This study involved preparation of Graphene oxide (GO) and reduced graphene oxide (RGO) using Hummer method and chemical method respectively. These carbon nanomaterials were used as starting material to make novel functionalize with thiocarbohydrazide (TCH) which was prepared by reacting CS2 with hydrazine to form GO or RGO- 4-amino,5-substituted 1H,1,2,4 Triazole 5(4H) thion (ASTT) ,(GOT) and( RGOT) respectively via cyclocondensation reaction. Also MnO2 nanorod was prepared to form hybridized with GOT and RGOT. A commercial multiwall carbon nanotube (MWCNT) and functionalization with carboxylic groups' (f-MWCNT) and its nanocomposite with GOT were also prepared. All carbon nanomaterials were characterized with different techniques such as

Background: Antibiotic resistance in Escherichia coli is a major concern for public health, as it reduces options to treat infections such at urinary tract infection (UTI). Green tea (Camellia sinensis) and other natural plant products have been of interest for their ability to modify antibiotic susceptibility of bacteria, a capacity largely attributed to bioactive polyphenols. Objective: The purpose of this work was to investigate in vitro the potential effect of green tea leaf ethanol extract on the antibiotic susceptibility of E. coli isolates. Materials and Methods: Green tea leaves was extracted using ethanol and re-dissolved 1% DMSO. Five concentrations of green tea leaves ethanol extract 0.60, 0.50, 0.30, 0.16 and 0

This research describes a new model inspired by Mobilenetv2 that was trained on a very diverse dataset. The goal is to enable fire detection in open areas to replace physical sensor-based fire detectors and reduce false alarms of fires, to achieve the lowest losses in open areas via deep learning. A diverse fire dataset was created that combines images and videos from several sources. In addition, another self-made data set was taken from the farms of the holy shrine of Al-Hussainiya in the city of Karbala. After that, the model was trained with the collected dataset. The test accuracy of the fire dataset that was trained with the new model reached 98.87%.

Engineering equipment is essential part in the construction project and usually manufactured with long lead times, large costs and special engineering requirements. Construction manager targets that equipment to be delivered in the site need date with the right quantity, appropriate cost and required quality, and this entails an efficient supplier can satisfy these targets. Selection of engineering equipment supplier is a crucial managerial process .it requires evaluation of multiple suppliers according to multiple criteria. This process is usually performed manually and based on just limited evaluation criteria, so better alternatives may be neglected. Three stages of survey comprised number of public a

Methotrexate (MTX) is a folate antagonist widely used in the treatment of neoplastic diseases; its biotransformation in the liver produced active metabolites that promote hepatotoxicity. The present study was designed to evaluate the hepatoprotective effect of aqueous extract of Camellia sinensis (Green tea) against MTX-induced liver damage in rats. A model of liver injury in rats was induced by intraperitoneal injection of 20mg/kg MTX as a single dose followed by saline and 1.25% and 2.5% aqueous extract of green tea (GTE) were orally administered 7 days prior and 5 days after MTX-intoxication as a sole source of drinking water. After killing the animals, blood samples were obtained for evaluation of serum levels of alanine and

The problem of internal sulfate attack in concrete is widespread in Iraq and neighboring countries.This is because of the high sulfate content usually present in sand and gravel used in it. In the present study the total effective sulfate in concrete was used to calculate the optimum SO3 content. Regression models were developed based on linear regression analysis to predict the optimum SO3 content usually referred as (O.G.C) in concrete. The data is separated to 155 for the development of the models and 37 for checking the models. Eight models were built for 28-days age. Then a late age (greater than 28-days) model was developed based on the predicted optimum SO3 content of 28-days and late age. Eight developed models were built for all