The present study develops an artificial neural network (ANN) to model an analysis and a simulation of the correlation between the average corrosion rate carbon steel and the effective parameter Reynolds number (Re), water concentration (Wc) % temperature (T o) with constant of PH 7 . The water, produced fom oil in Kirkuk oil field in Iraq from well no. k184-Depth2200ft., has been used as a corrosive media and specimen area (400 mm2) for the materials that were used as low carbon steel pipe. The pipes are supplied by Doura Refinery . The used flow system is all made of Q.V.F glass, and the circulation of the two –phase (liquid – liquid ) is affected using a Q.V.F pump .The input parameters of the model consists of Reynolds number , w

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet

The study was conducted in the Tigris River in Baghdad during May 2021 until March 2022 to follow the impact of climate change, rising temperatures, and the presence of pollutants on the dynamics of phytoplankton and some physicochemical variables from four sites. The results showed that the climatic conditions during different seasons, in addition to the nature of the sampling sites, have a clear and significant impact on the studied traits and, in turn, affect the phytoplankton community. The highest average temperature (30.67 ˚C) was recorded; the pH values ranged between 8.70 & 6.75; the electrical conductivity (1208.18-770.11 µS/cm ) and the total dissolved solids (TDS) (778.95- 439.49 mg/L) were evaluated. Upon measuring

The study was conducted to measure diatom species diversity in the lotic ecosystem across the Wasit Province for 12 months. The quantitative study of diatoms (phytoplankton) was investigated in the Tigris river. The density of algae was ranged from 60989 cell×103/l to 112780.82 cell×103/l in the five sites. These algae were belonging to 39 genera. The richness index values ranged from 1.53 at site 5 in January 2016 to 6.34 at site 1 and June2015. Shannon-Weiner diversity index (H´) was 2.33 in February 2016 and 3.72 in June 2015 both values at site 3, whereas Evenness index was 0.54 at site 5 in March2016 and 0.98 at site 1 in both August2015 and May2016. The lack of homogeneity of the appearance of species indicates the dominance of a

The main source of water supply in Iraq is the surface water, especially Tigris and Euphrates Rivers and their tributaries. In the recent years there was a great drop in the water levels of Tigris River within Baghdad City which had affected the operation of twelve water supply projects located on the banks of Tigris River in Baghdad City, due to significant climate changes, and the expansion of hydraulic construction (dams) and implementation of new irrigation projects in Turkey, these factors have greatly reduced the water flowrates of river by about 46%. In the present study the flow characteristics of Tigris River within Baghdad City was studied, the reach involved was about 49km in which it represents the urban zone

Studied Seen fungi water in nine stations or selected sites along the Tigris River began from the city of Mosul in the north to Qurna in episodes were measured some chemical agents and Alvezaúah water ranged pH values ??(11p) between 7.0 to 8.3 either temperatures ranged between 10to 28 m study showed isolated 22 species of 14 genera of fungi

The purpose of this research is to investigate the impact of corrosive environment (corrosive ferric chloride of 1, 2, 5, 6% wt. at room temperature), immersion period of (48, 72, 96, 120, 144 hours), and surface roughness on pitting corrosion characteristics and use the data to build an artificial neural network and test its ability to predict the depth and intensity of pitting corrosion in a variety of conditions. Pit density and depth were calculated using a pitting corrosion test on carbon steel (C-4130). Pitting corrosion experimental tests were used to develop artificial neural network (ANN) models for predicting pitting corrosion characteristics. It was found that artificial neural network models were shown to be