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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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Artificial intelligence in multimodal learning analytics: A systematic literature review

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computers and Education: Artificial Intelligence 2025年 8卷

作者： Mehrnoush Mohammadi Elham Tajik Roberto Martinez-Maldonado Shazia Sadiq Wojtek Tomaszewski Hassan Khosravi School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia Department of Educational Psychology and Learning Systems Florida State University Tallahassee USA Centre for Learning Analytics at Monash Faculty of Information Technology Monash University Melbourne Australia Australian Research Council Centre of Excellence for Children and Families Over the Life Course The University of Queensland Brisbane Australia Institute for Social Science Research The University of Queensland Brisbane Australia Institute for Teaching and Learning Innovation The University of Queensland Brisbane Australia

The proliferation of educational technologies has generated unprecedented volumes of diverse, multimodal learner data, offering rich insights into learning processes and outcomes. However, leveraging this complex, multimodal data requires advanced analytical methods. While Multimodal Learning Analytics (MMLA) offers promise for exploring this data, the potential of Artificial Intelligence (AI) to enhance MMLA remains largely unexplored. This paper bridges these two evolving domains by conducting the first systematic literature review at the intersection of AI and MMLA, analyzing 43 peer-reviewed papers from 11 reputable databases published between 2019 and 2024. The findings indicate a growing trend in AI-enhanced MMLA studies published predominantly in high-quality venues, led by education researchers with a predominant focus on tertiary education targeting diverse stakeholders. Guided by a novel conceptual framework, our analysis highlights the transformative role of AI across the MMLA process, particularly in model learning and feature engineering. However, it also uncovers significant gaps, including limited AI implementation in components requiring deep integration with learning theories, insufficient application of advanced AI techniques, and lack of large-scale studies in authentic learning environments. The review identifies key benefits, such as enhanced personalization and real-time feedback, while also addressing challenges related to ethical considerations, data integration, and scalability. Our study contributes by offering comprehensive recommendations for future research, emphasizing international collaboration, multi-level studies, and ethical AI implementation. These findings advance the theoretical understanding of AI's role in education, providing a foundation for developing sophisticated, interpretable, and scalable AI-enhanced MMLA approaches, potentially revolutionizing personalized learning across diverse educational settings.

关键词： AI Multimodal data Learning analytics Multimodal learning analytics Systematic review

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Using Firebase Cloud Messaging to Control Mobile Applications

Using Firebase Cloud Messaging to Control Mobile Application...

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International Conference on computer, Control, Electrical, and Electronics Engineering (ICCCEEE)

作者： Mohamed Abdalla Mokar Sallam Osman Fageeri Saif Eldin Fattoh Faculty of computer Science & Information Technology Alzaiem Alazhari University Sudan College of Economics Management and Information Systems Nizwa University Sultanate of Oman Department of Computer Science Emirates College for Science and Technology Sudan

ISBN: (数字)9781728110066

ISBN: (纸本)9781728110073

The system proposed in this paper is new framework that can be utilized by any application programmer to use it for control mobile Application by using FCM technology Stand for (Firebase Cloud Messaging), which is supported by Google. It is a very powerful technology in sending Notifications to Mobile applications through data messages that can be used to change the behavior of mobile applications that installed on the smart phones. The system is designed to deal with more than one Firebase application at the same time and send data messages that designed by the programmer either by creating these data manually or to get this data from another environment. This system was designed to save the information of the applications that need to get control and data Message information can be store inside the local system database. The proposed system is working as Rest Clint to send information that containing JSON Format with key-value pairs, which is created inside the system itself or by using existing Data message from the Database, this messages can be send to specific application and it is possible to send this data Messages and information to a specific user or specific group of users in the same group using (subscribe topic) or all users of the Mobile App The system is designed using technology of Dot Net, Web Service, Firebase Cloud Messaging and sql server database that use to manage the Data and firebase applications. this Data Messages Can be sent to specific Mobile Application or Group of Mobile Applications using same Data message, the proposed system is solved the main problem of this paper that difficult to manage the orientation of the functions within the applications to make it easy and fast as much as passible.

关键词： Mobile applications Servers Google Smart phones Databases Androids Humanoid robots

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Web Resources Management Method Based on Intelligent Technologies

Web Resources Management Method Based on Intelligent Technol...

