Background: Environmental tobacco smoking is produced by active smokers burning the tip of a cigarette and breathed by nonsmokers and measured by cotinine level. It has the potential to raise the risk of periodontal disease. One of the most frequent chronic diseases in adults is periodontal disease. The lower maternal-fetal attachment has been found to predict smoking status in previous studies, but no research has examined whether maternal-fetal attachment predicts environmental tobacco smoking. This study assessed the effects of maternal environmental tobacco smoke exposure on periodontal health and mother-infant bonding concerning salivary cotinine levels. Materials and methods: This is a comparative cross-sectional study comparing en

تعد المبارزة أحد الألعاب الرياضية التي يتأثر فيها الأداء بتطور القدرات الخاصة بالأداء ومنها تحمل (سرعة وقوة الأداء ),وأن أكثر الأساليب السابقة في تدريب تطوير تحمل(سرعة وقوة الأداء) بالمبارزة تكون على ارض صلبة مثل الخشب والألمنيوم آو الإسفلت وفي بعض القاعات يكون التارتان, وظل هذا الأسلوب لفترات طويلة في العراق ،حيث تستخدم تدريبات الإثقال التي تعمل على تنمية تحمل القوة . أما في الوقت الحاضر فقد ظهر اتجاه حديث في

Resulted in scientific and technological developments to the emergence of changes in the educational process and methods of teaching modern formats commensurate with the level of mental retardation. Which called for educational institutions, including the University of Baghdad / College of Fine Arts to urge and guide researchers to study and follow-up of recent developments in the educational process in order to develop in the fine arts in general and technical education in particular being play an important role in achieving educational goals. The educational methods of modern educational require effort-intensive and advanced for the development of technical skills among students, and thus worked researcher to employ computer technology

Gray-Scale Image Brightness/Contrast Enhancement with Multi-Model
Histogram linear Contrast Stretching (MMHLCS) method

Amputation of the upper limb significantly hinders the ability of patients to perform activities of daily living. To address this challenge, this paper introduces a novel approach that combines non-invasive methods, specifically Electroencephalography (EEG) and Electromyography (EMG) signals, with advanced machine learning techniques to recognize upper limb movements. The objective is to improve the control and functionality of prosthetic upper limbs through effective pattern recognition. The proposed methodology involves the fusion of EMG and EEG signals, which are processed using time-frequency domain feature extraction techniques. This enables the classification of seven distinct hand and wrist movements. The experiments conducte