Viscosity is one of the most important governing parameters of the fluid flow, either in the porous media or in pipelines. So it is important to use an accurate method to calculate the oil viscosity at various operating conditions. In the literature, several empirical correlations have been proposed for predicting crude oil viscosity. However, these correlations are limited to predict the oil viscosity at specified conditions. In the present work, an extensive experimental data of oil viscosities collected from different samples of Iraqi oil reservoirs was applied to develop a new correlation to calculate the oil viscosity at various operating conditions either for dead, satura

There are many methods of forecasting, and these methods take data only, analyze it, make a prediction by analyzing, neglect the prior information side and do not considering the fluctuations that occur overtime. The best way to forecast oil prices that takes the fluctuations that occur overtime and is updated by entering prior information is the Bayesian structural time series (BSTS) method. Oil prices fluctuations have an important role in economic so predictions of future oil prices that are crucial for many countries whose economies depend mainly on oil, such as Iraq. Oil prices directly affect the health of the economy. Thus, it is necessary to forecast future oil price with models adapted for emerging events. In this article, we st

Thermal evaporation method has used for depositing CdTe films
on corning glass slides under vacuum of about 10-5mbar. The
thicknesses of the prepared films are400 and 1000 nm. The prepared
films annealed at 573 K. The structural of CdTe powder and prepared
films investigated. The hopping and thermal energies of as deposited
and annealed CdTe films studied as a function of thickness. A
polycrystalline structure observed for CdTe powder and prepared
films. All prepared films are p-type semiconductor. The hopping
energy decreased as thickness increased, while thermal energy
increased.

The thirties and the early forties and the at end of the World War II of the last century
witnessed several attempts carried out by the Public Health Directorate and by the Iraqi
Ministry of Foreign Affairs to persuade the Rockefeller Foundation to fund Iraq with its
health program which is carried out in several regions in the world to promote the health and
social situations since Iraq during that period in history was badly in need to such plans and
programs because of this lack of financial and technical possibilities necessary for the
advancement of health and social dire situation.
The details of these attempts are deposited in the documents of the diplomatic mail
records of the United States of America , an

Background: During Ramadan, Muslims fast throughout daylight hours. There is a direct link between fasting and increasing incidence of infections. Antibiotic usage for treatment of infections should be based on accurate diagnosis, with the correct dose and dosing regimen for the shortest period to avoid bacterial resistance. This study aimed to evaluate the practices of physicians in prescribing suitable antibiotics for fasting patients and the compliance of the patients in using such antibiotics at regular intervals. Materials and methods: An observational study was carried out during the middle 10 days of Ramadan 2014 in two pharmacies at Baghdad. A total of 34 prescriptions (Rx) for adults who suffered from infections were examined. For

 In this paper, we propose a method using continuous wavelets to study the multivariate fractional Brownian motion through the deviations of the transformed random process to find an efficient estimate of Hurst exponent using eigenvalue regression of the covariance matrix. The results of simulations experiments shown that the performance of the proposed estimator was efficient in bias but the variance get increase as signal change from short to long memory the MASE increase relatively. The estimation process was made by calculating the eigenvalues for the variance-covariance matrix of Meyer’s continuous wavelet details coefficients.

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