Abstract The present work aims to study the performance of reinforced compacted clay soil by sand columns stabilized with sodium silicate to obtain more solid columns than the surrounding soil. The experimental work was carried out by using a lab model to evaluate the performance of both the floating and end bearing sand columns. The results showed that the improvement ratio for the soil reinforced with sand columns stabilized with sodium silicate reached 390% for the type of floating columns and 438% for end bearing columns.

The present work involved four steps: First step include reaction of acrylamide ,N-?-Methylen-bis(acryl amide) and N-tert Butyl acryl amide with poly acryloyl chloride in the presence of triethyl amine (Et3N) as catalyst, the second step include homopolymerization of all products of the first step by using benzoyl peroxide(BPO) as initiator in (80-90)Co in the presence of Nitrogen gas(N2). In the third step the poly acrylimide which prepare in second step was convert into potassium salt by using alcoholic potassium hydroxide solution. Fourth step include Alkylation of the prepared polymeric salts in third step by react it with different alkyl halides(benzyl chloride, allylbromide , methyl iodide) by using DMF as solvent for(10-12) hours.

The work includes synthesis of 1,2,3-triazoles via click conditions and using the microwave irradiation starting from two synthesized azides: 2,3,4,6-tetra-O-acetyl-β-D-glucopyranosyl azide (5) and perfluorobutylethyl azide (10) and different terminal alkynes. It also includes microwave enhanced synthesis of tetrazoles via the reaction of two synthesized azides i.e., perfluorobutylethyl azide (10) and 1,5-diazidopentane (13) with benzoyl cyanide. Most of the prepared compounds have been characterized by: TLC, FT-IR, 1H NMR, 13C NMR, LC-MS and microelemental analysis

   In this study, the bacteria from sputum specimens of patients with respiratory tract infections were isolated in   IbnSina Teaching Hospital, Mosul city, Iraq.  The bacteria  were subjected to phenotypic and biochemical tests necessary for identification. Twenty five isolates of six different bacterial species were obtained, they are : Staphylococcus aureus, Streptococcus pneumonia, Moraxella cattarhalis, Escherichia coli, Klebsiella pnuemoniae and Pseudomona saeruginosa with ratios (64%, 12%, 8%, 8%, 4% and 4% ) respectively. The sensitivity and resistance of these isolates to 12 antibiotic were studied, where the Gentamycin appear to be more effective on most of the isolates while all the isolates showe

Electro-kinetic remediation technology is one of the developing technologies that offer great promise for the cleanup of soils contaminated with heavy metals. A numerical model was formulated to simulate copper (Cu) transport under an electric field using one-dimensional diffusion-advection equations describing the contaminant transport driven by chemical and electrical gradients in soil during the electro-kinetic remediation as a function of time and space. This model included complex physicochemical factors affecting the transport phenomena, such as soil pH value, aqueous phase reaction, adsorption, and precipitation. One-dimensional finitedifference computer program successfully predicted meaningful values for soil pH profiles and Cu

This study aimed to explore the manufacture of high-fat pellets for obesity induction diets in male Wistar rats and determined its effect on lipid profiles and body mass index. It was an experimental laboratory method with a post-test randomized control group. Formulation of high-fat pellets (HFD) and physico-chemical characteristics of pellets were conducted in September 2019. This study used about 28 male Wistar white rats, two months old, and 150-200 g body weight. Rats were acclimatized for seven days, then divided into four groups: 7 rats were given a standard feed of Confeed PARS CP594 (P0), and three groups (P1, P2, P3) were given high-fat feed (HFD FII) 30 g/head/day. The result showed that the mean fat content of Formula II pell

Healthcare professionals routinely use audio signals, generated by the human body, to help diagnose disease or assess its progression. With new technologies, it is now possible to collect human-generated sounds, such as coughing. Audio-based machine learning technologies can be adopted for automatic analysis of collected data. Valuable and rich information can be obtained from the cough signal and extracting effective characteristics from a finite duration time interval that changes as a function of time. This article presents a proposed approach to the detection and diagnosis of COVID-19 through the processing of cough collected from patients suffering from the most common symptoms of this pandemic. The proposed method is based on adopt