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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
Computational materials science plays a crucial role in advancing new and improved materials. To leverage the advantages of local and nonlocal methods and aid in the advancement of predictive capabilities for materials, multiscale models have been introduced. Many such methods have been proposed to overcome computational challenges in accuracy and efficiency. In this work, I begin by presenting a review of some multiscale methods for crystalline modeling to provide context for this dissertation.
Together with my advisor Dr. Xingjie Helen Li, we explore the static behavior of a bottom-up nonlocal-to-local coupling method, Atomistic-to-Continuum coupling, and explore the dynamic behavior of a nonlocal method, Peridynamics, to explore a bimaterial interface.
Inspired by the blending method developed by \cite{Seleson2013} for nonlocal-to-local coupling, we create a symmetric and consistent blended force-based Atomistic-to-Continuum (AtC) scheme for one-dimensional atomistic chains. AtC coupling schemes have been introduced to utilize the accuracy of atomistic models near known defects and the computational efficiency of continuum models elsewhere. The conditions for the well-posedness of the underlying model are established by analyzing an optimal blending size and blending type to ensure the stability of the $H^1$ seminorm for the blended force-based operator. We present several numerical experiments to test and confirm the theoretical findings.
Then, we create a Peridynamics-to-Peridynamics scheme to model a bimaterial bar in one dimension. Peridynamics (PD) naturally allows for the simulation of crack propagation in its model due to its use of integro-differentials and time derivatives instead of the spatial derivatives typical of classical models. Although PD can be computationally intensive, its ability to accurately model fracture behavior, especially at material interfaces, makes it a valuable tool for achieving high accuracy in simulations, especially due to the susceptibility of fracture where differing materials meet. We prove the conservation laws, derive the dispersion relation, and estimate the coefficient of reflection near the interface for this nonlocal-to-nonlocal problem. We seek an optimal nonlocal interaction kernel in the governing equation for the cross-material interaction to reduce spurious artifacts when the kernel is assumed to be constant.
Lastly, I discuss potential future development in Atomistic-to-Continuum coupling and Peridynamics.
The COVID-19 pandemic abruptly changed educational institutions globally and challenged teachers and students. This immediate shift was difficult for K12 teachers because they were required to teach their courses online or using a blended learning (BL) model. As BL use continues to grow, concerns about student-content engagement have emerged. This study used a single case study to investigate experiences with facilitating student-content engagement in BL environments. Eleven teachers from two districts were surveyed and individually interviewed with semi-structured interviews. The complex adaptive blended learning systems (CABLS) was used as the theoretical framework to guide the data collection, data analysis, and interpretation of results. Findings of this study revealed that the learner demographics showed a diversity in economic status and academic abilities, with socioeconomic status emerging as a potential indicator affecting students’ access to technology within BL environments. Digital literacy skills varied among students, influencing their student-content engagement in BL environments. Teacher experiences with BL varied from embracing the mix of technology-mediated instruction with traditional F2F methods. Additionally, results showed that the support systems such as instructional coaches and professional learning communities (PLCs) played a crucial role in facilitating student-content engagement and enhancing pedagogical practices. Furthermore, findings showed that districts and institutions have demonstrated commitment to supporting BL environments through multiple layers of support. As technology continues to evolve, addressing challenges and leveraging collaborative efforts will be essential in ensuring that BL environments thrive and promote meaningful student-content engagement. Implications of this study inform the development of best practices, guidelines, and resources to enhance student engagement and foster a culture of continuous improvement in BL environments to improve student learning outcomes.
