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This research develops a new pipeline for large-scale point cloud registration by integrating chunked-based data processing within feature-based deep learning models to align aerial LiDAR and UAV photogrammetric data. By processing data in manageable chunks, this approach optimizes memory usage while retaining the spatial continuity essential for precise alignment across expansive datasets. Three models—DeepGMR, FMR, and PointNetLK—were evaluated within this framework, demonstrating the pipeline’s robustness in handling datasets with up to 49.73 million points. The models achieved average epoch times of 35 seconds for DeepGMR, 112 seconds for FMR, and 333 seconds for PointNetLK. Accuracy in alignment was also reliable, with rotation errors averaging 2.955, 1.966, and 1.918 degrees, and translation errors at 0.174, 0.191, and 0.175 meters, respectively. This scalable, high-performance pipeline offers a practical solution for spatial data processing, making it suitable for applications that require precise alignment in large, cross-source datasets, such as mapping, urban planning, and environmental analysis.

This comparative research identifies and analyzes recurring tropes in the novels Cobra (1972) by Cuban writer Severo Sarduy and La mucama de Omicunlé (2015) by Dominican writer Rita Indiana. Despite the years between the publication of these important Spanish-Caribbean works, they both reveal transformative processes through transgressive writing styles. Seemingly diverse, these novels present a similar plot: a series of violent events that surround the protagonists’ androgyny. Their stories bare a deeper significance as changes to the bodies provoke ruptures that unearth rhizomatic connections with the rest of the surrounding nature, which, of course, has its own histories, different from the ones recorded by humans. Moreover, the novels explore multiplicities and (re)occurrences through times and spaces imperceptibly interconnected. The androgynous rhizomatic trope in contemporary Spanish-Caribbean novels proves to be a significant contribution that leads readers to question biased historical records, conceived to perpetuate coloniality, and dispute heteropatriarchal visions of nature to bring about transcendental changes to the status quo.

Mobility monitoring in urban environments can provide valuable insights into pedestrian and vehicle movement – where people want to go, how they get there, and the challenges they face along the way. Today, local governments can automate the acquisition of such data using video surveillance to understand the potential impact of investment and policy decisions. However, public disapproval of computer vision due to privacy concerns opens opportunities for research into alternative tools built with privacy constraints at the core of the design. WiFi sensing emerges as a promising solution. Modern mobile devices ubiquitously support the 802.11 standard and regularly emit WiFi probe requests for network discovery. We can passively monitor this traffic to estimate the levels of congestion in public spaces.
In this dissertation, we address three fundamental research problems pertaining to developing streetscape-scale mobility intelligence: scalable infrastructure for WiFi signal capture, passive device localization, and device re-identification.

This thesis exhibition explores how Iranian women’s narratives might be reshaped for empowerment through the lens of Graphic Design. It challenges gender inequality by analyzing and examining historical and contemporary portrayals of women through case studies. To show women’s strength and resiliency, the thesis imagines an immersive experience that combines Iranian visual culture aesthetics with modern storytelling techniques. It promotes Graphic Design as a tool for social change. It adds to continuing conversations about women’s empowerment, cultural reclamation, and social advancement in Iran and, by extension, globally. The thesis exhibition envisions Graphic Design as a powerful tool for reshaping gender norms in Iranian society. It was inspired by the courage of women in movements such as Woman, Life, Freedom, which started in 2022 in Iran and other countries like Turkey, Syria, and Iraq, which demonstrate their fight for gender equality, self-determination, and the liberation of women from patriarchal and oppressive systems.

Every passenger vehicle must rely on a safe and optimal trajectory to eliminate traffic incidents and congestion as well as to reduce environmental impact, and travel time. Autonomous intersection management systems (AIMS) enable large scale optimization of vehicular trajectories with connected and autonomous vehicles (CAVs). The first contribution of this dissertation is the fastest trajectory planner (FTP) method which is geared for computing the fastest waypoint trajectories via performing graph search over a discretized space-time (ST) graph (Gt), thereby constructing collision-free space-time trajectories with variable vehicular speeds adhering to traffic rules and dynamical constraints of vehicles. The benefits of navigating a connected and autonomous vehicle (CAV) truly capture effective collaboration between every CAV during the trajectory planning step. This requires addressing trajectory planning activity along with vehicular networking in the design phase. For complementing the proposed FTP method in decentralized scenarios, the second contribution of this dissertation is an application layer V2V solution using a coordinator-based distributed trajectory planning method which elects a single leader CAV among all the collaborating CAVs without requiring a centralized infrastructure. The leader vehicular agent calculates and assigns a trajectory for each node CAV over the vehicular network for the collision-free management of an unsignalized road intersection. The proposed FTP method is tested in a simulated road intersection scenario for carrying out trials on scheduling efficiency and algorithm runtime. The resulting trajectories allow high levels of intersection sharing, high evacuation rate, with a low algorithm single-threaded runtime figures even with large scenarios of up to 1200 vehicles, surpassing comparable systems.

