The antibacterial activity of some extracts of A. eupatoria (aqueous and ethanolic) against some pathogenic bacteria (Staphylococcus aureus, Pseudomonas aeruginosa and Escherichia coli ) and their activity on wound healing in rats , also the presence of some active compounds in both extracts were detected . The results showed that the ethanolic extract was more effective on inhibiting tested bacteria than the aqueous extract . P.aeruginosa was the most resistant bacteria, while highest inhibition zone appeared on E.coli (20 mm) .There was a moderate activity against S.aureus with inhibition zone 15 mm. by using ethanolic extract (10 mg/ml) . The phytochemical analysis for detection of active compounds revealed the presence of Carbohydrate

The synthesis of zeolite NaX from locally available kaolin has been studied. The operating conditions for zeolite NaX production from kaolin with good crystallinity were as follows; a gel formation step of metakaolin in alkaline medium in presence of additional silica to crystallize the zeolite was achieved at 60 oC for 1 hr,and with stirring. In ageing step of the reactants at room temperature for 5 days and crystallization step at 87±2 oC for 24 hr. The catalytic activity of catalyst prepared from local kaolin was studied by using cumene cracking as a model for catalytic cracking and compared with standard HY zeolite and HX zeolite catalysts. The activity test was carried out in a laboratory continuous flow unit with fixed bed reactor

An oxidative polymerization approach was used to create polyaniline (PANI) and Fe₂O₃ /PANI nanoparticle combination. Various characterization approaches were used to investigate the structural, morphological, and Fe₂O₃ /PANI nanoparticle structures. The findings support the synthesis of polycrystalline nanoparticle PANI and Fe₂O₃ /PANI spherical nanoparticle composites. Gram-positive bacteria are tested for antibacterial activity. Various quantities of Nanoparticles of PANI and Fe₂O₃ /PANI nanoparticle composites were used to test Staph-aureus and gram-negative bacteria, E-coli, and candida species. PANI has antibacterial properties against all microo

This paper is concerned with introducing an explicit expression for orthogonal Boubaker polynomial functions with some important properties. Taking advantage of the interesting properties of Boubaker polynomials, the definition of Boubaker wavelets on interval [0,1) is achieved. These basic functions are orthonormal and have compact support. Wavelets have many advantages and applications in the theoretical and applied fields, and they are applied with the orthogonal polynomials to propose a new method for treating several problems in sciences, and engineering that is wavelet method, which is computationally more attractive in the various fields. A novel property of Boubaker wavelet function derivative in terms of Boubaker wavelet themsel

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

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

Metasurface polarizers are essential optical components in modern integrated optics and play a vital role in many optical applications including Quantum Key Distribution systems in quantum cryptography. However, inverse design of metasurface polarizers with high efficiency depends on the proper prediction of structural dimensions based on required optical response. Deep learning neural networks can efficiently help in the inverse design process, minimizing both time and simulation resources requirements, while better results can be achieved compared to traditional optimization methods. Hereby, utilizing the COMSOL Multiphysics Surrogate model and deep neural networks to design a metasurface grating structure with high extinction rat