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International Conference on computer science and information Technologies, CSIT 2018

作者： Gozhyj, Aleksandr Vysotska, Victoria Yevseyeva, Iryna Kalinina, Iryna Gozhyj, Victor Department of Computer Engineering Petro Mohyla Black Sea National University Nikolaev Ukraine Information Systems and Networks Department Lviv Polytechnic National University Lviv Ukraine Computing Science at the Faculty of Technology at de Montfort University Leicester United Kingdom

ISBN: (纸本)9783030010683

This study explores a method for managing web content based on intelligent technologies, as a stage of the content life cycle in e-commerce systems. The method of content management describes the processes of creating web resources and simplifies the technology of managing web resources in conditions of uncertainty. Also, the main problems of e-commerce and functional content processing services are analyzed. The proposed method makes it possible to develop tools for managing web resources and commercial content. © 2019, Springer Nature Switzerland AG.

关键词： Life cycle

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MACHINE LEARNING APPROACHES FOR COVID-19 DETECTION FROM CHEST X-RAY IMAGING: A SYSTEMATIC REVIEW

arXiv

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arXiv 2022年

作者： Arteaga-Arteaga, Harold Brayan delaPava, Melissa Mora-Rubio, Alejandro Bravo-Ortíz, Mario Alejandro Alzate-Grisales, Jesus Alejandro Arias-Garzón, Daniel López-Murillo, Luis Humberto Buitrago-Carmona, Felipe Villa-Pulgarín, Juan Pablo Mercado-Ruiz, Esteban Orozco-Arias, Simon Hassaballah, M. de la Iglesia-Vaya, Maria Cardona-Morales, Oscar Tabares-Soto, Reinel Department of Electronics and Automation Universidad Autónoma de Manizales Manizales Colombia Department of Chemical Engineering Universidad Nacional de Colombia Manizales Colombia Department of Computer Science Universidad Autónoma de Manizales Manizales Colombia Department of Systems and Informatics Universidad de Caldas Manizales Colombia Faculty of Computers and Information South Valley University Qena Egypt Unidad Mixta de Imagen Biomédica FISABIO-CIPF Fundación para el Fomento de la Investigación Sanitario y Biomédica de la Comunidad Valenciana Valencia Spain

There is a necessity to develop affordable, and reliable diagnostic tools, which allow containing the COVID-19 spreading. Machine Learning (ML) algorithms have been proposed to design support decision-making systems to assess chest X-ray images, which have proven to be useful to detect and evaluate disease progression. Many research articles are published around this subject, which makes it difficult to identify the best approaches for future work. This paper presents a systematic review of ML applied to COVID-19 detection using chest X-ray images, aiming to offer a baseline for researchers in terms of methods, architectures, databases, and current limitations. © 2022, CC BY.

关键词： COVID-19

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Predicting Length of Stay for Cardiovascular Hospitalizations in the Intensive Care Unit: Machine Learning Approach

Predicting Length of Stay for Cardiovascular Hospitalization...

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

作者： Belal Alsinglawi Fady Alnajjar Omar Mubin Mauricio Novoa Mohammed Alorjani Ola Karajeh Omar Darwish School of Computer Data and Mathematical Sciences Western Sydney University Penrith NSW Australia College of Information Technology United Arab Emirates University Al Ain 15551 UAE Department of Pathology and Microbiology King Abdullah University Hospital Faculty of Medicine Jordan University of Science and Technology Irbid Jordan Department of Computer Science Virginia Polytechnic Institute and State University Blacksburg VA United States Department of Computer Information Systems Ferrum College Ferrum Virginia United States

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Predicting Cardiovascular Length of stay based hospitalization at the time of patients' admitting to the coronary care unit (CCU) or (cardiac intensive care units CICU) is deemed as a challenging task to hospital management systems globally. Recently, few studies examined the length of stay (LOS) predictive analytics for cardiovascular inpatients in ICU. However, there are almost scarcely real attempts utilized machine learning models to predict the likelihood of heart failure patients length of stay in ICU hospitalization. This paper introduces a predictive research architecture to predict Length of Stay (LOS) for heart failure diagnoses from electronic medical records using the state-of-art- machine learning models, in particular, the ensembles regressors and deep learning regression models. Our results showed that the gradient boosting regressor (GBR) outweighed the other proposed models in this study. The GBR reported higher R-squared value followed by the proposed method in this study called Staking Regressor. Additionally, The Random forest Regressor (RFR) was the fastest model to train. Our outcomes suggested that deep learning-based regressor did not achieve better results than the traditional regression model in this study. This work contributes to the field of predictive modelling for electronic medical records for hospital management systems.

