In this study, the amounts of activity concentrations of naturally occurring in 10 soil samples of the Tigris river and surrounding areas collected from deferent city of Baghdad have been investigated. Tigris river is an important water source for irrigation and drinking in Iraq. This study was done during 2018 in Protection Center of the Iraqi Ministry of Health and Environment using a high purity germanium detector. The resolution of (HPGe) at 2keV and 30% efficiency. The results of soil sample obtained showed that the effective activity concentration of 40K are ranged from 181.4 Bq/kg in sample S6 to 286.4 Bq/kg in S7. For Raeq values are ranged from 6 Bq/m3 in sample S5 to 17 Bq/m3 in sample S3. The obtained data revealed that the me

Objective(s): To assess the level of depression and anxiety among school age children with acute lymphoblastic leukemia under chemotherapy treatment and to find out the relationship between the level of depression and anxiety among the affected children and their demographic characteristics.
Methodology: A cross-sectional study was conducted on school age children both gender having acute lymphoblastic leukemia under chemotherapy treated and their age between 6 years to 12 years. The study started from the period of September, 19th 2020 to March,1st 2021. Non-probability (Purposive) sample of (114) children with acute lymphoblastic leukemia under chemotherapy was selected in attending hospital wards, outpatient and counseling clinics

Objectives: The study aims to evaluate the effectiveness of the educational program on nurses’ knowledge towards nursing management for patients undergoing percutaneous coronary intervention (PCI), as well as to find out the relationship between nurses' knowledge and some of their demographic characteristics (age, gender, level of education, and years of experience in cardiac units).

Methodology:  A Quasi-experimental as one group (pre and post test) study was conducted at the Heart Center in Al-Diwaniyah city for the period from December 7, 2019 to February 23, 2020. A sample of (40) nurses working in the heart center was chosen from different nursing addresses. The sample covered one gro

ABSTRACT: Ultimate bearing capacity of soft ground reinforced with stone column was recently predicted using various artificial intelligence technologies such as artificial neural network because of all the advantages that they can offer in minimizing time, effort and cost. As well as, most of applied theories or predicted formulas deduced analytically from previous studies were feasible only for a particular testing environment and do not match other field or laboratory datasets. However, the performance of such techniques depends largely on input parameters that really affect the target output and missing of any parameter can lead to inaccurate results and give a false indicator. In the current study, data were collected from previous rel

Low bearing capacity of weak soil under shallow footings represents one of construction problems.
Kaolin with water content converges to liquid limit used to represent the weak soil under shallow
footing prototype. On the other hand, fly ash, which can be defined as undesirable industrial waste
material, was used to improve the bearing capacity of the soft soil considered in this research. The soft
soil was prepared in steel box (36×36×25) cm and shallow square footing prototype (6×6) cm were
used .Group of physical and chemical tests were conducted on kaolin and fly ash. The soft soil was
improved by a bed of compacted fly ash placed under the footing with dimensions equal to that of
footing but with different de

An analytical approach based on field data was used to determine the strength capacity of large diameter bored type piles. Also the deformations and settlements were evaluated for both vertical and lateral loadings. The analytical predictions are compared to field data obtained from a proto-type test pile used at Tharthar –Tigris canal Bridge. They were found to be with acceptable agreement of 12% deviation.

               Following ASTM standards D1143M-07e1,2010, a test schedule of five loading cycles were proposed for vertical loads and series of cyclic loads to simulate horizontal loading .The load test results and analytical data of 1.95

   Predicting permeability is a cornerstone of petroleum reservoir engineering, playing a vital role in optimizing hydrocarbon recovery strategies. This paper explores the application of neural networks to predict permeability in oil reservoirs, underscoring their growing importance in addressing traditional prediction challenges. Conventional techniques often struggle with the complexities of subsurface conditions, making innovative approaches essential. Neural networks, with their ability to uncover complicated patterns within large datasets, emerge as a powerful alternative. The Quanti-Elan model was used in this study to combine several well logs for mineral volumes, porosity and water saturation estimation. This model goes be