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IoT Empowered Early Warning of Transmission Line Galloping Based on Integrated Optical Fiber Sensing and Weather Forecast Time Series Data

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Computers, Materials & Continua 2025年第1期82卷 1171-1192页

作者： Zhe Li Yun Liang Jinyu wang Yang Gao State Grid Henan Electric Power Company Electric Power Scientific Research Institute State Grid Henan Electric Power CompanyZhengzhou450052China

Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power *** early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false *** address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather ***,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused ***,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network ***,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this ***,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.

关键词： Optical fiber sensing multi-source data fusion early warning of galloping time series data IoT adaptive weighted learning irregular time series perception closed-loop attention mechanism

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MoS2 synaptic transistor with one-step manufacture

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Science China(Information Sciences) 2025年第1期68卷 357-365页

作者： Yihao GUO Yang wang Wenjie DENG Yi WU Jingtao LI Kexin LI Yuehui ZHAO Songlin YU Xiaoting wang Yongzhe ZHANG Hui YAN Key Laboratory of Optoelectronics Technology of Education Ministry of China Faculty of Information TechnologyBeijing University of Technology Key Laboratory of Advanced Functional Materials Ministry of Education Faculty of Materials and ManufacturingBeijing University of Technology The 11th Research Institute of Electronics Technology Group Corporation

The synaptic transistor,which is a neuromorphic device with brain-like functions,has garnered significant attention due to characteristics such as low energy consumption,high integration,multiple functions,and high computational ***,the current synaptic transistor possesses a complicated structure and involves complex manufacturing steps,which is not conducive to low-cost,large-scale bionic ***,we introduce a simple MoS2synaptic transistor exhibiting robust synaptic *** transistor utilizes magnetron sputtering manufacture to combine the process of device metal electrode deposition and the introduction of defect states,which helps streamline the manufacturing process into a single *** long-term potentiation and long-term depression in photoelectric coordination are initially ***,biological functions,such as double pulse facilitation and the transformation behavior of short-term memory to long-term memory,are thoroughly evaluated,along with learning-forgetting-relearning ***,the device exhibits robust synaptic plasticity until incident light angles down to 30° to imitate the multi-to-one axon-cellular ***,a classical simulation of Pavlov's dog conditioned reflex is demonstrated along with the multiview extraction *** experimental results demonstrate a promising synaptic transistor with a simple structure,manufacturing technique,and excellent *** further indicates the potential for large-scale integration of bionic neuromorphic systems.

关键词： process optimization artificial synaptic device one-stepmanufacture synaptic plasticity bionic simulation

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Collaborative Trajectory Planning for Stereoscopic Agricultural Multi-UAVs Driven by the Aquila Optimizer

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Computers, Materials & Continua 2025年第1期82卷 1349-1376页

作者： Xinyu Liu Longfei wang Yuxin Ma Peng Shao School of Computer and Information Engineering Jiangxi Agricultural UniversityNanchang330045China

Stereoscopic agriculture,as an advanced method of agricultural production,poses new challenges for multi-task trajectory planning of unmanned aerial vehicles(UAVs).To address the need for UAVs to perform multi-task trajectory planning in stereoscopic agriculture,a multi-task trajectory planning model and algorithm(IEP-AO)that synthesizes flight safety and flight efficiency is *** on the requirements of stereoscopic agricultural geomorphological features and operational characteristics,the multi-task trajectory planning model is ensured by constructing targeted constraints at five aspects,including the path,slope,altitude,corner,energy and obstacle threat,to improve the effectiveness of the trajectory planning *** combined with the path optimization algorithm,an Aquila optimizer(IEP-AO)based on the interference-enhanced combination model is proposed,which can help UAVs to improve the trajectory search capability in complex operation space and large-scale operation tasks,and jump out of the locally optimal trajectory path region timely,to generate the optimal trajectory planning plan that can adapt to the diversity of the tasks and the flight ***,four simulated flights with different operation scales and different scene constraints were conducted under the constructed real 3Dimension scene,and the experimental results can show that the proposedmulti-task trajectory planning method canmeet themulti-task requirements in stereoscopic agriculture and improve the mission execution efficiency and agricultural production effect of UAV.

