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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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Study of Measurement Method in Inter-Vehicle Distance Using Hu Moment Invariants

Study of Measurement Method in Inter-Vehicle Distance Using ...

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International Conference on Control, Automation and systems ( ICCAS)

作者： Takeshi Funatsu Nobuo Sasaki Advanced Production Systems Engineering Course National Institute of Technology Gunma College 580 Toriba Maebashi Japan

ISBN: (纸本)9781538670804;9788993215168

This paper proposed a method to detect the license plate by using the Hu moment invariants and measure inter-vehicle distance. The method was aimed at practical driving-support system with single image sensor. Adaptive thresholding process made the detection robust against changes of brightness caused by weather changes, which have been a major problem of image processing in outdoor. Adaptive thresholding process achieved the detection rate of 93.2% This result shows that the robustness of the system has been improved. At a distance of 2 to 15 m, the error rate was about 4 to 6 %and the average absolute error rate was 4.75 % The average time for all processes was 44.67 ms, which shows that this method operates at a maximum speed of about 22.39 fps.

关键词： Licenses Thresholding (Imaging) Meteorology Error analysis Image sensors Distance measurement Image color analysis

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Does Adaptive Mode Transition Contribute to Better Driver Intervention in Highly Automated Driving?

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Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2019年第1期63卷 287 - 291页

作者： Huiping Zhou Makoto Itoh Satoshi Kitazaki Faculty of Engineering Information and Systems University of Tsukuba Tsukuba-city Japan {zhou.huiping.ga itoh.makoto.ge}@u.tsukuba.ac.jp Automotive Human Factors Research Center National Institute of Advanced Industrial Science and Technology Tsukuba-city Japan

This paper presents an adaptive mode (level) transition in highly combined driving automation in which the mode of a system could adaptively shift to any level including SAE level 3 (conditional automation, CA) to level 2 (partial automation) based on the driving environment. We show the effects of the adaptive transition on the take over of car control by a human driver and driving behavior after intervention when the system issues a response to intervene. A driving simulator experiment is conducted to collect data during the transition from automated control to manual driving in three scenes: obstacle on a driving lane, blurred lane mark, and stopped car ahead. Results indicate that the interventions of drivers who experience the adaptive transition are delayed in comparison to those who experience only the fixed transition. The adaptive transition is conducive for drivers to stop the car for preventing a potential collision with a stopped car ahead. Owing to the adaptive transition, drivers perceive a critical hazard after taking over car control and provide a rapid response. In addition, during the adaptive transition, drivers prefer verbal messages to the simple “beeping” message.

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Effect of impulse turbine geometry on the performance of a dual-turbine system for wave energy conversion

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International Journal of Fluid Machinery and systems 2018年第1期11卷 110-114页

作者： Takao, Manabu Katsube, Haruka Alam, M.M.A. Fukuma, Seisuke Okuhara, Shinya Kinoue, Yoichi Department of Mechanical Engineering National Institute of Technology Matsue College 14-4 Nishiikuma MatsueShimane690-8518 Japan Advanced Course of Production and Construction Systems Matsue College 14-4 Nishiikuma MatsueShimane690-8518 Japan Center to Support Practical Education National Institute of Technology Matsue College 14-4 Nishiikuma MatsueShimane690-8518 Japan Department of Mechanical Engineering Saga University 1 Honjo SagaSaga840-8502 Japan

Wells turbine, widely used to harness the wave energy, has high efficiency at a low flow rate. However, it has severe stall problem. In order to alleviate this problem, a dual-turbine system of Wells turbine with a booster impulse turbine was proposed. In the present study, the effect of impulse turbine geometries on the performance of combined turbine system was investigated by CFD analysis. As a result, the boosting effect of impulse turbine depends on the pressure differences that caused by the setting angles at a high flow rate. Moreover, the decrease of flow rate with the setting angle has a significant influence on the efficiency. © 2018, Turbomachinery Society of Japan. All rights reserved.

关键词： Wave energy conversion

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Measurement of the Iron Spectrum in Cosmic Rays from 10 GeV/n to 2.0 TeV/n with the Calorimetric Electron Telescope on the International Space Station

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Physical Review Letters 2021年第24期126卷 241101-241101页

作者： O. Adriani Y. Akaike K. Asano Y. Asaoka E. Berti G. Bigongiari W. R. Binns M. Bongi P. Brogi A. Bruno J. H. Buckley N. Cannady G. Castellini C. Checchia M. L. Cherry G. Collazuol K. Ebisawa H. Fuke S. Gonzi T. G. Guzik T. Hams K. Hibino M. Ichimura K. Ioka W. Ishizaki M. H. Israel K. Kasahara J. Kataoka R. Kataoka Y. Katayose C. Kato N. Kawanaka Y. Kawakubo K. Kobayashi K. Kohri H. S. Krawczynski J. F. Krizmanic J. Link P. Maestro P. S. Marrocchesi A. M. Messineo J. W. Mitchell S. Miyake A. A. Moiseev M. Mori N. Mori H. M. Motz K. Munakata S. Nakahira J. Nishimura G. A. de Nolfo S. Okuno J. F. Ormes N. Ospina S. Ozawa L. Pacini P. Papini B. F. Rauch S. B. Ricciarini K. Sakai T. Sakamoto M. Sasaki Y. Shimizu A. Shiomi P. Spillantini F. Stolzi S. Sugita A. Sulaj M. Takita T. Tamura T. Terasawa S. Torii Y. Tsunesada Y. Uchihori E. Vannuccini J. P. Wefel K. Yamaoka S. Yanagita A. Yoshida K. Yoshida Department of Physics University of Florence Via Sansone 1 50019 Sesto Fiorentino Italy INFN Sezione di Florence Via Sansone 1 50019 Sesto Fiorentino Italy Waseda Research Institute for Science and Engineering Waseda University 17 Kikuicho Shinjuku Tokyo 162-0044 Japan JEM Utilization Center Human Spaceflight Technology Directorate Japan Aerospace Exploration Agency 2-1-1 Sengen Tsukuba Ibaraki 305-8505 Japan Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa Chiba 277-8582 Japan Department of Physical Sciences Earth and Environment University of Siena via Roma 56 53100 Siena Italy INFN Sezione di Pisa Polo Fibonacci Largo B. Pontecorvo 3 56127 Pisa Italy Department of Physics and McDonnell Center for the Space Sciences Washington University One Brookings Drive St. Louis Missouri 63130-4899 USA Heliospheric Physics Laboratory NASA/GSFC Greenbelt Maryland 20771 USA Center for Space Sciences and Technology University of Maryland Baltimore County 1000 Hilltop Circle Baltimore Maryland 21250 USA Astroparticle Physics Laboratory NASA/GSFC Greenbelt Maryland 20771 USA Center for Research and Exploration in Space Sciences and Technology NASA/GSFC Greenbelt Maryland 20771 USA Institute of Applied Physics (IFAC) National Research Council (CNR) Via Madonna del Piano 10 50019 Sesto Fiorentino Italy Department of Physics and Astronomy Louisiana State University 202 Nicholson Hall Baton Rouge Louisiana 70803 USA Department of Physics and Astronomy University of Padova Via Marzolo 8 35131 Padova Italy INFN Sezione di Padova Via Marzolo 8 35131 Padova Italy Institute of Space and Astronautical Science Japan Aerospace Exploration Agency 3-1-1 Yoshinodai Chuo Sagamihara Kanagawa 252-5210 Japan Kanagawa University 3-27-1 Rokkakubashi Kanagawa Yokohama Kanagawa 221-8686 Japan Faculty of Science and Technology Graduate School of Science and Technology Hirosaki University 3 Bunkyo Hirosaki Aomori 036-856

The Calorimetric Electron Telescope (CALET), in operation on the International Space Station since 2015, collected a large sample of cosmic-ray iron over a wide energy interval. In this Letter a measurement of the iron spectrum is presented in the range of kinetic energy per nucleon from 10 GeV/n to 2.0 TeV/n allowing the inclusion of iron in the list of elements studied with unprecedented precision by space-borne instruments. The measurement is based on observations carried out from January 2016 to May 2020. The CALET instrument can identify individual nuclear species via a measurement of their electric charge with a dynamic range extending far beyond iron (up to atomic number Z=40). The energy is measured by a homogeneous calorimeter with a total equivalent thickness of 1.2 proton interaction lengths preceded by a thin (3 radiation lengths) imaging section providing tracking and energy sampling. The analysis of the data and the detailed assessment of systematic uncertainties are described and results are compared with the findings of previous experiments. The observed differential spectrum is consistent within the errors with previous experiments. In the region from 50 GeV/n to 2 TeV/n our present data are compatible with a single power law with spectral index −2.60±0.03.

关键词： Cosmic ray acceleration Cosmic ray composition & spectra Cosmic ray propagation Cosmic ray sources Cosmic rays & astroparticles

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What may happen or what you should do? Effects of knowledge representation regarding necessity of intervention on driver performance under level 2 automated driving 1

What may happen or what you should do? Effects of knowledge ...

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1st IEEE International Conference on industrial Cyber-Physical systems, ICPS 2018

作者： Itoh, Makoto Zhou, Huiping Kitazaki, Satoshi Faculty of Engineering Information and Systems University of Tsukuba Tsukuba Japan Tokyo Business Service Co. LTD Tokyo Japan Automotive Human Factors Research Center National Institute of Advanced Industrial Science and Technology Tsukuba Japan

ISBN: (纸本)9781538665312

Drivers of SAE Level 2 systems are expected to perform the Object and Event Detection and Response (OEDR) task even when the driving automation system is working. An ideal Human Machine Interface should dynamically inform the driver of all of the system's functional limitations and driver's role. However, systems currently available in the market rely to some extent on such information given to the driver as knowledge at a dealer shop prior to driving or through mass-media. Since information acquirable as knowledge by users is limited, it is necessary that the information is given in a simple and comprehendible form. In this study, we compare user-oriented and system-oriented expressions for the information that explains that there are cases where a driver needs to intervene. This study conducted an experiment with a high-fidelity driving simulator where the ego vehicle was equipped with a Level 2 driving automation system. The results of the experiment suggest that expressions such as 'you might need to intervene' (i.e., this is explained from the viewpoint of the driver) are better than expressions such as 'the system may go wrong' (i.e., this is explained from the viewpoint of the driving automation system). © 2018 IEEE.

