The Iraqi marshes are considered the most extensive wetland ecosystem in the Middle East and are located in the middle and lower basin of the Tigris and Euphrates Rivers which create a wetlands network and comprise some shallow freshwater lakes that seasonally swamped floodplains. Al-Hawizeh marsh is a major marsh located east of Tigris River south of Iraq. This study aims to assess water quality through water quality index (WQI) and predict Total Dissolved Solids (TDS) concentrations in Al-Hawizeh marsh based on artificial neural network (ANN). Results showed that the WQI was more than 300 for years 2013 and 2014 (Water is unsuitable for drinking) and decreased within the range 200-300 in years 2015 and 2016 (Very poor water). The developed ANN mode gave a high correlation coefficient reaching 0.927 for the prediction of TDS from the model and showed high levels of TDS in Al-Hawizeh marsh that pose threats to people using the marsh for drinking and other uses. The dissolved Oxygen concentration has the highest importance of 100% in the model because the water of the marsh is fresh water, while Turbidity had the lowest importance.
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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
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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
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Bodies of water are usually being polluted by wastes from domestic and industrial sources thereby making them unfit for use. Hence, this study aimed at assessing the water quality from Asa River, Ilorin, Nigeria in terms of bacteriological and physicochemical parameters. The bacteriological parameters assessed were heterotrophic bacterial count, total coliform, faecal coliform, identification of the isolates, antibiotic resistance patterns, and plasmid profile of the isolates. Whereas, the assessed physicochemical parameters were pH, total chloride, suspended solid, and total hardness. The heterotrophic bacterial count, total coliform, and faecal coliform counts ranged from 7.6 x 103 to 3.2 x 106 cfu/ml,
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This investigation aims to determine whether it is feasible to use the limestone rocks found in the Al-Samawa stone quarry in southern Iraq as the stationary phase in column chromatography separation. Together with the chromatographic application, the physical and chemical characteristics of the rocks are examined. SiO2, SO4, PO4, NO3, and Cl are the negative ions, while Ca, Mg, Na, K, and Li are the positive ions. The limestone samples are characterized via chromatographic analysis. The results suggest that limestone samples could be used as an adsorbent material for chromatographic separation techniques. Additionally, samples from the Nasiriyah refinery's crude oil can be used to sep
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Radiological assessment due to existing of natural occurring radioactive materials
(NORM) in South Rumaila oil field was achieved in this study. Different samples
including soil, sludge, scale, oil, and water were collected from different stages of
oil and gas production in Markazia Degassing Station (SDS) in South Rumaila oil
field. Radioactivity of Ra-226, Th-232 and K-40 were measured using gamma
spectrometry system based on HPGe detector with efficiency of 30%. The results
show that some locations within SDS are contaminated with NORM. The activity of
Ra-226, Th-232 and K-40 range between 18.4 to 312.8, 9.4 to 140.8 and 66.4 to
800.8 (Bq/kg) respectively. The places to be more contaminated among the other
p
This hydrochemical study of the surface and groundwater in Khan AL-Baghdadi area, western Iraq, included the interpretation of physical, chemical, and biological properties. Water samples were collected from wells (14 samples) and surface water of Euphrates River (6 samples) for the dry and wet periods of October 2018 and April 2019, respectively. The stable isotopes analysis was performed for the dry period only. The surface water samples were characterized by slightly alkaline, fresh, excessively mineralized, Ca-chloride type, and hard to very hard water class. While the groundwater samples were characterized by slightly alkaline, brackish, excessively mineralized, Ca-chloride and Na-Chloride type, and hard to very hard wat
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