The aim of this study was to assess the effectiveness of listening to music or Quran in reducing cancer patients’ anxiety before chemotherapy administration. Reducing anxiety in people with cancer, prior to chemotherapy administration, is a crucial goal in nursing care.

Cooking was of great importance in the Islamic Arabic culture and the
people of Morocco have shown great interest in this aspect and also in the
variety in the making of food. They used all kinds of meat of and have shown
interest in preserving and distributing it .The people of Morocco used the
additives in their cooking such as salt, saffron and many other kinds to add
special flavor and taste and their cooking a distinctive flavor.
Sweet and pastry, in addition to the drinks, represented another aspect of the
Moroccan kitchen. At that time women were brought as slaves from Sudan
and as a result they brought their experience in the making of sweets and
pastry with them to Morocco, they used sugar, fat, wheat

Objectives: To assess the knowledge and practice of thalassemic patients about desferal administration and
complications of iron overload.
Methodology: The present study composed of (50) thalssemic patient who are registered in center and was
performed in Ibn Al-Atheer center for congenital anemia for the period from 15/12/2006 to 1/4/2007.
Results: The result of the study showed highly significant difference at (160.05) for knowledge of thalassemic
patients and also appear highly significant difference at (P<O.O5) for practice of thalassemic patients.
Recommendations: The study recommends that there is necessity to increase the knowledge and practice of
thalassemic patient about desferal administration to minimiz

Convolutional Neural Networks (CNN) have high performance in the fields of object recognition and classification. The strength of CNNs comes from the fact that they are able to extract information from raw-pixel content and learn features automatically. Feature extraction and classification algorithms can be either hand-crafted or Deep Learning (DL) based. DL detection approaches can be either two stages (region proposal approaches) detector or a single stage (non-region proposal approach) detector. Region proposal-based techniques include R-CNN, Fast RCNN, and Faster RCNN. Non-region proposal-based techniques include Single Shot Detector (SSD) and You Only Look Once (YOLO). We are going to compare the speed and accuracy of Faster RCNN,

This study, which was conducted in the city of Mosul, through collected 1200 samples from the stool of patients with diarrhea attending hospitals and private clinics for the period from the beginning of January 2019 to the end of December 2019, those whose ages ranged from less than a year-60 year, and for both sexes and by reality 700 samples stool for males and 500 samples stool for females. Samples were collected in clean, sterile, and sealed 40ml plastic bottles. Patient information is noted, name of the parasite, history, sex, age, address. The result showed that climate and temperature have a significant effect on increase the incidence of intestinal parasites through the direct effect on the increase in infection rate. This effect wa

THE ROLE OF ELECTRONIC-PAYMENT SERVICE PROVIDERS IN THE DEVELOPMENT OF E-BANKING IN IRAQ - AN APPLIED RESEARCH IN CENTRAL BANK OF IRAQ

A common field development task is the object of the present research by specifying the best location of new horizontal re-entry wells within AB unit of South Rumaila Oil Field. One of the key parameters in the success of a new well is the well location in the reservoir, especially when there are several wells are planned to be drilled from the existing wells. This paper demonstrates an application of neural network with reservoir simulation technique as decision tool. A fully trained predictive artificial feed forward neural network (FFNNW) with efficient selection of horizontal re-entry wells location in AB unit has been carried out with maintaining a reasonable accuracy. Sets of available input data were collected from the exploited g