Research seeks to test the impact of the dimensions of mindfulness on  organizational Innovation, proposed in the light of the review literature on two variables of the research, which referred in General to the dynamic relationship between them, as result of weakness of mindfulness as one important factor driving the diversity of innovation and time, ways to sustain and preserve and then support innovations made by creators, weakness in the overall level of organizational Innovation, this represents the problem research, data collection over designing   identification, distributed to sample Formed from (30) head Department at a number of colleges of the University of Baghdad, results confirms the validity of resea

Various visual media are becoming an increasingly important and active instrument of communication. This fact has led some political parties and leading personalities in Iraq to make use of them as an accepted forum for the discussion of public affairs usually in a manner that conforms to their declared policy. They have to draw as much popular support as they could for the causes which they fight for. As a result, a state of great confusion has been created from the contradictory statements made by the contending parties and gave left grave consequences on all types of the audience receiving them. The problem of the study can be summarized in one major question: What is the opinions of the audience as regards the statements made by the

Good governance of service quality through the adoption of sustainable energy the study of A1- Karkh historic center of in Baghdad city

The study included selection six species of the fungi related to Pleurotus genus were evaluated for their ability to production of Pleurotin, one of them, Pleurotus ostreatus (P.11) was isolated and identified in the present study. Pleurotin was extracted with screening by Thin Layer Chromatography (TLC) and quantification High Performance Liquid Chromatography (HPLC). Cytotoxicity of Pleurotin extracted from P. ostreatus (P.11) grown in different sugar sources (galactose, mannitol, sucrose, dextrose and lactose) liquid media was test against three selected cancer cell lines, CaSki, MCF-7 and A549 addition to Human Non Cancer Fibroblast Cell Line (MRC-5). Pleurotin of P. ostreatus (P.11) grown in galactose induced the significant highest

Echocardiography is a widely used imaging technique to examine various cardiac functions, especially to detect the left ventricular wall motion abnormality. Unfortunately the quality of echocardiograph images and complexities of underlying motion captured, makes it difficult for an in-experienced physicians/ radiologist to describe the motion abnormalities in a crisp way, leading to possible errors in diagnosis. In this study, we present a method to analyze left ventricular wall motion, by using optical flow to estimate velocities of the left ventricular wall segments and find relation between these segments motion. The proposed method will be able to present real clinical help to verify the left ventricular wall motion diagnosis.

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with

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

Hepatitis B infection is a prominent infectious disease caused by hepatitis B virus (HBV), which infect liver and is considered as the main cause of liver cirrhosis, fibrosis and liver cancer worldwide. A pro-inflammatory cytokine Interleukin32 is believed to have a role in chronic HBV infections. Since its role in CHB infections is remain unclear, this study was done to detect IL-32 gene expression in CHB patients in order to identify its exact role. A total number of 110 blood samples were collected from Gastroenterology and Hepatology Teaching Hospital in Baghdad Medical City from CHB patients for both males and females with different age groups according to the research ethics form then sent to Central Public Health Laboratory (CPHL),