Background: Various fluids in the oral environment can affect the surface roughness of resin composites. This in vitro study was conducted to determine the influence of the mouth rinses on surface roughness of two methacrylate-based resin (nanofilled and packable composite) and siloraine-based resin composites.

Materials and methods: Disc-shaped specimens (12 mm in diameter and 2mm in height) were prepared from three types of composi

As an important resource, entanglement light source has been used in developing quantum information technologies, such as quantum key distribution(QKD). There are few experiments implementing entanglement-based deterministic QKD protocols since the security of existing protocols may be compromised in lossy channels. In this work, we report on a loss-tolerant deterministic QKD experiment which follows a modified “Ping-Pong”(PP) protocol. The experiment results demonstrate for the first time that a secure deterministic QKD session can be fulfilled in a channel with an optical loss of 9 dB, based on a telecom-band entangled photon source. This exhibits a conceivable prospect of ultilizing entanglement light source in real-life fiber-based

In this article, Convolution Neural Network (CNN) is used to detect damage and no damage images form satellite imagery using different classifiers. These classifiers are well-known models that are used with CNN to detect and classify images using a specific dataset. The dataset used belongs to the Huston hurricane that caused several damages in the nearby areas. In addition, a transfer learning property is used to store the knowledge (weights) and reuse it in the next task. Moreover, each applied classifier is used to detect the images from the dataset after it is split into training, testing and validation. Keras library is used to apply the CNN algorithm with each selected classifier to detect the images. Furthermore, the performa

The effect of approaching nozzle jet from the deposition surface
on structural, optical and morphology properties of copper oxide thin
films was studied. The film was prepared by homemade fully
computerized CNC spray pyrolysis deposition technique at
preparations speed (3, 4, 5, and 6 mm/sec). The repeated line mode
was used at deposition temperature equal 450 °C whereas the
spraying time was in the range of (15-30 min) according to the
deposition speed. The film exhibit polycrystalline structure with
preferred orientation along (-111), (022) and (011), (002) at a 2θ
value of (35.63o) and (38.8o) respectively. Optical band gaps were
recorded at these speed shows variance in value from (1.53-2.08 eV).
Fi

Optical properties of chromium oxide (Cr2O3) thin films which were prepared by pulse laser deposition method, onto glass substrates. Different laser energy (500-900) mJ were used to obtain Cr2O3 thin films with thickness ranging from 177.3 to 372.4 nm were measured using Tolansky method. Then films were annealed at temperature equal to 300 °C. Absorption spectra were used to determine the absorption coefficient of the films, and the effects of the annealing temperature on the absorption coefficient were investigated. The absorption edge shifted to red range of wavelength, and the optical constants of Cr2O3 films increases as the annealing temperature increased to 300 °C. X-ray diffraction (XRD) study reveals that Cr2O3 thin films are a

In order to take measures in controlling soil erosion it is required to estimate soil loss over area of interest. Soil loss due to soil erosion can be estimated using predictive models such as Universal Soil Loss Equation (USLE). The accuracy of these models depends on parameters that are used in equations. One of the most important parameters in equations used in both of models is (C) factor that represents effects of vegetation and other land covers. Estimating land cover by interpretation of remote sensing imagery involves Normalized Difference Vegetation Index (NDVI), an indicator that shows vegetation cover. The aim of this study is estimate (C) factor values for Part of Baghdad city using NDVI derived from satellite Image of Landsat-7