关键词： Stereoscopic agriculture unmanned aerial vehicle multi-task interference model Aquila optimizer

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Unmasking Social Robots’Camouflage:A GNN-Random Forest Framework for Enhanced Detection

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Computers, Materials & Continua 2025年第1期82卷 467-483页

作者： Weijian Fan Chunhua wang Xiao Han Chichen Lin School of Data Science and Intelligent Media Communication University of ChinaBeijing100024China School of Computer and Cyber Sciences Communication University of ChinaBeijing100024China Institute of Communication Studies Communication University of ChinaBeijing100024China State Key Laboratory of Media Convergence and Communication Communication University of ChinaBeijing100024China

The proliferation of robot accounts on social media platforms has posed a significant negative impact,necessitating robust measures to counter network anomalies and safeguard content *** robot detection has emerged as a pivotal yet intricate task,aimed at mitigating the dissemination of misleading *** graphbased approaches have attained remarkable performance in this realm,they grapple with a fundamental limitation:the homogeneity assumption in graph convolution allows social robots to stealthily evade detection by mingling with genuine human *** unravel this challenge and thwart the camouflage tactics,this work proposed an innovative social robot detection framework based on enhanced HOmogeneity and Random Forest(HORFBot).At the core of HORFBot lies a homogeneous graph enhancement strategy,intricately woven with edge-removal techniques,tometiculously dissect the graph intomultiple revealing ***,leveraging the power of contrastive learning,the proposed methodology meticulously trains multiple graph convolutional networks,each honed to discern nuances within these tailored *** culminating stage involves the fusion of these feature-rich base classifiers,harmoniously aggregating their insights to produce a comprehensive detection *** experiments on three social robot detection datasets have shown that this method effectively improves the accuracy of social robot detection and outperforms comparative methods.

关键词： Social robot detection graph neural networks random forest homophily heterophily

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Spatiotemporal evolution of wildfire activity during the PaleoceneEocene Thermal Maximum in China

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Science China Earth Sciences 2025年

作者： Xueting wang Zuoling CHEN Linlin CUI Xu wang State Key Laboratory of Lithospheric and Environmental Coevolution Institute of Geology and Geophysics Chinese Academy of Sciences University of Chinese Academy of Sciences College of Earth and Planetary Sciences Institute of Geology and Geophysics Chinese Academy of Sciences

The Paleocene-Eocene Thermal Maximum（PETM） was the most prominent warming event in the Cenozoic and serves as a geological analog for the current global warming driven by anthropogenic CO2emissions from burning fossil *** a primary disruptor of Earth's ecosystems, wildfire activity affects the balance of ecosystems, the global carbon cycle, and climate change. Therefore, studying the evolution and driving mechanisms of wildfires during the PETM is essential for predicting wildfire activity under global warming scenarios and for understanding the carbon cycle in the context of wildfire disturbances. Current research on wildfires during the PETM has mainly focused on the Northern Hemisphere, particularly Europe and North America, whereas studies on Chinese mainland remain limited. In this study, we analyzed black carbon（BC）concentration and carbon isotope(δ13C) in the Beigou section of the Nanyang Basin and the Xilutian section of the Fushun Basin to reconstruct the spatiotemporal evolution of wildfires in arid/semi-arid and humid zones. We integrated these findings with those of previous studies on the paleoclimate of the research area to elucidate the driving mechanisms behind wildfire occurrences. Our results indicate that wildfire activity weakened at the onset of the PETM, with the negative carbon isotope period characterized by low wildfire activity, except for a brief resurgence in the Nanyang Basin. During the recovery phase of the PETM, wildfire activity gradually increased, returning to the pre-PETM levels. The prolonged low wildfire state was likely caused by extremely high temperatures and precipitation as well as the resulting vegetation succession. During the PETM recovery phase, increased wildfire activity transformed “short-term carbon sinks” of the biological-atmospheric carbon cycle into “long-term carbon sinks” through the burial of BC. This process of carbon sink conversion may have acted as a negative feedback mechanism, contributing to carbo

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Anomaly Detection of Controllable Electric Vehicles through Node Equation against Aggregation Attack

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Computers, Materials & Continua 2025年第1期82卷 427-442页

作者： Jing Guo Ziying wang Yajuan Guo Haitao Jiang State Grid Jiangsu Electric Power Co. Ltd.Research InstituteState Grid Corporation of ChinaNanjing211100China

The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids *** study presents an innovative anomaly detection framework for EV charging stations,addressing the unique challenges posed by third-party aggregation *** approach integrates node equations-based on the parameter identification with a novel deep learning model,xDeepCIN,to detect abnormal data reporting indicative of aggregation *** employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter *** xDeepCIN model,incorporating a Compressed Interaction Network,has the ability to capture complex feature interactions in sparse,high-dimensional charging *** results on both proprietary and public datasets demonstrate significant improvements in anomaly detection performance,with F1-scores increasing by up to 32.3%for specific anomaly types compared to traditional methods,such as wide&deep and DeepFM(Factorization-Machine).Our framework exhibits robust scalability,effectively handling networks ranging from 8 to 85 charging ***,we achieve real-time monitoring capabilities,with parameter identification completing within seconds for networks up to 1000 *** research contributes to enhancing the security and reliability of renewable energy systems against evolving cyber threats,offering a comprehensive solution for safeguarding the rapidly expanding EV charging infrastructure.

关键词： Anomaly detection electric vehicle aggregation attack deep cross-network

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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

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Reconstruction characteristics of gut microbiota from patients with type 1 diabetes affect the phenotypic reproducibility of glucose metabolism in mice

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Science China Life Sciences 2025年第1期68卷 176-188页

作者： Zhiyi wang Mengxue Gong Yuanyuan Fang Huijuan Yuan Chenhong Zhang State Key Laboratory of Microbial Metabolism School of Life Sciences and BiotechnologyShanghai Jiao Tong University Department of Endocrinology Henan Provincial People's HospitalPeople's Hospital of Zhengzhou University

The human microbiota-associated(HMA) mice model, especially the germ-free(GF)-humanized mice, has been widely used to probe the causal relationships between gut microbiota and human diseases such as type 1 diabetes(T1D). However, most studies have not clarified the extent to which the reconstruction of the human donor microbiota in recipient mice correlates with corresponding phenotypic reproducibility. In this study, we transplanted fecal microbiota from five patients with T1D and four healthy people into GF mice, and microbiota from each donor were transplanted into 10 mice. Mice with similar microbiota structure to the donor exhibited better phenotypic reproducibility. The characteristics of the microbial community assembly of donors also influenced the phenotypic reproducibility in mice, and individuals with a higher proportion of stochastic processes showed more severe disorders. Microbes enriched in patients with T1D had a stronger colonization potential in mice with impaired glucose metabolism, and microbiota functional features related to T1D were better reproduced in these mice. This indicates that assembly traits and colonization efficacy of microbiota influence phenotypic reproducibility in GF-humanized mice. Our findings provide important insights for using HMA mice models to explore links between gut microbiota and human diseases.

关键词： gut microbiome human microbiota-associated animal model type 1 diabetes

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A Bayesian expectation maximization algorithm for state estimation of intelligent vehicles considering data loss and noise uncertainty

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Science China Technological Sciences 2025年

作者： Yan wang Feng TIAN Jianqiang wang Keqiang LI Department of Industrial and Systems Engineering The Hong Kong Polytechnic University School of Vehicle and Mobility Tsinghua University

Sideslip angle, yaw rate, and vehicle velocity are essential for intelligent vehicle control. Since these vehicle states are not measured directly, some Kalman-based approaches have been developed to estimate these states using in-vehicle sensors. However, the existing studies seldom account for the influence of sensor data loss on estimation accuracy. In addition,the process and measurement noise change during the estimation process because of the various driving conditions. To address these problems, an expectation-maximization robust extended Kalman filter (EMREKF) is proposed. Firstly, a robust extended Kalman filter (REKF) is developed to deal with the impact of missing measurements. Then, an improved expectation maximization (EM) algorithm that considers data loss is presented to update the noise parameter of the REKF dynamically. Finally,the improved EM is fused with the REKF to form the EMREKF to estimate vehicle state. The experimental results demonstrate that the EMREKF outperforms EKF, REKF, and maximum correntropy criterion EKF for various degrees of data loss and the proposed algorithm has a strong adaptive ability to different driving conditions.

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Design of a cationic accelerator enabling ultrafast ion diffusion kinetics in aqueous zinc-ion batteries

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Journal of Energy Chemistry 2025年第1期100卷 377-384页

作者： Yawei Xiao Qianqian Gu Haoyu Li Mengyao Li Yude wang National Center for International Research on Photoelectric and Energy Materials School of Materials and Energy Yunnan University Key Laboratory of Ministry of Education for Advanced Materials in Tropical Island Resources School of Chemistry and Chemical Engineering Hainan University College of Chemical Engineering Fuzhou University Yunnan Key Laboratory of Carbon Neutrality and Green Low-carbon Technologies Yunnan University

Aqueous zinc-ion batteries are highly favored for grid-level energy storage owing to their low cost and high safety,but their practical application is limited by slow ion *** address this,a strategy has been developed to create a cation-accelerating electric field on the surface of the cathode to achieve ultrafast Zn2+diffusion *** employing electrodeposition to coat MoS2on the surface of BaV6O16·3H2O nanowires,the directional built-in electric field generated at the heterointerface acts as a cation accelerator,continuously accelerating Zn2+diffusion into the active *** optimized Zn2+diffusion coefficient in CC@BaV-6O16·3H2O@MoS2(7.5×10～8cm2s-1) surpasses that of most reported V-based ***,MoS2serving as a cathodic armor extends the cycling life of the Zn-CC@BaV6O16·3H2O@MoS2full batteries to over 10000 *** work provides valuable insights into optimizing ion diffusion kinetics for high-performance energy storage devices.

关键词： Zinc alloys

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