research objectives to:
1. identify the social, economic and cultural factors affecting consumption.
2. detect the consumption culture among the population in the city of Erbil.
3. Identify the GATT consumer protection and rights.
The most important results:
1. that there is variation in the answers of respondents about keep up with modernity in the basic consumption (necessary), it swallowed the proportion of yes answers about keep up with modernity in food consumption (72%), and is an indication of growing consumer awareness of the individual in the side of nutrition. The clothing on the side of the proportion of yes answers amounted to (85%), in the health field note that the percentage of yes answers (83%), who are abr

The aim of the research is to shed light on the stages of developing the Iraqi virtual science Library(IVSL) project, and to define its distinctive role in providing all kinds of electronic resources to researchers from professors and graduate students, to identify its contents , the entry interfaces and applications of use, and provide them through electronic portals to publishing houses, research institutions and international universities, The research sample included the teaching staff and researchers participating in educational qualification courses at the Continuing Education Center at the University of Baghdad, The research population and its sample consisted of the category of (IVSL) users, and its sample (387) users. Analysis meth

Electrical Discharge Machining (EDM) is a non-traditional cutting technique for metals removing which is relied upon the basic fact that negligible tool force is produced during the machining process. Also, electrical discharge machining is used in manufacturing very hard materials that are electrically conductive. Regarding the electrical discharge machining procedure, the most significant factor of the cutting parameter is the surface roughness (Ra). Conventional try and error method is time consuming as well as high cost. The purpose of the present research is to develop a mathematical model using response graph modeling (RGM). The impact of various parameters such as (current, pulsation on time and pulsation off time) are studied on

In Libya, there are multiple sources of pollution, one of which is animal waste. The anaerobic digestion (AD) of organic wastes to produce biogas has the advantage of producing valuable, renewable energy while reducing the environmental impact of these wastes. Cowmanure have the potential to produce biogas due to their high organic content. This study aimed to study different concentrations for the feedstock (1:1 and 2:1 cow manure: water v/v) to monitor which one gives higher biogas production. A plastic tank with a capacity of 72 liters and a feedstock volume of 60 liters was used to create a pilot scale. The biogas was analyzed using a GC device at the end of the experiment in the Zawiya Oil Refining Company. The result indicated that th

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