Campaign analysis is an integral part of American democracy and has many complexities in its dynamics. Experts have long sought to understand these dynamics and evaluate campaign performance using a variety of techniques. We explore campaign financing and standing in the polls as two components of campaign performance in the context of the 2020 Democratic primaries. We show where these measures exhibit represent similar dynamics and where they differ. We focus on identifying change points in the trend for all candidates using joinpoint regression models. We find how these change points identify major events such as failure or success in a debate. Joinpoint regression reveals who the voters support when they stop supporting a specific candidate. This study demonstrates the value of joinpoint regression in political campaign analysis and it represents a crossover of this technique into the political domain building a foundation for continued exploration and use of this method.
Over the last few years, we have been working to improve the AUTOFIT program a and extend it to work on more complex spectra, especially spectra collected near room temperature. In this talk, we will discuss the problem of spectral complexity and the challenges it poses for moving to increasingly complicated systems. This will be highlighted by the cases of methoxyethanol, in which AUTOFIT was able to easily extract contributions from the ground state and four vibrationally excited states, and ethoxyethanol, in which AUTOFIT had difficulty identifying more than the ground vibrational state without the assistance of additional double resonance measurements.
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