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Computational-Analysis of the Non-Isothermal Dynamics of the Gravity-Driven Flow of Viscoelastic-Fluid-Based Nanofluids Down an Inclined Plane

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Fluid Dynamics & Materials Processing 2023年第3期19卷 767-781页

作者： Idrees Khan Tiri Chinyoka Andrew Gill Centre for Research in Computational&Applied Mechanics University of Cape TownRondebosch7701South Africa Department of Mathematics and Applied Mathematics University of Cape TownRondebosch7701South Africa Centre for High Performance and Computing Council for Scientific and Industrial ResearchRosebankCape Town7700South Africa

The paper explores the gravity-driven flow of the thin film of a viscoelastic-fluid-based nanofluids(VFBN)along an inclined plane under non-isothermal conditions and subjected to convective cooling at the *** Newton’s law of cooling is used to model the convective heat-exchange with the ambient at the *** Giesekus viscoelastic constitutive model,with appropriate modifications to account for non-isothermal effects,is employed to describe the polymeric *** unsteady and coupled non-linear partial differential equations(PDEs)describing the model problem are obtained and solved via efficient semi-implicit numerical schemes based on finite difference methods(FDM)implemented in *** response of the VFBN velocity,temperature,thermal-conductivity and polymeric-stresses to variations in the volume-fraction of embedded nanoparticles is *** is shown that these quantities all increase as the nanoparticle volume-fraction becomes higher.

关键词： Semi-implicit numerical scheme finite difference methods viscoelastic-fluid-based nanofluid(VFBN) nonisothermal viscoelastic flow giesekus constitutive model nanofluid thermal-conductivity gravity-driven flow

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Critical factors inhibiting information and communication technology (ICT) adoption in Côte d'Ivoire: a study of small and medium enterprises (SMEs) in metropolitan Abidjan

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International Journal of Technology, Policy and Management 2024年第1期24卷 19-49页

作者： Assiélou, N'Doli Guillaume Bourgault, Mario Department of Fundamental and Applied Sciences Mathematics and Computing Laboratory Nangui Abrogoua University Côte d'Ivoire 02 Abidjan 02 AbidjanBP 801 Ivory Coast Department of Mathematics and Industrial Engineering Polytechnique Montreal P.O. Box 6079 Station Centre-ville MontréalQCH3C 3A7 Canada

The paper aims to investigate and categorise the barriers influencing information and communication technology (ICT) adoption among small and medium-sized enterprises (SMEs). A questionnaire-based survey was used to collect data from 77 owners/managers of SMEs in metropolitan Abidjan, Côte d'Ivoire. A research framework based on the technology-organisationenvironment- individual-economy (T-O-E-I-E) model was used to guide the research effort. The results suggest that technological, economic and environmental factors such as unreliable basic fixed internet, cybercrime, a lack of government support and a high cost of access to fixed broadband internet are the most significant barriers to ICT adoption in the sample. Manufacturing firms had the most difficulties adopting ICTs;smaller enterprises also had more difficulties. The findings of this study offer valuable insights to SME owners/managers, policy-makers, financial institutions and local ICT companies wishing to understand why SMEs are slow to adopt ICTs, and actions that can be taken to strengthen the level of ICT adoption among SMEs. © 2024 Inderscience Enterprises Ltd.

关键词： ICT adoption small and medium-sized enterprises T-O-E-I-E barriers critical factors Côte d'Ivoire

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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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A Lightweight Feature Selection Method Based on Rankability

SSRN

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SSRN 2024年

作者： Kong, Lingping Velasquez, Juan D. Perfilieva, Irina Pant, Millie Pan, Jeng-Shyang Snášel, Václav Department of Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic Department of Industrial Engineering University of Chile Santiago Chile Santiago Chile Institute for Research and Applications of Fuzzy Modeling Centre of Excellence IT4Innovations division of The University of Ostrava Ostrava Czech Republic Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Uttarakhand Roorkee247667 India School of Artificial Intelligence Nanjing University of Information Science and Technology China Instituto Sistemas Complejos de Ingeniería Santiago Chile Santiago Chile Faculty of Computer Science and Telecommunications Cracow University of Technology Kraków Poland Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Uttarakhand Roorkee247667 India College of Computer Science and Engineering Shandong University of Science and Technology China

Within the realm of data mining and machine learning, feature selection stands out as an essential technique for dimensionality reduction, presenting both popularity and complexity. Feature selection involves a nuanced trade-off that results in information loss. This distinction underscores the necessity for the development of lightweight, efficient, and practical methods capable of minimizing information loss while executing dimensional reduction. In this paper, we introduce a rankability-based feature selection method. The concept of rankability, proposed in 2019 and similar to the entropy concept, remains relatively unexplored. Our proposed method distinguishes itself by its lightweight complexity, eliminating the need for iterative *** evaluate the effectiveness of our approach, we conducted experiments on forty-one datasets and compared the results with four other algorithms using refined data with 10\%, 30\%, and 50\% feature dimension reduction. The findings reveal that our rankability-based feature selection method consistently outperforms the other four methods across a majority of datasets in terms of accuracy, precision, recall, and F1-macro performance. Specifically, the classification score exceeds 0.9 on accuracy for 50\% feature dimension reduction in twelve datasets, with nine datasets achieving scores over 0.9 in recall, F1-macro, and precision. Overall, our proposed feature selection method demonstrates promising performance. © 2024, The Authors. All rights reserved.

关键词： Feature Selection

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Bridging HPC Communities through the Julia Programming Language

arXiv

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arXiv 2022年

作者： Churavy, Valentin Godoy, William F. Bauer, Carsten Ranocha, Hendrik Schlottke-Lakemper, Michael Räss, Ludovic Blaschke, Johannes Giordano, Mosè Schnetter, Erik Omlin, Samuel Vetter, Jeffrey S. Edelman, Alan Massachussetts Institute of Technology United States Oak Ridge National Laboratory United States Paderborn Center for Parallel Computing Paderborn University Germany Department of Mathematics University of Hamburg Germany Applied and Computational Mathematics RWTH Aachen University Germany University of Stuttgart Germany ETH Zurich Switzerland Birmensdorf Switzerland National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Centre for Advanced Research Computing University College London Gower Street LondonWC1E 6BT United Kingdom Perimeter Institute 31 Caroline St. N. WaterlooONN2L 2Y5 Canada Department of Physics and Astronomy University of Waterloo 200 University Avenue West WaterlooONN2L 3G1 Canada Center for Computation & Technology Louisiana State University Baton RougeLA70803 United States ETH Zurich Switzerland

The Julia programming language has evolved into a modern alternative to fill existing gaps in scientific computing and data science applications. Julia leverages a unified and coordinated single-language and ecosystem paradigm and has a proven track record of achieving high performance without sacrificing user productivity. These aspects make Julia a viable alternative to high-performance computing's (HPC's) existing and increasingly costly many-body workflow composition strategy in which traditional HPC languages (e.g., Fortran, C, C++) are used for simulations, and higher-level languages (e.g., Python, R, MATLAB) are used for data analysis and interactive computing. Julia's rapid growth in language capabilities, package ecosystem, and community make it a promising universal language for HPC. This paper presents the views of a multidisciplinary group of researchers from academia, government, and industry that advocate for an HPC software development paradigm that emphasizes developer productivity, workflow portability, and low barriers for entry. We believe that the Julia programming language, its ecosystem, and its community provide modern and powerful capabilities that enable this group's objectives. Crucially, we believe that Julia can provide a feasible and less costly approach to programming scientific applications and workflows that target HPC facilities. In this work, we examine the current practice and role of Julia as a common, end-to-end programming model to address major challenges in scientific reproducibility, data-driven AI/machine learning, co-design and workflows, scalability and performance portability in heterogeneous computing, network communication, data management, and community education. As a result, the diversification of current investments to fulfill the needs of the upcoming decade is crucial as more supercomputing centers prepare for the exascale era. © 2022, CC BY.

关键词： Productivity

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Preface

SpringerBriefs in Applied Sciences and Technology

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SpringerBriefs in applied Sciences and Technology 2024年 Part F2025卷 v页

作者： Ismail, Azman Zulkipli, Fatin Nur Daril, Mohd Amran Mohd Öchsner, Andreas Centre for Women Advancement and Leadership Universiti Kuala Lumpur Malaysian Institute of Marine Engineering Technology Perak Lumut Malaysia College of Computing Informatics and Mathematics Universiti Teknologi MARA Kelantan Machang Malaysia Malaysian Institute of Industrial Technology Universiti Kuala Lumpur Johor Masai Malaysia Faculty of Mechanical Engineering Esslingen University of Applied Sciences Esslingen am Neckar Baden-Württemberg Germany

