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Aliza Panjwani

Publications and source records attributed to Aliza Panjwani.

2 recordsLinked to original sources

Thermal Induced Structural Competitiveness and Metastability of Body-centered Cubic Iron under Non-Equilibrium Conditions

The structure and stability of iron near melting at multi-megabar pressures are of significant interest in high pressure physics and earth and planetary sciences. While the body-centered cubic (BCC) phase is generally recognized as unstable at lower temperatures, its stability relative to the hexagonal close-packed (HCP) phase at high temperatures (approximately 0.5 eV) in the Earth's inner core (IC) remains a topic of ongoing theoretical and experimental debate. Our ab initio calculations show a significant drop in energy, the emergence of a plateau and a local minimum in the potential energy surface, and stabilization of all phonon modes at elevated electron temperatures (>1-1.5 eV). These effects increase the competition among the BCC, HCP, and the face-centered cubic (FCC) phases and lead to the metastability of the BCC structure. Furthermore, the thermodynamic stability of BCC iron is enhanced by its substantial lattice vibration entropy. This thermally induced structural competitiveness and metastability under non-equilibrium conditions provide a clear theoretical framework for understanding iron phase relations and solidification processes, both experimentally and in the IC.

cond-mat.mtrl-sci

Latent class growth analysis for ordinal response data in the Distress Assessment and Response Tool: an evaluation of state-of-the-art implementations

Latent class growth analysis is a popular approach to identify underlying subpopulations. Several implementations, such as LCGA (Mplus), Proc Traj (SAS) and lcmm (R) are specially designed for this purpose. Motivated by data collection of psychological instruments over time in a large North American cancer centre, we compare these implementations using various simulated Edmonton Symptom Assessment System revised (ESAS-r) scores, an ordinal outcome from 0 to 10, as well as the real data consisting of more than 20,000 patients. We found that Mplus and lcmm lead to high correct classification rate, but Proc Traj over estimated the number of classes and failed to converge. While Mplus is computationally faster than lcmm, it does not allow more than 10 levels. We therefore suggest first analyzing data on the ordinal scale using lcmm. If computational time becomes an issue, then one can group the scores into categories and implement them in Mplus.

stat.AP