This research utilized natural asphalt (NA) deposits from sulfur springs in western Iraq. Laboratory tests were conducted to evaluate the performance of an asphalt mixture incorporating NA and verify its suitability for local pavement applications. To achieve this, a combination of two types of NA, namely soft SNA and hard HNA, was blended to create a binder known as Type HSNA. The resulting HSNA exhibited a penetration grade that adhered to Iraqi specifications. Various percentages of NA (20%, 40%, 60%, and 80%) were added to petroleum asphalt. The findings revealed enhanced physical properties of HSNA, which also satisfied the requirements outlined in the Iraqi specifications for asphalt cement. Consequently, HSNA can serve as an

Micro-perforated panel (MPP) absorber is increasingly gaining popularity as an alternative sound absorber in buildings compared to the well-known synthetic porous materials. A single MPP has a typical feature of a Helmholtz resonator with a high amplitude of absorption but a narrow absorption frequency bandwidth. To improve the bandwidth, a single MPP can be cascaded with another single MPP to form a double-layer MPP. This paper proposes the introduction of inhomogeneous perforation in the double-layer MPP system (DL-iMPP) to enhance the absorption bandwidth of a double-layer MPP. Mathematical models are proposed using the equivalent electrical circuit model and are validated with experiments with good agreement. It is revealed that the DL-

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Increasing the power conversion efficiency (PCE) of silicon solar cells by improving their junction properties or minimizing light reflection losses remains a major challenge. Extensive studies were carried out in order to develop an effective antireflection coating for monocrystalline solar cells. Here we report on the preparation of a nanostructured cerium oxide thin film by pulsed laser deposition (PLD) as an antireflection coating for silicon solar cell. The structural, optical, and electrical properties of a cerium oxide nanostructure film are investigated as a function of the number of laser pulses. The X-ray diffraction results reveal that the deposited cerium oxide films are crystalline in nature and have a cubic fluorite. The field

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

The present study aims to study the correlation between visfatin levels and metabolic syndrome in Iraqi obese adolescence (with and without metabolic syndrome) and its relation with other studied biochemical parameters. Sixty obese adolescences were depended in this study (with and without metabolic syndrome), compared with (30) non-obese children as control group. This study was done in the period from April 2020 until the end of December 2020, in the National Diabetes Centre/Mustansiriya University, Baghdad/Iraq. There were no significant differences in age, height, waist circumferences (WC), and diastolic blood pressure (DBP) in the patients' groups. In contrast, a significant increase differs (p<0.05) was recorded in the values of

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

The electrospun nanofibers membranes have gained considerable interest in water filtration applications. In this work, the fabrication and characterization of the electrospun polyacrylonitrile-based nonwoven nanofibers membrane are reported. Then, the membrane's performance and antifouling properties were evaluated in removing emulsified oil using a cross flow filtration system. The membranes were fabricated with different polyacrylonitrile (PAN) concentrations (8, 11, and 14 wt. %) in N, N-Dimethylformamide (DMF) solvent resulted in various average fiber sizes, porosity, contact angle, permeability, oil rejection, and antifouling properties. Analyses of surface morphology of the fabricated membranes before and after oil removal revealed