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Malicious Document Detection Based on GGE Visualization

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Computers, Materials & Continua 2025年第1期82卷 1233-1254页

作者： Youhe wang Yi Sun Yujie Li Chuanqi Zhou Henan Province Key Laboratory of Information Security Information Engineering UniversityZhengzhou450000China

With the development of anti-virus technology,malicious documents have gradually become the main pathway of Advanced Persistent Threat(APT)attacks,therefore,the development of effective malicious document classifiers has become particularly ***,detection methods based on document structure and behavioral features encounter challenges in feature engineering,these methods not only have limited accuracy,but also consume large resources,and usually can only detect documents in specific formats,which lacks versatility and *** address such problems,this paper proposes a novel malicious document detection method-visualizing documents as GGE images(Grayscale,Grayscale matrix,Entropy).The GGE method visualizes the original byte sequence of the malicious document as a grayscale image,the information entropy sequence of the document as an entropy image,and at the same time,the grayscale level co-occurrence matrix and the texture and spatial information stored in it are converted into grayscale matrix image,and fuses the three types of images to get the GGE color *** Convolutional Block Attention Module-EfficientNet-B0(CBAM-EfficientNet-B0)model is then used for classification,combining transfer learning and applying the pre-trained model on the ImageNet dataset to the feature extraction process of GGE *** shown in the experimental results,the GGE method has superior performance compared with other methods,which is suitable for detecting malicious documents in different formats,and achieves an accuracy of 99.44%and 97.39%on Portable Document Format(PDF)and office datasets,respectively,and consumes less time during the detection process,which can be effectively applied to the task of detecting malicious documents in real-time.

关键词： Malicious document visualization EfficientNet-B0 convolutional block attention module GGE image

High-temperature oxidation behavior and degradation mechanism of chromium-coated zirconium alloy cladding: a review

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Tungsten 2025年第1期7卷 50-70页

作者： Yi Li Bei-Qi wang Xin-Yin Zhang Shi-Jie wang Cong-Yi Li Hui-Long Yang Ran-Ran Su Tong Liu School of Mechanical Engineering Shanghai Jiao Tong University College and Graduate School of Arts and Sciences University of Virginia Global Institute of Future Technology Shanghai Jiao Tong University

Accident-tolerant fuels(ATF) have been extensively studied to reduce the production rate and total amount of heat and hydrogen generated from high-temperature steam oxidation during severe ***-coated zirconium alloy cladding has become one of the most promising candidates for ATF because of its excellent corrosion and oxidation *** paper provides a comprehensive overview of the research progress on oxidation behaviors and degradation mechanisms in chromium-coated zirconium alloy cladding under high-temperature *** techniques to strengthen the oxidation resistance are highlighted and ***,challenges and opportunities for various future directions are addressed.

关键词： Accident-tolerant fuel Chromium coating Degradation mechanism High-temperature oxidation behavior Zirconium alloy cladding

Has global warming already increased precipitation variability and disaster risk in past century?

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Science China Earth Sciences 2025年

作者： Huijun wang Nanjing University of Information Science and Technology

The global water cycle intensifies under climate warming,with an increase in global mean precipitation as well as extreme precipitation over many regions (Trenberth,1999;Chen and Sun,2017;Douville et al.,2021;Yin et al.,2024).In addition,precipitation has become increasingly volatile in recent *** example,both California and the Horn of Africa were hit by floods in 2023 immediately following prolonged multi-year ***,Henan province in eastern China experienced a severe drought-to-flood transition in the summer of 2024,causing extensive crop *** wild transitions between droughts and floods,in fact,reflect an increase in precipitation variability.

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SEFormer:A Lightweight CNN-Transformer Based on Separable Multiscale Depthwise Convolution and Efficient Self-Attention for Rotating Machinery Fault Diagnosis

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Computers, Materials & Continua 2025年第1期82卷 1417-1437页

作者： Hongxing wang Xilai Ju Hua Zhu Huafeng Li State Key Laboratory of Mechanics and Control for Aerospace Structures Nanjing University of Aeronautics and AstronauticsNanjing210016China School of Computer Science and Engineering Nanyang Technological UniversitySingapore639798Singapore

Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain ***,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis ***,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault ***,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative *** complexity results in high computational costs and limited industrial *** tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault ***,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration ***,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global ***,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and *** study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.

关键词： CNN-Transformer separable multiscale depthwise convolution efficient self-attention fault diagnosis

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

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

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

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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