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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.
Understanding and characterizing cancer patient outcomes is challenging and involves multiple clinical measurements (e.g., imaging and genomics biomarkers). Enabling multimodal analytics promises to reveal novel predictive patterns that are not available from singular data input. In particular, exploring histopathological and genomics sequencing data allows us to provide a path for us to understand the insights of cancer biology. In this dissertation, we first present a graph-based neural network (GNN) framework that allows multi-region spatial connection of tiles to predict molecular profile status in colorectal cancer. We demonstrate the validity of spatial connections of tumor tiles built upon the geometric coordinates derived from the raw histopathological images. These findings capture the interaction between histopathological characteristics and a panel of molecular profiles of treatment relevance. Second, we propose a multimodal transformer (PathOmics) integrating pathology and genomics insights into colorectal cancer survival prediction. The proposed unsupervised pretraining captures the intrinsic interaction between tissue microenvironments in WSI and a wide range of genomics data (e.g., miRNA-sequence, copy number variant, and methylation). After the multimodal knowledge aggregation in pretraining, the task-specific model finetuning expands the scope of data utility applicable to both multi- and single-modal data. Finally, we introduce a contrastive pathology-and-genomics pretraining to enhance patient survival prediction by extracting the multimodal interaction for each patient while distinguishing the differences among various patients. Together, the above methods provide an array of solutions for addressing the challenges in multimodal disease data understanding, leading to improved overall performance of patient outcome prediction in colorectal cancer.
Researchers have identified that inequitable learning experiences for African American students have negatively impacted their educational outcomes in the United States, and culturally sustaining practices offer great promises in supporting African American students. This meta-analysis investigated the effectiveness of culturally sustaining practices on African American students’ academic and behavioral outcomes. This study built on prior attempts to synthesize multiple definitions of culturally sustaining practices with recommendations from the literature aimed directly at African American students. In this dissertation, I first used the existing synthesis to establish a theoretical framework with an operational definition of culturally sustaining practices for African American students (CSPAAS). I then conducted a systematic review to identify group design studies aligned with the components of the CSPAAS framework. Effect sizes were extracted from each individual study and a random effects model was employed to determine the overall effectiveness of CSPAAS interventions. Additionally, I evaluated the included studies for methodological rigor using the Council for Exceptional Children (CEC, 2014, 2023) quality indicators to determine the extent to which CSPAAS interventions could be identified as evidence-based practices. Results revealed CSPAAS academic interventions were highly effective (n = 17; g = 1.01) and CSPAAS behavioral interventions were moderately effective (n = 5; g = 0.5). The CSPAAS practices for both academic and behavioral interventions also met CEC (2014, 2023) criteria to be categorized as evidence-based practices. Implications for future research are discussed.
In recent years, the global energy sector has been undergoing a significant transformation, characterized by an increasing shift towards data-driven operations and the widespread adoption of renewable energy such as solar photovoltaics (PV). This transition is largely motivated by the urgent need to address climate change and the realization of the potential that large-scale data collection and analysis hold for enhancing energy efficiency and sustainability. As the energy landscape becomes more complex and interconnected, the role of sophisticated energy forecasting techniques has grown in importance. These techniques are crucial for managing the variability and uncertainty inherent in renewable energy sources, such as wind and solar power, which are subject to fluctuations in weather and environmental conditions. Moreover, the integration of big data analytics into energy systems facilitates more accurate and timely predictions, thereby enabling more effective planning, operation, and maintenance of energy infrastructure. This dissertation introduces a novel, data-driven methodologies to address key challenges in energy forecasting: predicting weather-induced power outages, net load forecasting, and accurately estimating solar PV penetration.
In the first part of the study, a methodology to forecast weather-related power distribution outages one day ahead on an hourly basis is presented. A solution to address the data imbalance issue is proposed, where only a small portion of the data represents the hours impacted by outages, in the form of a weighted logistic regression model. Data imbalance is a key modeling challenge for small and rural electric utilities. The weights for outage and non-outage hours are determined by the reciprocals of their corresponding number of hours. To demonstrate the effectiveness of the proposed model, two case studies using data from a small electric utility company in the United States are presented. One case study analyses the weather-related outages aggregated up to the city level. The other case study is based on the distribution substation level, which has rarely been tackled in the outage prediction literature. Compared with two variants of ordinary logistic regression with equal weights, the proposed model shows superior performance in terms of geometric mean.
