The Mauddud reservoir, Khabaz oil field which is considered one of the main carbonate reservoirs in the north of Iraq. Recognizing carbonate reservoirs represents challenges to engineers because reservoirs almost tend to be tight and overall heterogeneous. The current study concerns with geological modeling of the reservoir is an oil-bearing with the original gas cap. The geological model is establishing for the reservoir by identifying the facies and evaluating the petrophysical properties of this complex reservoir, and calculate the amount of hydrocarbon. When completed the processing of data by IP interactive petrophysics software, and the permeability of a reservoir was calculated using the concept of hydraulic units then, there

Spatial Autoregressive Model (SAR) is one of the modeling frameworks that indicates a spatial dependence in the response variable. SAR model has a weakness, which is represented by the unknown variance of the residuals. Therefore, an alternative model has used titled Spatial Autoregressive Quantile Regression (SARQR) model That which is obtained by combining SAR and Quantile Regression (QR) models, is a regression method with the approach of dividing the data into particular quantiles that are likely to have different estimate values. This alternative model addresses the variance issues in SAR models. Additionally, the SARQR model not only resolves the issue of spatial variance but also serves as a solution for dealing with non-normal data

Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a

Objectives: To identify the impact of the brain consensus model on the acquisition of Arabic grammar concepts among students in the fourth grade, methodology: The pilot curriculum was used, and a partial control pilot design was adopted. There were 30 female students in the pilot group, 30 female students in the control group, and the two researchers were statistically rewarded among the two groups' students in some variables and used appropriate statistical means to analyse the results, including the test for two independent samples, the square (c2) and the Alpha Kronbach equation.Results: The pilot group outperformed the control group. The results showed that there is a significant statistical difference at the indicative level (0.05) for

The research aims to measure the sustainability of the Iraqi economy for the period 1990-2018 as well as to show the impact of fluctuations in the level of GDP on financial sustainability, where financial sustainability is the necessary and sufficient condition for achieving economic and financial balance in the country, as financial sustainability reflects the movement of the state budget and its relationship to GDP through the indicators of deficit, fiscal surplus and public debt internal and external, as well as reflecting the art of managing public debt, and the more managed public debt is achieved, the more the management of public debt is achieved financial sustainability. for the state in the sense that there is a reciprocal

Signification with its different aspects constitutes various aesthetic pictures for human and nature creations over different periods of time. It was a focal around which logical thinking loops on the interpretation level of the signification movement according to its structural context expresses its hidden entities with which a human being deals according to two symmetrical levels while interpreting to attain meaning. Sense was the first passing window for the picture of signification which thought deals with. While trying to decode it and because art in general and theatre in particular are considered as the basis of creating signification through its visual and aural elements, so it was necessary to consider one of the elements of cre

Bearing capacity of a concrete pile in fine grained cohesive soils is affected by the degree of saturation of the surrounding soil through the contribution of the matric suction. In addition, the embedded depth and the roughness of the concrete pile surface (expressed as British Pendulum Number BPN) also have their contribution to the shear strength of the concrete pile, consequently its bearing capacity. Herein, relationships among degree of saturation, pile depth, and surface roughness, were proposed as a mathematical model expressed as an equation where the shear strength of a pile can be predicted in terms of degree of saturation, depth, and BPN. Rel