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

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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Ensemble Reinforcement Learning: A Survey

arXiv

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arXiv 2023年

作者： Song, Yanjie Suganthan, Ponnuthurai Nagaratnam Pedrycz, Witold Ou, Junwei He, Yongming Chen, Yingwu Wu, Yutong College of Systems Engineering National University of Defense Technology Changsha China KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar Department of Electrical & Computer Engineering University of Alberta EdmontonAB Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Turkey Department of Analytics Operations and Systems University of Kent United Kingdom

Reinforcement Learning (RL) has emerged as a highly effective technique for addressing various scientific and applied problems. Despite its success, certain complex tasks remain challenging to be addressed solely with a single model and algorithm. In response, ensemble reinforcement learning (ERL), a promising approach that combines the benefits of both RL and ensemble learning (EL), has gained widespread popularity. ERL leverages multiple models or training algorithms to comprehensively explore the problem space and possesses strong generalization capabilities. In this study, we present a comprehensive survey on ERL to provide readers with an overview of recent advances and challenges in the field. Firstly, we provide an introduction to the background and motivation for ERL. Secondly, we conduct a detailed analysis of strategies such as model selection and combination that have been successfully implemented in ERL. Subsequently, we explore the application of ERL, summarize the datasets, and analyze the algorithms employed. Finally, we outline several open questions and discuss future research directions of ERL. By offering guidance for future scientific research and engineering applications, this survey significantly contributes to the advancement of ERL. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Large Model for Small Data: Foundation Model for Cross-Modal RF Human Activity Recognition

arXiv

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arXiv 2024年

作者： Weng, Yuxuan Wu, Guoquan Zheng, Tianyue Yang, Yanbing Luo, Jun Department of Computer Science and Engineering Southern University of Science and Technology China Smart City Center Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China College of Computer Science Sichuan University China College of Computing and Data Science Nanyang Technological University Singapore

Radio-Frequency (RF)-based Human Activity Recognition (HAR) rises as a promising solution for applications unamenable to techniques requiring computer visions. However, the scarcity of labeled RF data due to their non-interpretable nature poses a significant obstacle. Thanks to the recent breakthrough of foundation models (FMs), extracting deep semantic insights from unlabeled visual data become viable, yet these vision-based FMs fall short when applied to small RF datasets. To bridge this gap, we introduce FM-Fi, an innovative cross-modal framework engineered to translate the knowledge of vision-based FMs for enhancing RF-based HAR systems. FM-Fi involves a novel cross-modal contrastive knowledge distillation mechanism, enabling an RF encoder to inherit the interpretative power of FMs for achieving zero-shot learning. It also employs the intrinsic capabilities of FM and RF to remove extraneous features for better alignment between the two modalities. The framework is further refined through metric-based few-shot learning techniques, aiming to boost the performance for predefined HAR tasks. Comprehensive evaluations evidently indicate that FM-Fi rivals the effectiveness of vision-based methodologies, and the evaluation results provide empirical validation of FM-Fi’s generalizability across various environments. © 2024, CC BY.

关键词： Zero-shot learning

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GSLB: The Graph Structure Learning Benchmark 37

GSLB: The Graph Structure Learning Benchmark

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37th Conference on Neural Information Processing systems, NeurIPS 2023

作者： Li, Zhixun Wang, Liang Sun, Xin Luo, Yifan Zhu, Yanqiao Chen, Dingshuo Luo, Yingtao Zhou, Xiangxin Liu, Qiang Wu, Shu Yu, Jeffrey Xu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Automation University of Science and Technology of China China School of Cyberspace Security Beijing University of Posts and Telecommunications China Department of Computer Science University of California Los Angeles United States Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University United States

ISBN: (纸本)9781713899921

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Graphic methods

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Sentiment Analysis: Amazon Electronics Reviews Using BERT and Textblob

Sentiment Analysis: Amazon Electronics Reviews Using BERT an...

