Insulin-induced hyperglycemia is the hallmark of diabetes mellitus (DM), including various metabolic disorders. Diabetic people are more likely to develop dyslipidemia, hypertension, and obesity. Type 2 diabetes ‎(T2DM), the most common illness, is generally asymptomatic in its early stages and can go misdiagnosed for years. Diabetes screening may be beneficial in some cases since early identification and treatment can lessen the burden of diabetes and its consequences.‎ This study aimed to find the relationship between Glycated hemoglobin (HbA1c) ‎and lipid profile components in T2DM‎ patients. This descriptive-analytical and cross-sectional study was performed on the control group and T2DM patients in ‎Medical City in Ba

To evaluate and improve the efficiency of photovoltaic solar modules connected with linear pipes for water supply, a three-dimensional numerical simulation is created and simulated via commercial software (Ansys-Fluent). The optimization utilizes the principles of the 1st and 2nd laws of thermodynamics by employing the Response Surface Method (RSM). Various design parameters, including the coolant inlet velocity, tube diameter, panel dimensions, and solar radiation intensity, are systematically varied to investigate their impacts on energetic and exergitic efficiencies and destroyed exergy. The relationship between the design parameters and the system responses is validated through the development of a predictive model. Both single and mult

This research explored the performance of steel fiber concrete-filled stainless-steel tube columns stiffened with embedded carbon steel T-sections with various steel fiber ratios under biaxial bending conditions. A numerical parametric analysis was adopted, using finite element modeling with Abaqus CAE/2021 to evaluate the effects of the fiber ratio (ranging from 0% to 1.5%) on the load-bearing capacity and deflection behavior of columns. In addition, the compressive strength of concrete ranged between 45 and 65 MPa. An increase in the fiber ratio led to a substantial improvement in the ultimate load-bearing capacity (up to 24%), a reduction in deflection (of approximately 49%), and an improvement in column ductility, which were obt

Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model bas

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

This study focused on benthic algae (epipelic and attached algae on concrete lining stream) in Bani-Hassan stream in Holly Karbala, Iraq. The qualitative and quantitative studies of benthic algae were done by collecting 240 samples from five sites in the study area for the period from December 2012 to November 2013. Also, the environmental variables of the stream were examined in term of temporary and spatial. The results showed that the stream was alkaline, hard, oligohaline and a well aerated. The total nitrogen to the total phosphorus (TN: TP) ratio indicates nitrogen limitation. 129 species of benthic algae belonging to 57 genera were identified. Bacillariophyceae (diatoms) was the predominant taxon (95 species) followed by Chlorophyce