Dissertation Defense Announcements

Candidate Name: Johnine Willamson
 April 08, 2024  10:30 AM
Location: COED 259

Fifty percent of African American men with learning disabilities will not persist past their first year of college (Newman et al., 2011). A bachelor’s degree for an African American man means he is five times less likely to be incarcerated than his peers with a high school diploma and will make approximately $32,000 more per year on average than his counterparts without a bachelor’s degree (Trostel, 2015). Frequently neglected and inadequately represented in the existing literature on learning disabilities are the experiences of African American men with learning disabilities in higher education. The purpose of this phenomenological multi-case study was to examine the postsecondary educational experiences of African American men with learning disabilities by exploring the perspectives of both parents and students.

Ten semi-structured interviews were conducted; Six parent interviews and four student interviews. The study answered the following research questions (1) What are the psychosocial experiences of parents of African American young men with learning disabilities at the postsecondary level? (2) What are the primary roles of parents of African American young men with learning disabilities at the postsecondary level? (3) What do parents perceive about the intersecting identities of disability, race, and gender on the social and academic experiences of their African American young man with learning disabilities at the postsecondary level? (4) What are the psychosocial experiences of African American men with learning disabilities attending a Postsecondary Institution? (5) What are the experiences of African American men with learning disabilities attending a Postsecondary Institution regarding social and academic supports?

Based on the data analysis, three parent themes and two student themes emerged respectively: (1) Bubble Wrap Parenting, (2) The Changing of the Guard, and (3) In the Intersection of Black and Disabled; (1) Right in the Middle of the Dichotomy, and (2) The Juggling Act. The findings underscore that when Black men with learning disabilities receive services that segregate them from their peers, they face a forced choice between preserving their identity and accessing necessary support. One recommendation arising from these findings is to make support services universally available. This entails granting all students access to supports such as assistive technology and note-taking apps that have traditionally been exclusively available for the disabled population. By doing so, any stigma surrounding segregated support would be eliminated.

Candidate Name: Anthony Davis
Title: Student Conduct Administrators' Perceptions of Support
 April 09, 2024  1:00 PM
Location: COED 321C

Within the context of higher education, student conduct administration is drenched in risk, compliance with local and federal laws (Glick & Haug, 2020). In short, student conduct is a complex, and challenging functional area to work in, as administrators to balance educating students, protecting the campus community, and mitigating institutional risk (Miller & Sorochty, 2015; Lancaster & Waryold, 2008).

This qualitative, phenomenological study aimed to explore the lived professional experiences of student conduct administrators; to better understand their struggles and needs, as they would describe. Semi-structured interviews were used to capture depth in the shared experiences of ten participants and describe the meaning assigned to the phenomenon being explored.

The findings of this study were captured in 4 main themes: (1) Clashing with the Regime, which looks at SCAs challenges navigating political ecosystems within their respective institutions and states, (2) Encountering Turbulence, which captures common challenges SCAs experience while resolving cases (3) Nurtured by Leadership, which looks at the role of SCAs direct supervisor in fostering support and (4) Leaning on the Village, which captures the network of support SCAs receive outside of their direct supervisor.

Candidate Name: Shadab Anwar Shaikh
Title: Machine Learning-Based Approaches for Forward and Inverse Problems in Engineering Design
 April 03, 2024  12:00 PM
Location: DUKE 324

The battery enclosures of current electric vehicles are made of metallic alloys, specifically aluminum or steel. Replacing these metallic alloys with a lightweight material, such as carbon fiber composite, may offer significant weight savings due to its comparable strength-to-weight ratio. Carbon fiber is corrosion-resistant and can be engineered for fire resistance and electrical insulation. It can also be fine-tuned for specific applications and performance needs, such as "crashworthiness".

