Deposition of ZnO thin films with different powers using RF magnetron sputtering method: Structural, electrical and optical study.

Zinc Oxide thin films at room temperature with good crystallinity quality have been deposited at different Radio Frequency powers. Magnetron sputtering technique has been carried out on glass and oriented Si(100) substrates. The film structure has been characterized by X-ray Diffraction (XRD) and MicroRaman spectroscopy, which possesses a wurtzite structure with (002) preferential orientation selecting suitable conditions. Scanning electron microscope (SEM) and atomic force microscopy (AFM) have been utilized to determine the films surface morphology. The stoichiometry has been verified by Energy dispersive X-ray spectroscopy (EDX) analysis. The optical behaviors of the deposited films have been characterized by Ultraviolet Visible (UV-Vis) (optical transmittance measurements) as well as by Photoluminance characterization. Electrical properties, Current-Voltage (I-V) and Capacitance-Voltage (C-V) have been studied in details for Zinc Oxide on Silicon film that deposited at different Radio Frequency power. The high transparency, electrical behavior and smooth surface allow to use these Zinc Oxide films in photovoltaic cells and optoelectronics application.

Automatic coronary artery calcium scoring from unenhanced-ECG-gated CT using deep learning.

PURPOSE: The purpose of this study was to develop and evaluate an algorithm that can automatically estimate the amount of coronary artery calcium (CAC) from unenhanced electrocardiography (ECG)-gated computed tomography (CT) cardiac volume acquisitions by using convolutional neural networks (CNN). MATERIALS AND METHODS: The method used a set of five CNN with three-dimensional (3D) U-Net architecture trained on a database of 783 CT examinations to detect and segment coronary artery calcifications in a 3D volume. The Agatston score, the conventional CAC scoring, was then computed slice by slice from the resulting segmentation mask and compared to the ground truth manually estimated by radiologists. The quality of the estimation was assessed with the concordance index (C-index) on CAC risk category on a separate testing set of 98 independent CT examinations. RESULTS: The final model yielded a C-index of 0.951 on the testing set. The remaining errors of the method were mainly observed on small-size and/or low-density calcifications, or calcifications located near the mitral valve or ring. CONCLUSION: The deep learning-based method proposed here to compute automatically the CAC score from unenhanced-ECG-gated cardiac CT is fast, robust and yields accuracy similar to those of other artificial intelligence methods, which could improve workflow efficiency, eliminating the time spent on manually selecting coronary calcifications to compute the Agatston score.

Morphological modelling of three-phase microstructures of anode layers using SEM images.

A general method is proposed to model 3D microstructures representative of three-phases anode layers used in fuel cells. The models are based on SEM images of cells with varying morphologies. The materials are first characterized using three morphological measurements: (cross-)covariances, granulometry and linear erosion. They are measured on segmented SEM images, for each of the three phases. Second, a generic model for three-phases materials is proposed. The model is based on two independent underlying random sets which are otherwise arbitrary. The validity of this model is verified using the cross-covariance functions of the various phases. In a third step, several types of Boolean random sets and plurigaussian models are considered for the unknown underlying random sets. Overall, good agreement is found between the SEM images and three-phases models based on plurigaussian random sets, for all morphological measurements considered in the present work: covariances, granulometry and linear erosion. The spatial distribution and shapes of the phases produced by the plurigaussian model are visually very close to the real material. Furthermore, the proposed models require no numerical optimization and are straightforward to generate using the covariance functions measured on the SEM images.