Dissertation Defense Announcements

Optimally modulating a vehicle's speed profile through highway, urban, and suburban environments can result in pronounced fuel savings, particularly with heavy-duty vehicles. However, existing strategies for speed profile optimization traditionally rely on aspirational and often deterministic assumptions, which cease to be accurate in the presence of real-world features such as non-deterministic traffic and actuated signalized intersections. The research in this dissertation establishes a hierarchical Green-Light Approach Speed (h-GLAS) strategy for controlling vehicles traveling through non-deterministic highway, urban, and suburban environments. The h-GLAS strategy utilizes vehicle-to-infrastructure (V2I) communication to receive information about the route's topology, speed limits, and signal phase and timing (SPaT). For suburban environments that employ semi- and fully-actuated signalized intersections, past signal timing information is used forecast the future values of the signal phase lengths. This information is used to construct a desired velocity profile to be tracked by a semi-economic model predictive controller (MPC), which computes the optimal wheel force command for the vehicle. When traveling through highway environments, the desired velocity profile represents a globally optimal dynamic program solution, computed offline before the beginning of a trip. For urban and suburban environments, however, the velocity profile is constructed to allow the vehicle to arrive at upcoming intersections when the probability of a green signal is maximized. The semi-economic MPC minimizes a quadratic objective function, which reflects a trade-off between minimizing mechanical energy expenditure and braking effort, with tracking the supplied desired velocity profile. This MPC is unable to maintain vehicle following constraints without changing its convex nature, so a command governor (CG) is located downstream to maintain vehicle-following constraints efficiently. The CG modifies the MPC's control action by the minimal amount necessary to maintain safe vehicle following. Using the simulation packages from PTV VISSIM, the h-GLAS is validated against real signal timing algorithms within a stochastic traffic environment parameterized by real-world data. Simulation results show that the h-GLAS controller is capable of significantly reducing a vehicle's fuel consumption by 16%-26% when compared to a baseline control strategy traveling through the same suburban environment.

With the worldwide prevalence of obesity doubling over the past 30 years across a wide range of demographic and socioeconomic groups, the obesity epidemic is a major public health challenge today. Excessive food consumption and lack of exercise are two major contributors to obesity. In recent years, these two activities have been integrated into our daily lives together with social media. A growing body of literature delineated how social networks impact people's health-related behaviors and suggested social media has a role in affect people's obesity-related behaviors. This study aims to gain an insightful understanding of which online social factors are impacting users' obesogenic behaviors and explore computational methods to examine those behaviors using social media data.

Our work consists of three overall research aims. In the first aim, a systematic review was conducted to examine online social factors concerning obesity. A total of 1,608 studies that related to obesity and social media were collected from the three most popular electronic databases for the field. After close inspection, 50 studies were further examined and ten types of online social factors were identified within four-level social-ecological model, which was used to explain each factors' potential impact on an individual from varying levels of online social structure to user's connection to the real world. In the second aim, we learned how the local food environment influences state level obesity rate using social media data. Publicly available Yelp and MyFitnessPal data were collected via a novel approach to characterize the local food environment. A statistically significant correlation between the state's food environment and state obesity rate was observed. We further built a computational model to predict the state-level obesity rate using aforementioned data, in which we achieved a Pearson correlation of 0.791 across US states and the District of Columbia. In the third aim, we studied how a major social disrupting event, COVID-19 shutdown, affects users' dietary behavior using social media data. Tweets relating to people's dietary behavior with images from April to June of 2019, 2020, and 2021 were collected. An observational study of behavior patterns was conducted by using image classification models, visualization tools, and text analysis methods. People are found eating more healthier food during complete and partial shutdowns than before Covid-19. Results of this dissertation could help the public health agencies, policymakers, organizations and health researchers to better utilize social media to carry out obesity-related education, obesity surveillance, and develop public health policy to address this challenge.

Historically, standard English language ideologies have been perpetually ingrained in American educational practices and policies (Smitherman, 2017; Wong & Teuben-Rowe, 1997). These practices are not limited to K-12 studies and maintain a position of dominance in higher education (Álvarez-Mosquera & Marín-Gutiérrez, 2020). Calls for diversity in curriculum and pedagogical practices currently involve increasing demands for linguistic inclusion that reflects the diversity of student populations (CCCC, 2020). This study explores how Black women students across the diaspora who use home and/or native languages, dialects, and accents navigate their identities in academic spaces of higher learning where standard English language ideologies are often the only acceptable language varieties that are valued or encouraged. Data was collected through the use of virtual semi-structured interviews using Seidman’s (2006) method of three 90-minute interviews. A single focus group interview with all three participants was conducted as well. All interviews took place during the summer of 2021. Data was analyzed using a qualitative, narrative-based approach that included emphasis on both small stories (De Fina and Georgakopoulou, 2012) and dominant narratives (Lyotard, 1984). This research suggests the experiences of Black women in college composition and communications include themes like feelings of rejection, inadequacies, pressure to conform, and a lack of linguistic agency.

Supramolecular aggregates can have specific nanostructures that give them a variety of functionalities, making them useful for many applications in energy, catalysis, medicine, biotechnology and other scientific fields. These functional nanostructures are built via the self-assembly of building blocks with particular physicochemical properties. Different molecular interactions participate in the self-assembly processes such as metallic, ionic, van der Waals forces, electrostatic, hydrophobic, H-bonding, and π- π stacking. Therefore, the building blocks for self-assembly are molecules that are pre-designed to supply these interactions in a given environment; in addition, they can provide a desired functionality.
Polyhedral oligomeric silsesquioxane (POSS) is a promising scaffold to be used as delivery system. When covalently linked to a photosensitizer, POSS has an influence on the self-assembly behavior of the photosensitizer, modifying its properties, potentially enhancing its efficacy toward photodynamic therapy (PDT).
In this Thesis, I describe my work on the study of polyhedral oligomeric silsesquioxanes (POSS) porphyrin derivatives as building blocks, their self-assembly and application in photodynamic therapy (PDT). We envision that the chemical tunability of POSSs can be used as a promising option to improve the delivery and performance of photosensitizers.