Heavy metal ion removal from industrial wastewater treatment systems is still difficult because it contains organic contaminants. In this study, functional composite hydrogels with photo Fenton reaction activity were used to decompose organic contaminants. Fe₃O₄ Nanoparticle, chitosan (CS), and other materials make up the hydrogel. There are different factors that affected Photo-Fenton activity including (pH, H₂O₂ conc., temp., and exposure period). Atomic force microscopy was used to examine the morphology of the composite and its average diameter (AFM). After 60 minutes of exposure to UV radiation, CS/ Fe₃O₄ hydrogel composite had degraded methylene blue (M.B.)

To asses methylene blue as a cell marker, the cells of the buffy coat were labelled by incubating them in a medium containing a lable [Methylene blue] which is prepared in a concentration of 1%[1, 2, 3, 4, 5, 6] drops were tried at different periods of incubation [+/-,+/-, 1+/-, 1+/-] at 37 C degree. The results showed that monocytes and polymorphs are the main cells involved in the phagocytosis of this dye

In the literature, several correlations have been proposed for bubble size prediction in bubble columns. However these correlations fail to predict bubble diameter over a wide range of conditions. Based on a data bank of around 230 measurements collected from the open literature, a correlation for bubble sizes in the homogenous region in bubble columns was derived using Artificial Neural Network (ANN) modeling. The bubble diameter was found to be a function of six parameters: gas velocity, column diameter, diameter of orifice, liquid density, liquid viscosity and liquid surface tension. Statistical analysis showed that the proposed correlation has an Average Absolute Relative Error (AARE) of 7.3 % and correlation coefficient of 92.2%. A

A Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twenty four samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.

   In this study, the circulating fluidized bed was used to remove the Tetracycline from wastewater utilizing a pistachio shell coated with ZnO nanoparticles. Several parameters including, Tetracycline solution flowrate, initial static bed height, Tetracycline initial concentration and airflow rate were systematically examined to show their effect on the breakthrough curve and the required time to reach the adsorption capacity and thus draw the fully saturated curve of the adsorbent. Results showed that using ZnO nanoparticles will increase the adsorbent surface area and pores and as a result the adsorption increased, also the required time for adsorbent saturation increased and thus the removal efficiency may be achieved at mi

 The adsorption behavior of methyl orange from aqueous solution on Iraqi bentonite was investigated. The effects of various parameters such as initial concentration of methyl orange, amount of adsorbent, ionic strength and temperature on the adsorption capacity has been studied. The percentage removal of methyl orange increased with the decrease of initial concentration of methyl orange and it increased with the increase of dose of adsorbent. The adsorbed amount of methyl orange decrease with increasing ionic strength and an increase in temperature. The equilibrium adsorption isotherms have been analysed by the linear, Langmuir and Temkin models. The Langmuir isotherms have the highest correlation coefficients. Thermodynamic paramet

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet