Impact of Knowledge and Attitude on Saudis’ Physical Activity Practice and Inactivity Barriers: A Questionnaire-based Study

BACKGROUND: Community participation in physical activity is considered a major public health preference of WHO. Saudi Arabia in the last decades faced many tremendous economic changes leading to adoption of western dietary habits associated with sedentary lifestyle. AIM: We aimed to study the relationship between both physical activity knowledge and attitude of community to the practice of individuals. METHODS: We used a questionnaire consists of a mixture of closed-ended questions. Participants were recruited through direct meetings in local markets, schools, and workplaces. Seven hundred and sixty six individuals agreed to participate. RESULTS: Overall correct answers to questions about importance of physical activity were 76.58%. The predominance of participants’ attitude was to establish public places for physical activity in each neighborhood (92.1%). Participants acknowledged that they exercise to improve their health (47.5%). Participants mainly perform light exercises (47.2%) on basis of 1–3 times weekly (48.9%). About 90.8% of participants admitted that they like to increase duration of their physical activity. CONCLUSIONS: Overall physical activity practice of participants’ needs encourage overcoming obstacles that prevent individuals from practicing especially lack of time

Dermatological Lesions of Cholesterol Embolization Syndrome and Kaposi Sarcoma Mimic Primary Systemic Vasculitis: Case Report Study

Primary systemic vasculitis can present with a wide spectrum of manifestations ranging from systemic non-specific features such as fever, malaise, arthralgia, and myalgia to specific organ damage. We describe two cases of cholesterol embolization syndrome and Kaposi sarcoma mimicking primary systemic vasculitis, both of which were characterized by features such as livedo reticularis, blue toe syndrome, a brown, purpuric skin rash, and positive p-ANCA associated with Kaposi sarcoma. Establishing the right diagnosis was challenging, and thus we aim in this study to highlight the possible ways to distinguish them from primary systemic vasculitis. Keywords: Dermatological lesions, Cholesterol embolization syndrome, Kaposi sarcoma, vasculitis mimic

Automatic neonatal sleep stage classification:A comparative study

Sleep is an essential feature of living beings. For neonates, it is vital for their mental and physical development. Sleep stage cycling is an important parameter to assess neonatal brain and physical development. Therefore, it is crucial to administer newborn's sleep in the neonatal intensive care unit (NICU). Currently, Polysomnography (PSG) is used as a gold standard method for classifying neonatal sleep patterns, but it is expensive and requires a lot of human involvement. Over the last two decades, multiple researchers are working on automatic sleep stage classification algorithms using electroencephalography (EEG), electrocardiography (ECG), and video. In this study, we present a comprehensive review of existing algorithms for neonatal sleep, their limitations and future recommendations. Additionally, a brief comparison of the extracted features, classification algorithms and evaluation parameters is reported in the proposed study

Sufficiency and Efficiency of Field Training for Radiology Students During Internship Experience in Najran University, Saudi Arabia

Purpose: The study was design to evaluate the effectiveness and adequacy of the internship period employing quantitative study descriptive survey approach.   Theoretical framework: Internship is requirement of every student of radiology program of Radiological Sciences patch for the award of bachelor's degree at Najran University, Saudi Arabia. The competency level would demonstrate influence the sufficiency and efficiency of clinical training during internship period which represent six months after completing nine levels of radiology program.   Design\Methodology\Approach: The survey was distributed to the tow levels of the last year of radiological sciences which composed of 81 male and female students which gathered seventy-seven (77) participants. Data collected through a questionnaire and summarized as percentages, frequencies, means and standard deviations using SPSS version 20.0.   Findings: The study revealed un adequacy of the internship period and showed low efficiency due to its short duration.   Research, Practical, Social Implication:The research construct and variables are identified the effectiveness and adequacy of the internship period.this  study will be the modele of internship with a new qualitative change related to a period of time acceptable to students, similar to other universities.   Originality/Value: The originality and value in this study are the framework conceptance and questionnaire that prepared and proved for evaluating the effectiveness and adequacy of the internship period for student of radiology program.   Conclusion: In general internship period must be efficient and adequate to enhance sufficiency and efficiency experience by intern trainees

Energy efficiency considerations in software‐defined wireless body area networks

Wireless body area networks (WBAN) provide remote services for patient monitoring which allows healthcare practitioners to diagnose, monitor, and prescribe them without their physical presence. To address the shortcomings of WBAN, software-defined networking (SDN) is regarded as an effective approach in this prototype. However, integrating SDN into WBAN presents several challenges in terms of safe data exchange, architectural framework, and resource efficiency. Because energy expenses account for a considerable portion of network expenditures, energy efficiency has to turn out to be a crucial design criterion for modern networking methods. However, creating energy-efficient systems is difficult because they must balance energy efficiency with network performance. In this article, the energy efficiency features are discussed that can widely be used in the software-defined wireless body area network (SDWBAN). A comprehensive survey has been carried out for various modern energy efficiency models based on routing algorithms, optimization models, secure data delivery, and traffic management. A comparative assessment of all the models has also been carried out for various parameters. Furthermore, we explore important concerns and future work in SDWBAN energy efficiency

Sustainable Collaboration: Federated Learning for Environmentally Conscious Forest Fire Classification in Green Internet of Things (IoT)

Forests are an invaluable natural resource, playing a crucial role in the regulation of both local and global climate patterns. Additionally, they offer a plethora of benefits such as medicinal plants, food, and non-timber forest products. However, with the growing global population, the demand for forest resources has escalated, leading to a decline in their abundance. The reduction in forest density has detrimental impacts on global temperatures and raises the likelihood of forest fires. To address these challenges, this paper introduces a Federated Learning framework empowered by the Internet of Things (IoT). The proposed framework integrates with an Intelligent system, leveraging mounted cameras strategically positioned in highly vulnerable areas susceptible to forest fires. This integration enables the timely detection and monitoring of forest fire occurrences and plays its part in avoiding major catastrophes. The proposed framework incorporates the Federated Stochastic Gradient Descent (FedSGD) technique to aggregate the global model in the cloud. The dataset employed in this study comprises two classes: fire and non-fire images. This dataset is distributed among five nodes, allowing each node to independently train the model on their respective devices. Following the local training, the learned parameters are shared with the cloud for aggregation, ensuring a collective and comprehensive global model. The effectiveness of the proposed framework is assessed by comparing its performance metrics with the recent work. The proposed algorithm achieved an accuracy of 99.27 % and stands out by leveraging the concept of collaborative learning. This approach distributes the workload among nodes, relieving the server from excessive burden. Each node is empowered to obtain the best possible model for classification, even if it possesses limited data. This collaborative learning paradigm enhances the overall efficiency and effectiveness of the classification process, ensuring optimal results in scenarios where data availability may be constrained

Automatic neonatal sleep stage classification: A comparative study

Sleep is an essential feature of living beings. For neonates, it is vital for their mental and physical development. Sleep stage cycling is an important parameter to assess neonatal brain and physical development. Therefore, it is crucial to administer newborn's sleep in the neonatal intensive care unit (NICU). Currently, Polysomnography (PSG) is used as a gold standard method for classifying neonatal sleep patterns, but it is expensive and requires a lot of human involvement. Over the last two decades, multiple researchers are working on automatic sleep stage classification algorithms using electroencephalography (EEG), electrocardiography (ECG), and video. In this study, we present a comprehensive review of existing algorithms for neonatal sleep, their limitations and future recommendations. Additionally, a brief comparison of the extracted features, classification algorithms and evaluation parameters is reported in the proposed study

Head and Neck Surgery: A Differential Diagnosis in Otolaryngology

Introduction: In otolaryngology of the head and neck surgery; differential diagnosis is a practical and comprehensive guide that is organized uniquely by signs and symptoms instead of by diseases. Aim: This study will describe the keys to diagnostic evaluation and differential diagnosis of presenting symptoms for problems affecting each otolaryngology organ system.Methods: Each symptom opens with the patient’s presentation followed by an easily accessible list of potential diagnoses and supplementary data on the features of the different diseases to help correctly identify the problem. And identify features labeled by signs and symptoms, not by disease, and then enable quick clinical reference In-depth coverage of the diagnostic and treatment evaluation of all ENT disorders.

Perceived Risk of falls among Acute Care Patients

Purpose: In an effort to lower the number of falls that occur among hospitalized patients, several facilities have begun introducing various fall prevention programs. However, the efficacy of fall prevention programs is diminished if patients do not consider themselves to be at risk for falls and do not follow recommended procedures. The goal of this study was to characterize how patients in four different acute care specialist services felt about their risk of falling while in the hospital. Methods: One hundred patients admitted to the study hospital with a Morse Fall Scale score of 45 or higher were given the Patient Perception Questionnaire, a tool designed to assess a patient's perception of their own fall risk, fear of falling, and motivation to take part in fall prevention efforts. Scores on the Morse Fall Scale were gathered through a historical assessment of medical records. Descriptive statistics, Pearson's correlation coefficients, and independent sample t tests were used to examine the data. Results: The average age was 65, and around half (52%) were men and half (48%) were women. Based on their ratings on the Morse Fall Scale, all 100 participants were classified as being at high risk for falls. However, only 55.5% of the individuals agreed with this assessment. The likelihood that a patient would seek assistance and the degree to which they feared falling both declined as their faith in their mobility improved. Patients hospitalized after a fall exhibited considerably lower confidence scores and greater fear scores than patients who had not been injured in a fall. Conclusions: Patients who have a high fall risk assessment score may not believe they are at risk for falls and may not take any steps to reduce their risk. The prevalence of falls in hospitals might be mitigated by the creation of a fall risk assessment technique that takes into account both objective and subjective factors

TNN-IDS: Transformer neural network-based intrusion detection system for MQTT-enabled IoT Networks

The Internet of Things (IoT) is a global network that connects a large number of smart devices. MQTT is a de facto standard, lightweight, and reliable protocol for machine-to-machine communication, widely adopted in IoT networks. Various smart devices within these networks are employed to handle sensitive information. However, the scale and openness of IoT networks make them highly vulnerable to security breaches and attacks, such as eavesdropping, weak authentication, and malicious payloads. Hence, there is a need for advanced machine learning (ML) and deep learning (DL)-based intrusion detection systems (IDS). Existing ML-based IoT-IDSs face several limitations in effectively detecting malicious activities, mainly due to imbalanced training data. To address this, this study introduces a transformer neural network-based intrusion detection system (TNN-IDS) specifically designed for MQTT-enabled IoT networks. The proposed approach aims to enhance the detection of malicious activities within these networks. The TNN-IDS leverages the parallel processing capability of the Transformer Neural Network, which accelerates the learning process and results in improved detection of malicious attacks. To evaluate the performance of the proposed system, it was compared with various IDSs based on ML and DL approaches. The experimental results demonstrate that the proposed TNN-IDS outperforms other systems in terms of detecting malicious activity. The TNN-IDS achieved optimum accuracies reaching 99.9% in detecting malicious activities