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Device 2025年第5期3卷

作者： Zhong, Zhipeng Cheng, Xin Song, Wenqing Yang, Qianyi Li, Xiang Wang, Yuyang Wang, Wan Zhuang, Yezhao Chen, Yan Shi, Wu Lin, Tie Meng, Xiangjian Huang, Hai Wang, Jianlu Chu, Junhao State Key Laboratory of Photovoltaic Science and Technology Shanghai Frontiers Science Research Base of Intelligent Optoelectronic and Perception Institute of Optoelectronics and Department of Material Science Fudan University Shanghai 200433 China Zhangjiang Fudan International Innovation Center Fudan University Shanghai 201210 China State Key Laboratory of Surface Physics and Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai 200433 China State Key Laboratory of Infrared Physics Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai 200083 China Frontier Institute of Chip and System Fudan University Shanghai 200433 China

Reconfigurable logic devices that can perform multiple functions with fewer components offer a solution for increasing data processing demands. Achieving reconfigurability without complex designs, however, remains challenging. In this study, we present a two-terminal reconfigurable homojunction device based on ionic-conducting CuInP₂S₆ (CIPS). The device achieves tunable and reversible rectification by controlling Cu migration under an electric field. The homojunction is formed by modulating the contact potential of the CIPS channel, resulting in an asymmetric contact barrier. The resulting device has a rectification ratio of up to 4 × 10⁴, an ultra-low leakage current (∼100 fA), and a high breakdown voltage of 170 V, maintaining read stability over 3,000 cycles. It operates at frequencies up to 10 kHz and can be reconfigured as OR and AND logic states depending on the Cu migration direction of the CIPS homojunction. © 2025 The Author(s)

关键词： copper indium thiophosphate DTI-2: Explore ionic conductor photovoltaic reconfigurable homojunction

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PreZ-DGGAN: A Drug Graph GAN Based on Pre-Learning of Implicit Variables 2nd

PreZ-DGGAN: A Drug Graph GAN Based on Pre-Learning of Implic...

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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Liu, Yixin Fan, Yueqin Li, Zhipeng Zhang, Qinhu Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China School of Mechanical Engineering Guangxi University Nanning530004 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315201 China Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology School of Medicine Shanghai East Hospital Tongji University Shanghai200123 China College of Advanced Agricultural Sciences Zhejiang Agriculture and Forestry University Hangzhou311300 China

ISBN: (纸本)9789819619061

In the field of drug discovery and development, deep learning techniques have become a powerful tool to accelerate the discovery and development of new drugs. In the design and optimization of lead molecules, generative adversarial network and graph neural network are one of the prime techniques for accelerating research and development, because the former is based on the idea of two-player game to learn drug features to generate effective molecules, while the unstructured inputs of the latter are well-suited for the analysis of molecular data. Therefore, this study develops a new graph generative adversarial model, called PreZ-DGGAN, by inserting the graph neural network layer into the generative adversarial network. Within PreZ-DGGAN, the latent variable input of the generator is pre-learned by an improved graph variational autoencoder, which effectively avoids the mode collapse of the generative adversarial network, whereas the hidden layer of the discriminator is graph neural network layer, which is able to better learn the real molecules and judge the generated molecules as true or false. Experiments on two molecule datasets show that the molecules generated by PreZ-DGGAN have higher validity (100% on average) and uniqueness (91.5% on average), and the generated molecules are more diverse in structure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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MDH-NAS: Accelerating EEG Signal Classification with Mixed-Level Differentiable and Hardware-Aware Neural Architecture Search

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IEEE Internet of Things Journal 2025年

作者： Zhu, Lixian Wang, Su Jin, Xiaokun Zheng, Kai Zhang, Jian Sun, Shuting Tian, Fuze Cai, Ran Hu, Bin Key Laboratory of Brain Health Intelligent Evaluation and Intervention Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology School of Medical Technology Beijing100081 China Lanzhou University School of Information Science and Engineering Lanzhou730000 China Lanzhou University Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou China Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Shanghai China Chinese Academy of Sciences Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Lanzhou China

In non-invasive brain-computer interfaces (BCIs), EEG analysis plays a critical role, with neural networks serving as a cornerstone for signal decoding. Existing neural network approaches for EEG signal recognition require extensive manual design and hyperparameter tuning, leading to inefficiencies and making them impractical for embedded devices due to their large model size. To address these limitations, we propose Mixed-Level Differentiable and Hardware-Aware Neural Architecture Search (MDH-NAS), a framework that automatically generates lightweight neural networks tailored for EEG classification. Unlike traditional DARTS methods, MDH-NAS employs a hybrid optimization strategy that balances global and local search spaces, thereby accelerating and refining architecture discovery. It introduces explicit size constraints during the search process to ensure deployability on embedded devices. MDH-NAS demonstrates autonomous generation of architectures for tasks such as motor imagery (MI) and depression recognition, achieving 87.80% accuracy on the BCI-IV dataset and 90.09% on the MODMA dataset. When deployed on the EAIDK-610 board across heterogeneous tasks, it attains 85.37% accuracy on the EEG Motor Movement/Imagery dataset. This method reduces architecture discovery time by 89% and enhances prediction accuracy by 8.70% compared to baseline methods, highlighting its potential for scalable EEG analysis and real-world embedded deployment. © 2014 IEEE.

关键词： Electroencephalography

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Investigation of the Rutting Evolution of Double-Layered Heterogeneous Asphalt Pavement on a Steel Bridge Deck under the Coupling of Heavy Load and Variable Temperature

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Journal of Materials in Civil engineering 2025年第7期37卷

作者： Liu, Yang Qian, Zhendong Gong, Minghui Bo, Wu Zhang, Xiangfei Xu, Cai Intelligent Transportation System Research Center Southeast Univ. 2 Southeast University Rd. Nanjing211189 China Jiangsu Sobute New Materials Co. Ltd. 118 Liquan Rd. Nanjing211103 China School of Engineering Tibet Univ. 10 Tibet University East Rd. Lasa850000 China China Construction Eighth Engineering Division Co. Ltd. 1568 Century Ave. Shanghai200122 China

This paper studies the rutting evolution of a gussasphalt (GA) and epoxy asphalt (EA) composite structure under the coupling of heavy load and variable temperature for a double-layered heterogeneous pavement structure of long-span steel bridge. First, the rheological properties of the GA and EA mixtures were evaluated by conducting a permanent deformation test under triaxial repeated loads. The creep parameters for the GA and EA mixtures were determined by multiple linear regression based on the Bailey-Norton creep model. Then, the real traffic volume and temperature distribution of the GA+EA composite pavement structure on a long-span steel bridge in summer were obtained, and the permanent deformation evolution laws of the GA+EA composite pavement structure under different loading and temperature conditions were simulated using a finite-element (FE) method. The results show that the GA+EA structure has excellent rutting resistance performance. The rutting depth value estimated under constant temperature is exaggerated to some extent compared with that estimated under variable temperature condition. When the tire-pavement contact pressure increases from 0.7 MPa to 1.4 MPa, the rutting depth of GA+EA structure increases by about 110%. Findings from this study are helpful to investigate the rutting characteristics in the steel bridge deck pavement (SBDP) and may be used in the design of SBDP materials and structures. © 2025 American Society of Civil Engineers.

