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Thermodynamic properties of small flares in the quiet Sun observed by Hα and EUV: plasma motion of the chromosphere and time evolution of temperature / emission measure

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arXiv 2023年

作者： Kotani, Yuji Ishii, Takako T. Yamasaki, Daiki Otsuji, Kenichi Ichimoto, Kiyoshi Asai, Ayumi Shibata, Kazunari Astronomical Observatory Kyoto University Sakyo Kyoto606-8502 Japan Space Environment Laboratory Applied Electromagnetic Research Institute National Institute of Information and Communications Technology Koganei Tokyo184-8795 Japan Department of Environmental Systems Science Faculty of Science and Engineering Doshisha University 1-3 Tatara Miyakodani Kyotanabe City Kyoto610-0394 Japan Institute of Space and Astronautical Science Japan Aerospace Exploration Agency 3-1-1 Yoshinodai Kanagawa Chuo-ku Sagamihara252-5210 Japan

Small flares frequently occur in the quiet Sun. Previous studies have noted that they share many common characteristics with typical solar flares in active regions. However, their similarities and differences are not fully understood, especially their thermal properties. In this study, we performed imaging spectroscopic observations in the Hα line taken with the Solar Dynamics Doppler Imager on the Solar Magnetic Activity Research Telescope (SMART/SDDI) at the Hida Observatory and imaging observations with the Atmospheric Imaging Assembly onboard Solar Dynamics Observatory (SDO/AIA). We analysed 25 cases of small flares in the quiet Sun over the thermal energy range of 1024 − 1027 erg, paying particular attention to their thermal properties. Our main results are as follows: (1) We observe a redshift together with line centre brightening in the Hα line associated with more than half of the small flares. (2) We employ differential emission measure analysis using AIA multi-temperature (channel) observations to obtain the emission measure and temperature of the small flares. The results are consistent with the Shibata & Yokoyama (1999, 2002) scaling law. From the scaling law, we estimated the coronal magnetic field strength of small flares to be 5 –15 G. (3) The temporal evolution of the temperature and the density shows that the temperature peaks precede the density peaks in more than half of the events. These results suggest that chromospheric evaporations/condensations play an essential role in the thermal properties of some of the small flares in the quiet Sun, as does for large flares. Copyright © 2023, The Authors. All rights reserved.

关键词： Observatories

Monitoring System-Based Flying IoT in Public Health and Sports Using Ant-Enabled Energy-Aware Routing

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Journal of Healthcare Engineering 2021年 2021卷 1686946页

作者： Khan, Inam Ullah Hassan, Muhammad Abul Alshehri, Mohammad Dahman Ikram, Mohammed Abdulaziz Alyamani, Hasan J. Alturki, Ryan Hoang, Vinh Truong Department of Electronic Engineering School of Engineering and Applied Sciences Isra University Islamabad Pakistan Department of Computing and Technology Abasyn University Peshawar25000 Pakistan Department of Computer Science College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Computer Science Department University College in Al-Jamoum Umm Al-Qura University Makkah Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Jeddah Saudi Arabia Department of Information Science College of Computer and Information Systems Umm Al-Qura University Makkah Saudi Arabia Department of Information Technology Specialization Fpt University Hoa Lac High Tech Park Hanoi Viet Nam

In recent decades, the Internet of flying networks has made significant progress. Several aerial vehicles communicate with one another to form flying ad hoc networks. Unmanned aerial vehicles perform a wide range of tasks that make life easier for humans. However, due to the high frequency of mobile flying vehicles, network problems such as packet loss, latency, and perhaps disrupted channel links arise, affecting data delivery. The use of UAV-enabled IoT in sports has changed the dynamics of tracking and working on player safety. WBAN can be merged with aerial vehicles to collect data regarding health and transfer it to a base station. Furthermore, the unbalanced energy usage of flying things will result in earlier mission failure and a rapid decline in network lifespan. This study describes the use of each UAV's residual energy level to ensure a high level of safety using an ant-based routing technique called AntHocNet. In health care, the use of IoT-Assisted aerial vehicles would increase operational performance, surveillance, and automation optimization to provide a smart application of flying IoT. Apart from that, aerial vehicles can be used in remote communication for treatment, medical equipment distribution, and telementoring. While comparing routing algorithms, simulation findings indicate that the proposed ant-based routing protocol is optimal. © 2021 Inam Ullah Khan et al.

