Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item How do resource availability, information flow and targeted interactions shape eco-evolutionary processes in microbial organisms across scale?(2022) Swain, Anshuman; Fagan, William; Biology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This work explores the crucial link between biological organization and dynamics in microbes. I have developed and applied different sets of quantitative tools to investigate this theme using a multi-disciplinary approach. My first project uses flow optimization tools to explore microbial intra-cellular dynamics as a function of resources, cell structure, and trade-offs between major energy production processes. This model explains emergent cell-level properties such as the dependence of bacterial growth rates on cell shape, size patterns in long-term evolutionary experiments, and the intriguing Warburg effect – wherein cells use inefficient anaerobic energy production pathways instead of efficient aerobic processes, despite ample oxygen availability. Moving to the community-level, I explored the mechanisms by which high levels of diversity are maintained in natural systems. Via a cutting-edge agent-based model that incorporates biologically realistic higher-order interactions, a continuous species space in which microbes interact via antibiotics and defenses, and an evolution-via-mutation regime, I link the trajectory of community assembly to both spatial heterogeneity patterns and diversity dynamics, thereby providing a new perspective on the origin and maintenance of diversity in complex communities. At the ecosystem-level, I built a dynamical system model to explore how the availability of resources and oxygen—mediated by interactions among microbial functional groups—affect environmental production of major greenhouse gases, like methane, especially on early Earth. This work provides 1) the first ecosystem-based functional explanation of periodic global methane ‘hazes’ in Earth’s early atmosphere and 2) a way to predict oxygen concentrations of ancient environments in a novel way. Lastly, in research that can involve multiple scales of biological organization, I use novel network science and information theoretic metrics to understand differences in interaction patterns in networks through the lens of degeneracy, noise, and determinism. The tradeoff between degeneracy and determinism can help us define the most informative scale of a network, and this is explored in the project through the development of new openly accessible toolkits.Item Systematic Integration of PHM and PRA (SIPPRA) for Risk and Reliability Analysis of Complex Engineering Systems(2021) Moradi, Ramin; Groth, Katrina; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Complex Engineering Systems (CES) such as power plants, process plants, and manufacturing plants have numerous, interrelated, and heterogeneous subsystems with different characteristics and risk and reliability analysis requirements. With the advancements in sensing and computing technology, abundant monitoring data is being collected. This is a rich source of information for more accurate assessment and management of these systems. The current risk and reliability analysis approaches and practices are inadequate in incorporating various sources of information, providing a system-level perspective, and performing a dynamic assessment of the operation condition and operation risk of CES. In this dissertation, this challenge is addressed by integrating techniques and models from two of the major subfields of reliability engineering: Probabilistic Risk Assessment (PRA) and Prognostics and Health Management (PHM). PRA is very effective at modeling complex hardware systems, and approaches have been designed to incorporate the risks introduced by humans, software, organizational, and other contributors into quantitative risk assessments. However, PRA has largely been used as a static technology mainly used for regulation. On the other hand, PHM has developed powerful new algorithms for understanding and predicting mechanical and electrical device health to support maintenance. Yet, PHM lacks the system-level perspective, relies heavily on operation data, and its outcomes are not risk-informed. I propose a novel framework at the intersection of PHM and PRA which provides a forward-looking, model- and data-driven analysis paradigm for assessing and predicting the operation risk and condition of CES. I operationalize this framework by developing two mathematical architectures and applying them to real-world systems. The first architecture is focused on enabling online system-level condition monitoring. The second architecture improves upon the first and realizes the objectives of using various sources of information and monitoring operation condition together with operational risk.