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Numerical analysis of free convection under the influence of radiation and inclined MHD in a triangular cavity filled with hybrid nanofluid and a porous fin

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International Journal of Thermofluids 2024年 24卷

作者： Hassan, Ahmed M. Alomari, Mohammed Azeez Al-Salami, Qusay H. Alyousuf, Farah Q.A. Alqurashi, Faris Flayyih, Mujtaba A. Department of Mechanical Engineering University of Al-Qadisiyah Al-Qadisiyah58001 Iraq Department of Mechanical Engineering University of Al-Qadisiyah Ad-Diwaniyah58001 Iraq College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Department of Business Administration College of Administrative and Financial Sciences Cihan University Erbil Iraq Department of Computer Network College of Engineering and Computer Science Lebanese French University Kurdistan Region Erbil Iraq Mechanical Engineering Dept. College of Engineering University of Bisha Bisha Saudi Arabia Biomedical Engineering Department College of Engineering and Technologies Al-Mustaqbal University Hillah Iraq

This study presents a novel investigation into the thermal behavior of a hybrid nanofluid (MgO − Ag − H2O) in natural convection around a porous fin in a triangular enclosure under the influence of radiation and magnetohydrodynamic effects. The research uniquely combines these complex phenomena, addressing a significant gap in the literature. This configuration has potential applications in advanced solar thermal collectors, electronic cooling systems for high-power devices, and compact heat exchangers in various industries. The main objectives are to understand how various parameters influence heat transfer and fluid flow behavior and to optimize the design for enhanced thermal performance. The study considers a range of variables including Rayleigh number (103 − − 106), Hartmann number (0 – 50), Aspect ratio (0.3 – 0.6), radiation parameters (Rd = 1 − − 5, λ = 1 − 5), and volume concentration (0- 0.05), which have been numerically analyzed using the finite element method (FEM). The findings reveal that increasing the Darcy number (Da) enhances heat transfer at low Rayleigh numbers (Ra = 103,104). However, at higher Ra (Ra = 106), the impact of Da becomes more complex, with a critical Da beyond which heat transfer efficiency decreases due to an increase in flow resistance. The nanoparticle volume concentration plays a vital role, as higher concentrations lead to improved heat transfer efficiency, especially at higher Ra, through enhanced thermal conductivity and thermal dispersion. The length of the porous fin greatly impacts fluid flow patterns and heat transfer rates, with longer fins creating more complex flow patterns, promoting enhanced heat transfer and stronger thermal plumes. Thermal radiation, represented by the radiation parameters (Rd and λ), significantly influences both the heat transfer rate and the convective flow patterns within the enclosure. This study also incorporates a comprehensive entropy generation analysis, providing novel insights into sys

关键词： Rayleigh number

Novel Distributed informatics Platform to Support Machine Learning Discovery of Metabolic Biomarkers in Hypoxia Predisposition

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Novel Distributed Informatics Platform to Support Machine Le...

16th International Joint Conference on biomedical Engineering Systems and Technologies, BIOSTEC 2023

作者： Stell, Anthony Chauhan, Vedant Amador, Sandra Beuschlein, Felix Favier, Judith Gil, David Greenwood, Philip de Krijger, Ronald Kroiss, Matthias Ortuno, Samanta Patocs, Attila Walch, Axel School of Computing and Information Systems University of Melbourne Melbourne Australia Zurich Switzerland Universite Paris Cite PARCC INSERM Equipe Labellisee par la Ligue contre le Cancer Paris France Department of Computer Science Technology and Computation University of Alicante Alicante Spain Dept. of Pathology University Medical Center Utrecht and Princess Maxima Center for Pediatric Oncology Utrecht Netherlands University Hospital Munich Ludwig-Maximilians-Universitat Munchen Germany ELKH Hereditary Cancer Research Group Department of Laboratory Medicine Semmelweis University and Department of Molecular Genetics National Institute of Oncology Budapest Hungary Research Unit Analytical Pathology Helmholtz Zentmm Munchen Oberschleissheim Germany Department of Computer Science and Systems University of Murcia Murcia Spain Health and Biomedical Research Institute of Alicante Alicante Spain

To realise the scientific and clinical benefits of machine learning (ML) in a multi-centre research collaboration, a common issue is tire need to bring high-volnme data, complex analytical algorithms, and large-scale processing power, all together into one place. This paper describes tire detailed architecture of a novel platform that combines these features, in tire context of a proposed new clinical/bioinformatics project, Hypox-PD. Hypox-PD uses ML methods to identify new metabolic bionrarkers, through the analysis of high-volume data including mass spectrometry and imaging morphology of biobank tissue. The platform features three components: a content delivery network (CDN);a standardised orchestration application;and lrigh-specification processing power and storage. The central innovation of this platform is a distributed application that simultaneously manages the workflow between these components, provides a virtual mapping of the domain data dictionary, and presents the project data/metadata in a FAIR-compliant external interface. This paper presents the detailed design specifications of this platform, as well as initial test results in establishing the benchmark challenge of current direct transfer times without any specialised support. An initial costing of CDN usage is also presented, which indicates that significant performance improvement may be achievable at a reasonable cost to research budgets. © 2023 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Budget control

Multilingual chart-based constituency parse extraction from pre-trained language models

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arXiv 2020年

作者： Kim, Taeuk Li, Bowen Lee, Sang-Goo Dept. of Computer Science Hanyang University Dept. of Artificial Intelligence Hanyang University ILCC School of Informatics University of Edinburgh Dept. of Computer Science and Engineering Seoul National University

As it has been unveiled that pre-trained language models (PLMs) are to some extent capable of recognizing syntactic concepts in natural language, much effort has been made to develop a method for extracting complete (binary) parses from PLMs without training separate parsers. We improve upon this paradigm by proposing a novel chart-based method and an effective top-K ensemble technique. Moreover, we demonstrate that we can broaden the scope of application of the approach into multilingual settings. Specifically, we show that by applying our method on multilingual PLMs, it becomes possible to induce non-trivial parses for sentences from nine languages in an integrated and language-agnostic manner, attaining performance superior or comparable to that of unsupervised PCFGs. We also verify that our approach is robust to cross-lingual transfer. Finally, we provide analyses on the inner workings of our method. For instance, we discover universal attention heads which are consistently sensitive to syntactic information irrespective of the input language. Copyright © 2020, The Authors. All rights reserved.

关键词： Syntactics

Machine Learning Models Predict the Need of Amputation and/or Peripheral Artery Revascularization in Hypertensive Patients Within 7-Years Follow-Up

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Machine Learning Models Predict the Need of Amputation and/o...

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Konstantina Tsarapatsani Antonis I. Sakellarios Vassilis D. Tsakanikas Hans J. Trampisch Henrik Rudolf Nikolaos Tachos Marcus E. Kleber Winfried März Dimitrios I. Fotiadis Biomedical Research Institute Foundation for Research and Technology-Hellas (FORTH) Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina Greece Department of Medical Informatics Biometry and Epidemiology Ruhr University Bochum Bochum Germany Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Medical Clinic V Mannheim Medical Faculty University of Heidelberg Mannheim Germany Mannheim Institute of Public Health Social and Preventive Medicine Medical Faculty Mannheim University of Heidelberg Mannheim Germany

Lower extremity amputation and requirement of peripheral artery revascularization are common outcomes of undiagnosed peripheral artery disease patients. In the current work, prediction models for the need of amputation or peripheral revascularization focused on hypertensive patients within seven years follow up are employed. We applied machine learning (ML) models using classifiers such as Extreme Gradient Boost (XGBoost), Random Forest (RF) and Adaptive Boost (AdaBoost), that will allow clinicians to identify the patients at risk of these two endpoints using simple clinical data. We used the non-interventional cohort of the getABI study in the primary care setting, selecting 4,191 hypertensive patients out of 6,474 patients with age over 65 years old and followed up for vascular events or death up to 7 years. During this follow up period, 150 patients underwent either amputation or peripheral revascularization or both. Accuracy, Specificity, Sensitivity and Area under the receiver operating characteristic curve (AUC) were estimated for each machine learning model. The results demonstrate Random Forest as the most accurate model for the prediction of the composite endpoint in hypertensive patients within 7 years follow-up, achieving 73.27 % *** Relevance—This study assists clinicians to better predict and treat these serious outcomes, amputation and peripheral revascularization in hypertensive patients.

