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

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Case Studies in Thermal engineering 2025年 72卷

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

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

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

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On Aggregation in Ensembles of Multilabel Classifiers

arXiv

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arXiv 2020年

作者： Nguyen, Vu-Linh Hüllermeier, Eyke Rapp, Michael Mencía, Eneldo Loza Fürnkranz, Johannes Heinz Nixdorf Institute Department of Computer Science Paderborn University Germany Knowledge Engineering Group TU Darmstadt Germany Computational Data Analytics Group JKU Linz Austria

While a variety of ensemble methods for multilabel classification have been proposed in the literature, the question of how to aggregate the predictions of the individual members of the ensemble has received little attention so far. In this paper, we introduce a formal framework of ensemble multilabel classification, in which we distinguish two principal approaches: "predict then combine" (PTC), where the ensemble members first make loss minimizing predictions which are subsequently combined, and "combine then predict" (CTP), which first aggregates information such as marginal label probabilities from the individual ensemble members, and then derives a prediction from this aggregation. While both approaches generalize voting techniques commonly used for multilabel ensembles, they allow to explicitly take the target performance measure into account. Therefore, concrete instantiations of CTP and PTC can be tailored to concrete loss functions. Experimentally, we show that standard voting techniques are indeed outperformed by suitable instantiations of CTP and PTC, and provide some evidence that CTP performs well for decomposable loss functions, whereas PTC is the better choice for non-decomposable losses. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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A Continuous Information Gain Measure to Find the Most Discriminatory Problems for AI Benchmarking

A Continuous Information Gain Measure to Find the Most Discr...

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Congress on Evolutionary Computation

作者： Matthew Stephenson Damien Anderson Ahmed Khalifa John Levine Jochen Renz Julian Togelius Christoph Salge Department of Data Science and Knowledge Engineering Maastricht University Maastricht the Netherlands Computer and Information Sciences Department University of Strathclyde Glasgow UK Game Innovation Lab Tandon School of Engineering New York University New York USA Research School of Computer Science Australian National University Canberra Australia

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

This paper introduces an information-theoretic method for selecting a subset of problems which gives the most information about a group of problem-solving algorithms. This method was tested on the games in the General Video Game AI (GVGAI) framework, allowing us to identify a smaller set of games that still gives a large amount of information about the abilities of different game-playing agents. This approach can be used to make agent testing more efficient. We can achieve almost as good discriminatory accuracy when testing on only a handful of games as when testing on more than a hundred games, something which is often computationally infeasible. Furthermore, this method can be extended to study the dimensions of the effective variance in game design between these games, allowing us to identify which games differentiate between agents in the most complementary ways.

关键词： Games Correlation Artificial intelligence Testing Task analysis Noise measurement Technological innovation

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Optimizing novel thermal energy storage systems: Enhancing melting efficiency with tubes, stands, and advanced machine learning techniques

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Journal of Energy Storage 2025年 124卷

作者： Almadhor, Ahmad Basem, Ali Singh, Pradeep Kumar Othman, Nashwan Adnan Samad, Sarminah Alhomayani, Fahad M. Abduvalieva, Dilsora Ali, H. Elhosiny Akram, Muhammad Afzal, Abdul Rahman Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Saudi Arabia Faculty of Engineering Warith Al-Anbiyaa University Karbala56001 Iraq Department of Mechanical Engineering Institute of Engineering & Technology GLA University U.P. Mathura281406 India Department of Computer Engineering College of Engineering Knowledge University Erbil44001 Iraq Department of Computer Engineering Al-Kitab University Altun Kupri Iraq Department of Management College of Business Administration Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia College of Computers and Information Technology Taif University Saudi Arabia Applied College Taif University Saudi Arabia Department of Mathematics and Information Technologies Tashkent State Pedagogical University Bunyodkor avenue 27 Tashkent100070 Uzbekistan Physics Department Faculty of Science King Khalid University P.O. Box 9004 Abha Saudi Arabia Faculty of Design Informatics and Business University of Abertay Scotland Dundee United Kingdom Department of Industrial Engineering College of Engineering University of Business and Technology Jeddah21361 Saudi Arabia

The transition to renewable energy is crucial to meet growing global energy demands while minimizing environmental impact. Solar energy, a leading renewable source, faces limitations due to its intermittent nature, making energy storage systems essential for continuous supply. Thermal energy storage systems (TESSs) provide a compelling solution, especially by utilizing latent heat storage with phase change materials (PCMs), which efficiently store large amounts of energy. However, PCM-based systems suffer from low thermal conductivity and weak heat transfer performance. Numerous techniques to amend the thermal conductivity of PCMs, whether applied individually or together, show promise for improving the efficiency of TESSs. However, challenges like increased viscosity, decreased latent heat capacity, high costs, and complex manufacturing often limit their practical use. Thus, it is crucial to investigate more straightforward and more cost-effective solutions, like incorporating extended surfaces into the PCM container. In this study, a novel approach is taken to ameliorate the heat transfer surface area in TESSs by incorporating tubes and stands inside the PCM container rather than relying on conventional fins. A new compact TESS design was introduced, featuring fixed tubes supported by aluminum-made stands. Two artificial neural networks (ANN) models were trained with input parameters, including the length of the vertical stands, horizontal stands, and tilted stands, to anticipate the half-melting time and complete melting time of the PCM. In the end, two optimal designs of O-TESS1 and O-TESS2 were proposed by a single-objective optimization, focusing on minimizing the half and complete melting times of the PCM, respectively. A multi-objective optimization was also performed to balance the mentioned objectives. Using the generated data, a Pareto front and TOPSIS-selected designs were developed to visualize the trade-offs between these competing objectives. It took

关键词： Solar heating

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Local thermal non-equilibrium effects on Marangoni convective flow of Casson fluid with elastic deformation: Perspective of Cattaneo-Christov flux model

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International Journal of Thermofluids 2025年 27卷

作者： Abbas, Munawar Akgül, Ali Ibrahim, Talib K. Al-Mdallal, Qasem Turdialiyev, Umid Ghazi, Hafiz Muhammad Jastaneyah, Zuhair Garalleh, Hakim AL Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai602105 India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai India Siirt University Art and Science Faculty Department of Mathematics Siirt56100 Turkey Department of Computer Engineering Biruni University Istanbul Topkapı34010 Turkey Near East University Mathematics Research Center Department of Mathematics Near East Boulevard PC: 99138 Nicosia /Mersin 10 Turkey Department of Petroleum Engineering College of Engineering Knowledge University Erbil44001 Iraq Department of Computer Engineering Al-Kitab University Kupri Altun Iraq Department of Mathematical Sciences P.O. Box 15551 UAE University Al-Ain United Arab Emirates Doctor of Technical Sciences Department of Organic Chemistry Rector Andijan Machine-Building Institute Bobur Avenue 56 Andijan Uzbekistan Department of Information Engineering Technology National Skills University Islamabad Islamabad44310 Pakistan Department of Mechanical Engineering College of Engineeringx University of Business and Technology Jeddah21361 Saudi Arabia Department of Mathematical Science College of Engineering University of Business and Technology Jeddah21361 Saudi Arabia Department of Mathematics The Islamia University of Bahawalpur Bahawalpur63100 Pakistan Applied Science Research Center Applied Science Private University Amman Jordan

This work investigates the effects of local thermal non-equilibrium impacts on the chemical reactive flow of Casson fluid under elastic deformation over a sheet. The modified theory for mass and heat transport is the generalized CattaneoChristov theory, which takes into consideration the importance of relaxation times. Chemical reactors, heat exchangers, and geothermal systems are examples of engineering applications where precise temperature control between various materials or phases is required to maximize performance and efficiency. These applications depend on local thermal non-equilibrium conditions. In systems that use non-Newtonian fluids, as those found in polymer manufacturing, petroleum drilling, and food industry applications, this model can be applied to maximize heat and mass transmission. The CattaneoChristov heat flow theory provides a more accurate depiction of thermal behaviour in such fluids by accounting for thermal relaxation effects. In chemically reactive situations, these effects are crucial for increasing energy conversion, speeding up reaction rates, and creating efficient heat management systems. The relevant similarity variables are condensed, and then the model equation system is numerically resolved using the bvp4c method. The higher the interphase heat transfer value, the lower the thermal profile of the Casson fluid's solid and fluid phases. © 2025

关键词： Elastic deformation

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Liver Segmentation in Time-resolved C-arm CT Volumes Reconstructed from Dynamic Perfusion Scans using Time Separation Technique

Liver Segmentation in Time-resolved C-arm CT Volumes Reconst...

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International Image Processing, Applications and Systems Conference (IPAS)

作者： Soumick Chatterjee Hana Haseljić Robert Frysch Vojtěch Kulvait Vladimir Semshchikov Bennet Hensen Frank Wacker Inga Brüsch Thomas Werncke Oliver Speck Andreas Nürnberger Georg Rose Data and Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Magdeburg Magdeburg Germany Department of Biomedical Magnetic Resonance Faculty of Nature Science Otto von Guericke University Magdeburg Magdeburg Germany Faculty of Electrical Engineering and Information Technology Institute for Medical Engineering Otto von Guericke University Magdeburg Magdeburg Germany Faculty of Electrical Engineering and Information Technology Research Campus STIMULATE Otto von Guericke University Magdeburg Magdeburg Germany Institute of Materials Physics Helmholtz-Zentrum hereon Geesthacht Germany Institute of Diagnostic and Interventional Radiology Hannover Medical School Hannover Germany Research Campus STIMULATE Otto von Guericke University Magdeburg Magdeburg Germany Institute for Laboratory Animal Science Hannover Medical School Hannover Germany Department of Biomedical Magnetic Resonance Faculty of Nature Science Otto von Guericke University Magdeburg German Center for Neurodegenerative Disease Magdeburg Germany Center for Behavioral Brain Sciences Magdeburg Germany

ISBN: (纸本)9781665462204

Perfusion imaging is a valuable tool for diagnosing and treatment planning for liver tumours. The time separation technique (TST) has been successfully used for modelling C-arm cone-beam computed tomography (CBCT) perfusion data. The reconstruction can be accompanied by the segmentation of the liver - for better visualisation and for generating comprehensive perfusion maps. Recently introduced Turbolift learning has been seen to perform well while working with TST reconstructions, but has not been explored for the time-resolved volumes (TRV) estimated out of TST reconstructions. The segmentation of the TRVs can be useful for tracking the movement of the liver over time. This research explores this possibility by training the multi-scale attention UNet of Turbolift learning at its third stage on the TRVs and shows the robustness of Turbolift learning since it can even work efficiently with the TRVs, resulting in a Dice score of $0.864\pm 0.004$ .

关键词： Training Image segmentation Computed tomography Computational modeling Motion segmentation Liver Imaging

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Edge-minimum saturated k-planar drawings

arXiv

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arXiv 2020年

作者： Chaplick, Steven Klute, Fabian Parada, Irene Rollin, Jonathan Ueckerdt, Torsten Department of Data Science and Knowledge Engineering Maastricht University Netherlands Utrecht University Netherlands TU Eindhoven Netherlands Department of Mathematics and Computer Science FernUniversität in Hagen Germany Institute of Theoretical Informatics Karlsruhe Institute of Technology Germany

For a class D of drawings of loopless (multi-)graphs in the plane, a drawing D ∈ D is saturated when the addition of any edge to D results in D0 ∈/ D—this is analogous to saturated graphs in a graph class as introduced by Turán (1941) and Erdös, Hajnal, and Moon (1964). We focus on k-planar drawings, that is, graphs drawn in the plane where each edge is crossed at most k times, and the classes D of all k-planar drawings obeying a number of restrictions, such as having no crossing incident edges, no pair of edges crossing more than once, or no edge crossing itself. While saturated k-planar drawings are the focus of several prior works, tight bounds on how sparse these can be are not well understood. We establish a generic framework to determine the minimum number of edges among all n-vertex saturated k-planar drawings in many natural classes. For example, when incident crossings, multicrossings and selfcrossings are all allowed, the sparsest n-vertex saturated k-planar drawings have 2/k−(k mod 2) (n − 1) edges for any k ≥ 4, while if all that is forbidden, the sparsest such drawings have 2(k + 1)/k (k-1) (n − 1) edges for any k ≥ 6. Copyright © 2020, The Authors. All rights reserved.

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Slow Heat-Based Hybrid Simulated Annealing Algorithm in Vehicular Ad Hoc Network

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Computational intelligence and neuroscience 2023年第1期2023卷 9918748页

作者： Pavan Kumar Pagadala P Lalitha Surya Kumari Deepak Thakur Vivek Bhardwaj Mohammad Shahid Abdulrajak Buradi Abdul Razak Abiot Ketema Computer Science and Engineering Koneru Lakshmaiah Educational Foundation Hyderabad Telangana India. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India. School of Computer Science and Engineering Lovely Professional University Jalandhar 144411 India. Department of Electrical Engineering Galgotias College of Engineering and Technology 1 Knowledge Park Phase II Noida 201306 UP India. Department of Mechanical Engineering Nitte Meenakshi Institute of Technology Bangalore India. Department of Mechanical Engineering P. A. College of Engineering (Affiliated to Visvesvaraya Technological University Belagavi) Mangaluru 574153 India. Department of Biosystems Engineering Institute of Technology Hawassa University Hawassa Ethiopia.

Vehicular ad hoc networks (VANETs) using reliable protocols of routing have become crucial in identifying the changes to topology on a continuous basis for a large collection of vehicles. For this purpose, it becomes important to identify an optimal configuration of these protocols. There are several possible configurations that have been preventing the configuration of efficient protocols that do not make use of automatic and intelligent design tools. It can further motivate using the techniques of metaheuristics like the tools, which are well-suited to be able to solve these problems. The glowworm swarm optimization (GSO), simulated annealing (SA), and slow heat-based SA-GSO algorithms have been proposed in this work. The SA is a method of optimization, which imitates the manner in which the thermal system has been frozen down to its lowest state of energy. In the GSO, there is guidance to the rules of feasibility, where the swarm converges to its feasible regions very fast. Additionally, for overcoming any premature convergence, there is a local search strategy that is based on the SA and is used for making a search that is near to its true optimum solutions. Finally, this sluggish temperature-based SA-GSO algorithm will be employed to solve routing problems and problems of heat transfer. There is a hybrid slow heat SA-GSO algorithm with a faster speed of convergence and higher precision of computation that is more effective in solving problems of constrained engineering.

