The purpose of this research is to create predictive models for a leading sustainability certification - the B Corporation certification issued by the non-profit company B Lab based on the B Impact Assessment. This certification is one of many that are currently being used to assess sustainability in the corporate world, and this research seeks to understand the relationships between a corporation's characteristics (e.g. market, size, country) and the B Certification. The data used for the analysis comes from a B Lab upload to data.world, providing descriptive information on each company, current certification status, and B Impact Assessment scores. Further data engineering was used to include attributes on publicly traded status and years certified. Comparing Logistic Regression and Random Forest Classification machine learning methods, a predictive model was produced with 87.58% accuracy discerning between certified and de-certified B Corporations.

Since the 20th century, Arizona has undergone shifts in agricultural practices, driven by urban expansion and crop irrigation regulations. These changes present environmental challenges, altering atmospheric processes and influencing climate dynamics. Given the potential threats of climate change and drought on water availability for agriculture, further modifications in the agricultural landscape are expected. To understand these land use changes and their impact on carbon dynamics, our study quantified aboveground carbon storage in both cultivated and abandoned agricultural fields. To accomplish this, we employed Python and various geospatial libraries in Jupyter Notebook files, for thorough dataset assembly and visual, quantitative analysis. We focused on nine counties known for high cultivation levels, primarily located in the lower latitudes of Arizona. Our analysis investigated carbon dynamics across not only abandoned and actively cultivated croplands but also neighboring uncultivated land, for which we estimated the extent. Additionally, we compared these trends with those observed in developed land areas. The findings revealed a hierarchy in aboveground carbon storage, with currently cultivated lands having the lowest levels, followed by abandoned croplands and uncultivated wilderness. However, wilderness areas exhibited significant variation in carbon storage by county compared to cultivated and abandoned lands. Developed lands ranked highest in aboveground carbon storage, with the median value being the highest. Despite county-wide variations, abandoned croplands generally contained more carbon than currently cultivated areas, with adjacent wilderness lands containing even more than both. This trend suggests that cultivating croplands in the region reduces aboveground carbon stores, while abandonment allows for some replenishment, though only to a limited extent. Enhancing carbon stores in Arizona can be achieved through active restoration efforts on abandoned cropland. By promoting native plant regeneration and boosting aboveground carbon levels, these measures are crucial for improving carbon sequestration. We strongly advocate for implementing this step to facilitate the regrowth of native plants and enhance overall carbon storage in the region.

The cerebellum is recognized for its role in motor movement, balance, and more recently, social behavior. Cerebellar injury at birth and during critical periods reduces social preference in animal models and increases the risk of autism in humans. Social behavior is commonly assessed with the three-chamber test, where a mouse travels between chambers that contain a conspecific and an object confined under a wire cup. However, this test is unable to quantify interactive behaviors between pairs of mice, which could not be tracked until the recent development of machine learning programs that track animal behavior. In this study, both the three-chamber test and a novel freely-moving social interaction test assessed social behavior in untreated male and female mice, as well as in male mice injected with hM3Dq (excitatory) DREADDs. In the three-chamber test, significant differences were found in the time spent (female: p < 0.05, male: p < 0.001) and distance traveled (female: p < 0.05, male: p < 0.001) in the chamber with the familiar conspecific, compared to the chamber with the object, for untreated male, untreated female, and mice with activated hM3Dq DREADDs. A social memory test was added, where the object was replaced with a novel mouse. Untreated male mice spent significantly more time (p < 0.05) and traveled a greater distance (p < 0.05) in the chamber with the novel mouse, while male mice with activated hM3Dq DREADDs spent more time (p<0.05) in the chamber with the familiar conspecific. Data from the freely-moving social interaction test was used to calculate freely-moving interactive behaviors between pairs of mice and interactions with an object. No sex differences were found, but mice with excited hM3Dq DREADDs engaged in significantly more anogenital sniffing (p < 0.05) and side-side contact (p < 0.05) behaviors. All these results indicate how machine learning allows for nuanced insights into how both sex and chemogenetic excitation impact social behavior in freely-moving mice.

This review explores popular gambling strategies often believed to guarantee wins, such as card counting and taking advantage of arbitrage. We present a mathematical overview of these systems to evaluate their theoretical effectiveness in ideal conditions by presenting prior research and mathematical proofs. This paper then generates results from these models using Monte Carlo simulations and compares them to data from real-world scenarios. Additionally, we examine reasons that might explain the discrepancies between theoretical and real-world results, such as the potential for dealers to detect and counteract card counting. Ultimately, although these strategies may fare well in theoretical scenarios, they struggle to create long-term winning solutions in casino or online gambling settings.

Quantum entanglement, a phenomenon first introduced in the realm of quantum mechanics by the famous Einstein-Podolsky-Rosen (EPR) paradox, has intrigued physicists and philosophers alike for nearly a century. Its implications for the nature of reality, particularly its apparent violation of local realism, have sparked intense debate and spurred numerous experimental investigations. This thesis presents a comprehensive examination of quantum entanglement with a focus on probing its non-local aspects. Central to this thesis is the development of a detailed project document outlining a proposed experimental approach to investigate the non-local nature of quantum entanglement. Drawing upon recent advancements in quantum technology, including the manipulation and control of entangled particles, the proposed experiment aims to rigorously test the predictions of quantum mechanics against the framework of local realism. The experimental setup involves the generation of entangled particle pairs, such as photons or ions, followed by the precise manipulation of their quantum states. By implementing a series of carefully designed measurements on spatially separated entangled particles, the experiment seeks to discern correlations that defy explanation within a local realistic framework.

This thesis aims to advance healthcare and heart disease prevention by utilizing the Python programming language and various machine learning algorithms for heart disease detection. Being one of the main causes of death worldwide, cardiovascular disease is a serious global health concern. One person passes away from cardiovascular disease every 33 seconds in the United States alone. As the leading cause of death, early identification becomes critical for early intervention and prevention. The study addresses key research questions, including the role of machine learning in enhancing heart disease detection, comparative analysis of the six machine learning models, and the importance of predictive indicators. By leveraging machine learning algorithms for medical data interpretation, the thesis contributes insights into early disease detection.

Diffusion coefficients often vary across regions, such as cellular membranes, and quantifying their variation can provide valuable insight into local membrane properties such as composition and stiffness. Toward quantifying diffusion coefficient spatial maps and uncertainties from particle tracks, we use a Bayesian method and place Gaussian Process (GP) Priors on the maps. For the sake of computational efficiency, we leverage inducing point methods on GPs arising from the mathematical structure of the data giving rise to non-conjugate likelihood-prior pairs. We analyze both synthetic data, where ground truth is known, as well as data drawn from live-cell single-molecule imaging of membrane proteins. The resulting tool provides an unsupervised method to rigorously map diffusion coefficients continuously across membranes without data binning.

The characterization of spall microstructural damage metallic samples is critical to predicting and modeling modes of failure under blast, ballistic, and other dynamic loads. In this regard, a key step to improve models of dynamic damage is making appropriate connections between experimental characterization of actual damage in the form of discrete voids distributed over a given volume of the specimens, and the output of the models, which provide a continuous measure of damage, for example, void fraction as a function of position. Hence, appropriate homogenization schemes to estimate, e.g., continuous void fraction estimations from discrete void distributions, are key to calibration and validation of damage models. This project seeks to analyze 3D tomography data to relate the homogenization parameters for the discrete void distributions, i.e., homogenization volume size and step, as well as representative volume element size, to the local length scales, e.g., grain size as well as void size and spacing. Copper disks 10 mm in diameter and 1 mm thick with polycrystalline structures were subjected to flyer plate impacts resulting in shock stresses ranging from 2 to 5 GPa. The spall damage induced in samples by release waves was characterized using X-ray tomography techniques. The resulting data is thresholded to differentiate voids from the matrix and void fraction is obtained via homogenization using various parameterization schemes to characterize void fraction distributions along the shock and transverse directions. The representative volume element is determined by relating void fraction for varying parameterized window sizes to the void fraction in the overall volume. Results of this study demonstrate that the optimal representative volume element (RVE) to represent void fraction within 10% error of the overall sample void fraction for this Hitachi copper sample is .2304 mm3. The RVE is found to contain approximately 255 grains. Statistical volume elements of 1300 µm3 or smaller are used to quantify void fraction as a function of position and while the results along the shock direction, i.e., the presence of a clear peak at the expected location of the spall plane, are expected, the void fraction along the transverse direction show oscillatory behavior. The power spectra and predominant frequencies of these distributions suggest the periodicity of the oscillations relates to multiples of local material length scales such as grain size. This demonstrates that the grain size in the samples, about 120 µm, is too large compared to the sample size to try to capture spatial variability due to applied loads and the microstructure, since the microstructure itself produces variability on the order of a few grain sizes. These results may play a role for the design of experiments to collect real-world 3D damage data for validating and enhancing the accuracy and definition of simulation models for damage characterization by providing frameworks for microstructural strain variability when modeling spall behavior under dynamic damage.

This thesis attempts to answer the question ‘What changes in understanding occur as a student develops their way of understanding similarity using geometric transformations and what teacher interventions contribute to these changes in understanding?’ Similarity is a topic taught in school geometry usually alongside the related topic Congruence. The Common Core State Standards for Mathematics, upon which many states have based their state level educational standards, recommend teachers leverage transformational geometry to explain congruence and similarity using geometric transformations. "However, there is a lack of research studies regarding how transformational geometry can be taught as a productive way of understanding similarities and what challenges students might encounter when learning similarities via transformational geometry approaches." This study aims to further the efforts of teachers who are trying to develop their students’ transformational understandings of similarity. This study was conducted as exploratory teaching interviews in Spring 2023 at a large public university. The student was an undergraduate student who had not previously taken a transformational geometry-based Euclidean geometry at the university. I, as a teacher-researcher, designed a set of tasks for the exploratory teaching interviews, and implemented them over the course of 5 weeks. I, as a researcher, also analyzed the data to create a model for the student's understanding of similarity. Specifically, I was interested in sorting the ways of understanding expressed by the student into the categories pictorial, measurement-based, and transformational. By analyzing the videos from the interviews and tracking the students’ understandings from moment to moment, I was able to see a shift in her understanding toward a transformational understanding. Thus her way of understanding similarity using geometric transformations was strengthened and I was able to pinpoint key shifts in understanding that contribute to the strengthening of this understanding. Notably, the student developed a notion of dilation as coming from a single centerpoint, negotiated definitions from each way of understanding until eventually settling on a definition rooted in transformations, and applied similarity to an unfamiliar context using both her intuition about similarity and the definition she created. The implications of this being that a somewhat advanced understanding dilation is productive for understanding similarity using geometric transformations, and that to develop a student's way of understanding similarity using geometric transformations there must be a practical need for this created by tasks the student engages with.

For this study, my overarching goal was to understand the possibilities of humanity’s future in space exploration. Addressing the future of space exploration not only opens doors for a multitude of discoveries but may answer questions that can be essential to our survival on Earth. This study, more specifically, aimed to determine how college students at Arizona State University, engineering and astronomy students in particular, visualize the future of space exploration, as in the future, they will become the leading experts at the forefront of all space-related developments. The method through which I have conducted this study is a short survey, consisting of a variety of questions, designed to encourage students to develop their own unique interpretations of space exploration and ultimately, its imminent future. The results ultimately demonstrated that most participants in the study believed that political obstacles were the most prevalent concern in the further development of space exploration. There also appeared to be a moderate outlook on the future success and vitality of space exploration among student scientists and engineers. From a statistical standpoint, there appeared to be no alarming difference of opinion between these two ASU student groups.