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Evaluating user adaptations and perceptions of OpenELIS in sustaining its implementation in Côte d'Ivoire: A Qualitative Study
No full textThesis (Master's)--University of Washington, 2025Background:
Sustainability remains one of the most persistent challenges facing digital health interventions in low- and middle-income countries (LMICs). OpenELIS (OE), a laboratory information system introduced across Côte d’Ivoire’s public health laboratories with donor support, offers an opportunity to examine how digital health systems evolve in resource-constrained settings and what factors may shape their long-term adoption and institutionalization. Methods:This qualitative study analyzed stakeholder perspectives on the implementation and sustainability of OE across 37 laboratories in Côte d’Ivoire. In-depth interviews were conducted with laboratory personnel, information technology staff, and other stakeholders involved in OE use. A hybrid inductive-deductive thematic analysis was performed using the Consolidated Framework for Implementation Research (CFIR) and the Dynamic Sustainability Framework (DSF) to guide coding and interpretation. Analytic memoing and reflexivity practices, including a positionality statement, were used throughout the process to ensure rigor. Results:Findings were organized into four interconnected themes: (1) system readiness and infrastructure; (2) leadership and workforce capacity; (3) system adaptation and workflow alignment; and (4) perceptions of sustainability and donor dependency. Participants described how inconsistent infrastructure, informal leadership structures, staff turnover, and limited training disrupted OE implementation. However, adaptive transitions—such as the shift from OE Basic to OE Classic—improved usability and trust. Concerns about future sustainability were prevalent, especially in the absence of national transition plans and institutional ownership. These findings map onto CFIR’s Outer and Inner Setting domains and DSF’s emphasis on the dynamic interplay between intervention, setting, and broader ecological factors. Conclusions:The study highlights the complexity of sustaining donor-funded digital health tools in LMICs. Implementation success does not guarantee long-term sustainability without adequate investment in infrastructure, human capacity, and national ownership. Integrating implementation science frameworks such as CFIR and DSF can support more context-responsive digital health strategies that evolve with systems over time. Lessons from the OE experience in Côte d’Ivoire can inform broader efforts to strengthen digital health sustainability in similar settings
Mass Spectrometry-Based Analysis of Photochemical Cross-linking between Nitrile Imine and Trp, Phe, and Tyr Amino Acid Residues in Gas-Phase Ions
No full textThesis (Master's)--University of Washington, 2025In this study, conjugates of 2,5-diaryltetrazole and peptapeptides were photodissociated by 213 nm UV light. The nitrile imine group was generated from the tetrazole by losing a nitrogen molecule and cross-linked with carbonyl group of amide or amino acid side chain through intramolecular reactions. By analyzing the tandem mass spectra of ultraviolet photodissociation (UVPD) mass spectrometry (MS) and collision induced dissociation (CID) mass spectrometry, product structures were proposed. Theoretical calculations based on Born-Oppenheimer molecular dynamics (BOMD) were conducted for the conformational analyses of the proposed structures, and density-functional theory (DFT) based computations were used to evaluate isomer energies. Nitrile-imine cross-linking reaction mechanisms were suggested based on calculated structures of the peptide conjugates and product ions
Modularizing Emotion: The Rise of China’s Eryou Games and the Logic of the Emotional Engine
No full textThesis (Master's)--University of Washington, 2025This thesis explores the rise of Chinese Eryou (anime-style) mobile games from 2016 to 2024, focusing on how developers used emotional design to achieve rapid catch-up and global success. Using a dual-layered framework—Game_C (core mechanics) and Game_B (community and culture)—the study introduces the concept of an “Emotional Engine”: a modular system that integrates player emotions into gameplay, narrative, and fan interaction.Through case studies like Onmyoji and Genshin Impact, the thesis shows how emotional triggers such as gacha systems and character progression create structured player engagement. These emotions are amplified at the community level through co-creation and shared cultural practices.
Rather than copying Japanese models, Chinese developers pioneered a new emotional design paradigm, enabling them to dominate both domestic and international markets. This research contributes to game and innovation studies by highlighting how affective design can serve as a driver of industrial upgrading and cultural influence
Sleep, Cognition, Psychological Symptoms, and Health-Related Quality of Life Among Heart Transplant and Left Ventricular Assist Device Recipients
No full textThesis (Ph.D.)--University of Washington, 2025Heart failure (HF) is a chronic, progressive condition affecting an estimated 6.7 million Americans over age of 20. For individuals with advanced HF, treatment options such as heart transplantation (HTx) and left ventricular assist devices (LVADs) can significantly improve survival and health-related quality of life (HRQOL). Despite these interventions, many individuals continue to face persistent physiological and psychological challenges, including sleep disturbances, cognitive impairments, and psychological symptoms, which may hinder recovery and adversely affect overall well-being and HRQOL. The overarching purpose of this dissertation is to examine changes in sleep, cognition, psychological symptoms, and HRQOL from hospital discharge to 3 months post-discharge, and to explore interrelationships among these health outcomes in individuals who have received a LVAD or HTx. Specifically, the aims of this dissertation are to: 1) Conduct a comprehensive literature review on neurocognitive changes associated with HF, 2) Describe changes in objective and subjective sleep quality, cognition, psychological symptoms, and HRQOL from hospital discharge to 3 months post-discharge following a LVAD implant or HTx, 2) examine relationships between changes in sleep and changes in cognitive function, psychological symptoms, and HRQOL, and 3) Explore patient experiences, challenges, and perspectives on changes in sleep quality and its perceived impact on cognition, psychological wellbeing, and HRQOL during the initial 3 months post-discharge after LVAD or HTx. By characterizing these outcomes and their interconnections, this study provides novel insights into post-operative recovery in both LVAD and HTx recipients. The findings highlight critical gaps in current understanding, underscore the need for further research, and offer preliminary evidence to guide the design of future large-scale, multicenter studies. This work lays a foundation for improving care strategies and supporting multidimensional recovery in these growing patient populations
Leveraging Machine Learning to Uncover Integrated Impacts of Urban Environment Features on Human Health
No full textThesis (Master's)--University of Washington, 2025While human health is significant to sustainable development as cities keep increasing and are estimated to contain 70% of the population by 2050, the quantitative relationship of how the urban environments influence human health remains so poorly understudied due to the convoluted connection and co-effect among numerous urban factors, as well as the large-scale data integration difficulty. Previous researchers studying urban health mainly focus on finding specific urban environment feature’s influence or try to analyze the mechanisms of features co-effect on a coarse scale larger than census tract level or just with qualitative approach. Thus, this research set and reached the objectives to quantify the essential, specific and integrated relationship between urban environment and human health, and specify urban indicators’ co-effect at census tract level mainly from the urban environment’s perspective. With the development of data science, various statistical methods including Machine Learning (ML), SHAP analysis, and Pearson correlation analysis offer promising opportunities for analyzing such complex relationships, facilitated by the increasing availability of urban data by modern tools and the advancements in computational efficiency. By utilizing these methods, this research analyzes 6044 census tracts in 10 US metropolitans (New York City, Los Angeles, Chicago, Houston, Phoenix, Jacksonville, San Francisco Area, Seattle Area, Washington DC Area, Boston Area) with data from 2015 to 2024, builds a framework for census tract level urban health index developed from literature review and WHO urban health indicators framework (2014), quantifies 27 urban environment features’ influence on human health indicators especially on life expectancy, and builds a high performance urban health prediction model for future intervention suggestion for policy makers, making significant meaning towards the urban planning practice
Beyond Scaling: Frontiers of Retrieval-Augmented LMs
No full textThesis (Ph.D.)--University of Washington, 2025Language Models (LMs) have made significant progress by scaling training data and model sizes. However, they still face key limitations, including hallucinations and outdated knowledge, which undermine their reliability especially in expert domains like scientific research and software development. In this thesis, I argue that overcoming these challenges requires moving beyond monolithic LMs toward Augmented LMs: systems that are designed, trained, and deployed alongside complementary modules to improve reliability and efficiency. Specifically, my work has pioneered the field of Retrieval-Augmented LMs, which precisely locate relevant knowledge from large-scale text data and incorporate them at inference time. I begin by analyzing the limitations of current LMs and demonstrate how retrieval augmentation provides a more reliable, adaptable, and efficient path forward.
I then introduce our work on establishing new foundations for Retrieval-Augmented LMs, moving beyond simple post-hoc combinations of off-the-shelf models to tackle challenges driven by broader adoption.
Finally, I highlight the real-world impact of Retrieval-Augmented LMs through applications in domains such as scientific literature synthesis. Our fully open \osmodel system are now used by over 30,000 researchers.
I conclude by outlining our vision for the future of Augmented LMs, including better handling of heterogeneous modalities, flexible integration with diverse components, and rigorous interdisciplinary evaluation
Age-Dependent Variability in Immune Host Responses to SARS-CoV-2: Implications for Inclusive Clinical Trial Design
No full textThesis (Ph.D.)--University of Washington, 2025The COVID-19 pandemic, caused by SARS-CoV-2 has disproportionately affected older adults,who account for the majority of COVID-19-related hospitalizations and deaths. Although disease
severity has declined, periodic surges continue to pose significant risks to aging populations,
exacerbated by age-associated immune dysfunction, or immunosenescence. This dissertation
investigates the immune responses that contribute to protection against SARS-CoV-2 and how
these responses become dysregulated with increasing age. Given the evidence of impaired
antiviral immunity in aged models, we also evaluated the representation of older adults in
COVID-19 vaccine clinical trials to assess whether key age-associated immune differences are
being adequately captured in clinical research.
In Chapter 1, we examined the effects of IFNβ pre-treatment on SARS-CoV-2 infection
dynamics in lung epithelial cell models. We identified interferon-stimulated gene signatures
associated with antiviral protection, and found that pre-activation of the IFN pathway
significantly reduced viral replication and dampened inflammatory responses associated with
severe disease.
In Chapter 2, leveraging a cohort of young, mature, and aged mice infected with mouse-adapted
SARS-CoV-2, we performed temporally resolved transcriptomic analyses of lung tissues. Aged
mice exhibited impaired early cytokine and IFN signaling, dysregulated T cell activation during
recovery, and elevated baseline inflammatory signatures, all of which contributed to delayed
viral clearance and worsened disease outcomes. Moreover, these immune response are crucial for
effective vaccine-induced protection against COVID-19.
In Chapter 3, we conducted a cross-sectional analysis of COVID-19 vaccine clinical trials,
quantifying the underrepresentation of older adults and identifying exclusion criteria
disproportionately impacting this group. Our analysis reveals significant underrepresentation of
older adults in these trials, key barriers to their enrollment, and provides recommendations for
improving inclusivity of this group in clinical trials. Together, these studies provide new insights
into the molecular mechanisms underlying age-associated susceptibility to SARS-CoV-2 and
offer a framework for improving immune protection and clinical trial participation for older
adults
Understanding COVID-19 Induced Responses for Individuals and Businesses: Changes in Human Mobility Patterns and Resilience Tactics by Small Food Businesses
No full textThesis (Ph.D.)--University of Washington, 2025The COVID-19 pandemic has profoundly affected our daily lives, including the ways individuals access food, work, and carry out routine activities. From another perspective, small food businesses’ operations have also been affected by the unprecedented indoor service restrictions and reduced customer foot traffic. Understanding how people and businesses adapted is essential for addressing the wider effects on urban mobility, food access, and resilience. This dissertation is driven by the overarching question: How has the COVID-19 pandemic reshaped individual's mobility patterns and affected their food access, as well as small businesses' resilience tactics? To answer this, the study develops a comprehensive framework, combining advanced data modeling and empirical studies. First, the study introduces a modified Two-Step Floating Catchment Area (2SFCA) method to evaluate food accessibility through delivery services, addressing critical gaps in understanding disparities across regions and population groups. This methodology incorporates supply-demand interaction and the unique spatial characteristics of delivery services. Second, this dissertation investigates COVID and telecommuting-induced shifts in travel patterns using three waves of the Puget Sound household travel survey data and explores changes in the frequency, spatial and temporal distribution of maintenance and discretionary trips, as well as the resulting mode splits and vehicle miles traveled. Finally, this dissertation examines resilience strategies employed by small food businesses during disruptions. Using first-hand, qualitative and quantitative data collected from businesses in Seattle’s University District, it develops a mathematical optimization framework to evaluate and recommend effective strategies for coping with labor shortages, supply constraints, fluctuating demand, decreased production, and restricted capacity. In sum, the study provides a comprehensive picture of how individuals and small businesses adapted during the unprecedented COVID-19 Pandemic. This work contributes to creating resilient and adaptive urban communities in the post-pandemic era
Network Behavior Analysis of Spike Timing Dependent Plasticity (STDP) in Simulated Neural Networks
No full textThesis (Master's)--University of Washington, 2025The machine learning landscape is rapidly evolving with researchers often turning toward nature for inspiration. Understanding the development of neural networks \textit{in vivo} contributes significant transferable insight for advancing both neuroscience and computational research. This project applies a multiplicative Spike Timing Dependent Plasticity (STDP) model to the weighted graph output from neural growth simulations and analyzes the resulting spike and weight changes over time. This preliminary investigation establishes a baseline process for understanding the effects of STDP on a neural network and provides a framework for defining the resulting network behavior. Through rigorous data analysis, we examine bursting behavior during the refinement phase, analyze the progressive effects of STDP on synapse weights, and compare how the network behavior changes between the growth and refinement phases of neural development
Advancing Verbal Communication Skills Through Transcript-Based Feedback : Designing an AI-Assisted Language Learning Application, “Stepping Stones, ” to Support ESL Students in Academic and Professional Contexts
No full textThesis (Master's)--University of Washington, 2025This thesis investigates communication challenges faced by English as a Second Language (ESL) learners from high-context language backgrounds, such as Korean, Japanese, and Chinese, in online academic and professional settings. Cultural norms around indirectness, shared context, and cautious self-expression often influence how learners communicate in English, leading to subtle but persistent barriers to clarity, participation, and confidence. Adopting a research-through-design approach, the study combines interviews, diary studies, and online meeting transcript analysis to surface recurring pain points in learner speech. These insights informed the development of Stepping Stones, an AI-powered learning application that provides context-aware feedback based on learners’ actual spoken interactions. While many conventional language learning tools often emphasize grammar instruction and scripted practice, this system draws on learners’ real conversational data to provide personalized feedback and lesson flows. By analyzing naturally occurring speech rather than pre-written responses, it addresses individual communication patterns shaped by cultural context and supports growth in expressive clarity, pragmatic fluency, and habit awareness. This thesis presents a design-driven exploration of how culturally rooted communication barriers might be addressed through personalized feedback grounded in real learner speech, aiming to raise new possibilities for how interaction design can respond to the needs of high-context ESL learners