The aim of this dissertation is to develop a comprehensive framework for designing optimal AI/ML-driven waveform solutions to achieve autonomous interference avoidance in fixed frequency bands. In the age of advanced wireless communications, minimizing interference is critical for maximizing the signal-to-interference-plus-noise ratio (SINR), particularly in densely occupied frequency environments. The research presented here focuses on developing adaptive MIMO waveform optimization techniques that dynamically adjust to varying interference conditions, enhancing communication reliability and performance for future autonomous machine-to-machine (M2M) networks. In addition to the established adaptive MIMO waveform optimization techniques, this dissertation investigates the implementation of AI-enhanced methods, to improve real-time adaptability in interference-rich environments. By leveraging neural networks, the framework enables the MIMO system to autonomously learn optimal waveform adjustments, providing resilience and efficiency under unpredictable interference conditions. This approach is validated through extensive simulations and experimental setups, demonstrating significant gains in SINR and overall communication reliability, marking a robust advancement toward achieving fully autonomous interference-avoiding communication in 6G and beyond networks. The AI-driven techniques further enhance the adaptability of the MIMO system across diverse interference scenarios, contributing to more consistent performance. These improvements offer a scalable approach for interference avoidance, adaptable to various network configurations.

This dissertation investigated the influence of university presidents and business school deans on fundraising performance at public research universities. Drawing on strategic choice theory, upper echelons theory, and transformational leadership theory, this research suggests that managerial power and transformational leadership behaviors moderate the relationship between an institution’s fundraising strategy and its performance. To test these hypotheses, data on strategic choice, managerial power, leadership behaviors, and leader demographics were collected from 79 U.S. public research universities for fiscal years 2017–2021. Panel regression, specifically random-effects generalized least squares regression models, tested the hypotheses. The study found that university presidents and business school deans who exhibited transformational leadership behaviors, had higher fundraising outcomes. Furthermore, the study identified a significant positive effect of a dean’s prior work experience at prestigious institutions on a business school’s fundraising performance. This implies that deans hailing from elite institutions might possess qualities or networks that significantly enhance fundraising outcomes.

Improving skills in athletic competition often requires structured guidance from coaches, who direct performers’ intentions and attention toward achieving task objectives. Traditionally, coaching follows a linear top-down model, relying on technical demonstrations, extensive verbal instructions, and corrective feedback to promote the execution of "ideal" movement patterns for improved performance. In contrast, non-linear bottom-up coaching approaches, such as differential learning (DL), intentionally introduce unconventional movement variations that may initially appear random or counterproductive. These variations encourage performers to explore the perceptual-motor landscape and disrupt established movement solutions, thereby fostering adaptability and skill development. The current study explored these considerations by investigating an alternative to the traditional action selection model of the basal ganglia (BG). The proposed model suggests that the BG mediates cortical signals from cognitive and perceptual areas to perform analog computations to generate the appropriate velocity, direction, and force output through negative feedback mechanisms. According to this model, D1 and D2receptors within the direct and indirect pathways modulate adaptive gain in velocity control by fine tuning the final motor output.

Microbial partners provide beneficial and detrimental functions to their hosts and other microbes through the exchange of metabolites and info chemicals. Developing an understanding of these micro-interactions has considerable implications for human health, agriculture, and ecosystem protection. Here, the microbial interactions of two important marine organisms: the Forcepia sp. sponge, a source of the potential anticancer compound, lasonolide A (LSA), and Pyrodinium bahamense, a dinoflagellate which produces the potent neurotoxin, saxitoxin, were investigated. Chapter 1 introduces marine microbial interactions, their importance in the function of organisms and ecosystems, and their applications in human health, agriculture and ecosystem production. Chapter 2 describes the identification and capture of the lasonolide biosynthetic pathway from a metagenomic fosmid library. This chapter also describes the assembly of the pathway into an expression vector and attempts to sustainably produce LSA through heterologous expression. Chapter 3 describes the identification and characterization of the bacterial associates of Pyrodinium bahamense, a toxin producing dinoflagellate found in the northern Indian River Lagoon. This chapter also describes potential chemical and molecular interactions occurring between P. bahamense and its associated cultivable bacteria. Chapter 4 describes the investigation into the effects microbial associates have on the physiology of P. bahamense. The completion of this work further describes microbial interactions occurring in marine environments, their influences and functions in the physiology and evolution of marine organisms, and the tools available for their investigation and utilization for human and ecosystem benefit.

I investigate the link between real earnings management and customer satisfaction. Following the passing of the Sarbanes-Oxley Act, which requires public companies to ensure the accuracy of their financial reporting, the use of non-accrual based methods to achieve financial earnings targets has become commonplace. Nonaccrual-based methods consist of decisions that curtail firm activities, which increase net income; however, these methods come at the cost of future operations. These opportunistic behaviors in the service, retail, and hospitality industries impact customers and a firm's long-term value. I hypothesize that the outcome will be lower customer satisfaction and service quality.
I use firms’ publicly available financial information to detect real earning management (REM) of selling, general, and administrative (SG&A) expenses to understand how firms operate. Physical changes to a firm’s operating environment through these financial actions are immediately observable, generating negative electronic word of mouth. I use text analysis software to determine if the comment is positive or negative and the strength of the customers' sentiment, allowing me to observe the harmful effects of reducing SG&A expenses through the lens of the customer. In addition, I use American Customer Satisfaction Index (ACSI) scores to provide an overall assessment. I regress my customer satisfaction and service quality measures on the REM measure to test the hypotheses on the impact of REM. Further, I analyze and establish the link between REM's impact on the firm's sales growth rates.