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Targets capture by distributed active swarms via bio-inspired reinforcement learning

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 210-221页

作者： Kun Xu Yue Li Jun Sun Shuyuan Du Xinpeng Di Yuguang Yang Bo Li institute of Biomechanics and Medical Engineering Applied Mechanics Laboratory Department of Engineering MechanicsTsinghua University institute of Nuclear and New Energy Technology Tsinghua University Shanghai Aerospace Control Technology institute Shanghai Key Laboratory of Aerospace Intelligent Control Technology College of Automation Engineering Nanjing University of Aeronautics and Astronautics

Natural swarms arranged from cells to herds are usually decentralized but display intriguing collective intelligence in coordinating individuals across large scales to efficiently achieve their common goals. Learning from nature may provide new strategies for controlling collective dynamics of synthetic swarms to accomplish specific functions. Here, we present a bioinspired computational framework that steers distributed active swarms to collectively capture and merge targets via reinforcement learning. We exploit collective milling structures of natural herds to cage the targets, and adopt a switching control policy inspired by sperms' chiral dynamics to optimize the trajectories of individuals, through which the active swarms can selforganize to enclose single or multiple distant targets in a dynamical, adaptive and scalable manner. There exists a critical swarm size, beyond which the excessive competition between agents would generate large mechanical forces, leading to capture instability but enabling the transition from short-distance to long-distance merging capture of multiple targets. This work provides physical insights into distributed active swarms and could offer a multilevel, decentralized strategy toward controlling swarm robotics in wide applications such as bio-medical devices, machine immunity, and target clearance.

关键词： swarm intelligence robotics multi-tasks artificial intelligence bionics

Multi boron-doping effects in hard carbon toward enhanced sodium ion storage

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Journal of Energy Chemistry 2025年第1期100卷 730-738页

作者： Peng Zheng Wang Zhou Ying Mo Biao Zheng Miaomiao Han Qin Zhong Wenwen Yang Peng Gao Lezhi Yang Jilei Liu College of Materials Science and Engineering Hunan Joint International Laboratory of Advanced Materials and Technology for Clean Energy Hunan Province Key Laboratory for Advanced Carbon Materials and Applied Technology Hunan University Greater Bay Area institute for Innovation Hunan University Changsha Research institute of Mining & Metallurgy Co. LTD.

Hard carbon （HC） has been considered as promising anode material for sodium-ion batteries （SIBs）.The optimization of hard carbon’s microstructure and solid electrolyte interface （SEI） property are demonstrated effective in enhancing the Na+storage capability,however,a one-step regulation strategy to achieve simultaneous multi-scale structures optimization is highly ***,we have systematically investigated the effects of boron doping on hard carbon’s microstructure and interface chemistry.A variety of structure characterizations show that appropriate amount of boron doping can increase the size of closed pores via rearrangement of carbon layers with improved graphitization degree,which provides more Na+storage ***-situ Fourier transform infrared spectroscopy/electrochemical impedance spectroscopy （FTIR/EIS） and X-ray photoelectron spectroscopy （XPS） analysis demonstrate the presence of more BC3and less B–C–O structures that result in enhanced ion diffusion kinetics and the formation of inorganic rich and robust SEI,which leads to facilitated charge transfer and excellent rate *** a result,the hard carbon anode with optimized boron doping content exhibits enhanced rate and cycling *** general,this work unravels the critical role of boron doping in optimizing the pore structure,interface chemistry and diffusion kinetics of hard carbon,which enables rational design of sodium-ion battery anode with enhanced Na+storage performance.

关键词： X ray photoelectron spectroscopy

Distributed fiber optic sensing system for vibration monitoring of 3D printed bridges

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Optoelectronics Letters 2025年第1期21卷 28-34页

作者： ZHAO Shuai ZHOU Ran LUO Mingming LIU Jianfei LIU Xiongfei ZHOU Tao School of Electronic Information Engineering Hebei University of Technology Tianjin Key Laboratory of Electronic Materials and Devices School of Civil Engineering and Transportation Hebei University of Technology Tianjin Jinhang institute of technical Physics

The fiber optic sensing technology provides data support in structural health monitoring of the macro facilities, including design, construction, and maintenance of bridges, tunnels, ports and other infrastructures. In this paper, a distributed vibration sensing system is proved to be responsive to a single touch over a 1.8-m-long equivalent fiber segment, covering a vibration frequency from 5 Hz to 25 kHz. The sensing fiber was arranged as an S type layout on the bridge to recognize the standing state, windblown disturbance, and walking vibration. Moreover, the knocking and climbing events are recognized fiber laying spinning lines and hanging on the fences, respectively. The demonstration shows an accurate positioning and sensitive vibration monitoring applied on the automated three-dimensional(3D) printed bridge, which is applicable to all kinds of 3D printed facilities as intelligent sensory neuro-networks.

