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Anti-popery, political prejudice against Catholicism on the basis that it is not conducive to liberty, contributed to the American religious and political discourses of the Seven Years' War and the American Revolution. While some have argued that anti-popery diminished in New England during the Revolution, this paper shows that it

Anti-popery, political prejudice against Catholicism on the basis that it is not conducive to liberty, contributed to the American religious and political discourses of the Seven Years' War and the American Revolution. While some have argued that anti-popery diminished in New England during the Revolution, this paper shows that it persisted as a political assumption among New England Protestants and continued to be expressed in sermons and political debates of America's early republican period. The Franco-American alliance was a pragmatic alliance which did not ultimately do away with anti-papal sentiment. Following history to the nativist movement of the mid-nineteenth century, this paper then shows that the arguments deployed against Catholic Irish immigrants were of the same vein as those deployed by Protestant New Englanders before the American Revolution and that the assumption of religio-political anti-popery never truly faded in the early republic, allowing for it to be enlivened by the dramatic increase in New England's Catholic population in the 1820s and 1830s.
Created2024-05
Description
Diffusion coefficients often vary across regions, such as cellular membranes, and quantifying their variation can provide valuable insight into local membrane properties such as composition and stiffness. Toward quantifying diffusion coefficient spatial maps and uncertainties from particle tracks, we use a Bayesian method and place Gaussian Process (GP) Priors on

Diffusion coefficients often vary across regions, such as cellular membranes, and quantifying their variation can provide valuable insight into local membrane properties such as composition and stiffness. Toward quantifying diffusion coefficient spatial maps and uncertainties from particle tracks, we use a Bayesian method and place Gaussian Process (GP) Priors on the maps. For the sake of computational efficiency, we leverage inducing point methods on GPs arising from the mathematical structure of the data giving rise to non-conjugate likelihood-prior pairs. We analyze both synthetic data, where ground truth is known, as well as data drawn from live-cell single-molecule imaging of membrane proteins. The resulting tool provides an unsupervised method to rigorously map diffusion coefficients continuously across membranes without data binning.
ContributorsKumar, Vishesh (Author) / Presse, Steve (Thesis director) / Bryan IV, J. Shep (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Physics (Contributor)
Created2024-05
Description
With the growth of the additive manufacturing (AM) industry for metal components, there is an economic pressure for improved AM processes to overcome the shortcomings of current AM technologies (i.e., limited deposition rates, surface roughness, etc.). Unfortunately, the development of these processes can be time and capital-intensive due to the large

With the growth of the additive manufacturing (AM) industry for metal components, there is an economic pressure for improved AM processes to overcome the shortcomings of current AM technologies (i.e., limited deposition rates, surface roughness, etc.). Unfortunately, the development of these processes can be time and capital-intensive due to the large number of input parameters and the sensitivity of the process’s outputs to said inputs. There consequently has been a strong push to develop computational design tools (such as CFD models) which can decrease the time and cost of AM technology developments. However, many of the developments that have been made to simulate AM through CFD have done so on custom CFD packages (as opposed to commercially available packages), which increases the barrier to entry of employing computational design tools. For that reason, this paper has demonstrated a method for simulating fused deposition modeling using a commercially available CFD package (Fluent). The results from this implementation are qualitatively promising when compared to samples produced by existing metal AM processes, however additional work is needed to validate the model more rigorously and to reduce the computational cost. Finally, the developed model was used to perform a parameter sweep, thereby demonstrating a use case of the tool to help in parameter optimization.
Created2024-05
Description
Memory as whole is impacted by changes associated with aging and menopause. Different memory types are often tested preclinically utilizing rats in different task paradigms. Most studies have focused on understanding social recognition or working memory, however these memory types have yet to be studied together. This thesis focuses on

Memory as whole is impacted by changes associated with aging and menopause. Different memory types are often tested preclinically utilizing rats in different task paradigms. Most studies have focused on understanding social recognition or working memory, however these memory types have yet to be studied together. This thesis focuses on the process of creating and testing a new social recognition task that incorporates a working memory load. We tested different types of previously used social recognition paradigms with an increasing load and through qualitative and quantitative observations the task was modified until a final task was developed for a social working memory study. Young female rats were tested in this task in progressive, meaning a chronologically increasing load and nonprogressive, meaning non-chronological increase in load cognitions. It was found that young female rats had the ability to distinguish between the familiar and novel conspecifics before memory load exceeded four familiar and one novel conspecifics. Once validated through future studies, this task may be utilized to understand the impact of different types of menopause on social working memory.
ContributorsAsadifar, Sadaf (Author) / Bimonte-Nelson, Heather (Thesis director) / Corbin, William (Committee member) / Verpeut, Jessica (Committee member) / Barrett, The Honors College (Contributor) / School of Human Evolution & Social Change (Contributor) / Department of Psychology (Contributor)
Created2024-05
Description
In this comprehensive research, we have pursued a dual investigation within the scope of tissue engineering: firstly, to investigate the retention of nanoprobe siloxane emulsions in bio-compatible hydrogel matrices in order to be able to measure oxygen saturation within the hydrogel; secondly, to refine the design of 3D printed hydrogel

