الوصف The synthesis of 2 (N-phenyl dithio carboxamid) benzothiazol Ligand (L) from reaction of 2-Mercaptobenzothiozol with phenylisothiocyanate using ratio 1: 1. The ligand was characterized by elemental analysis (CHN),'H-NMR, IR and UV-Vis. The complexes with bivalent ions (Ni, Cu, Zn, Cd and Hg) have been prepared and characterized. The structural diagnosis was established using IR, UV–Visible spectro photometer, molar conductivity, atomic absorption and molar ratio with selected metal ions (Ni2+, Cu2+). The complexes of (Ni, Cu) gave octahedral structural while the complexes of (Zn, Cd, Hg) gave tetrahedral structural. The study of biological activity of the ligand (L) and its complexes (Ni, Cu, Hg) in two deferent concentration (

In this paper, integrated quantum neural network (QNN), which is a class of feedforward

neural networks (FFNN’s), is performed through emerging quantum computing (QC) with artificial neural network(ANN) classifier. It is used in data classification technique, and here iris flower data is used as a classification signals. For this purpose independent component analysis (ICA) is used as a feature extraction technique after normalization of these signals, the architecture of (QNN’s) has inherently built in fuzzy, hidden units of these networks (QNN’s) to develop quantized representations of sample information provided by the training data set in various graded levels of certainty. Experimental results presented here show that

Quality control of well logs has always been an important objective in reservoir studies because of the key role played by well logs as input data. This study aims to make a quality control on well logs data for two wells of Yamama formation in southern Iraqi field to ensuring and enhancing the measurement accuracy. In the beginning, the calibration data of before and after surveys are applied as initial evaluation for the quality of density log in well R-1. Then, depth matching is used to fit the depth of all logs in each well. After that, the comparison between the main and repeat sections is helped to check the repeatability. Finally, all uncorrected logs are environmentally corrected to remove the effects of the borehole conditi

This paper presents a hybrid approach for solving null values problem; it hybridizes rough set theory with intelligent swarm algorithm. The proposed approach is a supervised learning model. A large set of complete data called learning data is used to find the decision rule sets that then have been used in solving the incomplete data problem. The intelligent swarm algorithm is used for feature selection which represents bees algorithm as heuristic search algorithm combined with rough set theory as evaluation function. Also another feature selection algorithm called ID3 is presented, it works as statistical algorithm instead of intelligent algorithm. A comparison between those two approaches is made in their performance for null values estima