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Ultrastable supported oxygen evolution electrocatalyst formed by ripening-induced embedding

Science (New York, N.Y.) 2025年第6735期387卷 791-796页

作者： Shi, Wenjuan Shen, Tonghao Xing, Chengkun Sun, Kai Yan, Qisheng Niu, Wenzhe Yang, Xiao Li, Jingjing Wei, Chenyang Wang, Ruijie Fu, Shuqing Yang, Yong Xue, Liangyao Chen, Junfeng Cui, Shiwen Hu, Xiaoyue Xie, Ke Xu, Xin Duan, Sai Xu, Yifei Zhang, Bo State Key Laboratory of Molecular Engineering of Polymers Department of Macromolecular Science Fudan University Shanghai China Collaborative Innovation Center of Chemistry for Energy Materials Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials MOE Key Laboratory of Computational Physical Sciences Department of Chemistry Fudan University Shanghai China Department of Chemistry and Department of Electrical and Computer Engineering Northwestern University Evanston IL United States Hefei National Laboratory Hefei China

The future deployment of terawatt-scale proton exchange membrane water electrolyzer (PEMWE) technology necessitates development of an efficient oxygen evolution catalyst with low cost and long lifetime. Currently, the stability of the most active iridium (Ir) catalysts is impaired by dissolution, redeposition, detachment, and agglomeration of Ir species. Here we present a ripening-induced embedding strategy that securely embeds the Ir catalyst in a cerium oxide support. Cryogenic electron tomography and all-atom kinetic Monte Carlo simulations reveal that synchronizing the growth rate of the support with the nucleation rate of Ir, regulated by sonication, is pivotal for successful synthesis. A PEMWE using this catalyst achieves a cell voltage of 1.72 volts at a current density of 3 amperes per square centimeter with an Ir loading of just 0.3 milligrams per square centimeter and a voltage degradation rate of 1.33 microvolts per hour, as demonstrated by a 6000-hour accelerated aging test.

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Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles 59

Generative AI-Enhanced Cooperative MEC of UAVs and Ground St...

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59th Annual Conference on Information Sciences and systems, CISS 2025

作者： You, Jiahao Jia, Ziye Dong, Chao Wu, Qihui Han, Zhu Nanjing University of Aeronautics and Astronautics The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

ISBN: (纸本)9798331513269

The increasing deployment of unmanned surface vehicles (USVs) require computational support and coverage in applications such as maritime search and rescue. Unmanned aerial vehicles (UAVs) can offer low-cost, flexible aerial services, and ground stations (GSs) can provide powerful supports, which can cooperate to help the USVs in complex scenarios. However, the collaboration between UAVs and GSs for USVs faces challenges of task uncertainties, USVs trajectory uncertainties, heterogeneities, and limited computational resources. To address these issues, we propose a cooperative UAV and GS based robust multi-access edge computing framework to assist USVs in completing computational tasks. Specifically, we formulate the optimization problem of joint task offloading and UAV trajectory to minimize the total execution time, which is in the form of mixed integer nonlinear programming and NP-hard to tackle. Therefore, we propose the algorithm of generative artificial intelligence-enhanced heterogeneous agent proximal policy optimization (GAI-HAPPO). The proposed algorithm integrates GAI models to enhance the actor network ability to model complex environments and extract high-level features, thereby allowing the algorithm to predict uncertainties and adapt to dynamic conditions. Additionally, GAI stabilizes the critic network, addressing the instability of multi-agent reinforcement learning approaches. Finally, extensive simulations demonstrate that the proposed algorithm outperforms the existing benchmark methods, thus highlighting the potentials in tackling intricate, cross-domain issues in the considered scenarios. © 2025 IEEE.

关键词： Unmanned surface vehicles

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Design of a Rat Robotic Forelimb 13th

Design of a Rat Robotic Forelimb

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13th International Conference on Biomimetic and Biohybrid systems, Living Machines 2024

作者： Pham, Huy Jackson, Clayton B. Armstead, Benjamin Nourse, William R. P. Andrada, Emanuel Quinn, Roger D. Department of Mechanical and Aerospace Engineering Case Western Reserve University ClevelandOH44106 United States Institute of Zoology and Evolutionary Research Friedrich-Schiller University Jena Germany Department of Electrical Computer and Systems Engineering Case Western Reserve University ClevelandOH44106 United States

ISBN: (纸本)9783031725968

This paper presents the design of robotic rat forelimbs, scaled to 2.5 times the size of a female Sprague-Dawley rat. Based on a previous rat hindlimb model, significant enhancements were implemented to facilitate manufacturing, robustness, improve overall performance, and enhance biological accuracy. The two front legs are each equipped with five motors to enable sagittal rotations of the scapula, elbow, and wrist joints, along with scapula abduction and adduction. The leg segment dimensions are based on a scanned rat bones model, but the shapes are modified for 3D printing and assembly. Parts are printed using micro carbon fiber filled nylon, strengthened by various amounts of continuously inlaid carbon fiber. Revolute joints are driven by motors, and a pulley-belt transmission system is incorporated to move wrist and elbow motors higher up in order to reduce the inertia of the leg. Furthermore, the foot structure comprises two segments that are passively controlled by two torsion springs to enhance flexibility and facilitate a more naturalistic movement. Additionally, a new electrical board is integrated to enhance operational smoothness and align with the control scheme. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Machine design

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Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 246-262页

作者： Zhao, Xiyu Cui, Qimei Li, Weicai Ni, Wei Hossain, Ekram Sheng, Quan Z. Tao, Xiaofeng Zhang, Ping School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China School of Computing Macquarie University SydneyNSW2109 Australia Department of Broadband Communication Peng Cheng Laboratory Shenzhen518055 China School of Electrical and Data Engineering University of Technology Sydney SydneyNSW2007 Australia Department of Electrical and Computer Engineering University of Manitoba WinnipegMBR3T 2N2 Canada

Personalized federated learning (PFL), e.g., the renowned Ditto, strikes a balance between personalization and generalization by conducting federated learning (FL) to guide personalized learning (PL). While FL is unaffected by personalized model training, in Ditto, PL depends on the outcome of the FL. However, the clients’ concern about their privacy and consequent perturbation of their local models can affect the convergence and (performance) fairness of PL. This paper presents PFL, called DP-Ditto, which is a non-trivial extension of Ditto under the protection of differential privacy (DP), and analyzes the trade-off among its privacy guarantee, model convergence, and performance distribution fairness. We also analyze the convergence upper bound of the personalized models under DP-Ditto and derive the optimal number of global aggregations given a privacy budget. Further, we analyze the performance fairness of the personalized models, and reveal the feasibility of optimizing DP-Ditto jointly for convergence and fairness. Experiments validate our analysis and demonstrate that DP-Ditto can surpass the DP-perturbed versions of the state-of-the-art PFL models, such as FedAMP, pFedMe, APPLE, and FedALA, by over 32.71% in fairness and 9.66% in accuracy. © 2023 CCBY.

关键词： Federated learning

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Virtual Resistance Control for an Active Battery Management System 14

Virtual Resistance Control for an Active Battery Management ...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Thurlbeck, Alastair P. Siddiquee, Ashraf Kisacikoglu, Mithat John Sozer, Yilmaz National Renewable Energy Laboratory Center for Integrated Mobility Sciences GoldenCO80401 United States The University of Akron Department of Electrical and Computer Engineering AkronOH44325 United States

ISBN: (纸本)9798331516116

A virtual resistance control scheme is proposed for an active state-of-charge (SOC) balancing system within a behind-the-meter-storage (BTMS) battery pack. The proposed control enables the balancing system converters to simultaneously track a reference current while also regulating a shared 12 V bus. This enables the system to balance the cell SOCs while supporting auxiliary system loads on the 12 V bus. The advantage of the virtual resistance technique lies in its robustness to the actual resistances of the system. Without virtual resistance, the reference current tracking and 12 V bus load sharing among the converters would be dictated by the varying resistances between each converter and the 12 V bus, which are unequal and difficult to measure accurately. However, by applying the virtual resistance technique, these differences are negated such that the expected reference current tracking and equal load sharing are achieved. © 2025 IEEE.

