Background: The marginal adaptation has a key role in the success and longevity of the fixed dental restoration, which is affected by the impression and the fabrication techniques .The objective of this in vitro study was to evaluate and compare the marginal fitness of lithium disilicate crowns using two different digital impression techniques (direct and indirect techniques) and two different fabrication techniques (CAD/CAM and Press techniques). Materials and Methods: Thirty two sound upper first premolar teeth of comparable size extracted for orthodontic reason were selected in this study .Standardized preparation of all teeth samples were carried out with modified dental surveyor to receive all ceramic crown restoration with 1 mm deep

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

تهدف الدراسة إلى استعراض ظهور كراسي اليونسكو في الجامعات العراقية والأنشطة التي تم تنفيذها لتحقيق أهداف التنمية المستدامة، حيث تم إنشاء كل كرسي لتحقيق أحد هذه الأهداف التي تركت أثرها على بعض القطاعات التعليمية والاجتماعية ، وبالتالي تنبع أهمية الدراسة من الحاجة إلى الإشارة إلى إنشاء كراسي اليونسكو في الجامعات العراقية والأنشطة التي عززت إنشائها ونشاطها ومبادراتها المحلية والدولية, وكذلك تحديد ما حقق

Agricultural development occupies an important position in the economies of developing countries, and its role is greater for the Arab countries. The first task is to provide food security for all the population through sustainable development, which includes the investment of available natural resources and employment opportunities for the rural population, As well as the provision of raw materials for agricultural processing in order to increase agricultural exports to reduce the balance of payments deficit. Sustainable development is linked to increased production and improvement, as well as to food security. On the one hand, it has to cope with the increase in population and if it is possible to achieve a surplus allocated for export, a

Purpose – The research aims to introduce sustainable agricultural development and the possibility of its application in the Iraqi agricultural sector by setting a proposed plan by which to overcome obstacles and then advance the reality of the agricultural sector in Iraq and the fact that the process of achieving agricultural development in the Iraqi agricultural sector today has become more sophisticated and more distant than before. The study adopted the descriptive analytical approach based on the principles of economic theory to clarify the shortcomings in the process of harmony between the three main elements of sustainable agricultural development, which are natural, social, and manufactured.

Abstract Background Deep learning (DL) has attracted increasing attention in orthodontics, with many studies reporting high diagnostic and predictive accuracy. However, despite these promising results, routine clinical adoption remains limited. Objectives This scoping review aimed to explore the main barriers that restrict the translation of DL systems from research environments into everyday orthodontic practice. Methodology The review was conducted in accordance with PRISMA-ScR guidelines. Electronic searches were performed in PubMed, Scopus, Web of Science, and IEEE Xplore for English-language studies published between 2015 and June 2025. Studies reporting quantitative performance of DL-based orthodontic applications were included. Dat

Background: Legionella pneumophila (L. pneumophila) is gram-negative bacterium, which causes Legionnaires’ disease as well as Pontiac fever. Objective: To determine the frequency of Legionella pneumophila in pneumonic patients, to determine the clinical utility of diagnosing Legionella pneumonia by urinary antigen testing (LPUAT) in terms of sensitivity and specificity, to compares the results obtained from patients by urinary antigen test with q Real Time PCR (RT PCR) using serum samples and to determine the frequency of serogroup 1 and other serogroups of L. pneumophila. Methods: A total of 100 pneumonic patients (community acquired pneumonia) were enrolled in this study during a period between October 2016 to April 2017; 92 sam