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The state-of-the-art review on resource allocation problem using artificial intelligence methods on various computing paradigms

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arXiv 2022年

作者： Joloudari, Javad Hassannataj Mojrian, Sanaz Saadatfar, Hamid Nodehi, Issa Fazl, Fatemeh Shirkharkolaie, Sahar Khanjani Alizadehsani, Roohallah Dipu Kabir, H.M. Tan, Ru-San Rajendra Acharya, U. Department of Computer Engineering Faculty of Engineering University of Birjand Birjand*** Iran Department of Information Technology Mazandaran University of Science and Technology Babol Iran Department of Computer Engineering Qom University of Technology Qom Iran Department of Electronic Engineering Faculty of Electrical and Computer Engineering University of Birjand Birjand Iran Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC3216 Australia School of Science and Technology Singapore University of Social Sciences Singapore Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan

With the increasing growth of information through smart devices, increasing the quality level of human life requires various computational paradigms presentation including the Internet of Things, fog, and cloud. Between these three paradigms, the cloud computing paradigm as an emerging technology adds cloud layer services to the edge of the network so that resource allocation operations occur close to the end-user to reduce resource processing time and network traffic overhead. Hence, the resource allocation problem for its providers in terms of presenting a suitable platform, by using computational paradigms is considered a challenge. In general, resource allocation approaches are divided into two methods, including auction-based methods (goal, increase profits for service providers-increase user satisfaction and usability) and optimization-based methods (energy, cost, network exploitation, Runtime, reduction of time delay). In this paper, according to the latest scientific achievements, a comprehensive literature study (CLS) on artificial intelligence methods based on resource allocation optimization without considering auction-based methods in various computing environments are provided such as cloud computing, Vehicular Fog Computing, wireless, IoT, vehicular networks, 5G networks, vehicular cloud architecture, machine-to-machine communication (M2M), Train-to-Train(T2T) communication network, Peer-to-Peer (P2P) network. Since deep learning methods based on artificial intelligence are used as the most important methods in resource allocation problems;Therefore, in this paper, resource allocation approaches based on deep learning are also used in the mentioned computational environments such as deep reinforcement learning, Q-learning technique, reinforcement learning, online learning, and also Classical learning methods such as Bayesian learning, Cummins clustering, Markov decision process. © 2022, CC BY-SA.

关键词： Reinforcement learning

J-WAVE: AN OPEN-SOURCE DIFFERENTIABLE WAVE SIMULATOR

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arXiv 2022年

作者： Stanziola, Antonio Arridge, Simon R. Cox, Ben T. Treeby, Bradley E. Dept. of Medical Physics and Biomedical Engineering University College London LondonWC1E 6BT United Kingdom Department of Computer Science University College of London LondonWC1E 6BT United Kingdom

We present an open-source differentiable acoustic simulator, j-Wave, which can solve time-varying and time-harmonic acoustic problems. It supports automatic differentiation, which is a program transformation technique that has many applications, especially in machine learning and scientific computing. j-Wave is composed of modular components that can be easily customized and reused. At the same time, it is compatible with some of the most popular machine learning libraries, such as JAX and TensorFlow. The accuracy of the simulation results for known configurations is evaluated against the widely used k-Wave toolbox and a cohort of acoustic simulation software. j-Wave is available from https://***/ucl-bug/jwave. Copyright © 2022, The Authors. All rights reserved.

关键词： Application programs

An Automated Detection of Amyotrophic Lateral Sclerosis from Resting-State MEG Data Using 3D Deep Convolutional Neural Network

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An Automated Detection of Amyotrophic Lateral Sclerosis from...

IEEE International Conference on Systems, Man and Cybernetics

作者： Kaniska Samanta Sujit Roy Veronique Marchand-Pauvert Shirin Dora Stephanie Duguez Muskaan Singh Girijesh Prasad Saugat Bhattacharyya Intelligent System Research Center Ulster University United Kingdom Brainalive Research Pvt. Ltd. Kanpur India CNRS Inserm Laboratoire d'Imagerie Biomédicale Sorbonne Universités UPMC Univ Paris 06 France Dept. of Computer Science Loughborough University United Kingdom School of Biomedical Sciences Ulster University United Kingdom

A novel 3D deep convolutional neural network (3D-CNN) model called MEGNet3D has been proposed in the paper. MEGNet3D is designed to differentiate between amyotrophic lateral sclerosis (ALS) and healthy individuals from their resting state (eyes open and eyes closed condition) sensor-level magnetoencephalography (MEG) data. The raw MEG data is initially transformed into their time-frequency representation, which are then used as inputs to MEGNet3D. Both magnetometer and gradiometer recordings have been investigated separately. The proposed model exhibits an accuracy of over 75% for most classification conditions. Thus, MEGNet3D is capable of handling high subject variability and shows that spectral-temporal representation of resting-state MEG data yields relevant neural markers related to the existence of ALS. Furthermore, it has also been observed resting state with eyes closed yields better classification accuracy as compared to the resting state with eyes open condition.

