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Thick 3D-Printed Hierarchical Li4Ti5O12@MOF anode and Grid-Lined micropores for High-Performance Lithium-Ion batteries

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Journal of Colloid and Interface Science 2025年 696卷 137883页

作者： Mwizerwa, Jean Pierre Li, Jie Nsengiyumva, Walter Li, Chen Xu, Kun Rasaki, Sefiu Abolaji Liu, Changyong School of Materials Science and Engineering Fujian University of Technology Fuzhou350118 China Additive Manufacturing Institute College of Mechatronics & Control Engineering Shenzhen University Shenzhen518060 China Fujian Provincial Key Laboratory of Terahertz Functional Devices and Intelligent Sensing School of Mechanical Engineering and Automation Fuzhou University Fuzhou China The Institute of Precision Instrument and Intelligent Measurement & Control Fuzhou University Fuzhou China Institute: Department of Chemical and Petroleum Engineering University of Calgary AlbertaT2N 1N4 Canada

Li4Ti5O12 (LTO) anode is a promising candidate for high-energy–density lithium-ion batteries (LIBs), but achieving high mass loading, a porous structure, and efficient ion transport remains a challenge. Herein, we present a high-mass-loading, hierarchically porous LTO anode with a conductive network and grid-lined micropores, embedded with a metal–organic framework (MOF) as both an electrode additive and surface protective layer. This novel structure is fabricated using extrusion-based three-dimensional (3D) printing technology. The self-standing framework provides mechanical stability and high conductivity, enabling a thick (316 μm) 3D-printed LTO@UiO-66-MOF (3D-LTO@U) anode with a mass loading of 11 mg/cm2. It delivers a high-rate capability of 161 mAh/g at 5C, an areal specific capacity of 5.37 mAh/cm2, and 78.9 % areal capacity retention after 150 cycles. Additionally, it achieves a high specific energy density of 382.35 Wh/kg. The UiO-66 MOF provides strong binding affinity, suppressing side reactions and enhancing Li-ion/electron transport within the 3D-printed interconnected channels. This improves active material utilization during charge and discharge. Furthermore, a 3D-printed full cell integrating a grid-lined 3D-LTO@U anode and a 3D-printed LiFePO4 cathode exhibits enhanced electrochemical performance. This work demonstrates an effective strategy for designing thick, high-mass-loading, and porous conductive network anodes for advanced LIBs. © 2025 Elsevier Inc.

关键词： Lithium-ion batteries

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Domain Adaptation-based Radio Frequency Fingerprint Identification for Industrial Cyber-Physical Systems

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IEEE Network 2025年

作者： Wang, Yatong Cao, Bin Wen, Zhongyi Yan, Mu Song, Changqing Beijing Institute of Technology School of Information and Electronics Beijing100081 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Technology Beijing100191 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100191 China

With the widespread adoption of cloud and fog platforms, security issues facing Industrial Cyber-Physical Systems (ICPSs) are increasingly prominent. Radio Frequency Fingerprinting (RFFI) emerges as a promising physical layer security technique for user authentication, effectively enhancing ICPS security over wireless networks by exploiting inherenthardware imperfections in transmitters. However, as technologies evolve and user environments become denser and more complex, existing RFFI techniques encounter significant domain shift challenges, leading to performance degradation. Therefore, developing domain-adaptive RFFI techniques is crucial for the effectiveness of ICPSs. In this article, we first provide a comprehensive review of the RFFI framework, emphasizing its applications in ICPSs and the key processes involved. Next, we focus on the domain shift issues faced by RFFI in ICPSs and present a comprehensive survey of domain adaptation methods. To tackle these challenges, we propose a novel feature-alignment based domain adaptation (FADA) framework for RFFI, which is designed to mitigate the performance degradation caused bycross-domain shifts by aligning the feature representations of the source and target domain. Finally, we discuss the inherent challenges and future directions for developing domain adaptation based RFFI to enhance the security of ICPSs. © 2025 IEEE. All rights reserved.

关键词： Radio frequency identification (RFID)

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Robust Training in Multiagent Deep Reinforcement Learning Against Optimal Adversary

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年

作者： Guo, Weiran Liu, Guanjun Zhou, Ziyuan Wang, Jiacun Tang, Ying Wang, Miaomiao Tongji University School of Computer Science and Technology Shanghai201804 China Monmouth University Department of Computer Science and Software Engineering West Long BranchNJ07764 United States Rowan University Department of Electrical and Computer Engineering GlassboroNJ08028 United States Beijing Institute of Control Engineering Space Optoelectronic Measurement and Perception Lab Beijing100190 China

Industry 5.0 enhances manufacturing ability through efficient human-machine interaction, combining human resources and robots to complete tasks more accurately and effectively. Artificial intelligence (AI) plays an essential role in Industry 5.0. As a branch in AI, multiagent deep reinforcement learning (MADRL) attracts vast attention in both academia and industry. However, there is a gap between virtual and physical environments in terms of how clean an observed state is. In addition, state adversarial attacks can seriously impact the performance of MADRL. Hence, how to improve the robustness of MADRL algorithms is an important research topic. In this article, we propose an optimal policy-based state adversary attack method that would make the MADRL algorithm more robust when it is applied in the training process of agents. Two case studies related to Industry 5.0 and a general case study are presented in which robustness training against the optimal adversarial attack is tested. The MADRL algorithms involved in the experiments include centralized training and decentralized execution (CTDE) framework and shared experience actor-critic (SEAC) to demonstrate the universality of our method. © 2013 IEEE.

关键词： Deep reinforcement learning

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The fabrication of "8"-shape microstructured optical fiber and its application in pressure/temperature sensing

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Journal of Lightwave technology 2025年

作者： Xue, Pingsheng Fu, Yongqing Wu, Qiang Ng, Wai Pang Binns, Richard Liu, Qiang Northeastern University College of Information Science and Engineering Shenyang110819 China Northumbria University Faculty of Engineering and Environment Newcastle upon TyneNE1 8ST United Kingdom Northeastern University at Qinhuangdao School of Control Engineering Qinhuangdao066004 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China

A microstructured optical fiber was designed. The internal structure of the fiber consists of two round channels, and its cross-section resembles the shape of "8". This fiber can be applied in the fields of pressure sensing and temperature sensing. The stress induced by pressure on the core increases the birefringence due to the photo-elastic effect. When an appropriate liquid material, such as glycerol, is filled into the two round channels of the fiber, temperature sensing can be achieved based on the coupling between the fiber core and the liquid rod. Both pressure and temperature sensing characteristics were investigated in detail in experiments. By constructing a Sagnac interferometer, the pressure sensitivity reached 109.78 nm/MPa. Based on the coupling between the core and the infiltrated glycerol in the round channels, the temperature sensitivity was as high as -47.15 nm/°C. Their cross-sensitivities were also analyzed. The pressure or temperature sensors based on this fiber are highly suitable for scenarios such as the oil and gas industries. © 1983-2012 IEEE.

关键词： Gas industry

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ZnO Nanostructure-Based Flexible Pressure Sensors Deposited on Filter Paper for Wearable Application

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Langmuir 2025年 2025 May 15页

作者： Ding, Jijun Zhao, Pengfei Chen, Haixia Shaanxi Engineering Research Centre of Oil and Gas Resource Optical Fiber Detection Shaanxi Key Laboratory of Measurement and Control Technology for Oil and Gas Wells School of Science Xi’an Shiyou University Xi’an 710065 China

Flexible pressure sensors have broad prospects in smart wearables, healthcare, and human-computer interaction. Nevertheless, flexible pressure sensors still face numerous thorny challenges. It has become a crucial problem to skillfully design and successfully achieve flexible pressure sensors with both a high sensing range and ultrahigh sensitivity. The sensor is designed and realized with inspiration drawn from the layered microstructure of human skin, and hierarchical structure flexible pressure sensors are fabricated, where PDMS microstructures/MWCNTs act as the top electrode, filter paper/ZnO nanostructures/MWCNTs act as the intermediate active layer, and an Ag interdigitated electrode acts as the bottom electrode. The sensing performance of the sensor is investigated to develop the application of pressure sensors for human health detection in daily life, and a pressure sensor array is prepared to investigate the detection of spatial pressure distribution. Sensors based on paper and PDMS can achieve low-pressure detection (30 Pa), high sensitivity (261.38 kPa^-1), fast response time (∼73.8 ms), and excellent cyclic stability (10 000 cycles). Finally, the sensor demonstrates its functionality by lighting up a small lamp, which confirms that the as-prepared pressure sensor has excellent application scenarios and is beneficial for the development of flexible electronic devices. © 2025 American Chemical Society.

关键词： Pressure sensors

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Multi-Modal Multi-Kernel Graph Learning for Autism Prediction and Biomarker Discovery

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 842-854页

作者： Jin Liu Junbin Mao Hanhe Lin Hulin Kuang Shirui Pan Xusheng Wu Shan Xie Fei Liu Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi China Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China School of Science and Engineering University of Dundee Dundee U.K. School of Information and Communication Technology Griffith University Gold Coast QLD Australia Shenzhen Health Development Research and Data Management Center Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Graph learning-based multi-modal integration and classification is one of the most challenging tasks for disease prediction. To effectively offset the negative impact among modalities in the process of multi-modal integration and heterogeneous information extractions from graphs, we propose a novel method called Multi-modal Multi-Kernel Graph Learning (MMKGL). To solve the problem of negative impact among modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging Data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology.

关键词： Diseases Autism Random access memory Data mining Kernel Electronic mail Training Neuroimaging Graph convolutional networks Bioinformatics

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STARS Assisted Semi-Grant-Free NOMA communications

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IEEE Transactions on Wireless communications 2025年

作者： Xie, Jin Yue, Xinwei Zhao, Jingjing Zou, Yixuan Liu, Yuanwei Liu, Rongke Ding, Zhiguo Key Laboratory of Information and Communication Systems China Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing100101 China Beihang University School of Electronic and Information Engineering Beijing100191 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Khalifa University Department of Computer and Information Engineering Abu Dhabi United Arab Emirates

This paper investigates the performance of simultaneously transmitting and reflecting surface (STARS) assisted semi-grant-free non-orthogonal multiple access network with randomly distributed users. By deploying STARS, the transmit signals of grant-based user (GBU) and grant-free users (GFUs) can be exquisitely adjusted to reduce interference. We propose a maximum channel scheduling (MCS) protocol that allows a GFU to access GBU’s channel with the assistance of STARS. In particular, the impacts of perfect/imperfect successive interference cancellation (pSIC/ipSIC) on MCS protocol are taken into account. To characterize the performance of STARS aided MCS (STARS-MCS) network, we derive the expressions of outage probability for GBU and GFU with pSIC/ipSIC. By applying convolution theorem and Laplace transform, the asymptotic expressions of outage probability and diversity orders for GBU and GFU are attained. We further design a STARS-based power control (SPC) strategy to eliminate the outage probability error floor and improve the outage performance. Numerical results show that: 1) The performance of STARS-MCS outperforms the existing benchmarks in terms of outage probability and system throughput;2) The SPC strategy can effectively improve the performance of the STARS-MCS network and eliminate the outage probability error floor at high signal-to-noise ratios;and 3) By adjusting reflection and transmission coefficients of STARS, the outage performance of GBU and GFU can be greatly enhanced. © 2025 IEEE. All rights reserved.

关键词： Laplace transforms

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A Profit-Oriented Attack by Virtual Power Plants for Economic Risk Transfer: Profitability Analysis and Defense Strategy

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IEEE Systems Journal 2025年

作者： Wang, Luyu Wu, Jiping Ding, Zhetong Zhang, Kaifeng Sikdar, Biplab Southeast University Key Laboratory of Measurement and Control of CSE School of Automation Nanjing 210096 China National University of Singapore Department of Electrical and Computer Engineering 119077 Singapore NARI Technology Limited Company Nanjing 211100 China

Virtual power plants (VPP) may face the risk of economic losses arising from the deviation between day-ahead schedule and real-time energy delivery. This article introduces a novel profit-oriented attack by some greedy VPP (G-VPP) for the first time, which aims to transfer its risk of economic losses to other normal VPPs (N-VPPs) and obtain a risk-free profit. Particularly, the G-VPP launches the load-altering attack (LAA) on N-VPPs to fail their real-time (RT) generation schedule via modifying the generation instructions of the managed resources. The optimal LAA strategy considering stealthiness is proposed to avoid priori detection and the optimal bidding strategy using CVaR is proposed to quantify bidding risk and maximize profit. To defend against such profit-oriented attacks, this article constitutes the first attack traceability research. A defense scheme with attack detection and attacker identification is proposed, which is executed by VPPs and the independent system operator (ISO). VPPs employ an attack detection algorithm based on complex features, and if any, report it to the ISO. The ISO constructs a knowledge graph of electricity market transactions and embeds a rules-based identification algorithm to identify suspected attackers. Simulation results show the effectiveness of the defense scheme and Neo4j is used to visualize. © 2007-2012 IEEE.

关键词： Cyber-physical system security electricity market profit-oriented cyberattack

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UAV-Assisted IoT Data Collection Scheme for IRS-Enabled Backscatter communication Networks

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green communications and Networking 2025年

作者： Han, Shuai Wang, Jinming Xu, Sai Wang, Shiji Li, Cheng Harbin Institute of Technology School of Electronics and Information Engineering China 5GIC and 6GIC GuildfordGU2 7XH United Kingdom Southeast University National Mobile Communications Research Laboratory Nanjing211111 China Beijing Aerospace Measurement and control Technology CO.LTD Beijing China Simon Fraser University School of Engineering Science BurnabyBCV5A 1S6 Canada

Data collection schemes assisted by unmanned aerial vehicle (UAV) play a key role in Internet-of-Things (IoT) networks. Nonetheless, the massive IoT devices bring new challenges, especially the high energy consumption. The backscatter communication (BackCom) technique can utilize ambient signals for passive transmission, thus being applicable to IoT systems. Due to its numerous reflecting elements, Intelligent Reflecting Surface (IRS) can accomplish backscatter with high beamforming gain. Thus, IRS is suitable to alleviate the double-fading effect, which significantly restrains the development of BackCom networks. This paper utilizes the IRS-based BackCom technique to achieve efficient IoT data collection with the help of a UAV. The UAV provides carriers for multiple IRSs, which can emit data through backscatter. Then, the UAV collects the backward data dynamically. To maximize the collected data, the UAV's transmit power, trajectory, and the IRS's beamformers are jointly optimized. The optimization is realized through an alternative algorithm based on block coordinate descent (BCD) and reinforcement learning (RL). The Lagrangian dual transformation and semi-definite relaxation (SDR) approaches are employed to address the non-convexity of the problem effectively. Finally, simulation results demonstrated the effectiveness and feasibility of the proposed optimization scheme in the introduced system model. © 2017 IEEE.

关键词： Reinforcement learning

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Simulation-Based Metrological Temperature Ensuring and control of the Temperature Field in an Induction Furnace Based on Various Parameters

Simulation-Based Metrological Temperature Ensuring and Contr...

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International Conference on Computer and Automation engineering, ICCAE

作者： Jan Rybář Samuel Emebu Sayed Yousuf Sayed Sohaibullah Zarghoon Lukáš Bartalský Peter Onderčo Andrej Smetánka Radek Matušů Faculty of Mechanical Engineering Institute of Automation Informatization and Measurement Slovak University of Technology Bratislava Slovakia Department of Separation Science LUT School of Engineering Science Lappeenranta-Lahti University of Technology Lappeenranta Finland Department of Auto mechanical Engineering Faculty of Electro-Mechanic Kabul Polytechnic University Kabul Afghanistan Department of Automation and Control Engineering Faculty of Applied Informatics Tomas Bata University in Zlín Zlín Czech Republic

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

Induction heating is widely used in industrial furnaces because of its rapid response time and high energy efficiency. To design and optimize these furnaces, computer-aided simulations are essential. This study aims to develop an accurate temperature field model for a square cross-section steel billet in an induction furnace by leveraging various parameters in COMSOL Multiphysics software. The simulation analysis incorporated temperature dynamics, considering skin depth, temperaturedependent, and constant parameters of the steel billet. The datadriven model was converted into a state-space representation utilizing MATLAB's System Identification Toolbox. This conversion facilitated the design of a Linear Quadratic Regulator (LQR) with integral gain and pole placement. Further, these controllers' performance was analyzed, and controllers were successfully implemented to regulate the temperature field of the billet from initial temperature to reference temperature, utilizing the distributed parameter system control circuit.

关键词： Billets Analytical models Temperature Accuracy Computational modeling Furnaces Mathematical models Temperature control Steel Induction heating

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