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.
Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D
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To identify the fungi associated with water hyacinth (Eichhornia crassipes [Mart.] Solms), an aquatic weed, which presents in Tigris river from Baghdad south ward. Five regions from middle and south of Iraq (Al-Noumanya, Saeid Bin-Jubier, Al-Azizia, Al-Reyfay and Al-Hay) were selected for this study. Twelve fungal species were isolated. Alternaria alternata, Acremonium sp and Cladsporium herbarum, were the most frequently species (91.66 % ,50 % and 25 %) respectively. The fungi Alternaria alternata, Acremonium sp. and Phoma eupyrena were more aggressive to water hyacinth as (91.66%,83,33%, and 75%) in pathogenicity test.
The purpose of this study is to identify the extent of the EFL teachers’ implementationoftotal quality management (TQM)principles through teaching English as a foreign language in Iraqi secondary schools.A total of 180 EFL teachers fromsecondary schools in Baghdad during the academic year 2010/ 2011 participated in the study. Data was collected using a scale to identify the extent of implementation of total quality management principles by 140 EFL teachers in their classroom. To support the data collected from the scale,40 teachers were observed by EFL educational supervisors during their lessons. The collected data was analyzed using frequency, means and standard deviation to identify the extent of implementation of (TQM) principles.
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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
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The Central Marshes are one of southern Iraq's most important wetlands and ecosystems. A study on evaluating soil quality and water quality in terms of chemical properties at certain sites in the southern Iraqi Central Marshes has been conducted to investigate their types and suitability for enhancing the agricultural reality of most field crops. Soil and water samples were collected from 15 sites and transferred to the laboratory. In the lab, the following parameters were determined: electrical conductivity (EC), total dissolved salts (TDS), organic materials (OM), pH, gypsum, and total sulfate content (SO3). The tests conducted on the samples indicated that it could be said that the soil of the Central Marshes
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Groundwater quality deterioration due to anthropogenic natural activities and its immense utilization in various sectors is considered a great concern. The aim of this study is to determine the groundwater quality parameters at various sources in and around Dhaka city and compare them with Bangladesh drinking water standards. In this study, six groundwater quality parameters (pH, DO, COD, TS, TDS, and arsenic) and ten groundwater samples are analyzed to determine the water quality. The collected samples have maximum and minimum pH values of 6.9 and 6.4, respectively. Maximum and minimum DO values are 0.3 and 0.1 mg/L, respectively. The arsenic concentration is 0 mg/L for all collected groundwater samples. The maximum and minimum COD
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