关键词： Asphalt pavements

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A comparative study of the performance of ten metaheuristic algorithms for parameter estimation of solar photovoltaic models

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PeerJ Computer Science 2025年 11卷 e2646-e2646页

作者： Zga, Adel Zitouni, Farouq Harous, Saad Sallam, Karam Almazyad, Abdulaziz S. Xiong, Guojiang Mohamed, Ali Wagdy Department of Computer Science and Information Technology Laboratory of Artificial Intelligence and Information Technology Kasdi Merbah University Ouargla Algeria Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical Engineering Guizhou University Guiyang China Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza Egypt Applied Science Research Center Applied Science Private University Amman Jordan

This study conducts a comparative analysis of the performance of ten novel and wellperforming metaheuristic algorithms for parameter estimation of solar photovoltaic models. This optimization problem involves accurately identifying parameters that reflect the complex and nonlinear behaviours of photovoltaic cells affected by changing environmental conditions and material inconsistencies. This estimation is challenging due to computational complexity and the risk of optimization errors, which can hinder reliable performance predictions. The algorithms evaluated include the Crayfish Optimization Algorithm, the Golf Optimization Algorithm, the Coati Optimization Algorithm, the Crested Porcupine Optimizer, the Growth Optimizer, the Artificial Protozoa Optimizer, the Secretary Bird Optimization Algorithm, the Mother Optimization Algorithm, the Election Optimizer Algorithm, and the Technical and Vocational Education and Training-Based Optimizer. These algorithms are applied to solve four well-established photovoltaic models: the singlediode model, the double-diode model, the triple-diode model, and different photovoltaic module models. The study focuses on key performance metrics such as execution time, number of function evaluations, and solution optimality. The results reveal significant differences in the efficiency and accuracy of the algorithms, with some algorithms demonstrating superior performance in specific models. The Friedman test was utilized to rank the performance of the various algorithms, revealing the Growth Optimizer as the top performer across all the considered models. This optimizer achieved a root mean square error of 9.8602187789E−04 for the single-diode model, 9.8248487610E−04 for both the double-diode and triplediode models and 1.2307306856E−02 for the photovoltaic module model. This consistent success indicates that the Growth Optimizer is a strong contender for future enhancements aimed at further boosting its efficiency and effectiveness. Its

关键词： Mean square error

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Compression and thermal conduction performance of bioinspired sandwich structures fabricated by laser powder bed fusion

Additive Manufacturing Frontiers

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Additive Manufacturing Frontiers 2025年第1期4卷

作者： Lin, Kaijie Hu, Kaiming Gu, Dongdong Yang, Jiankai Zhou, Mingdong Xiong, Yi College of Materials Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing 210016 China Jiangsu Provincial Research Center for Laser Additive Manufacturing of High-Performance Components Nanjing University of Aeronautics and Astronautics Nanjing 210016 China Shanghai Aircraft Manufacturing Co. Ltd. Shanghai 201324 China School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai 200093 China Shanghai Key Laboratory of Digital Manufacture for Thin-walled Structures School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen 518055 China

After millions of years of natural evolution, horsetails have evolved unique stem structures that enable survival in harsh environments. Inspired by the cross-sectional characteristics of horsetail stems, a series of bioinspired sandwich structures were designed and fabricated using the laser powder bed fusion (LPBF) process. By combining experimental and finite element simulation methods, the formability, mechanical properties, deformation behavior, and thermal conduction performance of these structures were determined. Results show that the surface morphology of the bioinspired sandwich structures was smooth, with no cracks observed. The bioinspired sandwich structure with an inner tube diameter of 1.9 mm (D_1.9) exhibited optimal comprehensive mechanical properties, with a specific strength of 64.2 MPa/(g/cm³), and specific energy absorption of 3.3 J/g. Stress distribution results revealed that the D_1.9 structures had the most uniform stress distribution. Furthermore, increasing the internal conduction paths improved heat transfer;therefore, the thermal conductivities of the D_1.4, D_1.9, and D_2.4 structures were higher than that of the D₀ structure. This study demonstrates that a bioinspired design approach, combined with additive manufacturing technology, enables the development of high-performance structures with both load-bearing and thermally insulating capabilities. © 2025

关键词： Additive manufacturing Bioinspired Laser powder bed fusion Mechanical properties Thermal conduction

