This work deals with preparation of Sulfated Zirconia catalyst (SZ) for isomerization of n-hexane model and refinery light naphtha, as well as enhanced the role of promoters to get the target with the mild condition, stability, and to prevent formation of coke precursors on strong acidic sites of the catalyst. The prepared SZ catalysts were characterization by fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), Brunauer –Emmett-Teller (BET) surface area analysis, Thermogravimetric Analysis (TGA), Scanning Electron Microscope (SEM) and atomic force microscopy (AFM) Analyzer. The results illustrate that the maximum conversion and selectivity for n-hexane isomerization with Ni-WSZ and operating temperature of 150 °C

Calcium-Montmorillonite (bentonite) [Ca-MMT] has been prepared via cation exchange reaction using benzalkonium chloride [quaternary ammonium] as a surfactant to produce organoclay which is used to prepare polymer composites. Functionalization of this filler surface is very important factor for achieving good interaction between filler and polymer matrix. Basal spacing and functional groups identification of this organoclay were characterized using X-Ray Diffraction (XRD) and Fourier Transform Infrared (FTIR) spectroscopy respectively.  The (XRD) results showed that the basal spacing of the treated clay (organoclay) with the benzalkonium chloride increased to 15.17213 ⁰A, this represents an increment of about 77.9% in the

A total of 200 samples (180 fecal materials and 20 organ samples) were collected from (5 different poultry farms, 10 local poultry shops, 5 houses poultry, 5 Eggs stores shops and 5hand slaughters centers) in Ibb city, Yemen, 2014. According to morphological, cultural, as well as biochemical characterization and serological tests, 59(29.5%) isolates were identified as Salmonella spp. and all Salmonella isolates were categorized by serotype, which comprised of, 37(62.71%) Salmonella Typhimurium serovar, 21(35.59%). Salmonella Enteritidis serovar and 1(1.69%) Salmonella Heidlberg serovar. Antibiotic sensitivity test was done for bacterial isolates and the results showed there were clear differences in antibiotic resistant. Antimicrobial

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