the development trend of computer networks.Network intrusion detection faces challenges in dealing with ... With the birth of Software-Defined Networking(SDN),integration of both SDN and traditional architectures becomes the development trend of computer networks.Network intrusion detection faces challenges in dealing with complex attacks in SDN environments,thus to address the network security issues from the viewpoint of Artificial Intelligence(AI),this paper introduces the Crayfish Optimization Algorithm(COA)to the field of intrusion detection for both SDN and traditional network architectures,and based on the characteristics of the original COA,an Improved Crayfish Optimization Algorithm(ICOA)is proposed by integrating strategies of elite reverse learning,Levy flight,crowding factor and parameter modification.The ICOA is then utilized for AI-integrated feature selection of intrusion detection for both SDN and traditional network architectures,to reduce the dimensionality of the data and improve the performance of network intrusion detection.Finally,the performance evaluation is performed by testing not only the NSL-KDD dataset and the UNSW-NB 15 dataset for traditional networks but also the InSDN dataset for SDN-based networks.Experimental results show that ICOA improves the accuracy by 0.532%and 2.928%respectively compared with GWO and COA in traditional networks.In SDN networks,the accuracy of ICOA is 0.25%and 0.3%higher than COA and PSO.These findings collectively indicate that AI-integrated feature selection based on the proposed ICOA can promote network intrusion detection for both SDN and traditional architectures. 展开更多
关键词 Software-defined networking(SDN) intrusion detection artificial intelligence(AI) feature selection crayfish optimization algorithm(COA)
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Complex Behavior in a Selective Aging Neuron Model Based on Small World Networks 认领 引用 被引量：2
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作者 ZHANG Gui-Qing CHEN Tian-Lun 《Communications in Theoretical Physics》 SCIE CAS 2008年第2期409-413,共5页
Complex behavior in a selective aging simple neuron model based on small world networks is investigated.The basic elements of the model are endowed with the main features of a neuron function.The structure of the sele... Complex behavior in a selective aging simple neuron model based on small world networks is investigated.The basic elements of the model are endowed with the main features of a neuron function.The structure of the selective aging neuron model is discussed.We also give some properties of the new network and find that the neuron model displays a power-law behavior.If the brain network is small world-like network,the mean avalanche size is almost the same unless the aging parameter is big enough. 展开更多
关键词 selective aging self-organized criticality small world networks finite-size-scaling analysis power-law
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A New Selective Neural Network Ensemble Method Based on Error Vectorization and Its Application in High-density Polyethylene (HDPE) Cascade Reaction Process 认领 引用 被引量：1
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作者 朱群雄 赵乃伟 徐圆 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2012年第6期1142-1147,共6页
Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy o... Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy of networks, but there are some problems, e.g., lacking of unified definition of diversity among component neural networks and difficult to improve the accuracy by selecting if the diversities of available networks are small. In this study, the output errors of networks are vectorized, the diversity of networks is defined based on the error vectors, and the size of ensemble is analyzed. Then an error vectorization based selective neural network ensemble (EVSNE) is proposed, in which the error vector of each network can offset that of the other networks by training the component networks orderly. Thus the component networks have large diversity. Experiments and comparisons over standard data sets and actual chemical process data set for production of high-density polyethylene demonstrate that EVSNE performs better in generalization ability. 展开更多
关键词 high-density polyethylene modeling selective neural network ensemble diversity definition error vectorization
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I-AOMDV:QoS-Aware Multipath Routing Protocol for Enhanced in Dynamic Multi-Node Vehicle Area Network 认领 引用
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作者 Zhu Mingqiang Zhou Ying +4 位作者 Chu Jianxiang Chen Ziyang Ai Huilin Li Jundi Han Jinze 《China Communications》 SCIE EI CSCD 2026年第3期286-297,共12页
