In this study a new ligand ,(potassium 2-carbomethoxy amino-5-trithiocarbonate 1,3,4-thiadiazole) (L) has been prepared from 2-carbomethoxy amino-5-mercapto 1,3,4-thiadiazole with CS2 in alkali media . The product has been isolated and characterized by appropriate physical measurements, vibrational and electronic spectroscopy. The ligand was used to prepare a number of complexes with some metal ions Co(II), Ni(II) and Cu(II). These complexes have been characterized by FT-IR, UV-Vis spectra, molar conductivity, magnetic susceptibility, melting points and atomic absorption measurements. The nickel and copper complexes have an octahedral geometry while cobalt complex has a tetrahedral geometry. The nature of bonding between the metal ion

Generally the a.c. conductivity shows a power law in frequency s  ()  where the exponent s ≤ 1. As the frequency goes to zero the conductivity become frequency independent. The a.c. conductivity was studied for the Ge1-xSex thin films to see how the selenium contents affect the permittivity and the permeability for the Ge1-x Sex. The thin films prepared by thermal evaporation at room temperature and under vacuum (~2 x10-5toor) using Edward coating unit model 306A. From the relation between ln conductivity and ln w, the effect of selenium contents in Ge1-x Sex thin films on the exponent value, the relaxation time and the maximum barrier height. An algebric fitting method for circles and circular arcs was used to find the permit

Most dental works require a diagnostic impression; alginate is contemplated as the most popular material used for this purpose. Titanium dioxide nanoparticles show evidence of antimicrobial activity in the recent era, for this purpose, this study aimed to evaluate the effect of adding Titanium dioxide nanoparticles on antimicrobial activity and surface detail reproduction of alginate impression material. Materials and methods: Titanium dioxide nanoparticles (purity = 99%, size= 20nm) was added to alginate at three different concentrations (2%, 3% and 5%). 84 samples were prepared in total. Samples were tested for antimicrobial activity using a disc diffusion test, and surface detail reproduction was done using (ISO 21563:2021). One-way A

New complexes of the [M(Ura)(Phen)(OH2)Cl2]Cl.2H2O type, where (Ura) uracil ; (Phen) 1,10-phenanthroline hydrate; M (Cr+3 , Fe+3 and La+3) were synthesized from mix ligand and characterized . These complexes have been characterized by the elemental micro analysis, spectral (FT-IR., UV-Vis, 1HNMR, 13CNMR and Mass) and magnetic susceptibility as well the molar conductive mensuration. Cr+3, Fe+3 and La+3- complexes of six–coordinated were proposed for the insulated for three metal(III) complexes for molecular formulas following into uracil property and 1,10-phenanthroline hydrate present . The proposed molecular structure for all metal (III) complexes is octahedral geometries .The biological activity was tested of metal(III) salts, ligands

Objectives To compare the clinical efficacy of microvascular decompression surgery (MVD) and gamma knife radiosurgery (GKR) as a treatment for patients with primary trigeminal neuralgia (TN) and evaluate the outcome regarding pain relief, recurrence, and complications with both modalities of treatment. Patients and Methods A randomized prospective study conducted in SaadAlwitry Neurosciences Hospital, Baghdad, Iraq. Eighty-four patients with TN from January 2016 to January 2018, 45 patients had GKR while 39 patients treated with MVD. The pain evaluated pre-and post-operatively using the Barrow Neurological Institute Pain Intensity scale (BNIPI), visual analog scale (VAS) and Brief Pain Inventory Facial (BPI-Facial) scoring systems. In GKR p

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet