Adsorption is one of the most important technologies for the treatment of polluted water from dyes. Theaim of this study is to use a low-cost adsorbent for this purpose. A novel and economical adsorbent was used to remove methyl violet dye (MV) from aqueous solutions. This adsorbent was prepared from bean peel, which is an agricultural waste. Batch adsorption experiments were conducted to study the ability of the bean peel adsorbent (BPA) to remove the methyl violet (MV) dye. The effects of different variables, such as weight of the adsorbent, pH of the MV solution, initial concentration of MV, contact time and temperature, on the adsorption behaviour were studied. It was found experimentally that the time required to achieve equilibrium

Crude soybean peroxidase (SBP), isolated from soybean seed coats (hulls) at unusually low concentrations, catalyses the oxidative polymerisation of hazardous aqueous benzidine and its 3,3′-dichloro, 3,3′-dimethyl and 3,3′-dimethoxy derivatives in the presence of hydrogen peroxide. The optimum operating conditions for oxidation of 0·10 mM benzidine were investigated. At pH 5, the hydrogen peroxide-to-substrate concentration ratio was 1·5 and the minimum SBP concentration required to achieve at least 95% conversion of the benzidine in synthetic wastewater was 0·43 mU/ml. Progress curves were established for the conversion of the four substrates, and apparent first-order rate constants were derived. Enzyme-catalysed polym

New heterocyclic compounds derived from 2-Morpholino-1,8-naphthyridine-4-carboxylic acid such as oxadiazolo, thiadiazolo – thione and triazolo-thione have been prepared and characterized on the basis of IR and 1H NMR spectra data. The hydrizide compound was utilized as a starting material for preparing of these compounds. The second part of this study involves the biological studies of some of these naphthyridine derivatives by using three different kinds of bacteria namely: Staphylococcus aureus, Pseudomonas aeruglnosa and Escherichia coli. The data indicated that some of these compounds have a good activity against the tested bacteria in comparison to antibiotics.

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

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

Detergent is one of the pollutants that poses significant threats to ecological systems. Detergents can also dissolve in wastewater and negatively impact the efficiency of wastewater treatment facilities. They are used for a variety of functions, most notably hygiene, and are an integral aspect of human life. This means that there are a variety of routes by which detergent components can reach the environment. In this Study, twenty-three detergent samples from local markets in Baghdad. The aim of this study is to investigate the concentration of heavy metals Cobalt (Co), Chromium (Cr),Lead (Pb),Zinc (Zn), Iron (Fe) and Cadmium (Cd) in some detergents using Atomic Absorption Spectrophotometer. The results of the concentration of heavy elemen

Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c