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Rim Sghiri,1,&ast; Hana Ben Hassine,2,&ast; Mariam Ghozzi,2 Nejla El Amri,3 Khadija Baccouche,3 Rihab Sarraj,3 Zahid Shakoor,1 Adel Almogren,1 Elyes Bouajina3 1Immunology Unit, Department of Pathology, College of Medicine, King Saud University, Riyadh, Saudi Arabia; 2Immunology Laboratory, Farhat Hached Hospital, Sousse, Tunisia; 3Department of Rheumatology, Farhat Hached Hospital, Sousse, Tunisia&ast;These authors contributed equally to this workCorrespondence: Rim Sghiri, Immunology Unit, Department of Pathology, College of Medicine, King Saud University, Riyadh, Saudi Arabia, Email rimsghiri2023@gmail.comPurpose: FRAX® is a tool used for evaluation of risk of fracture in RA and non-RA patients and to identify those eligible for intervention. One of the limitations of FRAX in RA settings is that it does not consider factors known to contribute to osteoporosis such as autoantibodies. This study analysed the association of anti-mutated citrullinated vimentin antibody (anti-MCV), anti-cyclic citrullinated peptide antibody (anti-CCP), IgM rheumatoid factor (RF), IgA RF with 10-year risk of major osteoporosis and hip fracture.Methods: FRAX® tool was used to estimate 10-year risk of major osteoporosis fracture and hip fracture in 189 RA patients over 40 years of age. Anti-MCV, anti-CCP, IgM RF and IgA RF were tested using enzyme immunoassay and analysed at different levels. Results were adjusted for various confounders including disease activity.Results: Fifty-one (26.9%) RA patients had high (≥ 20%) 10-year risk of major osteoporosis fracture and 67 (35.4%) had high (> 3%) 10-year risk of hip fracture. Among all the tested autoantibodies, only IgM RF at elevated levels was associated with high 10-year risk of major osteoporosis fracture (adjusted OR = 4.1, 95% CI = 1.5– 11.3, p = 0.006) and of hip fracture (adjusted OR = 17.4, 95% CI = 3.7– 81.3, p < 0.0001). There was no agreement between FRAX and femoral neck (FN) BMD. None of the autoantibodies tested were associated with FN osteopenia or osteoporosis including IgM RF at high levels.Conclusion: Our study highlights the importance of quantitative measurement of autoantibodies in assessment of risk for fractures among RA patients. Our preliminary findings need to be assessed in prospective studies to determine the actual predictive value of high IgM RF levels among patients with RA.Keywords: osteoporosis, rheumatoid arthritis, 10-years risk of major osteoporosis fracture, 10-years risk of hip fracture, FRAX, anti-mutated citrullinated vimentin antibody, anti-cyclic citrullinated peptide antibody, rheumatoid factor, rheumatoid factor isotypes

Mobile learning has ushered in a transformative era in education, compelling educational institutions to reimagine their pedagogical approaches. This shift is particularly evident in teacher training programs, where mobile learning is increasingly integrated into traditional education. The success of these integrated systems hinges on educators' willingness to adapt to these evolving paradigms. Nevertheless, a notable gap exists in the research landscape concerning the challenges of adopting mobile learning in teacher training and its consequential impact on teachers' professional capabilities. This study aims to bridge this gap by identifying the factors influencing teachers' satisfaction and performance within mobile learning training courses and establishing the intricate relationships between these variables. This study was conducted in a quantitative research framework and collected data from 563 schoolteachers through an online survey. These respondents were actively engaged in mobile-based training courses at the Provincial Institute for Teacher Education (PITE) Sindh during their mandatory training programs in the academic year 2022-23. Structural equation modeling was employed to analyze the proposed hypotheses rigorously. The study's findings unveil a robust and significant nexus between several critical factors and educators' experiences when utilizing mobile learning for training. Specifically, content quality, information quality, system quality, prior experiences, and mobile self-efficacy contributed strongly to task-technology fit, ultimately enhancing teachers' engagement, and yielding improved outcomes. Moreover, the study elucidates a clear correlation between factors encompassing understanding, Instructors' prompt feedback, teachers' expectations, and instructor quality within the context of training course design. These factors collectively positively influence teachers' satisfaction and performance, enhancing content knowledge, pedagogical skills, and professional dispositions. This holistic approach to mobile learning positively influences teachers' satisfaction and, ultimately, enhances teachers' overall performance. This study provides valuable insights to guide educators, institutions, and policymakers in effectively embracing and implementing mobile learning to benefit teachers and, ultimately, the broader field of education.

Abstract Cervical cancer is one of the biggest challenges in global health, thus it forms a critical need for early detection technologies that could improve patient prognosis and inform treatment decisions. This development in the form of an early detection mechanism increases the chances of successful treatment and survival, as early diagnosis promptly offers interventions that can dramatically reduce the rate of deaths attributed to this disease. Here, a customized Convolutional Neural Network (CNN) model is proposed for cervical cancerous cell detection. It includes three convolutional layers with increasing filter sizes and max-pooling layers, followed by dropout and dense layers for improved feature extraction and robust learning. By using ResNet models as inspiration, the model further innovates by incorporating skip connections into the CNN design. By enabling direct feature transmission from earlier to later layers, skip links enhance gradient flow and help preserve important spatial information. By boosting feature propagation, this integration increases the model’s ability to recognize minute patterns in cervical cell images, hence increasing classification accuracy. In our methodology, the SIPaKMeD dataset has been employed which contains 4049 cervical cell images that are arranged into five different categories. To address class imbalance, Generative Adversarial Networks (GANs) have been applied for data augmentation; that is, synthetic images have been created, that improve the diversity of the dataset and further enhance the robustness of the same. The present model is astonishingly accurate in classifying five cervical cell types: koilocytes, superficial-intermediate, parabasal, dyskeratotic, and metaplastic, thus significantly enhancing early detection and diagnosis of cervical cancer. The model gives an excellent performance because it has a validation accuracy of 99.11% and a training accuracy of 99.82%. It is a reliable model in the diagnosis of cervical cancerous cells because it ensures advancement in the computer-assisted cervical cancer detection system.

In modern healthcare monitoring, wearable sensors play a crucial role in collecting patient data, especially for individuals with disabilities. However, analyzing this data presents significant challenges, such as high energy consumption, latency, and task allocation inefficiencies. Healthcare monitoring systems face computational demands due to the processing of data from wearable sensors, which are constrained by limited resources and often affected by connectivity issues for data offloading. To address these challenges, this paper proposes a Cooperative Dew-Computing for Healthcare (CCH) framework, which integrates wearable sensors and dew devices within a cloud-fog-dew infrastructure. The CCH framework ensures efficient task distribution and computational offloading by selecting appropriate anchor devices using probabilistic and fuzzy logic techniques, taking into account energy efficiency and computational capabilities. Cooperative communication among anchor devices enables efficient task management, allowing tasks to be offloaded or distributed among multiple anchor devices. The proposed framework enhances healthcare monitoring systems by improving computational efficiency, optimizing resource utilization, and mitigating connectivity issues. This is particularly beneficial for disabled patients who rely on continuous monitoring and efficient data processing. Our evaluations demonstrate that the CCH framework outperforms existing techniques in terms of energy efficiency, latency, and task delay reduction, underscoring its effectiveness in providing timely and efficient data processing for healthcare monitoring systems.

The purpose of this study was to look at factors that might influence whether or not academics will use e-learning during the Corona Virus Disease 2019 (COVID-19) pandemic. The goal of this paper is to look into the effect of perceived interaction, educational materials, playfulness, perceived enjoyment, self-efficacy, perceived usefulness, and perceived ease of use on students' attitudes toward and intentions to use e-learning in Saudi Arabia higher education during the COVID-19 pandemic, as well as the indirect impact of student satisfaction with their actual use of e-learning. The research model was evaluated using structural equation modeling (SEM) and route analysis, which were based on e-learning user data collected through a survey. Student happiness and desire to use e-learning both had a beneficial impact on actual use, according to the statistics. During the COVID-19 epidemic, perceived interaction and self-efficacy were shown to be the most important factors influencing perceived usefulness and ease of use, which affected students' intentions and satisfaction with e-learning. The study's participants were e-learning users from a government university in Saudi Arabia. Previous studies on e-learning in developing nations have seldom taken a holistic approach. This paper also aims to include a literature review of recent published studies in the field of e-learning usage during the COVID-19 outbreak.

The proliferation of fake news on social media platforms necessitates the development of reliable datasets for effective fake news detection and veracity analysis. In this article, we introduce a veracity dataset of Arabic tweets called "VERA-ARAB", a pioneering large-scale dataset designed to enhance fake news detection in Arabic tweets. VERA-ARAB is a balanced, multi-domain, and multi-dialectal dataset, containing both fake and true news, meticulously verified by fact-checking experts from Misbar. Comprising approximately 20,000 tweets from 13,000 distinct users and covering 884 different claims, the dataset includes detailed information such as news text, user details, and spatiotemporal data, spanning diverse domains like sports and politics. We leveraged the X API to retrieve and structure the dataset, providing a comprehensive data dictionary to describe the raw data and conducting a thorough statistical descriptive analysis. This analysis reveals insightful patterns and distributions, visualized according to data type and nature. We also evaluated the dataset using multiple machine learning classification models, exploring various social and textual features. Our findings indicate promising results, particularly with textual features, underscoring the dataset's potential for enhancing fake news detection. Furthermore, we outline future work aimed at expanding VERA-ARAB to establish it as a benchmark for Arabic tweets in fake news detection. We also discuss other potential applications that could leverage the VERA-ARAB dataset, emphasizing its value and versatility for advancing the field of fake news detection in Arabic social media. Potential applications include user veracity assessment, topic modeling, and named entity recognition, demonstrating the dataset's wide-ranging utility for broader research in information quality management on social media. [ABSTRACT FROM AUTHOR]

Recent advances in auditory attention detection from multichannel electroencephalography (EEG) signals encounter the challenges of the scarcity of available online EEG data and the detection of auditory attention with low latency. To this end, we propose a complete deep auditory generative adversarial network auxiliary, named auditory-GAN, designed to handle these challenges while generating EEG data and executing auditory spatial detection. The proposed auditory-GAN system consists of a spectro-spatial feature extraction (SSF) module and an auditory generative adversarial network auxiliary (AD-GAN) classifier. The SSF module extracts the spatial feature maps by learning the topographic specificity of alpha power from EEG signals. The designed AD-GAN network addresses the need for extensive training data by synthesizing augmented versions of original EEG data. We validated the proposed method on the widely used KUL dataset. The model assesses the quality of generated EEG images and the accuracy of auditory spatial attention detection. Results show that the proposed auditory-GAN can produce convincing EEG data and achieves a significant i.e., 98.5% spatial attention detection accuracy for a 10-s decision window of 64-channel EEG data. Comparative analysis reveals that the proposed neural approach outperforms existing state-of-the-art models across EEG data ranging from 64 to 32 channels. The Auditory-GAN model is available at https://github.com/tasleem-hello/Auditory-GAN-/tree/Auditory-GAN. [ABSTRACT FROM AUTHOR]

Medical image security is paramount in the digital era but remains a significant challenge. This paper introduces an innovative zero-watermarking methodology tailored for medical imaging, ensuring robust protection without compromising image quality. We utilize Sped-up Robust features for high-precision feature extraction and singular value decomposition (SVD) to embed watermarks into the frequency domain, preserving the original image's integrity. Our methodology uniquely encodes watermarks in a non-intrusive manner, leveraging the robustness of the extracted features and the resilience of the SVD approach. The embedded watermark is imperceptible, maintaining the diagnostic value of medical images. Extensive experiments under various attacks, including Gaussian noise, JPEG compression, and geometric distortions, demonstrate the methodology's superior performance. The results reveal exceptional robustness, with high Normalized Correlation (NC) and Peak Signal-to-noise ratio (PSNR) values, outperforming existing techniques. Specifically, under Gaussian noise and rotation attacks, the watermark retrieved from the encrypted domain maintained an NC value close to 1.00, signifying near-perfect resilience. Even under severe attacks such as 30% cropping, the methodology exhibited a significantly higher NC compared to current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The usage of Internet of Underwater Things (IoUT) Networks allows for the detection of a variety of aquatic factors like temperature, pressure, pollution, etc. They are also used to forecast the ocean’s weather to collect information about natural disasters. However, they are easily compromised by attackers due to deployment in unattended environments. To overcome these issues, security is required in IoUT networks to avoid unauthorized access and ensure network credibility. This work proposes an authentication and a malicious node detection mechanism to restrict the unauthorized external nodes from accessing the network and the internal nodes from acting maliciously, respectively. Moreover, blockchain stores the hashes of sensor nodes’ credentials during the registration process to make the system secure and traceable. Meanwhile, for data aggregation and malicious nodes’ detection, a weighted trust evaluation mechanism is introduced. Moreover, an additive increase multiplicative decrease algorithm puts malicious nodes in an intensive observation queue to verify the data of malicious nodes before aggregating. Moreover, weights are assigned to sensor nodes based on their behaviour. If the weight of a sensor node becomes zero, it is revoked by the blockchain. Besides, the proposed malicious node detection mechanism’s enhanced efficiency is proved via extensive simulations.