The present work aimed to study the efficiency of nanofiltration (NF) and reverse osmosis (RO) membrane for heavy metal removal from wastewater and study the factors affecting the performance of these two membranes: feed concentrations for heavy metal ions, pressure, and flow rate. The experimental results showed, heavy metals concentration in permeate increase with raise in feed concentrations, decline with increase in flow rate. The raise of pressure, heavy metals concentration decreases for RO membrane, but for NF membrane the concentration decrease and then at high pressure increase. The rejection percentage for chromium in NF and RO is 99.7% and 99.9%, for copper is 98.4% and 99.3%, for zinc is 97.9% and 99.5%, for nickel is 97.2% and

This work was conducted to study the ability of locally prepared Zeolite NaY for the reduction of sulfur compounds from Iraqi natural gas by a continuous mode adsorption unit. Zeolite Y was hydrothermally synthesized using abundant kaolin clay as aluminum precursor. Characterization was made using chemical analysis, XRD and BET surface area. Results of the adsorption experiments showed that zeolite Y is an active adsorbent for removal H2S from natural gas and other gas streams. The effect of temperature was found inversely related to the removal efficiency. Increasing bed height was found to increase the removal efficiency at constant flow rate of natural gas. The adsorption capacity was evaluated and its maximum uptake was 5.345 mg H2S/g z

One of the recent significant but challenging research studies in computational biology and bioinformatics is to unveil protein complexes from protein-protein interaction networks (PPINs). However, the development of a reliable algorithm to detect more complexes with high quality is still ongoing in many studies. The main contribution of this paper is to improve the effectiveness of the well-known modularity density ( ) model when used as a single objective optimization function in the framework of the canonical evolutionary algorithm (EA). To this end, the design of the EA is modified with a gene ontology-based mutation operator, where the aim is to make a positive collaboration between the modularity density model and the proposed

Bacteria strain H8, which produces high amount of exopolysaccharide (EPS), was isolated from soil, and identified as strain of Azotobacter chrococcum by its biochemical /physiological characteristics, EPS was extracted, partially purified and used as bioflocculant. The biochemical analysis of the partially purified EPS revealed that it was an alginate. analysis of EPS by Fourier transform infrared spectrometry (FTIR) show that the -OH groups present in bioflocculant are clearly seen at 3433.06 cm-1, the peaks attributed to the -CH3 groups present at 2916.17 cm-1 , and some distinct peaks such as carboxyl group showed strong absorption bands at 1604.66 cm-1, 1411.80 cm-1 and 1303.79 cm-1 indicate the chemical structure of alginate. The effe

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.

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob