Research has shown that being a female athlete in a male-dominated sports world is an oppressive burden, yet the experiences of being a black female athlete have been largely ignored. To combat this lack of attention, this paper invokes communication and feminist theorist Bell Hook's concept of moving black women from margin to center to reveal the intersectional oppression of gender and racial narratives that they face in sports. By outlining the difference between white and black femininity and studying media portrayals of popular black female athletes such as Venus and Serena Williams and others, it becomes obvious how black women are typecast into certain social and athletic roles. This research also includes an auto-ethnographic component of my own experience as a black female lacrosse player at the NCAA Division I level. This component functions as a point of comparison and contrast of the ideas and concepts I discuss. Lastly, I offer recommendations and suggestions as to how to empower young black female athletes and retain them in a variety of sports. The goal of my thesis is to place special attention onto black women in an area which there is an extreme lack of representation. My own empirical research has led me to the conclusion that not only is such a discussion important, but it is absolutely necessary. If we are to fight back against hegemonic social structures such as racism and gender roles in the sports world, we must first understand what we are up against. My thesis gives us a glimpse into our imposing opponents, and I hope that future research continues this trend so that black female athletes like myself may one day be considered an athlete in the same sense that our white peers are.

In 2020, the world was swept by a global pandemic. It disrupted the lives of millions; many lost their jobs, students were forced to leave schools, and children were left with little to do while quarantined at their houses. Although the media outlets covered very little of how children were being affected by COVID-19, it was obvious that their group was not immune to the issues the world was facing. Being stuck at home with little to do took a mental and physical toll on many kids. That is when EVOLVE Academy became an idea; our team wanted to create a fully online platform for children to help them practice and evolve their athletics skills, or simply spend part of their day performing a physical and health activity. Our team designed a solution that would benefit children, as well as parents that were struggling to find engaging activities for their kids while out of school. We quickly encountered issues that made it difficult for us to reach our target audience and make them believe and trust our platform. However, we persisted and tried to solve and answer the questions and problems that came along the way. Sadly, the same pandemic that opened the widow for EVOLVE Academy to exist, is now the reason people are walking away from it. Children want real interaction. They want to connect with other kids through more than just a screen. Although the priority of parents remains the safety and security of their kids, parents are also searching and opting for more “human” interactions, leaving EVOLVE Academy with little room to grow and succeed.

In 2020, the world was swept by a global pandemic. It disrupted the lives of millions; many lost their jobs, students were forced to leave schools, and children were left with little to do while quarantined at their houses. Although the media outlets covered very little of how children were being affected by COVID-19, it was obvious that their group was not immune to the issues the world was facing. Being stuck at home with little to do took a mental and physical toll on many kids. That is when EVOLVE Academy became an idea; our team wanted to create a fully online platform for children to help them practice and evolve their athletics skills, or simply spend part of their day performing a physical and health activity. Our team designed a solution that would benefit children, as well as parents that were struggling to find engaging activities for their kids while out of school. We quickly encountered issues that made it difficult for us to reach our target audience and make them believe and trust our platform. However, we persisted and tried to solve and answer the questions and problems that came along the way. Sadly, the same pandemic that opened the widow for EVOLVE Academy to exist, is now the reason people are walking away from it. Children want real interaction. They want to connect with other kids through more than just a screen. Although the priority of parents remains the safety and security of their kids, parents are also searching and opting for more “human” interactions, leaving EVOLVE Academy with little room to grow and succeed.

In 2020, the world was swept by a global pandemic. It disrupted the lives of millions; many lost their jobs, students were forced to leave schools, and children were left with little to do while quarantined at their houses. Although the media outlets covered very little of how children were being affected by COVID-19, it was obvious that their group was not immune to the issues the world was facing. Being stuck at home with little to do took a mental and physical toll on many kids. That is when EVOLVE Academy became an idea; our team wanted to create a fully online platform for children to help them practice and evolve their athletics skills, or simply spend part of their day performing a physical and health activity. Our team designed a solution that would benefit children, as well as parents that were struggling to find engaging activities for their kids while out of school. We quickly encountered issues that made it difficult for us to reach our target audience and make them believe and trust our platform. However, we persisted and tried to solve and answer the questions and problems that came along the way. Sadly, the same pandemic that opened the widow for EVOLVE Academy to exist, is now the reason people are walking away from it. Children want real interaction. They want to connect with other kids through more than just a screen. Although the priority of parents remains the safety and security of their kids, parents are also searching and opting for more “human” interactions, leaving EVOLVE Academy with little room to grow and succeed.