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Predicting upper limb disability progression in primary progressive multiple sclerosis using machine learning and statistical methods

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Predicting upper limb disability progression in primary prog...

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Mostafa, Sally Song, Isabel Hyo Jung Metwally, Ahmed A. Strauli, Nicolas Sewde, Nehemiah Friesenhahn, Michel Usdin, Maxime Jia, Xiaoming San Francisco State University Department of Computer Science San FranciscoCA United States Cairo University Biomedical Engineering Dept Giza Egypt OMNI Biomarker Development Genentech Inc South San FranciscoCA United States DevSci Informatics Genentech Inc South San FranciscoCA United States PHC Biostatistics Genentech Inc South San FranciscoCA United States DevSci Informatics Genentech Inc South San FranciscoCA United States

ISBN: (纸本)9781665401265

Multiple sclerosis (MS) is a heterogeneous immune-mediated disorder of the central nervous system, and primary progressive MS (PPMS) is characterized by insidious injury independent of clinical relapses from disease onset. Predicting disease progression remains an unmet need across the MS disease spectrum. We evaluated statistical and machine learning (ML) models for predicting progression in 596 patients with PPMS from ORATORIO, a randomized, placebo (PBO)-controlled, phase 3 clinical study of ocrelizumab (OCR). Using baseline clinical, magnetic resonance imaging, and biomarker (neurofilament light chain [NfL]) data, we trained 6 ML models to predict a progression event (logistic regression, support vector machines, random forest, neural network, gradient boosting, and extreme gradient boosting) and the time to progression (Cox proportional hazards [PH]) on upper limb function, as measured by 20% confirmed worsening on the 9-hole peg test (9HPT). Predictive performance was similar across the six ML models (area under the curve = 0.63 to 0.67) and was modest using the Cox PH model (C-index = 0.76, an improvement over previous studies). In the Cox PH model, treatment arm, baseline 9HPT, neurologic functional system scores, and serum NfL levels were identified as factors associated with progression;model performance was similar across the OCR, PBO, and combined OCR + PBO treatment arms. Accounting for potential interactions with treatment arm resulted in similar predictive performance. To our knowledge, this is the first study to systematically evaluate ML models and statistical methods for predicting progression in PPMS using a well-characterized clinical trial cohort. © 2021 IEEE.

关键词： Support vector machines

A Learnable Prior Improves Inverse Tumor Growth Modeling

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arXiv 2024年

作者： Weidner, Jonas Ezhov, Ivan Balcerak, Michal Metz, Marie-Christin Litvinov, Sergey Kaltenbach, Sebastian Feiner, Leonhard Lux, Laurin Kofler, Florian Lipkova, Jana Latz, Jonas Rueckert, Daniel Menze, Bjoern Wiestler, Benedikt Dept. of Computer Science The Center for Translational Cancer Research TranslaTUM Technical University of Munich Germany Dept. of Neuroradiology School of Medicine and Health Technical University of Munich Germany School of Engineering and Applied Sciences Harvard University MA United States Dept. of Quantitative Biomedicine University of Zurich Zurich Switzerland Helmholtz AI Helmholtz Zentrum Munich Germany Center for Translational Cancer Research TranslaTUM Technical University of Munich Germany Department of Pathology University of California IrvineCA United States Department of Mathematics University of Manchester United Kingdom Department of Computing Biomedical Image Analysis Group Imperial College London United Kingdom Klinikum rechts der Isar Technical University Munich Germany

Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a substantial challenge, either due to the high computational requirements of model-based approaches or the limited robustness of deep learning (DL) methods. We propose a novel framework that leverages the unique strengths of both approaches in a synergistic manner. Our method incorporates a DL ensemble for initial parameter estimation, facilitating efficient downstream evolutionary sampling initialized with this DL-based prior. We showcase the effectiveness of integrating a rapid deep-learning algorithm with a high-precision evolution strategy in estimating brain tumor cell concentrations from magnetic resonance images. The DL-Prior plays a pivotal role, significantly constraining the effective sampling-parameter space. This reduction results in a fivefold convergence acceleration and a Dice-score of 95%. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems