Oxidative stress is oxidative damage caused by free radicals and reactive oxygen species (ROS). These ROS can cause oxidative damage to cellular components, including membrane lipids, receptors, enzymes, proteins, and nucleic acids. It would eventually lead to cell apoptosis and the appearance of certain pathological conditions. This work investigates the antioxidant potentials of chamomile extract in vitro by evaluating the extract activity to scavenge 2,2-Diphenyl-1-picrylhydrazyl (DPPH), also in vivo by investigating its effects on oxidative stress-induced rats by assessing the total oxidant status (TOS) and total antioxidant capacity in the radiation exposed rats with and without the treatment with chamomile extract. The results

This study was carried out to evaluate the antioxidant activity of Iraqi sumac seeds (Rhus coriaria. L) (Anacardiaceae). Total phenolic compounds and flavoniods were determined in three different sumac seed extracts (SSE) (aqueous,ethanolic and methanolic extract). For extraction Antioxidant activity of SSE were evaluated by various antioxidant assays, including total antioxidant capacity, reducing power,by using 1,1-diphenyl-2-picryl hydrazyl (DPPH) radical scavenging, nitric oxide scavenging, Hydroxyl radical scavenging, and metal ion chelating activities. These various antioxidant activities were compared with ascorbic acid as a standard antioxidant.The results showed that the three(SSE), contained large amounts of phenolic and flavonio

The medicinal plants (Astragalus species) have been used traditionally as anti-inflammatory, antioxidant, and Anti-diabetics. The current research investigates the phytochemistry and some biological activity of methanol extract of different parts of Astragalus bruguieri Bioss., a wild medicinal plant grows on Safeen mountain, Erbil, Iraq. The methanol extracts of A. bruguieri were analyzed for total phenolic, flavonoid, and saponin contents. In-vitro antioxidant activity was analyzed by 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) assays. Furthermore, the plant extracts were examined for in-vitro enzyme inhibitory activity and in-v

This review examines how artificial intelligence (AI) including machine learning (ML), deep learning (DL), and the Internet of Things (IoT) is transforming operations across exploration, production, and refining in the Middle Eastern oil and gas sector. Using a systematic literature review approach, the study analyzes AI adoption in upstream, midstream, and downstream activities, with a focus on predictive maintenance, emission monitoring, and digital transformation. It identifies both opportunities and challenges in applying AI to achieve environmental and economic goals. Although adoption levels vary across the region, countries such as Saudi Arabia, the UAE, and Qatar are leading initiatives that align with global sustainability targets.

Erosion and deposition are natural phenomena in many estuaries that can cause morphological changes, leading to navigation and offshore structure problems. Many previous studies have focused on investigating the morphological changes of estuaries depending on different parameters and their combined effects with erosion and deposition processes, such as maximum flow amplitude, waves, currents, tidal flow, storms, rising sea elevation, maximum turbidity, salinity, and bed roughness height as input parameters. These studies are based on field measurements and numerical simulations by using topographic surveys, soil samples, satellite images, geological data, and bathymetric maps with the aid of ArcGIS. Some of these studies examine the

Copper (I) complex containing folic acid ligand was prepared and characterized on the basis of metal analyses, UV-VIS, FTIR spectroscopies and magnetic susceptibility. The density functional theory (DFT) as molecular modeling calculations was used to determine the donor atoms of folic acid ligand which appear clearly at oxygen atoms binding to hydrogen. Detection of donation sights is supported by theoretical parameters such as geometry, mulliken population, mulliken charge and HOMO-LUMO gap obtained by DFT calculations.

In recent years, the field of research around the congestion problem of 4G and 5G networks has grown, especially those based on artificial intelligence (AI). Although 4G with LTE is seen as a mature technology, there is a continuous improvement in the infrastructure that led to the emergence of 5G networks. As a result of the large services provided in industries, Internet of Things (IoT) applications and smart cities, which have a large amount of exchanged data, a large number of connected devices per area, and high data rates, have brought their own problems and challenges, especially the problem of congestion. In this context, artificial intelligence (AI) models can be considered as one of the main techniques that can be used to solve ne

Face recognition and identity verification are now critical components of current security and verification technology. The main objective of this review is to identify the most important deep learning techniques that have contributed to the improvement in the accuracy and reliability of facial recognition systems, as well as highlighting existing problems and potential future research areas. An extensive literature review was conducted with the assistance of leading scientific databases such as IEEE Xplore, ScienceDirect, and SpringerLink and covered studies from the period 2015 to 2024. The studies of interest were related to the application of deep neural networks, i.e., CNN, Siamese, and Transformer-based models, in face recogni