The compound [K1] was synthesized from the reaction of dichloromethane with linear alkyl benzene (Lab9) using ethanol as a solvent, and from(chloro methyl)-4-nonylbenzene) [K1] it was possible to synthesize the compound Z(4-(nonan-3-yl)phenyl) methane amine) [K2] containing the amine group by synthesized from [K2] reaction with appropriate phenolic aldehydes and using Ethanol as a solvent in the preparation of vinyl chloride4-(((4-nonylbenzyl)imino)methyl)phenol-4-(((4-nonylbenzyl)imino methyl)benzene-1,3diol) [K3-K4] bases has been used. Preparation of a number of Phenolic polymers4-(2- hydroxy-3.5-dimethylbenzyl)-2-methyl-6-(((4-4-(2hyroxy-3, 5-dimethylbenzyl)-2-methyl-6(((4 nonylbenzyl) imino) methyl) benzene-phenolnonylbenzyl) imino) me

Five isolates of Gram negative bacteria (Klebsiella pneumoniae, Psuedomonas auroginosa, proteus mirabilis and two strains of E.coli) were in quested for the ability of bearing silver nanoparticles by using LB medium, all the isolates of bacteria were buttered brown color just as soon as mixed the supernatant of bacterial culture with AgNO3 solution, that refered the biosynthesis of Silver nanoparticles (Ag NPs). UV–visible spectrophotometer and Fourier transform infrared (FTIR) spectroscopy were utilized for estimation of (Ag NPs). The five isolates of bacteria were tendered to produce spontaneous mutants by using different kinds of antibiotics, Ampicillin put to use for making mutant in E.coli and Proteus mirabillis, while Pseudom

In this research, the degradation of Dazomet has been studied by using thermal Fenton process and photo-Fenton processes under UV and lights sun. The optimum values of amounts of the Fenton reagents have been determined (0.07g FeSO4 .7H2O, 3.5µl H2O2) at 25 °C and at pH 7 where the degradation percentages of Dazomet were recorded high. It has been found that solar photo Fenton process was more effective in degradation of Dazomet than photo-Fenton under UV-light and thermal Fenton processes, the percentage of degradation of Dazomet by photo-Fenton under sun light are 88% and 100% at 249 nm and 281 nm respectively, while the percentages of degradation for photo-Fenton under UV-light are 87%, 96% and for thermal Fenton are 70% and 66

In this research, the degradation of Dazomet has been studied by using thermal Fenton process and photo-Fenton processes under UV and lights sun. The optimum values of amounts of the Fenton reagents have been determined (0.07g FeSO4 .7H2O, 3.5µl H2O2) at 25 °C and at pH 7 where the degradation percentages of Dazomet were recorded high. It has been found that solar photo Fenton process was more effective in degradation of Dazomet than photo-Fenton under UV-light and thermal Fenton processes, the percentage of degradation of Dazomet by photo-Fenton under sun light are 88% and 100% at 249 nm and 281 nm respectively, while the percentages of degradation for photo-Fenton under UV-light are 87%, 96% and for thermal Fenton are 70% and 66.8% at 2

Most approaches to combat antibiotic resistant bacteria concentrate on discovering new antibiotics or modifying existing ones. However, one of the most promising alternatives is the use of bacteriophages. This study was focused on the isolation of bacteriophages that are specific to some of commonly human pathogens namely E. coli, Streptococcus pyogenes, Staphylococcus aureus, Proteus mirabilis, Pseudomonas aeruginosa, Salmonella spp. and Klebsiella pneumoniae. These bacteriophages were isolated from sewages that were collected from four different locations in Kirkuk City. Apart from S. pyogenes, bacteriophages specific to all tested bacteria were successfully isolated and tested for their effectiveness by spot test. The most effective

Optimum perforation location selection is an important study to improve well production and hence in the reservoir development process, especially for unconventional high-pressure formations such as the formations under study. Reservoir geomechanics is one of the key factors to find optimal perforation location. This study aims to detect optimum perforation location by investigating the changes in geomechanical properties and wellbore stress for high-pressure formations and studying the difference in different stress type behaviors between normal and abnormal formations. The calculations are achieved by building one-dimensional mechanical earth model using the data of four deep abnormal wells located in Southern Iraqi oil fields. The magni

Recent growth in transport and wireless communication technologies has aided the evolution of Intelligent Transportation Systems (ITS). The ITS is based on different types of transportation modes like road, rail, ocean and aviation. Vehicular ad hoc network (VANET) is a technology that considers moving vehicles as nodes in a network to create a wireless communication network. VANET has emerged as a resourceful approach to enhance the road safety. Road safety has become a critical issue in recent years. Emergency incidents such as accidents, heavy traffic and road damages are the main causes of the inefficiency of the traffic flow. These occurrences do not only create the congestion on the road but also increase the fuel consumption and p

The analytic solution for the unsteady flow of generalized Oldroyd- B fluid on oscillating rectangular duct is studied. In the absence of the frequency of oscillations, we obtain the problem for the flow of generalized Oldroyd- B fluid in a duct of rectangular cross- section moving parallel to its length. The problem is solved by applying the double finite Fourier sine and discrete Laplace transforms. The solutions for the generalized Maxwell fluids and the ordinary Maxwell fluid appear as limiting cases of the solutions obtained here. Finally, the effect of material parameters on the velocity profile spotlighted by means of the graphical illustrations

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par