关键词： Unmanned aerial vehicles (UAV)

Towards improving Alzheimer's intervention: a machine learning approach for biomarker detection through combining MEG and MRI pipelines

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arXiv 2024年

作者： Ahmad, Alwani Liyana Sanchez-Bornot, Jose Sotero, Roberto C. Coyle, Damien Idris, Zamzuri Faye, Ibrahima Department of Fundamental and Applied Sciences Faculty of Science and Information Technology Universiti Teknologi PETRONAS Perak Malaysia Department of Neurosciences Hospital Universiti Sains Malaysia Kelantan Malaysia Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Kelantan Malaysia Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Magee campus Derry~Londonderry BT48 7JL United Kingdom Department of Radiology Hotchkiss Brain Institute University of Calgary CalgaryAB Canada The Bath Institute for the Augmented Human University of Bath BathBA2 7AY United Kingdom Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Kelantan Malaysia Universiti Teknologi PETRONAS Perak Malaysia

Background: Magneto/eletro-encephalography (MEG/EEG) are non-invasive neuroimaging techniques that offer excellent temporal and spatial resolution that can be used to study brain functioning. It has been proven effective in research on dementia and Alzheimer's disease (AD), where MEG/EEG analyses can identify changes in brain activity at various stages of AD progression, including preclinical and prodromal. Research has shown differences in the power spectrum properties among healthy controls (HC) and individuals with mild cognitive impairment (MCI) or AD. It has also been conjectured that MEG/EEG analyses have the capability to identify pathological changes before first clinical symptoms and may provide biomarkers for interventions designed to reverse or slow the progression of AD. Objective: To conduct a comprehensive analysis by evaluating the performance of various classification techniques using features extracted by following different MEG methodologies. This study will focus on selecting MEG-based biomarkers/features that enhance performance in distinguishing between healthy control (HC) and mild cognitive impairment (MCI) participants from the BioFIND study. Furthermore, we will compare this with the effectiveness of using MRI-based anatomical features extracted from same participants, independently or in combination with the MEG features;thus, extending from previous study where similar analyses were performed using ADNI and OASIS-3 datasets. Methods: MEG and MRI data are obtained from the BioFIND dataset, comprising 324 participants with 158 individuals diagnosed as MCI and 166 HC. Sensor and source-based MEG analyses are performed using custom MATLAB code based on SPM12 and OSL toolboxes, for co-registration, preprocessing including filtering, and automatic artifact rejection. Source-based analyses were applied through different source localization methods as implemented in SPM12 and DAiSS toolboxes. We conducted analogous machine learning analyses on sen

关键词： Neurodegenerative diseases

An evaluation of lightweight deep learning techniques in medical imaging for high precision COVID-19 diagnostics

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Healthcare Analytics 2022年 2卷 100096-100096页

作者： Ukwandu, Ogechukwu Hindy, Hanan Ukwandu, Elochukwu Department of Computer Communication and Information Systems School of Engineering and Built Environment Glasgow Caledonian University United Kingdom Computer Science Department Faculty of Computer and Information Sciences Ain Shams University Cairo 11566 Egypt Department of Applied Computing and Engineering Cardiff School of Technologies Cardiff Metropolitan University Cardiff CF5 2YB United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom

Timely and rapid diagnoses are core to informing on optimum interventions that curb the spread of COVID-19. The use of medical images such as chest X-rays and CTs has been advocated to supplement the Reverse-Transcription Polymerase Chain Reaction (RT-PCR) test, which in turn has stimulated the application of deep learning techniques in the development of automated systems for the detection of infections. Decision support systems relax the challenges inherent to the physical examination of images, which is both time consuming and requires interpretation by highly trained clinicians. A review of relevant reported studies to date shows that most deep learning algorithms utilised approaches are not amenable to implementation on resource-constrained devices. Given the rate of infections is increasing, rapid, trusted diagnoses are a central tool in the management of the spread, mandating a need for a low-cost and mobile point-of-care detection systems, especially for middle- and low-income nations. The paper presents the development and evaluation of the performance of lightweight deep learning technique for the detection of COVID-19 using the MobileNetV2 model. Results demonstrate that the performance of the lightweight deep learning model is competitive with respect to heavyweight models but delivers a significant increase in the efficiency of deployment, notably in the lowering of the cost and memory requirements of computing resources. © 2022 The Author(s)

关键词： COVID-19 detection Diagnostic analytics Lightweight deep learning techniques Machine learning Point-of-care Resource-constrained devices

DEEPCO-RIS: Joint BD-RIS and hybrid NOMA/OMA optimization for energy-efficient vehicular networks

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Sustainable computing: Informatics and systems 2025年 47卷

作者： Nada Alzaben Nadhem Nemri Wahida Mansouri Othman Alrusaini Mukhtar Ghaleb Jihen Majdoubi Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science and Information Technology Faculty of Sciences and Arts Turaif Northern Border University Arar 91431 Saudi Arabia Department of Engineering and Applied Sciences Applied College Umm Al-Qura University Makkah Saudi Arabia College of Computing and Information Technology University of Bisha Bisha 61922 Saudi Arabia Engineering and Technology Unit Applied College Majmaah University Al Majmaah 11952 Saudi Arabia

Next-generation vehicular networks require wireless infrastructures that deliver ultra-reliable, energy-efficient, and low-latency communication under highly dynamic conditions. Traditional RIS-aided and hybrid NOMA/OMA designs face critical limitations, including rigid phase control, high successive interference cancellation (SIC) complexity, and limited adaptability to rapid vehicular mobility. To address these challenges, this paper proposes DEEPCO-RIS (Dinkelbach-Enhanced Energy-Efficient Optimization with Beyond-Diagonal RIS), a unified optimization framework that integrates BD-RIS phase configuration, hybrid NOMA/OMA access mode selection, user scheduling, and power allocation. These components are jointly optimized under realistic constraints, including SIC feasibility, power budgets, RIS energy costs, and QoS guarantees. The energy efficiency maximization problem is formulated as a mixed-integer non-convex program and solved using a modular approach combining Dinkelbach’s method, block coordinate descent, successive convex approximation, and manifold-based optimization for BD-RIS tuning. Extensive simulations demonstrate that DEEPCO-RIS achieves up to 22 Mbits/Joule energy efficiency, maintains outage probabilities below 6% even under stringent QoS targets, and exhibits strong robustness against SIC imperfections and network load variations. These results establish DEEPCO-RIS as a scalable and sustainable solution for next-generation vehicular communication networks.

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Two classes of narrow-sense BCH codes and their duals

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arXiv 2022年

作者： Wang, Xiaoqiang Wang, Jiaojiao Li, Chengju Wu, Yansheng Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Hubei University Wuhan430062 China Data Science and Information Technology Research Center Tsinghua-Berkeley Shenzhen Institute Tsinghua Shenzhen Inter national Graduate School Shenzhen China The Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China

