The purpose of this mixed methods action research study was to implement digital outreach strategies, which would enhance students’ motivation to complete financial aid requirements and scale the departments’ more time-consuming counseling efforts. Using self-determination theory as the primary framework, I implemented the use of a series of emails and text messages sent by students’ admissions recruiters to a group of first-year students admitted to a large, public Land Grant Institution. The messages were framed to enhance students’ autonomy, competence, and relatedness the summer before they enrolled. The digital campaign was also supported by supplemental opportunities, including virtual appointments, a targeted webpage, and virtual workshops. Following the intervention, I compared the enrollment and financial outcomes of participants and a comparison group. Intervention and comparison groups were also surveyed about their perceived levels of self-determination and satisfaction prior to high school graduation and the summer before enrolling at the university. Additionally, selected students from both groups were interviewed during their first semester at the university. There were no statistically significant differences in students’ perceived self-determination, satisfaction, enrollment, and financial aid outcomes following the intervention. Relatedness increased significantly across the two times of assessment indicating all students developed stronger relationships with those from the university’s financial aid and admissions offices, which boded well for students just entering the university. In logistic regression analyses, Pell Grant eligibility was a significant factor associated with negative financial aid outcomes of owing a student account balance of $500 or greater and not completing financial aid requirements on time. Taken together with qualitative interviews, these findings suggest a need for additional one-on-one or other high-touch support methods, to support admitted students in the financial aid process.

This thesis critically examines the dominant narrative constructed in US-based news media about the United States’ institutions of violence, the Black Lives Matter movement (BLM), and the Immigrant Rights (IR) movements. Engaging multiple disciplines across the social science that engage race, immigration, the media, and American politics, the thesis situates the media’s role in racial injustice and nation-state violence against the Black and Immigrant communities. White Supremacy is deeply part of the United States' past and present, and the news media plays a critical role in capturing and framing the challenges to entrenched systemic racism led by social movement activism. The news media is situated in a powerful public position, capable of leading or supporting social justice work as well as further entrenching systems of oppression and injustice. This thesis explores whether the media challenges or reinforces the nation-state’s violent and racist institutions and practices. To operationalize the empirical work, the thesis asks how the news media (de)centers and (de)legitimizes social movements, impacted communities, and the nation-state when reporting on BLM and IR. Two original datasets of 8,742 news articles (for BLM) and 3,372 news articles (for IR), covering 2013 to 2023, are analyzed using machine learning techniques like named entity recognition and semantic networks of text, along with qualitative content analysis of select months as critical case studies. The thesis reveals how news media serves as a governance tool capable of stifling dissent by decentering the racial injustices that legitimize movement tactics and simultaneously centering partisan politics and the nation-state.

This dissertation presents a volume filtering framework to solve particle-laden flows. Particle-laden flows are studied, employing the well-established Euler-Lagrange method, using the point-particle approximation. This approach requires the filter width to be much larger than the particle diameter. The method assumes that the particle is smaller than the Kolmogorov length scale. This thesis investigates how inertial particles at semi-dilute volume fractions modulate the flow characteristics for particles smaller than 1 in wall units, when dispersed within wall-bounded channel flows at friction Reynolds number of 180. The simulations are performed with 4 way coupling in order to account for high local concentration of particles, to capture mechanisms such as turbophoresis and preferential concentration. We show that drag attenuation or augmentation is determined by the particle inertia. As particle size is increased greater than 1 in wall units, the regime becomes finite-sized, requiring an interface-resolved description. To do this a novel Immersed Boundaries (IB) framework based on the concept of volume-filtering called the Volume-Filtered Immersed Boundary (VF-IB) method is presented. Transport equations are obtained by volume-filtering the Navier-Stokes equation and accounting for the stresses at the solid-fluid interface. Boundary conditions are transformed into bodyforces that appear as surface integrals on the right hand side of the filtered equation. The approach requires the filter width to be much smaller than the particle diameter in order to accurately resolve the interfacial dynamics. Several canonical tests are conducted for both stationary and moving immersed solids and report comparable results to the experimental and/or body-fitted simulations. Keep in mind, the VF-IB method reverts back to the Euler-Lagrange formulation if the filter width is significantly greater than the particle diameter. An artifact of volume-filtering is the emergence of unclosed terms we define as the sub-filter scale term. In order to characterize the contribution of this term on the solution, a more simpler case of a 2-D varying coefficient hyperbolic equation that has an exact solution is looked into. It is observed that the sub-filter scale term scales inversely with the square of the filter width. For fine interface resolution (i.e. small filter width), this value can be ignored with negligible effect to the accuracy of the numerical solution. However for coarse interface resolution (i.e. large filter width), including the sub-filter scale term significantly increases the accuracy of the numerical solution