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Data quantity governance for machine learning in materials science

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National Science Review 2023年第7期10卷 234-250页

作者： Yue Liu Zhengwei Yang Xinxin Zou Shuchang Ma Dahui Liu Maxim Avdeev Siqi Shi School of Computer Engineering and Science Shanghai University Shanghai Engineering Research Center of Intelligent Computing System Australian Nuclear Science and Technology Organisation School of Chemistry The University of Sydney State Key Laboratory of Advanced Special Steel School of Materials Science and Engineering Shanghai University Materials Genome Institute Shanghai University

Data-driven machine learning(ML) is widely employed in the analysis of materials structure-activity relationships,performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate ***,because of the laborious process of materials data acquisition,ML models encounter the issue of the mismatch between a high dimension of feature space and a small sample size(for traditional ML models) or the mismatch between model parameters and sample size(for deep-learning models),usually resulting in terrible ***,we review the efforts for tackling this issue via feature reduction,sample augmentation and specific ML approaches,and show that the balance between the number of samples and features or model parameters should attract great attention during data quantity *** this,we propose a synergistic data quantity governance flow with the incorporation of materials domain *** summarizing the approaches to incorporating materials domain knowledge into the process of ML,we provide examples of incorporating domain knowledge into governance schemes to demonstrate the advantages of the approach and *** work paves the way for obtaining the required high-quality data to accelerate materials design and discovery based on ML.

关键词： machine learning data governance data quantity materials science

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV systems

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IEEE Transactions on Mobile computing 2025年

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science Faculty of Environment Science and Economy ExeterEX4 4QF United Kingdom Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China University of Oslo Department of Informatics Oslo0316 Norway

Mobile Edge computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. © 2025 IEEE.

关键词： Convex optimization

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An anti-aliasing filtering of quantum images in spatial domain using a pyramid structure

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Chinese Physics B 2024年第5期33卷 223-237页

作者：吴凯周日贵罗佳 College of Information Engineering Shanghai Maritime UniversityShanghai 201306China Research Center of Intelligent Information Processing and Quantum Intelligent Computing Shanghai 201306China School of Mathematics and Computational Science Shangrao Normal UniversityShangrao 334001China

As a part of quantum image processing,quantum image filtering is a crucial technology in the development of quantum ***-pass filtering can effectively achieve anti-aliasing effects on ***,most quantum image filterings are based on classical domains and grayscale images,and there are relatively fewer studies on anti-aliasing in the quantum *** paper proposes a scheme for anti-aliasing filtering based on quantum grayscale and color image scaling in the spatial *** achieves the effect of anti-aliasing filtering on quantum images during the scaling ***,we use the novel enhanced quantum representation(NEQR)and the improved quantum representation of color images(INCQI)to represent classical *** aliasing phenomena are more pronounced when images are scaled down,this paper focuses only on the anti-aliasing effects in the case of ***,we perform anti-aliasing filtering on the quantum representation of the original image and then use bilinear interpolation to scale down the image,achieving the anti-aliasing *** constructed pyramid model is then used to select an appropriate image for upscaling to the original image ***,the complexity of the circuit is *** to the images experiencing aliasing effects solely due to scaling,applying anti-aliasing filtering to the images results in smoother and clearer ***,the anti-aliasing filtering allows for manual intervention to select the desired level of image smoothness.

关键词： quantum color image processing anti-aliasing filtering algorithm quantum multiplier pyramid model

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Provably Secure Efficient Key-Exchange Protocol for intelligent Supply Line Surveillance in Smart Grids

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IEEE Internet of Things Journal 2025年

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J.F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan 250353 China

intelligent supply line surveillance is critical for modern smart grids. Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the smart grid. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage, and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the Random or Real (ROR) model. Furthermore, informal security analysis demonstrates the protocol's robustness in rigorously resisting various attacks, including physical tampering, impersonation, ephemeral secret leakage and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol's superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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