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Language Engineering (ESOLEC), International Conference on

作者： Abdulrahman Mahgoub Hesham Atef Abdulrahman Nasser Mohamed Yasser Walaa M. Medhat M. Saeed Darweesh Passent M. El-Kafrawy Computing and Information Systems Research Center (CIS) School of Information Technology and Computer Science Nile University Egypt Faculty of Computer and AI Benha University Cairo Egypt Wireless Intelligent Networks Center (WINC) School of Engineering and Applied Sciences Nile University Egypt Computer Science Department Effat College of Engineering SA

ISBN: (纸本)9781665453233

The market needs a deeper and more comprehensive grasp of its insight, where the analytics world and methodologies such as “Sentiment Analysis” come in. These methods can assist people especially “business owners” in gaining live insights into their businesses and determining wheatear customers are satisfied or not. This paper plans to provide indicators by gathering real world Amazon reviews from Egyptian customers. By applying both Bidirectional Encoder Representations from Transformers “Bert” and “Text Blob” sentiment analysis methods. The processes shall determine the overall satisfaction of Egyptian customers in the electronics department - in order to focus on a specific domain. The two methods will be compared for both the Arabic and English languages. The results show that people in *** are mostly satisfied with the percentage of 47%. For the performance, BERT outperformed Textblob indicating that word embedding model BERT is more superior than rule-based model Textblob with a difference of 15% - 25%.

关键词： Sentiment analysis Bit error rate Transformers Consumer electronics Business

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AAAI 2008 workshop reports

AAAI 2008 workshop reports

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作者： Anand, Sarabjot Singh Bunescu, Razvan Carvcdho, Vitor Chomicki, Jan Conitzer, Vincent Cox, Michael T. Dignum, Virginia Dodds, Zachary Dredze, Mark Furcy, David Gabrilovich, Evgeniy Göker, Mehmet H. Guesgen, Hans Hirsh, Haym Jannach, Dietmar Junker, Ulrich Ketter, Wolfgang Kobsa, Alfred Koenig, Sven Lau, Tessa Lewis, Lundy Matson, Eric Metzler, Ted Mihalcea, Rada Mobasher, Bamshad Pineau, Joelle Poupart, Pascal Raja, Anita Ruml, Wheeler Sadeh, Norman Shani, Guy Shapiro, Daniel Smith, Trey Taylor, Matthew E. Wagstaff, Kiri Walsh, William Zhou, Rong Department of Computer Science University of Warwick United Kingdom School of Electrical Engineering and Computer Science Ohio University United States Microsoft Live Labs United States computer science and engineering University at Buffalo United States Computer science and economics Duke University United States Intelligent Computing group of BBN Technologies Utrecht University Netherlands Department of Computer science Harvey Mudd College United States University of Pennsylvania United States University of Wisconsin United States Yahoo Research PricewaterhouseCoopers Center for Advanced Research United States Department of Computer science School of Engineering and Advanced Technology Massey University New Zealand Departent of Computer science Rutgers University United States Department of Computer Science Dortmund University of Technology. Germany ILOG Rotterdam School of Management Erasmus University Netherlands Donald Bren School of Information and Computer Sciences University of California Irvine United States Depatrment of Computer science University of Southern California United States IBM Almaden Research Center United States Department of Computer Information Technology Southern New Hampshire University United States Computer and Information Technology Program College of Technology Purdue University United States Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computer Science and Engineering University of North Texas United States School of Computing DePaul University United States Department of Computer Science McGill University Canada School of Computer Science University of Waterloo Canada Department of Software and Information Systems University of North Carolina Charlotte United States University of New Hampshire United States Microsoft Research Institute for the Study of Learning and Expertise Applied Reactivity Inc. United States NASA Ames Research Center Carnegie Mellon University West

AAAI was pleased to present the AAAI-08 Workshop Program, held Sunday and Monday, July 13-14, in Chicago, Illinois, USA. The program included the following 15 workshops: Advancements in POMDP Solvers;AI Education Workshop Colloquium;Coordination, Organizations, Institutions, and Norms in Agent systems, Enhanced Messaging;Human Implications of Human-Robot Interaction;Intelligent Techniques for Web Personalization and Recommender systems;Metareasoning: Thinking about Thinking;Multidisciplinary Workshop on Advances in Preference Handling;Search in Artificial Intelligence and Robotics;Spatial and Temporal Reasoning;Trading Agent Design and Analysis;Transfer Learning for Complex Tasks;What Went Wrong and Why: Lessons from AI research and Applications;and Wikipedia and Artificial Intelligence: An Evolving Synergy. Copyright © 2009, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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A practical approximation algorithm for the LMS line estimator 97

A practical approximation algorithm for the LMS line estimat...

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Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms

作者： David M. Mount Nathan S. Netanyahu Kathleen Romanik Ruth Silverman Angela Y. Wu Department of Computer Science and Institute for Advanced Computer Studies University of Maryland College Park MD Center for Automation Research University of Maryland College Park MD and Center of Excellence in Space Data and Information Sciences Code 930.5 Space Data and Computing Division NASA Goddard Space Flight Center Greenbelt MD Center for Automation Research University of Maryland College Park MD and National Institute of Standards and Technology Gaithersburg MD Department of Computer Science University of the District of Columbia Washington DC and Center for Automation Research University of Maryland College Park MD Department of Computer Science and Information Systems The American University Washington DC

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RAxML-Cell: Parallel Phylogenetic Tree Inference on the Cell Broadband Engine

RAxML-Cell: Parallel Phylogenetic Tree Inference on the Cell...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Filip Blagojevic Alexandros Stamatakis Christos D. Antonopoulos Dimitrios S. Nikolopoulos Center for High-end Computing Systems Department of Computer Science Virginia Polytechnic Institute and State University Blacksburg VA USA School of Computer & Communication Sciences Advanced Micro Devices Inc. Lausanne Switzerland Department of Computer Science Division of Research and Informatics College of William and Mary USA

Computational phylogeny is a challenging application even for the most powerful supercomputers. It is also an ideal candidate for benchmarking emerging multiprocessor architectures, because it exhibits fine- and coarse-grain parallelism at multiple levels. In this paper, we present the porting, optimization, and evaluation of RAxML on the cell broadband engine. RAxML is a provably efficient, hill climbing algorithm for computing phylogenetic trees, based on the maximum likelihood (ML) method. The cell broadband engine, a heterogeneous multi-core processor with SIMD accelerators which was initially marketed for set-top boxes, is currently being deployed on supercomputers and high-end server architectures. We present both conventional and unconventional, cell-specific optimizations for RAxML's search algorithm on a real cell multiprocessor. While exploring these optimizations, we present solutions to problems related to floating point code execution, complex control flow, communication, scheduling, and multilevel parallelization on the cell.

关键词： Phylogeny Engines Parallel processing Concurrent computing Microprocessors computer architecture Military computing computer science Supercomputers Multicore processing

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FIRE MAIN CONTROL

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NAVAL ENGINEERS JOURNAL 1985年第6期97卷 35-48页

作者： MALKOFF, DB MOY, MC WILLIAMS, HL Dr. Donald B. Malkoff majored in physics as an undergraduate at Harvard University. He received an M.D. degree from the University of Pittsburgh School of Medicine in 1960. This was followed by an internship and residency in neurology at University Hospital in Ann Arbor Michigan. He spent several years at the National Institutes of Health engaged in gerontology research has practiced and taught clinical neurology and in 1983 received an M.S. degree in computer science at the University of California San Diego. Currently Dr. Malkoff is employed by the Navy Personnel Research and Development Center in San Diego California where he is senior investigator in a human factor/computer display-and-control project involving the DDG-51 gas turbine propulsion unit. He is a member of the American Academy of Neurology the Society for Neuroscience the American Association for Artificial Intelligence and the Association for Computing Machinery. Dr. Malkoff is certified by the American Board of Psychiatry and Neurology has taught computer science at UCSD and published in several research areas including magnetoencephalography and electron microscopy. His basic interests are in the areas of artificial intelligence and learning expert systems particularly as they apply to the problems of fault-detection and control. Dr. Melvyn C. Moy received his undergraduate training in mathematics and chemistry at the University of Texas Austin. He studied experimental psychology at the University of Wisconsin Madison receiving his M.S. in 1970 and Ph.D. in 1972. He served as an assistant professor at the University of South Dakota where he taught experimental design and methodology for a year before joining the Navy Personnel Research and Development Center in 1973. His work and research since then spans across many application areas such as the development of manpower planning models for the Navy the design of operational decision aids the human engineering of interactive large-scale war gaming systems and the evaluation o

