New substituted anthraquinones with amino derivations fragments were synthesized through the substitution of bromine atom by different amines using the Ullmann coupling reaction. Obtained compounds based on anthraquinone used for experimental antimicrobial studies. The structure of the synthesized compounds was confirmed by LC-MS and ¹H, ¹³C NMR spectroscopy. Studies on planktonic microorganisms have shown that the first synthesized anthraquinone derivatives have an inhibitory effect against bacteria and fungi. The triazene 1-(3-(benzoic acid(triaz-1-en-1-ol(-4-(1H-imidazol-1-yl(-9,10-dioxo-9,10-dihydroanthracene -2-sulfonic acid, have wide spectrum of activity, growth retardation zones against gram-positive micro

In this work, we focused on studying 1,4-naphthoquinones and their derivatives, and knowing the methods of preparing them using different auxiliary agents and forming derivatives containing heterocyclic rings, active groups and saturated rings containing heterogeneous elements . In addition, due to their strong antibacterial, antifungal and anticancer activity, 1,4-naphthoquinone compounds biological importance and are considered a source of various pharmaceutical compounds.

The compound [G1] was prepared from the reaction of thiosemicarbazide with para-hydroxyphenylmethyl ketone in ethanol as a solvent. Then by sequence reactions prepared [G2] and [G3] compounds. The compound [G4] reaction with ethyl acetoacetoneto synthesized compound [G6] and acetyl acetone to synthesized compound [G5]. Reaction the [G3] with two different types of aldehydes in the present of pipredine to form new alkenes compounds [G7]and [G8].The compound [G3] reacted with hydrazine hydrate to formation[G4] with present the hydrazine hydrade 80% in (10) ml of absolute ethanol. Latter the compound [G4]reacted with different aldehydes with present the glacial acetic acid and the solvent was ethanol to formed the Schiff bases compounds[G9] an

Antibiotic resistance is the major growing threat facing the pharmacological treatment of bacterial infections. Therefore, bioprospecting the medicinal plants could provide potential sources for antimicrobial agents. Mimusops, the biggest and widely distributed plant genus of family Sapotaceae, is used in traditional medicines due to its promising pharmacological activities. This study was conducted to elucidate the antimicrobial effect of three unexplored Mimusops spp. (M. kummel, M. laurifolia and M. zeyheri). Furthermore, the mechanisms underlying such antibacterial activity were studied. The Mimusops leaf extracts revealed significant antibacterial activities against the five tested bacter

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

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%.

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