The study involved preparing a new compound by combining Schiff bases generated from compounds for antipyrine, including lanthanide ions (lanthanum, neodymium, erbium, gadolinium, and dysprosium). The preparation of the ligand from condensation reactions (4-antipyrinecarboxaldehyde with ethylene di-amine) at room temperature, and was characterization using spectroscopic and analytical studies ( FT-IR, UV-visible spectra, ¹H-NMR, mass spectrometry, (C.H.N.O), thermogravimetric analysis (TGA), in addition to the magnetic susceptibility and conductivity measurement of the synthesis complexes, among the results we obtained from the tests, we showed that the ligand behaves with the (triple Valence) lanthanide ions, the multidentate

A multistep synthesis was established for the preparation of a new vanillic acid-1, 2, 4-1triazole-3-thiol conjugate (4). Finally, several aromatized aldehydes reacted with compound (4) to produce Schiff bases derivatives (5–11). The purpose of this research is to prepare new vanillic acid derivatives with 1, 2, 4-triazole-3-thiol heterocyclic ring structures and to evaluate their antimicrobial activity in a preliminary assessment. Fourier-transform infrared (FT-IR) and proton nuclear magnetic resonance spectroscopy (¹H-NMR) were used to verify the structures of the newly synthesized compounds. all the final synthesized compounds (

In this research an Artificial Neural Network (ANN) technique was applied for the prediction of Ryznar Index (RI) of the flowing water from WTPs in Al-Karakh side (left side) in Baghdad city for year 2013. Three models (ANN1, ANN2 and ANN3) have been developed and tested using data from Baghdad Mayoralty (Amanat Baghdad) including drinking water quality for the period 2004 to 2013. The results indicate that it is quite possible to use an artificial neural networks in predicting the stability index (RI) with a good degree of accuracy. Where ANN 2 model could be used to predict RI for the effluents from Al-Karakh, Al-Qadisiya and Al-Karama WTPs as the highest correlation coefficient were obtained 92.4, 82.9 and 79.1% respe

In this research an Artificial Neural Network (ANN) technique was applied for the prediction of Ryznar Index (RI) of the flowing water from WTPs in Al-Karakh side (left side) in Baghdad city for year 2013. Three models (ANN1, ANN2 and ANN3) have been developed and tested using data from Baghdad Mayoralty (Amanat Baghdad) including drinking water quality for the period 2004 to 2013. The results indicate that it is quite possible to use an artificial neural networks in predicting the stability index (RI) with a good degree of accuracy. Where ANN 2 model could be used to predict RI for the effluents from Al-Karakh, Al-Qadisiya and Al-Karama WTPs as the highest correlation coefficient were obtained 92.4, 82.9 and 79.1% respectively. For

Background: Molar Incisor hypomineralisation (MIH) is one of the biggest challenges with great clinical interest. Currently, the etiology of MIH remains unclear. There is no previous study concerning school children aged 7 – 9 years in Al-Najaf governorate in order to estimate the prevalence and severity of molar incisor hypomineralisation and the possible associated risk factors. This study aimed to estimate the prevalence, severity and the possible associated etiological factors of molar incisor hypomineralisation and also to study the correlation between body mass index and molar incisor hypomineralisation. Material and Methods: Across sectional study conducted at Al-Najaf Governorate. A total of 600 children were enrolled those