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Thermal Analysis of Magnetized Bioconvective Flow of a Casson Nanofluid With Temperature Dependent Thermal Conductivity: A Numerical Study

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International Journal of Energy Research 2025年第1期2025卷

作者： Shahzad Khattak Muhammad Naveed Khan Mohammad Yar Mohamed Hussien Taoufik Saidani Kaouther Ghachem Lioua Kolsi Department of Mathematics Faculty of Engineering and Computing National University of Modern Languages Islamabad Pakistan numl.edu.pk Department of Mathematics Quaid-i-Azam University Islamabad Pakistan qau.edu.pk Department of Mathematics Kabul Polytechnic University Kabul Afghanistan kpu.edu.af Department of Chemistry Faculty of Science King Khalid University Abha Saudi Arabia kku.edu.sa Center for Scientific Research and Entrepreneurship Northern Border University 73213 Arar Saudi Arabia nbu.edu.sa Department of Industrial and Systems Engineering College of Engineering Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia pnu.edu.sa Department of Mechanical Engineering College of Engineering University of Ha’il Ha’il City 81451 Saudi Arabia uoh.edu.sa

The current study has applications in various technical fields, including energy generation, fluid dynamics, geophysical fluid mechanics, and biomedical and industrial engineering. This study aims to investigate the explicit solution of a two-dimensional Casson nanofluid flow model incorporating an exponential heat exchanger and activation energy. The fluid motion is induced by an exponentially stretching surface under a convective boundary condition and variable thermal conductivity. Additionally, the influence of microorganisms and magnetohydrodynamics (MHDs) is examined. The governing system of coupled partial differential equations (PDEs) is transformed into a set of ordinary differential equations (ODEs) and numerically solved using the bvp4c solver in MATLAB. A comprehensive graphical and tabulated analysis is conducted for various governing parameters. The findings reveal that an increase in the Casson fluid and porosity parameters leads to a reduction in fluid velocity. Moreover, the microorganism density profile declines with increasing bioconvection Lewis number and Peclet number.

关键词： casson nanofluid model exponential stretching surface microorganism variable heat exchanger

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An improved simulation model for pedestrian crowd evacuation

arXiv

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arXiv 2020年

作者： Muhammed, Danial A. Rashid, Tarik A. Alsadoon, Abeer Bacanin, Nebojsa Fattah, Polla Mohammadi, Mokhtar Banerjee, Indradip Computer Department College of Science University of Sulaimani SulaymaniyahKRG46001 Iraq Computer Science and Engineering Department University of Kurdistan Hewler ErbilKRG44001 Iraq Department of Information Technology School of Computing and Mathematics Charles Sturt University Sydney BathurstNSW2795 Australia Faculty of Informatics and Computing Vice-dean Singidunum University Belgrade11000 Serbia Software and Informatics Engineering Dept Engineering College Salahaddin University-Erbil ErbilKRG44001 Iraq Department of Information Technology Lebanese French University ErbilKRG44001 Iraq Department of Computer Science and Engineering University Institute of Technology The University of Burdwan BurdwanWest Bengal713101 India

This paper works on one of the most recent pedestrian crowd evacuation models, i.e., "a simulation model for pedestrian crowd evacuation based on various AI techniques", developed in late 2019. This study adds a new feature to the developed model by proposing a new method and integrating it with the model. This method enables the developed model to find a more appropriate evacuation area design, among others regarding safety due to selecting the best exit door location among many suggested locations. This method is completely dependent on the selected model's output, i.e., the evacuation time for each individual within the evacuation process. The new method finds an average of the evacuees’ evacuation times of each exit door location;then, based on the average evacuation time, it decides which exit door location would be the best exit door to be used for evacuation by the evacuees. To validate the method, various designs for the evacuation area with various written scenarios were used. The results showed that the model with this new method could predict a proper exit door location among many suggested locations. Lastly, from the results of this research using the integration of this newly proposed method, a new capability for the selected model in terms of safety allowed the right decision in selecting the finest design for the evacuation area among other designs. © 2020, CC BY.

关键词： Location

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Real-Time Emission Monitoring and Display to Reduce Vehicle Pollution Using Wireless Network

Real-Time Emission Monitoring and Display to Reduce Vehicle ...

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International Conference on Developments in eSystems engineering (DESE)

作者： M. Soufian Mae. Irshaidat M. Babaie G. Elliott D. Mahood L. Pendlebury B. Hughes N. Darbyshire J. Williams M. B. Lupu J. W. Jenkins D. Jones N. Havery G. Abadaki T. Raynes Faculty of Arts Science and Technology Wrexham Glyndŵr University Wrexham Wales UK School of Computing Science and Engineering University of Salford Manchester UK

ISBN: (数字)9781728130217

ISBN: (纸本)9781728130224

There are several sensors operating in a car to control different aspects of the car performance. Among those, “NOx exhaust gas sensors” are responsible for catalyst performance, which helps the emission reduction of the vehicles. The focus of this paper is to design cost effective industrial wireless networks for the use of data transfer from NOx sensors to an on-board visual display. The instantaneous and cumulative pollution produced by a car are valuable data for environment and studying the correlation between fuel consumption and driving behavior. In this paper we study how above data can be accessed, transmitted and provided to the driver. Various design strategies are presented. The implementation of the developed hardware and software are shown to produce good verification results.

关键词： Automobiles Temperature sensors Temperature measurement Receivers Engines Monitoring

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Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

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Infectious Disease Modelling 2024年第2期9卷 501-518页

作者： Marta C.Nunes Edward Thommes Holger Fröhlich Antoine Flahault Julien Arino Marc Baguelin Matthew Biggerstaff Gaston Bizel-Bizellot Rebecca Borchering Giacomo Cacciapaglia Simon Cauchemez Alex Barbier-Chebbah Carsten Claussen Christine Choirat Monica Cojocaru Catherine Commaille-Chapus Chitin Hon Jude Kong Nicolas Lambert Katharina B.Lauer Thorsten Lehr Cédric Mahe Vincent Marechal Adel Mebarki Seyed Moghadas Rene Niehus Lulla Opatowski Francesco Parino Gery Pruvost Andreas Schuppert Rodolphe Thiébaut Andrea Thomas-Bachli Cecile Viboud Jianhong Wu Pascal Crépey Laurent Coudeville Center of Excellence in Respiratory Pathogens(CERP) Hospices Civils de Lyon(HCL)and Centre International de Recherche en Infectiologie(CIRI)Équipe SantéPubliqueÉpidémiologie etÉcologieÉvolutive des Maladies Infectieuses(PHE3ID)Inserm U1111CNRS UMR5308ENS de LyonUniversitéClaude Bernard Lyon 1LyonFrance South African Medical Research Council Vaccines&Infectious Diseases Analytics Research UnitFaculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa New Products and Innovation(NPI) Sanofi Vaccines(Global)TorontoOntarioCanada Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Fraunhofer Institute for Algorithms and Scientific Computing(SCAI) Department of BioinformaticsSchloss BirlinghovenSankt AugustinGermany University of Bonn Bonn-Aachen International Center for IT(b-it)BonnGermany Institute of Global Health Faculty of MedicineUniversity of GenevaGenevaSwitzerland and Swiss School of Public HealthZürichSwitzerland Department of Mathematics University of ManitobaWinnipegManitobaCanada MRC Centre for Global Infectious Disease Analysis School of Public HealthImperial College LondonLondonUK Centre for Mathematical Modelling of Infectious Diseases Department of Infectious Disease EpidemiologyLondon School of Hygiene&Tropical MedicineLondonUK National Center for Immunization and Respiratory Diseases(NCIRD) US Centers for Disease Control and Prevention(CDC)AtlantaGAUSA Departement of Computational Biology Departement of Global HealthInstitut PasteurParisFrance Institut de Physique des Deux Infinis de Lyon(IP2I) UMR5822IN2P3/CNRSUniversitéClaude Bernard Lyon 1VilleurbanneFrance Mathematical Modelling of Infectious Diseases Unit Institut PasteurUniversitéParis CitéUMR2000 CNRSParisFrance Decision and Bayesian Computation Institut PasteurUniversitéParis CitéCNRS UMR 3571France Fraunhofer-Institute for Translational Medicine and Pharmacology HamburgGermany Institute of Global Health Faculty of MedicineUniversity of

In July 2023,the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

关键词： Modelling Covid-19 Infectious diseases Pandemic preparedness Workshop

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Evidence for particle acceleration approaching PeV energies in the W51 complex