Designing a carbon fiber-based battery enclosure for crash performance through trial-and-error experiments can be extremely laborious and inefficient. This inefficiency can be alleviated by using virtual manufacturing and structural analysis software. A simulation software chain allows for the virtual manufacturing and crash-testing of the battery enclosure in a single process. However, these numerical simulations are computationally expensive, time-consuming, and may require significant user interaction. Finding optimal design parameters within a reasonable time-frame can be extremely challenging.

The first part of this dissertation addresses the forward problem of accelerating the design of battery enclosures for crash performance. It involves developing a machine learning-based surrogate model of the simulation workflow that can provide quick, approximate results in a fraction of seconds. This can further support design space exploration studies.

Physical phenomena in engineering design are governed by differential equations, typically solved in a forward manner with known physical parameters, initial and/or boundary conditions, and a source term. However, there is often a need to reconstruct the source term from available measurement data, which may be corrupted with noise, along with the initial and/or boundary conditions, and physical parameters. These types of problems are known as inverse problems, more specifically, inverse source problems. Inverse source problems are often ill-posed and are usually solved by iterative schemes and optimization techniques with regularization, which can be time-consuming. In recent years, machine learning approaches have shown promise in managing ill-posed problems and handling noisy data.

The second part of this dissertation addresses a specific type of inverse source problem, known as the dynamic load identification problem, which involves determining the time-varying forces acting on a mechanical system from the sensor measurements. The study begins with the development of a deep learning model that leverages physics information to infer the forcing functions of both linear and nonlinear oscillators from observational data. Furthermore, the study leads up to a development of a physically consistent surrogate model that is capable of providing robust predictions from the noisy observations without the need to explicitly solve the differential equation.

Candidate Name: Paula Shuping Williams
 April 08, 2024  1:00 PM
Location: COED 110

Family engagement with schools has been shown to be a predictor of student success (Powell et at., 2010) and federal statute supports school/ family relationships through the Family Engagement in Education Act. For students with disabilities (SWD), family engagement may be even more critical. Unfortunately, data has suggested that family engagement may be limited due to barriers families of SWD may face (Van Haren & Fiedler, 2008). Teacher invitation, teacher beliefs about family involvement and quality of communication are factors related to family engagement. The purpose of this study was to investigate an in-service teacher's use of a step-by-step strategy during family/ teacher conferences to increase family engagement during the conference, improve quality teacher communication and positively impact teacher beliefs on family involvement. The step-by-step conferencing strategy was called PIQUE and was developed through a review of prior research and feedback from experts in the field. This case study used both quantitative and qualitative methods to determine the effectiveness of the PIQUE strategy. Within an AB single-case design, I noted an increase in the 5-second intervals of the family speaking during the conference from baseline to post-intervention phase. This increase was immediate and demonstrated an accelerating trend. The teacher and parent completed surveys and interviews, which were analyzed thematically alongside descriptive and inferential field notes recorded by the researcher. Through this analysis, two primary themes were identified as Misunderstanding Communication as Equal to Engagement and Bias as a Barrier to Engagement. A secondary theme of Lack of Confidence When Engaging with Families was also identified. Triangulation was achieved across quantitative and qualitative data sources. Conclusions point to an increase in equity of power during conferences and positive change in teacher beliefs about family involvement and engagement after the implementation of the intervention. A conclusion that PIQUE implementation led to these changes should be interpreted with caution due to the threats to internal and external validity of case studies. The study concluded with implications for practice, limitations and suggestions for future research.

Candidate Name: Jesse Redford
Title: Interpretable Methods for Quantitative Measurement and Classification of Surface Topography
 April 04, 2024  4:00 PM
Location: Duke 324

The functionality of manufactured components is intricately linked to their surface topography, a characteristic profoundly shaped by the fabrication process. Repeatable quantitative characterization of surfaces is essential for detecting variations, defects, and predicting performance. However, the plethora of surface descriptors presents challenges in optimal selection of the correct assessment metric. This work addresses two of these aspects: automatic selection of surface descriptors for classification and an application-specific approach targeting scan path strategies in laser-based powder bed fusion (LPBF) additive manufacturing.

A framework, titled Surface Quality and Inspection Descriptors (SQuID), was developed and shown to provide an effective systematic approach for identifying surface descriptions capable of classifying textures based on process or user-defined differences. Using a form of univariate analysis rooted in signal detection theory, the predictive capability of a discriminability value, d', is demonstrated in the classification of mutually exclusive surface states. A discrimination matrix that offers a robust feature selection algorithm for multiclass classification challenges is also introduced. The generality of the approach is validated on two datasets. The first is the open-source Northeastern University dataset consisting of intensity images from six different surface classes commonly found in cold-rolled steel strip operations. The application of signal detection theory's measure, d', proved successful in quantifying a texture parameter's ability to discriminate between surfaces, even amidst violations of normality and equal variance assumptions regarding the data.

To further validate the approach, SQuID is leveraged to classify different grades of surface finish appearances. ISO 25178-2 areal surface metrics extracted from bandpass filtered measurements of a set of ten visual smoothness standards obtained from low magnification coherent scanning interferometry are used to quantify different grades of powder-coated surface finish. The highest classification accuracy is achieved using only five multi-scale descriptions of the surface determined by the SQuID selection algorithm. In this case, spatial and hybrid parameters were selected over commonly prescribed height parameters such as Sa, which proved ineffective in characterizing differences between the surface grades.

Expanding surface metrology capabilities into LPBF additive manufacturing, additional studies developed a methodology to comprehend the relationship between scanning strategies, interlayer residual heat effects, and atypical surface topography formation. Using a single process-informed surface measurement, a critical cooling constant is derived to link surface topography signatures directly to process conditions that can be calculated before part fabrication. Twelve samples were manufactured and measured to validate the approach. Results indicate that the methodology enables accurate isolation of areas within the parts known to elicit heterogeneity in microstructure and surface topography due to overheating. This approach provides not only a new surface measurement technique but also a scalable parameterization of LPBF scan strategies to quantify track-to-track process conditions. The methodology demonstrates a powerful application of surface texture metrology to characterize LPBF surface quality and predict process outcomes.

Overall, this thesis contributes a systematic approach for identifying discriminatory parameters for surface classification and a novel process-informed surface measurement for predicting track-scale overheating during LPBF-AM of a nickel superalloy.

Candidate Name: Chelse Spinner
Title: Striving for optimal care: Understanding the determinants and experiences of Black women after cesarean birth using a public health critical race praxis lens
 April 05, 2024  11:00 AM
Location: https://charlotte-edu.zoom.us/j/93158630404?pwd=N2RzVnJOeFZmcFF5Njh4bnRHRnZ5QT09

In the United States (U.S.), Black women are more likely to undergo a cesarean birth in comparison to other racial and ethnic groups. Previous research has identified individual-level factors, such as health behaviors, comorbidities, and socioeconomic status to be associated with cesarean birth among Black women. However, those individual-level factors do not fully account for the variation in cesarean births. The three-manuscript dissertation explores factors that influence cesarean rates among Black women in the US. The first manuscript provided a scoping review of peer reviewed research on the risk and protective factors associated with cesarean birth among Black women in the U.S. In the second manuscript, logistic regression was utilized to examine the association between experiencing racial discrimination and delivery method using data from the 2016-2021 Pregnancy Risk Monitoring System (PRAMS). The third manuscript applied a qualitative, phenomenological approach to understand the experiences, perceptions, and needs of Black women following a cesarean birth. The findings contribute to the understanding of racial disparities in cesarean births and can inform evidence-based practice and research. There is opportunity to provide all women with the chance to receive optimal maternity care and Black women are no exception.