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Image classification by combining quantum kernel learning and tensor networks

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Physical Review A 2025年第1期111卷 012630-012630页

作者： Nathan X. Kodama Alex Bocharov Marcus P. da Silva Department of Electrical Computer and Systems Engineering Case Western Reserve University Cleveland Ohio 44106 USA Microsoft Azure Quantum Redmond Washington 98052 USA

Several variational quantum circuits for machine learning have been proposed in recent years, with one promising class of variational algorithms involving tensor networks operating on states resulting from local feature maps. In contrast, a random feature approach known as quantum kitchen sinks (C. M. Wilson et al., Quantum kitchen sinks: An algorithm for machine learning on near-term quantum computers, arXiv:1806.08321) provides comparable performance, but leverages nonlocal feature maps. Here we combine these two approaches by proposing a circuit Ansatz where a tree tensor network coherently processes the nonlocal feature maps of quantum kitchen sinks and we run numerical experiments to evaluate the performance of the Ansatz on image classification. From the perspective of classification performance, we find that simply combining quantum kitchen sinks with tensor networks yields no qualitative improvements. However, the addition of feature optimization greatly boosts performance, leading to state-of-the-art quantum circuits for image classification, requiring only shallow circuits and a small number of qubits, both well within reach of near-term quantum devices.

关键词： Tensors

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A Vertical Federated Multiview Fuzzy Clustering Method for Incomplete Data

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IEEE Transactions on Fuzzy systems 2025年第5期33卷 1510-1524页

作者： Li, Yan Hu, Xingchen Yu, Shengju Ding, Weiping Pedrycz, Witold Kiat, Yeo Chai Liu, Zhong National University of Defense Technology Laboratory for Big Data and Decision Changsha410073 China National University of Defense Technology School of Computer Changsha410073 China Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China Gliwice44-100 Poland University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Nanyang Technological University College of Computing and Data Science 639798 Singapore

Multiview fuzzy clustering (MVFC) has gained widespread adoption owing to its inherent flexibility in handling ambiguous data. The proliferation of privatization devices has driven the emergence of new challenge in MVFC research works. Federated learning, a technique that can jointly train without directly using raw data, has gain significant attention in decentralized MVFC. However, their applicability depends on the assumptions of data integrity and independence between different views. In fact, while within distributed environments, data typically exhibits two challenging problems: 1) multiple views within a single client;2) incomplete data. Existing methods exhibit limitations in effectively addressing these challenges. Hence, in this study, we aim at achieving the effective clustering for incomplete data by a novel vertical federated MVFC framework. Specifically, a unified clustering framework is designed to capture both local client learning and global server training. For the local client learning, the data reconstruction strategy and prototype alignment strategy are introduced to ensure the preservation of data structure and refinement of clustering relationships, which mitigates the impact of incomplete data. Meanwhile, the global training process implements aggregation based on client-specific information. The whole process is realized based on the unified fuzzy clustering framework, promoting collaborative learning between client-specific and server information. Theoretical analyses and extensive experiments are carefully conducted to validate the effectiveness and efficiency of the proposed method from multiple perspectives. © 1993-2012 IEEE.

关键词： Data integrity

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Achievable Rate Analysis and Optimization of Double-RIS Assisted Spatially Correlated MIMO with Statistical CSI

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China Communications 2025年第1期22卷 139-157页

作者： Xu Kaizhe Guo Jiajia Zhang Jun Jin Shi Ma Shaodan Department of Communications and Networking Xi’an Jiaotong-Liverpool UniversitySuzhou 215123China State Key Laboratory of Internet of Things for Smart City University of MacaoMacao SARChina Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and TelecommunicationsNanjing 210003China National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China State Key Laboratory of Internet of Things for Smart City and the Department of Electrical and Computer Engineering University of MacaoMacao SARChina

Reconfigurable intelligent surface(RIS)is a novel meta-material which can form a smart radio environment by dynamically altering reflection directions of the impinging electromagnetic *** the prior literature,the inter-RIS links which also contribute to the performance of the whole system are usually neglected when multiple RISs are *** this paper we investigate a general double-RIS assisted multiple-input multiple-output(MIMO)wireless communication system under spatially correlated non line-of-sight propagation channels,where the cooperation of the double RISs is also *** design objective is to maximize the achievable ergodic rate based on full statistical channel state information(CSI).Specifically,we firstly present a closedform asymptotic expression for the achievable ergodic rate by utilizing replica method from statistical *** a full statistical CSI-enabled optimal design is proposed which avoids high pilot training overhead compared to instantaneous CSI-enabled *** further reduce the signal processing overhead and lower the complexity for practical realization,a common-phase scheme is proposed to design the double *** results show that the derived asymptotic ergodic rate is quite accurate even for small-sized antenna *** the proposed optimization algorithm can achieve substantial gain at the expense of a low overhead and ***,the cooperative double-RIS assisted MIMO framework is proven to achieve superior ergodic rate performance and high communication reliability under harsh propagation environment.

关键词： common reflection pattern double RISs large system analysis MIMO replica method

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Practical aberration correction using deep transfer learning with limited experimental data

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Optics Express 2025年第6期33卷 14431-14444页

作者： Kok, Yong En Bentley, Alexander Parkes, Andrew J. Somekh, Michael G. Wright, Amanda J. Pound, Michael P. University of Nottingham School of Computer Science NottinghamNG8 1BB United Kingdom Optics and Photonics Group Department of Electrical and Electronic Engineering University of Nottingham NottinghamNG7 2RD United Kingdom Research Center for Humanoid Sensing Zhejiang Laboratory Hangzhou3111100 China

Adaptive optics is a technique for correcting aberrations and improving image quality. When adaptive optics was first used in microscopy, it was common to rely on iterative approaches to determine the aberrations present. It is advantageous to avoid iteration, and therefore there has been a shift to deep learning for aberration prediction. However, issues remain regarding the practicalities of machine learning for adaptive optics, an important one being the requirement for a large training dataset. Here, we explore transfer learning to overcome this need for data by pre-training a network on a large simulated dataset and fine-tuning it with reduced experimental data for application in an experimental setting. We demonstrate that the pre-trained network can make noticeable improvements with fine-tuning on just 24 experimental samples. To further enhance practicality, we significantly extend the range of aberrations present, predicting up to 25 Zernike modes with each coefficient ranging from −1 to 1, and perform a thorough analysis of the type and magnitude of phase-diversity required in the input data for a successful network. Our approach demonstrates substantial aberration reduction on experimental data for 10 Zernike modes, with an average 73% decrease in RMS wavefront error from 1.81 to 0.48 rad when correction is applied. This method achieves complete experimental image capture and aberration inference at rates comparable to the image acquisition time of a typical laser scanning microscope. Additionally, we consider the benefits of further improvements via an iterative step. As such, this work addresses some of the key practical hurdles that remain in the use of deep learning for aberration prediction and correction. © 2025 Optica Publishing Group (formerly OSA). All rights reserved.

关键词： Adaptive optics

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Sparse Point Clouds Assisted Learned Image Compression

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IEEE Transactions on Circuits and systems for Video Technology 2025年第5期35卷 4000-4010页

作者： Jiang, Yiheng Zhang, Haotian Li, Li Liu, Dong Li, Zhu University of Science and Technology of China MoE Key Laboratory of Brain-Inspired Intelligent Perception and Cognition Hefei230027 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230026 China University of Missouri-Kansas City Department of Computer Science and Electrical Engineering Kansas CityMO64110 United States

In the field of autonomous driving, a variety of sensor data types exist, each representing different modalities of the same scene. Therefore, it is feasible to utilize data from other sensors to facilitate image compression. However, few techniques have explored the potential benefits of utilizing inter-modality correlations to enhance the image compression performance. In this paper, motivated by the recent success of learned image compression, we propose a new framework that uses sparse point clouds to assist in learned image compression in the autonomous driving scenario. We first project the 3D sparse point cloud onto a 2D plane, resulting in a sparse depth map. Utilizing this depth map, we proceed to predict camera images. Subsequently, we use these predicted images to extract multi-scale structural features. These features are then incorporated into learned image compression pipeline as additional information to improve the compression performance. Our proposed framework is compatible with various mainstream learned image compression models, and we validate our approach using different existing image compression methods. The experimental results show that incorporating point cloud assistance into the compression pipeline consistently enhances the performance. © 1991-2012 IEEE.

关键词： Image compression

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