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Correction to: Next-generation UWB antennas gadgets for human health care using SAR

EURASIP Journal on Wireless Communications and Networking 2021年第1期2021卷 1-1页

作者： Ali, Aysha Maryam Al Ghamdi, Mohammed A. Iqbal, Muhammad Munwar Khalid, Shehzad Aldabbas, Hamza Saeed, Saqib University of Engineering and Technology Taxila Pakistan Computer Science Department Umm Al-Qura University Makkah City Saudi Arabia Computer Engineering Department Bahria University Islamabad Pakistan Software Engineering Department Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Al-Salt Jordan Department of Computer Information Systems College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Kingdom of Saudi Arabia

An amendment to this paper has been published and can be accessed via the original article.

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Mobile-based deep learning system for early detection of diabetic retinopathy

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Intelligence-Based Medicine 2025年 12卷

作者： El-Mehdi Chakour Zineb Sadok Rostom Kachouri Anass Mansouri Idriss Benatiya Andaloussi Mohamed Akil Ali Ahaitouf Laboratory of Intelligent Systems Geo-resources and Renewable Energies Faculty of Sciences and Technology Sidi Mohamed Ben Abdellah University Fez Morocco Gaspard Monge Computer Science Laboratory ESIEE Paris Gustave Eiffel University F-77454 Marne-la-Vallée Paris France Laboratory of Intelligent Systems Geo-resources and Renewable Energies National School of Applied Sciences Sidi Mohamed Ben Abdellah University Fez Morocco Ophthalmology Department Hassan II Hospital Sidi Mohamed Ben Abdellah University Fez Morocco

Diabetic retinopathy (DR) is a leading cause of vision loss globally, especially in regions with limited access to eye care. Early detection is essential to prevent irreversible damage and improve patient outcomes. In this study, a portable, real-time Assisted Mobile Diagnostic (AMD) system for DR detection, which integrates an optimized deep learning model into a mobile platform, is presented. Unlike conventional AI-based approaches that require high-performance computing and stationary fundus cameras, our system combines a non-mydriatic retinal camera with a mobile device, enabling point-of-care diagnostics. Captured retinal images are preprocessed using techniques such as blurring and contrast enhancement before being analyzed by a fine-tuned DenseNet-121 model. The model is trained using a private dataset along with two large public datasets: APTOS (Asia Pacific Tele-Ophthalmology Society) and EyePACS (Eye Picture Archive Communication System). The proposed approach achieved a high accuracy: 97.38% on APTOS, 90.90% on EyePACS, and 98.61% on the private dataset. The system delivers real-time performance on mobile devices, with an average processing time of 162.5 ms, making it well-suited for rapid screening. This Deep learning-based mobile application is part of a multi-platform tele-ophthalmology framework that includes both tablet and desktop integrations, facilitating accessible and remote DR diagnosis.

关键词： Deep learning-based mobile application Earlier screening of diabetic retinopathy Real-time eye diagnosis And handheld fundus camera

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Thermal performance optimization of microchannel heat sinks with triangle wave fin designs and various heat transfer fluids using GA/RSM/TOPSIS

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Case Studies in Thermal Engineering 2025年 72卷

作者： Amr S. Abouzied Ali Basem Talib K. Ibrahim Ahmad Almadhor Khalid Almutairi Hind Albalawi Salem Alkhalaf Dilsora Abduvalieva Ibrahim Elbadawy Samah G. Babiker Department of Pharmaceutical Chemistry College of Pharmacy University of Hail Hail 81442 Saudi Arabia Faculty of Engineering Warith Al-Anbiyaa University Karbala 56001 Iraq Department of Petroleum Engineering College of Engineering Knowledge University Erbil 44001 Iraq Department of Petroleum Engineering Al-Kitab University Altun Kupri Iraq Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Saudi Arabia Applied College Mechanical Engineering Technology University of Hafr Al Batin Hafr Al Batin Saudi Arabia Department of Physics College of Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Computer Engineering College of Computer Qassim University Buraydah Saudi Arabia Department of Mathematics and Information Technologies Tashkent State Pedagogical University Bunyodkor avenue 27 Tashkent 100070 Uzbekistan College of Engineering and Technology American University of the Middle East Kuwait Department of Electronic Physics Faculty of Applied Science Red Sea University Port Sudan Sudan

Microchannel heat sinks (MHSs) stand out as a promising solution for dissipating heat in high-performance systems. The development and optimization of these systems are vital to meeting the growing demands of modern, power-dense electronics. The present study introduces a novel approach to enhance heat dissipation in microchannel systems. The design incorporates two sets of fins with a distinctive triangle wave pattern within the microchannels. The acquired results for the introduced fins were compared with the data of a non-finned MHS and a device with straight fins. The maximum temperature of the non-finned MHS was 307.222 K, while the incorporation of the mentioned fins reduced the temperature by approximately 2 K. This reduction is significant, as lower maximum temperatures improve the reliability and longevity of electronic components. Response surface methodology (RSM) was utilized to develop predictive models, reducing reliance on complex simulations and costly prototypes. The effects of fin folding angle ( θ ) and spacing from side walls ( S ) on thermo-hydraulic performance were systematically analyzed. Optimization was achieved by integrating genetic algorithms with the predictive models. Three distinct optimization targets (maximizing the Nusselt number, minimizing the pressure drop, and maximizing the device's overall efficiency) were considered, and three optimum designs were proposed for each target. Additionally, Pareto front analysis and TOPSIS evaluation provided a comprehensive multi-objective assessment. In RSM-based anticipation models, the R 2 values were 0.971 for the Nusselt number and 0.977 for pressure drop. The remarkable R 2 values indicated that the models for both responses had a high level of accuracy. Based on the results, heat sink design 3 (HSD3) with geometric parameters of θ = 97.785° and S = 70 μm was recommended for enhancing the overall performance of the MHS. By utilizing just two triangle wave fins configured according to th

关键词： TOPSIS and Pareto fronts Optimization Response surface methodology Microchannel heat sink design Triangle wave fins Genetic Algorithm

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Hopf algebra of multi-decorated rooted forests, free matching Rota-Baxter algebras and Gröbner-Shirshov bases

arXiv

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arXiv 2020年

作者： Gao, Xing Guo, Li Zhang, Yi School of Mathematics and Statistics Key Laboratory of Applied Mathematics and Complex Systems Lanzhou University Gansu Lanzhou730000 China Department of Mathematics and Computer Science Rutgers University NewarkNJ07102 United States School of Mathematics and Statistics Nanjing University of Information Science & Technology Jiangsu Nanjing210044 China

Recent advances in stochastic PDEs, Hopf algebras of typed trees and integral equations have inspired the study of algebraic structures with replicating operations. To understand their algebraic and combinatorial nature, we first use rooted forests with multiple decoration sets to construct free Hopf algebras with multiple Hochschild 1-cocycle conditions. Applying the universal property of the underlying operated algebras and the method of Gröbner-Shirshov bases, we then construct free objects in the category of matching Rota-Baxter algebras which is a generalization of Rota-Baxter algebras to allow multiple Rota-Baxter operators. Finally the free matching Rota-Baxter algebras are equipped with a cocycle Hopf algebra *** Codes 16T30, 17B38, 05E16, 16Z10, 05C05, 16W99, 16S10, 05A05 Copyright © 2020, The Authors. All rights reserved.

关键词： Algebra

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A reconfigurable quantum local area network over deployed fiber

arXiv

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arXiv 2021年

作者： Alshowkan, Muneer Williams, Brian P. Evans, Philip G. Rao, Nageswara S.V. Simmerman, Emma M. Lu, Hsuan-Hao Lingaraju, Navin B. Weiner, Andrew M. Marvinney, Claire E. Pai, Yun-Yi Lawrie, Benjamin J. Peters, Nicholas A. Lukens, Joseph M. Quantum Information Science Group Oak Ridge National Laboratory Oak RidgeTN37831 United States Autonomous and Complex Systems Group Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Applied Physics Stanford University StanfordCA94305 United States School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Materials Science and Technology Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

Practical quantum networking architectures are crucial for scaling the connection of quantum resources. Yet quantum network testbeds have thus far underutilized the full capabilities of modern lightwave communications, such as flexible-grid bandwidth allocation. In this work, we implement flex-grid entanglement distribution in a deployed network for the first time, connecting nodes in three distinct campus buildings time-synchronized via the Global Positioning System (GPS). We quantify the quality of the distributed polarization entanglement via log-negativity, which offers a generic metric of link performance in entangled bits per second. After demonstrating successful entanglement distribution for two allocations of our eight dynamically reconfigurable channels, we demonstrate remote state preparation—the first realization on deployed fiber—showcasing one possible quantum protocol enabled by the distributed entanglement network. Our results realize an advanced paradigm for managing entanglement resources in quantum networks of ever-increasing complexity and service demands. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum entanglement

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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： Mohammadi, Mehrnoush Tajik, Elham Martinez-Maldonado, Roberto Sadiq, Shazia Tomaszewski, Wojtek Khosravi, Hassan School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia Department of Educational Psychology and Learning Systems Florida State University Tallahassee United States 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. © 2025

关键词： Educational robots

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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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