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Distantly Supervised Relation Extraction via Recursive Hierarchy-Interactive Attention and Entity-Order Perception

arXiv

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arXiv 2021年

作者： Han, Ridong Peng, Tao Han, Jiayu Cui, Hai Liu, Lu College of Computer Science and Technology Jilin University Jilin Changchun130012 China College of Software Jilin University Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China Department of Linguistics University of Washington SeattleWA98195 United States

Wrong-labeling problem and long-tail relations severely affect the performance of distantly supervised relation extraction task. Many studies mitigate the effect of wrong-labeling through selective attention mechanism and handle long-tail relations by introducing relation hierarchies to share knowledge. However, almost all existing studies ignore the fact that, in a sentence, the appearance order of two entities contributes to the understanding of its semantics. Furthermore, they only utilize each relation level of relation hierarchies separately, but do not exploit the heuristic effect between relation levels, i.e., higher-level relations can give useful information to the lower ones. Based on the above, in this paper, we design a novel Recursive Hierarchy-Interactive Attention network (RHIA) to further handle long-tail relations, which models the heuristic effect between relation levels. From the top down, it passes relation-related information layer by layer, which is the most significant difference from existing models, and generates relation-augmented sentence representations for each relation level in a recursive structure. Besides, we introduce a newfangled training objective, called Entity-Order Perception (EOP), to make the sentence encoder retain more entity appearance information. Substantial experiments on the popular New York Times (NYT) dataset are conducted. Compared to prior baselines, our RHIA-EOP achieves state-of-the-art performance in terms of precision-recall (P-R) curves, AUC, Top-N precision and other evaluation metrics. Insightful analysis also demonstrates the necessity and effectiveness of each component of RHIA-EOP. © 2021, CC BY.

关键词： Semantics

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Application of advanced neural network approaches to investigate the heat transfer features of Powell-Eyring hybrid nanofluids

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Tribology International 2025年 211卷

作者： Wenbo Li Amjad Ali Pasha Zahoor Shah Ali Algahtani Vineet Tirth Nashwan Adnan Othman Mohamed H. Ahmed Maryam Jawaid Waqar Azeem Khan College of Science Shenyang Aerospace University Shenyang 110136 PR China Aerospace Engineering Department King Abdulaziz University Jeddah 21589 Saudi Arabia Department of Mathematics COMSATS University Islamabad Islamabad Campus Pakistan Mechanical Engineering Department College of Engineering King Khalid University Abha Asir 61421 Saudi Arabia Research Center for Advanced Materials Science (RCAMS) King Khalid University Guraiger PO Box 9004 Abha Asir 61413 Saudi Arabia Centre for Engineering and Technology Innovations King Khalid University Abha Asir 61421 Saudi Arabia Department of Computer Engineering College of Engineering Knowledge University Erbil 44001 Iraq Department of Computer Engineering Al-Kitab University Altun Kupri Iraq Mechanical Engineering Department King Abdulaziz University P.O. Box 80204 Jeddah 21589 Saudi Arabia School of Mathematics and Statistics Beijing Institute of Technology Beijing 100081 PR China

Possibilities from artificial intelligence are emerging swiftly in many fields, offering novel approaches as well as greatly enhanced ability to analyze complex situations and patterns of various facets. The focus of this paper is to extend the utilization of neural networks with the Levenberg-Marquardt Scheme (NNs-LMS) to explore entropy analysis of Powell–Eyring hybrid nanofluid, which considers the impacts of linear thermal radiation and viscous dissipation (EPNF-LTR-VD). The critical parameters like Prandtl number, suction/injection parameter, Eckert number, and material parameter are taken into consideration in order to understand the flow characteristics and heat transfer rates in this research. By employing the right similarity transformations, the set of nonlinear PDEs is reduced to set of nonlinear ODEs, thereby enabling the lowering the order of the system complexity. A similar dataset is then obtained with the help of the Adam numerical method for analyzing the impacts that these critical parameters can have on more extensive EPNF-LTR-VD scenarios. The dataset is utilized to test, train, and validate the EPNF-LTR-VD, proving its accuracy in predicting fluid system behavior. The empirical studies do not only provide evidence of effectiveness and accuracy of the proposed approach but also identify a high level of correspondence with reference data. Additional support for the model performance is provided by sophisticated performance graphs, error histograms, as well as regression evaluations. This research shows how AI can be used to advance the modeling of fluids; it helps to bring a new perspective into how fluid dynamics can be analyzed and differs from the previous works.

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