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International Scientific-Professional Conference on Information Technology (IT)

作者： Aniko Simon Luka Laković Pavle Kovačević Peter A. Kara Ivana Ognjanović Ramo Šendelj Christoph Reich Milovan Roganović John Mantas Laszlo Bokor Sigma Technology 1093 Budapest Hungary Faculty for Information Systems and Technologies University of Donja Gorica 81000 Podgorica Montenegro Budapest University of Technology and Economics 1111 Budapest Hungary Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics 1111 Budapest Hungary Department of Computer Science School of Computer Science and Mathematics Faculty of Science Engineering and Computing Wireless Multimedia and Networking Research Group Kingston University London KT1 2EE UK Humanistic Studies University of Donja Gorica 81000 Podgorica Montenegro Institute of Data Science Cloud Computing and IT Security Furtwangen University 78120 Furtwangen Germany Clinic for Neurology Clinical Centre of Montenegro 81000 Podgorica Montenegro Faculty of Nursing School of Health Sciences National and Kapodistrian University of Athens 11527 Goudi Greece

With the advancement of information technology in the recent decades, digital and online services have become essential components of everyday life in Europe. Montenegro is no different in this regard. However, as digital literacy in the country is still in a developing phase in comparison with most countries of the EU, a higher portion of its citizens may rely on assistive technical communication. In this paper, we provide a case study on the assistive technical communication of Montenegrin eServices. The work addresses the sectors of online banking, healthcare services, higher education, national administrative services, and utilities. Our findings highlight the diversity in terms of implemented assistive technical communication, and distinguish usage contexts on the level of adequacy. Yet as the functionalities of Montenegrin eServices are expected to gradually expand in the upcoming years, assistive technical communication must aim to handle the higher extents of service complexity.

关键词： Online banking Education Europe Medical services Complexity theory Information technology

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G3SR: Global Graph Guided Session-based Recommendation

arXiv

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arXiv 2022年

作者： Deng, Zhi-Hong Wang, Chang-Dong Huang, Ling Lai, Jian-Huang Yu, Philip S. School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China College of Mathematics and Informatics South China Agricultural University Guangzhou China Guangdong Provincial Key Laboratory of Public Finance and Taxation with Big Data Application China University of Illinois at Chicago ChicagoIL United States

Session-based recommendation tries to make use of anonymous session data to deliver high-quality recommendation under the condition that user-profiles and the complete historical behavioral data of a target user are unavailable. Previous works consider each session individually and try to capture user interests within a session. Despite their encouraging results, these models can only perceive intra-session items and cannot draw upon the massive historical relational information. To solve this problem, we propose a novel method named G3SR (Global Graph Guided Session-based Recommendation). G3SR decomposes the session-based recommendation workflow into two steps. First, a global graph is built upon all session data, from which the global item representations are learned in an unsupervised manner. Then, these representations are refined on session graphs under the graph networks, and a readout function is used to generate session representations for each session. Extensive experiments on two real-world benchmark datasets show remarkable and consistent improvements of the G3SR method over the state-of-the-art methods, especially for cold items. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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IMPROVING PERFORMANCE OF CONTOUR INTEGRAL-BASED NONLINEAR EIGENSOLVERS WITH INFINITE GMRES

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SIAM Journal on Scientific Computing 2025年第3期47卷 B595-B617页

作者： Liu, Yuqi Roman, Jose E. Shao, Meiyue School of Mathematical Sciences Fudan University Shanghai200433 China D. Sistemes Informatics i Computacio Universitat Politecnica de Valencia Cam ide Vera s/n Valencia46022 Spain School of Data Science Shanghai Key Laboratory for Contemporary Applied Mathematics Fudan University Shanghai200433 China

