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Precedents for the Experiential Turn in American Art Museums: A Case Study of Paul Gardner, Chick Austin, Leslie Cheek, and the Museums They Directed, 1932-1968
No full textThis dissertation examines the historical precedents for the experiential turn in American art museums by analyzing the directorships of Paul Gardner, Chick Austin, and Leslie Cheek Jr. between 1932 and 1968. Drawing on archival research and museum studies scholarship, this study reevaluates how these directors innovated museum practice through theatricality, mass media, and audience engagement—long before contemporary museological discourse framed such strategies as revolutionary. Gardner’s use of radio broadcasts at the Nelson-Atkins Museum, Austin’s integration of circus spectacle at the Ringling Museum, and Cheek’s scenographic exhibition designs at the Virginia Museum of Fine Arts illustrate how mid-century museum leaders actively redefined visitor experience while navigating tensions between curatorial authority and public accessibility. By situating these case studies within the broader evolution of museum education and exhibition design, this dissertation challenges dominant narratives that position the experiential turn as a recent phenomenon. Instead, it highlights a longer history of institutional experimentation that anticipated early twenty-first-century debates on participatory museum models. In doing so, it provides valuable insights for contemporary museum professionals grappling with the ongoing negotiation between connoisseurship and entertainment, scholarly expertise and public engagement
Application of Industry 5.0 Attributes for Successful Implementation: Framework of Manufacturing Excellence in Aerospace and Defense Industries
Get PDFAs a novel concept, Industry 5.0 is making its rounds around the academic community and various industries. Despite its introduction over five years ago, definitions and perceptions are still under deliberation. Applications, methods of implementation and values are still in the conceptual phase. This study introduces a framework inclusive of human-centricity, sustainability and resilience, the pillars associated with Industry 5.0. It aims to differentiate the revolution from its predecessor, Industry 4.0, as it dives into the technologies associated with Industry 4.0 including but not limited to the Internet of Things (IoT), autonomous robots, and artificial intelligence. As those technologies evolve through time, this study captures the various practices, and cultures of people and technology through the noted Industrial revolutions. Alongside the literature, this study further identifies Industry 5.0 attributes. It aims to bridge the gap by understanding the necessity for implementing Industry 5.0 in Aerospace and Defense Sectors with inputs provided by industry professionals and subject matter experts (SMEs). Further providing input to the gap in research for this “trailblazing” revolution and a theoretical framework with the possibility to serve as a guiding star for alternative industries
Transformative Pedagogies Through Iterative Instructional Design
Get PDFPedagogical reforms have fallen short, limited to strategies and symptomatic approaches instead of systemic restructuring. Addressing learning environments as reciprocal communities, this dissertation is a critical making project where I developed a model integrating relationship-based practices. I applied Indigenous pedagogies guided by my research question: How does combining Indigenous methods, intersectional feminisms, and digital humanities transform course development and deployment as a communal process of collective learning? Using design justice principles and digital learning affordances, this work seeks to increase learning engagement and outcomes and participant experiences by explicitly embedding community-building along with content. Building with the liberation pedagogies of bell hooks along with the relational methodologies of feminist and Indigenous scholars, I applied critical making as research creation through procedural and iterative development of an open-access community course. A process-based methodology is well-suited to this investigation as I designed tasks, activities, and multimodal content with collective inquiry for digital humanities course components. Through reflexive design, I embedded mutual co-learning, applying Indigenous and intersectional frameworks acknowledging students and instructors are engaged in a symbiotic system, generating an educational experience that expands freedom for all. I facilitated an open access unaffiliated version of the College Board’s AP African American Studies course during the 2023-2024 academic year, with hundreds of registered participants studying Black history, arts, and culture. I accompanied learners by facilitating asynchronous modules and hybrid live discussions, as well as supplying robust resources for support and further study. By incorporating these architectures, redistributing power, and collaboratively supporting community, I demonstrate that the execution of innovative pedagogies can drive a significant increase in overall learning gains and academic experiences, as demonstrated by participants’ responses through surveys. I detail how these theories and models are transformative for instruction and pedagogical practice. The results of my work are instrumental to the necessary reimagining and (re)configuring of courses
Learning-Based Ethereum Phishing Detection: Evaluation, Robustness, And Improvement
Get PDFPhishing attacks continue to pose a significant threat to the Ethereum ecosystem, accounting for a major share of Ethereum-related cybercrimes. To enhance the detection of such fraudulent transactions, this dissertation develops a comprehensive framework for machine learning-based phishing detection in Ethereum transactions. The framework addresses critical aspects such as feature selection, class imbalance, model robustness, and the vulnerability of detection models to adversarial attacks. By systematically evaluating these key practices, this work contributes to the development of more effective detection methods. The first part of the dissertation assesses the current state of phishing detection methods, identifying gaps in feature selection, dataset composition, and model optimization. We propose a systematic framework that evaluates these factors, providing a foundation for improving the overall performance and reliability of detection models. The second part explores the vulnerability of machine learning models, including Random Forest, Decision Tree, and K-Nearest Neighbors, to single-feature adversarial attacks. Through extensive experimentation, we analyze the impact of various adversarial strategies on model performance and uncover alarming weaknesses in existing models. However, the varied effects of these attacks across different algorithms present opportunities for mitigation through adversarial training and improved feature selection. Finally, the dissertation investigates how phishing detection models generalize across datasets, focusing on the role of preprocessing techniques such as feature engineering and class balancing. Our findings show that optimizing these techniques enhances model accuracy and robustness, making detection methods more adaptable to evolving threats. Overall, this work presents a comprehensive framework that addresses the critical elements of phishing detection in Ethereum transactions, offering valuable insights for the development of more robust and generalizable machine learning-based security models. The proposed framework has broad implications for improving blockchain security and advancing the field of phishing detection
& Everything Else
No full text& EVERYTHING ELSE is a collection of stories that explores various forms of obsession. In “BEG FOR SCRAPS,” Pickle, a former bomb-sniffing dog, fixates on his owner, who’s fallen into a cycle of neglect post-divorce. “LET THEM FIGHT” follows Casey, a 20-something on a date to see, for the umpteenth time, a kaiju movie that conjures their escapist tendencies from a traumatic childhood. “DINNER WITH THE FOOL,” narrated by a state-appointed clown obsessed with jokes, describes a family contending with their matriarch’s planned suicide. “BIRDWATCHING” follows three characters, each with their own odd obsession: Jodie who wants to photograph a legendary bird, Mary-Esther who sees God in her cul de sac, and George Chandler who fakes an alien abduction to sell his story. Finally, “WHAT CAME OF OLD VAN HELSING?” depicts Lyle and Caroline, each consumed by the specter of violent childhood wrongs and vengeful political actions. These stories, intentionally bizarre and darkly funny, explore our strange and compulsive ways of confronting emotional neglect
Advancing Responsible AI: Disparity Mitigation Strategies for Human-Centered AI Systems
No full textIn recent years, the widespread adoption of machine learning (ML) has driven the expansion of automated decision-making across various real-world applications. While ML models improve efficiency and predictive accuracy in domains such as healthcare, finance, and criminal justice, biases inherited from training data can potentially lead to unintended disparities. Growing awareness of these biases has raised concerns within the human-centered AI and responsible AI communities, highlighting the importance of promoting fairness in AI systems. Aiming for equitable outcomes in automated decisions is not only a technical goal but also an ethical consideration, as models influenced by biases may inadvertently reinforce societal inequalities and lead to disparities among different groups.
