The study aims to predict Total Dissolved Solids (TDS) as a water quality indicator parameter at spatial and temporal distribution of the Tigris River, Iraq by using Artificial Neural Network (ANN) model. This study was conducted on this river between Mosul and Amarah in Iraq on five positions stretching along the river for the period from 2001to 2011. In the ANNs model calibration, a computer program of multiple linear regressions is used to obtain a set of coefficient for a linear model. The input parameters of the ANNs model were the discharge of the Tigris River, the year, the month and the distance of the sampling stations from upstream of the river. The sensitivity analysis indicated that the distance and discharge have the most significant affect on the predicted TDS concentrations. The results showed that a network with (8) hidden neurons was highly accurate in predicting TDS concentration. The correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE) between measured data and model outputs were calculated as 0.975, 113.9 and 11.51%, respectively for testing data sets. Comparisons between final results of ANNs and multiple linear regressions (MLR) showed that the ANNs model could be successfully applied and provides high accuracy to predict TDS concentrations as a water quality parameter.
Allowing Iraqi companies to use multiple systems and policies leads to varying levels of disclosure and no high symmetry between report preparers and users, and that the adoption of integrated reporting can reduce information asymmetry. The theoretical side addressed the concepts of these variables, and in the practical side the binary variable (0, 1) was used. To compensate for the value of the independent variable (integrated reporting) based on the Central Bank of Iraq’s classification of banks according to the (CAMLES) index, and the dependent variable (information asymmetry) was measured through two measures (price difference, unusual return), the research community was represented by (5) Banks out of the total of banks li
... Show MoreAs long as Islamic banks cannot operate in an environment that deals with traditional banking instruments based on the interest rate (bank interest) that is prohibited by Article No. (6) of the Islamic Banking Law No. (43) of 2015 which stipulates that (do not deal in any activity that carries Interest (neither taking nor giving)). Therefore it seeks to provide an alternative strategic solution within the framework of the provisions and rules of Islamic Sharia, on the basis of participating in profit and loss to avoid the method of interest taking and giving, to find investment methods to provide new financial products, such as Islamic certificates of deposit and investment funds according to the method Murabaha, speculation (Mudar
... Show MoreThis paper is a review of the genus Sitta in Iraq, Five species of this genus are recognized
Sitta kurdistanica, S. neumayr, S. europaea, S.dresseri and S. tephronota. Geographical
distribution and systematic nots were given for separation and identification, also some notes
on nest building and nest sites of S. tephronota supporting by figures are presented.
In this study, vegetable tanned leather waste of cow (VTLW-C) is used as adsorbent for removing methyl violet 10B dye from aqueous solution. The VTLW-C adsorbent was characterized by FTIR and SEM in order to evaluate its surface properties before using in adsorption experiments. Batch adsorption method was applied to study the effect of different factors such as weight of leather waste, time of shaking, and starting concentration of methyl violet 10B dye. Different isothermal models such as Langmuir, Freundlich, Temkin and Dubinin-Radushkevich (D–R) were used to analyze the experimental data. Kinetic study proceeds using (PFO) kinetic model and (PSO) kinetic model. The results showed better agreement with the Freundlich model; this means
... Show Moreسعي المجتمع العراقي منذ أكثر من نصف قرن مضى لإعادة استثمار عشرات المليارات من الدولارات من الإيرادات النفطية في القطاع الزراعي وهياكله وبنياته التحية، كإنشاء السدود والخزانات المائية واستصلاح الأراضي والمشاريع الإنتاجية الحيوانية والنباتية وبطاقات كادت تقترب او تتجاوز حاجز طلب السكان من الأغذية والمنتوجات الزراعية التي تغذي الصناعة الا ان الزيادة السكانية وتحسن مستوى الدخل النفطي شكلا انتقالا جدي
... Show MoreThis study specifically contributes to the urgent need for novel methods in Training of Trainers (ToT) programs which can be more effective and efficient through incorporation of AI tools. By exploring scenarios in which AI could be used to dramatically advance trainer preparation, knowledge-sharing, and skill-building across sectors, the research aims to understand the possibility. This study uses a mixed-methods approach, it surveys 500 trainers and conducts in-depth interviews with a further 50 ToT program directors across diverse industries to evaluate the impact of AI-enhanced ToT programs. The results showcase that the use of AI has a substantial positive effect on trainer performance and program outcomes. AI-enhanced ToT programs, fo
... Show MoreThis paper presents a robust algorithm for the assessment of risk priority for medical equipment based on the calculation of static and dynamic risk factors and Kohnen Self Organization Maps (SOM). Four risk parameters have been calculated for 345 medical devices in two general hospitals in Baghdad. Static risk factor components (equipment function and physical risk) and dynamics risk components (maintenance requirements and risk points) have been calculated. These risk components are used as an input to the unsupervised Kohonen self organization maps. The accuracy of the network was found to be equal to 98% for the proposed system. We conclude that the proposed model gives fast and accurate assessment for risk priority and it works as p
... Show MoreThe 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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