In this work, the infinite GMRES algorithm, recently proposed by [Jarlebring and Correnty, SIAM J. Matrix Anal. Appl., 43 (2022), pp. 1382-1405] is employed in contour integral-based nonlinear eigensolvers, avoiding the computation of costly factorizations at each quadrature node to solve the linear systems efficiently. Several techniques are applied to make the infinite GMRES memory-friendly, computationally efficient, and numerically stable in practice. More specifically, we analyze the relationship between polynomial eigenvalue problems and their scaled linearizations, and provide a novel weighting strategy which can significantly accelerate the convergence of infinite GMRES in this particular context. We also adopt the technique of TOAR to infinite GMRES to reduce the memory footprint. Theoretical analysis and numerical experiments are provided to illustrate the efficiency of the proposed algorithm. © 2025 Society for Industrial and Applied mathematics.

关键词： Eigenvalues and eigenfunctions

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Observation of gamma rays from the northern celestial sky up to the sub-PeV range with the Tibet air shower array and its underground muon detector array 38

Observation of gamma rays from the northern celestial sky up...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Kato, S. Amenomori, M. Bao, Y.W. Bi, X.J. Chen, D. Chen, T.L. Chen, W.Y. Chen, Xu Chen, Y. Cirennima Cui, S.W. Danzengluobu Ding, L.K. Fang, J.H. Fang, K. Feng, C.F. Feng, Zhaoyang Feng, Z.Y. Gao, Qi Gou, Q.B. Guo, Y.Q. Guo, Y.Y. Hayashi, Y. He, H.H. He, Z.T. Hibino, K. Hotta, N. Hu, Haibing Hu, H.B. Hu, K.Y. Huang, J. Jia, H.Y. Jiang, L. Jiang, P. Jin, H.B. Kasahara, K. Katayose, Y. Kato, C. Kato, S. Kawahara, I. Kawashima, T. Kawata, K. Kozai, M. Labaciren Le, G.M. Li, A.F. Li, H.J. Li, W.J. Li, Y. Lin, Y.H. Liu, B. Liu, C. Liu, J.S. Liu, L.Y. Liu, M.Y. Liu, W. Lu, H. Makishima, T. Masuda, Y. Matsuhashi, S. Matsumoto, M. Meng, X.R. Meng, Y. Mizuno, A. Munakata, K. Nakamura, Y. Nanjo, H. Ning, C.C. Nishizawa, M. Noguchi, R. Ohnishi, M. Okukawa, S. Ozawa, S. Qian, X. Qian, X.L. Qu, X.B. Saito, T. Sakata, M. Sako, T. Sako, T.K. Sasaki, T. Shao, J. Shibasaki, T. Shibata, M. Shiomi, A. Sugimoto, H. Takano, W. Takita, M. Tan, Y.H. Tateyama, N. Torii, S. Tsuchiya, H. Udo, S. Usui, R. Wang, H. Wang, S.F. Wang, Y.P. Wangdui Wu, H.R. Wu, Q. Xu, J.L. Xue, L. Yang, Z. Yao, Y.Q. Yin, J. Yokoe, Y. Yu, Y.L. Yuan, A.F. Zhai, L.M. Zhang, H.M. Zhang, J.L. Zhang, X. Zhang, X.Y. Zhang, Y. Zhang, Yi Zhang, Ying Zhao, S.P. Zhaxisangzhu Zhou, X.X. Zou, Y.H. Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa277-8582 Japan Department of Physics Hirosaki University Hirosaki036-8561 Japan School of Astronomy and Space Science Nanjing University Nanjing210093 China Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Department of Mathematics and Physics Tibet University Lhasa850000 China Department of Physics Hebei Normal University Shijiazhuang050016 China University of Chinese Academy of Sciences Beijing100049 China Shandong University Qingdao266237 China Institute of Modern Physics SouthWest Jiaotong University Chengdu610031 China Department of Physics Shinshu University Matsumoto390-8621 Japan Faculty of Engineering Kanagawa University Yokohama221-8686 Japan Faculty of Education Utsunomiya University Utsunomiya321-8505 Japan Faculty of Systems Engineering Shibaura Institute of Technology Omiya330-8570 Japan Faculty of Engineering Yokohama National University Yokohama240-8501 Japan Institute for Cosmic Ray Research University of Tokyo Kashiwa277-8582 Japan Polar Environment Data Science Center Joint Support-Center for Data Science Research Research Organization of Information and Systems Tachikawa190-0014 Japan National Center for Space Weather China Meteorological Administration Beijing100081 China School of Information Science and Engineering Shandong Agriculture University Taian271018 China Department of Astronomy School of Physical Sciences University of Science and Technology of China Hefei230026 China National Institute of Informatics Tokyo101-8430 Japan National Institute of Information and Communications Technology Tokyo184-8795 Japan Department of Mechanical and Electrical Engineering Shangdong Management University Jinan250357 China College of Science China University of Petroleum Qingdao266555

Gamma-ray observation in the sub-PeV range helps us search for Galactic PeV cosmic-ray accelerators, PeVatrons. Measurement of the energy spectrum of gamma rays from astrophysical sources is especially crucial to discuss their hadronic or leptonic origin. In this study, gamma rays from the sky region of HESS J1843−033 are observed by the Tibet air shower array equipped with the underground muon detector array. A new gamma-ray source TASG J1844−038 is detected above 25 TeV with a 6.2 σ level in the region of interest, the position of which is consistent with HESS J1843−033, eHWC J1842−035, and LHAASO J1843−0338. The gamma-ray energy spectrum of TASG J1844−038 is successfully measured in 25−130 TeV for the first time;it smoothly connects to that of HESS J1843−033 and gives consistent flux levels with those of eHWC J1842−035 and LHAASO J1843−0338 at 56 and 100 TeV, respectively. The analysis of the combined gamma-ray spectra between the H.E.S.S., LHAASO, and TASG sources indicates the cutoff at about 50 TeV. The distribution of the molecular clouds around TASG J1844−038 is investigated by analyzing public CO line-emission data;our study finds the clouds in the velocity range of 75−95 km s−1 lying flat over the region with a 50 K km s−1 level. The association of the observed gamma rays with a nearby supernova remnant SNR G28.6−0.1 is discussed, and it is proposed that cosmic rays accelerated by SNR G28.6−0.1 up to about 500 TeV generate the observed gamma rays from the collisions with the ambient gas found in the analysis. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic ray detectors

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Measurement of the primary cosmic-ray proton spectrum between 40 TeV and a few hundred TeV with the Tibet hybrid experiment (Tibet-III + MD) 38

Measurement of the primary cosmic-ray proton spectrum betwee...

引用

38th International Cosmic Ray Conference, ICRC 2023

作者： Katayose, Y. Amenomori, M. Bao, Y.W. Bi, X.J. Chen, D. Chen, T.L. Chen, W.Y. Chen, Xu Chen, Y. Cirennima Cui, S.W. Danzengluobu Ding, L.K. Fang, J.H. Fang, K. Feng, C.F. Feng, Zhaoyang Feng, Z.Y. Gao, Qi Gou, Q.B. Guo, Y.Q. Guo, Y.Y. Hayashi, Y. He, H.H. He, Z.T. Hibino, K. Hotta, N. Hu, Haibing Hu, H.B. Hu, K.Y. Huang, J. Jia, H.Y. Jiang, L. Jiang, P. Jin, H.B. Kasahara, K. Katayose, Y. Kato, C. Kato, S. Kawahara, I. Kawashima, T. Kawata, K. Kozai, M. Labaciren Le, G.M. Li, A.F. Li, H.J. Li, W.J. Li, Y. Lin, Y.H. Liu, B. Liu, C. Liu, J.S. Liu, L.Y. Liu, M.Y. Liu, W. Lu, H. Makishima, T. Masuda, Y. Matsuhashi, S. Matsumoto, M. Meng, X.R. Meng, Y. Mizuno, A. Munakata, K. Nakamura, Y. Nanjo, H. Ning, C.C. Nishizawa, M. Noguchi, R. Ohnishi, M. Okukawa, S. Ozawa, S. Qian, X. Qian, X.L. Qu, X.B. Saito, T. Sakata, M. Sako, T. Sako, T.K. Sasaki, T. Shao, J. Shibasaki, T. Shibata, M. Shiomi, A. Sugimoto, H. Takano, W. Takita, M. Tan, Y.H. Tateyama, N. Torii, S. Tsuchiya, H. Udo, S. Usui, R. Wang, H. Wang, S.F. Wang, Y.P. Wangdui Wu, H.R. Wu, Q. Xu, J.L. Xue, L. Yang, Z. Yao, Y.Q. Yin, J. Yokoe, Y. Yu, Y.L. Yuan, A.F. Zhai, L.M. Zhang, H.M. Zhang, J.L. Zhang, X. Zhang, X.Y. Zhang, Y. Zhang, Yi Zhang, Ying Zhao, S.P. Zhaxisangzhu Zhou, X.X. Zou, Y.H. Faculty of Engineering Yokohama National University Yokohama240-8501 Japan Department of Physics Hirosaki University Hirosaki036-8561 Japan School of Astronomy and Space Science Nanjing University Nanjing210093 China Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Department of Mathematics and Physics Tibet University Lhasa850000 China Department of Physics Hebei Normal University Shijiazhuang050016 China University of Chinese Academy of Sciences Beijing100049 China Shandong University Qingdao266237 China Institute of Modern Physics SouthWest Jiaotong University Chengdu610031 China Department of Physics Shinshu University Matsumoto390-8621 Japan Faculty of Engineering Kanagawa University Yokohama221-8686 Japan Faculty of Education Utsunomiya University Utsunomiya321-8505 Japan Faculty of Systems Engineering Shibaura Institute of Technology Omiya330-8570 Japan Institute for Cosmic Ray Research University of Tokyo Kashiwa277-8582 Japan Polar Environment Data Science Center Joint Support-Center for Data Science Research Research Organization of Information and Systems Tachikawa190-0014 Japan National Center for Space Weather China Meteorological Administration Beijing100081 China School of Information Science and Engineering Shandong Agriculture University Taian271018 China Department of Astronomy School of Physical Sciences University of Science and Technology of China Hefei230026 China National Institute of Informatics Tokyo101-8430 Japan National Institute of Information and Communications Technology Tokyo184-8795 Japan Department of Mechanical and Electrical Engineering Shangdong Management University Jinan250357 China College of Science China University of Petroleum Qingdao266555 China Tokyo Metropolitan College of Industrial Technology Tokyo116-8523 Japan Depart

We have been observing extensive air showers using the Tibet-III air shower array and the underground water-Cherenkov muon detector array (MD) to determine the chemical composition of cosmic rays with energies corresponding to the knee energy region. In previous studies, we developed a method to acquire the proton spectrum in the energy range from 40 to 630 TeV via a hybrid experiment using the Tibet-III and MD and investigated its performance. We conducted Monte Carlo simulations showing that this method can separate protons with a purity of 90% and that the maximum total systematic error of the proton flux depending on interaction models in air-shower development is ±37%. In the present study, analysis results for data acquired over a period of 12 days in 2014 are presented. The abundances of proton-like showers to whole well-reconstructed showers, including model dependence, are 9.1%–14.5% at ∼35 TeV and 1.8%–3.1% at ∼450 TeV. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic rays

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A Brief Review of Explainable Artificial Intelligence in Healthcare

SSRN

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SSRN 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalo, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Swinburne University of Technology HawthornVIC3122 Australia Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, The Authors. All rights reserved.

关键词： Decision making

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2828 The influence of ethnicity and social disadvantage on frailty in a United Kingdom (UK) populationGet access

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Age and Ageing 2025年第SUPPLEMENT_1期54卷

作者： Heald, AH Lu, W Williams, R McCay, K Stedman, M O’Neill, TW The School of Medicine and Manchester Academic Health Sciences Centre University of Manchester Department of Endocrinology and Diabetes Salford Royal Hospital Salford Department of Computing & Mathematics Faculty of Science and Engineering Manchester Metropolitan University Division of Informatics Imaging and Data Science University of Manchester RES Consortium Andover Centre for Epidemiology Versus Arthritis University of Manchester Department of Rheumatology Salford UK

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Towards deployment-centric multimodal AI beyond vision and language

arXiv

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arXiv 2025年

作者： Liu, Xianyuan Zhang, Jiayang Zhou, Shuo van der Plas, Thijs L. Vijayaraghavan, Avish Grishina, Anastasiia Zhuang, Mengdie Schofield, Daniel Tomlinson, Christopher Wang, Yuhan Li, Ruizhe van Zeeland, Louisa Tabakhi, Sina Demeocq, Cyndie Li, Xiang Das, Arunav Timmerman, Orlando Baldwin-McDonald, Thomas Wu, Jinge Bai, Peizhen Al Sahili, Zahraa Alwazzan, Omnia Do, Thao N. Suvon, Mohammod N.I. Wang, Angeline Cipolina-Kun, Lucia Moretti, Luigi A. Farndale, Lucas Jain, Nitisha Efremova, Natalia Ge, Yan Varela, Marta Lam, Hak-Keung Celiktutan, Oya Evans, Ben R. Coca-Castro, Alejandro Wu, Honghan Abdallah, Zahraa S. Chen, Chen Danchev, Valentin Tkachenko, Nataliya Lu, Lei Zhu, Tingting Slabaugh, Gregory G. Moore, Roger K. Cheung, William K. Charlton, Peter H. Lu, Haiping Centre for Machine Intelligence University of Sheffield Sheffield United Kingdom School of Computer Science University of Sheffield Sheffield United Kingdom The Alan Turing Institute London United Kingdom Department of Metabolism Digestion and Reproduction Imperial College London London United Kingdom Department of Applied AI Simula Research Laboratory Oslo Norway Information School University of Sheffield Sheffield United Kingdom NHS England Leeds United Kingdom Institute of Health Informatics University College London London United Kingdom Department of Engineering King’s College London London United Kingdom Department of Computing Science University of Aberdeen Aberdeen United Kingdom School of Informatics University of Edinburgh Edinburgh United Kingdom School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom Department of Informatics King’s College London London United Kingdom Department of Earth Sciences University of Cambridge Cambridge United Kingdom Department of Computer Science University of Manchester Manchester United Kingdom Department of Computer Science Queen Mary University of London London United Kingdom Digital Environment Research Institute Queen Mary University of London London United Kingdom Department of Computer Science University of Bath Bath United Kingdom Department of Classics King’s College London London United Kingdom School of Electrical Electronic and Mechanical Engineering University of Bristol Bristol United Kingdom School of Engineering University of the West of England Bristol United Kingdom Cancer Research UK Scotland Institute Glasgow United Kingdom School of Business and Management Queen Mary University of London London United Kingdom City St George’s University of London London United Kingdom British Antarctic Survey Cambridge United Kingdom School of Health and Wellbeing University of Glasgow Glasgow United Kingdom Chief Data & AI Office Lloyds Banking Group Lo

Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as healthcare, science, and engineering. However, most multimodal AI advances focus on models for vision and language data, while their deployability remains a key challenge. We advocate a deployment-centric workflow that incorporates deployment constraints early to reduce the likelihood of undeployable solutions, complementing data-centric and model-centric approaches. We also emphasise deeper integration across multiple levels of multimodality and multidisciplinary collaboration to significantly broaden the research scope beyond vision and language. To facilitate this approach, we identify common multimodal-AI-specific challenges shared across disciplines and examine three real-world use cases: pandemic response, self-driving car design, and climate change adaptation, drawing expertise from healthcare, social science, engineering, science, sustainability, and finance. By fostering multidisciplinary dialogue and open research practices, our community can accelerate deployment-centric development for broad societal impact. © 2025, CC BY.

关键词： Decision making

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Indirect reciprocity in the public goods game with collective reputations

arXiv

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arXiv 2023年

作者： Wei, Ming Wang, Xin Liu, Longzhao Zheng, Hongwei Jiang, Yishen Hao, Yajing Zheng, Zhiming Fu, Feng Tang, Shaoting School of Mathematical Sciences Beihang University Beijing100191 China Institute of Artificial Intelligence Beihang University Beijing100191 China Key laboratory of Mathematics Informatics and Behavioral Semantics Beihang University Beijing100191 China Institute of Medical Artificial Intelligence Binzhou Medical University Yantai264003 China Zhongguancun Laboratory Beijing100094 China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang University Beijing100191 China PengCheng Laboratory Shenzhen518055 China State Key Lab of Software Development Environment Beihang University Beijing100191 China Beijing Academy of Blockchain and Edge Computing Beijing100085 China Department of Mathematics Dartmouth College HanoverNH03755 United States Department of Biomedical Data Science Geisel School of Medicine at Dartmouth LebanonNH03756 United States

Indirect reciprocity unveils how social cooperation is founded upon moral systems. Within the frame of dyadic games based on individual reputations, the "leading-eight" strategies distinguish themselves in promoting and sustaining cooperation. However, in the real-world societies, there are widespread interactions at the group level, where individuals need to make a singular action choice when facing multiple individuals with different reputations. Here, through introducing the assessment of collective reputations, we develop a framework that embeds group-level reputation structure into public goods game to study the evolution of group-level indirect reciprocity. We show that changing the criteria of group assessment destabilize the reputation dynamics of leading-eight strategies. In a particular range of social assessment criteria, all leading-eight strategies can break the social dilemma in public goods games and sustain cooperation. Specifically, there exists an optimal, moderately set assessment criterion that is most conducive to promoting cooperation. Moreover, in the evolution of assessment criteria, the preference of the leading-eight strategies for social strictness is inversely correlated with the payoff level. Our work reveals the impact of social strictness on prosocial behavior, highlighting the importance of group-level interactions in the analysis of evolutionary games and complex social dynamics. © 2023, CC BY.

关键词： Game theory

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Simulation of single particle flowing in a microfluidic device using molecular dynamics method 3

Simulation of single particle flowing in a microfluidic devi...

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3rd Annual Scientific Meeting on Medical Physics and Biophysics, PIT-FMB in conjunction with the 17th South-East Asia Congress of Medical Physics, SEACOMP 2019

作者： Viridi, Sparisoma Haryanto, Freddy Anshori, Isa Haekal, Mohammad Department of Physics Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jalan Ganesha 10 Bandung40132 Indonesia Department of Biomedical Engineering School of Electrical Engineering and Informatics Institut Teknologi Bandung Jalan Ganesha 10 Bandung40132 Indonesia Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Sukolilo Surabaya60111 Indonesia

Blood cells are modeled as spherical particles that flow through a microfluidic device with one inlet and two outlet channels, which is designed as a separator of blood particles. Molecular dynamics (MD) method was used intuitively in the simulation with the help of Semi-Circle Segmented Path Generator (SCSPG) as an approximation in creating fluid profile along the device channel. The trajectories generated from SCSPG was advanced using a fully developed Poiseuille flow with maximum fluid speed on the trajectories and the tails of speed distribution which was extended to the size of the channel with speed of zero at the channel walls. It has been observed that for a single particle trajectory the outlet channel was chosen by the particle depends on the axial position of the particles. Mass of particle determines how hard the particle deflects due to fluid profile. A better design is proposed in this work for separating two groups of particles with different size. © Published under licence by IOP Publishing Ltd.

关键词： Trajectories

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