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science Bulletin 2025年第6期 934-942页

作者：李灏萱江泽铧管洲榆包玉倩刘月星胡婷婷李佳佳刘茹涵吴量程棣纪宏伟王勇王亚星 Carol Y.Cheung 郑颖丰王继红李震吴卫兵 Cynthia Ciwei Lim Yong Mong Bee Hong Chang Tan Elif I.Ekinci David C.Klonoff Justin B.Echouffo-Tcheugui Nestoras Mathioudakis Leonor Corsino Rafael Simó Charumathi Sabanayagam Gavin Siew Wei Tan 程景煜黄天荫李华婷蔡淳毛丽娟 Lee-Ling Lim 覃宇宗盛斌贾伟平 Shanghai University of Sport School of Clinical Medicine Beijing Tsinghua Changgung Hospital Tsinghua Medicine Tsinghua University Beijing Visual Science and Translational Eye Research Institute(BERI) Tsinghua Medicine Tsinghua University Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes MoE Key Lab of Artificial Intelligence Artificial Intelligence Institute Shanghai Jiao Tong University Department of Cardiology the Affiliated Hospital of Qingdao University CEMS NCMIS HCMS MADIS Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematics University of Chinese Academy of Sciences Chinese Academy of Sciences Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Beijing Institute of Ophthalmology Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Sciences Key Laboratory Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular Diseases Department of Renal Medicine Singapore General Hospital Sing Health-Duke Academic Medical Centre Department of Endocrinology Singapore General Hospital Department of Endocrinology Austin Health Department of Medicine The University of Melbourne(Austin Health) Australian Centre for Accelerating Diabetes Innovations(ACADI) The Univ

Diabetes poses a considerable global health challenge, with varying levels of diabetes knowledge among healthcare professionals, highlighting the importance of diabetes training. Large Language Models(LLMs)provide new insights into diabetes training, but their performance in diabetes-related queries remains uncertain, especially outside the English language like Chinese. We first evaluated the performance of ten LLMs: Chat GPT-3.5, Chat GPT-4.0, Google Bard, Lla MA-7B, Lla MA2-7B, Baidu ERNIE Bot, Ali Tongyi Qianwen, Med GPT, Huatuo GPT, and Chinese Lla MA2-7B on diabetes-related queries, based on the Chinese National Certificate Examination for Primary Diabetes Care in China(NCE-CPDC) and the English Specialty Certificate Examination in Endocrinology and Diabetes of Membership of the Royal College of Physicians of the United Kingdom. Second, we assessed the training of primary care physicians(PCPs) without and with the assistance of Chat GPT-4.0 in the NCE-CPDC examination to ascertain the reliability of LLMs as medical assistants. We found that Chat GPT-4.0 outperformed other LLMs in the English examination, achieving a passing accuracy of 62.50%, which was significantly higher than that of Google Bard, Lla MA-7B, and Lla MA2-7B. For the NCE-CPFC examination, Chat GPT-4.0, Ali Tongyi Qianwen, Baidu ERNIE Bot, Google Bard, Med GPT, and Chat GPT-3.5 successfully passed, whereas Lla MA2-7B, Huatuo GPT,Chinese LLa MA2-7B, and Lla MA-7B failed. Chat GPT-4.0(84.82%) surpassed all PCPs and assisted most PCPs in the NCE-CPDC examination(improving by 1%–6.13%). In summary, LLMs demonstrated outstanding competence for diabetes-related questions in both the Chinese and English language, and hold great potential to assist future diabetes training for physicians globally.

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Implementation of Mobile game for Religion Learning

Implementation of Mobile game for Religion Learning

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IEEE international Conference on Teaching, Assessment and Learning for engineering (TALE)

作者： Edy Sulistiyo Vindy Nizirwan Anwar Diana Teresia Spits Warnars Setya Chendra Wibawa Goenawan Brotosaputro Dedy Prasetya Kristiadi Harco Leslie Hendric Spits Warnars Kiyota Hashimoto Electrical Engineering Department Universitas Negeri Surabaya Jawa Timur Indonesia Game Application & Technology Program School of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Computer Science Esa Unggul University Jakarta Indonesia Information Systems Department Bina Nusantara University Jakarta Indonesia Informatics Department Universitas Negeri Surabaya Jawa Timur Indonesia Information System Department Universitas Budi Luhur Jakarta Indonesia Computer Science department Raharja University Tangerang Indonesia Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Technology and Environment Prince Songkla University Phuket Campus Thailand

