Physical Sciences and Mathematics Commons^™

Nonparametric Bayesian Deep Learning For Scientific Data Analysis, Devanshu Agrawal

Doctoral Dissertations

Deep learning (DL) has emerged as the leading paradigm for predictive modeling in a variety of domains, especially those involving large volumes of high-dimensional spatio-temporal data such as images and text. With the rise of big data in scientific and engineering problems, there is now considerable interest in the research and development of DL for scientific applications. The scientific domain, however, poses unique challenges for DL, including special emphasis on interpretability and robustness. In particular, a priority of the Department of Energy (DOE) is the research and development of probabilistic ML methods that are robust to overfitting and offer reliable …

Exploration Of Mid To Late Paleozoic Tectonics Along The Cincinnati Arch Using Gis And Python To Automate Geologic Data Extraction From Disparate Sources, Kenneth Steven Boling

Doctoral Dissertations

Structure contour maps are one of the most common methods of visualizing geologic horizons as three-dimensional surfaces. In addition to their practical applications in the oil and gas and mining industries, these maps can be used to evaluate the relationships of different geologic units in order to unravel the tectonic history of an area. The construction of high-resolution regional structure contour maps of a particular geologic horizon requires a significant volume of data that must be compiled from all available surface and subsurface sources. Processing these data using conventional methods and even basic GIS tools can be tedious and very …

Unifying Chemistry And Machine Learning For The Study Of Noncovalent Interactions, Jacob A. Townsend

Doctoral Dissertations

Gas separations are in great demand for carbon emission reduction, natural gas purification, oxygen isolation, and much more. Many of these separations rely on cost-prohibitive methods such as cryogenic distillation or strong-binding solvents. As a result, novel materials are being developed to subvert the energetic expense of gas separation processes. These studies focus on improving the performance of alternative materials, including (but not limited to) metal-organic frameworks, covalent organic frameworks, dense polymeric membranes, porous polymers, and ionic liquids.

In this work, the atomistic effects of functional units are explored for gas separations processes using electronic structure theory and machine learning. …

Carbon Metabolism In Cave Subaerial Biofilms, Victoria E. Frazier

Masters Theses

Subaerial biofilms (SABs) grow at the interface between the atmosphere and rock surfaces in terrestrial and subterranean environments around the world. Multi-colored SABs colonizing relatively dry and nutrient-limited cave surfaces are known to contain microbes putatively involved in chemolithoautotrophic processes using inorganic carbon like carbon dioxide (CO₂) or methane (CH₄). However, the importance of CO₂ and CH₄ to SAB biomass production has not been quantified, the environmental conditions influencing biomass production and diversity have not been thoroughly evaluated, and stable carbon and nitrogen isotope compositions have yet to be determined from epigenic cave SABs. …

Mobile Location Data Analytics, Privacy, And Security, Yunhe Feng

Doctoral Dissertations

Mobile location data are ubiquitous in the digital world. People intentionally and unintentionally generate numerous location data when connecting to cellular networks or sharing posts on social networks. As mobile devices normally choose to communicate with nearby cell towers outdoor, it is reasonable to infer human locations based on cell tower coordinates. Many social networking platforms, such as Twitter, allow users to geo-tag their posts optionally, publishing personal locations to friends or everyone. These location data are particularly useful for understanding mobile usage behaviors and human mobility patterns. Meanwhile, the public expresses great concern about the privacy and security of …