When They See Us in the Pages: The Representation of Black and Brown Males in Children’s Literature

This study seeks to explore ways in which Black and Brown males are depicted in picturebooks. This study is guided by the concept of cultural code-meshing-— the blending and concurrent use” (Young, 2009, p. 50) of all the cultural languages that are mine (e.g., rap quotes, artistic media, and academic writing). The following question guided this study: How are race, racism, and power depicted in Crown: An Ode to the Fresh Cut and Windows? The theoretical framework that guides the study is Critical Race Theory (CRT), which aids in understanding how ideas of race, racism, and power are operationalized in various spaces. The sample consists of two picturebooks, Windows authored by Julia Denos and illustrated by E.B. Goodale, and Crown: An Ode to the Fresh Cut, authored by Derrick Barnes and illustrated by Gordon C. James. I use the critical content analysis approach as it is effective for understanding how various forms of privilege and oppression exist in society. I use textual and visual analysis to make visible various discourses operating through the print text and visual imagery within the two picturebooks. Through a deductive approach, I observe data from both books and analyze the data through the tenets of CRT to understand how race, racism, and power are communicated through print text and visual imagery. Of the six tenets of CRT, there were four tenets found operationalized in the study: counter storytelling/unique voices of color, race as a social construction, permanence of racism, and the critique of colorblindness. Exploring the representation of Black and Brown males in children’s literature will contribute to the field of education—and by extension the role of children’s book publishing in education—by advancing conversations around the selection and utilization by adults of picturebooks for young children. Understanding how messages surrounding Black and Brown males are communicated through the words and images within picturebooks can inform the ways in which future picturebooks are created, published, awarded, selected, and used in various spaces, including classrooms

Inspiring Change Agents through Technology

A capstone submitted in partial fulfillment of the Requirements for the degree of Doctor of Education in the College of Education at Morehead State University by Edith J. Bostic and Timothy R. Edwards Gallipolis on April 1, 2016

Comparative Models of Hydrocarbon Emissions for a Diesel Engine Operating at Constant Loads and Speeds

Linear multiple regression (LMR) and nonlinear polynomial network (NPN) models were developed from data collected from ISO 8178‐4 (1996) test cycle B‐type tests (ISO) and an expanded set of tests (EXP) to predict hydrocarbon (HC) emissions from a diesel engine. LMR using the ISO training data (R2 = 0.94) resulted in overfitting of the model as applied to the evaluation data (R2 = 0.49). LMR based on the expanded data (R2 = 0.68) was a better LMR model when applied to the evaluation data (R2 = 0.64). NPN using the expanded training data (R2 = 0.99) resulted in the best model when applied to the evaluation data (R2 = 0.98) and is preferred for predicting HC when the larger set of test mode data are available. NPN using the ISO training data (R2 = 0.99) resulted in a satisfactory fit for the evaluation data (R2 = 0.91), although with a higher average absolute error (0.52 vs. 0.42 g/kWh) than NPN using the EXP training data. This model was also considered suitable for predicting HC. Results of this initial study suggest that data could be collected during ISO 8178‐4 emission tests and modeled with NPN to predict HC emissions for a diesel engine operating at various constant speeds and loads

Modeling NO Emissions of an Off-road Diesel Engine Based on Emission Tests

Emissions values determined by the ISO 8178 emission certification tests do not necessarily represent emissions of a tractor in operation (Hansson et al., 2001). Rather than using ISO 8178 tests solely for certification, data collected during the tests may be suitable for predicting nitrogen oxide (NOx) emissions of an engine operating at constant loads and speeds. Linear multiple regression (LMR) and nonlinear polynomial network (NPN) models were developed with data collected from ISO 8178-4 (1996) test cycle B-type tests (ISO) and an expanded set of tests (EXP) to predict NOx emissions from a diesel engine. LMR using the ISO training data (R2 = 0.94) resulted in over-training of the model, as applied to the evaluation data (R2 = 0.51). LMR based on the expanded data (R2 = 0.60) was a better LMR model, when applied to the evaluation data (R2 = 0.73). NPN using the ISO training data (R2 = 0.99) resulted in a considerable improvement over the LMR models for the evaluation data (R2 = 0.81). NPN using the EXP training data (R2 = 0.96) resulted in the best model when applied to the evaluation data (R2 = 0.95). When applied to the evaluation data, the mean absolute error of the NPN EXP based model was significantly less than from the NPN ISO based model. The NPN model based on EXP data is recommended for predicting NOx. Data could be collected during ISO 8178-4 emission tests that included additional test modes and modeled with NPN to predict NOx emissions for a diesel engine operating at various constant speeds and loads

Editors\u27 Notes

Editors\u27 Note

Editors\u27 Notes

Editors\u27 Note

Editors\u27 Notes

Editors\u27 Note

Editors\u27 Notes

Editors\u27 Note

Reviewer Acknowledgements 2019

Reviewer Acknowledgement

Editors\u27 Notes

Editors\u27 Note