Short-circuit faults can cause significant damage to power grid infrastructure, resulting in costly maintenance for utility providers. Rapid identification of fault locations can help mitigate these damages and associated expenses. Recent studies have demonstrated that graph neural network (GNN) models, using phasor data from various points in a power grid, can accurately locate fault events by accounting for the grid’s topology—a feature not typically leveraged by other machine learning methods. However, despite their high performance, GNN models are often viewed as ”black-box” systems, making their decision logic difficult to interpret. This thesis demonstrates that explanation methods can be applied to GNN models to enhance their transparency by clarifying the reasoning behind fault location predictions. By systematically benchmarking several explanation techniques for a GNN model trained for fault location detection, we assess and recommend the most effective methods for elucidating fault detection predictions in power grid systems.

This study applies the Response Escalation Paradigm (REP) to examine jealousy scores across five stages of increasing relationship threat (Huelsnitz et al., 2018). Participants rated their jealousy in scenarios involving a romantic partner with either a human or AI rival in virtual reality (VR), among other conditions. Consistent with hypothesis 1, jealousy rates increased across the stages in each condition. Consistent with hypothesis 2, people reported higher jealousy for the human rival in VR compared to the AI rival in VR at Stage 1. Inconsistent with hypothesis 3, participants did not experience faster escalation (the rate of increase in jealousy as the level of threat increases across the scenarios) for the human rival in VR relative to the AI rival in VR. Exploratory analyses did not reveal significant gender differences in jealousy responses. Overall, results indicate that individuals feel jealous about AI rivals as they do with human rivals.

There is a growing interest and attention for organizations to move from a shareholder capitalist to stakeholder capitalist business model, in which delivering environmental, social, and governance (ESG) performance is becoming more pronounced. Because of this transition, there is growing pressure for managers at all levels to act in sustainable, socially responsible, and ethical (SRE) ways; yet the current management models are based on maximizing economic value and scientific management. Therefore, the attempts to try and manage 21st century organizations with 20th century management practices and the aspirations of responsible management education have not demonstrated the desired effect (Laasch & Gherardi, 2019), as seen by continued business failures. It is not yet understood whether it is possible for the everyday manager to effectively navigate the tensions and contradictions of trying to deliver economic value to shareholders, social and ecological value to other stakeholders, while simultaneously attending to regulatory policies and, in the process, avoiding burnout for overtasked managers. Conducted in a debt remediation call center in the Dominican Republic, this study used an eclectic design integrating abductive analysis with a cultural-historical activity theory methodology to explore responsible management practices at the lowest levels of management. The findings describe responsible managers as pressure moderators enacting four primary elements: 1) embracing a pluralistic logic of responsibility, 2) accepting tensions with resilience, 3) engaging practices dynamically to moderate pressures, and 4) learning primarily on the job amid real work. In this organization, moderating the pressures of emotional labor was the primary object of their attention.

Major academic institutions in the United States house unidentified human skeletal remains in their collections as a result of unethical obtainment, poor documentation, and lack of resources. This thesis explores the possible geographic provenances associated with two skulls, A11 and A12, kept in the Florida Atlantic University (FAU) Anthropology Department. This thesis utilizes strontium and oxygen isotope analysis collected from dental and enamel tissues to explore possible geographic provenance for A11 and A12.
Analyzing isotopic composition of human skeletal remains is an established method to reconstruct human processes, history, events, and lifeways. Strontium (expressed by the ratio: 87Sr/86Sr) and oxygen (expressed by the ratio: δ18O) stable isotope analysis is used to determine place of origin for human remains with unknown origin. Strontium and oxygen isotopes express geographic signatures, of an individual's food and drinking water ingested during childhood which can reflect the isotope signature of the environment (soil, water, geology) from where it originates.

The increasing integration of renewable energy sources (RES) and electric vehicles (EVs) into microgrids presents both opportunities and challenges in terms of optimizing energy use and minimizing electricity costs. This dissertation explores the development of an advanced optimization framework using artificial intelligence (AI) to enhance battery operation in microgrids. The proposed solution leverages AI techniques to dynamically manage the charging and discharging of batteries, considering fluctuating energy demands, variable electricity pricing, and intermittent RES generation.
By employing a fuzzy logic-based control algorithm, the system intelligently allocates energy from solar power, grid electricity, and battery storage, while coordinating EV charging schedules to reduce peak demand charges. The optimization framework integrates predictive modeling for energy consumption and generation, alongside real-time data from weather forecasts and electricity markets, to make informed decisions. Additionally, the approach considers the trade-off between maximizing renewable energy usage and minimizing reliance on costly grid power during peak hours.