ISBN: (数字)9781728126654

ISBN: (纸本)9781728126661

Currently, the growth of information and technology is rapid. It makes a lot of things in various fields including education becoming more effective and efficient. In education, one of its implementation which is a game is proven to be a useful tool to support conventional teaching methods and bring a more natural understanding of learning materials. Moreover, as a product of the popular culture in modern society, video game mirrors the general culture practice in real life and reflects it via its own culture inside the game. This makes a video game can give a contribution to the social construction of reality as it affects the player's view towards learning in real life and vice versa. From there, we see that there is an opportunity for learning about religion to be supported by its utilization to provide an interactive and fun learning experience. In this paper, we discuss how a video game is implemented to support religious learning. The game was developed with the scrum method where we surveyed to gather the user requirements before the design step. The game design was made by using the use case diagram and storyboard, and it was built using Unity version 2017.3.0f3.

关键词： Games Land mobile radio Education computer science Operating systems Information systems Media

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Building a quantum engineering undergraduate program

arXiv

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arXiv 2021年

作者： Asfaw, Abraham Blais, Alexandre Brown, Kenneth R. Candelaria, Jonathan Cantwell, Christopher Carr, Lincoln D. Combes, Joshua Debroy, Dripto M. Donohue, John M. Economou, Sophia E. Edwards, Emily Fox, Michael F.J. Girvin, Steven M. Ho, Alan Hurst, Hilary M. Jacob, Zubin Johnson, Blake R. Johnston-Halperin, Ezekiel Joynt, Robert Kapit, Eliot Klein-Seetharaman, Judith Laforest, Martin Lewandowski, H.J. Lynn, Theresa W. McRae, Corey Rae H. Merzbacher, Celia Michalakis, Spyridon Narang, Prineha Oliver, William D. Palsberg, Jens Pappas, David P. Raymer, Michael G. Reilly, David J. Saffman, Mark Searles, Thomas A. Shapiro, Jeffrey H. Singh, Chandralekha IBM Quantum IBM T. J. Watson Research Center Yorktown HeightsNY United States Institut Quantique Département de Physique Université de Sherbrooke SherbrookeQCJ1K 2R1 Canada Canadian Institute for Advanced Research TorontoONM5G 1M1 Canada The Duke Quantum Center Department of Physics Department of Electrical and Computer Engineering Department of Chemistry Duke University DurhamNC27708 United States SystemX Program Department of Electrical Engineering Stanford University StanfordCA94305 United States Department of Physics and Astronomy University of Southern California Los AngelesCA90089 United States The Quantum Engineering Program Department of Physics Colorado School of Mines GoldenCO80401 United States The Department of Electrical Computer and Energy Engineering University of Colorado Boulder BoulderCO80309 United States The Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada The Department of Physics Virginia Tech BlacksburgVA24061 United States The IQUIST University of Illinois Urbana-Champaign UrbanaIL61801 United States JILA National Institute of Standards and Technology The University of Colorado BoulderCO80309 United States Department of Physics University of Colorado Boulder BoulderCO80309 United States The Department of Physics Imperial College London Prince Consort Road LondonSW7 2AZ United Kingdom The Yale Quantum Institute Department of Physics Yale University New HavenCT06520 United States Google Research VeniceCA90291 United States The Department of Physics & Astronomy San José State University San JoséCA95192 United States The School of Electrical and Computer Engineering Purdue University Indiana West Lafayatte47907 United States The Department of Physics The Ohio State University ColumbusOH43210 United States The Department of Physics University of Wisconsin-Madison 1150 University Avenue MadisonWI53706 United States The Quantitative Biosciences and Engineering

The rapidly growing quantum information science and engineering (QISE) industry will require both quantum-aware and quantum-proficient engineers at the bachelor's level. We provide a roadmap for building a quantum engineering education program to satisfy this need. For quantum-aware engineers, we describe how to design a first quantum engineering course accessible to all STEM students. For the education and training of quantum-proficient engineers, we detail both a quantum engineering minor accessible to all STEM majors, and a quantum track directly integrated into individual engineering majors. We propose that such programs typically require only three or four newly developed courses that complement existing engineering and science classes available on most larger campuses. We describe a conceptual quantum information science course for implementation at any post-secondary institution, including community colleges and military schools. QISE presents extraordinary opportunities to work towards rectifying issues of inclusivity and equity that continue to be pervasive within engineering. We present a plan to do so and describe how quantum engineering education presents an excellent set of education research opportunities. Finally, we outline a hands-on training plan on quantum hardware, a key component of any quantum engineering program, with a variety of technologies including optics, atoms and ions, cryogenic and solid-state technologies, nanofabrication, and control and readout electronics. Our recommendations provide a flexible framework that can be tailored for academic institutions ranging from teaching and undergraduate-focused two- and four-year colleges to research-intensive universities. © 2021, CC BY.

