This paper considers approximate solution of the hyperbolic one-dimensional wave equation with nonlocal mixed boundary conditions by improved methods based on the assumption that the solution is a double power series based on orthogonal polynomials, such as Bernstein, Legendre, and Chebyshev. The solution is ultimately compared with the original method that is based on standard polynomials by calculating the absolute error to verify the validity and accuracy of the performance.

Oscillation criterion is investigated for all solutions of the first-order linear neutral differential equations with positive and negative coefficients. Some sufficient conditions are established so that every solution of eq.(1.1) oscillate. Generalizing of some results in [4] and [5] are given. Examples are given to illustrated our main results.

A fracture is a damage to bone tissue that causes damage to the tissue surrounding the bone and may penetrate the skin. Subjects and methods: the present study included (80) fractured Iraqi patients (and 40) patients with DM2 and (40) without DM2 and compared them with (40) healthy control. Patients and control are matched in age. This study showed a significant increase in retinol-binding protein 4 (RBP4) and a considerable decrease in Vit .A GPT and GOT in fracture patients with and without DM2. In addition, there was a significant negative correlation between RBP4 with (GPT and GOT) in fracture patients with DM2 and a significant positive correlation between RBP4 with (GPT and GOT) in fracture patients without DM2.       

Background: Recent research indicates that persistent inflammatory responses may contribute to the rise of diabetic nephropathy (DN) and diabetic cardiovascular disease (DCVD) in type 2 diabetes mellitus patients (DM2). Numerous molecules associated with inflammation and angiogenesis have been implicated in the development and progression of DN and DCVD, respectively. Methods: The subjects were separated into five groups: healthy controls (n= 25), type 2 diabetes mellitus patients (n= 30), type 2 diabetes mellitus patients with nephropathy DN (n= 30), and type 2 diabetes mellitus patients with cardiovascular disease DCVD (n= 30). The blood levels of irisin, IL-8, HbA1C, urea, and creatinine were determined. Results: In current study there w

Objectives: This study aimed to identify and study most properties of the specific and general health-related
quality-of-life (HRQoL) in prostate cancer patients, as well as creating a new measurement scale for assessing QoL
among prostate cancer patients.
Methodology: A cross sectional (descriptive) study was conducted to evaluate General Quality of life in patients
with prostate cancer. A sample of 100 prostate cancer patients from Al-Amal National hospital for cancer
management and Oncology Center in Baghdad Medical City. This study applied format of General World Health
Organization Quality of Life-BERF questionnaire. The methods used descriptive statistics to evaluate the General
QoL-Improvements, as well as inf

Various theories have been proposed since in last century to predict the first sighting of a new crescent moon. None of them uses the concept of machine and deep learning to process, interpret and simulate patterns hidden in databases. Many of these theories use interpolation and extrapolation techniques to identify sighting regions through such data. In this study, a pattern recognizer artificial neural network was trained to distinguish between visibility regions. Essential parameters of crescent moon sighting were collected from moon sight datasets and used to build an intelligent system of pattern recognition to predict the crescent sight conditions. The proposed ANN learned the datasets with an accuracy of more than 72% in comp

       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial