Dissertation Defense Announcements

Candidate Name: Chengying Hua
Title: Dynamic Speed Harmonization and Synergistic Performance Evaluation in A Connected and Automated Vehicle Environment
 June 05, 2024  9:00 AM
Location: EPIC 3344, Zoom ID: 918 9672 4029

Due to inherent restrictions in human driving behavior and information access, freeway congestion and stop-and-go behavior are nearly unavoidable. The adverse impacts include increased safety risks, longer travel times, and excessive fuel consumption. Various techniques (e.g., Variable Speed Limit (VSL), which is also known as Dynamic Speed Harmonization (DSH)), have been proposed to dampen traffic oscillation and smooth traffic speed. However, the effectiveness of the VSL is related to the compliance rates of drivers. Fortunately, new opportunities are emerging with the development of Connected and Automated Vehicles (CAVs) that can completely comply with the control system. The objective of this study is to investigate the effects of coordinated speed control in mixed traffic flow involving Human-Driven Vehicles (HDVs) and CAVs on the freeway. Therefore, a control strategy based on Deep Reinforcement learning (DRL) is developed to better understand how CAVs can improve operational performance. To evaluate and quantify the impact, a comprehensive performance framework is formulated. A series of numerical experiments will be conducted under different market penetration rates (MPRs) under various simulated scenarios. The overall intent of this study is to inform practitioners about the potential interactions between MOEs in implementing specific control strategies in a CAV environment.

Candidate Name: Jennifer Kant
Title: Impact of Evidence-Based Teaching Practices on SVSM Nanoscale Science Course and Transforming STEM Teaching and Learning Academy
 June 19, 2024  3:30 PM
Location: Burson 118

Quality STEM education in secondary and post-secondary schools is vital to the advancement of knowledge and technology for the United States. Quality STEM education is found where evidence-based teaching practices are implemented in the classroom. This dissertation focuses on two programs and their impact on their participants with the goals of providing quality STEM education. The STEM Academy is a faculty learning community with goals to support faculty members in implementing new evidence-based teaching practices at UNC Charlotte. The second program is Summer Ventures in Science and Mathematics (SVSM) Nanoscale Science course for high school students in North Carolina. The data for investigating the experiences of the participants in the STEM Academy was collected through semi-structured individual interviews as well as focus groups. The transcripts of these were thematically coded to find consensus on the benefits of the STEM Academy and the barriers to implementing new evidence-based teaching practices. The data for investigating the impact of the SVSM Nanoscale Science course and its revisions was collected through the grading of student final papers using a rubric specific to the Big Ideas in Nanoscale Science (BINS) and the experimental design process. Also, students were given the Student Attitudes towards STEM (S-STEM) survey at the beginning and end of the course. The results of the STEM Academy interviews and focus groups yielded 11 themes; six describing benefits and five describing barriers identified by the participants. These results can help to reform and grow the STEM Academy for future participants to meet its goals of supporting faculty members in implementing evidence-based teaching practices in STEM classrooms at UNC Charlotte. The results of grading the student final papers from the SVSM Nanoscale Science course showed significant improvements to writing research questions, designing experiments, and writing conclusions about their findings for students in the second cohort compared to the first cohort. These findings indicate the revisions to the course had a positive impact on student outcomes. The S-STEM survey results show the students maintained or slightly improved their positive attitudes towards STEM after participating in the SVSM course.
Key words: Nanoscale Science, Evidence-Based Teaching Practices, Faculty Learning Community

Candidate Name: Andrew McBride
Title: The Strategic Ambiguity of Leadership: Implications for the Science and Practice of Leadership Development
 June 10, 2024  10:30 AM
Location: Dubois Center Room 501

Leadership is a conceptually ambiguous term, which creates a challenge for the practice of leadership development: How do we develop something without knowing what that “something” means? Prior research has not explored this challenge, and its existence has not stopped organizations from spending billions of dollars a year on leadership development. In this dissertation, I start by asking how individual leadership development programs can function in light of leadership’s ambiguity. I use qualitative theory-building methods in study 1 to generate an explanation for this question that is grounded in the concept of strategic ambiguity. In study 2, I followed up on one of the key implications of this explanation—potential competing incentives between science and practice—and asked how the science and practice of leadership development (mis)aligns. For this study, I used inductive coding on two sources: academic recommendations drawn from leadership development and leader behavior articles, and practitioner claims drawn from client/customer-facing websites. In the closing chapter, I develop big picture implications and questions for the science and practice of leadership and its development.

Candidate Name: Lena Etzel
Title: Executive Functioning and Anterior Cingulate Cortex Volume as Potential Moderators of the Combat Exposure-PTSD Relationship
 June 04, 2024  1:00 PM
Location: https://zoom.us/j/98196152929?pwd=NkJveXN0ejZ4VlZWcDUwNkxhWkNxZz09 Meeting ID: 981 9615 2929 Passcode: 687968

Combat, a common source of trauma in the military, is consistently predictive of post-traumatic stress disorder (PTSD) among service members deployed to Iraq and Afghanistan. PTSD has detrimental effects on post-deployment health and psychosocial functioning. The cognitive model of PTSD posits that automatic threat appraisals maintain PTSD when they generalize to safe situations. As a result, the ability to modify this automatic response may support re-adaptation to the civilian context following deployment. Executive functioning (EF) includes suppressing automatic, incorrect responses (inhibition), generating and holding on to alternative, more context-appropriate perspectives (working memory), and flexibly shifting toward them (cognitive flexibility) and may act as a buffer by enabling re-consideration of trauma appraisals that otherwise maintain the combat exposure-PTSD relationship. Additionally, the anterior cingulate cortex (ACC), a brain region within the ventromedial prefrontal cortex, supports decision-making in uncertain contexts, regulating emotion to prevent incorrect automatic threat responses. Consequently, a smaller ACC volume may be associated with a diminished ability to adjust incorrect automatic threat appraisals. Using data from the Chronic Effects of Neurotrauma Consortium Study 34 (CENC-34) examining health outcomes following combat exposure and neurotrauma, the present study examined the factor structure of EF, and examined the resulting EF components and ACC volume as moderators in the relationship between combat exposure and PTSD, including PTSD symptom severity as well as diagnostic status. Participants were Iraq and Afghanistan Veterans (N = 241) who passed performance and symptom validity thresholds. Factor analysis of EF tests yielded two components, Cognitive Flexibility and Working Memory. After adjusting for age, sex, years of education, time since trauma, current Major Depressive Disorder (MDD) diagnosis, and presence of deployment mTBI, EF components were not associated with PTSD symptoms or diagnosis, and no support was found for an interaction between either component and combat exposure. In these models, combat exposure was significantly associated with PTSD symptoms and PTSD diagnosis. Similarly, after adjusting for age, current MDD diagnosis, presence of deployment mTBI, and total intracranial volume, combat exposure and ACC volume were not associated with PTSD symptoms or diagnosis and results did not support an interaction effect between combat exposure and ACC volume. Unexpectedly, across all models, current MDD diagnosis was the most consistently predictive of PTSD symptoms and PTSD diagnostic status. The present work was an initial foray toward advancing theoretical, empirical, and clinical understandings of the factors contributing to persistent PTSD in Veterans. Our replication of the association between MDD and PTSD underscores the need to comprehensively assess for relevant comorbidities in clinical settings. Additionally, with combat exposure as a significant predictor of PTSD symptom severity, individuals exposed to combat may benefit from periodic screenings to enable early detection and intervention.

Candidate Name: Alfred Hubbard
Title: Genomic Epidemiology for Malaria: Novel application of geospatial methods, new genomic markers, and population-level insights
 May 21, 2024  9:30 AM
Location: Bioinformatics 408

Genomic epidemiology is the use of genetic data to characterize and explain disease occurrence and transmission. Application of these methods to malaria has already yielded substantial benefits, such as identification and surveillance of drug resistance genotypes. However, the potential for genomic epidemiology to accelerate progress towards malaria eradication is far from fully realized. This dissertation demonstrates new applications of genomic information to questions that are impossible to address with conventional epidemiological data. First, the value of correlating genetic and environmental distances to understand the drivers of Plasmodium falciparum transmission is showcased with microsatellite data from 44 sites in Western Kenya. Second, the design and validation of a new panel of genetic markers, microhaplotypes with multiple SNPs on each short read, is presented for P. vivax, enabling sensitive, scalable characterization of within-host diversity in multi-strain infections. Finally, a similar panel of microhaplotype markers for P. falciparum is applied to samples from eight countries throughout Africa, yielding insights into continent scale transmission dynamics. The analysis of environmental drivers revealed the Winam Gulf of Lake Victoria as a barrier to malaria transmission, a conclusion that would be impossible to reach rigorously without this novel methodology. The new P. vivax panel yielded quality sequences and detected expected patterns of genetic relatedness, indicating this tool is ready for broad application. The P. falciparum microhaplotype analysis identified subtle patterns of genetic relatedness and surprisingly little relationship between within-host diversity and incidence, highlighting the potential of these markers but also a need for future work on the interpretation of the resulting data. This dissertation expands the scope of questions about malaria epidemiology that can be answered with genomic data and argues that routine application of these methods could accelerate progress towards malaria eradication.

Candidate Name: Maria G Alessi
 May 22, 2024  1:00 PM
Location: Colvard 4078

Stressed young adults are at greater risk of lifetime higher morbidity and mortality, highlighting a need for feasible and effective approaches to improve stress management in this population. Mindfulness, defined as intentional awareness of the present with an attitude of nonjudgment, is a promising intervention to improve a host of physical and mental health outcomes. The stress-buffering hypothesis posits that mindfulness may mitigate harmful consequences of chronic stress through top-down modulation of stress perception as well as bottom-up regulation of stress response systems (Creswell & Lindsay, 2014). Further, Lindsay & Creswell’s Monitor & Acceptance Theory (MAT; 2017) proposes that experiential acceptance is a key component of these effects, without which stress reactivity may be exacerbated. This study sought to investigate the stress-buffering mechanisms of mindfulness and replicate prior research supporting MAT using a single session dismantling study comparing regulated breathing control, monitor-only, and monitor + accept conditions in a sample of 33 stressed young adults who completed an in-lab social evaluative stressor. A series of linear hierarchical regressions and multilevel models were utilized to compare condition effects on physiological and psychological stress responsivity and reactivity, respectively. Individual-level predictors (e.g., trait mindfulness, self-compassion) that may moderate stress-buffering effects were also examined, and exploratory qualitative analysis of participants’ perceptions was conducted. No hypotheses were supported by the study’s findings, most likely due to the underpowered sample. Qualitative results further suggest that the study’s active control was potentially equally efficacious to the mindfulness conditions in buffering stress. Future research directions include clarifying the minimal amount of mindfulness practice needed to observe stress-buffering effects as well as investigating how mindfulness is most effectively learned in stressed populations.

Candidate Name: Kristin M. Lenoir
Title: Exploring the Role of the Electronic Frailty Index (eFI) in Identifying Vulnerable Older Adults in a Healthcare Setting
 May 23, 2024  11:00 AM
Location: Zoom meeting ID: 948 3521 7906, Passcode: 367907

Healthcare organizations play a key role in supporting health for a growing population of older adults. With the emergence of electronic health records, routinely collected data can be leveraged to identify vulnerable older adults more easily. Healthcare organizations can employ risk stratification, interventions, population management strategies, and community partnerships to enhance health and care for high-risk populations. Frailty, an internationally recognized indicator of vulnerability associated with numerous adverse outcomes, has received attention as a viable target for intervention as it provides a multidimensional view of an individual’s health status. This dissertation contains three studies that examine how structured data from the electronic medical record might be leveraged to identify older adults with elevated risk of experiencing adverse events. The first study explores the joint association of frailty and neighborhood disadvantage with emergency and inpatient utilization and considers how area-level variables may contribute to recognizing older adults with unmet needs across functional, medical, and social domains. The second and third studies leverage longitudinal frailty measures to explore frailty transitions in a unique healthcare context to inform strategies for the prevention, delay, or even reversal of frailty. The third manuscript considers how rural residence modifies the associations between frailty state transitions and individual-level predisposing and need factors as well as contextual-level predisposing factors.

Candidate Name: Abimbola R. Ogungbire
Title: Novel Machine Learning Techniques for Weather-Related Crash Prediction
 May 15, 2024  10:30 AM
Location: EPIC 3344

This dissertation addresses critical aspects of traffic safety, focusing on novel approaches for weather-related crash prediction—a significant concern in the transportation field. It is divided into three interconnected studies: geospatial risk mapping of weather-related crashes, addressing data imbalanced in machine learning for weather-related crash severity analysis, and analytics for future weather-related crash prediction. In the first study, the dissertation advances a novel approach to hotspot mapping by developing a spatio-temporal cube that incorporates both the spatial and temporal dimensions of crash data, providing a dynamic and comprehensive analysis of crash hotspots. In the second study, the dissertation tackles the challenge of imbalanced data, which can bias machine learning model outputs, making them less adept at predicting crash severity. By extending methods such as Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN), the dissertation evaluates the effectiveness of these methods in datasets with a prevalence of nominal predictors, aiming to enhance the predictive accuracy of machine learning models for crash severity. Lastly, the dissertation proposes the use of Spatially Ensembled ConvLSTM algorithm for predicting a weather-related traffic crash. This approach aims address the limitations of traditional predictive models by leveraging the ability of LSTMs to retain relevant information over extended time frames across different heterogenous spaces. The proposed technique was compared with existing methods to test if it outperform conventional predictive models and the standard ConvLSTM in accuracy.

Candidate Name: Abdulrahman Aldkheel
Title: Design and Evaluation of a Conversational Agent to Support Domestic Violence Survivors
 May 24, 2024  10:00 AM
Location: https://charlotte-edu.zoom.us/j/92315191825

Domestic violence (DV) is widely recognized as a significant problem with detrimental impacts on the mental, physical, and socio-economic well-being of individuals, families, and the broader community. Despite various resources designated to support survivors, they may not be easily accessible or readily available. More importantly, DV survivors are often reluctant to divulge their experiences with others and may even refrain from seeking assistance because of social, emotional, privacy, and cultural concerns. As the immediate response to DV is crucial to survivors' physical and psychological well-being, they need prompt and non-blaming first responders.
Technological advancement, particularly in automated Conversational Agents (CAs), is progressing rapidly. CAs are gaining attention as a promising tool for providing counseling and support, addressing the above-mentioned challenges with survivors using traditional supporting resources. The primary objective of this dissertation research is to design, develop, and evaluate a CA-based solution that assists DV survivors in receiving support, enhancing their awareness, and increasing their access to services. To this end, we first identify the meta requirements and design principles of CA for survivors by interview-ing DV professionals, then design and develop SafeHaven, a CA-based prototype for supporting DV survivors, by following the design principles and meeting the meta requirements, and finally evaluate the effectiveness and user perception of SafeHavan by conducting user experiments.
Our findings suggest that CAs should empathize with survivors' experiences and provide them with meaningful informational, tangible, and emotional support, ensuring their safety as well as maintaining a transparent, private, and trustworthy dialogue. An evaluation of the CA was carried out with 36 participants, including DV survivors, their friends, family, and professionals, evaluating, among other metrics, the emotional, informational, and instrumental support provided. It was discovered that the CA outperformed traditional online searches and ChatGPT in terms of providing emotional, informational, and instrumental support, high information quality, and user trust.
This research identifies meta-requirements and design principles for designing a CA for DV survivors. This is the first research to evaluate the effectiveness of CAs in assisting individuals with DV, providing tailored, context-sensitive assistance that is superior to the capabilities of traditional search engines and general AI platforms like ChatGPT. In a broader sense, our results will be instrumental in guiding the development of future CA-driven support systems for DV survivors. This dissertation emphasizes the transformative potential of CAs for survivors of DV, as well as significant implications for CA developers, DV organizations, and support groups, proposing innovative strategies for enhancing anonymity, accessibility, and support for survivors.

Candidate Name: Natasha D. Lipscomb
 May 06, 2024  12:00 PM
Location: Contact : clewis64@charlotte.edu

Racially minoritized students (RMS) face substantial disparities in college persistence and completion rates (Museus & Saelua, 2017). In particular, Black student enrollment at public two-year or community colleges has declined significantly, dipping below 13% in 2020, while for-profit institutions have maintained enrollment of Black students at roughly 28% over a 10-year period (AACC, 2023). Because community colleges have a reputation for being low-cost, high-quality institutions with more than 60 % of its graduates free of student loan debt (AACC, 2019), proper attention must be given and action taken to identify and address the needs of RMS in the community college settings to increase persistence and graduation rates. Sociological research on community colleges highlights the stratified tension between the increased provision of access as open-door institutions against low rates of successful completion (Schudde & Goldrick-Rab, 2014). While culturally relevant education practices have been most successfully implemented in the K-12 space (Ladson-Billings, 1995), an amplified call goes out to responsible community college leaders for the creation of culturally relevant campus environments. Using the culturally relevant leadership practices framework (Jones et al., 2016), this cross-case study explores the roles and practices of presidents and executive leaders within the context of their community colleges to determine how they create spaces for Black student achievement.