The ship fire main has undergone considerable development throughout the past 2,000 years, resulting in a system that is critical both for normal ship function and for ship survivability in emergencies. Because of its complexity, the modern firemain system is highly vulnerable to malfunction and to damage during combat. Firemain fault detection and fault recovery are currently conducted manually by damage control teams. The advantages and disadvantages of this method are discussed, and alternative methods of fault detection and recovery are explored. An interactive computer program is introduced which uses central control over remotely situated valves to facilitate fault detection and recovery, significantly reducing recovery-time and manpower requirements. These reductions may result in savings of lives, ship systems, and ships themselves. The computer program is based upon an algorithm which is, in effect, a prescription that can be followed manually by the operator or be completely automated. The color graphic display which is used for monitoring can also be utilized for the training of damage control operators or for the evaluation of other algorithms for firemain control. Alternative firemain hardware and configurations could lead to even more efficient methods of fault detection and recovery as well as improved firemain water supply management in general.

关键词： NAVAL VESSELS

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Reports of the AAAI 2011 conference workshops

Reports of the AAAI 2011 conference workshops

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作者： Agmon, Noa Agrawal, Vikas Aha, David W. Aloimonos, Yiannis Buckley, Donagh Doshi, Prashant Geib, Christopher Grasso, Floriana Green, Nancy Johnston, Benjamin Kaliski, Burt Kiekintveld, Christopher Law, Edith Lieberman, Henry Mengshoel, Ole J. Metzler, Ted Modayil, Joseph Oard, Douglas W. Onder, Nilufer O'Sullivan, Barry Pastra, Katerina Precup, Doina Ramachandran, Sowmya Reed, Chris Sariel-Talay, Sanem Selker, Ted Shastri, Lokendra Singh, Satinder Smith, Stephen F. Srivastava, Siddharth Sukthankar, Gita Uthus, David C. Williams, Mary-Anne Department of Computer Science University of Texas at Austin United States Center for Knowledge Driven Information Systems Infosys Labs India Adaptive Systems Section Naval Research Laboratory Washington DC United States Department of Computer Science University of Maryland College Park United States EMC's Center of Excellence in Ireland Research Europe at EMC Ireland Department of Computer Science University of Georgia United States School of Informatics University of Edinburgh United Kingdom Department of Computer Science University of Liverpool United Kingdom Department of Computer Science University of North Carolina Greensboro United States Faculty of Engineering and Information Technology University of Technology Sydney Australia VeriSign Inc. United States Department of Computer Science University of Texas at El Paso United States Machine Learning Department Carnegie Mellon University United States Massachusetts Institute of Technology Media Laboratory United States Carnegie Mellon University's Silicon Valley Campus United States Darrell W. Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computing Science University of Alberta Canada College of Information Studies Institute for Advanced Computer Studies University of Maryland College Park United States Department of Computer Science Michigan Technological University United States Department of Computer Science University College Cork Ireland Cognitive Systems Research Insitute Athens Greece School of Computer Science McGill University Canada Stottler Henke Inc. United States School of Computing University of Dundee United Kingdom Department of Computer Engineering Istanbul Technical University Turkey Convergence Lab Infosys Technologies Ltd. United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States Robotics Institute Carnegie Mellon University United States University of Wisconsin

The AAAI-11 workshop program was held Sunday and Monday, August 7-18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages;Analyzing Microtext;Applied Adversarial Reasoning and Risk Modeling;Artificial Intelligence and Smarter Living: The Conquest of Complexity;Artifiicial Intelligence for Data center Management and Cloud computing;Automated Action Planning for Autonomous Mobile Robots;Computational Models of Natural Argument;Generalized Planning;Human Computation;Human-Robot Interaction in Elder Care;Interactive Decision Theory and Game Theory, 2010;Language-Action Tools for Cognitive Artificial Agents: Integrating Vision, Action, and Language;Lifelong Learning from Sensorimotor Experience;Plan, Activity, and Intent Recognition;and Scalable Integration of Analytics and Visualization. This article presents short summaries of those events. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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