In this study water quality index (WQI) was calculated to classify the flowing water in the Tigris River in Baghdad city. GIS was used to develop colored water quality maps indicating the classification of the river for drinking water purposes. Water quality parameters including: Turbidity, pH, Alkalinity, Total hardness, Calcium, Magnesium, Iron, Chloride, Sulfate, Nitrite, Nitrate, Ammonia, Orthophosphate and Total dissolved solids were used for WQI determination. These parameters were recorded at the intakes of the WTPs in Baghdad for the period 2004 to 2011. The results from the annual average WQI analysis classified the Tigris River very poor to polluted at the north of Baghdad (Alkarkh WTP) while it was very poor to very polluted in t

PDBNRSIA Asst, International Journal of Research in Social Sciences and Humanities, 2018

The need to constantly and consistently improve the quality and quantity of the educational system is essential. E-learning has emerged from the rapid cycle of change and the expansion of new technologies. Advances in information technology have increased network bandwidth, data access speed, and reduced data storage costs. In recent years, the implementation of cloud computing in educational settings has garnered the interest of major companies, leading to substantial investments in this area. Cloud computing improves engineering education by providing an environment that can be accessed from anywhere and allowing access to educational resources on demand. Cloud computing is a term used to describe the provision of hosting services

Background: Transplantation has revolutionized
treatment of end- stage renal disease (ESRD) by proving
more cost effective than hemodialysis, with a lower
morbidity and improved quality of life.
Objective: To evaluate the development of these
complications in the first month postoperatively and
correlate their development to the type of donation
whether related or unrelated.
Methods: Fifty (50) patients aged (15-62) years, with a
mean age (34.46 ± 12.4 SD) years with (ESRD), who
underwent renal transplantation from September 2000 to
October 2002, were followed-up for one month
postoperatively clinically and by assessment of renal
function tests, sonographic and Doppler examinations.
Ureteral obs

The present work aims to study the efficiency of using aluminum refuse, which is available locally (after dissolving it in sodium hydroxide), with different coagulants like alum [Al2 (SO4)3.18H2O], Ferric chloride FeCl3 and polyaluminum chloride (PACl) to improve the quality of water. The results showed that using this coagulant in the flocculation process gave high results in the removal of turbidity as well as improving the quality of water by precipitating a great deal of ions causing hardness. From the experimental results of the Jar test, the optimum alum dosages are (25, 50 and 70 ppm), ferric chloride dosages are (15, 40 and 60 ppm) and polyaluminum chloride dosages were (10, 35 and 55 ppm) for initial water turbidity (100, 500 an

Soil invertebrates community an important role as part of essential food chain and responsible for the decomposition in the soil, helps soil aeration , nutrients recycling and increase agricultural production by providing the essential elements necessary for photosynthesis and energy flow in ecosystems.The aim of the present study was to investigate the soil invertebrates community in one of the date palms plantation in Aljaderia district South of Baghdad, , and their relationships with some physical and chemical properties of the soil , as Five randomly distributed replicates of soil samples were collected monthly. Invertebrates samples were sorted from the soil with two methods, direct method to isolate large invertebrates and indirec

Irisin is a novel myokine and adipokine, its role during pregnancy and its association with some metabolic risk factors especially pre-pregnancy body mass index (pre-BMI) need more evaluation. The aim of the study is to find whether the pre-BMI could predict irisin levels during normal pregnancy and to clarify associations of irisin with some pathological parameters.

Irisin levels were estimated by ELISA in sera of 59 normal pregnant women who enrolled from December 2016 to May 2017 at Maternity Hospital, Zakho city, Kurdistan region (Iraq). Thirty-two normal-weight pregnant (pre-BMI≤24.9 kg/m², Age=24.03 mean±3.7standard deviation) and 27 overweight/obese-pregnant (pre-BMI>25 kg/m², Age=27.6 mean±3.9

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c

The following study was conducted to investigate the correlation between the expression of three different genes (NOB1, DDX47, CD101( with the occurrence and development of chronic myeloid leukemia (CML) in Iraq. The difference in the expression of these genes between patients and healthy controls was studied. Moreover the correlation of age and gender with CML occurrence and comparing with control was also examined. Results showed significant increases in mean of gene expression level (ΔCt) of patient groups for all genes compared to the corresponding ΔCt means in control group, also the gene expression folding (2-ΔΔCt) reflect significant differences in the expression of these genes and CD101, mRNA showed the highest level in CML pati

Diagnosing heart disease has become a very important topic for researchers specializing in artificial intelligence, because intelligence is involved in most diseases, especially after the Corona pandemic, which forced the world to turn to intelligence. Therefore, the basic idea in this research was to shed light on the diagnosis of heart diseases by relying on deep learning of a pre-trained model (Efficient b3) under the premise of using the electrical signals of the electrocardiogram and resample the signal in order to introduce it to the neural network with only trimming processing operations because it is an electrical signal whose parameters cannot be changed. The data set (China Physiological Signal Challenge -cspsc2018) was ad