In this comprehensive research, we have pursued a dual investigation within the scope of tissue engineering: firstly, to investigate the retention of nanoprobe siloxane emulsions in bio-compatible hydrogel matrices in order to be able to measure oxygen saturation within the hydrogel; secondly, to refine the design of 3D printed hydrogel molds to enhance structural integrity of hydrogels for cell encapsulation. We evaluated the retention capabilities of these nanoemulsions, tagged with fluorescent dyes, across varying concentrations, and further advanced the mold design to prevent hydrogel unraveling and ensure complete filling. The findings suggest pivotal implications for the application of these hydrogels in cell transplantation and set a methodological precedent for future empirical studies.
ContributorsMazboudi, Jad (Author) / Weaver, Jessica (Thesis director) / Alamin, Tuhfah (Committee member) / Barrett, The Honors College (Contributor) / Watts College of Public Service & Community Solut (Contributor) / Harrington Bioengineering Program (Contributor)
Created2024-05
Description
This thesis aims to advance healthcare and heart disease prevention by utilizing the Python programming language and various machine learning algorithms for heart disease detection. Being one of the main causes of death worldwide, cardiovascular disease is a serious global health concern. One person passes away from cardiovascular disease every

This thesis aims to advance healthcare and heart disease prevention by utilizing the Python programming language and various machine learning algorithms for heart disease detection. Being one of the main causes of death worldwide, cardiovascular disease is a serious global health concern. One person passes away from cardiovascular disease every 33 seconds in the United States alone. As the leading cause of death, early identification becomes critical for early intervention and prevention. The study addresses key research questions, including the role of machine learning in enhancing heart disease detection, comparative analysis of the six machine learning models, and the importance of predictive indicators. By leveraging machine learning algorithms for medical data interpretation, the thesis contributes insights into early disease detection.
ContributorsLa, Nikki (Author) / Sheehan, Connor (Thesis director) / Connor, Dylan (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2024-05
Description
This thesis provides an analysis of the potential issues of using ChatGPT, as despite its benefits it does have its concerns that may deter societal progress. The thesis first provides insight into how ChatGPT generates text and provides insight into how the process of generating its outputs can lead to

This thesis provides an analysis of the potential issues of using ChatGPT, as despite its benefits it does have its concerns that may deter societal progress. The thesis first provides insight into how ChatGPT generates text and provides insight into how the process of generating its outputs can lead to a variety of issues in the output such as hallucinated and biased output. After explaining how these issues occur, the thesis focuses on the impact of these issues in important industries such as medicine, education, and security, comparing them to popular open-source models such as Llama and Falcon.
ContributorsTsai, Brandon (Author) / Martin, Thomas (Thesis director) / Shakarian, Paulo (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
DescriptionThis project takes a keyboard-only input program and transforms it into a GUI Application, a point-and-click application much like a web browser, file manager, or video game.
ContributorsScheder, Linden (Author) / Roumina, Kavous (Thesis director) / Guggemos, Michael (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor) / Dean, W.P. Carey School of Business (Contributor)
Created2024-05
DescriptionThis project asks the question: Are significant accounting crimes prevented from occurring through new legislation, increased prosecution of existing legislation, or a different solution? Three of the major accounting scandals (Enron, Tyco, and WorldCom) are reviewed, the Sarbanes-Oxley Act of 2002 is explained, and accounting scandals since 2002 are explored.
ContributorsGriedl, Christianna (Author) / Shields, David (Thesis director) / Jordan, Erin (Committee member) / Barrett, The Honors College (Contributor) / School of Accountancy (Contributor)
Created2024-05
DescriptionI researched the children's book industry in order to effectively write and illustrate my own children's book. This book is about a child's experience with the anxiety disorder Selective Mutism that draws from my own personal experience.
ContributorsLove, Rosalia (Author) / Westover, Chad (Thesis director) / Williams, Wendy (Committee member) / Elliott, Steve (Committee member) / Barrett, The Honors College (Contributor) / Graphic Information Technology (Contributor)
Created2024-05