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

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

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Design of a cationic accelerator enabling ultrafast ion diffusion kinetics in aqueous zinc-ion batteries

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Journal of Energy Chemistry 2025年第1期100卷 377-384页

作者： Yawei Xiao Qianqian Gu Haoyu Li Mengyao Li Yude Wang national Center for International Research on Photoelectric and Energy Materials School of Materials and Energy Yunnan University Key Laboratory of Ministry of Education for Advanced Materials in Tropical Island Resources School of Chemistry and Chemical Engineering Hainan University College of Chemical Engineering Fuzhou University Yunnan Key Laboratory of Carbon Neutrality and Green Low-carbon Technologies Yunnan University

Aqueous zinc-ion batteries are highly favored for grid-level energy storage owing to their low cost and high safety,but their practical application is limited by slow ion *** address this,a strategy has been developed to create a cation-accelerating electric field on the surface of the cathode to achieve ultrafast Zn2+diffusion *** employing electrodeposition to coat MoS2on the surface of BaV6O16·3H2O nanowires,the directional built-in electric field generated at the heterointerface acts as a cation accelerator,continuously accelerating Zn2+diffusion into the active *** optimized Zn2+diffusion coefficient in CC@BaV-6O16·3H2O@MoS2(7.5×10～8cm2s-1) surpasses that of most reported V-based ***,MoS2serving as a cathodic armor extends the cycling life of the Zn-CC@BaV6O16·3H2O@MoS2full batteries to over 10000 *** work provides valuable insights into optimizing ion diffusion kinetics for high-performance energy storage devices.

关键词： Zinc alloys

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Interannual variability of boreal summer intraseasonal oscillation over the northwestern Pacific influenced by the Pacific Meridional Mode

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Atmospheric and Oceanic science Letters 2025年第1期18卷 6-11页

作者： Haoyu Zhou Pang-Chi Hsu Lin Chen Yitian Qian Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environmental Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science and TechnologyNanjingChina

During the boreal summer,intraseasonal oscillations exhibit significant interannual variations in intensity over two key regions:the central-western equatorial Pacific(5°S-5°N,150°E-150°W)and the subtropical Northwestern Pacific(10°-20°N,130°E-175°W).The former is well-documented and considered to be influenced by the ENSO,while the latter has received comparatively less attention and is likely influenced by the Pacific Meridional Mode(PMM),as suggested by partial correlation analysis *** elucidate the physical processes responsible for the enhanced(weakened)intraseasonal convection over the subtropical northwestern Pacific during warm(cold)PMM years,the authors employed a moisture budget *** findings reveal that during warm PMM years,there is an increase in summer-mean moisture over the subtropical northwestern *** increase interacts with intensified vertical motion perturbations in the region,leading to greater vertical moisture advection in the lower troposphere and consequently resulting in convective *** a process is pivotal in amplifying intraseasonal convection *** observational findings were further verified by model experiments forced by PMM-like sea surface temperature patterns.

关键词： Boreal summer intraseasonal oscillation Interannual variability Pacific Meridional Mode Moisture budget analysis

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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

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Quickly obtaining densely dispersed coherent particles in steel matrix and its related mechanical property

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International Journal of Minerals,Metallurgy and Materials 2025年第1期32卷 111-118页

作者： Xiaoxiao Wang Qingsong Huang School of Chemical Engineering Sichuan UniversityChengdu 610065China

Densely distributed coherent nanoparticles(DCN)in steel matrix can enhance the work-hardening ability and ductility of steel *** the routes to this end can be generally classified into the liquid-solid route and the solid-solid ***,the formation of DCN structures in steel requires long processes and complex *** far,obtaining steel with coherent particle enhancement in a short time remains a bottleneck,and some necessary steps remain ***,we show a high-efficiency liquid-phase refining process reinforced by a dynamic magnetic ***-Y-Mn-O particles had an average size of around(3.53±1.21)nm and can be obtained in just around 180 *** small nanoparticles were coherent with the matrix,implying no accumulated dislocations between the particles and the steel *** findings have a potential application for improving material machining capacity,creep resistance,and radiation resistance.

关键词： ferritic steels coherent particles microstructure compression test work hardening

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Dielectric-ion-conductive ZnNb2O6 layer enabling rapid desolvation and diffusion for dendrite-free Zn metal batteries

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Journal of Energy Chemistry 2025年第1期100卷 693-701页

作者： Haifeng Yang Jian Wang Panpan Zhang Xiaomin Cheng Qinghua Guan Jing Dong Bixian Chen Lujie Jia Jing Zhang Yongzheng Zhang Yunjian Liu Hongzhen Lin School of Materials science and Engineering Jiangsu University i-Lab & CAS Key Laboratory of Nanophotonic Materials and Devices Suzhou Institute of Nano-Tech and Nano-Bionics Chinese Academy of Sciences Helmholtz Institute Ulm (HIU) Karlsruhe Institute of Technology (KIT) School of Materials science and Engineering Xi'an University of Technology State Key Laboratory of Chemical Engineering East China University of Science and Technology

Rechargeable aqueous zinc-metal batteries （AZMBs） are promising candidates for large-scale energy storage systems due to their low cost and high ***,their performance and sustainability are significantly hindered by the sluggish desolvation kinetics at the electrode/electrolyte interface and the corresponding hydrogen evolution reaction where active water molecules tightly participate in the Zn（H2O）62+solvation ***,learnt from self-generated solid electrolyte interphase （SEI） in anodes,the dielectric but ion-conductive zinc niobate nanoparticles artificial layer is constructed on metallic Zn surface （ZNB@Zn）,acting as a rapid desolvation *** zincophilic and dielectric-conductive properties of ZNB layer accelerate interfacial desolvation/diffusion and suppress surface corrosion or dendrite formation,achieving uniform Zn plating/stripping behavior,as confirmed by electronic/optical microscopies and interface spectroscopical measurements together with theoretical ***,the as-prepared ZNB@Zn electrode exhibits excellent cycling stability of over 2000 h and robust reversibility （99.54%） even under high current density and depth of discharge ***,the assembled ZNB@Zn-based full cell displays high capacity-retention rate of 80.21%after 3000 cycles at 5 A g-1and outstanding rate performance up to 10 A *** large-areal pouch cell is stabilized for hundreds of cycles,highlighting the bright prospects of the dielectric but ion-conductive layer in further application of AZMBs.

关键词： Zn metal battery Dielectric artificial layer Rapid ion diffusion Zincophilic diffusion Active water inhibition

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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)

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Attention Eraser and Quantitative Measures for Automated Bone Age Assessment

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Computers, Materials & Continua 2025年第1期82卷 627-644页

作者： Liuqiang Shu Lei Yu College of Computer and Information science Chongqing Normal UniversityChongqing401331China

Bone age assessment(BAA)aims to determine whether a child’s growth and development are normal concerning their chronological *** predict bone age more accurately based on radiographs,and for the left-hand X-ray images of different races model can have better adaptability,we propose a neural network in parallel with the quantitative features from the left-hand bone measurements for *** this study,a lightweight feature extractor(LFE)is designed to obtain the featuremaps fromradiographs,and amodule called attention erasermodule(AEM)is proposed to capture the fine-grained ***,the dimensional information of the metacarpal parts in the radiographs is measured to enhance the model’s generalization capability across images fromdifferent *** is trained and validated on the RSNA,RHPE,and digital hand atlas datasets,which include images from various racial *** model achieves a mean absolute error(MAE)of 4.42 months on the RSNA dataset and 15.98 months on the RHPE *** to ResNet50,InceptionV3,and several state-of-the-art methods,our proposed method shows statistically significant improvements(p<0.05),with a reduction in MAE by 0.2±0.02 years across different racial ***,t-tests on the features also confirm the statistical significance of our approach(p<0.05).

关键词： Bone age assessment attention eraser quantitative feature metacarpal bones

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Glucocorticoid receptor signaling in the brain and its involvement in cognitive function

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Neural Regeneration Research 2025年第9期20卷 2520-2537页

作者： Chonglin Su Taiqi Huang Meiyu Zhang Yanyu Zhang Yan Zeng Xingxing Chen Brain science and Advanced Technology Institute Hubei Province Key Laboratory of Occupational Hazard Identification and ControlSchool of MedicineWuhan University of Science and TechnologyWuhanHubei ProvinceChina

The hypothalamic-pituitary-adrenal axis regulates the secretion of glucoco rticoids in response to environmental *** the brain,a nuclear receptor transcription fa ctor,the glucocorticoid recepto r,is an important component of the hypothalamicpituitary-a d renal axis's negative feedback loop and plays a key role in regulating cognitive equilibrium and *** glucoco rticoid receptor influences cognitive processes,including glutamate neurotransmission,calcium signaling,and the activation of brain-derived neurotrophic factor-mediated pathways,through a combination of genomic and non-genomic *** interactions within the central nervous system can alter the expression and activity of the glucocorticoid receptor,there by affecting the hypothalamic-pituitary-a d renal axis and stress-related cognitive *** appropriate level of glucocorticoid receptor expression can improve cognitive function,while excessive glucocorticoid receptors or long-term exposure to glucoco rticoids may lead to cognitive *** with cognitive impairment-associated diseases,such as Alzheimer's disease,aging,depression,Parkinson's disease,Huntington's disease,stroke,and addiction,often present with dysregulation of the hypothalamic-pituitary-adrenal axis and glucocorticoid receptor *** review provides a comprehensive overview of the functions of the glucoco rticoid receptor in the hypothalamic-pituitary-a d renal axis and cognitive *** emphasizes that appropriate glucocorticoid receptor signaling fa cilitates learning and memory,while its dysregulation can lead to cognitive *** provides clues about how glucocorticoid receptor signaling can be targeted to ove rcome cognitive disability-related disorders.

关键词： brain-derived neurotrophic factor calcium signaling glucocorticoid receptor glucocorticoid glutamate transmission hypothalamic-pituitary-adrenal axis long-term potentiation neurocognitive disorders neuroplasticity stress

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The detection, extraction and parameter estimation of extreme-mass-ratio inspirals with deep learning

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science china(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 86-96页

作者： Qianyun Yun Wen-Biao Han Yi-Yang Guo He Wang Minghui Du Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Shanghai Astronomical Observatory Chinese Academy of Sciences School of Physics and Astronomy Shanghai Jiao Tong University School of Astronomy and Space science University of Chinese Academy of Sciences Taiji Laboratory for Gravitational Wave Universe (Beijing/Hangzhou) University of Chinese Academy of Sciences Lanzhou Center for Theoretical Physics Key Laboratory of Theoretical Physics of Gansu Province Key Laboratory of Quantum Theory and Applications of MoE Lanzhou University Institute of Theoretical Physics & Research Center of Gravitation Lanzhou University International Centre for Theoretical Physics Asia-Pacific (ICTP-AP) University of Chinese Academy of Sciences (UCAS) Center for Gravitational Wave Experiment National Microgravity Laboratory Institute of Mechanics Chinese Academy of Sciences

One of the primary goals of space-borne gravitational wave detectors is to detect and analyze extreme-mass-ratio inspirals(EMRIs). This task is particularly challenging because EMRI signals are complex, lengthy, and faint. In this work, we introduce a2-layer convolutional neural network(CNN) approach to detect EMRI signals for space-borne detectors, achieving a true positive rate(TPR) of 96.9% at a 1% false positive rate(FPR) for signal-to-noise ratio(SNR) from 50 to 100. Especially, the key intrinsic parameters of EMRIs such as the mass, spin of the supermassive black hole(SMBH) and the initial eccentricity of the orbit can also be inferred directly by employing a neural network. The mass and spin of the SMBH can be determined at 99% and 92% respectively. This will greatly reduce the parameter spaces and computing cost for the following Bayesian parameter *** model also has a low dependency on the accuracy of the waveform model. This study underscores the potential of deep learning methods in EMRI data analysis, enabling the rapid detection of EMRI signals and efficient parameter estimation.

关键词： gravitational wave EMRIs deep-learing

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