The dissertation then explores net load forecasting in the context of increasing behind-the-meter (BTM) solar PV system adoption. This adoption introduces complexities to grid management, especially concerning net load-the difference between demand and PV generation. The intermittent nature of PV generation, influenced by weather and time, adds to net load volatility, posing challenges to grid reliability. This dissertation presents a review of state-of-the-art net load forecasting with a focus on forecasting approaches, techniques, explanatory variables, and the impact of PV penetration on net load forecasting. Additionally, the study conducts a critical analysis of existing literature to identify gaps in the field of net load forecasting and PV integration. To address some of these gaps, a benchmark net load forecasting model is proposed. The proposed model uses publicly available data from ISO New England. Through the case study, it is demonstrated that the proposed net load forecasting model outperforms the current benchmark load forecast model significantly in terms of forecasting accuracy, as measured by Mean Absolute Percentage Error. Moreover, the case study also demonstrates the effectiveness of the proposed model over a range of PV penetration, which is an important consideration as the use of solar energy continues to grow.
Furthermore, the dissertation addresses two critical questions regarding PV integration: (1) How much PV is there in the system?; (2) Which meters have BTM PV? To address the challenge of estimating PV penetration in systems, existing supervised and unsupervised methods are reviewed, which reveal common limitations, especially when PV installation information is limited or completely unavailable. To overcome these challenges, a regression-based approach is developed by leveraging the difference in performance in the benchmark load and net load forecasting models in forecasting net load. The proposed framework is deployed for real-world data from an ISO and a medium-sized in the United States. The results validate the effectiveness of the proposed method in accurately estimating PV penetration levels, even without explicit PV installation data, using only historical load data.
The final part of the study focuses on identifying meters with BTM PV installations. Again by, leveraging the performance disparities between load forecasting models and net load forecasting models, a methodology is devised to differentiate meters with and without PV installations. The effectiveness of the proposed frameworks is confirmed using an empirical case study at a medium-sized US utility with meter-level load data meters. The results illustrate that accurate identification of meters with PV installations was achieved while maintaining a low rate of false identifications. This methodology provides valuable insights for utilities, empowering them to comprehend the adoption and impact of distributed solar energy within their service territories.
Overall, this study contributes significantly to the field of energy system forecasting by developing data-driven models that enhance the understanding and management of weather-induced outages, net load variability, and solar PV integration. These advancements enable utilities to make informed decisions for grid planning, capacity management, and service customization, paving the way for more resilient and efficient energy systems.
Recurrent events are commonly encountered in medical and epidemiological studies. It is often of interest what and how risk factors influence the occurrence of events. While much research on recurrent events has addressed both time-independent and time-dependent effects, there is a possibility that these effects also vary with certain covariates.
In this dissertation, we develop novel estimation and inference procedures for two intensity models for recurrent event data—a class of semiparametric models and a nonparametric frailty model. Both models allow for the simultaneous measurement of time-varying and covariate-varying effects, with covariates potentially depend on event history. The proposed semiparametric models offer much flexibility through the choice of different link functions and parametric functions. Two hypothesis tests have been developed to assess the parametric functions of the covariate-varying effects. For the proposed nonparametric intensity model with gamma frailty, estimation procedure involves using an Expectation-Maximization (EM) algorithm and local linear estimation techniques. Variance estimators are obtained through a weighted bootstrap procedure. Both of the proposed models have been applied to a malaria vaccine efficacy trial (MAL-094) to assess the efficacy of the RTS,S/AS01 vaccine.
This dissertation explores notions of belonging among minority Honors students through student self-identifying questionnaires and semi-structured interviews. One objective of this study is to explore how the Honors educational environment impacts minority student populations and their overall sense of belonging. Another objective of this study is to examine the influence of race, class, gender, culture, and educational experiences prior to entering the Honors College. In the context of this study, a minority classification refers to the student’s self-identification as one or more of the following groups: LatinX, Indigenous American, Black/African American, Pacific Islander, and/or Middle Eastern. The findings indicate that having a fostered identity before entering the Honors College, minority representation, community, and social/emotional safety are aspects of the Honors educational experience that contribute to the participants’ notions of belonging. The study presents implications for diversity, equity, and inclusion in Honors programs, as well as institutional and systemic changes to help promote minority student success.
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.
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.
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
