LO-phonon–plasmon–coupled modes in n-type 4H– and 6H–SiC single crystals with free-carrier concentrations of 1016–1018 cm−3 have been measured by Raman scattering at room temperature. The axial-type mode for which plasma oscillation and atomic displacement are parallel to the c axis, and the planar-type mode for which these oscillations lie in the c plane, have been individually observed. From a line-shape analysis of the observed spectra, the plasmon frequency, carrier damping, and phonon damping have been deduced. These quantities have large differences between the axial- and planar-type mode in 6H–SiC, indicating its large crystal anisotropy. On the contrary, 4H–SiC shows small anisotropy. The longitudinal and transverse effective mass components of the electron have been determined from the plasmon frequency using carrier densities derived from Hall measurements. The deduced values are m∥=1.4m0 and m⊥=0.35m0 for 6H–SiC, and m∥=0.48m0 and m⊥=0.30m0 for 4H–SiC. The carrier mobility obtained from the analysis is also anisotropic. This is consistent with reported electrical measurements.
Because of the rapid spread and wide range of the clinical manifestations of the coronavirus disease 2019 (COVID-19), fast and accurate estimation of the disease progression and mortality is vital for the management of the patients. Currently available image-based prognostic predictors for patients with COVID-19 are largely limited to semi-automated schemes with manually designed features and supervised learning, and the survival analysis is largely limited to logistic regression. We developed a weakly unsupervised conditional generative adversarial network, called pix2surv, which can be trained to estimate the time-to-event information for survival analysis directly from the chest computed tomography (CT) images of a patient. We show that the performance of pix2surv based on CT images significantly outperforms those of existing laboratory tests and image-based visual and quantitative predictors in estimating the disease progression and mortality of COVID-19 patients. Thus, pix2surv is a promising approach for performing image-based prognostic predictions.
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