The degradation of Toluidine Blue dye in aqueous solution under UV irradiation is investigated by using photo-Fenton oxidation (UV/H2O2/Fe+). The effect of initial dye concentration, initial ferrous ion concentration, pH, initial hydrogen peroxide dosage, and irradiation time are studied. It is found put that the removal rate increases as the initial concentration of H2O2 and ferrous ion increase to optimum value ,where in we get more than 99% removal efficiency of dye at pH = 4 when the [H2O2] = 500mg / L, [Fe + 2 = 150mg / L]. Complete degradation was achieved in the relatively short time of 75 minutes. Faster decolonization is achieved at low pH, with the optimal value at pH 4 .The concentrations of degradation dye are detected by spectr

The pollution producing from textile industries effluents is growing since the years, due to at discharged lots of it in water without treatment. The resulting effluent is colourful, highly toxic, and poses a significant environmental hazard. This problem can be solved by using enzymic biological treatment, where the Congo red dye was used with concentrations (100,200,300,500) mg /L, pH values (3,4,5,6,7,8), and variable temperatures (25,35,45)°C, the best removal of Congo red (CR) dye  under optimum conditions for degradation was at  concentration of 100 mg/L, at (pH 6, 25 °C) with efficiency of 99.85 % using the peroxidase enzyme extracted from red radish plant, while the removal percentage decreased when increase dye concentration

Cr2O3 thin films have been prepared by spray pyrolysis on a glass substrate. Absorbance and transmittance spectra were recorded in the wavelength range (300-900) nm before and after annealing. The effects of annealing temperature on absorption coefficient, refractive index, extinction coefficient, real and imaginary parts of dielectric constant and optical conductivity were expected. It was found that all these parameters increase as the annealing temperature increased to 550°C.

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d

In this paper, Bayes estimators of Poisson distribution have been derived by using two loss functions: the squared error loss function and the proposed exponential loss function in this study, based on different priors classified as the two different informative prior distributions represented by erlang and inverse levy prior distributions and non-informative prior for the shape parameter of Poisson distribution. The maximum likelihood estimator (MLE) of the Poisson distribution has also been derived. A simulation study has been fulfilled to compare the accuracy of the Bayes estimates with the corresponding maximum likelihood estimate (MLE) of the Poisson distribution based on the root mean squared error (RMSE) for different cases of the