This work studied the facilitation of the transportation of Sharqi Baghdad heavy crude oil characterized with high viscosity 51.6 cSt at 40 °C, low API 18.8, and high asphaltenes content 7.1 wt.%, by reducing its viscosity from break down asphaltene agglomerates using different types of hydrocarbon and oxygenated polar solvents such as toluene, methanol, mix xylenes, and reformate. The best results are obtained by using methanol because it owns a high efficiency to reduce viscosity of crude oil to 21.1 cSt at 40 °C. Toluene, xylenes and reformate decreased viscosity to 25.3, 27.5 and 28,4 cSt at 40 °C, respectively. Asphaltenes content decreased to 4.2 wt. % by using toluene at 110 °C. And best improvement in API of the heavy crude o

In this paper a stirred-bed performed of the copper catalyzed synthesis of ethylchlorosilanes from silicon and ethyl chloride was described. A Si-catalyst mixture prepared by reaction of CuCl and Si was employed. The compositions of products were mainly ethyltrichlorosilane, diethyldichlorosilane, and ethyldichlorosilane and mainly depended on the extent of Cu in the mixture and the reaction temperature. A promoting effect on the extent of adsorption was observed on the addition of certain additives. The kinetic data revealed the direct depended of the reaction rate on C2H5Cl pressure.

The Al-Kindy College Medical Journal (KCMJ) is an Iraqi scholarly journal published by the Al-Kindy College of Medicine, University of Baghdad. It was officially founded in 2004. It is a peer-reviewed journal, published in both online and printed forms. It has a mission to offer a publication platform that mirrors recent knowledge and findings in the field of medicine and medical sciences. It publishes various types of articles, including editorial, review article, research article, brief report, case report, and letter to editor. It accepts articles in the English language. It was biannually published till 2021 when it started to launch three issues per year. The journal is registered with numerous partners, including Iraqi Academi

Abstract The study aimed at demonstrating the reality of sectarian coexistence in Iraq, which was characterized by the tolerance and coercion caused by the successive government policies to govern Iraq and to this day. The study was based on the hypothesis that coexistence between Islamic sects in Iraq can be achieved as long as there are strong bonds linking its components, and these bonds can produce coexistence between the sects based on peace. The study concluded that the hypothesis is correct, in addition to drawing a set of observations aimed at identifying weaknesses for advancing them through the adoption of mechanisms that address these weaknesses to yield towards a genuine peaceful coexistence among Islamic sects in Iraq.

Coumarin is a natural substance isolated from different plants. It belonges to a group of benzobyrones which consists of a benzene ring joined to a pyrone nucleus. In the present research, a new series of coumarin derivatives were formed. Compound (1) (7-hydroxy-4-methyl Coumarin) was converted into 4-methylquinolin-2(H) derivative (2) by reaction with acetamide, and then reaction of (2) with thiosemicarbazide in ethanol leads to the synthesize of hydrazincarbothioamide derivative (3).The reaction of (3) with ethylchloroacetate in presence of sodium acetate leads to closure ring to get [(1-(5-oxo-2-thioxoimidazolidin-1-ylimino) ethyl)]quinolin-2(1H)-one (4). Mannich bases were prepared through the reaction of (4) with primary

Prediction of penetration rate (ROP) is important process in optimization of drilling due to its crucial role in lowering drilling operation costs. This process has complex nature due to too many interrelated factors that affected the rate of penetration, which make difficult predicting process. This paper shows a new technique of rate of penetration prediction by using artificial neural network technique. A three layers model composed of two hidden layers and output layer has built by using drilling parameters data extracted from mud logging and wire line log for Alhalfaya oil field. These drilling parameters includes mechanical (WOB, RPM), hydraulic (HIS), and travel transit time (DT). Five data set represented five formations gathered

The adsorption behavior of congo red dye from its aqueous solutions was investigated onto natural and modified bauxite clays. Both bauxite and modified bauxite are primarily characterized by using, FTIR, SEM, AFM, and XRD. Several variables are studied as a function of adsorption including contact time, adsorbent weight, pH, ionic strength, particle size and temperature under batch adsorption technique. The absorbance of the solution before and after adsorption was measured spectrophotometrically. The equilibrium data fit with Langmuir model of adsorption and the linear regression coefficient R2 is found to be 0.9832 and 0.9630 for natural and modified bauxite respectively at 37.5°C which elucidate the best fitting isotherm model. The gene

Modified unsaturated polyester (MUPE) was blended with Cellulose (Cls) and with ethyl cellulose (ECls) at ambient conditions in the presence of ethyl methyl ketone peroxide (EMKP) as hardener. The blends containing different weight percentages (5-25 %) of Cls or ECls. Mechanical properties (impact strength, hardness, and bending) and dielectric constant were determined. The results observed that Cls increases the impact strength, hardness, and dielectric constant and decreases the bending of the MUPS, while ECls causes an increase in the three mechanical behaviours and a decrease in the dielectric constant of the MU-PS.

Face recognition and identity verification are now critical components of current security and verification technology. The main objective of this review is to identify the most important deep learning techniques that have contributed to the improvement in the accuracy and reliability of facial recognition systems, as well as highlighting existing problems and potential future research areas. An extensive literature review was conducted with the assistance of leading scientific databases such as IEEE Xplore, ScienceDirect, and SpringerLink and covered studies from the period 2015 to 2024. The studies of interest were related to the application of deep neural networks, i.e., CNN, Siamese, and Transformer-based models, in face recogni