关键词： engineering education

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Digital twin technology advancing industry 4.0 and industry 5.0 across sectors

Results in Engineering

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Results in engineering 2025年 26卷

作者： Ocident Bongomin Mwewa Chikonkolo Mwape Nonsikelelo Sheron Mpofu Brendah Kembabazi Bahunde Richard Kidega Ibrahim Luqman Mpungu Godias Tumusiime Cynthia Awuor Owino Yannick Marnaigue Goussongtogue Aregawi Yemane Proscovia Kyokunzire Clement Malanda Jimmy Komakech Dan Tigalana Onesmas Gumisiriza George Ngulube Department of Mechanical Engineering Faculty of Engineering and Survey Ndejje University P.O. Box 7088 Kampala Uganda Department of Manufacturing Textile and Industrial Engineering School of Engineering Moi University P.O. Box 30100-3900 Eldoret Kenya Egypt Solid Waste Management Center of Excellence Faculty of Engineering Ain Shams University Cairo 11566 Egypt Department of Agricultural and Biosystems Engineering University of Kassel Nordbahnhoftstraße 1a Witzenhausen 37213 Germany Department of Crop Inspection and Certification Ministry of Agriculture Animal Industry and Fisheries P.O Box 102 Entebbe Uganda Department of Mining Engineering Faculty of Engineering and Technology Busitema University P.O. Box 236 Tororo Uganda Department of Mining and Mineral Processing Engineering School of Mines and Engineering Taita Taveta University P.O. Box 635-80300 Voi Kenya Department of Technical Teacher and Instructor Education School of Education Kyambogo University P.O. Box 1 Kyambogo Kampala Uganda Department of Polymer Textile and Industrial Engineering Faculty of Engineering and Technology Busitema University P.O. Box 236 Tororo Uganda Department of Electrical and Computer Engineering School of Engineering Makerere University P.O. Box 7062 Kampala Uganda Department of Civil and Structural Engineering School of Engineering Moi University P.O. Box 30100-3900 Eldoret Kenya Department of Industrial Engineering School of Mechanical and Industrial Engineering Mekelle University Mekelle Ethiopia Department of Polymer Engineering Graduate School Chonnam National University Gwangju 61186 South Korea Department of Applied Studies Malawi University of Science and Technology P.O. Box 5196 Limbe Malawi College of Education and Human Development Western Michigan University 1903W MI United States Department of Oil and Gas Industry Facility Design and Construction Faculty of Pipeline Construction Ufa State Petroleum Technological University 450064 Street Kosmo

Digital Twin (DT) technology is transforming industrial systems by integrating physical assets with digital models, enabling real-time monitoring, predictive analytics, and process optimization, particularly within the framework of Industry 4.0 (I4.0). As the global industrial landscape shifts toward Industry 5.0 (I5.0), DTs are increasingly being redefined to support human-centric innovation, sustainability, and system resilience. This review examines the evolving role of DTs in bridging the efficiency-driven goals of I4.0 with the inclusive, sustainable objectives of I5.0. It explores ten enabling technologies such as artificial intelligence (AI), internet of things, blockchain, cloud and edge computing, and extended reality, while discussing both the opportunities and challenges posed by I5.0. The study emphasizes key principles of DTs, including real-time synchronization, feedback mechanisms, and lifecycle integration. A detailed sectorial analysis across manufacturing, infrastructure, energy, transportation, mining, agriculture, and healthcare illustrates how DTs are being applied in diverse contexts to enhance operational efficiency, product quality, and decision-making. The mapping of applications by country, sector, and industrial focus reveals growing trends toward I5.0 in areas such as logistics and infrastructure. Common application domains include monitoring, optimization, prediction, and decision support. Despite their potential, DT adoption faces challenges including high implementation costs, data integration issues, cybersecurity concerns, and lack of standardization. The review discusses these barriers alongside the importance of validation and security for trusted deployment. It concludes by identifying future directions, including cognitive twins, industrial metaverse integration, and ethical AI. DTs are positioned as foundational technologies for advancing sustainable, resilient, and human-centered industrial systems.

关键词： Digital twins Industry 4.0 Industry 5.0 Human-centric innovation Sustainability Artificial intelligence Predictive analytics

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Magnetic Control of Soft Chiral Phonons in PbTe

arXiv

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arXiv 2021年

作者： Baydin, Andrey Hernandez, Felix G.G. Rodriguez-Vega, Martin Okazaki, Anderson K. Tay, Fuyang Timothy Noe, G. Katayama, Ikufumi Takeda, Jun Nojiri, Hiroyuki Rappl, Paulo H.O. Abramof, Eduardo Fiete, Gregory A. Kono, Junichiro Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Smalley-Curl Institute Rice University HoustonTX77005 United States Instituto de Física Universidade de São Paulo SP São Paulo05508-090 Brazil Theoretical Division Los Alamos National Laboratory Los AlamosNM87545 United States Laboratório Associado de Sensores e Materiais Instituto Nacional de Pesquisas Espaciais SP São José dos Campos12201-970 Brazil Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Physics Graduate School of Engineering Science Yokohama National University Yokohama240-8501 Japan Institute for Materials Research Tohoku University Sendai980-8577 Japan Department of Physics Northeastern University BostonMA02115 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Department of Material Science and NanoEngineering Rice University HoustonTX77005 United States

PbTe crystals have a soft transverse optical phonon mode in the terahertz frequency range, which is known to efficiently decay into heat-carrying acoustic phonons, resulting in anomalously low thermal conductivity. Here, we studied this phonon via polarization-dependent terahertz spectroscopy. We observed softening of this mode with decreasing temperature, indicative of incipient ferroelectricity, which we explain through a model including strong anharmonicity with a quartic displacement term. In magnetic fields up to 25 T, the phonon mode splits into two modes with opposite handedness, exhibiting circular dichroism. Their frequencies display Zeeman splitting together with an overall diamagnetic shift with increasing magnetic field. Using a group-theoretical approach, we demonstrate that these observations are the result of magnetic field-induced morphic changes in the crystal symmetries through the Lorentz force exerted on the lattice ions. Thus, our study reveals a novel process of controlling phonon properties in a soft ionic lattice by a strong magnetic field. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic fields

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PixelSteganalysis: Pixel-wise Hidden Information Removal with Low Visual Degradation

arXiv

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arXiv 2019年

作者： Jung, Dahuin Bae, Ho Choi, Hyun-Soo Yoon, Sungroh The Dept. of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Dept. of Cyber Security Ewha Womans University Seoul03760 Korea Republic of Dept. of Computer Science & Engineering Interdisciplinary Graduate Program in Medical Bigdata Convergence Kangwon National University Chuncheon 24341 Korea Republic of Ziovision Chuncheon24341 Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of

Recently, the field of steganography has experienced rapid developments based on deep learning (DL). DL based steganography distributes secret information over all the available bits of the cover image, thereby posing difficulties in using conventional steganalysis methods to detect, extract or remove hidden secret images. However, our proposed framework is the first to effectively disable covert communications and transactions that use DL based steganography. We propose a DL based steganalysis technique that effectively removes secret images by restoring the distribution of the original images. We formulate a problem and address it by exploiting sophisticated pixel distributions and an edge distribution of images by using a deep neural network. Based on the given information, we remove the hidden secret information at the pixel level. We evaluate our technique by comparing it with conventional steganalysis methods using three public benchmarks. As the decoding method of DL based steganography is approximate (lossy) and is different from the decoding method of conventional steganography, we also introduce a new quantitative metric called the destruction rate (DT). The experimental results demonstrate performance improvements of 10-20% in both the decoded rate and the DT. Copyright © 2019, The Authors. All rights reserved.

关键词： Steganography

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Coupling Rendering and Generative Adversarial Networks for Artificial SAS Image Generation

Coupling Rendering and Generative Adversarial Networks for A...

引用

OCEANS

作者： Albert Reed Isaac D. Gerg John D. McKay Daniel C. Brown David P. Williamsk Suren Jayasuriya School of Electrical Computer & Energy Engineering Arizona State University Tempe AZ USA School of Electrical Engineering and Computer Science Pennsylvania State University State College PA USA Applied Research Laboratory Pennsylvania State University State College PA USA Graduate Program in Acoustics Pennsylvania State University State College PA USA NATO STO Centre for Maritime Research & Experimentation La Spezia Italy School of Arts Media & Engineering Arizona State University Tempe AZ USA

There is a growing demand for large-scale Synthetic Aperture Sonar (SAS) datasets. This demand stems from data-driven applications such as Automatic Target Recognition (ATR) [1]-[3], segmentation [4] and oceanographic research of the seafloor, simulation for sensor prototype development and calibration [5], and even potential higher level tasks such as motion estimation [6] and micronavigation [7]. Unfortunately, the acquisition of SAS data is bottlenecked by the costly deployment of SAS imaging systems, and even when data acquisition is possible, the data is often skewed towards containing barren seafloor rather than objects of interest. This skew introduces a data imbalance problem wherein a dataset can have as much as a 1000-to-1 ratio of seafloor background to object-of-interest SAS image chips.

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Exciton Localization of High-Quality ZnO/Mg_xZn_1-xO Multiple Quantum Wells on Si (111) with a Y₂O₃ Buffer Layer (vol 1, pg 3829, 2018)

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ACS APPLIED NANO MATERIALS 2018年第10期1卷 5958-5958页

作者： Liu, Wei-Rein Huang, Wei-Lun Wu, Yung-Chi Lai, Liang-Hsun Hsu, Chia-Hung Hsieh, Wen-Feng Chiang, Tsung-Hung Wan, H. W. Hong, M. Kao, J. National Synchrotron Radiation Research Center Hsinchu 30076 Taiwan Program for Science and Technology of Accelerator Light Source National Chiao Tung University Hsinchu 30010 Taiwan Department of Photonics College of Electrical and Computer Engineering National Chiao Tung University Hsinchu 30010 Taiwan Department of Photonics College of Electrical and Computer Engineering National Chiao Tung University Hsinchu 30010 Taiwan Department of Materials Science & Engineering National Tsing Hua University Hsinchu 30013 Taiwan Graduate Institute of Applied Physics and Department of Physics National Taiwan University Taipei 10617 Taiwan Department of Physics National Tsing Hua University Hsinchu 30013 Taiwan

We report the structural and optical properties of ten-period ZnO/MgxZn1–xO multiple quantum wells (MQWs) prepared on the most widely used semiconductor material, Si. The introduction of a nanometer thick high-k Y2O3transition layer between Si (111) substrate and a ZnO buffer layer significantly improves the structural perfection of the MQWs grown on top of it. The high structural quality of the ZnO/MgxZn1–xO MQWs is evidenced by the appearance of pronounced high order satellite peaks in X-ray crystal truncation rods; high resolution cross-sectional transmission electron microscopy images also confirmed the regularly arranged well and barrier layers. When the well width is less than ∼2.7 nm, the quantum-confined Stark effect in MQWs can be negligible. Not only the increasing exciton-binding energy but also reducing exciton–phonon coupling determined in temperature-dependent photoluminescence spectra indicate quantum-size effect. Our results demonstrate that ZnO/MgxZn1–xO MQWs integrated on Si have great potential in UV optoelectronic device applications.

关键词： Excitons Phonons Oxides Binding energy Layers

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The Potential of Generative Artificial Intelligence Across Disciplines: Perspectives and Future Directions

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The Journal of Data Education 2025年第1期65卷

作者： Keng-Boon Ooi Garry Wei-Han Tan Mostafa Al-Emran Mohammed A. Al-Sharafi Alexandru Capatina Amrita Chakraborty Yogesh K. Dwivedi Tzu-Ling Huang Arpan Kumar Kar Voon-Hsien Lee Xiu-Ming Loh Adrian Micu Patrick Mikalef Emmanuel Mogaji Neeraj Pandey Ramakrishnan Raman Nripendra P. Rana Prianka Sarker Anshuman Sharma Ching-I Teng Samuel Fosso Wamba Lai-Wan Wong a UCSI Graduate Business School UCSI University Kuala Lumpur Malaysiab FORE School of Management New Delhi Indiac Faculty of Business Design and Arts Swinburne University of Technology Sarawak Campus Kuching Malaysia a UCSI Graduate Business School UCSI University Kuala Lumpur Malaysiac Faculty of Business Design and Arts Swinburne University of Technology Sarawak Campus Kuching Malaysiad College of Business Administration Adamson University Manila Philippines e Faculty of Engineering & IT The British University in Dubai Dubai UAEf Department of Computer Techniques Engineering Dijlah University College Baghdad Iraq g Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Selangor Malaysia h Department of Business Administration “Dunarea de Jos” University of Galati Galati Romania i Tech Talk New Delhi India j Digital Futures for Sustainable Business & Society Research Group School of Management Swansea University Bay Campus Swansea UKk Department of Management Symbiosis International (Deemed University) Pune India l Department of Information Management National Central University Taoyuan Taiwan m Department of Management Studies Indian Institute of Technology Delhi New Delhi India n Faculty of Business and Finance Universiti Tunku Abdul Rahman Kampar Malaysia o Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim Norwayp Department of Technology Management SINTEF Digital Trondheim Norway q Keele Business School Keele University Keele UK r Indian Institute of Management Mumbai Mumbai India k Department of Management Symbiosis International (Deemed University) Pune India s College of Business and Economics Qatar University Doha Qatar t Business School Manchester Metropolitan University Manchester UK u Department of Marketing College of Business Administration Ajman university Ajman UAE v Graduate Institute of Management Chang Gung University Taoyuan Taiw

ABSTRACTIn a short span of time since its introduction, generative artificial intelligence (AI) has garnered much interest at both personal and organizational levels. This is because of its potential to cause drastic and widespread shifts in many aspects of life that are comparable to those of the Internet and smartphones. More specifically, generative AI utilizes machine learning, neural networks, and other techniques to generate new content (e.g. text, images, music) by analyzing patterns and information from the training data. This has enabled generative AI to have a wide range of applications, from creating personalized content to improving business operations. Despite its many benefits, there are also significant concerns about the negative implications of generative AI. In view of this, the current article brings together experts in a variety of fields to expound and provide multi-disciplinary insights on the opportunities, challenges, and research agendas of generative AI in specific industries (i.e. marketing, healthcare, human resource, education, banking, retailing, the workplace, manufacturing, and sustainable IT management).

关键词： Generative artificial intelligence machine learning large language model ChatGPT Bard

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Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-Informed Machine Learning

引用

ACS nano 2025年第23期19卷 2025 Jun 3页

作者： Joseph Cave Anne Christiono Carmine Schiavone Henry J Pownall Vittorio Cristini Daniela I Staquicini Renata Pasqualini Wadih Arap C Jeffrey Brinker Matthew Campen Zhihui Wang Hien Van Nguyen Achraf Noureddine Prashant Dogra Mathematics in Medicine Program Department of Medicine Houston Methodist Research Institute Houston Texas 77030 United States. Physiology Biophysics and Systems Biology Program Graduate School of Medical Sciences Weill Cornell Medicine New York New York 10065 United States. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 United States. Department of Chemical Materials and Industrial Production Engineering University of Naples Federico II Naples 80138 Italy. Department of Medicine Houston Methodist Houston Texas 77030 United States. Department of Medicine Weill Cornell Medicine New York New York 10065 United States. Neal Cancer Center Houston Methodist Research Institute Houston Texas 77030 United States. Department of Imaging Physics University of Texas M.D. Anderson Cancer Center Houston Texas 77030 United States. Rutgers Cancer Institute Newark New Jersey 08901 United States. Division of Cancer Biology Department of Radiation Oncology Rutgers New Jersey Medical School Newark New Jersey 08901 United States. Division of Hematology/Oncology Department of Medicine Rutgers New Jersey Medical School Newark New Jersey 08901 United States. Department of Chemical and Biological Engineering University of New Mexico Albuquerque New Mexico 87106 United States. College of Pharmacy University of New Mexico Albuquerque New Mexico 87106 United States. Department of Physiology and Biophysics Weill Cornell Medical College New York New York 10065 United States. Department of Electrical and Computer Engineering University of Houston Houston Texas 77204 United States.

The safety of inorganic nanoparticles (NPs) remains a critical challenge for their clinical translation. To address this, we developed a machine learning (ML) framework that predicts NP toxicity both and , leveraging physicochemical properties and experimental conditions. A curated cytotoxicity dataset was used to train and validate binary classification models, with top-performing models undergoing explainability analysis to identify key determinants of toxicity and establish structure-toxicity relationships. External testing with diverse inorganic NPs validated the predictive accuracy of the framework for settings. To enable organ-specific toxicity predictions , we integrated a physiologically based pharmacokinetic (PBPK) model into the ML pipeline to quantify NP exposure across organs. Retraining the ML models with PBPK-derived exposure metrics yielded robust predictions of organ-specific nanotoxicity, further validating the framework. This PBPK-informed ML approach can thus serve as a potential alternative approach to streamline NP safety assessment, enabling the rational design of safer NPs and expediting their clinical translation.

关键词： PBPK artificial intelligence cytotoxicity machine learning mathematical modeling nanoparticle nanotoxicity

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