关键词： Predictive models Heart Machine learning Hospitals Biological system modeling Mathematical model Tuning

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An efficient internet traffic classification system using deep learning for IoT

arXiv

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arXiv 2021年

作者： Umair, Muhammad Basit Iqbal, Zeshan Bilal, Muhammad Almohamad, Tarik Adnan Nebhen, Jamel Mehmood, Raja Majid Department of Computer Science University of Engineering and Technology Taxila47050 Pakistan Division of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies Yongin-si Korea Republic of Electrical-Electronics Engineering Department Faculty of Engineering Karabük University Karabük78050 Turkey Department of Electronics and Communication Engineering A'Sharqiyah University Ibra400 Oman Prince Sattam bin Abdulaziz University College of Computer Engineering and Sciences P.O. Box 151 Alkharj 11942 Saudi Arabia Information and Communication Technology Department School of Electrical and Computer Engineering Xiamen University Malaysia Sepang43900 Malaysia

Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify internet traffic due to improper feature selection. In this research, an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic. To examine the performance of the proposed technique, Moore-dataset is used for training the classifier. The proposed scheme takes the preprocessed data and extracts the flow features using a deep neural network (DNN). In particular, the maximum entropy classifier is used to classify the internet traffic. The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification, i.e., 99.23%. Furthermore, the proposed algorithm achieved the highest accuracy compared to the support vector machine (SVM) based classification technique and k-nearest neighbours (KNNs) based classification *** Codes 62M45, 68T07 © 2021, CC BY.

关键词： Internet of things

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Foreword

Advances in Intelligent Systems and Computing

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Advances in Intelligent systems and Computing 2020年 1017卷 v-vi页

作者： Abawajy, Jemal H. Choo, Kim-Kwang Raymond Islam, Rafiqul Xu, Zheng Atiquzzaman, Mohammed Faculty of Science Deakin University GeelongVIC Australia Department of Information Systems and Cyber Security The University of Texas at San Antonio San AntonioTX United States School of Computing and Mathematics Charles Sturt University AlburyNSW Australia Shanghai University Shanghai China School of Computer Science University of Oklahoma NormanOK United States

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Packaging research artefacts with RO-Crate

arXiv

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arXiv 2021年

作者： Soiland-Reyes, Stian Sefton, Peter Crosas, Mercè Castro, Leyla Jael Coppens, Frederik Fernández, José M. Garijo, Daniel Grüning, Björn Rosa, Marco La Leo, Simone Carragáin, Eoghan Ó. Portier, Marc Trisovic, Ana Groth, Paul Goble, Carole Department of Computer Science University of Manchester United Kingdom Informatics Institute University of Amsterdam Netherlands Faculty of Science University Technology Sydney Australia Institute for Quantitative Social Science Harvard University CambridgeMA United States ZB MED Information Centre for Life Sciences Cologne Germany VIB-UGent Center for Plant Systems Biology Gent Belgium Barcelona Supercomputing Center Barcelona Spain Ontology Engineering Group Universidad Politécnica de Madrid Madrid Spain Bioinformatics Group Department of Computer Science Albert-Ludwigs-University Freiburg Freiburg Germany PARADISEC Melbourne Australia Italy University College Cork Ireland Vlaams Instituut voor de Zee Oostende Belgium Institute for Quantitative Social Science Harvard University CambridgeMA United States Informatics Institute University of Amsterdam Netherlands Department of Computer Science University of Manchester United Kingdom

An increasing number of researchers support reproducibility by including pointers to and descriptions of datasets, software and methods in their publications. However, scientific articles may be ambiguous, incomplete and difficult to process by automated systems. In this paper we introduce RO-Crate, an open, community-driven, and lightweight approach to packaging research artefacts along with their metadata in a machine readable manner. RO-Crate is based on *** annotations in JSON-LD, aiming to establish best practices to formally describe metadata in an accessible and practical way for their use in a wide variety of situations. An RO-Crate is a structured archive of all the items that contributed to a research outcome, including their identifiers, provenance, relations and annotations. As a general purpose packaging approach for data and their metadata, RO-Crate is used across multiple areas, including bioinformatics, digital humanities and regulatory sciences. By applying"just enough" Linked Data standards, RO-Crate simplifies the process of making research outputs FAIR while also enhancing research reproducibility. © 2021, CC BY.