Candidate Name: Corey M. Shores
 March 26, 2024  9:00 AM
Location: Friday Building - Room 222

Recent scholarly attention has turned towards evaluations of harmful or “dark” leadership traits and behaviors. However, prevailing literature on destructive leaders primarily delves into leader-centric evaluations of traits, antecedents, and consequences, leaving a significant gap in understanding follower-driven perspectives on evaluations of destructive leaders. This study advocates for a second-order meta-analysis (SOMA) to scrutinize the interplay between evaluations of destructive leaders, the nomological network of concepts surrounding such evaluations, and the relative importance of potential predictors of such evaluations. While primary meta-analytic inquiries abound in the field, their findings sometimes present conflicting results, necessitating a secondary meta-analytic exploration encompassing diverse variables, including follower traits and various manifestations of destructive leadership. This dissertation takes stock of the limitations and opportunities in the extant literature. It presents a roadmap for a cleaned-up concept space, which will allow more robust future research by systematically searching through 256 articles and retaining 30 articles for the initial inclusion before additional searches to fill the remaining SOMA effect size estimates in the correlates in matrices for follower and leader individual differences, leadership construct correlates, and potential outcomes of DLB. Although I successfully coded over 37 follower differences, 68 DLB outcomes, and five destructive leadership constructs as correlates, many missing correlates were primarily tied to outcome relationships, demographics, and personality measures. These missing correlates were initially substantial, with over 70% of the meta-analytic correlation matrices bank. Moreover, the selection process prioritized meta-analytic estimates with the largest sample sizes to mitigate random sampling errors, resulting in comprehensive matrices comprising 182 meta-analytic estimates (total k = 10,818 & total sample size (n) = 2,384,935) not including any Metabus.org derived meta-analytic estimates. Some key statistically significant results include a robust model using eleven follower individual differences (i.e., gender, age, race, five-factor personality traits, positive affect, narcissism, trait anger) with R2 = 0.239 and all incremental correlate additions measured by Change in R Squared with p < 0.05 for all predictor additions excluding age and gender variables. Also, the relative weights and regression coefficients supported these findings. Emotional Stability emerged as a dominant predictor across the personality and demographic traits for followers at RW% = 0.46 with a coefficient β = - 0.652, p < 0.001. Additionally, Trait Anger yielded RW% = 0.23 with a coefficient β = - 0.514, p < 0.001. Additionally, this study suggests the most robust leadership construct relationships to destructive leadership, ethical leadership with ρ = - 0.63 (k = 2; n = 8,186), and unethical leadership ρ = 0.58 (k = 3, n = 2,702).

Candidate Name: Micheal McLamb
Title: Two- and Three-Dimensional Metamaterials for the Infrared Spectral Range
 April 04, 2024  12:00 PM
Location: Grigg 131

Plasmonic metamaterials are artificially structured materials with the inclusion of metallic elements regarded as macroscopically uniform mediums. These materials showcase adaptable optical characteristics achieved through manipulation of the materials' intrinsic geometries at scales much finer than the wavelength of the incident electromagnetic radiation under consideration.

This dissertation focuses on the fabrication methodologies and applications of plasmonic metamaterials in perfect absorption and plasmonic sensing. Plasmonic metamaterials, distinguished by their ability to manipulate electromagnetic radiation through engineered subwavelength structures, have garnered significant attention for their potential in various fields, including photonics, sensing, and energy harvesting.

The dissertation examines current fabrication techniques for plasmonic metamaterials, focusing on additive manufacturing approaches. The advantages of two-photon polymerization for the fabrication of plasmonic metamaterials is discussed in detail along with more traditional techniques like electron beam vapor deposition and atomic layer deposition. The advantages and limitations of each approach are scrutinized, laying the groundwork for subsequent investigations into tailored designs for specific applications.

Building upon the foundation of fabrication techniques, two distinct applications of plasmonic metamaterials are examined. Firstly, the concept of perfect absorption, wherein the metamaterial is engineered to efficiently absorb incident electromagnetic radiation across a narrow spectral range. Through theoretical modeling and experimental validation, novel designs for achieving perfect absorption are proposed and characterized. The investigated designs leverage the unique optical properties of plasmonic metamaterials to enhance light-matter interactions.

