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.

Submit to DRUM

Submit to DRUM

To submit an item to DRUM, login using your UMD credentials. Then select the "Submit Item to DRUM" link in the navigation bar. View DRUM policies and submission guidelines.
Equitable Access Policy

Equitable Access Policy

The University of Maryland Equitable Access Policy provides equitable, open access to the University's research and scholarship. Faculty can learn more about what is covered by the policy and how to deposit on the policy website.
Theses and Dissertations

Theses and Dissertations

DRUM includes all UMD theses and dissertations from 2003 forward.

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UM Community-managed Collections

Recent Submissions

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    A deisobutanizer heat-pump retrofit for improved economic, water, and carbon sustainability
    (2026-05-27) Adomaitis, Raymond
    This manuscript accompanies a Jupyter Notebook file and Python module used for a modeling study assessing the economic, water, and carbon sustainability improvements to a commercial-scale deisobutanizer. This study was developed as part of a senior-level Chemical Engineering capstone design class in Spring of 2026 at the University of Maryland to demonstrate how sustainability can be quantified. The retrofit considered in this study consists of replacing the cooling water and low-pressure steam utilities of the distillation unit total condenser and reboiler, respectively, with an electric-powered heat pump that transfers heat removed by the condenser and upgrades it to heat the reboiler. The retrofitted process uses less than 10% of the total power of the base-case design, has a discounted cash-flow rate of return of over 40%, and results in significantly reduced CO2 production and essentially no water consumption when renewable electricity is used for the heat pump system.
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    Effects of Auditory Masking on Children's Use of Semantic Context
    (2026-05) Staiger, Lauren; Newman, Rochelle; Kane, Stacey; Goupell, Matthew; Blomquist, Christina
    More often than not, children spend their lives in noisy environments where they must learn and listen to language. Although spoken word recognition is a well-documented process in quiet conditions, it remains unclear how background noise influences the mechanisms involved in child speech perception The current study investigated how children’s ability to use informative semantic sentence cues to facilitate lexical access of an upcoming word and suppress similar sounding competitors is impacted by speech-shaped noise and two-talker babble noise. We hypothesized that children would use semantic context in the informative condition to facilitate lexical access of the target and suppress cohort competitors despite the presence of maskers. Additionally, we hypothesized that over time, children would be slower to fixate on the target image and would sustain fixations to the cohort image for longer in the two-talker babble condition. Eye tracking equipment monitored eye gazes across four images during the presentation of a sentence stimulus masked with background noise. In both the two-talker babble and speech-shaped noise maskers, children were able to use semantic context to facilitate access of a target word and suppress cohort competition. Although children demonstrated a trend toward prolonged consideration of the cohort competitor in the two-talker babble condition, there was no significant effect of noise type when considering target fixations, potentially suggesting that at high signal-to-noise ratios, children are able to benefit from semantic context when masked by both two-talker babble and speech-shaped noise. Overall, these findings highlight the importance of researching complex auditory environments where children spend much of their time learning and listening to spoken speech.
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    Greenhouse Gas Inventory and Analysis for the United States: 1990-2024
    (2026-04-15) Desai, Mausami; Camobreco, Vincent; Hedger, Toby; Irving, William; Rewcastle, Kenna; Steller, John; Barbieri, Lindsay; Weitz, Melissa; Murumkar, Tanvi; Fawcett, Allen; Lou, Jiehong; Cui, Ryna; Hultman, Nate
    The Greenhouse Gas Inventory and Analysis for the United States (GHGIA) presents comprehensive, economy-wide greenhouse gas (GHG) sources and sinks estimates covering the United States. The data are presented for each year from 1990 through 2024, the latter being the most recent year when comprehensive data are available for the entire economy. Along with detailed results for single years and analyses of trends over time, this report presents methodological descriptions, data inputs, a characterization of uncertainties, recalculations, and improvements. The report adheres to good practice and methodological guidance as defined by the Intergovernmental Panel on Climate Change (IPCC) and to international standards for transparency, accuracy, completeness, comparability, and consistency. In 2024, total gross emissions were 6,205.3 million metric tons of carbon dioxide equivalent (MMT CO2 Eq.). Gross emissions in 2024 decreased by 5.1 percent relative to 1990 and were 0.04 percent higher than gross emissions in 2023. The decreasing trend from around 2005 is associated primarily with a decrease in CO2 emissions from fossil fuel combustion. Total net U.S. GHG emissions in 2024, including emissions and sinks from the land use, land-use change and forestry (LULUCF) sector, were 5,298.8 MMT CO2 Eq. This net emissions estimate is a 3.8 percent decrease from 1990 and a 0.2 percent annual increase from 2023. Sequestration of CO2 in the LULUCF sector offset the equivalent of 14.6 percent of gross emissions in 2024 (906.5 MMT CO2 Eq.).This report has been developed in a manner that supports comparability and continuity with past official U.S. inventories prepared by the U.S. Environmental Protection Agency. The GHGIA is not an official national inventory on behalf of the United States, nor is it an official submission in the context of the United Nations Framework Convention on Climate Change (UNFCCC).
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    Exploring Innovative Funding Options for The MARC Growth and Transformation Plan
    (2026-05-15) Mears, Daniel; Brown, Paul
    This report explores innovative funding strategies to support the Maryland Transit Administration’s (MTA) Maryland Area Regional Commuter (MARC) Growth and Transformation Plan, which outlines major investments to expand capacity, improve reliability, and strengthen regional rail connections. With traditional transportation revenues under strain, new and sustainable funding approaches are needed. The study analyzes the MARC Plan’s financial requirements, reviews existing research on transportation finance, and conducts case studies of Connecticut, Massachusetts, Virginia, and Utah, states that have developed stable and innovative rail transit systems. Through document review, data analysis, and expert interviews, the project identifies practical funding mechanisms that can be adapted for Maryland. These findings inform a set of recommendations to help MTA advance the MARC Growth and Transformation Plan and build a more sustainable financial framework for Maryland’s commuter rail system.
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    What’s That: Whole Object and Taxonomic Constraint in Children with Autism
    (2026-05) Eisenfeld, Hannah; Newman, Rochelle
    This paper focuses on determining how children with autism (ASD) approach word learning. Prior research has determined that non-autistic (NA) children exhibit word learning biases to assist with paring down potential or unlikely referent alternatives to effectively learn new word meanings. Some children with autism exhibit some of these word learning biases, but it is unclear if they exhibit two specific biases: the taxonomic and whole object biases. This project aims to investigate if children with ASD exhibit these biases, and if children with ASD perform similarly on the experimental tasks to their NA peers. 22 NA children ages 2-10 years participated in an analysis comparing their performance to that of children in previous studies and 11 ASD-NA pairs were matched for receptive language. Results suggest that children with ASD exhibit a taxonomic bias regardless of a label being presented or not, and that they exhibit a whole object bias. No statistically significant differences between groups arose for the taxonomic or whole object tasks, suggesting that children with ASD perform similarly to their NA peers. We anticipate this research will enhance our knowledge of how children with autism engage in the word learning process.