ZnS nanoparticles were prepared by a simple microwave irradiation method under mild condition. The starting materials for the synthesis of ZnS quantum dots were zinc acetate (R & M Chemical) as zinc source, thioacetamide as a sulfur source and ethylene glycol as a solvent. All chemicals were analytical grade products and used without further purification. The quantum dots of ZnS with cubic structure were characterized by X-ray powder diffraction (XRD), the morphology of the film is seen by scanning electron microscopy (SEM). The particle size is determined by field effect scanning electron microscopy (FESEM), UV-Visible absorption spectroscopy and XRD. UV-Visible absorption spectroscopy analysis shows that the absorption peak of the as-prep

This work studies with produce of light fuel fractions of gasoline, kerosene and gas oil from treatment of residual matter that will be obtained from the solvent extraction process as by product from refined lubricate to improve oil viscosity index in any petroleum refinery. The percentage of this byproduct is approximately 10% according to all feed (crude oil) in the petroleum refinery process. The objective of this research is to study the effect of the residence time parameter on the thermal cracking process of the byproduct feed at a constant temperature, (400 °C). The first step of this treatment is the thermal cracking of this byproduct material by a constructed batch reactor occupied with control device at a selective range of re

This paper aims to decide the best parameter estimation methods for the parameters of the Gumbel type-I distribution under the type-II censorship scheme. For this purpose, classical and Bayesian parameter estimation procedures are considered. The maximum likelihood estimators are used for the classical parameter estimation procedure. The asymptotic distributions of these estimators are also derived. It is not possible to obtain explicit solutions of Bayesian estimators. Therefore, Markov Chain Monte Carlo, and Lindley techniques are taken into account to estimate the unknown parameters. In Bayesian analysis, it is very important to determine an appropriate combination of a prior distribution and a loss function. Therefore, two different

Background: Educational environment is one of the most important determinants of an effective curriculum. Students' perceptions of their educational environment have a significant impact on their behavior and academic progress. Objective: 1. To identify students’ perception to the educational environment.2. To identify any gender or class level differences in the students’ perception.Type of the study: This is a descriptive cross-sectional studyMethodology: The study was carried out on convenient sample of 150 students of 2nd and 5th grade. This study was done in Al Kindy Medical College, Baghdad, Iraq and conducted during the period from the 1st of October 2013 till the end of March 2014, by using DREEM questionnaire a validated uni

New vision of Mr. Abdul Mahdi Al-Muttafiqi in the light of unpublished documents

Background Depression is one of the most common medical illnesses worldwide. Medical students during their training will be subjected to many stressors, both mentally and emotionally. Objectives Assess the prevalence of depressive symptoms among medical students and to identify any association between depressive symptoms and some socio-demographic factors.

The research aims to improve operational performance through the application of the Holonic Manufacturing System (HMS) in the rubber products factory in Najaf. The problem was diagnosed with the weakness of the manufacturing system in the factory to meet customers' demands on time within the available resources of machines and workers, which led to time delays of Processing and delivery, increased costs, and reduced flexibility in the factory, A case study methodology used to identify the reality of the manufacturing system and the actual operational performance in the factory. The simulation was used to represent the proposed (HMS) by using (Excel 2010) based on the actual data and calculate the operational performance measures