This abstract focuses on the significance of wireless body area networks (WBANs) as a cutting-edge and self-governing technology, which has garnered substantial attention from researchers. The central challenge faced by WBANs revolves around upholding quality of service (QoS) within rapidly evolving sectors like healthcare. The intricate task of managing diverse traffic types with limited resources further compounds this challenge. Particularly in medical WBANs, the prioritization of vital data is crucial to ensure prompt delivery of critical information. Given the stringent requirements of these systems, any data loss or delays are untenable, necessitating the implementation of intelligent algorithms. These algorithms play a pivotal role in expediting diagnosis and treatment processes during medical emergencies. This study introduces an innovative protocol termed collaborative binary Naive Bayes decision tree (CBNBDT) designed to enhance packet classification and transmission prioritization. Through the utilization of this protocol, incoming packets are categorized based on their respective classes, enabling subsequent prioritization. Thorough simulations have demonstrated the superior performance of the proposed CBNBDT protocol compared to baseline approaches.
The research aimed to identify and build two specialized scales for cognitive load and mental stress and to identify the level of each of them among 110-meter steeplechase runners among youth, and to prepare a psychological counseling approach to reduce the level of cognitive load and mental stress among 110-meter steeplechase runners among youth, so that the two research hypotheses are that there are differences. There are statistically significant differences between the results of the pre- and post-tests of the experimental group in measuring cognitive load. There are statistically significant differences between the results of the pre- and post-tests of the experimental group in measuring mental stress. The experimental method w
... Show MoreIn this study, gold nanoparticles were synthesized in a single step biosynthetic method using aqueous leaves extract of thymus vulgaris L. It acts as a reducing and capping agent. The characterizations of nanoparticles were carried out using UV-Visible spectra, X-ray diffraction (XRD) and FTIR. The surface plasmon resonance of the as-prepared gold nanoparticles (GNPs) showed the surface plasmon resonance centered at 550[Formula: see text]nm. The XRD pattern showed that the strong four intense peaks indicated the crystalline nature and the face centered cubic structure of the gold nanoparticles. The average crystallite size of the AuNPs was 14.93[Formula: see text]nm. Field emission scanning electron microscope (FESEM) was used to s
... Show MoreThe present paper aims at evaluating the vailability quality and future horizons of potable water in the city of Shatra as a model. This is done in accordance with certain subjective and objective factors alongside the classification map of Shatra as a residential area. This system follows geographical studies specialized in urban construction. The problem of the present paper as well as the data approaching that problem have been chosen from the records of 2018. The researcher offered (919) questionnaire forms to be answered by a sample of dwellers in that area. Besides, the researcher also followed lab analysis of water samples collected from districts in the city of Shatra. GIS technology was also used to arrive at the real water shar
... Show MoreThat corruption with all its forms, has prevailed over the whole world, but with different degrees relaying on the one who leads these countries of rulers, followers and officials who have been deemed the main reason for that corruption, but if these rulers were righteous, These countries would have blessed and elevated and were corrupt unjust tyrants who were the disaster that befell the chiefs of those countries with ruin, misery, and backwardness. This is what we sought to prove and clarify by considering the verses of the Holy Qur’an in respect with this topic. The research includes an introduction, two topics, and a conclusion.
 
... Show MoreBackground: Non-alcoholic fatty liver disease (NAFLD) is the most common liver disorder globally. The prevalence is 25% worldwide, distributed widely in different populations and regions. The highest rates are reported for the Middle East (32%). Due to modern lifestyles and diet, there has been a persistent increase in the number of NAFLD patients. This increase occurred at the same time where there were also increases in the number of people considered being obese all over the world. By analyzing fatty liver risk factors, studies found that body mass index, one of the most classical epidemiological indexes assessing obesity, was associated with the risk of fatty liver. Objectives: To assess age, sex, and body mass index (BMI) as
... Show MoreBackground: Non-alcoholic fatty liver disease (NAFLD) is the most common liver disorder globally. The prevalence is 25% worldwide, distributed widely in different populations and regions. The highest rates are reported for the Middle East (32%). Due to modern lifestyles and diet, there has been a persistent increase in the number of NAFLD patients. This increase occurred at the same time where there were also increases in the number of people considered being obese all over the world. By analyzing fatty liver risk factors, studies found that body mass index, one of the most classical epidemiological indexes assessing obesity, was associated with the risk of fatty liver.
Objectives: To assess age, sex, and body
... Show MoreLandSat Satellite ETM+ image have been analyzed to detect the different depths of regions inside the Tigris river in order to detect the regions that need to remove sedimentation in Baghdad in Iraq Country. The scene consisted of six bands (without the thermal band), It was captured in March ٢٠٠١. The variance in depth is determined by applying the rationing technique on the bands ٣ and ٥. GIS ٩. ١ program is used to apply the rationing technique and determined the results.
In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial
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