Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature

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

Removal of Congo red, Rhodamine B, and Dispers Blue dyes from water solution have been achieved using Flint Clay as an adsorbent. The adsorption was studied as a function of contact time, adsorbent dose, pH, and temperature under batch adsorption technique. The equilibrium data fit with Langmuir, Freundlich and Toth models of adsorption and the linear regression coefficient R2 was used to elucidate the best fitting isotherm model. Different thermodynamic parameters, namely Gibb’s free energy, enthalpy and entropy of the on-going adsorption process have also been evaluated. Batch technique has been employed for the kinetic measurements and the adsorption of the three dyes follows a second order rate kinetics. The kinetic investigations al

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.

In this paper the process of metal ions extraction (Zn(II) and Cu(II)) was studied in PEG-KCl aqueous two phase system was investigated without using an extracting agent. The experimental runs were performance at constant temperature (25 ^oC), constant mixing time (30 min), and constant PH of the solution (about 3). The effect of KCl salt concentration (from 10% to 25%), volumetric phase ratio of PEG solution to KCl solution (from 0.5 to 2), and the initial metal ion concentration (from 0.25 ml to 2 ml of 1 gm/L solution) were investigated on the percent extraction of Zn(II) and Cu(II). The results indicated that the percent extraction of metal ions increase with increasing of salt concentration and phase ratio, and slightly de

In this study, nickel cobaltite (NC) nanoparticles were created using the sol-gel process and used as an adsorbent to adsorb methyl green dye (MG) from aqueous solutions. The adequate preparation of nickel cobaltite nanoparticles was verified using FT-IR, SEM, and X-ray diffraction (XRD) studies. The crystalline particle size of NC nanoparticles was 10.53 nm. The effects of a number of experimental variables, such as temperature, adsorbent dosage, and contact time, were examined. The optimal contact time and adsorbent dosage were 120 minutes and 4.5 mg/L, respectively. Four kinetic models—an intraparticle diffusion, a pseudo-first-order equation, a pseudo-second-order equation, and the Boyd equation—were employed to monitor the adsorpti

The present study aimed to assess the antibacterial activity of peanut (Arachis hypogaea L.) skin extracts. The phytochemical analysis of the peanut skin extracts was investigated, the result showed a strong presence of flavonoids, phenols, alkaloids and tannins in methanol and ethyl acetate extracts. Antibiotic susceptibility of the bacterial isolates was performed on seven antibiotics represented by Amikacin, Tetracycline, Ciprofloxacin, Chloramphenicol, Ticarcillin, Cefotaxime and Gentamicin by disc diffusion method. The antibiogram for studied isolates revealed high level resistance of A. baumannii to all of the antibiotics under test except amikacin, while Staph. aurous was resistance to Chloramphenicol and Cefotxime and sensitive to A