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Journal of Energy Chemistry 2025年第1期100卷 612-620页

作者： Weiwei Yu Xinbao Zhang Hongyu Chen Yanan Wang Shaoguo li Fucun Chen Zhenni liu Xiujie li Xiangxue Zhu State Key Laboratory of Catalysis Dalian Institute of Chemical Physics Chinese Academy of Sciences

Selective hydrogenation of furfural to furfuryl alcohol is a great challenge in the hydrogenation field due to thermodynamic preference for hydrogenation of C=C over C=***,a novel Al2O3/C-u hybrid catalyst,composed of N-modified dendritic carbon networks supporting Al2O3nanoparticles,was successfully prepared via carbonizing the freeze-dried gel from spontaneous cross-linking of alginate,Al3+and *** obtained carbon-supported Al2O3hybrid catalyst has a high ratio （31%） of Al species in pentahedral-coordinated *** introduction of urea enhances the surface N content,the ratio of pyrrolic N,and specific surface area of catalyst,leading to improved adsorption capacity of C=O and the accessibility of active *** the furfural hydrogenation reaction with isopropyl alcohol as hydrogen donor,Al2O3/C-u catalyst achieved a 90%conversion of furfural with 98.0% selectivity to furfuryl alcohol,outperforming that of commercial γ-***,Al2O3/C-u demonstrates excellent catalytic stability in the recycling tests attributed to the synergistic effect of abundant weak Lewis acid sites and the anchoring effect of the carbon network on *** work provides an innovative and facile strategy for fabrication of carbon-supported Al2O3hybrid catalysts with rich AlVspecies,serving as a high selective hydrogenation catalyst through MPV reaction route.

关键词： Selective hydrogenation Furfural Furfuryl alcohol Cross-linking Carbon supported Al 2 O 3 MPV route

Ultrastrong MXene films for bone regeneration

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Science China Materials 2025年

作者： Jinghong li Department of Chemistry Center for Bioanalytical Chemistry Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education) Tsinghua University

Guided bone regeneration (GBR),as an important clinical treatment of bone defects,has long been a great challenge in modern *** addition to biological scaffolds,growth factors,and stem cells,barrier membranes for GBR application also play a key role in preventing the invasion of cells from the surrounding epithelium and connective tissue[1–3],to favor osteoprogenitor cells to proliferate and form new bone tissue in the site of *** commercial GBR membranes,such as polytetrafluoroethylene,titanium,collagen,and polyesters,exhibit low osteogenesis performance because of the lack of suitable antioxidant ability.

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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

IDSSCNN-XgBoost:Improved Dual-Stream Shallow Convolutional Neural Network Based on Extreme Gradient Boosting Algorithm for Micro Expression Recognition

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Computers, Materials & Continua 2025年第1期82卷 729-749页

作者： Adnan Ahmad Zhao li Irfan Tariq Zhengran He School of Information Science and Engineering Southeast UniversityNanjing210016China

Micro-expressions(ME)recognition is a complex task that requires advanced techniques to extract informative features fromfacial *** deep neural networks(DNNs)with convolutional structures have been ***,unlike DNNs,shallow convolutional neural networks often outperform deeper models in mitigating overfitting,particularly with small ***,many of these methods rely on a single feature for recognition,resulting in an insufficient ability to extract highly effective *** address this limitation,in this paper,an Improved Dual-stream Shallow Convolutional Neural Network based on an Extreme Gradient Boosting Algorithm(IDSSCNN-XgBoost)is introduced for ME *** proposed method utilizes a dual-stream architecture where motion vectors(temporal features)are extracted using Optical Flow TV-L1 and amplify subtle changes(spatial features)via EulerianVideoMagnification(EVM).These features are processed by IDSSCNN,with an attention mechanism applied to refine the extracted effective *** outputs are then fused,concatenated,and classified using the XgBoost *** comprehensive approach significantly improves recognition accuracy by leveraging the strengths of both temporal and spatial information,supported by the robust classification power of *** proposed method is evaluated on three publicly available ME databases named Chinese Academy of Sciences Micro-expression Database(CASMEII),Spontaneous Micro-Expression Database(SMICHS),and Spontaneous Actions and Micro-Movements(SAMM).Experimental results indicate that the proposed model can achieve outstanding results compared to recent *** accuracy results are 79.01%,69.22%,and 68.99%on CASMEII,SMIC-HS,and SAMM,and the F1-score are 75.47%,68.91%,and 63.84%,*** proposed method has the advantage of operational efficiency and less computational time.

关键词： ME recognition dual stream shallow convolutional neural network euler video magnification TV-L1 XgBoost

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

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

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

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

Optimizing Fine-Tuning in Quantized Language Models:An In-Depth Analysis of Key Variables

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Computers, Materials & Continua 2025年第1期82卷 307-325页

作者： Ao Shen Zhiquan Lai Dongsheng li Xiaoyu Hu National Key Laboratory of Parallel and Distributed Computing National University of Defense TechnologyChangsha410073China Strategic Assessments and Consultation Institute Academy of Military ScienceBeijing100091China

Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning *** this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader ***-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational *** these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA *** these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains *** study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM *** investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do *** insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized ***,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller *** study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM

关键词： Large-scale Language Model Parameter-Efficient Fine-Tuning parameter quantization key variable trainable parameters experimental analysis

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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