DRUM - Digital Repository at the University of Maryland
DRUM collects, preserves, and provides public access to the scholarly output of the university. Faculty and researchers can upload research products for rapid dissemination, global visibility and impact, and long-term preservation.
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DRUM includes all UMD theses and dissertations from 2003 forward.
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CHARACTERISTICS AND HEALTH INSURANCE COVERAGE AMONG ADOPTED CHILDREN IN THE UNITED STATES
(2024) Fleishman, Jamie Lin; Yue, Dahai; Health Services Administration; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The practice of adopting a child is a common occurrence within the United States. Research on adoption, birth parents, and adoptive parents has traditionally focused on psychology and social work outcomes. This master’s thesis shifts the focus of adoption outcomes to a public health perspective, examining the demographics, socioeconomic conditions, and health insurance rates of domestic and international adoptees. To help determine the rates of any health insurance, public health insurance, and private health insurance, this thesis examined American Community Survey 2022 5-year data, a nationally representative study of adoptees and their counterparts—non-adoptees who are biologically related to their parents or head of household. It was determined that domestic adoptees and non-adoptees had similar demographic and socioeconomic statuses whereas international adoptees were predominantly non-Hispanic Asians, females, and have wealthier and White heads of households. After controlling for demographic and socioeconomic variables, it was discovered that international adoptees have higher uninsured rates compared to non-adoptees and domestic adoptees, which could be attributed to geographic differences in health insurance coverage. Further research is needed to examine health insurance coverage rates in the United States for adoptees.
Second Wave Mechanics
(2024) Fabbri, Anthony; Herrmann, Jeffrey W; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The COVID-19 pandemic experienced very well-documented "waves" of the virus's progression, which can be analyzed to predict future wave behavior. This thesis describes a data analysis algorithm for analyzing pandemic behavior and other, similar problems. This involves splitting the linear and sinusoidal elements of a pandemic in order to predict the behavior of future "waves" of infection from previous "waves" of infection, creating a very long-term prediction of a pandemic. Common wave shape patterns can also be identified, to predict the pattern of mutations that have recently occurred, but have not become popularly known as yet, to predict the remaining future outcome of the wave. By only considering the patterns in the data that could possibly have acted in tandem to generate the observed results, many false patterns can be eliminated, and, therefore, hidden variables can be estimated to a very high degree of probability. Similar mathematical relationships can reveal hidden variables in other underlying differential equations.
TRANSITIONING VISUALLY IMPAIRED USERS TO UTILIZE ACCESSIBILITY TECHNOLOGY
(2024) Jo, Hyejin; Reitz, Galina; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
In a world increasingly driven by visual information, this research develops the Transition Experience Interface (TEI), dedicated to supporting individuals adapting to visual impairments with advanced accessibility technologies. TEI features a user-centric design with a mobile user interface that includes tutorials, updates on new features, a voice command guide, and a progress dashboard. These components aim to reduce dependency on visual cues, enhancing digital inclusivity and promoting independence by encouraging the use of built-in accessibility features on smartphones. TEI educates users on their devices’ capabilities and fosters habitual use of these features, preparing them to rely less on vision and more on voice commands and other settings. This proactive approach helps users operate their smartphones confidently and independently as their visual function changes, bridging the gap between traditional tools and user needs, and highlighting the potential of inclusive design.
A Framework for Remaining Useful Life Prediction and Optimization for Complex Engineering Systems
(2024) Weiner, Matthew Joesph; Azarm, Shapour; Groth, Katrina M; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Remaining useful life (RUL) prediction plays a crucial role in maintaining the operational efficiency, reliability, and performance of complex engineering systems. Recent efforts have primarily focused on individual components or subsystems, neglecting the intricate relationships between components and their impact on system-level RUL (SRUL). The existing gap in predictive methodologies has prompted the need for an integrated approach to address the complex nature of these systems, while optimizing the performance with respect to these predictive indicators.
This thesis introduces a novel methodology for predicting and optimizing SRUL, and demonstrates how the predicted SRUL can be used to optimize system operation. The approach incorporates various types of data, including condition monitoring sensor data and component reliability data. The methodology leverages probabilistic deep learning (PDL) techniques to predict component RUL distributions based on sensor data and component reliability data when sensor data is not available. Furthermore, an equation node-based Bayesian network (BN) is employed to capture the complex causal relationships between components and predict the SRUL. Finally, the system operation is optimized using a multi-objective genetic algorithm (MOGA), where SRUL is treated as a constraint and also as an objective function, and the other objective relates to mission completion time. The validation process includes a thorough examination of the component-level methodology using the C-MAPSS data set. The practical application of the proposed methodology in this thesis is through a case study involving an unmanned surface vessel (USV), which incorporates all aspects of the methodology, including system-level validation through qualitative metrics. Evaluation metrics are employed to quantify and qualify both component and system-level results, as well as the results from the optimizer, providing a comprehensive understanding of the proposed approach’s performance.
There are several main contributions of this thesis. These include a new deep learning structure for component-level PHM, one that utilizes a hybrid-loss function for a multi-layer long short-term memory (LSTM) regression model to predict RUL with a given confidence interval while also considering the complex interactions among components. Another contribution is the development of a new framework for computing SRUL from these predicted component RULs, in which a Bayesian network is used to perform logic operations and determine the SRUL. These contributions advance the field of PHM, but also provide a practical application in engineering. The ability to accurately predict and manage the RUL of components within a system has profound implications for maintenance scheduling, cost reduction, and overall system reliability. The integration of the proposed method with an optimization algorithm closes the loop, offering a comprehensive solution for offline planning and SRUL prediction and optimization. The results of this research can be used to enhance the efficiency and reliability of engineering systems, leading to more informed decision-making.
TEACHER PRACTICES AROUND REPERTOIRE LEARNING AND SELECTION IN THE ELEMENTARY GENERAL MUSIC CLASSROOM
(2024) Nixon, Elizabeth Rivera; Elpus, Kenneth; Music; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
In music education, it is sometimes said that “the repertoire is the curriculum.” This research seeks to study the ways that elementary general music teachers select repertoire for use in their classrooms, and how the repertoire that they choose in turn influences what students are taught. By interviewing four elementary general music teachers about their practices around repertoire selection, with a focus on one specific lesson, I attempted in this research to answer the following questions:What resources (textbooks, digital resources, etc.) do teachers report utilizing when seeking repertoire for use in their classrooms?
When selecting repertoire for their classrooms, what musical and extra-musical factors do elementary general music teachers consider?
How do teachers describe the influence of repertoire selection on the concepts taught in elementary general music classrooms?
After conducting these interviews and analyzing the data collected, I have determined that the teachers in this study report drawing repertoire from a variety of different resources, including digital resources, print books, and their colleagues. From these resources, they select repertoire based on a variety of overlapping selection criteria, including content-based, aesthetic, functional, and repertoire that provides opportunities for learning about different cultures. The repertoire selected has impacts on the remainder of the lesson, whether through pedagogical or structural impacts. Finally, this paper discusses the impacts of teacher pedagogical approaches on repertoire selection, and the ways in which repertoire is transmitted between colleagues within the world of elementary general music education.