Subsequently, the utilization of these architectures for sensing applications is demonstrated. By exploiting the sensitivity of surface plasmon resonance to changes in the local refractive index, plasmonic metamaterials offer unprecedented opportunities for label-free, real-time detection of biomolecules, gases, and other analytes.

This dissertation showcases the potential practical applications of plasmonic metamaterials in perfect absorption and plasmonic sensing. It contributes to the ongoing advancement of plasmonic metamaterials and their seamless integration into cutting-edge photonics and sensing technologies.

Candidate Name: Zhi Li
Title: Informing Evaluation Practice through Research on Evaluation
 April 04, 2024  3:00 PM
Location: Mebane Hall Room 061, Cato College of Education

This dissertation advances research on evaluation (RoE) through a trio of studies focusing on the role of context and the innovative use of Linguistic Inquiry and Word Count (LIWC) software in formative evaluation in a qualitative research project. The initial study maps out how evaluation context dimensions—evaluator, stakeholder, organizational/program, and historical/political—affect evaluation, providing a nuanced understanding of these impacts. Subsequent research demonstrates LIWC's potential to monitor and formatively evaluate interviewer effects in data collection using LIWC's summary variable (authenticity and emotional tone), revealing that interviewer-interviewee demographic alignment has no significant effect in this specific qualitative research's data collection process. The final paper broadens LIWC's application, employing all built-in variables to pinpoint linguistic indicators of data richness, thereby refining data collection techniques. Together, these investigations shed light on contextual influences in RoE and validate LIWC as a pivotal tool for evaluators to assess evaluation context and provide strategies to evaluate qualitative data collection efforts ethically and efficiently, advocating for informed and adaptive evaluation practices to enhance research quality.
Key Words: Research on evaluation (RoE), evaluation context, Linguistic Inquiry and Word Count (LIWC), formative evaluation, interviewer effect, data collection, data richness

Candidate Name: Providence Adu
Title: Analyzing Housing Market Dynamics and Neighborhood Change: A Case Study of Charlotte, North Carolina
 April 02, 2024  9:30 AM
Location: In-person: McEniry 329 (3rd floor conference room), Virtual: https://charlotte-edu.zoom.us/j/96219890756

This research contributes to understanding the effects of local government urban regulatory policy and actions of private actors on a neighborhood’s housing market using the fast-growing city of Charlotte, North Carolina, as a case study.

The first article of this research examines private actors in the rental housing market and their impact on neighborhood outcomes. The analysis focuses on how exclusionary criteria used in online rental advertisements vary spatially and how they potentially impact neighborhood outcomes. It also focuses on how various factors such as race, income, and platform (Zillow vs. Craigslist) influence the presence of exclusionary criteria in rental advertisements.

The second article situates private actors' actions within the scope of a neighborhood’s changing characteristics and their effects on a neighborhood’s capital investment exhibited through housing renovation activity. The analysis employs 10-year longitudinal parcel-level permitting data on housing renovation activity, housing and neighborhood-specific variables, and spatial statistical techniques to assess if a change in a neighborhood’s prevailing characteristics influences housing renovation activity.

The third article analyzes the effects of local government regulatory policies on a neighborhood's housing market, specifically housing code violations that are resolved with repairs. The chapter hypothesizes that housing code violations, when solved with repairs, will significantly affect a neighborhood’s housing market by increasing home sales and rental prices or contribute to the loss of affordable housing as landlords withdraw their property from the housing market. To test this hypothesis, the research uses longitudinal data on home sales prices, gross rent, housing code violations, and other housing and neighborhood-specific variables. It employs spatial statistics techniques to model their longitudinal relationships.

These three articles collectively contribute to our understanding of neighborhood housing markets analyzed through the lens of private investments and practices and urban regulatory policy adopted by local governments in fast-growing cities like Charlotte. Furthermore, these chapters create a framework that shows how spatial statistics tools, natural language processing techniques, and novel and traditional data can be used to understand the relationship between a neighborhood’s housing market and neighborhood change.