2-(2-amino-5-nitro-phenylazo) -phenol was ready by grouping the diazonium salt of 2-aminophenol with 4-nitroaniline.Thegeometry of azo ligand(HL)was resolved on the origin of (C.H.N) analysis, 1H and 13CNMR spectra, infrared spectra and UV–vis electronic absorption spectra. Dealing with the azo ligand produced with Nd+3,Cd+3,Dy+3 and Er+3at aqueous ethanol for a 1:2 metal: ligand rate, and in perfect ph. The formation for compounds have been described by utilizing flame atomic absorption,(C.H.N) Analyses, conductivity, infrared spectra and UV–vis spectral procedures. Nature in the produced compounds have been studied obey the ratio of mole and continuous variance manners, Beer's law yielded up a concentration rate (1×10-4 - 3×10-4M) .

Ortho amino hydrazobenzene (L) has been prepared from the reaction of ortho amino phenyl thiol with phenyl hyrazan in mole ratio(1:1). It has been characterized by elemental analysis (C, H, N), IR, UV–Vis. The complexes of the bivalent ions (Co, Ni, Cu, Zn, Pd, Cd, Hg and Pb) and the trivalent (Cr) have been prepared and characterized too. The structural have been established by elemental analysis(C,H,N), IR , UV – Vis spectra , conductivity measurements , atomic absorption and magnetic susceptibility . The complexes showed characteristic behaviour of octahedral geometry around the metal ion and the( N,N) ligand coordinated in bidentate modeexcept with pd showed square planer. ? ,kf , ?max for the complexes were estimated too . ? for Co

Many researchers tried to prevent or reduce moisture damage and its sensitivity to temperature to improving the performance of hot mix asphalt because it is decreasing the functional and structural life of fixable pavement due to the moisture damage had exposed to it.

The main objective of this study is to inspect the effect of (fly ash “3%, 6%, 12%”, hydrated lime”5%, 10%, 20%” and silica fumes”1%, 2%, 4%) referring to previous research by the net weight asphalt cement as a modified material on the moisture and temperature sensitivity of hot mix asphalt. This was done using asphalt from AL-Nasiria refinery with penetration grade 40-50, nominal maximum size (12.5) mm (surface course) of aggregate and on

Liquid-Liquid Extraction of Cu(II) ion in aqueous solution by dicyclohexyl-18-crown-6 as extractant in dichloroethane was studied .The extraction efficiency was investigated by a spectrophometric method. The reagent form a coloured complex which has been a quantitatively extracted at pH 6.3. The method obeys Beer`s law over range from (2.5-22.5) ppm with the correlation coefficient of 0.9989. The molar absorptivity the stoichiometry of extracted complex is found to be 1:2. the proposed method is very sensitive and selective.

6-(2-benzathiazolyl azo),-3,5-dimethylphenol was formed by grouping the 2- benzothiazole diazonium chloride with 3,5-dimethylphenol. Azo ligand(L) was resolved on the origin by 1H and 13CNMR, FTIR and UV-V is spectral analysis. Complexation of tridentate ligand (L) with Co2+, Ni2+, Cu2+ and Zn2+ in aqueous of ethyl alcohol with a 1:2 metal:ligand, and at ideal pH.. The formation of metal chelates are assigned using flame atomic, absorption, FTIR, and UV-Vis spectral analysis, other than conductivity and magnetic estates. The nature of the metal chelates were carried out by mole ratio and continuous, variation mechanism, Beer's law, followed the rate (0.0001 - 3×0.0001 M) concentration., High molar, absorptivity, for the complex solutions w

Salicylaldehyde was reacting with 2-amino benzoic acid to produce the Schiff base ligand benzoic acid 2-salicylidene (L). The prepared ligand was identified by Microelemental Analysis, FT.IR and UV-Vis spectroscopic techniques. A new complexes of Co(II),Ni(II),Cu(II) and Zn(II) with Schiff base was prepared in aqueous ethanol with a (1:1) M:L. The prepared complexes were characterized using flame atomic absorption, (C.H.N) Analysis, FT.IR and UV-Vis spectroscopic methods as well as magnetic susceptibility and conductivity measurements. Biological activity of the ligand and complexes against three selected types of bacteria were also examined. Some of the complexes exhibit good bacterial activities. From the obtained data the tetrahedral str

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv