Culturally responsive teaching refers to an approach to teaching and learning that facilitates the achievement of all students by including content that is relatable to all cultures and creating a culturally supported and learner-centered environment. The CSE 110 course at ASU would greatly benefit from the incorporation of culturally relevant learning, as it would help them thrive in their chosen field of study while being able to uphold and value cultural relevance. The incorporation of culturally relevant pedagogy would further help students from marginalized communities feel more accepted and capable of thriving in STEM education. We began our research by first understanding the foundations of culturally responsive pedagogy, including how it is currently being used in classrooms. Concurrently, we studied the CSE 110 curriculum to see where we can implement this teaching strategy. Our research helped us develop a set of worksheets. In the second semester of our research, we distributed these worksheets and a set of control worksheets. Students were randomly assigned to an experiment or control group each of the four weeks of the study. We then analyzed this information to quantitatively see how culturally responsive pedagogy affects their outcomes. To follow up we also conducted a survey to get some qualitative feedback about student experience. Our final findings consisted of an analysis of how culturally responsive pedagogy affects learning outcomes in an introductory computer science course.

This thesis explores the nature of Type II diabetes and Alzheimer’s disease to better understand their symptoms, treatments, and the factors that lead to further development; with a specific focus on the role that diet plays. Alternate connections outside of diet, including amyloidosis and inflammation, are additionally analyzed, but the emphasis remains on introducing diets that may be helpful in terms of preventative measures and controlling symptoms of diseases. Two major diets investigated are the Mediterranean diet and DASH, and it will be discussed why these diets are better suited for overall health, in comparison to a diet based primarily in high-fat or ultra-processed foods. This paper integrates information from scientists, medical professionals, and even a personal testimony from a diabetic patient. This thesis will recommend legislation to better regulate ultra-processed foods based on the current models of the United States FDA and the EU and will further recommend better education implementation discussing healthier diets and overall disease prevention. Despite the current research and statistics outlined on these two diseases, with their respective connections to diet, deeper investigation and analysis remains to be done in order to better understand diet-disease links.

In the realm of supply chain management, student organizations play a crucial role in shaping the future leaders of the field. The Supply Chain Management Association at ASU (SCMA at ASU) stands as a testament to the impact such organizations can have. Recognized as a powerhouse within the highly competitive landscape of student-run organizations at the W.P. Carey School of Business, SCMA at ASU not only facilitates networking opportunities but also serves as a channel for industry insights, professional growth, and the development of an engaged student community. The purpose of this guidebook is to distill the collective experience of SCMA at ASU into a comprehensive resource that can guide and inspire the establishment, maintenance, and growth of student-led supply chain organizations at universities nationwide. To create the most informative guidebook possible, this resource will not only draw upon the rich experiences and achievements of SCMA at ASU but also incorporate insights and information from supply chain student organizations across various universities. This inclusion ensures a diverse range of successful strategies, innovative practices, and practical advice, reflecting what has worked best for these organizations in different academic and operational contexts. By pooling knowledge from a broad spectrum of successful SCM student organizations, this guidebook aims to serve as an essential tool for any university looking to enhance its supply chain program through student-led initiatives, fostering a new generation of supply chain professionals equipped to navigate and lead in an ever-changing global landscape.

Recent satellite and remote sensing innovations have led to an eruption in the amount and variety of geospatial ice data available to the public, permitting in-depth study of high-definition ice imagery and digital elevation models (DEMs) for the goal of safe maritime navigation and climate monitoring. Few researchers have investigated texture in optical imagery as a predictive measure of Arctic sea ice thickness due to its cloud pollution, uniformity, and lack of distinct features that make it incompatible with standard feature descriptors. Thus, this paper implements three suitable ice texture metrics on 1640 Arctic sea ice image patches, namely (1) variance pooling, (2) gray-level co-occurrence matrices (GLCMs), and (3) textons, to assess the feasibly of a texture-based ice thickness regression model. Results indicate that of all texture metrics studied, only one GLCM statistic, namely homogeneity, bore any correlation (0.15) to ice freeboard.

Deforestation in the Amazon rainforest has the potential to have devastating effects on ecosystems on both a local and global scale, making it one of the most environmentally threatening phenomena occurring today. In order to minimize deforestation in the Amazon and its consequences, it is helpful to analyze its occurrence using machine learning architectures such as the U-Net. The U-Net is a type of Fully Convolutional Network that has shown significant capability in performing semantic segmentation. It is built upon a symmetric series of downsampling and upsampling layers that propagate feature information into higher spatial resolutions, allowing for the precise identification of features on the pixel scale. Such an architecture is well-suited for identifying features in satellite imagery. In this thesis, we construct and train a U-Net to identify deforested areas in satellite imagery of the Amazon through semantic segmentation.

This thesis focuses on solving separable nonlinear least squares (SNLLS) problems and explores how the so-called Variable Projection (VarPro) method can be used to solve this particular type of problem. First, there is a brief discussion on curve fitting methods and SNLLS models. Then, an overview of the VarPro algorithm is discussed, along with the optimization concepts that facilitate the method's success. We examine how to derive the Jacobian for the nonlinear solvers and consider different ways to approximate it numerically. This leads into a section focusing on a variety of numerical experiments that illustrate the effectiveness of the VarPro method. The tests demonstrate how different initial guesses, noise levels, and Jacobian approximations affect the accuracy and efficiency of the computations. The thesis also briefly talks through some of the many applications of VarPro across a wide spectrum of topics, which include numerical analysis, biomedical imaging, spectroscopy, and chemistry.

With uses in fields such as medicine, agriculture, and biotechnology, halogenases are useful enzymes in nature which add or substitute halogens onto other molecules. By doing so, they become necessary for biosynthesis and cross-coupling reactions. Halogenases can be classified by three main types of mechanisms: nucleophilic, radical, and electrophilic. From there, they can be further broken down by the halogen involved, the substrate needed, other proteins used, or molecules generated. A notable example is PrnA which is a tryptophan-7 halogenase that falls under the flavin-dependent definition with an electrophilic mechanism. Historically, research on these enzymes was slow until the use of bioinformatics rapidly accelerated discoveries to the point where halogenases like VirX1 can be identified from viruses. By reviewing the literature available on halogenase since their first analysis, a better understanding of their functions can be obtained. Also, with the application of bioinformatics, a phylogenetic analysis on the halogenases present in cyanobacteria can be conducted and compared.

This project involved research into solar thermal and geothermal energy generation as possible solutions to the growing U.S. energy crisis. Background research into this topic revealed the effects of climate and environmental impacts as major variables in determining optimal states. Delving into thermodynamic engineering analyses, the main deliverables of this research were mathematical models to analyze plant efficiency improvements in order to optimize the cost of operating solar thermal and geothermal power plants. The project concludes with possible future research areas relating to this field.