关键词： Spinning (fibers)

Unleashing the Potential of Electroactive Hybrid Biomaterials and Self‑Powered Systems for Bone Therapeutics

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Nano-Micro Letters 2025年第2期17卷 463-497页

作者： Shichang Liu Farid Manshaii Jinmiao Chen Xinfei Wang Shaolei Wang Junyi Yin Ming Yang Xuxu Chen Xinhua Yin Yunlei Zhou Honghui Hospital Xi’an Jiaotong UniversityXi’an 710018People’s Republic of China Department of Bioengineering Henry Samueli School of Engineering and Applied ScienceUniversity of California Los AngelesLos Angeles 90095USA Hangzhou institute of Technology Xidian UniversityHangzhou 311231People’s Republic of China

The incidence of large bone defects caused by traumatic injury is increasing worldwide,and the tissue regeneration process requires a long recovery time due to limited self-healing *** bioelectrical phenomena have been well recognized as critical biophysical factors in bone remodeling and *** by bioelectricity,electrical stimulation has been widely considered an external intervention to induce the osteogenic lineage of cells and enhance the synthesis of the extracellular matrix,thereby accelerating bone *** ongoing advances in biomaterials and energy-harvesting techniques,electroactive biomaterials and self-powered systems have been considered biomimetic approaches to ensure functional recovery by recapitulating the natural electrophysiological microenvironment of healthy bone *** this review,we first introduce the role of bioelectricity and the endogenous electric field in bone tissue and summarize different techniques to electrically stimulate cells and ***,we highlight the latest progress in exploring electroactive hybrid biomaterials as well as self-powered systems such as triboelectric and piezoelectric-based nanogenerators and photovoltaic cell-based devices and their implementation in bone tissue ***,we emphasize the significance of simulating the target tissue’s electrophysiological microenvironment and propose the opportunities and challenges faced by electroactive hybrid biomaterials and self-powered bioelectronics for bone repair strategies.

关键词： Electroactive biomaterials Self-powered bioelectronics Bone regeneration Bone tissue

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Bandgap characteristics analysis and graded design of a novel metamaterial for flexural wave suppression

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applied Mathematics and Mechanics(English Edition) 2025年第1期46卷 1-24页

作者： Fan YANG Zhaoyang MA Xingming GUO Shanghai Key Laboratory of Mechanics in Energy Engineering Shanghai Institute of Applied Mathematics and Mechanics School of Mechanics and Engineering ScienceShanghai University Shanghai institute of Aircraft Mechanics and Control

A novel elastic metamaterial is proposed with the aim of achieving lowfrequency broad bandgaps and bandgap regulation. The band structure of the proposed metamaterial is calculated based on the Floquet-Bloch theorem, and the boundary modes of each bandgap are analyzed to understand the effects of each component of the unit cell on the bandgap formation. It is found that the metamaterials with a low elastic modulus of ligaments can generate flexural wave bandgaps below 300 Hz. Multi-frequency vibrations can be suppressed through the selective manipulation of bandgaps. The dual-graded design of metamaterials that can significantly improve the bandgap width is proposed based on parametric studies. A new way that can regulate the bandgap is revealed by studying the graded elastic modulus in the substrate. The results demonstrate that the nonlinear gradient of the elastic modulus in the substrate offers better bandgap performance. Based on these analyses, the proposed elastic metamaterials can pave the way for multi-frequency vibration control, low-frequency bandgap broadening, and bandgap tuning.

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Evolutionary Particle Swarm Optimization Algorithm Based on Collective Prediction for Deployment of Base Stations

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Computers, Materials & Continua 2025年第1期82卷 345-369页

作者： Jiaying Shen Donglin Zhu Yujia Liu Leyi Wang Jialing Hu Zhaolong Ouyang Changjun Zhou Taiyong Li School of Computer Science and Technology Zhejiang Normal UniversityJinhua321004China School of Future Technologies Jiangxi Institute of Applied Science and TechnologyNanchang330000China School of Computing and Artificial Intelligence Southwestern University of Finance and EconomicsChengdu611130China

The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real *** development of the Internet of Things(IoT)relies on the support of base stations,which provide a solid foundation for achieving a more intelligent way of *** a specific area,achieving higher signal coverage with fewer base stations has become an urgent ***,this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization(EPSO)algorithm based on collective prediction,referred to herein as *** a new strategy called neighbor-based evolution prediction(NEP)addresses the issue of premature convergence often encountered by *** also employs a strengthening evolution(SE)strategy to enhance the algorithm’s global search capability and efficiency,ensuring enhanced robustness and a faster convergence speed when solving complex optimization *** better adapt to the actual communication needs of base stations,this article conducts simulation experiments by changing the number of base *** experimental results demonstrate thatunder the conditionof 50 ormore base stations,ECPPSOconsistently achieves the best coverage rate exceeding 95%,peaking at 99.4400%when the number of base stations reaches *** results validate the optimization capability of the ECPPSO algorithm,proving its feasibility and *** ablative experiments and comparisons with other algorithms highlight the advantages of ECPPSO.

