In this work, an important sugar alkynyl ether has been synthesized in two subsequent steps starting from commercially available D-galactose (3). This kind of compounds is highly significant in the synthesis of biologically active molecules such as 1,2,3-triazole and isoxazoles. In the first step, galactose (3) was reacted with acetone in the presence of anhydrous copper (II) sulfate to produce 1,2:3,4-di-O-isopropylidene-α-D-galactose (4) in good yield. The latter was reacted with excess of 3-bromoprop-1-yne in DMF in the presence of NaOH pellets to afford the target molecule 5 in a very good yield. The temperature of this step is crucial in determining the reaction yield. The exact structure of compound 5 is identified using NMR technique and DFT calculations.
thin films of se:2.5% as were deposited on a glass substates by thermal coevaporation techniqi=ue under high vacuum at different thikness
The present study aims to detect CTX-M-type ESBL from Escherichia coli clinical isolates and to analyze their antibotic susceptibility patterns. One hundred of E. coli isolates were collected from different clinical samples from a tertiary hospital. ESBL positivity was determined by the disk diffusion method. PCR used for amplification of CTX-M-type ESBL produced by E. coli. Out of 100 E. coli isolates, twenty-four isolates (24%) were ESBL-producers. E. coli isolated from pus was the most frequent clinical specimen that produced ESBL (41.66%) followed by urine (34.21%), respiratory (22.23%), and blood (19.05%). After PCR amplification of these 24 isolates, 10 (41.66%) isolates were found to possess CTX-M genes. The CTX-M type ESBL
... Show MoreFace Recognition Systems (FRS) are increasingly targeted by morphing attacks, where facial features of multiple individuals are blended into a synthetic image to deceive biometric verification. This paper proposes an enhanced Siamese Neural Network (SNN)-based system for robust morph detection. The methodology involves four stages. First, a dataset of real and morphed images is generated using StyleGAN, producing high-quality facial images. Second, facial regions are extracted using Faster Region-based Convolutional Neural Networks (R-CNN) to isolate relevant features and eliminate background noise. Third, a Local Binary Pattern-Convolutional Neural Network (LBP-CNN) is used to build a baseline FRS and assess its susceptibility to d
... Show MoreThis paper is an attempt to investigate the syntactic and semantic features of the English phrasal verbs. In this paper, phrasal verbs were classified into subgroups according to their syntactic and semantic characteristics. After giving a survey of literature written on the meaning and definition of phrasal verbs, two sections have been devoted to tackle the most important issues in this category of English verbs. Section one sheds light on the basic definitions of the term ‘phrasal verb’ which are, according to the researcher’s point of view, sufficient to cover the area of the study. In addition, it studies the number and the importance of phrasal verbs in English. Section two deals with the syntactic and semantic features of Engli
... Show MoreThe aim of this paper is introducing the concept of (ɱ,ɳ) strong full stability B-Algebra-module related to an ideal. Some properties of (ɱ,ɳ)- strong full stability B-Algebra-module related to an ideal have been studied and another characterizations have been given. The relationship of (ɱ,ɳ) strong full stability B-Algebra-module related to an ideal that states, a B- -module Ӽ is (ɱ,ɳ)- strong full stability B-Algebra-module related to an ideal , if and only if for any two ɱ-element sub-sets and of Ӽɳ, if , for each j = 1, …, ɱ, i = 1,…, ɳ and implies Ạɳ( ) Ạɳ( have been proved..
Abstract
Objective(s): To evaluate housekeeping services staff work environment and their health status, as well as to determine the impact of the work environment upon their health status.
Methodology: A descriptive design is employed throughout the present study to evaluate housekeeping services staff work environment and their health status, as well as to determine the impact of the work environment upon their health status from November 3rd 2017 to June 30th 2018. A purposive “nonprobability” sample of (101) housekeeping staff is selected for the present study. An instrument is constructed for the purpose of the study and it is consists of (2) parts: (I) Evaluation of work environment, and (II) Evaluation of housekeeping st