arXiv

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arXiv 2024年

作者： Cao, Zhen Aharonian, F. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Bian, W. Bukevich, A.V. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, H.X. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, J.H. Fang, K. Feng, C.F. Feng, H. Feng, L. Feng, S.H. Feng, X.T. Feng, Y. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. Hasan, M. He, H.H. He, H.N. He, J.Y. He, Y. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Karpikov, I. Kuleshov, D. Kurinov, K. Li, B.B. Li, C.M. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, Jian Li, Jie Li, K. Li, S.D. Li, W.L. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, D.B. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Luo, Q. Luo, Y. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ou, L.J. Pattarakijwanich, P. Pei, Z.Y. Qi, J.C. Qi, M.Y. Qiao, B.Q. Qin, J.J. Raza, A. Ruffolo, D. Sáiz, A. Saeed, M. Semikoz, D. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu V. Stepanov, V. Su, Y. Sun, D.X. Sun, Q.N. Sun, X.N. Sun, Z.B. Takata, J. Tam, P.H.T. Key Laboratory of Particle Astrophysics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 Ireland Max-Planck-Institut for Nuclear Physics 69029 Heidelberg103980 Germany School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Chengdu610031 China School of Astronomy and Space Science Nanjing University Nanjing210023 China Center for Astrophysics Guangzhou University Guangzhou510006 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Institute for Nuclear Research of Russian Academy of Sciences Moscow117312 Russia Hebei Normal University Shijiazhuang050024 China University of Science and Technology of China Hefei230026 China State Key Laboratory of Particle Detection and Electronics China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China Research Center for Astronomical Computing Zhejiang Laboratory Hangzhou311121 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China School of Physics and Astronomy Yunnan University Kunming650091 China Ministry of Education Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Zhuhai 519000 Guangdong Guangzhou510275 China Institute of Frontier and Interdisciplinary Science Shandong University Qingdao266237 China APC Université Paris Cité CNRS IN2P3 CEA/IRFU Observatoire de Paris Paris119 75205 France Department of Engineering Physics Tsinghua Universit

The γ-ray emission from the W51 complex is widely acknowledged to be attributed to the interaction between the cosmic rays (CRs) accelerated by the shock of supernova remnant (SNR) W51C and the dense molecular clouds in the adjacent star-forming region, W51B. However, the maximum acceleration capability of W51C for CRs remains elusive. Based on observations conducted with the Large High Altitude Air Shower Observatory (LHAASO), we report a significant detection of γ rays emanating from the W51 complex, with energies from 2 TeV to 200 TeV. The LHAASO measurements, for the first time, extend the γ-ray emission from the W51 complex beyond 100 TeV and reveal a significant spectrum bending at tens of TeV. By combining the "π0-decay bump" featured data from Fermi-LAT, the broadband γ-ray spectrum of the W51 region can be well-characterized by a simple pp-collision model. The observed spectral bending feature suggests an exponential cutoff at ∼ 400 TeV or a power-law break at ∼ 200 TeV in the CR proton spectrum, most likely providing the first evidence of SNRs serving as CR accelerators approaching the PeV regime. Additionally, two young star clusters within W51B could also be theoretically viable to produce the most energetic γ rays observed by LHAASO. Our findings strongly support the presence of extreme CR accelerators within the W51 complex and provide new insights into the origin of Galactic CRs. © 2024, CC BY.

关键词： Cosmic rays

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Data science in Public Mental Health: A New Analytic Framework

Data Science in Public Mental Health: A New Analytic Framewo...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Charith Silva Mahsa Saraee Mo Saraee School of Computing Science and Engineering University of Salford-Manchester School of Cardiovascular Sciences Faculty of Biology Medicine and Health University of Manchester

Understanding public mental health issues using data science and finding solutions based on the findings from the data science projects can be complex and requires advanced techniques, compared to conventional data analysis projects. It is important to have a comprehensive project management process to ensure that project associates are competent and have enough knowledge to implement the data science process. Therefore, this paper presents a new framework that mental health professionals can use to solve challenges they face using data science. Although a large number of research papers have been published on public mental health, few have addressed the use of data science in public mental health. Recently, Data science has changed the way we manage, analyze and leverage data in healthcare industry. Data science projects differ from conventional data analysis, primarily because of the scientific approach used during data science projects. One of the motives for introducing a new framework is to motivate healthcare professionals to use "Data science" to address the challenges of mental health. Having a good data analysis framework and clear guidelines for a comprehensive analysis is always a plus point. It also helps to predict the time and resources needed in the early in the process to get a clear idea of the problem to be solved.

关键词： Mental health Data science Data visualization Data mining Medical services Visualization Knowledge discovery

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An improved simulation model for pedestrian crowd evacuation

TechRxiv

引用

TechRxiv 2020年

作者： Muhammed, Danial A. Rashid, Tarik A. Alsadoon, Abeer Bacanin, Nebojsa Fattah, Polla Mohammadi, Mokhtar Banerjee, Indradip Computer Department College of Science University of Sulaimani KRG Sulaymaniyah46001 Iraq Computer Science and Engineering Department University of Kurdistan Hewler KRG Erbil44001 Iraq Department of Information Technology School of Computing and Mathematics Charles Sturt University Bathurst SydneyNSW2795 Australia Faculty of Informatics and Computing Vice-dean Singidunum University Belgrade11000 Serbia Software and Informatics Engineering Dept Engineering College Salahaddin University-Erbil KRG Erbil44001 Iraq Department of Information Technology Lebanese French University KRG Erbil44001 Iraq Department of Computer Science and Engineering University Institute of Technology The University of Burdwan West Bengal Burdwan713101 India

关键词： Location

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A Framework for Conditional Statement Technical Debt Identification and Description

arXiv

引用

arXiv 2020年

作者： Alhefdhi, Abdulaziz Dam, Hoa Khanh Nugroho, Yusuf Sulistyo Hata, Hideaki Ishio, Takashi Ghose, Aditya School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Teknik Informatika Universitas Muhammadiyah Surakarta Jawa Tengah Surakarta57102 Indonesia Faculty of Engineering Shinshu University Wakasato Nagano380-8553 Japan Division of Information Science Nara Institute of Science and Technology Nara Ikoma630-0192 Japan

Technical Debt (TD) occurs when development teams favour short-term operability over long-term stability. Since this places software maintainability at risk, technical debt requires early attention to avoid paying for accumulated interest. Most of the existing work focuses on detecting technical debt using code comments, known as Self-Admitted Technical Debt (SATD). However, there are many cases where technical debt instances are not explicitly acknowledged but deeply hidden in the code. In this paper, we propose a framework that caters for the absence of SATD comments in code. Our Self-Admitted Technical Debt Identification and Description (SATDID) framework determines if technical debt should be self-admitted for an input code fragment. If that is the case, SATDID will automatically generate the appropriate descriptive SATD comment that can be attached with the code. While our approach is applicable in principle to any type of code fragments, we focus in this study on technical debt hidden in conditional statements, one of the most TD-carrying parts of code. We explore and evaluate different implementations of SATDID. The evaluation results demonstrate the applicability and effectiveness of our framework over multiple benchmarks. Comparing with the results from the benchmarks, our approach provides at least 21.35%, 59.36%, 31.78%, and 583.33% improvements in terms of Precision, Recall, F-1, and Bleu-4 scores, respectively. In addition, we conduct a human evaluation to the SATD comments generated by SATDID. In 1-5 and 0-5 scales for Acceptability and Understandability, the total means achieved by our approach are 3.128 and 3.172, respectively. © 2020, CC BY.

关键词： Machine learning

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Federated learning for 6G communications: Challenges, methods, and future directions

arXiv

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arXiv 2020年

作者： Liu, Yi Yuan, Xingliang Xiong, Zehui Kang, Jiawen Wang, Xiaofei Niyato, Dusit School of Data Science of Technology Heilongjiang University Harbin China Faculty of Information Technology Monash University ClaytonVIC3800 Australia School of Computer Science and Engineering Nanyang Technological University Singapore Singapore College of Intelligence and Computing Tianjin University Tianjin China Energy Research Institute Nanyang Technological University Singapore

—As the 5G communication networks are being widely deployed worldwide, both industry and academia have started to move beyond 5G and explore 6G communications. It is generally believed that 6G will be established on ubiquitous Artificial Intelligence (AI) to achieve data-driven Machine Learning (ML) solutions in heterogeneous and massive-scale networks. However, traditional ML techniques require centralized data collection and processing by a central server, which is becoming a bottleneck of large-scale implementation in daily life due to significantly increasing privacy concerns. Federated learning, as an emerging distributed AI approach with privacy preservation nature, is particularly attractive for various wireless applications, especially being treated as one of the vital solutions to achieve ubiquitous AI in 6G. In this article, we first introduce the integration of 6G and federated learning and provide potential federated learning applications for 6G. We then describe key technical challenges, the corresponding federated learning methods, and open problems for future research on federated learning in the context of 6G communications. Copyright © 2020, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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Predictive Modelling in Mental Health: A Data science Approach

Predictive Modelling in Mental Health: A Data Science Approa...

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IEEE Conference on Sustainable Utilization and Development in engineering and Technology (STUDENT)

ISBN: (数字)9781728132761

ISBN: (纸本)9781728132778

In national and regional level, understanding of factors associated with public health issues like mental health is paramount important to improve the awareness. This study aims to use the data mining techniques such as association rule mining to improve the degree of understanding the mental health among various geographical areas by identifying various vital behavioural factors associated with mental health issues. The study will produce interesting relationships among the behavioural factors in form of Association rules. The outcomes of this research will be beneficial to organisations that work in public health sector to improve mental health among the citizens. Also, this proposed new data science approach will be beneficial to improve the degree of understanding by identifying factors associated with mental health within the different geographical areas such as city or state level. The study found that states in US which have low excessive drinking percentage and high obesity and high smoking percentage has the highest frequent of mental distress. Also, these rules have shown high confidence threshold among females rather than males. Mental health related authorities who works in local and national governments may be used these findings to improve awareness of mental health.

关键词： Data science Data mining Public healthcare Obesity Sociology Statistics Machine learning algorithms

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