In this study, polymeric coating was developed by incorporating nano graphene in the polymer blend with applications to oil storage tanks. The oil storage tanks samples were brought from the oil Pipeline Company / Doura refinery in Baghdad. The coating polymer was formed with a blend (epoxy resin and repcoat ZR). The proportion of mixing the mixture was 3:1:1 epoxy resin 21.06 gm: repcoat ZR 10.53 gm: hardener 10.53 gm. The blend/graphene was prepared using in stui-polymerization method with different weight percentage 1, 3, 5, and 7 wt % added to blend. The resulting solution was put in a glass tube on a magnetic stirrer for one hour at a temperature of 40 °C. The result of contact angle and water absorption the best ratio of 3wt

حزب العمال الكردستاني ودوره في تطوير القضية الكردية في تركيا من 1991-2013

The current research aimed to identify psychological stability and its relationship to university integration and spiritual intelligence among university students. The research sample consisted of (158) students from the College of Education - Al-Mustansiriya University.

A scale was applied: psychological stability, university integration, and spiritual intelligence,   and by using the (Pearson) correlation coefficient, and the t-test, the results showed: the sample members enjoy psychological stability, university integration, and spiritual intelligence, and there is a positive, statistically significant correlation between the research variables, and the results resulted in some recommendations and proposals.

T-joints are common structures encountered in the assemblage of many industrial applications due to their advantages. However, joining these structures when using Friction Stir Welding (FSW) could be prone to defects that cause severe consequences like loss of strength and fracture. The current paper implements an experimental procedure to assess the effect of geometrical tool shape on void formation in friction stir welded AA 6061-T6 T-joint configuration. Taguchi optimization method was put into service to minimize the number of experiments and fulfil the goal of discovering the optimal FSW parameters that allow the manufacturing of such configurations with high mechanical properties. X-ray radiography and micrograph images were u

    Two dwarf snakes were discovered, Eirenis thospitis Schmidtler & Lanza from Sereen mountain, north east of Arbil  and E. rothii Jan from Saffin mountain North of Arbil  city  North of Iraqi Kurdistan. Supported by description and important notes on variation. In addition summarized list for 9 species of the genus Eirenis Jan in Iraq is also presented.  

Home Computer and Information Science 2009 Chapter The Stochastic Network Calculus Methodology Deah J. Kadhim, Saba Q. Jobbar, Wei Liu & Wenqing Cheng Chapter 568 Accesses 1 Citations Part of the Studies in Computational Intelligence book series (SCI,volume 208) Abstract The stochastic network calculus is an evolving new methodology for backlog and delay analysis of networks that can account for statistical multiplexing gain. This paper advances the stochastic network calculus by deriving a network service curve, which expresses the service given to a flow by the network as a whole in terms of a probabilistic bound. The presented network service curve permits the calculation of statistical end-to-end delay and backlog bounds for broad

Power switches require snubbing networks for driving single – phase industrial heaters. Designing these networks, for controlling the maximum allowable rate of rise of anode current (di/dt) and excessive anode – cathode voltage rise (dv/dt) of power switching devices as thyristors and Triacs, is usually achieved using conventional methods like Time Constant Method (TCM), resonance Method (RM), and Runge-Kutta Method (RKM). In this paper an alternative design methodology using Fuzzy Logic Method (FLM) is proposed for designing the snubber network to control the voltage and current changes. Results of FLM, with fewer rules requirements, show the close similarity with those of conventional design methods in such a network of a Triac drivin

Various theories have been proposed since in last century to predict the first sighting of a new crescent moon. None of them uses the concept of machine and deep learning to process, interpret and simulate patterns hidden in databases. Many of these theories use interpolation and extrapolation techniques to identify sighting regions through such data. In this study, a pattern recognizer artificial neural network was trained to distinguish between visibility regions. Essential parameters of crescent moon sighting were collected from moon sight datasets and used to build an intelligent system of pattern recognition to predict the crescent sight conditions. The proposed ANN learned the datasets with an accuracy of more than 72% in comp