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Metasurface-Refractive Hybrid Lens Design
No full textRefractive optics are widely used in imaging systems while optical aberrations can limit their imaging performance and the typical solution to correct is cascading additional refractive optics with varying materials and shapes. Still, this scheme can result in bulky and costly lenses. Metasurfaces (MSs), with their compactness and ability to locally manipulate wavefronts, offer additional degrees of freedom in aberration correction. Integrating MSs with refractive optics creates MS-refractive hybrid lenses, enabling advanced optical performance while maintaining a compact design. Various methods have been proposed for designing aberration-correcting MSs in hybrid lenses, which often rely on predefined target phase profiles or basis function expansions. However, these approaches typically neglect critical factors such as polarization-dependent responses and phase dispersion inherent to MS meta-atoms. This dissertation presents a framework for designing and optimizing MS-refractive hybrid lenses, incorporating physical optics modeling to overcome these limitations. A key contribution is the implementation of an adjoint optimization method, allowing free-form optimization of all MS parameters to minimize image spot size across multiple field angles and wavelengths. To enable this optimization, a ray-wave hybrid propagation method is developed for scalar fields, providing accurate field propagation through refractive optics. Furthermore, this work extends hybrid lens design to vector field modeling, introducing a vector field physical optics propagation scheme to analyze polarization effects. This capability is essential for designing hybrid lenses with polarization-sensitive MSs or coated refractive surface. Several examples are presented to demonstrate the proposed method’s versatility, including hybrid lenses (1) designed to generate and focus cylindrically polarized beams, (2) incorporating multifunctional MSs developed through adjoint gradient optimization, and (3) for full-Stokes polarization sensing
Not Just Playing Pretend: Story Drama as Queer and Humanist Praxis
No full textWhile conventional story time typically involves passive listening and enforced stillness, Story Drama transforms the storytelling experience into a participatory process where children use their voices, bodies, and imaginations to shape the narrative collaboratively. Indeed, Story Drama, combined with queer and humanist learning theory, fosters an inclusive, student-centered environment where youth practice critical thinking, problem-solving, and self-realization. Using the techniques of Story Drama transforms storytelling into a tool for cultivating socially engaged, independent learners, encouraging critical thinking and activism by nurturing empathy, community awareness, and creative agency from an early age.
This thesis explores the reimagining of traditional storytelling practices in educational settings through the lenses of queering pedagogy, which resists normative hierarchies and embraces fluid identity and storytelling, and humanist learning theory, centered on intrinsic motivation, emotional connection, and student autonomy. Drawing from my experience teaching intergenerational Story Stroll and Baby and Me lessons at the Orlando Family Stage, I demonstrate how queer and humanist theory resist the restrictive norms of traditional storytelling. These practices cultivate social engagement, community solidarity, and early literacy skills by prioritizing students’ autonomy and empowering them to lead creative, child-driven role-play. This thesis contends that integrating pedagogical methods, queer theory, and humanist learning theory can transform storytelling into a tool for fostering independent learners and conscious members of society
A Depth-Integrated Investigation of Hydrodynamic Habitat Preferences of Seagrass in Estuarine Environments
No full textSubmerged aquatic vegetation shapes morphodynamic, hydrodynamic, and ecological attributes of shallow, coastal waters. Though seagrasses have been shown to exhibit threshold tolerances in response to varying light, salinity, and temperature, hydrodynamic thresholds have yet to be described. In this study, seagrass distribution data observed over 25 years were combined with modeled wave energy to quantify the hydrodynamic preferences of seagrass. The frequency distributions of wind-wave heights in the study area (Mosquito Lagoon, Indian River Lagoon, and Lake Worth Lagoon, Florida) were characterized using the Simulating WAves Nearshore (SWAN) numerical model. Seagrass distribution data, collected through direct observation and aerial imagery analysis backed by repeated ground truthing of fixed transects, were combined with the modeled hydrodynamic data to identify hydrodynamic preferences of seagrass. Analysis revealed that these preferences vary by water depth. Hydrodynamic thresholds at various depths were estimated using a logistic regression model. In shallow water (0.2 – 0.5 m), seagrass likelihood was maximized (\u3e50%) where the 80th percentile significant wave height (H80) was greater than 5.9 cm (95% CI: 5.73-6.07 cm). When water depths were 0.5 – 1.0 m, the 50% probability threshold was observed at greater wave heights; when H80 exceeded 11.1 cm (95% CI: 11.33-11.73 cm). In shallow and moderate depths, seagrass likelihood increased with H80, indicating that seagrasses growing at those depths preferentially sought environments with greater wave energy. In deeper water (1.0 – 1.5 m), tolerance to greater wave heights was observed (50% probability threshold at H80 = 16.2 cm, 95% CI: 15.84-16.58 cm); however, the opposite preference was observed; seagrass likelihood increased as H80 decreased. Considering the findings across depth, a zone of seagrass hydrodynamic preference can be described where H80 is greater than 5.91 cm and less than 16.2 cm. Including hydrodynamic tolerance by depth in restoration planning will increase the success of seagrass planting efforts
Advanced Subspace Estimation Techniques for Statistical Analysis and Machine Learning
Get PDFThe rapid expansion of data volume and complexity across diverse domains has underscored the need for robust and efficient techniques capable of extracting meaningful information from high-dimensional datasets. Subspace estimation—identifying the underlying low-dimensional structures within high-dimensional data—has emerged as a cornerstone method in modern data analysis and machine learning. This dissertation aims to advance both the theoretical and practical aspects of subspace estimation problems.
In the first part, we investigate Fréchet central subspace estimation, a critical component of sufficient dimension reduction (SDR) challenges. Traditional SDR methods often fall short when dealing with non-Euclidean responses. To address this, we propose a novel Fréchet SDR method that leverages kernel distance covariance, specifically designed for metric space-valued responses such as count data, probability densities, and other complex structures. By employing a kernel-based transformation to map these intricate responses into a suitable feature space, our approach facilitates efficient and accurate dimension reduction while accommodating the diverse and non-Euclidean characteristics inherent in modern datasets.
The second part of the dissertation focuses on robust subspace recovery, a fundamental task with applications in clustering, anomaly detection, and image processing, among others. Although Iteratively Reweighted Least Squares (IRLS) has demonstrated strong empirical performance, its theoretical foundations have remained largely unexplored. We rigorously establish that, under a set of deterministic conditions, a variant of IRLS augmented with dynamic smoothing regularization converges linearly to the true underlying subspace from any initialization. Additionally, we extend our theoretical guarantees to the more general setting of affine subspace estimation, offering novel insights and recovery guarantees in an area where existing theory is notably sparse
Performance and Operability of Additively Manufactured Solid Fuels in Airbreathing Propulsion
Get PDFSolid fuels have primarily been investigated in the context of non-airbreathing systems such as hybrid rockets and solid rocket boosters. In both cases, the vehicle must carry an oxidizer— either as gaseous or liquid oxygen in hybrid rockets or embedded within the fuel grain in solid rocket boosters. Airbreathing engines, such as ramjets and scramjets, offer a distinct advantage over these technologies by extracting the necessary oxygen for combustion from the surrounding atmosphere, greatly increasing range. Solid fuels are optimal for many ramjet and scramjet applications due to their high volumetric energy density and long shelf life compared to liquid and gaseous fuels. However, operating these engines at high altitudes presents challenges due to low pressure, which leads to ignition difficulties and blowout. To implement these technologies, advanced fuel formulations with wider flammability limits are necessary. Additive manufacturing offers unparalleled flexibility in constructing solid fuel grains, providing precise control over geometry and internal composition compared to traditional casting methods. This paper examines the potential of additively manufactured solid fuels—PLA, PETG, ABS, and PMMA—for use in ramjets and scramjets, characterizing key performance parameters such as specific impulse, combustion efficiency, and regression rate. Fuels were tested under realistic ramjet and scramjet flight conditions in multiple engine configurations, including an optically accessible small-scale ramjet and scramjet, as well as a full-scale, axially symmetric ramjet
Leyendo Pasión de historia desde una mirada poscolonial
No full textThe short story “Pasión de historia” by Ana Lydia Vega can be interpreted as a sharp reflection on Latin American postmodernity, immersed in a cultural reality shaped by colonialism. This context raises questions of identity in the face of a globalized economy, making the story a text with a complex framework and multiple levels of interpretation. Among these, postcolonialism stands out as the central axis of the analysis presented in this essay.
The frequent references to the Puerto Rican nation and its independence struggle, the depiction of gastronomy as a unifying national element, the use of popular language infused with vernacular expressions influenced by English, the allusions to a universal literature with a distinctly Eurocentric and bourgeois character, and the ideologies that juxtapose civilization and barbarism reveal a fragmented notion of the Puerto Rican nation. This vision is rooted in heteroglossia, transcending traditional notions of identity.
Through the construction of the plot, the narrative voice, the parodic rhythm, and the ironic and mocking language, a worldview emerges that is deeply marked by the subaltern condition of the colonized. Within this framework, the story desacralizes the idea of a homogeneous, developing nation while simultaneously challenging globalizing Eurocentric supremacism.
The relevance of this work, despite being written nearly 40 years ago, lies in the persistence of the issues it addresses, which remain unresolved in the 21st Century still bound by postcolonial structures. In this sense, the research question guiding this critical essay is: Can “Pasión de historia” be read from a postcolonial perspective? The short answer is “yes,” and the arguments developed in the following pages will expand and substantiate this assertion
Bent Never Broken: A Study of Multidimensional Perfectionism Through Animation
No full textBent Never Broken is a 3D animated short film about a martial artist that strives to gain his mother’s love through perfection. The film focuses on maladaptive multidimensional perfectionism and how it affects parent-child relationships. Bent Never Broken utilizes stylized rendering and animation to maintain maturity and appeal. It has a mature storyline with an ending that enforces the idea that perfection does not equate to love, and that it is okay to walk away from maladjusted parent-child relationships
A Deep Learning Framework for Last-Mile Delivery Enhancement Using Social Media
No full textOver the past decade, people have been spending more time online. Almost anything can be done from a laptop or cellphone. This is one of the reasons why e-commerce has been in a constant boom, as it is easier to buy something online and have it delivered to the front door than to go to the store. As more people engage in this activity, e-commerce platforms\u27 challenges are more complicated and need to be addressed faster. However, these challenges escape the company\u27s scope when external factors influence the objective of optimized deliveries, for example, traffic issues or bad weather during the last mile, pushing the company to fill the gap of developing different types of routes depending on the area. On the other hand, Intelligent Transportation Systems (ITS) also have a budget challenge that interferes with the need for delivery companies for traffic sensors in urban areas. This research aims to investigate a solution that closes the gap for accurate traffic prediction tailored for last-mile delivery logistics using social media analysis. The novel proposed methodology can be divided into two stages: (1) social media analysis: to get an idea of the overall sentiment around the city regarding traffic, and (2) traffic prediction: uses deep learning tools like Graph Convolutional and Long-Short Term Memory Neural Networks and data from social media and other influential factors
Determining Essential Attributes for Experience and Satisfaction in the Customer 4.0 Era: a Kano Model Approach
Get PDFThe world is constantly changing, with rapid technological advancements and increased digital connectivity shaping daily activities. In this evolving landscape, customers have transformed into what is known as Customer 4.0—a new generation of consumers who expect high levels of personalization, digital engagement, and greater transparency from organizations. These expectations extend to higher education, where students demand more customized learning experiences, interactive content, and digital accessibility. This research investigates engineering students\u27 preferences regarding educational attributes aligned with Customer 4.0 characteristics using the Kano model. While the Kano model has been widely applied across industries, its use in higher education remains limited. This study aims to classify educational attributes based on their impact on student satisfaction. A Kano questionnaire was developed to assess fifteen attributes derived from an extensive literature review. Data was collected from 419 engineering students from UCF. The findings revealed that six attributes surveyed were considered must-be attributes, including transparency, innovation, sustainability, mental health, applicable knowledge, and accessible materials. Additionally, six attributes, including interactive content and open educational resources, were identified as attractive features. Chi-square was used to determine associations between the demographics and attributes. Fisher’s exact test was applied to ensure statistical validity, and Cramer’s V was used to measure the strength of association between demographic factors and attribute classifications. The results indicate that gender influences students\u27 perceptions and expectations of some attributes. This research contributes to the body of knowledge on the use of the Kano model in higher education. It provides insights into how higher education students align with Customer 4.0 principles. Institutions should understand and implement those essential and attractive attributes to enhance student experience in the digital era. Future research could explore these attributes across different institutions, disciplines, and student demographics to further validate and expand upon these findings
Solving the National Security Threat of the United States Drug Crisis
No full textThis thesis analyzes the current drug crisis in the United States with the intention of forming a sound National Security Report and Strategy to equip current and future administrations with the necessary tools and information to adapt to the modern crisis. The number of drug induced and related deaths are rising in the U.S., which arguably is cause for alarm at the federal level. With the country divided on how to properly attack this crisis, which affects hundreds of thousands of lives across the nation, this thesis aims to provide a solution to a drug crisis that is unlike any other in the history of the U.S. This thesis aims to study the history of the evolution of drug use in the United States, as well as how previous and current administrations have responded to the way in which their specific drug crisis was impacting the nation at the time. This analysis is designed to fully understand the level of success to which the drug problem has been handled by former presidential administrations so as to propose a well educated solution to the constantly evolving drug problems of today and tomorrow. In order to properly understand the modern drug challenges in today’s societal and pharmaceutical conditions, research will be conducted on current drug trends that will range from drug usage to drug arrests to societal drug beliefs. This research, in conjunction with the analysis of past American counter-drug initiatives, will be used to craft a new National Security Report and Strategy that mimic the format of legitimate intelligence and national security documents