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Enhanced heat transfer in nanofluid flow via prabhakar fractional models for solar thermal systems

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Journal of Radiation Research and applied Sciences 2025年第3期18卷

作者： Shajar Abbas Ghada Ragheb Elnaggar Ahmad Shafique Ioan-Lucian Popa S. Saleem Muhammad Ramzan Mumtaz Ahmad Centre for Advanced Studies in Pure and Applied Mathematics Bahauddin Zakariya University Multan Pakistan AS (Ayub Shah) Institute of Mathematics and Research Centre Multan Pakistan Industrial and Systems Engineering Department College of Engineering Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Computing Mathematics and Electronics “1 Decembrie 1918” University of Alba Iulia 15 510009 Alba Iulia Romania Faculty of Mathematics and Computer Science Transilvania University of Brasov Iuliu Maniu Street 50 500091 Brasov Romania Department of Mathematics College of Science King Khalid University Abha 61413 Saudi Arabia Center for Artificial Intelligence (CAI) King Khalid University Abha 61421 Saudi Arabia Department of Mathematics University of Sargodha Sargodha Pakistan

This study presents a comprehensive thermal analysis of non-Newtonian nanofluid flow influenced by solar thermal radiation, with direct applications in industrial engineering, renewable energy systems, and thermal management. The objective is to evaluate the performance of a second-grade nanofluid in a parabolic trough solar collector (PTSC), focusing on the impacts of chemical reaction, mass and heat transport. The model assumes a copper–PVA-water nanofluid infused with gyrotactic microorganisms to enhance thermal conductivity and bioconvection. The dimensionless governing equations for velocity, temperature, concentration, and bioconvection are formulated using Fourier's and Fick's laws and calculated analytically via the Laplace integral transform. Results reveal that a 10 % increase in nanoparticle volume fraction leads to a 15 % improvement in temperature distribution, while the electric field parameter increases fluid velocity by 12 %. Aditionally, increase in bioconvection Lewis and Peclet numbers significantly reduce microorganism concentration by approximately 20 %. The study demonstrates that fractional-order models offer better control of transport mechanisms and that second-grade nanofluids improve heat transfer efficiency. These findings highlights the promise of of such fluids in solar thermal systems, industrial engineering processes, and energy-efficient system design.

关键词： Solar radiation Prabhakar derivative Gyrotactic microorganisms Laplace integral transform

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Preface

Advanced Structured Materials

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Advanced Structured Materials 2024年 208卷 v页

作者： Ismail, Azman Zulkipli, Fatin Nur Daril, Mohd Amran Mohd Öchsner, Andreas Maritime Engineering Technology and Centre for Women Advancement and Leadership Malaysian Institute of Marine Engineering Technology Universiti Kuala Lumpur Perak Lumut Malaysia Information Science Studies College of Computing Informatics and Mathematics Universiti Teknologi MARA Kelantan Machang Malaysia Malaysian Institute of Industrial Technology Universiti Kuala Lumpur Johor Masai Malaysia Faculty of Mechanical and Systems Engineering Esslingen University of Applied Sciences Esslingen am Neckar Baden-Württemberg Germany

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Foundational Competencies and Responsibilities of a Research Software Engineer

arXiv

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arXiv 2023年

作者： Goth, Florian Alves, Renato Braun, Matthias Castro, Leyla Jael Chourdakis, Gerasimos Christ, Simon Cohen, Jeremy Druskat, Stephan Erxleben, Fredo Grad, Jean-Noël Hagdorn, Magnus Hodges, Toby Juckeland, Guido Kempf, Dominic Lamprecht, Anna-Lena Linxweiler, Jan Löffler, Frank Martone, Michele Schwarzmeier, Moritz Seibold, Heidi Thiele, Jan Philipp Waldow, Harald von Wittke, Samantha Institute for Theoretical Physics and Astrophysics University of Würzburg Germany European Molecular Biology Laboratory Heidelberg Germany Cluster of Excellence IntCDC University of Stuttgart Germany ZB MED Information Centre for Life Sciences Cologne Germany School of Computation Information and Technology Technical University of Munich Garching Germany Institute for Parallel and Distributed Systems University of Stuttgart Stuttgart Germany Leibniz University Hannover Department of Cell Biology and Biophysics Computational Biology Germany Imperial College London London United Kingdom Institute for Software Technology Berlin Germany Humboldt-Universität zu Berlin Department of Computer Science Berlin Germany Helmholtz-Zentrum Dresden-Rossendorf Germany Institute for Computational Physics University of Stuttgart Germany Geschäftsbereich IT Charité Universitätsmedizin Berlin Germany The Carpentries United States Heidelberg University Scientific Software Center Germany Institute of Computer Science University of Potsdam Germany Technische Universität Braunschweig Germany Michael Stifel Center Jena Friedrich Schiller University Jena Germany Leibniz Supercomputing Centre Garching Germany Mathematical Modeling and Analysis TU Darmstadt Germany IGDORE Munich Germany Weierstrass Institute Berlin Germany Leibniz University Hannover Institute of Applied Mathematics Scientific Computing Hannover Germany Johann Heinrich von Thünen Institute Centre for Information Management Germany