关键词： Particle swarm optimization effective coverage area global optimization base station deployment

Machine Learning Techniques in Predicting Hot Deformation Behavior of Metallic Materials

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 713-732页

作者： Petr Opela Josef Walek Jaromír Kopecek Department of Metallurgical Technologies Faculty of Materials Science and TechnologyVSB–Technical University of OstravaOstrava70800Czech Republic FZU—institute of Physics of the Czech Academy of Sciences Prague18200Czech Republic

In engineering practice,it is often necessary to determine functional relationships between dependent and independent *** relationships can be highly nonlinear,and classical regression approaches cannot always provide sufficiently reliable ***,Machine Learning(ML)techniques,which offer advanced regression tools to address complicated engineering issues,have been developed and widely *** study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic *** ML-based regression methods of Artificial Neural Networks(ANNs),Support Vector Machine(SVM),Decision Tree Regression(DTR),and Gaussian Process Regression(GPR)are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon *** the GPR method has not been used for such a regression task before,the results showed that its performance is the most favorable and practically unrivaled;neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.

关键词： Machine learning Gaussian process regression artificial neural networks support vector machine hot deformation behavior

Morphology engineering of ZnO micro/nanostructures under mild conditions for optoelectronic application

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International Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 498-503页

作者： Liang Chu Haoyu Shen Hudie Wei Hongyu Chen Guoqiang Ma Wensheng Yan institute of Carbon Neutrality and New Energy&School of Electronics and Information Hangzhou Dianzi UniversityHangzhou 310018China School of applied Physics and Materials Wuyi UniversityJiangmen 529020China

Zinc oxide(ZnO)serves as a crucial functional semiconductor with a wide direct bandgap of approximately 3.37 *** reaction is commonly used in the synthesis of ZnO micro/nanostructures,given its low cost,simplicity,and easy ***,ZnO morphology engineering has become desirable through the alteration of minor conditions in the reaction process,particularly at room *** this work,ZnO micro/nanostructures were synthesized in a solution by varying the amounts of the ammonia added at low temperatures(including room temperature).The formation of Zn^(2+)complexes by ammonia in the precursor regulated the reaction rate of the morphology engineering of ZnO,which resulted in various structures,such as nanoparticles,nanosheets,microflowers,and single ***,the obtained ZnO was used in the optoelectronic application of ultraviolet detectors.

关键词： morphology engineering low temperature ZnO nanosheets microflowers ultraviolet detector

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Research progress in metal-organic frameworks and their derivatives in electrochemistry

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Science China Chemistry 2025年

作者： Lingling Chen Yuxin Liu Yichun Su Wenchen Zhu Guangyu Du Huan Pang School of Chemistry and Chemical Engineering Yangzhou University Department of applied Physics The Hong Kong Polytechnic University Interdisciplinary Materials Research Center Institute for Advanced Study Chengdu University State Key Laboratory of Coordination Chemistry Nanjing University

Metal-organic frameworks (MOFs) are distinguished by their unique porosity and meticulously ordered framework structures,setting them apart from conventional complexes. These materials are defined by their highly organized crystalline and pore structures, extensive surface areas, robust porosity, and superior adsorption capabilities. Owing to their exceptional structural and functional attributes, MOFs and their derivatives are leveraged across a broad spectrum of applications, demonstrating significant potential across diverse sectors. In recent times, the scientific community has increasingly focused on MOFs, underscoring their growing importance in research. Despite the promising array of applications for MOFs and their derivatives,challenges persist in practical deployments, and the pathway to research advancement remains daunting. This document delves into the synthesis and various applications of MOFs and their derivatives, particularly in the domains of energy storage, catalysis,sensing, and water treatment. It also highlights the ongoing research progress in the field of electrochemistry, including developments in lithium ion batteries (LIBs), lithium-sulfur batteries (LSBs), zinc-ion batteries (ZIBs), aqueous nickel-zinc batteries (NZBs), and supercapacitors. Finally, it provides an outlook on the future prospects and potential hurdles facing the development of MOF materials.

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