关键词： Knowledge representation

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Pedestrian Action Recognition Using Motion Sensor and k-NN Classifier

Pedestrian Action Recognition Using Motion Sensor and k-NN C...

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2018 2nd International Conference on Artificial Intelligence: Technologies and Applications（ICAITA2018）

作者： Ningping Sun Toru Tsuruoka Dept. of Human-Oriented Information System Engineering National Institute of Technology Kumamoto College Advanced Electronics and Information Systems Engineering Course National Institute of Technology Kumamoto College

We focused on the recognition of pedestrian action using motion sensor and machine learning. In this paper, we present our method how to define feature of actions from acquired data, and how to use k nearest neighborh... 详细信息

关键词： motion sensor action recognition machine learning feature definition k nearest neighborhood classifier

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Human Motion Modeling from Complementary Skeleton Joints of Multiple Kinects

Human Motion Modeling from Complementary Skeleton Joints of ...

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2018 International Conference on Mathematics, Modelling, Simulation and Algorithms（MMSA2018）

作者： Ningping Sun Shunsuke Murakami Dept.of Human-Oriented Information System Engineering National Institute of Technology Kumamoto College Advanced Electronics and Information Systems Engineering Course National Institute of Technology Kumamoto College

ISBN: (纸本)9781510863064

Human motion modeling with the proposed algorithm of complementary skeleton joints can help us to solve the problem of visual blind spot when the motion are captured by Kinects. The shooting model can be remodeled and reconstructed with the recombination and complementation of the skeleton joints by a series of three-dimensional coordinate transformation according to the different Kinects and a virtual monocular camera. The reconstructed human skeleton model can be applied to further motion simulation from the virtual monocular camera.

关键词： motion capture skeleton joint of Kinect three-dimensional coordinate transformation complementation

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Discovery and affinity maturation of antibody fragments from an unfavorably enriched phage display selection by deep sequencing and machine learning

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Journal of Bioscience and Bioengineering 2025年

作者： Kawada, Sakiya Kurumida, Yoichi Ito, Tomoyuki Nguyen, Thuy Duong Nishi, Hafumi Nakazawa, Hikaru Saito, Yutaka Kameda, Tomoshi Tsuda, Koji Umetsu, Mitsuo Department of Biomolecular Engineering of Engineering Tohoku University 6-6-11 Aoba Aramaki Aoba-ku Sendai 980-8579 Japan Department of Data Science School of Frontier Engineering Kitasato University 1-15-1 Kitazato Minami-ku Kanagawa Sagamihara 252-0373 Japan Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology (AIST) 2-4-7 Aomi Koto-ku Tokyo 135-0064 Japan Department of Applied Information Sciences of Information Sciences Tohoku University 6-3-09 Aoba Aramaki Aoba-ku Sendai 980-8579 Japan Tohoku Medical Megabank Organization Tohoku University 2-1 Seiryo-machi Aoba-ku Sendai 980-8573 Japan Faculty of Core Research Ochanomizu University 2-1-1 Ohtsuka Bunkyo-ku Tokyo 112-8610 Japan Department of Computational Biology and Medical Sciences of Frontier Sciences The University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa 277-8561 Japan Center for Advanced Intelligence Project RIKEN 1-4-1 Nihombashi Chuo-ku Tokyo 103-0027 Japan Research and Services Division of Materials Data and Integrated Systems National Institute for Materials Science 1-2-1 Sengen Tsukuba Ibaraki 305-0047 Japan

Phage display selection has been used for directed evolution of antibody fragments. However, variants with binding affinity cannot be always identified due to undesirable enrichment of target-unrelated variants in the biopanning process. Here, our goal was to obtain functional variants by deep sequencing and machine learning from a phage display library where functional variants were not appropriately enriched. Deep sequencing of the previously biopanned pools revealed that amplification bias might have prevented the enrichment of target-binding phages. We performed a sequence similarity search based on the deep sequencing analysis so that the influence of bias was decreased, leading to discovery of a variant with binding affinity, which could not be discovered by a conventional screening method alone. We applied machine learning to the deep sequencing data;the machine learning proposed effective mutations for increasing affinity, allowing us to identify a variant with improved affinity (EC50 = 3.46 μM). In summary, we present the possibility of obtaining functional variants even from unfavorably enriched phage libraries by using deep sequencing and machine learning. © 2025 The Society for Biotechnology, Japan

关键词： Clustering analysis Deep sequencing Galectin-3 Machine learning Phage display VHH

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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