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Cindy Kim

Publications and source records attributed to Cindy Kim.

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First Principle Analysis of the Magnetism and Electronic Structure of Fe2XSi (X=Ti, V)

The electronic, magnetic, and mechanical properties of Fe2XSi, where X is titanium (Ti) and vanadium (V), are investigated using computational methods. Volume optimization reveals that the ground state of Fe2TiSi is a nonmagnetic narrow-band gap semiconductor, and that of Fe2VSi is a ferrimagnetic metal. The negative formation energies of both materials confirm the stability of their crystal structures. Mechanical properties confirm the static stability of the crystal structure and suggest that the titanium compound is ductile; however, the vanadium material is brittle. Density of states and band structure studies confirm the nonmagnetic semiconducting nature with an indirect band gap at {\Gamma} and X for the titanium material and the ferrimagnetic-metallic nature of the vanadium material. Electronic and magnetic properties of the materials were investigated with applied tension (negative pressure) and compression (positive pressure) to the crystal structure. The pressure study on Fe2TiSi shows a tunable band gap and a possible semiconductor-metal transition, and Fe2VSi shows a tunable magnetic moment and a possible low-spin/high-spin magnetic transition.

cond-mat.mtrl-sci

Exploration of COVID-19 Discourse on Twitter: American Politician Edition

The advent of the COVID-19 pandemic has undoubtedly affected the political scene worldwide and the introduction of new terminology and public opinions regarding the virus has further polarized partisan stances. Using a collection of tweets gathered from leading American political figures online (Republican and Democratic), we explored the partisan differences in approach, response, and attitude towards handling the international crisis. Implementation of the bag-of-words, bigram, and TF-IDF models was used to identify and analyze keywords, topics, and overall sentiments from each party. Results suggest that Democrats are more concerned with the casualties of the pandemic, and give more medical precautions and recommendations to the public whereas Republicans are more invested in political responsibilities such as keeping the public updated through media and carefully watching the progress of the virus. We propose a systematic approach to predict and distinguish a tweet's political stance (left or right leaning) based on its COVID-19 related terms using different classification algorithms on different language models.

cs.CL