Optical properties of Si/Si0.87Ge0.13 multiple quantum well wires

Nanometer-scale wires cut into a Si/Si0.87Ge0.13 multiple quantum well structure were fabricated and characterized by using photoluminescence and photoreflectance at temperatures between 4 and 20 K. It was found that, in addition to a low-energy broadband emission at around 0.8 eV and other features normally observable in photoluminescence measurements, fabrication process induced strain relaxation and enhanced electron-hole droplets emission together with a new feature at 1.131 eV at 4 K were observed. The latter was further identified as a transition related to impurities located at the Si/Si0.87Ge0.13 heterointerfaces

Degeneracy of Ground State in Two-dimensional Electron-Lattice System

We discuss the ground state of a two dimensional electron-lattice system described by a Su-Schrieffer-Heeger type Hamiltonian with a half-filled electronic band, for which it has been pointed out in the previous paper [J. Phys. Soc. Jpn. 69 (2000) 1769-1776] that the ground state distortion pattern is not unique in spite of a unique electronic energy spectrum and the same total energy. The necessary and sufficient conditions to be satisfied by the distortion patterns in the ground state are derived numerically. As a result the degrees of degeneracy in the ground state is estimated to be about $N^{N/4}$ for $N \gg 1$ with $N$ the linear dimension of the system.Comment: 2pages, 2figure

After heat distribution of a mobile nuclear power plant

A computer program was developed to analyze the transient afterheat temperature and pressure response of a mobile gas-cooled reactor power plant following impact. The program considers (in addition to the standard modes of heat transfer) fission product decay and transport, metal-water reactions, core and shield melting and displacement, and pressure and containment vessel stress response. Analyses were performed for eight cases (both deformed and undeformed models) to verify operability of the program options. The results indicated that for a 350 psi (241 n/sq cm) initial internal pressure, the containment vessel can survive over 100,000 seconds following impact before creep rupture occurs. Recommendations were developed as to directions for redesign to extend containment vessel life

SATMC: Spectral Energy Distribution Analysis Through Markov Chains

We present the general purpose spectral energy distribution (SED) fitting tool SED Analysis Through Markov Chains (SATMC). Utilizing Monte Carlo Markov Chain (MCMC) algorithms, SATMC fits an observed SED to SED templates or models of the user's choice to infer intrinsic parameters, generate confidence levels and produce the posterior parameter distribution. Here we describe the key features of SATMC from the underlying MCMC engine to specific features for handling SED fitting. We detail several test cases of SATMC, comparing results obtained to traditional least-squares methods, which highlight its accuracy, robustness and wide range of possible applications. We also present a sample of submillimetre galaxies that have been fitted using the SED synthesis routine GRASIL as input. In general, these SMGs are shown to occupy a large volume of parameter space, particularly in regards to their star formation rates which range from ~30-3000 M_sun yr^-1 and stellar masses which range from ~10^10-10^12 M_sun. Taking advantage of the Bayesian formalism inherent to SATMC, we also show how the fitting results may change under different parametrizations (i.e., different initial mass functions) and through additional or improved photometry, the latter being crucial to the study of high-redshift galaxies.Comment: 17 pages, 11 figures, MNRAS accepte

Severity classification of ground-glass opacity via 2-D convolutional neural network and lung CT scans: a 3-day exploration

Ground-glass opacity is a hallmark of numerous lung diseases, including patients with COVID19 and pneumonia, pulmonary fibrosis, and tuberculosis. This brief note presents experimental results of a proof-of-concept framework that got implemented and tested over three days as driven by the third challenge entitled "COVID-19 Competition", hosted at the AI-Enabled Medical Image Analysis Workshop of the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023). Using a newly built virtual environment (created on March 17, 2023), we investigated various pre-trained two-dimensional convolutional neural networks (CNN) such as Dense Neural Network, Residual Neural Networks (ResNet), and Vision Transformers, as well as the extent of fine-tuning. Based on empirical experiments, we opted to fine-tune them using ADAM's optimization algorithm with a standard learning rate of 0.001 for all CNN architectures and apply early-stopping whenever the validation loss reached a plateau. For each trained CNN, the model state with the best validation accuracy achieved during training was stored and later reloaded for new classifications of unseen samples drawn from the validation set provided by the challenge organizers. According to the organizers, few of these 2D CNNs yielded performance comparable to an architecture that combined ResNet and Recurrent Neural Network (Gated Recurrent Units). As part of the challenge requirement, the source code produced during the course of this exercise is posted at https://github.com/lisatwyw/cov19. We also hope that other researchers may find this light prototype consisting of few Python files based on PyTorch 1.13.1 and TorchVision 0.14.1 approachable