In this work, silver nanoparticles (AgNPs) were biosynthesized from leaves of Ziziphus mauritiana Lam. jujube plant in Iraq and tested against fungal pathogens. Extract of leaves of Z. mauritiana mixed with 10-3 M AgNO3exposed to slight sunlight for 3 days. Characterization of AgNPs was done using UV-visible spectroscopy, SPM (scanning probe microscopy) and atomic force microscopy (AFM). The change of solution color from pale brown to dark brown and the exhibited maximum peak at 445 nm accepted as an indicator to biosynthesized AgNPs. Aqueous extract of Ziziphus mauritiana is considered as biological reduced and stabilized agent for Ag+ to Ag0. AFM showed the formation of irregular shapes of AgNPs. The biosynthesized silver nanoparticles ha

Objectives Dental implant is a revolution in dentistry; some shortages are still a focus of research. This study use long duration of radiofrequency (RF)–magnetron sputtering to coat titanium (Ti) implant with hydroxyapatite (HA) to obtain a uniform, strongly adhered in a few micrometers in thickness. Materials and Methods Two types of substrates, discs and root form cylinders were prepared using a grade 1 commercially pure (CP) Ti rod. A RF–magnetron sputtering device was used to coat specimens with HA. Magnetron sputtering was set at 150 W for 22 hours at 100°C under continuous argon gas flow and substrate rotation at 10 rpm. Coat properties were evaluated via field emission scanning electron microscopy (FESEM), scanning electro

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

 A mixture model is used to model data that come from more than one component. In recent years, it became an effective tool in drawing inferences about the complex data that we might come across in real life. Moreover, it can represent a tremendous confirmatory tool in classification observations based on similarities amongst them. In this paper, several mixture regression-based methods were conducted under the assumption that the data come from a finite number of components. A comparison of these methods has been made according to their results in estimating component parameters. Also, observation membership has been inferred and assessed for these methods. The results showed that the flexible mixture model outperformed the

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 A mixture model is used to model data that come from more than one component. In recent years, it became an effective tool in drawing inferences about the complex data that we might come across in real life. Moreover, it can represent a tremendous confirmatory tool in classification observations based on similarities amongst them. In this paper, several mixture regression-based methods were conducted under the assumption that the data come from a finite number of components. A comparison of these methods has been made according to their results in estimating component parameters. Also, observation membership has been inferred and assessed for these methods. The results showed that the flexible mixture model outperformed the others