BCH codes and their dual codes are two special subclasses of cyclic codes and are the best linear codes in many cases. A lot of progress on the study of BCH cyclic codes has been made, but little is known about the minimum distances of the duals of BCH codes. Recently, a new concept called dually-BCH code was introduced to investigate the duals of BCH codes and the lower bounds on their minimum distances in [12]. For a prime power q and an integer (formula presented), or (formula presented). In this paper, some sufficient and necessary conditions in terms of the designed distance will be given to ensure that the narrow-sense BCH codes of length n are dually-BCH codes, which extended the results in [12]. Lower bounds on the minimum distances of their dual codes are developed for (formula presented). As byproducts, we present the largest coset leader δ1 modulo n being of two types, which proves a conjecture in [28] and partially solves an open problem in [21]. We also investigate the parameters of the narrow-sense BCH codes of length n with design distance δ1. The BCH codes presented in this paper have good parameters in general. © 2022, CC BY.

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DieT Transformer model with PCA-ADE integration for advanced multi-class brain tumor classification

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Intelligence-Based Medicine 2025年 11卷 100192-100192页

作者： Amin, Mohammad Nahar, Khalid M.O. Gharaibeh, Hasan Nasayreh, Ahmad Alsalmanc, Neda'a Alomar, Alaa Malkawi, Majd Alqasem, Noor Smerat, Aseel Zitar, Raed Abu Nusier, Shawd Ezugwu, Absalom E. Abualigah, Laith Department of Information Technology and Computer Sciences Yarmouk University Irbid 211633 Jordan Faculty of Computer Studies Arab Open University P.O.Box 84901 Riyadh 11681 Saudi Arabia Department of Computer Science Faculty of Information Technology Jordan University of Science and Technology Irbid 22110 Jordan Department of Medicine Faculty of Medicine Jordan University of Science and Technology Irbid 22110 Jordan Department of Biomedical Systems and Informatics Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid 21163 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates Department of Health and Clinical Outcomes Research Saint Louis University St. Louis MO United States Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Early and accurate diagnosis of brain tumors is crucial to improving patient outcomes and optimizing treatment strategies. Long-term brain injury results from aberrant proliferation of either malignant or nonmalignant tissues in the brain. MRIs, or magnetic resonance imaging, are one of the most used approaches for detecting brain tumors. Professionals physically evaluate people after they have had MRI filtering, the process of enhancing MRI scans for radiologist interpretation, to establish if they have a brain tumor. Because different specialists use different frames to make judgments on the same MRI image, their analyses may yield contradictory results. Furthermore, simply detecting a tumor is insufficient. Inconsistent diagnoses can lead to delays in treatment, impacting survival rates and quality of care. It is also crucial to diagnose the patient's tumor so that treatment can begin as soon as possible. In this research, we investigate the multi-class classification of brain tumors utilizing a cutting-edge methodology that includes feature extraction from pictures using the DieT Transformer model, dimensionality reduction with PCA, and feature selection using the ADE algorithm. The proposed model, known in the publication as ADE_DieT, obtained an accuracy of 96.09 %. In addition, this article analyzes the performance of various pre-trained models, including MobileNetV3, NasNet, ResNet50, VGG16, VGG19, and DeiT. The proposed approach shortens the time required for manual diagnosis by clinicians by assisting in the rapid and accurate identification of brain tumors using MRI data. In oncology, this is important since it allows for early treatment. Integrating ADE_DieT into clinical workflows can support radiologists by reducing diagnosis time and enhancing diagnostic consistency. © 2024 The Authors

关键词： Brain tumor Medical image analysis MRI Vision Transformer

Machine Learning Approach for Identifying Anatomical Biomarkers of Early Mild Cognitive Impairment

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arXiv 2024年

作者： Ahmad, Alwani Liyana Sanchez-Bornot, Jose Sotero, Roberto C. Coyle, Damien Idris, Zamzuri Faye, Ibrahima Department of Fundamental and Applied Sciences Faculty of Science and Information Technology Universiti Teknologi PETRONAS Perak Malaysia Department of Neurosciences Hospital Universiti Sains Malaysia Kelantan Malaysia Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Kelantan Malaysia Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Magee campus Derry LondonderryBT48 7JL United Kingdom Department of Radiology Hotchkiss Brain Institute University of Calgary CalgaryAB Canada The Bath Institute for the Augmented Human University of Bath BathBA2 7AY United Kingdom Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Kelantan Malaysia Universiti Teknologi PETRONAS Perak Malaysia Universiti Teknologi PETRONAS Perak32610 Malaysia

