The polycystic ovary syndrome is an endocrine condition. One of the leading causes of female infertility and the most common disorder among women. The work was being carried out on 100 Iraqi women (50 cases confirmed with PCOS and 50 controls). Between October 2019 and March 2020, blood samples were collected from the Advanced Institute of Infertility Diagnosis and Assisted Reproductive Technology at AL-Nahrain University and a private laboratory. ELISA was used to evaluate the biochemical parameters of preptin, FSH, insulin, LH, and CCL 18 in serum samples from the AFIAS-6 (AFIAS Automated Immunoassay System). The findings of the analysis indicate that, as opposed to the control group, values of prolactin (ng/ml), LH (mIU/ml), Preptin (

Carbon nanotubes were prepared by an arc-discharge method,
under different values of pressure of oxygen gas. The structure of
multi-walled carbon nanotubes powders has been characterized by
low-angle X-ray diffraction .The morphology of carbon nanotube
powder was examined by transmission electron microscope. The
capacitance-voltage and current- voltage (dark and illumination
current) characterization were measured under different values of
pressure (10-3, 10-4, 10-5) mbar of oxygen gas

The performance of sewage pumps stations affected by many factors through its work time which produce undesired transportation efficiency. This paper is focus on the use of artificial neural network and multiple linear regression (MLR) models for prediction the major sewage pump station in Baghdad city. The data used in this work were obtained from Al-Habibia sewage pump station during specified records- three years in Al-Karkh district, Baghdad. Pumping capability of the stations was recognized by considering the influent input importance of discharge, total suspended solids (TSS) and biological oxygen demand (BOD).  In addition, the chemical oxygen demands (COD), pH and chloride (Cl). The proposed model performanc

Background: Neonatal septicemia is a major health problem in developing countries furthermore data on bacteriological profile in early onst sepses (EOS) and late neonatal sepsis (LOS) are lacking in context of  continuous change in bacteriological profile and increasing resistant strains. Objectives: The study done to determine the pattern of organisms implicated in neonatal septicemia in a neonatal care unit and to measure the degree of bacterial resistance to some antibiotics.

Type of the study : cross –sectional study.

Methods: Confirmed cases of neonatal septicemia admitted at Al-Alwyia pediatric teaching hospital for the period from January 20

خلفية البحث: مرض السكري هو عامل خطر لأمراض القلب والأوعية الدموية وتصلب الشرايين وسبب مهم للوفاة. يرتبط خلل الدهون في الدم بشكل شائع بمرض السكري من النوع الثاني ويعتبر مؤشر تصلب الشرايين في البلازما علامة قوية للتنبؤ بخطر الإصابة بتصلب الشرايين وأمراض القلب التاجية. الهدف من البحث: دراسة ارتباط المؤشرات الدهنية لتصلب الشرايين لدى المرضى العراقيين المصابين بالسكري من النوع الثاني ولديهم أمراض قلبية وعائ

A linear and nonlinear theoretical and experimental aeroelastic investigation of a wing-flap-tab typical section model undergoing two-dimensional incompressible airflow is described. The linear flutter velocity (LFV) and frequency are predicted using linear analysis. Then a freeplay structural nonlinearity is considered in the tab. The structural equations of motion have been coupled with Theodorsen aerodynamic theory to produce the theoretical aeroelastic model which is analyzed by a state space method to predict the LFV and flutter frequency. Linear piecewise function has been used to introduce the tab spring stiffness in the freeplay state. The ground vibration test is used to measure the model structural dynamic characteristics. Then th

Alzheimer's disease (AD) increasingly affects the elderly and is a major killer of those 65 and over. Different deep-learning methods are used for automatic diagnosis, yet they have some limitations. Deep Learning is one of the modern methods that were used to detect and classify a medical image because of the ability of deep Learning to extract the features of images automatically. However, there are still limitations to using deep learning to accurately classify medical images because extracting the fine edges of medical images is sometimes considered difficult, and some distortion in the images. Therefore, this research aims to develop A Computer-Aided Brain Diagnosis (CABD) system that can tell if a brain scan exhibits indications of