The Tigris River is one of the main rivers and an important resource for the population of Iraq. The present study aimed to quantify the concentration of surface-active substances in the Tigris River and to investigate the dynamics of this ecosystem. Five sampling sites were selected along the river within Baghdad city (Al-Muthanna Bridge, Al-Greaat Bridge, Al-Sarrafia Bridge, Al-Jadriyah Bridge, and Al-Za'franiya Area) for the period from July 2020 to April 2021. The study examined the relationship between the concentrations of surface-active materials (surfactants, including anionic and nonionic types) and their potential interaction with nutrients—nitrate (NO₃⁻), phosphate (PO₄), and sulfate (SO₄²⁻)—as well as the

This study aimed to explore and separate the phytochemicals of the whole plant Conyza canadensis, a naturally growing plant in Iraq, since no phytochemical research was done previously in Iraq. The whole plant of C. canadensis was defatted by maceration in hexane for 24 hours. The defatted plant materials were extracted using Soxhlet apparatus, the aqueous ethanol 85% as a solvent extraction for 9 hours, and fractionated by petroleum ether, chloroform, ethyl acetate, and n-butanol. The petroleum ether, chloroform, and ethyl acetate fractions were analyzed by high-performance liquid chromatography (HPLC) for their steroids, alkaloids, and polyphenolic (phenolic acids and flavonoids) contents. One alkaloid was isolated from chloroform fractio

Researchers have recently increased their focus on the link between autoimmune diseases and infections. Most of the recent research indicates that silent human cytomegalovirus (HCMV), may have diverse roles in the initiation, development, and exacerbation of autoimmune diseases, such as coeliac Disease (CD) and inflammatory bowel disease. The aim of this study is to evaluate the role of HCMV infection in Iraqi patients with CD. Serum samples were obtained from 60 patients with CD, and from 60 healthy subjects. Enzyme-linked immunosorbent assay was used to determine the Anti-Transglutaminase IgG/IgA, Anti-gliadin IgA/ IgG, as well as the HCMV IgM/ IgG levels in the serum samples. Significantly higher percentage of positivity for seru

Hierarchical temporal memory (HTM) is a biomimetic sequence memory algorithm that holds promise for invariant representations of spatial and spatio-temporal inputs. This article presents a comprehensive neuromemristive crossbar architecture for the spatial pooler (SP) and the sparse distributed representation classifier, which are fundamental to the algorithm. There are several unique features in the proposed architecture that tightly link with the HTM algorithm. A memristor that is suitable for emulating the HTM synapses is identified and a new Z-window function is proposed. The architecture exploits the concept of synthetic synapses to enable potential synapses in the HTM. The crossbar for the SP avoids dark spots caused by unutil

A new and hybrid deep learning-based approach for diagnosing faults in electric vehicle (EV) drive motors is proposed in this article. This article presents a new and hybrid deep learning-based method of diagnosing faults in the drive motors of electric vehicles (EV). In contrast to standard CNNLSTM approaches that depend on SoftMax classification, the introduced framework combines a Random Forest (RF) classifier to enhance the generalization, interpretability, and robustness of fault prediction. Furthermore meant for use on edge computing equipment with IoT integration, the design allows for real-time monitoring in resource-limited settings. The introduced algorithm utilizes a Random Forest (RF) classifier for accurate fault classification

Background Due to the intermittent, nonlinear, and uncertain behavior of renewable energy sources (res) such as solar and wind, grid stability and reliability require very high forecasting and optimization skills as widely reported in the literature. Traditional optimization methods work very well in small or static systems but are suffer difficulty on large-scale, dynamic and stochastic renewable environment due to their NP-hard nature. Methods The framework introduces the concept of a Machine Learning-Assisted Hybrid Cuckoo Search (ML-HCS) that combines CS with a hybrid metaheuristic and integrates Long Short-Term Memory (LSTM) networks for forecasting based on both regression models of LSTMs and hybrid optimization algorithm

Breast cancer is highlighted in recent research as one of the most prevalent types of cancer. Timely identification is essential for enhancing patient results and decreasing fatality rates. Utilizing computer-assisted detection and diagnosis early on may greatly improve the chances of recovery by accurately predicting outcomes and developing suitable treatment plans. Grading breast cancer properly, especially evaluating nuclear atypia, is difficult owing to faults and inconsistencies in slide preparation and the intricate nature of tissue patterns. This work explores the capability of deep learning to extract characteristics from histopathology photos of breast cancer. The research introduces a new method called SMOTE-based Convolut

BOOK REVIEW

ناقش البحث في طياته عدداً من القضايا الرئيسة المتعلقة بالتقييم الاستراتيجي والإطار العام للخطة الاستراتيجية المقترحة لشركة نفط ميسان للسنوات الخمس المقبلة (2020_2024)، وهدف هذا البحث يتمحور في تقييم عملية صياغة استراتيجية شركة نفط ميسان لتحديد نقاط القوة وتعضيدها ومواطن الضعف ومحاولة معالجتها لتجنب الوقوع بها عند وضع استراتيجية للسنوات القادمة، وعلى هذا الاساس فان مشكلة البحث تكمن في مدى نجاح الاستراتي

مشكلة البحث

    لقد تفاقمت مشكلة تلوث البيئة في السنوات الأخيرة من العقد الأخير والعقد الأول من القرنين العشرين والحادي والعشرين على التوالي بشكل يلفت النظر. إذ انتشر الاستعمال غير العقلاني للموارد الطبيعية من مياه ملوثة وسكن غير صحي وغير ملائم وسؤ التغذية وظروف الصحة العامة الرديئة وانتشار العوادم التي تحدثها المركبات والمولدات التي انتشرت في أحياء المدن العراقية لتزوي