Coagulation - flocculation are basic chemical engineering method in the treatment of metal-bearing industrial wastewater because it removes colloidal particles, some soluble compounds and very fine solid suspensions initially present in the wastewater by destabilization and formation of flocs. This research was conducted to study the feasibility of using natural coagulant such as okra and mallow and chemical coagulant such as alum for removing Cu and increase the removal efficiency and reduce the turbidity of treated water. Fourier transform Infrared (FTIR) was carried out for okra and mallow before and after coagulant to determine their type of functional groups. Carbonyl and hydroxyl functional groups on the surface of

         In the present work, the magnetic dipole and electric quadrupole moments for some sodium isotopes have been calculated using the shell model, considering the effect of the two-body effective interactions and the single-particle potentials. These isotopes are; ²¹Na (3/2+), ²³Na (3/2+), ²⁵Na (5/2+), ²⁶Na (3+), ²⁷Na (5/2+), ²⁸Na (1+) and, ²⁹Na (3/2+). The one-body transition density matrix elements (OBDM) have been calculated using the (USDA, USDB, HBUMSD and W) two-body effective interactions carried out in the sd-shell model space. The sd shell model space consists of the active 2s_1/2, 1d_5/2,

The quality of groundwater should be improved by keeping safe water sources from contaminants in protective way by doing regular measuring and checkup before it supplied for usage. Private Wells do not receive the same services that wells supplying the public do. Well owners are responsible for protecting their drinking water. This work was carried out in Badra city, Iraq from December 2017 to May 2018, six wells water were investigated to determine the general characteristics of wells as well as studying the effect of environmental factors on the quality of water. The average of six wells were eleven parameters that is out of permissible limits were EC, Sal., Alk., TH, TDS, Na, Ca, Cl, SO₄, Fe, Zn (4402-5183 /cm, 2.76-3.9 ppt

Election study included four sites for the waters in area of Youssifiyah south of Baghdad (river water, tank water, liquefactions water, and water filtrate and seethed and purifier by alum and chloride), the samples were collected during the month of June in 2007. Temperature, electrical conductivity and acidity are measured. Also , the concentration of chloride , sulphate , carbonate , nitrate , sodium , calcium , magnesium , hard total and total dissolved substances are determined , as well as heavy metals assess environmental risk (such as Cu, Pb, Zn, Fe). It was also a study of bacterial totals included both total Bacteria (TB) and Total Coliform Bacteria (TC) and Fecal Coliform (FC) and Fecal Streptococci (FS). The stu

Mercury, arsenic, cadmium and lead, were measured in sediment samples of river and marine environmental of Basra governorate in southern of Iraq. Sixteen sites of sediment were selected and distributed along Shatt Al-Arab River and the Iraqi marine environment. The samples were distributed among one station on Euphrates River before its confluence with Tigris River and Shatt Al-Arab formation, seven stations along Shatt Al-Arab River and eight stations were selected from the Iraqi marine region. All samples were collected from surface sediment in low tide time. ICP technique was used for the determination of mercury and arsenic for all samples, while cadmium and lead were measured for the same samples by using Atomic Absorption Spectrosc

In this study three inorganic nano additives, namely; CaCO3, Al2O3 and SiO2 were used to prepare nanocomposites of unsaturated polyester in order to modify their mechanical properties, i.e. tensile strength, elongation, impact and hardness. The results indicated that all the three additives were effective to improve the mechanical properties up to 4% by weight. The effectiveness of them follows the order : CaCO3 > Al2O3 > SiO2 This is due to their particle size in which CaCO3 (13nm), Al2O3 (20-30nm) and SiO2 (15-20nm).

Artificial intelligence (AI) is entering many fields of life nowadays. One of these fields is biometric authentication. Palm print recognition is considered a fundamental aspect of biometric identification systems due to the inherent stability, reliability, and uniqueness of palm print features, coupled with their non-invasive nature. In this paper, we develop an approach to identify individuals from palm print image recognition using Orange software in which a hybrid of AI methods: Deep Learning (DL) and traditional Machine Learning (ML) methods are used to enhance the overall performance metrics. The system comprises of three stages: pre-processing, feature extraction, and feature classification or matching. The SqueezeNet deep le