For much of the 20th century, mariners in the United States were able to utilize the radio beacon system to aid in navigation; however, in spite of its importance in U.S. nautical history, there has been very little historical or archaeological research published about the system. The Jupiter Inlet Light Station Radio Beacon Building, located at what is today known as the Jupiter Inlet Lighthouse Outstanding Natural Area (JILONA), was part of this coastal network of radio beacons. This thesis involves the methodologies of historical research and terrestrial laser scanning and serves several purposes: to provide JILONA with information about and a digital point cloud of the radio beacon building for future use in a planned museum onsite, to create a much-needed historical narrative of the U.S. radio beacon system, and to aid the Florida Atlantic University Department of Anthropology in future terrestrial laser scanner and modeling efforts. Because the project was undertaken at the request of JILONA, this thesis is to be considered a work of public archaeology.

This thesis exhibition explores how digital culture affects identity and connection through a series of paintings made through collage, mixed media, and reflective surfaces. It looks at how identity is fragmented in the digital age when we construct personas online that are selected and, therefore, less authentic. The paintings juxtapose the use of analog methods with digital imagery, in order to ask about the tension between vulnerability and performance and the authenticity of online interactions. The series is about emotional exhaustion, curated personas, and the search for genuine connection. Reflective elements and textures mounted and layered encourage viewers to engage in a dialectic between themselves and the mediated world, between 'digital self' and the 'authentic self.' The thesis hopes that this work can provoke discourse regarding the ramifications of digital culture on self-perceiving and interpersonal relations in recognizing the human dependence on depth and vulnerability in our fragmented reality.

Microplastics are a ubiquitous pollutant that has emphasized major concern for several benthic ecosystems and for the species that inhabit them especially as temperatures have begun to warm at an exponential rate. This study has investigated the abundance and trophic transfer intensity of microplastics through exposure experimentation to two different benthal organisms, the stone crab (Menippe mercenaria) and hard clam (Mercenaria mercenaria), under three different temperature gradients. Within a laboratory setting, hard clams were exposed to a concentration of different sizes and types of microplastics in three different temperatures to observe the accumulation rate of these particles from direct ingestion. The exposed clams were then fed to predatory stone crabs from the Indian River Lagoon, under the same three temperature treatments, to detect MP trophic transfer. To examine the disposition of ingested plastics, histology and fluorescent microscopy were used to quantify the locations and numbers of microplastics in the tissues.

This study investigates the spatial disparities in flood risk and social vulnerability across 66,543 census tracts in the Conterminous United States (CONUS), emphasizing urban–rural differences. Utilizing the American Community Survey (ACS) 2016–2020 data, we focused on 16 social factors representing socioeconomic status, household composition, racial and ethnic minority status, and housing and transportation access. Principal Component Analysis (PCA) reduced these variables into five principal components: Socioeconomic Disadvantage, Elderly and Disability, Housing Density and Vehicle Access, Youth and Mobile Housing, and Group Quarters and Unemployment. An additive model created a comprehensive Social Vulnerability Index (SVI). Statistical analysis, including the Mann–Whitney U test, indicated significant differences in flood risk and social vulnerability between urban and rural areas. Spatial cluster analysis using Local Indicators of Spatial Association (LISA) revealed significant high flood risk and social vulnerability clusters, particularly in urban regions along the Gulf Coast, Atlantic Seaboard, and Mississippi River. Global and local regression models, including Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR), highlighted social vulnerability’s spatial variability and localized impacts on flood risk. The results showed substantial regional disparities, with urban areas exhibiting higher flood risks and social vulnerability, especially in southeastern urban centers.

We propose an approach to the quantization of the interior of a Schwarzschild black hole, represented by a Kantowski-Sachs (KS) framework, by requiring its covariance under a notion of residual diffeomorphisms. We solve for the family of Hamiltonian constraint operators satisfying the associated covariance condition, in addition to parity covariance, preservation of the Bohr Hilbert space of Loop Quantum KS and a correct (naïve) classical limit. We further explore imposing minimality of the number of terms, and compare the solution with other Hamiltonian constraints proposed for Loop Quantum KS in the literature, with special attention to a most recent case. In addition, we discuss a lapse commonly chosen to decouple the evolution of the two degrees of freedom of the model, yielding exact solubility of the model, and we show that such choice can indeed be quantized as an operator densely defined on the Bohr Hilbert space, but must include an infinite number of shift operators. Also, we show the reasons why we call the classical limit “naïve”, and point this out as a reason for one limitation of some present prescriptions.