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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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Uniaxial stretch-induced axonal injury thresholds for axonal dysfunction and disruption and strain rate effects on thresholds for mouse neuronal stem cells

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Journal of Biomechanical Science and engineering 2017年第1期12卷 16-00598-16-00598页

作者： Kurtoglu, Evrim Nakadate, Hiromichi Kikuta, Kazuhiro Aomura, Shigeru Kakuta, Akira Graduate School of System Design Tokyo Metropolitan University Tokyo Japan Advanced Course of Mechanical and Computer Systems Engineering Tokyo National College of Technology 1220-2 Kunugida-machi Hachioji-shi Tokyo193-0997 Japan

In this paper, the proposed study aims to achieve a better understanding of neuronal tolerance and contribute to the prediction of the secondary degeneration of diffuse axonal injury (DAI). Therefore, a uniaxial stretching device which subjected cultured neurons to uniaxial stretch was employed to evaluate the effect of strain and strain rate along axon to realize the injury threshold. Neurons differentiated from mouse neuronal stem cells were injured and the morphology was observed before and after stretching with strains of 0.10, 0.12, 0.18, 0.23 at strain rates of 8, 11, 19, 26 s-1 respectively. Results suggest that the threshold for axonal dysfunction is around 0.18 strain whereas the threshold for axonal disruption is around 0.23 and the results of strain rate effect investigations on axonal dysfunction and disruption around these threshold values indicated that higher strain rate values such as 50 s-1 may have diminishing effects on threshold for axonal disruption. © 2017 The Japan Society of mechanical Engineers.

关键词： Stem cells

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Statistical estimation of S-N curves for high-strength steels based on their static mechanical properties

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Zairyo/Journal of the Society of Materials Science, Japan 2019年第3期68卷 258-264页

作者： Ito, Tsutomu Okada, Kenji Onosaka, Masaki Sakaida, Akiyoshi Mukoyama, Kazutaka Hanaki, Koushu Nakamura, Yuki Takahashi, Jun Sakai, Tatsuo Department of Mechanical System Engineering Faculty of Engineering Toyama Prefectural University Kurokawa Imizu939-0398 Japan National Institute of Technology Kagawa College Chokushi-Cho Takamatsu761-8058 Japan Mechanical Engineering Course Faculty of Advanced Engineering National Institute of Technology Kagawa College Chokushi-Cho Takamatsu761-8058 Japan Department of Mechanical Engineering National Institute of Technology Akashi College Uozumi-Cho Akashi674-8501 Japan Graduate School of Engineering Osaka University Yamadaoka Suita565-0871 Japan Department of Mechanical Engineering National Institute of Technology Toyota College Eisei-Cho Toyota471-8525 Japan Materials Characterization Department Denka Co. Ltd. 3-5-1 Asahi-machi Machida Tokyo194-8560 Japan Research Organization of Science and Technology Ritsumeikan University Nojihigashi Kusatsu525-8577 Japan

In this study, a statistical estimation of S-N curves for high-strength steels from their static mechanical properties was attempted. First, the fatigue data of high-strength steels were extracted from the "Database on fatigue strength of metallic materials" published by the Society of Materials Science, Japan (JSMS), and the S-N curves of respective steels were determined from the JSMS standard, that is, the "Standard evaluation method of fatigue reliability for metallic materials: Standard regression method of S-N curve". The correlations between the regression parameters obtained here and the static mechanical properties were investigated. Thus, significant correlations were found between the regression parameters and static mechanical properties (e.g., fatigue limit E and tensile strength sB). Based on these correlations, the S-N curves of high-strength steels were successfully predicted from their static mechanical properties, similar to structural carbon steels and aluminum alloys in previous studies. Moreover, from the distribution of the fatigue limit E, the percentile points for the estimated S-N curve were predicted. It was finally confirmed that 84 % of the S-N data series fall within the estimated interval of ±2s, where s represents the standard deviation of the fatigue limit. © 2019 Society of Materials Science *** right reserved.

关键词： High strength steel

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A Research on Document Summarization and Presentation system Based on Feature Word Extraction from Stored Informations

A Research on Document Summarization and Presentation System...

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International Conference on Technologies and Applications of Artificial Intelligence (TAAI)

作者： Kei Matsubayashi Akihiro Yamashita Hidetoshi Nonaka Yohko Konno Advanced Course of Mechanical and Computer Systems Engineering Tokyo College Hachioji Tokyo Japan Department of Computer Science Tokyo College Hachioji Tokyo Japan CHOWA GIKEN Corporation Sapporo Hokkaido Japan

This study examines a method of effective utilization as knowledge in organizational stored information of business activities. More specifically, the purpose of this study is developing a system that supports efficient finding of appropriate knowledge from the stored information according to a question sentence input from a user. As an approach, we used automatic documents summarization technology to obtain valuable information from the stored information and we evaluated the effectiveness based on the real bulletin board system data from a certain company.

关键词： Hafnium Artificial intelligence

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In-situ measurements of strain in soft body armor with FBG sensors during ballistic impacts

In-situ measurements of strain in soft body armor with FBG s...

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Optical Fiber Sensors, OFS 2018

作者： Hackney, Drew Goode, Tyler Seng, Frederick Shoemaker, George Schultz, Steven Pankow, Mark Peters, Kara Department of Mechanical and Aerospace Engineering North Carolina State University RaleighNC27695 United States Department of Electrical and Computer Engineering Brigham Young University ProvoUT84602 United States Naval Undersea Warfare Center Advanced Instrumentation System Technology AIST Executing Agent Code 709 NewportRI02841 United States

ISBN: (纸本)9781943580507

The strain on a single FBG sensor, integrated into a single layer of Kevlar mounted behind a Kevlar soft body armor coupon, is measured throughout a ballistic impact event. The strain is collected through high-speed, full-spectral interrogation of the FBG during the event. The use of the wavelength shift and spectral distortion of the FBG provides a description of the strain profile and interaction between the optical fiber and Kevlar fabric. The measurements are correlated to high-speed camera images taken from the rear and side of the armor coupon. In future testing, this information can be applied to identify the sequence of energy dissipation mechanisms enacted within the armor to resist the ballistic impact. © OSA 2018 © 2018 The Author(s)

关键词： Fiber Bragg gratings

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Application of quaternion neural network to EMG-based estimation of forearm motion

引用

2017 International Conference on Artificial Intelligence, ICAI 2017 at 2017 World Congress in computer Science, computer engineering and Applied Computing, CSCE 2017

作者： Firdaus, H. Ogawa, T. Mechanical and Electronic Systems Engineering Course Takushoku University Tokyo Japan Department of Electronics and Computer System Takushoku University Tokyo Japan

ISBN: (纸本)9781601324603

This paper presents the Application of Quaternion Neural Network (QNN) to Electromyography (EMG) based Estimation of Forearm Motion. Motion of human body can be modeled as a set of rotations in three-dimensional space by various joints. The aim of this research is to show the efficiency of QNN in estimation from EMG. We are trying to learn, estimate and simulate combined motion using QNN as well as comparing the estimation efficiency with the feedforward neural network (FNN). We are measuring EMG signal of target muscles while performing basic forearm motion. The efficiency of the estimation then determines by simulation with combine motion of forearm. It is expected in better performance of estimation in QNN compare to FNN. CSREA Press ©.

关键词： Efficiency

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Smart radio environments

arXiv

引用

arXiv 2021年

作者： Gradoni, Gabriele Renzo, Marco Di Díaz-Rubio, Ana Tretyakov, Sergei A. Caloz, Christophe Gradoni, Gabriele Peng, Zhen Alu, Andrea Lerosey, Geoffroy Fink, Mathias Galdi, Vincenzo Cui, Tie Jun Frazier, Benjamin W. Anlage, Steven M. Salucci, Marco Massa, Andrea Cheng, Qiang Wang, Jinghe Jin, Shi Dardari, Davide Decarli, Nicoló Yurduseven, Okan Matthaiou, Michail Kenney, Mitchell Gordon, George Georgiou, Orestis Nguyen, Cam Ly Martini, Enrica Maci, Stefano Wakatsuchi, Hiroki Phang, Sendy School of Mathematical Sciences Department of Electrical and Electronic Engineering University of Nottingham Nottingham United Kingdom Laboratoire des Signaux et Systémes CNRS CentraleSupélec Université Paris-Saclay Gif-sur-Yvette Paris France Department of of Electronics and Nanoengineering Aalto University Espoo Finland META Research Group KU Leuven Belgium School of Mathematical Sciences Department of Electrical and Electronic Engineering University of Nottingham University Park NG72RD United Kingdom Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign IL United States Advanced Science Research Center City University of New York United States Greenerwave 6 rue Jean Calvin Paris75005 France Institut Langevin 1 rue Jussieu Paris75005 France Fields & Waves Lab Department of Engineering University of Sannio Benevento Italy State Key Laboratory of Millimeter Waves Southeast University Nanjing China Applied Physics Laboratory Johns Hopkins University LaurelMD20723 United States Department of Electrical and Computer Engineering Department of Physics Quantum Materials Center University of Maryland College ParkMD20742 United States Trento Italy Chengdu China Beijing China Institute of Electromagnetic Space State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China National Mobile Communications Research Laboratory Southeast University Nanjing China University of Bologna Bologna Italy Bologna Italy Centre for Wireless Innovation Institute of Electronics Communications and Information Technology Queen’s University Belfast Belfast United Kingdom Department of Electrical and Electronic Engineering University of Nottingham Nottingham United Kingdom Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Wireless System Laboratory Corporate Research & Development Center Toshiba Corporation Kawasaki Japan Department of Information Engineering an

This Roadmap takes the reader on a journey through the research in electromagnetic wave propagation control via reconfigurable intelligent surfaces. Meta-surface modelling and design methods are reviewed along with physical realisation techniques. Several wireless applications are discussed, including beam-forming, focusing, imaging, localisation, and sensing, some rooted in novel architectures for future mobile communications networks towards 6G. © 2021, CC BY.

关键词： Electromagnetic wave propagation

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Strain-rate Dependency of Axonal Tolerance for Uniaxial Stretching 61

Strain-rate Dependency of Axonal Tolerance for Uniaxial Stre...

引用

61st SAE Stapp Car Crash Conference, STAPP 2017

作者： Nakadate, Hiromichi Kurtoglu, Evrim Furukawa, Hidenori Oikawa, Shoko Aomura, Shigeru Kakuta, Akira Matsui, Yasuhiro Graduate School of System Design Tokyo Metropolitan Univers Japan Advanced Course of Mechanical and Computer Systems Engineeri Japan National Traffic Safety and Environment Laboratory Japan

This study aims to clarify the relation between axonal deformation and the onset of axonal injury. Firstly, to examine the influence of strain rate on the threshold for axonal injury, cultured neurons were subjected to 12 types of stretching (strains were 0.10, 0.15, and 0.20 and strain rates were 10, 30, 50, and 70 s-1). The formation of axonal swellings and bulbs increased significantly at strain rates of 50 and 30 s-1 with strains of 0.15 and 0.20, respectively, even though those formations did not depend on strain rates in cultures exposed to a strain of 0.10. Then, to examine the influence of the strain along an axon on axonal injury, swellings were measured at every axonal angle in the stretching direction. The axons that were parallel to stretching direction were injured the most. Finally, we proposed an experimental model that subjected an axon to more accurate strain. This model observed the process of axonal injury formation by detecting the same neuron before and after stretching. These results suggest that the strain-rate dependency of axonal tolerance is induced by a higher magnitude of loading strain and an experiment focusing on axonal strain is required for obtaining more detailed injury criteria for an axon. © Copyright 2017 The Stapp Association.

关键词： Strain rate

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MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for computer Vision

arXiv

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arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

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Self-Induced Backaction Optical Pulling Force

引用

Physical Review Letters 2018年第12期120卷 123901-123901页

作者： Tongtong Zhu Yongyin Cao Lin Wang Zhongquan Nie Tun Cao Fangkui Sun Zehui Jiang Manuel Nieto-Vesperinas Yongmin Liu Cheng-Wei Qiu Weiqiang Ding Department of Physics Harbin Institute of Technology Harbin 150001 China Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117583 Singapore Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education and Shanxi Province College of Physics and Optoelectronics Taiyuan University of Technology Taiyuan 030024 China Department of Biomedical Engineering Dalian University of Technology Dalian 116024 China Instituto de Ciencia de Materiales de Madrid Consejo Superior de Investigaciones Científicas Campus de Cantoblanco Madrid 28049 Spain Departments of Mechanical and Industrial Engineering and Electrical and Computer Engineering Northeastern University Boston Massachusetts 02115 USA

We achieve long-range and continuous optical pulling in a periodic photonic crystal background, which supports a unique Bloch mode with the self-collimation effect. Most interestingly, the pulling force reported here is mainly contributed by the intensity gradient force originating from the self-induced backaction of the object to the self-collimation mode. This force is sharply distinguished from the widely held conception of optical tractor beams based on the scattering force. Also, this pulling force is insensitive to the angle of incidence and can pull multiple objects simultaneously.

关键词： Electromagnetism mechanical effects of light on material media Photonic crystals Optical microcavities Optical tweezers

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