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Unspoken Bonds
Get PDFThis thesis explores the art of non-verbal storytelling through 3D animation, focusing on a short narrative about the blossoming friendship between a young boy and girl. The story revolves around a boy who uses magical elements to attract the attention of a girl absorbed in her book, set in a simple environment featuring a single bench.
This project examines the unique challenges and opportunities presented by “Unspoken” 3D animation. It delves into the techniques of creating expressive characters and a compelling narrative arc without relying on spoken language. The study also explores how body language, facial expressions, posing, and composition combine to communicate emotions and plot developments within a simple yet powerful environment.
Furthermore, this thesis investigates the role of magical effects in 3D animation as both a narrative device and a visual enhancement. It examines how these fantasy elements represent the characters\u27 emotions and experiences.
The project also emphasizes on character posing and facial expressions to convey meaning and emotion. It addresses the importance of timing and posing sequences to portray an “Unspoken” story.
By analyzing this approach to 3D animated storytelling, the thesis showcases the power of visual narratives that overcome language barriers, highlighting non-verbal communication as an effective and engaging method of storytelling in animation
A Dutch Approach to Charleston’s Water Management
Get PDFCoastal cities across the world face increasing threats from climate change impacts such as flooding, drought, and sea level rise. The East Coast of the United States, including Charleston, South Carolina is among the most vulnerable cities due to its low elevation and exposure to extreme weather events. The Netherlands, with a big part of its country below sea level, have become accustomed generating solutions to mitigate climate change impacts.
This thesis examines how elements of Dutch water management, which is known for its multi-layered, proactive, and integrated governance system, can be adapted to improve Charleston’s resilience against climate change impacts.
To find this answer, this research compares governance structures, mitigation and adaptation strategies, and water management frameworks in both the Netherlands, and the United States. The findings indicate that the fragmented and reactive approach in the U.S. water management creates barriers to implement an effective climate adaptation strategy. In contrast, the Dutch model involves long-term planning practices, stakeholder collaboration, and a wide variety of solutions. The research highlights the main practices that can be integrated into Charleston’s water management system, while also keeping in mind the limitations within its current governmental system. Since the research focuses on governance structures and their role in shaping water management strategies, it bridges the gap between Dutch and U.S. water management approaches. The findings provide concrete recommendations not only for Charleston but also for other historic East Coast cities that face similar climate threats, while also contributing to a broader discussion about water management policies and frameworks in the United States
Reinforcement Learning for High-Dimensional Tradespace Exploration in Multi-Objective Optimization
No full textEngineering design optimization often requires balancing multiple competing objectives while navigating complex, high-dimensional tradespaces. Despite significant advances in tradespace exploration techniques, current methods face critical challenges in efficiently navigating high-dimensional design spaces. As the number of design variables and competing objectives increases, traditional multi-criteria decision-making and optimization methods struggle to explore the vast solution space comprehensively. The curse of dimensionality exacerbates this issue, making it impractical to analyze all possible solutions and effectively identify trade-offs between objectives. Additionally, the computational complexity of high-dimensional spaces further complicates optimization, making exhaustive solution analysis infeasible. Traditional multi-objective optimization methods rely on static Pareto-front calculations, which are computationally expensive and inflexible in handling evolving constraints. This research introduces a reinforcement learning (RL)-based decomposition and coordination (DC) framework to systematically explore and optimize multi-objective tradespaces. The study applies this RL-driven methodology to a four-dimensional tradespace, where the agent learns optimal relaxation strategies to balance competing objectives. Through iterative relaxation, the RL agent discovers efficient designs and evaluates their Pareto efficiency in the feasible region. Unlike conventional optimization approaches, this method enables adaptive exploration by refining constraints and improving design efficiency through continuous learning. The findings demonstrate that RL can effectively optimize engineering trade-offs, reducing reliance on heuristic-based decision-making and manual iterations. By automating the exploration of high-dimensional tradespaces, this study provides a foundation for Machine Learning (ML)-driven design optimization, offering a structured and scalable approach to multi-objective decision-making in engineering design
Migration and Diaspora: An Intersectional Feminist Reading of Lucy by Jamacia Kincaid.
Get PDFThis thesis explores how migration and diaspora shape the intersectional experience of Black women through a close reading of Jamaica Kincaid\u27s Lucy. Drawing on Black feminist thought, I make a case using analytical and theoretical frameworks to expand intersectionality to account for global movement and memory. Intersectionality must move beyond national borders to account for how power travels with people. I argue that migration works externally through movement and displacement, and diaspora works internally through memory. There is a location in the United States, but there is always a look back through memory and the two conflict. Lucy\u27s migration from the Caribbean to the United States brings her into contact with new racial and gender hierarchies. At the same time, her memories of home continue to shape how she sees herself. Rather than viewing migration as a one-way shift or diaspora as nostalgia, I present both as active forces that complicate identity. It positions Lucy\u27s story as evidence that the intersections of race, gender, and class cannot be fully understood without considering the influence of movement and memory. I propose a framework of global intersectionality that incorporates the destabilizing effects of transnational movement and diasporic memory into intersectional feminist analysis
Hemodynamic Indicators of Stroke Risk Due To Carotid Stenosis: Computational Fluid Dynamics Approach
No full textStroke remains a leading cause of morbidity and mortality worldwide, with carotid artery stenosis as a major contributor. Current risk assessment primarily relies on stenosis severity, yet many strokes occur in patients with moderate stenosis. This highlights the need for improved predictive metrics. This study aims to identify key hemodynamic metrics for developing multi-parametric indices to improve stroke risk assessment beyond stenosis severity. We performed computational fluid dynamics (CFD) analysis on patient-specific carotid artery models derived from computed tomography angiography (CTA) scans of 22 patients (12 stroke, 10 non-stroke). We extracted hemodynamic parameters, including wall shear stress (WSS), wall shear stress gradient (WSSG), velocity, vorticity, and pressure. We used the normalization techniques to standardize the values, and statistical analysis to evaluate their ability to differentiate stroke from non-stroke cases. While individual hemodynamic parameters showed limited discriminatory power, multi-parametric indices significantly differentiated stroke from non-stroke patients. Mean velocity and minimum pressure frequently appeared in the most differentiating multi-parametric indices, highlighting their relevance in stroke risk assessment. Among the multi-parametric indices, the Velocity-Pressure-WSS (VPW) index (p = 0.0362, two tail T-test) and Velocity-Pressure-Vorticity (VPV) index (p = 0.0470, two tail T-test) demonstrated the strongest discriminatory power. This study reveals the importance of assessment by multi-parametric hemodynamic indices over single-parameter evaluation and establishes a foundation for hemodynamic-based stroke risk assessment by identifying four key parameters—mean velocity, minimum pressure, WSS threshold (WSSthres), and vorticity threshold (Vortthres)—that effectively differentiate stroke from non-stroke cases when combined. The VPW and VPV indices highlight the potential of multi-parametric approaches to improve risk stratification, setting the stage for future research in personalized stroke prediction models
