Permeability is an essential parameter in reservoir characterization because it is determined hydrocarbon flow patterns and volume, for this reason, the need for accurate and inexpensive methods for predicting permeability is important. Predictive models of permeability become more attractive as a result.

   A Mishrif reservoir in Iraq's southeast has been chosen, and the study is based on data from four wells that penetrate the Mishrif formation. This study discusses some methods for predicting permeability. The conventional method of developing a link between permeability and porosity is one of the strategies. The second technique uses flow units and a flow zone indicator (FZI) to predict the permeability of a rock mass u

Background: Insufficient sleep due to excessive media use is linked to decrease physical activity, poor nutrition, obesity, and decreased overall health-related quality of life.

Objectives: To assess the effect of using the internet and social media on the sleep of 4th-stage secondary school students.

Subjects and Methods: Cross-sectional study with the analytic element; for 500 secondary school students, obtained by choosing two schools randomly from each of the six educational directorates, by using a structured questionnaire.

Result: Secondary scho

Methotrexate (MTX) is one of the most effective medications to treat rheumatoid arthritis (RA).Aserum of 60 Iraqi male patients suffering from RA as (G1) was newly diagnosis and the same patient in G1 after taking MTX as G2 and 40 Iraqi male healthy control as G3. Nesfatin-1 (Nf-1) is belong to the adipokine family withpleiotropic effect. Nf-1 has been found in different tissues, including stomach, pancreas, bone cells, cartilage and heart. Retinol binding protein (RBP4) was known as transpoter of retinol from its storage sites in the liver to the extrahepatic tissues. Moreover, RBP4 acts as adipokine and contributes in the pathophsyology of prototypic inflammatory disease, rheumatoid arthritis (RA). The results showed a significant increas

Generally, radiologists analyse the Magnetic Resonance Imaging (MRI) by visual inspection to detect and identify the presence of tumour or abnormal tissue in brain MR images. The huge number of such MR images makes this visual interpretation process, not only laborious and expensive but often erroneous. Furthermore, the human eye and brain sensitivity to elucidate such images gets reduced with the increase of number of cases, especially when only some slices contain information of the affected area. Therefore, an automated system for the analysis and classification of MR images is mandatory. In this paper, we propose a new method for abnormality detection from T1-Weighted MRI of human head scans using three planes, including axial plane, co