This dissertation presents four key frameworks to enhance fairness in AI models. In the first part, we propose an ensemble learning-based framework that leverages multiple deep learning models with different sampling strategies to improve fairness. We then develop a fair representation learning framework that removes sensitive information while preserving relevant non-sensitive features, ensuring adaptable representations across classification tasks. Next, we introduce a contrastive learning framework that employs supervised and self-supervised strategies to mitigate bias in tabular datasets by strategically selecting positive pair samples. Finally, we investigate fairness in large language models (LLMs), assessing their susceptibility to social biases in zero-shot, few-shot, and fine-tuned settings while exploring mitigation strategies. This dissertation contributes to the advancement of responsible AI by introducing novel methodologies that address bias at different stages of model training and evaluation. Our work helps improve fairness-aware learning techniques, fostering more inclusive AI-driven decision-making and provides insights into mitigating disparities in machine learning models
NASA Flight Crew Operations Patch
No full textA patch typically worn by NASA flight crews, such as pilots.https://stars.library.ucf.edu/scuacoloring-images/1004/thumbnail.jp
A Systematic Review Of Dietary Trends, Influencing Factors, And Their Impacts On Cardiovascular Health In Turkish Populations
No full textCardiovascular disease (CVD) is the leading cause of death globally, including in Türkiye. One key factor influencing CVD prevention is dietary patterns, which directly impacts cardiovascular health. Türkiye traditionally follows the Mediterranean diet (MedDiet), which is acclaimed for its cardiovascular benefits. Despite this, Türkiye continues to experience high rates of CVD, with projections indicating that CVD mortality will double for both men and women by 2030. It is crucial to address diet-related factors, including potential nutritional deficiencies, to reduce cardiovascular health risks.
This research aims to analyze the Turkish community\u27s adherence to the Mediterranean diet to understand why CVD prevalence remains high despite following a well-established, beneficial dietary pattern. A systematic review was conducted using EBSCO and the UCF Library as primary databases, resulting in the inclusion of twenty-three articles based on predefined inclusion and exclusion criteria. These studies highlighted moderate adherence to the MedDiet and identified several factors affecting adherence, including nutritional imbalances, varying eating habits across age groups, meal frequency, and the impact of the COVID-19 pandemic. Furthermore, social influences, food prices, nutritional literacy, government initiatives, and location were found to play significant roles in shaping dietary behaviors.
Future research could explore the deep-rooted factors behind adherence or non-adherence to the Mediterranean diet and apply to a broader population. Additionally, comparing dietary habits in Turkish migrants could indicate how migration, cultural adaptation, and access to diverse food environments influence dietary patterns and their potential link to CVD prevalence
The Effects of Nurse-Led Physical Activity on the Aging Population
No full textThe aging population in the United States is increasing in proportion, yet older adults\u27 physical activity levels have not increased much. The lack of physical activity leads to numerous health complications, such as an increased risk for cardiovascular disease, increased risk for falls and fractures, as well as lead to obesity and diabetes. Unfortunately, few healthcare facilities monitor or audit physical activity. Older adults are frequently placed on fall risk precautions, which heavily encourages a sedentary hospital stay. This commonly leads to older adults with declining abilities to accomplish their activities of daily living (ADLs) after hospitalization. Nurses, spending the most time at the bedside, should take the initiative to help encourage and support their patients in daily ambulation and physical activity. A literature review of studies published between 2020-2025 found from various online databases shows that not only is the implementation of nurse-driven activity interventions feasible, but it also shows to have numerous benefits for the aging population, including an increase in quality of life, better disease management, a decrease in additional health risk factors, as well as an increase in confidence, independence, and safety. This literature review hopes to analyze the information currently available to have a better understanding as to why the implementation of nurse-driven mobility interventions has not gone through widespread adoption
Ableism Under the Veil of Ignorance: Does Rawls Render Justice to the Disabled?
No full textThis thesis focuses on Rawls’ neglect of disabled people in A Theory of Justice. Rawls claims that disabled people are too complicated for this theory and thus depriving them of their equality. This thesis looks at how other scholars have approached Rawls’ claim. This is meant for advanced undergraduates and new graduate students in political science, political theory/philosophy, and philosophy, who are likely being first introduced to Rawls or have never heard of him before. These students are the future of our society, and they will make a change in the future. This thesis serves to educate them on Rawls’ theory and how disabled people fit into his theory and political theory in general. It advocates for students to learn more about disabled people from disabled voices to ensure that when they get the chance to enact change, they will consider disabled people. This thesis offers an introduction to the field of disability studies to guide students on where they can learn more about disability. Then, it uses Rawls’ theory as an example of how disabled people should not be treated in political theory. This is done through a review of how scholars approached Rawls’ treatment of disabled people, with many defending him or offering solutions without challenging Rawls’ rhetoric towards disabled people. This thesis is meant to be a guide for future students of political theory on how to best incorporate disabled people into their ideas. Disabled people’s needs have been long overlooked in this field. Now is the time to change that, and this thesis aids in that change