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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.
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.
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.
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).
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.
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
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.
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.
This dissertation explores the intersection of Geographic Information Science (GIScience) and Artificial Intelligence (AI), specifically focusing on the enhancement of 3D deep learning models by spatial principles for understanding 3D geospatial data. With the rapid advancement in geospatial technologies and the proliferation of 3D data acquisition methods, there is a growing necessity to improve the capability of AI models to interpret complex 3D geospatial data effectively. This work seeks to leverage spatial principles, particularly spatial autocorrelation, to address the challenges pertaining to 3D geospatial object detection.
The research is structured around three pivotal questions: the utility of spatial autocorrelation features for understanding 3D geospatial data, the approach to derive content-adaptive spatial autocorrelation features, and the enhancement of post-processing in the task of 3D geospatial object detection. Through a series of experiments and model developments, this dissertation demonstrates that incorporating spatial autocorrelation features, such as semivariance, significantly enhances the performance of 3D deep learning models in geospatial object detection. A novel spatial autocorrelation encoder is introduced, integrating spatial contextual features into the 3D deep learning workflow and thereby improving accuracy in detecting objects within complex urban and natural environments. Further, the dissertation delves into the challenges brought by data partitioning and sampling in large-scale 3D point clouds, as evidenced in the DeepHyd project focusing on the detection of hydraulic structures (i.e., bridge and its components). The findings highlight the critical role of spatial dependency patterns in optimizing object detection accuracy and pave the way for future improvement of the 3D deep learning frameworks.