In vehicular networking applications,Mobile Ad hoc networks(MANETs)enable dynamic,infrastructure-free connectivity for multi-node mobile scenarios.Frequent topology changes,however,challenge routing protocols in deliv... In vehicular networking applications,Mobile Ad hoc networks(MANETs)enable dynamic,infrastructure-free connectivity for multi-node mobile scenarios.Frequent topology changes,however,challenge routing protocols in delivering quality of service(QoS)for diverse applications.We propose inhanced Ad-hoc on-demand distance vector multipath(I-AOMDV),an enhanced multipath routing protocol using a primary-backup strategy to meet stringent QoS demands in dynamic vehicular environments.Whereas AOMDV relies on hop count,I-AOMDV integrates hop count,bandwidth,and path stability into a QoS-aware framework for optimized path selection.Extensive NS-2 simulations demonstrate that IAOMDV surpasses AOMDV under high mobility,improving data packet delivery by up to 22%and cutting voice service latency by 8–15%,even in high-mobility scenarios.By addressing vehicular networking needs for latency,reliability,and bandwidth,I-AOMDV delivers a scalable,efficient routing solution. 展开更多
关键词 mobile ad hoc networks multipath routing path selection primary-cackup strategy
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Beyond Wi-Fi 7:Enhanced Decentralized Wireless Local Area Networks with Federated Reinforcement Learning 认领 引用
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作者 Rashid Ali Alaa Omran Almagrabi 《Computers, Materials & Continua》 SCIE EI 2026年第3期391-409,共19页
Wi-Fi technology has evolved significantly since its introduction in 1997,advancing to Wi-Fi 6 as the latest standard,with Wi-Fi 7 currently under development.Despite these advancements,integrating machine learning in... Wi-Fi technology has evolved significantly since its introduction in 1997,advancing to Wi-Fi 6 as the latest standard,with Wi-Fi 7 currently under development.Despite these advancements,integrating machine learning into Wi-Fi networks remains challenging,especially in decentralized environments with multiple access points(mAPs).This paper is a short review that summarizes the potential applications of federated reinforcement learning(FRL)across eight key areas of Wi-Fi functionality,including channel access,link adaptation,beamforming,multi-user transmissions,channel bonding,multi-link operation,spatial reuse,and multi-basic servic set(multi-BSS)coordination.FRL is highlighted as a promising framework for enabling decentralized training and decision-making while preserving data privacy.To illustrate its role in practice,we present a case study on link activation in a multi-link operation(MLO)environment with multiple APs.Through theoretical discussion and simulation results,the study demonstrates how FRL can improve performance and reliability,paving the way for more adaptive and collaborative Wi-Fi networks in the era of Wi-Fi 7 and beyond. 展开更多
关键词 Artificial intelligence reinforcement learning channels selection wireless local area networks 802.11ax 802.11be Wi-Fi
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Selective PNC in Satellite-Terrestrial Network over Non-Identical Fading Channels 认领 引用
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作者 Zhen Li Mingchuan Yang +1 位作者 Gang Wang Donglai Zhao 《China Communications》 SCIE CSCD 2022年第12期197-206,共10页
We analyze the performance of a twoway satellite-terrestrial decode-and-forward(DF) relay network over non-identical fading channels.In particular,selective physical-layer network coding(SPNC) is employed in the propo... We analyze the performance of a twoway satellite-terrestrial decode-and-forward(DF) relay network over non-identical fading channels.In particular,selective physical-layer network coding(SPNC) is employed in the proposed network to improve the average end-to-end throughput performance.More specifically,by assuming that the DF relay performs instantaneous throughput comparisons before performing corresponding protocols,we derive the expressions of system instantaneous bit-error-rate(BER),instantaneous end-to-end throughput,average end-to-end throughput,single node detection(SND)occurrence probability and average end-to-end BER over non-identical fading channels.Finally,theoretical analyses and Monte Carlo simulation results are presented.Evaluations show that:1) SPNC protocol outperforms the conventional physical-layer network coding(PNC) protocol in infrequent light shadowing(ILS),average shadowing(AS) and frequent heavy shadowing(FHS) Shadowed-Rician fading channels.2) As the satellite-relay channel fading gets more sewere,SPNC protocol can achieve more performance improvement than PNC protocol and the occurrence probability of SND protocol increases progressively.3) The occurrence probability increase of SND has a beneficial effect on the average end-to-end throughput in low signal-to-noise ratio(SNR) regime,while the occurrence probability decrease of SND has a beneficial effect on the average end-to-end BER in highSNR regime. 展开更多
关键词 satellite-terrestrial relay network selective physical-layer network coding(SPNC) average end-to-end throughput Shadowed-Rician fading
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Enhancing operational planning of active distribution networks considering effective topology selection and thermal energy storage 认领 引用 被引量：1
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作者 Vineeth Vijayan Ali Arzani Satish M.Mahajan 《iEnergy》 EI 2025年第2期98-106,共9页
