Autorías: Wafaa Sabah Mohammed Al-Khafaji, Fatimah Hameed Kzar Al-Masoodi, Suadad Ibrahim Suhail Al-Kinani. Localización: Revista iberoamericana de psicología del ejercicio y el deporte. Nº. 3, 2023. Artículo de Revista en Dialnet.

        The experimental and theoretical methods were studied for inhibition of the corrosion titanium in HCl by using neomycin sulfate drug. The results of neomycin sulfate drug had good corrosion protection for titanium in hydrochloric acid and the inhibition efficiency (%IE) increasing with increasing concentration of drug because the neomycin sulfate drug had adsorption from acid solution on surface of titanium metal. The program of hyperchem-8.07 was used for theoretical study of the drug by molecular mechanics and semi-empirical calculations. Quantum chemical was studied drug absorption and electron transferred from the drug to the Titanium metal, also inhibition potentials of drug attachment with the (LUMO-HOMO) energy gap,

Background: The aim of the study was to investigate the effect of surface treatments of zirconia (grinding and sandblast with 50μm, 100 μm) on shear bond strength between zirconia core and veneering ceramic. Material and methods: Twenty-eight presintered Y-TZP ceramic specimens (IPS e.max ZirCAD, Ivoclar vivadent) were fabricated and sintered according to manufacturer’s instructions. The core specimens were divided randomly in to 4 groups, group 1: no surface treatment, group2: zirconia specimens were ground with silicon carbide paper up to1200 grit under water cooling, group3: zirconia specimens were ground and sandblast with 100 μm alumina, group 4: zirconia specimens were ground and sandblast with 50 μm alumina. Surfa

Background: Adjustment of any premature occlusal contact of any zirconia restoration requires its polishing or glazing in order to restore the smoothness of the restoration. The objective of this in vitro study was to evaluate the effects of different polishing systems and glazing on the surface roughness of full-contour zirconia. Material and methods: Forty disks (diameter: 8 mm, thickness: 6.4 mm) were prepared from pre-sintered full-contoured zirconia block; they were colored and sintered in a high-temperature furnace at 1500ËšC for 8 hours. The specimens were then leveled and finished using grinding and polishing machine and adjusted using diamond disk. The specimens were then randomly divided into four groups (n=10), group I involves

Introduction: This study was performed to compare the effect of Fractional CO2 laser or Q switched Nd:YAG laser of surface treatment on the shear bond strength of zirconia-porcelain interface. Methods: Fractional CO2 laser at 30 W, 2 ms, time interval 1 ms, distance between spots 0.3 mm, and number of scans is (4) or Q switched Nd:YAG laser at 30 J/mm2 and 10 Hz were used to assess the shear bond strength of zirconia to porcelain. Pre-sintered zirconia specimens were divided into three groups (n = 10) according to the surface treatment technique used: (a) untreated (Control) group; (b) CO2 group; (c) Nd:YAG group. All samples were then sintered and veneered with porcelain according to the manufacturer’s instructions. Surface morph

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class