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Local magnetic moment oscillation around an Anderson impurity on graphene

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 192-201页

作者： Shuai Li Zhen Ma Jin-Hua Gao School of Science Hebei University of Science and Technology School of Electronics and information Zhengzhou University of Light Industry School of Physics and Wuhan National High Magnetic Field center Huazhong University of Science and Technology

We theoretically investigate the spin resolved Friedel oscillation （FO） and quasiparticle interference （QPI） in graphene induced by an Anderson *** the impurity becomes magnetic,the resulted FO becomes spin dependent,which gives rise to a local magnetic moment oscillation with an envelop decaying as r-2in real space in the doping ***,at half filling,the electron density and local magnetic moment will not oscillate but decay as *** spin resolved FO has both sublattice and spin ***,the local magnetic moment decay at half filling only occurs at one sublattice of graphene,which is quite like the phenomenon observed in the STM experiment (***ález-Herrero et al.,Science 352,437 （2016）).We further give an analytic formula about such spin dependent FO based on the stationary phase ***,we study the interference of quasiparticles around the magnetic impurity by calculating the spin dependent Fourier-transformed local density of states （FT-LDOS）.Our work gives a comprehensive understanding about the local magnetic moment oscillation around an Anderson impurity on graphene.

关键词： Friedel oscillation local magnetic moment graphene Anderson impurity

Interannual variability of boreal summer intraseasonal oscillation over the northwestern Pacific influenced by the Pacific Meridional Mode

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Atmospheric and Oceanic Science Letters 2025年第1期18卷 6-11页

作者： Haoyu Zhou Pang-Chi Hsu Lin Chen Yitian Qian Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environmental Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science and TechnologyNanjingChina

During the boreal summer,intraseasonal oscillations exhibit significant interannual variations in intensity over two key regions:the central-western equatorial Pacific(5°S-5°N,150°E-150°W)and the subtropical Northwestern Pacific(10°-20°N,130°E-175°W).The former is well-documented and considered to be influenced by the ENSO,while the latter has received comparatively less attention and is likely influenced by the Pacific Meridional Mode(PMM),as suggested by partial correlation analysis *** elucidate the physical processes responsible for the enhanced(weakened)intraseasonal convection over the subtropical northwestern Pacific during warm(cold)PMM years,the authors employed a moisture budget *** findings reveal that during warm PMM years,there is an increase in summer-mean moisture over the subtropical northwestern *** increase interacts with intensified vertical motion perturbations in the region,leading to greater vertical moisture advection in the lower troposphere and consequently resulting in convective *** a process is pivotal in amplifying intraseasonal convection *** observational findings were further verified by model experiments forced by PMM-like sea surface temperature patterns.

关键词： Boreal summer intraseasonal oscillation Interannual variability Pacific Meridional Mode Moisture budget analysis

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Industrial Control Anomaly Detection Based on Distributed Linear Deep Learning

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Computers, Materials & Continua 2025年第1期82卷 1129-1150页

作者： Shijie Tang Yong Ding Huiyong Wang School of Computer Science and information Security Guilin University of Electronic TechnologyGuilin541004China School of Electronic Engineering and Automation Guilin University of Electronic TechnologyGuilin541004China Guangxi Engineering Research center of Industrial Internet Security and Blockchain Guilin University of Electronic TechnologyGuilin541004China Institute of Cyberspace technology HKCT Institute for Higher EducationHong Kong999077China School of Mathematics&Computing Science Guilin University of Electronic TechnologyGuilin541004China

As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and fast and accurate attack detection techniques are *** key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time *** address this issue,we propose an anomaly detection method based on distributed deep *** method uses a bilateral filtering algorithm for sequential sequences to remove noise in the time series,which can maintain the edge of discrete *** use a distributed linear deep learning model to establish a sequential prediction model and adjust the threshold for anomaly detection based on the prediction error of the validation *** method can not only detect abnormal attacks but also locate the sensors that cause *** conducted experiments on the Secure Water Treatment(SWAT)and Water Distribution(WADI)public *** experimental results show that our method is superior to the baseline method in identifying the types of attacks and detecting efficiency.

关键词： Anomaly detection CPS deep learning MLP(multi-layer perceptron)

A Cross-Multi-Domain Trust Assessment Authority Delegation Method Based on Automotive Industry Chain

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Computers, Materials & Continua 2025年第1期82卷 407-426页

作者： Binyong Li Liangming Deng Jie Zhang Xianhui Deng School of Cybersecurity(Xin Gu Industrial College) Chengdu University of Information TechnologyChengdu610225China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225China SUGON Industrial Control and Security center Chengdu610225China

To solve the challenges of connecting and coordinating multiple platforms in the automotive industry and to enhance collaboration among different participants,this research focuses on addressing the complex supply relationships in the automotive market,improving data sharing and interactions across various platforms,and achieving more detailed integration of data and *** propose a trust evaluation permission delegation method based on the automotive industry *** proposed method combines smart contracts with trust evaluation mechanisms,dynamically calculating the trust value of users based on the historical behavior of the delegated entity,network environment,and other factors to avoid malicious node attacks during the permission delegation *** also introduce strict control over the cross-domain permission granting and revocation mechanisms to manage the delegation path,prevent information leakage caused by malicious node interception,and effectively protect data integrity and *** analysis shows that this method meets the realtime requirements of collaborative interaction in the automotive industry chain and provides a feasible solution to permission delegation issues in the automotive industry chain,offering dynamic flexibility in authorization and scalability compared to most existing solutions.

关键词： Automotive industry chain cross-domain access block chain delegate authority trust assessment

Role of the Greenland Sea ice anomaly in the late-spring drought over Northwest China

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Atmospheric and Oceanic Science Letters 2025年第1期18卷 1-5页

作者： Yang Liu Huopo Chen Nansen-Zhu International Research Centre Institute of Atmospheric Physics Chinese Academy of SciencesBeijingChina Collaborative Innovation center on Forecast and Evaluation of Meteorological Disasters Nanjing University for Information Science and TechnologyNanjingChina

Drought across Northwest China in late spring has exerted a vital effect on the local climate and agricultural production,and has been alleviated during the past *** study explored the influence of the preceding Arctic sea ice on the May drought in Northwest China caused by the precipitation *** analysis indicated that when the Greenland Sea ice concentration is abnormally high during February to April,the dry conditions in Northwest China tend to be *** increase of sea ice in the Greenland Sea can excite a meridional circulation,which causes sea surface temperature(SST)anomalies in the North Atlantic via the sea-air interaction,manifested as significant warm SST anomalies over the south of Greenland and the subtropical North Atlantic,but negative SST anomalies over the west of the *** abnormal SST pattern maintains to May and triggers a zonal wave train from the North Atlantic through Scandinavia and Central Asia to Northwest China,leading to abnormal cyclones in Northwest ***,Northwest China experiences a more humid climate than usual.

关键词： Arctic sea ice Greenland Sea North Atlantic SST Drought Northwest China

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A Latency-Aware and Fault-Tolerant Framework for Resource Scheduling and Data Management in Fog-Enabled Smart City Transportation Systems

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Computers, Materials & Continua 2025年第1期82卷 1377-1399页

作者： Ibrar Afzal Noor ul Amin Zulfiqar Ahmad Abdulmohsen Algarni Department of Computer Science and information technology Hazara UniversityMansehra21300Pakistan Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation *** computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end *** of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud *** smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system *** address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog *** framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation *** FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud *** simulation-based executions,tasks are allocated to the nearest available nodes with minimum *** the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of *** successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.

关键词： Fog computing smart cities smart transportation data management fault tolerance resource scheduling

A Multi-Objective Particle Swarm Optimization Algorithm Based on Decomposition and Multi-Selection Strategy

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Computers, Materials & Continua 2025年第1期82卷 997-1026页

作者： Li Ma Cai Dai Xingsi Xue Cheng Peng School of Computer Science Shaanxi Normal UniversityXi’an710119China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China information Construction and Management center and Institute of Artificial Intelligence and Educational New Productivity Ningxia Normal UniversityGuyuan756099China

The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization *** the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search ***,two update strategies based on decomposition are used to update the evolving population and external archive,***,a multiselection strategy is *** first strategy is for the subspace without a non-dominated *** the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global *** second strategy is for the subspace with a non-dominated *** the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local *** third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated *** the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to ***,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search ***,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test *** results show that the proposed algorithm has better performance.

关键词： Multi-objective optimization multi-objective particle swarm optimization decomposition multi-selection strategy

Lightweight Underwater Target Detection Using YOLOv8 with Multi-Scale Cross-Channel Attention

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Computers, Materials & Continua 2025年第1期82卷 713-727页

作者： Xueyan Ding Xiyu Chen Jiaxin Wang Jianxin Zhang School of Computer Science and Engineering Dalian Minzu UniversityDalian116600China Research center of Multimodal information Perception and Intelligent Processing Dalian Minzu UniversityDalian116600China

Underwater target detection is extensively applied in domains such as underwater search and rescue,environmental monitoring,and marine resource *** is crucial in enabling autonomous underwater robot operations and promoting ocean ***,low imaging quality,harsh underwater environments,and obscured objects considerably increase the difficulty of detecting underwater targets,making it difficult for current detection methods to achieve optimal *** order to enhance underwater object perception and improve target detection precision,we propose a lightweight underwater target detection method using You Only Look Once(YOLO)v8 with multi-scale cross-channel attention(MSCCA),named *** the proposed multiscale cross-channel attention module,multi-scale attention(MSA)augments the variety of attentional perception by extracting information from innately diverse sensory *** cross-channel strategy utilizes RepVGGbased channel shuffling(RCS)and one-shot aggregation(OSA)to rearrange feature map channels according to specific *** aggregates all features only once in the final feature mapping,resulting in the extraction of more comprehensive and valuable feature *** experimental results show that the proposed YOLOv8-UOD achieves a mAP50 of 95.67%and FLOPs of 23.8 G on the Underwater Robot Picking Contest 2017(URPC2017)dataset,outperforming other methods in terms of detection precision and computational cost-efficiency.

关键词： Deep learning underwater target detection attention mechanism

RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

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Science China(information Sciences) 2025年

作者： Wenyu CAI Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National Engineering Research center for Big Data technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

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