Grid-scale energy storage systems provide effective solutions to address challenges such as supply-load imbalances and voltage violations resulting from the non-coinciding nature of renewable energy generation and pea... Grid-scale energy storage systems provide effective solutions to address challenges such as supply-load imbalances and voltage violations resulting from the non-coinciding nature of renewable energy generation and peak demand incidents.While battery and hydrogen storage are commonly used for peak shaving,ice-based thermal energy storage systems(TESSs)offer a direct way to reduce cooling loads without electrical conversion.This paper presents a multi-objective planning framework that optimizes TESS dispatch,network topology,and photovoltaic(PV)inverter reactive power support to address operational issues in active distribution networks.The objectives of the proposed scheme include minimizing peak demand,voltage deviations,and PV inverter VAr dependency.The mixed-integer nonlinear programming problem is solved using a Pareto-based multi-objective particle swarm optimization(MOPSO)method.The MATLAB-OpenDSS simulations for a modified IEEE-123 bus system show a 7.1%reduction in peak demand,a 13%reduction in voltage deviation,and a 52%drop in PV inverter VAr usage.The obtained solutions confirm minimal operational stress on control devices such as switches and PV inverters.Thus,unlike earlier studies,this work combines all three strategies to offer an effective solution for the operational planning of the active distribution network. 展开更多
关键词 Operational planning power distribution network PV inverters thermal energy storage systems topology selection
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Microporous Organic Nanotube Networks from Hyper Cross-linking Core-shell Bottlebrush Copolymers for Selective Adsorption Study 认领 引用 被引量：2
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作者 Tian-Qi Wang Yang Xu +2 位作者 Zi-Dong He Ming-Hong Zhou Kun Huang 《Chinese Journal of Polymer Science》 SCIE EI CAS CSCD 2018年第1期98-105,共8页
We report a synthesis of microporous organic nanotube networks（MONNs） by a combination of hyper cross-linking and molecular templating of core-shell bottlebrush copolymers. The intrabrush and interbrush cross-linkin... We report a synthesis of microporous organic nanotube networks（MONNs） by a combination of hyper cross-linking and molecular templating of core-shell bottlebrush copolymers. The intrabrush and interbrush cross-linking of polystyrene（PS） shell layer in the core-shell bottlebrush copolymers led to the formation of micropores and large-sized nanopores（meso/macrospores） in MONNs, respectively, while selective removal of polylactide（PLA） core layer generated mesoporous tubular structure. The size of PLA-templated mesoporous cores and porous structure both at micro-and meso-scale could be controlled by simple tuning of the ratio of core/shell or the PLA core fraction in the bottlebrush precursors. Moreover, the resultant MONNs showed a highly selective adsorption capacity for the positively charged dyes on the basis of multi-porosity and carboxylate group-rich structure. In addition, MONNs also exhibited effective performance in size-selective adsorption of biomacromolecules. This work represents a new avenue for the preparation of MONNs and also provides a new application for molecular bottlebrushes in nanotechnology. 展开更多
关键词 Microporous organic nanotube networks Hyper cross-linking Core-shell bottlebrush copolymers Meso/Macrospores Selective adsorption capacity
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Voxel event graph neural network for event-based human gait recognition 认领 引用
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作者 Guangyuan MA Xiaolin GONG +2 位作者 Jiangtao XU Jiandong GAO Zhaoxuan GUO 《Optoelectronics Letters》 EI 2026年第3期167-173,共7页
To improve the accuracy of event-based gait recognition,a method called voxel event graph neural network(VEGNN)is proposed.This method voxelizes the event stream and selects representative voxels as vertices of the gr... To improve the accuracy of event-based gait recognition,a method called voxel event graph neural network(VEGNN)is proposed.This method voxelizes the event stream and selects representative voxels as vertices of the graph.Then the edges are constructed based on spatio-temporal distance and temporal order constraints so that the event stream is constructed as a graph structure.Finally,a lightweight feature extraction network based on graph neural networks(GNNs)is used to efficiently capture spatio-temporal information and motion cues from the event graph.To evaluate our method,an event-based gait recognition dataset called Celex-Gait is created.Experimental results show that VEGNN achieves a gait recognition accuracy of 95.8%and 93.9%on the Celex-Gait dataset and the DVS128-Gait-Day dataset,respectively.Furthermore,compared to the state-of-the-art methods,VEGNN reduces the number of model parameters by 30%.This indicates that VEGNN achieves higher recognition accuracy with lower model complexity. 展开更多
关键词 selects representative voxels voxel event graph neural network vegnn event stream lightweight feature extraction network voxel event graph neural network graph structurefinallya graph neural networks gnns gait recognitiona
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FedCW: Client Selection with Adaptive Weight in Heterogeneous Federated Learning 认领 引用
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作者 Haotian Wu Jiaming Pei Jinhai Li 《Computers, Materials & Continua》 SCIE EI 2026年第1期1551-1570,共20页