Background. Alzheimer's Disease (AD) represents a significant challenge in neurodegenerative disorders, necessitating early detection for effective intervention. Among neuroimaging methods, magnetic resonance imaging (MRI) is widely used because it is easy to apply in clinical practice and cost-effective, making it crucial for studying AD. Objective. This study aims to perform a comprehensive analysis of machine learning (ML) methods used in MRI-based biomarker selection and classification analysis. The goal is to study AD-related early cognitive decline by discriminating between healthy control (HC) participants who stayed stable and those unstable (uHC) who developed mild cognitive impairment (MCI) within five years. Methods. We utilized 3-Tesla (3T) MRI data from the Alzheimer's Disease Neuroinformatic Initiative (ADNI) and the Open Access Series of Imaging Studies 3 (OASIS-3), focusing on HC and uHC. Freesurfer’s recon-all, among other tools, was used to extract MRI-based anatomical biomarkers corresponding to semi-automatic segmented subcortical and cortical brain regions. We applied various ML techniques to select features and classify the data. These included methods from preliminary analysis performed in the MATLAB Classification Learner (MCL) app and more sophisticated methods like nested cross-validation and Bayesian optimization implemented in a customized pipeline to enhance classification performance for balanced and imbalanced datasets. Our pipeline was applied to both original imbalanced and randomly balanced datasets within a Monte Carlo analysis. Moreover, we implemented data harmonization approaches based on polynomial regression that enhanced the performance of ML and statistical methods. Complementary performance metrics, such as Accuracy (Acc), area under receiver operating characteristic curve (AROC), F1 score, and Matthew’s correlation coefficient (MCC), were used to evaluate the assessed methodologies. Results. In feature selection analyses,

关键词： Neurodegenerative diseases

Factors affecting team social networking and performance: The moderation effect of team size and tenure

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Journal of Open Innovation: Technology, Market, and Complexity 2023年第2期9卷

作者： Kurdi, Barween Al Alshurideh, Muhammad Turki Akour, Iman Alzoubi, Haitham M. Obeidat, Zaid Mohammad Hamadneh, Samer Joghee, Shanmugan Department of Marketing Faculty of Business The Hashemite University Zarqa Jordan Department of Marketing School of Business The University of Jordan Amman 11942 Jordan Department of Management College of Business Administration University of Sharjah Sharjah 27272 United Arab Emirates Information Systems Department College of Computing & Informatics University of Sharjah Sharjah United Arab Emirates School of Business Skyline University College Sharjah United Arab Emirates Applied Science Research Center Applied Science Private University Amman 11937 Jordan Department of Marketing School of Business The University of Jordan Amman Jordan School of Business Skyline University College Sharjah United Arab Emirates

As it derives from information technology, the internet revolution, and their consequences for business operations, social networking has received wide research interest. Therefore, this study aims to analyze the factors influencing team social networking and performance with the moderating effect of team size and tenure in SMEs in Jordan. Using a survey questionnaire, the study collected the data from this sector and analyzed it with SamartPLS to test the proposed research model. The results showed a mostly significant positive effect from seven factors (team communication, team conflict, team power, team regulation, team collaboration, team engagement, and team motivation). The study also revealed a moderating effect of both team size and tenure in the relationship between team social networking and SME performance in Jordan. The contributions provided through this work expand understanding of the factors’ importance while addressing the issue of team social networking and performance. In practice, this enables team leaders to focus on team size and tenure to develop their business performance. © 2023

关键词： Moderation Performance Social networking Team size Team tenure