THE SOULS OF BLACK GHOSTS: CENTERING INTERGENERATIONAL TRAUMA OF BLACK AMERICANS FROM HARRIET TUBMAN TO BELOVED

Intergenerational trauma is being debated and researched in fields such as neuroscience and sociology, and yet many question whether one can genuinely feel the negative effects of a lived experience from a distant ancestor. This project aims to raise awareness on the attempts at erasing Harriet Tubman’s heritage through scholarship on her disability, and to highlight the presence of intergenerational trauma (as defined by Dr. DeGruy’s observations in Post-Traumatic Slave Syndrome) in Toni Morrison’s Beloved. I note the ripple effects of chattel slavery in the United States seen in the consciousness and unconsciousness of Black Americans within a literary context by bringing in Orlando Patterson’s concept of social death in conversation with intergenerational trauma. The goal of this project is to encourage continued research into intergenerational trauma of Black Americans and to caution against scholarship that may promote the erasure of their trauma and heritage

Exploring Infant Toy Interactions in Free Play Environments

Infants’ interactions with toys play a crucial role in their early cognitive and motor development, yet much of the research on this topic has been conducted in controlled laboratory settings, which may not accurately reflect the diversity of toys and interactions infants experience in their natural environments. This study uniquely examined these interactions in a home setting by utilizing an online video conferencing platform. This study investigated how toy properties influence manual infant-object interactions during dyadic tabletop free play, where mothers selected the toys for the session. Within the confines of this study, results show that infants spend significantly more time manipulating traditional organizational/fine motor toys compared to other toy categories (i.e., responsive, art, etc.). This study highlights the importance of considering the natural context in which toy interactions occur and provides insights into how different types of toys impact early behaviors and exploration

“THIS GENERATION OF WOMEN IS FINISHED”: MISOGYNY AS SOCIAL BONDING ON SOCIAL MEDIA AND PATRIARCHAL CONSTRUCTIONS OF FEMININITY

This thesis explores online discourse between users on social media platforms, specifically Instagram, in an effort to better understand the patriarchal influence on American social culture. Users on the platform regularly write off misogynistic comments as simply “dark humor” or an extreme expression of one’s “freedom of speech.” At the same time, supportive comments are also influenced by those same misogynistic expectations. I argue that misogynistic thinking in comments and a willingness to uphold them for social bonding provides deeper insights into our society’s attitudes towards women, their roles, and conceptions of femininity in general. I conducted a content analysis that analyzed social interactions within comment sections of randomly selected posts on Instagram that featured women in an attempt to articulate the patriarchal “logic” within the discussions. Determining what within the post instigated specific comments from users can help to provide insight into the ways patriarchy normalizes itself in our social interactions on and offline

THE QUIET GNOSTICISM OF SAMUEL BECKETT’S ENDGAME

This thesis draws on and extends previous Gnostic readings of Samuel Beckett’s fiction which has located the figure of the demiurge mostly in the background and finds through close reading that in Endgame the demiurge takes the foreground. This thesis proposes that Endgame’s gnosticism goes further than recreating gnostic themes and tropes. Rather, Endgame’s gnosticism fuses the gnostic laughter of the gnostic Christ, Schopenhauerian tragedy, and Quietist paradoxes to create a system of progressive enlightenment which culminates in a revelation evidenced by a laugh that, in Beckett’s Watt, is called the mirthless laugh. This laugh, I propose, is the laugher’s recognition that they play both the demiurge and the trapped gnostic soul to themselves. The demiurge, then, is no longer just in the foreground and background. This gnosticism, I argue, is closer to Beckett’s artistic vision as it incorporates Schopenhauerian concepts of the tragic and the will-to-live

META’S AI BIAS TO WHAT EXTENT DO META\u27S ALGORITHMIC PROCESSES FOR CLASSIFYING AND PROCESSING INFORMATION RELATED TO ACTS OF RACISM IMPACT FAIRNESS, BIAS, AND SOCIAL JUSTICE ACROSS ITS PLATFORMS?

Algorithmic processing and classification of racism-related content have become increasingly prevalent across Meta\u27s platforms. While these algorithms aim to create safer online environments, their effectiveness and impact on fairness remain understudied. This research examines how Meta\u27s algorithmic processes for classifying racist content affect fairness, bias, and social justice across Facebook, Instagram, and WhatsApp. Using qualitative research methodology, the study conducted focus groups with diverse platform users to understand their experiences with content moderation. The research also analyzed theoretical frameworks related to algorithmic bias, fairness metrics, and social justice in digital spaces. Key findings revealed significant variations in algorithmic effectiveness across different languages and cultural contexts, with implications for fairness and user experience. The study identified patterns in false positives and negatives, transparency issues, and challenges in handling intersectional content. These findings will add to the increasing research base on algorithmic fairness and provide recommendations for improving content moderation systems. The findings offer valuable insights for technology companies, policymakers, and civil rights advocates working to create more equitable digital spaces

Reexamining the Second Amendment: The Impact of Police Militarization on Civilian Gun Ownership

The increasing militarization of police forces in the United States contains profound implications on citizens’ Second Amendment rights, specifically concerning the ownership of weapons capable of mass violence. The original intent of the Second Amendment was not only to guarantee self-defense but also to safeguard citizens’ ability to resist a potentially tyrannical government. As police forces acquire military grade weaponry, some argue that civilians should have access to similar arms to maintain the balance of power between the state and its citizens, as outlined by the purpose of the Second Amendment. The historic use of violent police force to suppress peaceful organizers calls into question the necessity of a well-armed citizenry to act as a critical check on government overreach. Ultimately, demilitarizing police forces, rather than increasingly arming civilian forces is the most efficient solution to maintaining the parity of civilian and government forces. This demilitarization would include measures such as limiting access to military-grade weaponry, reevaluating use of force protocols, reducing Special Weapons and Tactics (SWAT) deployments, and changes in police training and culture. This paper seeks to advance research on Second Amendment rights by recontextualizing the civilian-centric discussion on gun ownership to focus on its relationship to increasing police militarization

HUMAN-AI INTERACTION IN DRIVING BEHAVIOR ANALYSIS: MACHINE LEARNING AND PATTERN MINING FOR THE UNOBTRUSIVE COGNITIVE ASSESSMENT

Creating unobtrusive, intelligent sensing systems that can be embedded into daily life and assess changes in the physical and cognitive functions of people has garnered significant attention. This dissertation presents a data-driven pattern recognition framework that integrates human-AI interaction and machine learning to analyze and quantify cognitive decline in older drivers based on the data stream of their driving data. This study harnesses real in-vehicle sensing data collected from +65 older drivers. Various AI-enabled data analytics modeling techniques are developed and tested to understand the driving patterns of older drivers, the relations between driving data and their cognitive functions, and detect cognitively impaired drivers. First, an in-vehicle sensing platform is employed to collect and preprocess telematics data from older drivers, capturing critical driving metrics such as speed, acceleration, braking, and steering behavior. Second, pattern mining techniques using unsupervised learning methods, including Self-Organizing Maps (SOM) and Deep Embedded Clustering (DEC), are applied to identify driving behavior patterns. These patterns are then analyzed to differentiate between normal aging-related behavior and behavior indicative of MCI. Third, pattern recognition and quantitative analysis are conducted to examine the relationship between cognitive decline and driving features. Statistical and machine learning models are used to assess how driving behaviors are altered due to cognitive impairment. Finally, a novel two-stage self-supervised deep contrastive learning framework is developed to detect MCI from telematics data. This framework first leverages self-supervised learning to extract meaningful driving behavior representations, followed by supervised classifiers to identify MCI cases with uncertainty quantification. This study identifies critical driving behaviors—particularly long-term patterns involving trip frequency, nighttime and peak-hour driving exposure, and throttle control—as significant digital biomarkers of MCI, contrasting with traditional assumptions that short-term driving variability is the primary indicator of cognitive decline. This approach significantly contributes to non-invasive, scalable, and privacy-preserving cognitive health assessments, highlighting how AI-driven behavioral analytics can complement traditional clinical assessments effectively. It establishes a pipeline for early detection and personalized interventions in healthcare, transportation safety, behavioral science, and aging research

EXPLORING A MODERN DEEP LEARNING TECHNIQUE FOR WETLAND MAPPING AND MONITORING USING WORLDVIEW-2 SATELLITE PRODUCTS

Wetlands play a significant role in the world’s hydrology, climate, and biodiversity. Even with the benefits and values wetlands provide to the environment, they have been undergoing loss and degradation due to natural and anthropogenic processes. To protect wetlands from loss and degradation and to restore their function, it is essential to develop a sustainable wetland monitoring system. One of the key elements of a wetland monitoring system is wetland mapping. This dissertation research developed an object-based deep learning protocol for mapping heterogeneous wetlands with many communities from a high-resolution WV-2 satellite image. To test this developed protocol, an object-based machine learning ensemble approach was selected as a benchmark for comparison. To effectively apply the developed protocol, feature selection techniques were applied, optimal spectral and spatial features were identified, and the benefit of four additional bands of WV-2 products were evaluated. The study also applied a post classification change detection technique to delineate the change between 2017 and 2021. The developed object-based deep learning protocol has been proven superior to the object-based machine learning ensemble approach. The feed-forward neural network (FNN) deep learning classifier achieved an overall accuracy of 91.2% and 88.6% for 2017 and 2021 imagery, respectively. On the other hand, the ensemble analysis approach achieved an overall accuracy of 87.8% and 85.5% for 2017 and 2021 imagery, respectively. The FNN improved (\u3e3%) the classification accuracy compared to the ensemble analysis, and the difference between classification results was statistically significant. The deep learning classifier not only increased the overall accuracy, but it also helped identify minor communities more accurately than the ensemble analysis technique. The additional four bands, object-based texture measures, and NDVI values of WV-2 satellite imagery showed the potential to map heterogeneous wetlands with many communities. The change map provided valuable insights into temporal changes in wetlands, which can aid in the formulation of adaptive management strategies. Exploration of automated/semi-automated deep learning methods contributed by this dissertation research will not only advance modern deep learning in wetland applications, but also assist with regional land managers to make efficient decisions by generating timely map products

CAN SERVANT LEADERSHIP REINVIGORATE COMMUNITIES OF FAITH

This study examined the relationship between servant leadership characteristics and church growth in Episcopal congregations. Using quantitative analysis, the research assessed whether leadership characteristics—such as emotional healing, creating value for the community, conceptual skills, empowering followers, helping followers succeed, putting followers first, and behaving ethically—were significant predictors of numerical growth. Data were collected from 40 churches (20 growing, 20 declining), with leadership perceptions measured through a validated servant leadership instrument. The findings indicate that while growing churches exhibited slightly higher mean scores across several servant leadership characteristics, none of the differences were statistically significant. Logistic regression analysis further demonstrated that no individual leadership characteristic significantly predicted church growth at the p \u3c .05 level. These results suggest that church expansion may be influenced more by external factors—such as congregational engagement, denominational policies, and regional demographics—than by leadership characteristics alone. This study contributes to church leadership research and servant leadership theory by highlighting the complexity of leadership effectiveness in faith-based organizations. The findings reinforce the need for a context-dependent approach to leadership, where contextual, cultural, and structural factors are considered alongside leadership behaviors. Given the lack of statistically significant findings, future research should incorporate qualitative methods to explore how servant leadership manifests in different congregational settings and whether leadership practices align with broader church growth strategies

MAPLESS AUTONOMOUS NAVIGATION USING MONOCULAR VISION

This thesis investigates monocular vision-based navigation for autonomous ground robots in indoor environments. It explores the effectiveness of mapless navigation strategies using image inputs and evaluates both modular and end-to-end deep learning techniques. A comprehensive implementation pipeline was developed on a Clearpath Dingo robot, comparing the approaches: classical optical flow (Farneback), modular deep learning with motion planning, and an end-to-end model. The modular methods involve predicting region-wise obstacle depths using a ResNet 18 based model trained on a custom indoor dataset, followed by a motion planner. The end-to-end approach leverages a ResNet-18 architecture to jointly predict steering angles and collision probabilities. All models were trained and evaluated using custom datasets collected in varied indoor trajectories, totaling over 100,000 images. Experimental evaluation focused on both offline performance (e.g., RMSE, F1- score) and real-time deployment on the Dingo platform. Results indicate that while classical vision methods lack robustness, deep learning-based approaches demonstrate higher reliability and adaptability. Notably, modular models allow easier interpretability and integration with planning systems, while end-to-end models offer faster inference and smoother navigation. This study validates the feasibility of deploying monocular vision-driven systems for indoor mapless navigation, highlighting trade-offs between interpretability, performance, and real-time constraints. The outcomes contribute to the broader field of robot autonomy using vision-only sensing, especially in low-cost, compute-constrained settings