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arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han 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 Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task learning

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The Use of Large Language Models and Their Association With Enhanced Impact in Biomedical Research and Beyond

MedComm - Future Medicine

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MedComm - Future Medicine 2025年第2期4卷

作者： Cheng, Huzi Shi, Wen Sheng, Bin Lee, Aaron Y. Car, Josip Chaudhary, Varun Atanasov, Atanas G. Liu, Nan Qiu, Yue Chen, Qingyu Wong, Tien Yin Tham, Yih-Chung Zheng, Ying-Feng Department of Psychological and Brain Sciences Indiana University Bloomington IN United States Department of Ophthalmology the First Affiliated Hospital of Guangzhou Medical University Guangzhou China Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology University of Washington School of Medicine Seattle WA United States Centre for Population Health Sciences Lee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore Department of Primary Care and Public Health School of Public Health Imperial College London London United Kingdom Department Health Research Methods Evidence and Impact McMaster University Hamilton ON Canada Department of Surgery McMaster University Hamilton ON Canada Ludwig Boltzmann Institute Digital Health and Patient Safety Medical University of Vienna Vienna Austria Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences Jastrzebiec Poland Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Tsinghua Medicine Tsinghua University Beijing China Yale School of Medicine Yale University United States Singapore Eye Research Institute Singapore National Eye Centre Singapore School of Clinical Medicine Beijing Tsinghua Changgung Hospital Beijing China Ophthalmology and Visual Sciences Academic Clinical Program Duke-NUS Medical School Singapore Centre for Innovation and Precision Eye Health and Department of Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Singapore State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China

The release of ChatGPT in 2022 has catalyzed the adoption of large language models (LLMs) across diverse writing domains, including academic writing. However, this technological shift has raised critical questions regarding the prevalence of LLM usage in academic writing and its potential influence on the quality and impact of research articles. Here, we address these questions by analyzing preprint articles from arXiv, bioRxiv, and medRxiv between 2022 and 2024, employing a novel LLM usage detection tool. Our study reveals that LLMs have been widely adopted in biomedical and other types of academic writing since late 2022. Notably, we noticed that LLM usage is linked to an enhanced impact of research articles after examining their correlation, as measured by citation numbers. Furthermore, we observe that LLMs influence specific content types in academic writing, including hypothesis formulation, conclusion summarization, description of phenomena, and suggestions for future work. Collectively, our findings underscore the potential benefits of LLMs in scientific communication, suggesting that they may not only streamline the writing process but also enhance the dissemination and impact of research findings across disciplines. © 2025 The Author(s). MedComm - Future Medicine published by John Wiley & Sons Australia, Ltd on behalf of Sichuan International Medical Exchange & Promotion Association (SCIMEA).

关键词： academic writing chatGPT large language models LLM

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Learning graphons via structured gromov-wasserstein barycenters

arXiv

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arXiv 2020年

作者： Xu, Hongteng Luo, Dixin Carin, Lawrence Zha, Hongyuan Gaoling School of Artificial Intelligence Renmin University of China 59 Zhongguancun St Haidian District Beijing100872 China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China School of Computer Science and Technology Beijing Institue of Technology 5 Zhongguancun St Haidian District100811 China Department of ECE Duke University DurhamNC27708 United States School of Data Science Shenzhen Research Institute of Big Data Chinese University of Hong Kong Hong Kong

We propose a novel and principled method to learn a nonparametric graph model called graphon, which is defined in an infinite-dimensional space and represents arbitrary-size graphs. Based on the weak regularity lemma from the theory of graphons, we leverage a step function to approximate a graphon. We show that the cut distance of graphons can be relaxed to the Gromov-Wasserstein distance of their step functions. Accordingly, given a set of graphs generated by an underlying graphon, we learn the corresponding step function as the Gromov-Wasserstein barycenter of the given graphs. Furthermore, we develop several enhancements and extensions of the basic algorithm, e.g., the smoothed Gromov-Wasserstein barycenter for guaranteeing the continuity of the learned graphons and the mixed Gromov-Wasserstein barycenters for learning multiple structured graphons. The proposed approach overcomes drawbacks of prior state-of-the-art methods, and outperforms them on both synthetic and real-world data. The code is available at https://***/HongtengXu/SGWB-Graphon. © 2020, CC BY.

关键词： Graph theory

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Correction to: Global surface features contribute to human haptic roughness estimations

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Experimental brain research 2022年第4期240卷 1293页

作者： Huazhi Li Jiajia Yang Yinghua Yu Wu Wang Yulong Liu Mengni Zhou Qingqing Li Jingjing Yang Shiping Shao Satoshi Takahashi Yoshimichi Ejima Jinglong Wu Graduate School of Interdisciplinary Science and Engineering in Health Systems Okayama University 3-1-1 Tsushima-Naka Kita-ku Okayama 700-8530 Japan. Graduate School of Interdisciplinary Science and Engineering in Health Systems Okayama University 3-1-1 Tsushima-Naka Kita-ku Okayama 700-8530 Japan. yang@okayama-u.ac.jp. Section On Functional Imaging Methods National Institute of Mental Health Bethesda MD USA. yang@okayama-u.ac.jp. Section On Functional Imaging Methods National Institute of Mental Health Bethesda MD USA. School of Psychological and Cognitive Sciences Peking University Beijing China. Department of Teacher Education Wenzhou University Wenzhou China. School of Computer Science and Technology Changchun University of Science and Technology Changchun China. School of Social Welfare Yonsei University Seoul Korea. School of Mechatronical Engineering Beijing Institute of Technology Beijing China.

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Training of the Russian Federation’s national guard officers in information security 6th

Training of the Russian Federation’s national guard officer...

引用

6th International Congress on Interdisciplinary Behavior and Social sciences, ICIBSoS 2017

作者： Streltsov, R.V. Lavina, T.A. Bosova, L.L. Tsaregorodtsev, E.I. Department of Designs Armored Techniques Perm Military Institute of Russian troops of the National Guard Perm Russia Department of Computer Technology Chuvash State University Russia Faculty of Theory and Methods of Teaching Computer Science Moscow State University of Education Moscow Russia Faculty of Economics and Finance Mari State University Yoshkar-Ola Russia

ISBN: (纸本)9781138553804

The aim of this study was to develop scientific methodological approaches for training Russian troops in information security. We deal with problems related to the fundamentals of information security of the troops of the Russian Federation’s National Guard. A literature analysis was conducted to understand the importance of training troops in information security in the world community. The results of the initial pedagogical experiment on the level of training that officers receive in information security are presented. The development of graduates’ military professional competencies is ensured by obtaining a body of expertise, knowledge, and skills to guide units in a modern combined-arms battle in an armed conflict using Information and Communication Technologies (ITC). © 2018 Taylor & Francis Group, London.

关键词： Security of data

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Association Rules Transformation for Knowledge Integration and Warehousing 17th

Association Rules Transformation for Knowledge Integration a...

引用

17th International Conference on Intelligent Systems Design and Applications, ISDA 2017

作者： Ayadi, Rim Hachaichi, Yasser Feki, Jamel Multimedia Information Systems and Advanced Computing Laboratory University of Sfax Sfax Tunisia Department of Computer Science and Quantitative Methods Higher Institute of Business Administration of Sfax Sfax Tunisia Faculty of Computing and IT University of Jeddah Jeddah Saudi Arabia

ISBN: (纸本)9783319763477

Knowledge management process is a set of procedures and tools applied to facilitate capturing, sharing and effectively using knowledge. However, knowledge collected from organizations is generally expressed in various formalisms, therefore it is heterogeneous. Thus, a Knowledge Warehouse (KW), which is a solution for implementing all phases of the knowledge management process, should solve this structural heterogeneity before loading and storing knowledge. In this paper, we are interested in knowledge normalization. More accurately, we firstly introduce our proposed architecture for a KW, and then we present the MOT (Modeling with Object Types) language for knowledge representation. Since our objective is to transform heterogeneous knowledge into MOT, as a pivot model, we suggest a meta-model for the MOT and another for the explicit knowledge extracted through the association rules technique. Thereafter, we define eight transformation rules and an algorithm to transform an association rules model into the MOT model. © 2018, Springer International Publishing AG, part of Springer Nature.

关键词： Data mining

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Palladium nanoparticles supported over Green tea extract-modified Zn(Al)O mixed metal oxide as a heterogeneous catalyst for cyanation of aryl halides

引用

Journal of Organometallic Chemistry 2025年

作者： Ali B.M. Ali Abdulla A. Al-dulaimi Shaxnoza Saydaxmetova Narinderjit Singh Sawaran Singh Elangovan Muniyandy S. Sunitha M.A. Diab Aseel Smerat Syed Waheedullah Ghori Saiful Islam Air Conditioning Engineering Department College of Engineering University of Warith Al-Anbiyaa Karbala Iraq College of Nursing University of Al Maarif Al Anbar 31001 Iraq Department of Chemistry and Its Teaching Methods Tashkent State Pedagogical University Tashkent Uzbekistan Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nilai 71800 Malaysia Department of Biosciences Saveetha School of Engineering. Saveetha Institute of Medical and Technical Sciences Chennai 602105 Tamil Nadu India Applied Science Research Center Applied Science Private University Amman Jordan Department of Chemistry Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Biotechnology Yeungnam University Gyeongsan Gyeongbuk 38541 Republic of Korea Water Analysis Department Central Public Health Laboratories Ministry of Health and Population Cairo 11613 Egypt Faculty of Educational Sciences Al-Ahliyya Amman University Amman 19328 Jordan. Computer Technologies Engineering Mazaya University College Nasiriyah Iraq Mechanical Engineering Department College of Engineering King Khalid University Abha Saudi Arabia Civil Engineering Department College of Engineering King Khalid University Abha 61421 Saudi Arabia

Recently, modified hydrotalcite-like materials featuring layers of metal oxides have gained significant attention as improved supports for nanoparticles (NPs) due to their well electronic effects that intrinsically stabilize the deposited NPs. This research presents a refined methodology for synthesizing palladium nanoparticles (Pd NPs) using green tea extract-modified zinc-aluminum mixed oxides (Zn(Al)O/GT extract/Pd NPs). Green tea extract serves as a platform and a green reducing agent, enabling the in situ formation of Pd NPs without the use of harsh chemicals. This innovative approach represents the first occasion of designing and bio-generating Pd NPs coordinated on Zn(Al)O mixed oxides, along with their catalytic effects for the cyanation of aryl halides. The physicochemical properties of the resulting materials were evaluated utilizing a range of techniques including FE-SEM, elemental mapping, EDX, TEM, and ICP-OES analysis. The Zn(Al)O/GT extract/Pd NPs nanocatalyst showed exceptional catalytic efficiency as a recyclable platform for the cyanation of iodo- and bromo-benzene derivatives, employing K 4 [Fe(CN) 6 ] as the cyanating sorce. The obtained benzonitriles were produced in good yields, and the catalyst demonstrated the capacity to be reused up to 7 runs with only a minimal reduction in its efficacy.

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Hackers vs. Security: Attack-Defence Trees as Asynchronous Multi-Agent Systems

arXiv

引用

arXiv 2019年

作者： Arias, Jaime Budde, Carlos E. Penczek, Wojciech Petrucci, Laure Stoelinga, Marielle LIPN CNRS UMR 7030 Universite Paris 13 Sorbonne Paris Cite Villetaneuse France Institute of Computer Science PAS Warsaw Poland University of Natural Sciences and Humanities II Siedlce Poland Formal Methods and Tools University of Twente Enschede Netherlands Department of Software Science Radboud University Nijmegen Netherlands

Attack-Defence Trees (ADTs) are well-suited to assess possible attacks to systems and the efficiency of counter-measures. In this paper, we first enrich the available constructs with reactive patterns that cover further security scenarios, and equip all constructs with attributes such as time and cost to allow quantitative analyses. Then, ADTs are modelled as (an extension of) Asynchronous Multi-Agents Systems- EAMAS. The ADT-EAMAS transformation is performed in a systematic manner that ensures correctness. The transformation allows us to quantify the impact of different agents configurations on metrics such as attack time. Using EAMAS also permits parametric verification: we derive constraints for property satisfaction. Our approach is exercised on several case studies using the Uppaal and IMITATOR tools. Copyright © 2019, The Authors. All rights reserved.

关键词： Multi agent systems

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National identity predicts public health support during a global pandemic (vol 13, 517, 2022)

引用

NATURE COMMUNICATIONS 2022年第1期13卷 517-517页

作者： Van Bavel, Jay J. Cichocka, Aleksandra Capraro, Valerio Sjastad, Hallgeir Nezlek, John B. Pavlovic, Tomislav Alfano, Mark Gelfand, Michele J. Azevedo, Flavio Birtel, Michele D. Cislak, Aleksandra Lockwood, Patricia L. Ross, Robert Malcolm Abts, Koen Agadullina, Elena Aruta, John Jamir Benzon Besharati, Sahba Nomvula Bor, Alexander Choma, Becky L. Crabtree, Charles David Cunningham, William A. De, Koustav Ejaz, Waqas Elbaek, Christian T. Findor, Andrej Flichtentrei, Daniel Franc, Renata Gjoneska, Biljana Gruber, June Gualda, Estrella Horiuchi, Yusaku Huynh, Toan Luu Duc Ibanez, Agustin Imran, Mostak Ahamed Israelashvili, Jacob Jasko, Katarzyna Kantorowicz, Jaroslaw Kantorowicz-Reznichenko, Elena Krouwel, Andre Laakasuo, Michael Lamm, Claus Leygue, Caroline Lin, Ming-Jen Mansoor, Mohammad Sabbir Marie, Antoine Mayiwar, Lewend Mazepus, Honorata McHugh, Cillian Minda, John Paul Mitkidis, Panagiotis Olsson, Andreas Otterbring, Tobias Packer, Dominic J. Perry, Anat Petersen, Michael Bang Puthillam, Arathy Riano-Moreno, Julian C. Rothmund, Tobias Santamaria-Garcia, Hernando Schmid, Petra C. Stoyanov, Drozdstoy Tewari, Shruti Todosijevic, Bojan Tsakiris, Manos Tung, Hans H. Umbres, Radu G. Vanags, Edmunds Vlasceanu, Madalina Vonasch, Andrew Yucel, Meltem Zhang, Yucheng Abad, Mohcine Adler, Eli Akrawi, Narin Mdarhri, Hamza Alaoui Amara, Hanane Amodio, David M. Antazo, Benedict G. Apps, Matthew Ay, F. Ceren Ba, Mouhamadou Hady Barbosa, Sergio Bastian, Brock Berg, Anton Bernal-Zarate, Maria P. Bernstein, Michael Bialek, Michal Bilancini, Ennio Bogatyreva, Natalia Boncinelli, Leonardo Booth, Jonathan E. Borau, Sylvie Buchel, Ondrej Cameron, C. Daryl Carvalho, Chrissie F. Celadin, Tatiana Cerami, Chiara Chalise, Hom Nath Cheng, Xiaojun Cian, Luca Cockcroft, Kate Conway, Jane Cordoba-Delgado, Mateo Andres Crespi, Chiara Crouzevialle, Marie Cutler, Jo Cypryanska, Marzena Dabrowska, Justyna Daniels, Michael A. Davis, Victoria H. Dayley, Pamala N. Delouvee, Sylvain Denkovski, Ognjan Dezecache, Guillaume Dhaliwal, Nathan A. Diato, Department of Psychology and Neural Science New York University New York NY USA School of Psychology University of Kent Canterbury England Department of Economics Middlesex University London London England Department of Strategy and Management Norwegian School of Economics Bergen Norway SWPS University of Social Sciences and Humanities Poznań Poland Department of Psychological Sciences College of William and Mary Williamsburg VA USA Institute of Social Sciences Ivo Pilar Zagreb Croatia Department of Philosophy Macquarie University Sydney NSW Australia Stanford Graduate School of Business Stanford University Stanford CA USA Institute of Communication Science Friedrich-Schiller University Jena Jena Germany School of Human Sciences Institute for Lifecourse Development University of Greenwich London England Department of Experimental Psychology University of Oxford Oxford England Center for Human Brain Health School of Psychology University of Birmingham Birmingham England Department of Psychology Macquarie University Sydney NSW Australia KU Leuven Leuven Belgium National Research University Higher School of Economics (HSE) Moscow Russia De La Salle University Manila Philippines Department of Psychology University of the Witwatersrand Johannesburg South Africa Department of Political Science Aarhus University Aarhus Denmark X University Toronto Canada Department of Government Dartmouth College Hanover NH USA Department of Psychology University of Toronto Toronto ON Canada Gatton College of Business and Economics University of Kentucky Lexington KY USA Department of Mass Communication National University of Science and Technology (NUST) Islamabad Pakistan Department of Management Aarhus University Aarhus Denmark Faculty of Social and Economic Sciences Comenius University Bratislava Slovakia IntraMed Buenos Aires Argentina Macedonian Academy of Sciences and Arts North Macedonia Republic of North Macedonia University of Colorado Boulder Bould

Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national samples. Study 2 (N = 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic (r = −0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics. Understanding collective behaviour is an important aspect of managing the pandemic response. Here the authors show in a large global study that participants that reported identifying more strongly with their nation reported greater engagement in public health behaviours and support for public health policies in the context of the pandemic.

关键词： Viral infection Human behaviour

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Effects of the Cattaneo–Christov flux model on thermally radiative flow of tetra hybrid nanofluid with surface tension gradient

引用

Case Studies in Thermal Engineering 2025年 72卷

作者： Mostafa Mohamed Okasha Munawar Abbas Faiza Benabdallah Talib K. Ibrahim Tatyana Orlova Ali Akgül Murad Khan Hassani Abdullah A. Faqihi Hakim AL. Garalleh Ahmed M. Galal Department of Mechanical Engineering College of Engineering Northern Border University Arar Saudi Arabia Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai 602105 Tamil Nadu India Department of Mathematics The Islamia University of Bahawalpur Bahawalpur 63100 Pakistan Department of Industrial and Systems Engineering College of Engineering Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Petroleum Engineering College of Engineering Knowledge University Erbil 44001 Iraq Department of Petroleum Engineering Al-Kitab University Altun Kupri Iraq Department of Physics and Its Teaching Methods Tashkent State Pedagogical University Tashkent Uzbekistan Siirt University Art and Science Faculty Department of Mathematics 56100 Siirt Turkey Department of Computer Engineering Biruni University Topkapı 34010 Istanbul Turkey Near East University Mathematics Research Center Department of Mathematics Near East Boulevard PC: 99138 Mersin 10 Nicosia Turkey Applied Science Research Center Applied Science Private University Amman Jordan Ghazni University Department of Mathematics Afghanistan Department of Industrial Engineering College of Engineering and Computer Science Jazan University P. O. Box 706 Jazan 45142 Saudi Arabia Department of Mathematical Science College of Engineering University of Business and Technology Jeddah 21361 Saudi Arabia Department of Mechanical Engineering College of Engineering in Wadi Alddawasir Prince Sattam Bin Abdulaziz University Saudi Arabia Production Engineering and Mechanical Design Department Faculty of Engineering Mansoura University P.O 35516 Mansoura Egypt

The importance of thermal radiation and Stefan blowing on the boundary layer flow of tetra hybrid nanofluid flow across a disk are examined in the present work employing the Cattaneo-Christov flux model. Additionally, the Marangoni convection effect and Stefan blowing are examined. Tetra-hybrid nanofluids are employed in sophisticated thermal systems like heat exchangers, solar energy systems, and cooling technologies because of their improved thermal conductivity and heat transmission properties. Under microgravity conditions, a surface tension differential in the plasma causes the intriguing scientific phenomenon known as Marangoni convection. Important uses of the Marangoni effect include welding, convection or Bernard cells, crystal growth, electron beam melting of metals, soap film stability, and more. In order to improve heat exchangers in chemical processes, optimize thermal management in power plants, and provide effective cooling systems for intricate machinery, the model is essential. Furthermore, it is a flexible tool for contemporary engineering solutions that advance sustainable practices by improving the energy efficiency of renewable technologies. To solve the problem, the Homotopy analysis approach is applied, and graphs are used to show the analytical outcomes. The temperature and velocity fields rise as the Stefan blowing parameter rises, while the solutal profile falls.

关键词： Thermal radiation Cattaneo-Christov model Tetra hybrid nanofluid Stefan blowing Heat generation

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