With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy... With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments. 展开更多
关键词 Federated learning non-IID client selection weight allocation vehicular networks
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A Novel Synthetic Dataset for Effective Detection of Replay Attacks in SDN-Enabled IoT Networks 认领 引用
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作者 Nader Karmous Leila Bousbia +2 位作者 Mohamed Ould-Elhassen Aoueileyine Imen Filali Ridha Bouallegue 《Computers, Materials & Continua》 SCIE EI 2026年第7期700-735,共36页
This study proposes an intelligent Intrusion Detection and Prevention System(IDPS)integrated into a centralized Ryu Software-Defined Networking(SDN)controller to mitigate replay attacks within Internet of Things(IoT)e... This study proposes an intelligent Intrusion Detection and Prevention System(IDPS)integrated into a centralized Ryu Software-Defined Networking(SDN)controller to mitigate replay attacks within Internet of Things(IoT)environments.To address the scarcity of specialized datasets,a comprehensive dataset was generated using a real-time SDN-IoT testbed encompassing Mininet,multiple OpenFlow 1.3 switches,and a single Ryu controller.The experimental setup featured the exchange of legitimate and malicious Message Queuing Telemetry Transport(MQTT)traffic between hosts and IoT devices to simulate realistic network behaviors and attack vectors.Our methodology introduces a novel feature engineering framework by evaluating three distinct configurations,including:(1)preprocessed features,(2)data reduced through Principal Component Analysis(PCA),and(3)latent representations extracted via a Variational Autoencoder(VAE).Four distinct classifiers were rigorously benchmarked,including Random Forest(RF),Support Vector Machine(SVM),Extreme Gradient Boosting(XGBoost),and a Convolutional Neural Network(CNN).Performance metrics were derived from 50 independent runs and validated through paired t-tests and Wilcoxon signed-rank tests.The results demonstrate that VAE-based deep feature extraction significantly improves detection accuracy.Notably,the CNN trained on these features achieved a peak accuracy of 99.91%and a false alarm rate of 0.19%.The framework’s real-time effectiveness and scalability were validated through live deployment,offering a robust and reproducible solution for securing SDN-enabled IoT infrastructures.Ultimately,our proposed CNN-VAE approach demonstrates superior performance and higher detection precision compared to existing related works in the field of IoT intrusion detection. 展开更多
关键词 Replay attacks man in the middle cybersecurity software defined networking Internet of Things machine learning artificial intelligence deep learning variational autoencoder feature selection principal component analysis Wilcoxon signed-rank
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Selective area growth of high-quality in-plane InAs nanowires and nanowire networks by molecular-beam epitaxy on Ge substrates 认领 引用 被引量：1
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作者 Lei Liu Xiyu Hou +6 位作者 Chenkai Liu Lianjun Wen Donghai Li Miaoling Lin Ping-Heng Tan Dong Pan Jianhua Zhao 《Science China Materials》 SCIE EI CAS CSCD 2026年第4期2076-2085,共10页
In-plane InAs nanowires and nanowire networks have garnered significant attention in electronics,optoelectronics,and quantum computation due to their small electron effective mass,narrow bandgap,high electron mobility... In-plane InAs nanowires and nanowire networks have garnered significant attention in electronics,optoelectronics,and quantum computation due to their small electron effective mass,narrow bandgap,high electron mobility,strong spin-orbit coupling interaction,and large Landég factor.To date,in-plane InAs nanowires and nanowire networks have been primarily grown on III–V substrates.However,few studies have demonstrated the selective area growth of in-plane InAs nanowires and nanowire networks on CMOS-compatible group-IV Si or Ge substrates.In this work,we first employed conventional selective-area epitaxy to grow in-plane InAs nanostructures on Ge(111)substrates by molecular beam epitaxy.This approach,however,fails to concurrently achieve good selectivity and continuity.To overcome this limitation,we introduced a metal-sown,single-indium-source two-step growth method,which attains both selectivity and continuity but yields nanowires with rough surfaces and limited lengths(<10µm).We subsequently proposed an upgraded metal-sown,dual-indium-source two-step growth method,successfully fabricating in-plane InAs nanowires and nanowire networks with smooth surface morphology and lengths exceeding 60µm.Furthermore,by optimizing the As beam equivalent pressure,overgrowth at network junctions is effectively suppressed,resulting in uniform nanowire networks.High-resolution transmission electron microscopy and Raman spectroscopy confirm the high-quality single-crystalline nature and pure zinc-blende structure of the nanowires and networks.This work establishes a foundation for fabricating high-quality in-plane InAs/superconductor hybrid nanowires and nanowire networks. 展开更多
关键词 InAs nanowire nanowire network selective area growth molecular-beam epitaxy dual-indium-source
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