关键词： Metadata

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Roadmap for Unconventional Computing with Nanotechnology

arXiv

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arXiv 2023年

作者： Finocchio, Giovanni Incorvia, Jean Anne C. Friedman, Joseph S. Yang, Qu Giordano, Anna Grollier, Julie Yang, Hyunsoo Ciubotaru, Florin Chumak, Andrii Naeemi, Azad J. Cotofana, Sorin D. Tomasello, Riccardo Panagopoulos, Christos Carpentieri, Mario Lin, Peng Pan, Gang Joshua Yang, J. Todri-Sanial, Aida Boschetto, Gabriele Makasheva, Kremena Sangwan, Vinod K. Trivedi, Amit Ranjan Hersam, Mark C. Camsari, Kerem Y. McMahon, Peter L. Datta, Supriyo Koiller, Belita Aguilar, Gabriel H. Temporão, Guilherme P. Rodrigues, Davi R. Sunada, Satoshi Everschor-Sitte, Karin Tatsumura, Kosuke Goto, Hayato Puliafito, Vito Åkerman, Johan Takesue, Hiroki Ventra, Massimiliano Di Pershin, Yuriy V. Mukhopadhyay, Saibal Roy, Kaushik Wang, I-Ting Kang, Wang Zhu, Yao Kaushik, Brajesh Kumar Hasler, Jennifer Ganguly, Samiran Ghosh, Avik W. Levy, William Roychowdhury, Vwani Bandyopadhyay, Supriyo Department of Mathematical and Computer Sciences Physical Sciences and Earth Sciences University of Messina Messina98166 Italy Chandra Family Department of Electrical and Computer Engineering University of Texas at Austin AustinTX78712 United States Department of Electrical and Computer Engineering University of Texas at Dallas RichardsonTX75080 United States Department of Electrical and Computer Engineering National University of Singapore Singapore117576 Singapore Department of Engineering University of Messina Messina98166 Italy Laboratoire Albert Fert CNRS Thales Université Paris-Saclay Palaiseau91767 France IMEC Leuven3001 Belgium Faculty of Physics University of Vienna Vienna1090 Austria School of Electrical & Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Quantum and Computer Engineering Department Delft University of Technology DelftCD 2628 Netherlands Department of Electrical and Information Engineering Politecnico di Bari Bari70125 Italy Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore639798 Singapore College of Computer Science and Technology Zhejiang University Hangzhou310013 China Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States Electrical Engineering Department Eindhoven University of Technology AZ Eindhoven5612 Netherlands Microelectronics Department LIRMM Université de Montpellier Montpellier34095 France Laplace CNRS UT3 INPT University of Toulouse Toulouse31062 France Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Electrical and Computer Engineering University of Illinois at Chicago ChicagoIL60607 United States Department of Electrical and Computer Engineering University of California at Santa Barbara Santa BarbaraCA93106 United States School of Applied and

In the "Beyond Moore's Law" era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for a roadmap for unconventional computing with nanotechnologies to guide future research, and this collection aims to fill that need. The authors provide a comprehensive roadmap for neuromorphic computing using electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets, and various dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain-inspired computing for incremental learning and problem-solving in severely resource-constrained environments. These approaches have advantages over traditional Boolean computing based on von Neumann architecture. As the computational requirements for artificial intelligence grow 50 times faster than Moore's Law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon, and this roadmap will help identify future needs and challenges. In a very fertile field, experts in the field aim to present some of the dominant and most promising technologies for unconventional computing that will be around for some time to come. Within a holistic approach, the goal is to provide pathways for solidifying the field and guiding future impactful discoveries. © 2023, CC BY-NC-SA.

关键词： Nanotechnology

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