The term Research Software Engineer, or RSE, emerged a little over 10 years ago as a way to represent individuals working in the research community but focusing on software development. The term has been widely adopted and there are a number of high-level definitions of what an RSE is. However, the roles of RSEs vary depending on the institutional context they work in. At one end of the spectrum, RSE roles may look similar to a traditional research role. At the other extreme, they resemble that of a software engineer in industry. Most RSE roles inhabit the space between these two extremes. Therefore, providing a straightforward, comprehensive definition of what an RSE does and what experience, skills and competencies are required to become one is challenging. In this community paper we define the broad notion of what an RSE is, explore the different types of work they undertake, and define a list of foundational competencies as well as values that outline the general profile of an RSE. Further research and training can build upon this foundation of skills and focus on various aspects in greater detail. We expect that graduates and practitioners will have a larger and more diverse set of skills than outlined here. On this basis, we elaborate on the progression of these skills along different dimensions. We look at specific types of RSE roles, propose recommendations for organisations, and give examples of future specialisations. An appendix details how existing curricula fit into this framework. © 2023, CC BY.

关键词： Computer aided software engineering

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A framework for the construction of generative models for mesoscale structure in multilayer networks

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Physical Review Research 2020年第2期2卷 023100-023100页

作者： Marya Bazzi Lucas G. S. Jeub Alex Arenas Sam D. Howison Mason A. Porter Oxford Centre for Industrial and Applied Mathematics Mathematical Institute University of Oxford Oxford OX2 6GG United Kingdom The Alan Turing Institute London NW1 2DB United Kingdom Warwick Mathematics Institute University of Warwick Coventry CV4 7AL United Kingdom Center for Complex Networks and Systems Research School of Informatics and Computing Indiana University Bloomington Indiana 47408 USA ISI Foundation Turin Italy Departament d'Enginyeria Informàtica i Matemàtiques Universitat Rovira i Virgili 43007 Tarragona Spain CABDyN Complexity Centre University of Oxford Oxford OX1 1HP United Kingdom Department of Mathematics University of California Los Angeles Los Angeles California 90095 USA

Multilayer networks allow one to represent diverse and coupled connectivity patterns—such as time-dependence, multiple subsystems, or both—that arise in many applications and which are difficult or awkward to incorporate into standard network representations. In the study of multilayer networks, it is important to investigate mesoscale (i.e., intermediate-scale) structures, such as dense sets of nodes known as communities, to discover network features that are not apparent at the microscale or the macroscale. The ill-defined nature of mesoscale structure and its ubiquity in empirical networks make it crucial to develop generative models that can produce the features that one encounters in empirical networks. Key purposes of such models include generating synthetic networks with empirical properties of interest, benchmarking mesoscale-detection methods and algorithms, and inferring structure in empirical multilayer networks. In this paper, we introduce a framework for the construction of generative models for mesoscale structures in multilayer networks. Our framework provides a standardized set of generative models, together with an associated set of principles from which they are derived, for studies of mesoscale structures in multilayer networks. It unifies and generalizes many existing models for mesoscale structures in fully ordered (e.g., temporal) and unordered (e.g., multiplex) multilayer networks. One can also use it to construct generative models for mesoscale structures in partially ordered multilayer networks (e.g., networks that are both temporal and multiplex). Our framework has the ability to produce many features of empirical multilayer networks, and it explicitly incorporates a user-specified dependency structure between layers. We discuss the parameters and properties of our framework, and we illustrate examples of its use with benchmark models for community-detection methods and algorithms in multilayer networks.

关键词： Clustering Community structure Interconnected & interdependent networks Multilayer & multiplex networks Block models

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