Large-scale molecular dynamics (MD) simulations, along with bond-order interatomic potentials, have been applied to study the defect production for lattice atom recoil energies from 500 eV to 20 keV in gallium arsenide (GaAs). At low energies, the most surviving defects are single interstitials and vacancies, and only 20% of the interstitial population is contained in clusters. However, a direct-impact amorphization in GaAs occurs with a high degree of probability during the cascade lifetime for Ga PKAs (primary knock-on atoms) with energies larger than 2 keV. The results reveal a non-linear defect production that increases with the PKA energy. The damage density within a cascade core is evaluated, and used to develop a model that describes a new energy partition function. Based on the MD results, we have developed a model to determine the non-ionizing energy loss (NIEL) in GaAs, which can be used to predict the displacement damage degradation induced by space radiation on electronic components. The calculated NIEL predictions are compared with the available data, thus validating the NIEL model developed in this study.

Using a Bayesian approach to epidemiological compartmental modeling, we demonstrate the "bomb-like" behavior of exponential growth in COVID-19 cases can be explained by transmission of asymptomatic and mild cases that are typically unreported at the beginning of pandemic events due to lower prevalence of testing. We studied the exponential phase of the pandemic in Italy, Spain, and South Korea, and found the R0 to be 2.56 (95% CrI, 2.41-2.71), 3.23 (95% CrI, 3.06-3.4), and 2.36 (95% CrI, 2.22-2.5) if we use Bayesian priors that assume a large portion of cases are not detected. Weaker priors regarding the detection rate resulted in R0 values of 9.22 (95% CrI, 9.01-9.43), 9.14 (95% CrI, 8.99-9.29), and 8.06 (95% CrI, 7.82-8.3) and assumes nearly 90% of infected patients are identified. Given the mounting evidence that potentially large fractions of the population are asymptomatic, the weaker priors that generate the high R0 values to fit the data required assumptions about the epidemiology of COVID-19 that do not fit with the biology, particularly regarding the timeframe that people remain infectious. Our results suggest that models of transmission assuming a relatively lower R0 value that do not consider a large number of asymptomatic cases can result in misunderstanding of the underlying dynamics, leading to poor policy decisions and outcomes.

The objective of this research is to demonstrate how similarity metrics can be used to quantify differences between sets of diffraction patterns. A set of 49 similarity metrics is implemented to analyze and quantify similarities between different Gaussian-based peak responses, as a surrogate for different characteristics in X-ray diffraction (XRD) patterns. A methodological approach was used to identify and demonstrate how sensitive these metrics are to expected peak features. By performing hierarchical clustering analysis, it is shown that most behaviors lead to unrelated metric responses. For instance, the results show that the Clark metric is consistently one of the most sensitive metrics to synthetic single peak changes. Furthermore, as an example of its utility, a framework is outlined for analyzing structural changes because of size convergence and isotropic straining, as calculated through the virtual XRD patterns.

Abstract