In the realm of medical diagnostics, achieving heightened accuracy is paramount, leading to the meticulous refinement of AI Models through expert-guided tuning aiming to bolster the precision by ensuring their adaptability to complex datasets and optimizing outcomes across various healthcare sectors. By incorporating expert knowledge into the fine-tuning process, these advanced models become proficient at navigating the intricacies of medical data, resulting in more precise and dependable diagnostic predictions. As healthcare practitioners grapple with challenges presented by conditions requiring heightened sensitivity, such as cardiovascular diseases, continuous blood glucose monitoring, the application of nuanced refinement in Transformer Models becomes indispensable. Temporal data, a common feature in medical diagnostics, presents unique challenges for Transformer Models characterized by sequential observations over time, requiring models to capture intricate temporal dependencies and complex patterns effectively. In the study, two pivotal healthcare scenarios are delved into: the detection of Coronary Artery Disease (CAD) using Stress ECGs and the identification of psychological stress using Continuous Glucose Monitoring (CGM) data. The CAD dataset was obtained from the Mayo Clinic Integrated Stress Center (MISC) database, which encompassed 100,000 Exercise Stress ECG signals (n=1200), sourced from multiple Mayo Clinic facilities. For the CGM scenario, expert knowledge was utilized to generate synthetic data using the Bergman minimal model, which was then fed to the transformers for classification. Implementation in the CAD example yielded a remarkable 28% Positive Predictive Value (PPV) improvement over the current state-of-the-art, reaching an impressive 91.2%. This significant enhancement demonstrates the efficacy of the approach in enhancing diagnostic accuracy and underscores the transformative impact of expert-guided fine-tuning in medical diagnostics.

Repertoire for solo bassoon is becoming more common as music advances and evolves. There is a vast array of works for solo bassoon; however, only a small percentage of those are by composers from an underrepresented community, with an even smaller percentage written by composers with East Asian or Southeast Asian heritage. Furthermore, these works have little to no high-quality studio recordings. Additionally, these works often include contemporary techniques such as multiphonics, difficult tremolos, flutter tonguing, pitch bends, and glissandi, among others. This adds another layer of inaccessibility for those bassoonists who are unfamiliar with how to perform these techniques and therefore may be afraid to “take the plunge” into contemporary works that utilize them. I have created performance guides for Tilt by Tonia Ko, Legend of the Sea by Xinyan Li, and Jeu des Cinq Éléments II by Tôn-Thât Tiêt, in hopes of promoting and raising the accessibility of works by living composers with East Asian and Southeast Asian backgrounds.

Physical inactivity is a major contributor to chronic illnesses and mortality globally. However, most interventions to address it rely on static, aggregate models that overlook idiographic (i.e., individual-level) dynamics, limiting intervention effectiveness. Leveraging mobile technology and control systems engineering principles, this dissertation provides a novel, comprehensive framework for personalized behavioral interventions that have been tested experimentally under the Control Optimization Trial (COT) paradigm. Through careful design of experiments, elaborate signal processing and model estimation, and judicious formulation of behavior intervention optimization as a control system problem, this dissertation develops tools to overcome challenges faced in the large-scale dissemination of mobile health (mHealth) interventions. A novel Three-Degrees-of-Freedom Kalman Filter-based Hybrid Model Predictive Control (3DoF-KF HMPC) controller is formulated for physical activity interventions and evaluated in a clinical trial, demonstrating its effectiveness. Furthermore, this dissertation expands on understanding the underlying dynamics influencing behavior change. Engineering principles are applied to develop a conceptual approach to generate dynamic hypotheses and translate these into first-principle dynamic models. The generated models are used in concert with system identification principles to enhance the design of experiments that yield dynamically informative data sets for behavioral medicine applications. Additionally, sophisticated search, filtering, and model estimation algorithms are applied to optimize and personalize model structures and estimate dynamic models that account for nonlinearities and “Just-in-Time” (JIT; moments of need, receptivity, and opportunity) context in behavior change systems. In addition, the pervasive issue of data missingness in interventions is addressed by integrating system identification principles with a Bayesian inference model-based technique for data imputation. The findings in this dissertation extend beyond physical activity, offering insights for promoting healthy behaviors in other applications, such as smoking cessation and weight management. The integration of control systems engineering in behavioral medicine research, as demonstrated in this dissertation, offers broad impacts by advancing the field's understanding of behavior change dynamics, enhancing accessibility to personalized behavioral health interventions, and improving patient outcomes. This research has the potential to radically improve behavioral interventions, increase affordability and accessibility, inspire interdisciplinary collaboration, and provide behavioral scientists with tools capable of addressing societal challenges in mHealth and preventive medicine.