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A significant increase in the incidence of non-O157 verotoxigenic Escherichia coli (VTEC) infections have become a serious health issues, and this situation is worsening due to the dissemination of plasmid mediated multidrug-resistant microorganisms worldwide. This study aims to investigate the presence of plasmid-mediated verotoxin gene in non-O157 E. coli. Standard microbiological techniques identified a total of 137 E. coli isolates. The plasmid was detected by Perfectprep Plasmid Mini preparation kit. These isolates were subjected to disk diffusion assay, and plasmid curing with ethidium bromide treatment. The plasmid containing isolates were subjected to a polymerase chain reaction (PCR) for investigating

The remove of direct blue (DB71) anionic dye on flint clay in aqueous solution was investigated by using a batch system for various dye concentrations. The contact time, pH, adsorbent dose, and temperature was studied under batch adsorption technique. The data of adsorption equilibrium fit with isotherm Langmuar and Freiundlich ,when the correlation coefficient used to elucidate the best fitting isotherm model. The thermodynamic parameters such as, ?Hº ,?Sº and ?Gº. Thermodynamic analysis indicated that the sorption of the dyes onto Flint clay was endothermic and spontaneous.

Environmental pollution is experiencing an alarming surge within the global ecosystem, warranting urgent attention. Among the significant challenges that demand immediate resolution, effective treatment of industrial pollutants stands out prominently, which for decades has been the focus of most researchers for sustainable industrial development aiming to remove those pollutants and recover some of them. The liquid membrane (LM) method, specifically electromembrane extraction (EME), offers promise. EME deploys an electric field, reducing extraction time and energy use while staying eco-friendly. However, there's a crucial knowledge gap. Despite strides in understanding and applying EME, optimizing it for diverse industrial pollutant

Bearing capacity of soil is an important factor in designing shallow foundations. It is directly related to foundation dimensions and consequently its performance. The calculations for obtaining the bearing capacity of a soil needs many varying parameters, for example soil type, depth of foundation, unit weight of soil, etc. which makes these calculation very variable–parameter dependent. This paper presents the results of comparison between the theoretical equation stated by Terzaghi and the Artificial Neural Networks (ANN) technique to estimate the ultimate bearing capacity of the strip shallow footing on sandy soils. The results show a very good agreement between the theoretical solution and the ANN technique. Results revealed that us