The research presented in this Honors Thesis provides development in machine learning models which predict future states of a system with unknown dynamics, based on observations of the system. Two case studies are presented for (1) a non-conservative pendulum and (2) a differential game dictating a two-car uncontrolled intersection scenario. In the paper we investigate how learning architectures can be manipulated for problem specific geometry. The result of this research provides that these problem specific models are valuable for accurate learning and predicting the dynamics of physics systems.<br/><br/>In order to properly model the physics of a real pendulum, modifications were made to a prior architecture which was sufficient in modeling an ideal pendulum. The necessary modifications to the previous network [13] were problem specific and not transferrable to all other non-conservative physics scenarios. The modified architecture successfully models real pendulum dynamics. This case study provides a basis for future research in augmenting the symplectic gradient of a Hamiltonian energy function to provide a generalized, non-conservative physics model.<br/><br/>A problem specific architecture was also utilized to create an accurate model for the two-car intersection case. The Costate Network proved to be an improvement from the previously used Value Network [17]. Note that this comparison is applied lightly due to slight implementation differences. The development of the Costate Network provides a basis for using characteristics to decompose functions and create a simplified learning problem.<br/><br/>This paper is successful in creating new opportunities to develop physics models, in which the sample cases should be used as a guide for modeling other real and pseudo physics. Although the focused models in this paper are not generalizable, it is important to note that these cases provide direction for future research.

High-entropy alloys possessing mechanical, chemical, and electrical properties that far exceed those of conventional alloys have the potential to make a significant impact on many areas of engineering. Identifying element combinations and configurations to form these alloys, however, is a difficult, time-consuming, computationally intensive task. Machine learning has revolutionized many different fields due to its ability to generalize well to different problems and produce computationally efficient, accurate predictions regarding the system of interest. In this thesis, we demonstrate the effectiveness of machine learning models applied to toy cases representative of simplified physics that are relevant to high-entropy alloy simulation. We show these models are effective at learning nonlinear dynamics for single and multi-particle cases and that more work is needed to accurately represent complex cases in which the system dynamics are chaotic. This thesis serves as a demonstration of the potential benefits of machine learning applied to high-entropy alloy simulations to generate fast, accurate predictions of nonlinear dynamics.

Robots are often used in long-duration scenarios, such as on the surface of Mars,where they may need to adapt to environmental changes. Typically, robots have been built specifically for single tasks, such as moving boxes in a warehouse or surveying construction sites. However, there is a modern trend away from human hand-engineering and toward robot learning. To this end, the ideal robot is not engineered,but automatically designed for a specific task. This thesis focuses on robots which learn path-planning algorithms for specific environments. Learning is accomplished via genetic programming. Path-planners are represented as Python code, which is optimized via Pareto evolution. These planners are encouraged to explore curiously and efficiently. This research asks the questions: “How can robots exhibit life-long learning where they adapt to changing environments in a robust way?”, and “How can robots learn to be curious?”.

Colorimetric assays are an important tool in point-of-care testing that offers several advantages to traditional testing methods such as rapid response times and inexpensive costs. A factor that currently limits the portability and accessibility of these assays are methods that can objectively determine the results of these assays. Current solutions consist of creating a test reader that standardizes the conditions the strip is under before being measured in some way. However, this increases the cost and decreases the portability of these assays. The focus of this study is to create a machine learning algorithm that can objectively determine results of colorimetric assays under varying conditions. To ensure the flexibility of a model to several types of colorimetric assays, three models were trained on the same convolutional neural network with different datasets. The images these models are trained on consist of positive and negative images of ETG, fentanyl, and HPV Antibodies test strips taken under different lighting and background conditions. A fourth model is trained on an image set composed of all three strip types. The results from these models show it is able to predict positive and negative results to a high level of accuracy.

The ongoing Global Coronavirus Pandemic has been upheving social norms for over a year at this point. For countless people, our lives look very different at this point in time than they did before the pandemic began. Quarantine, Shelter in Place, Work from Home, and Online classes have led global populations to become less active leading to an increase in sedentary lifestyles. The final impact of this consequence is unknown, but emerging studies have led to concrete evidence of decreased physical and mental wellbeing, particularly in children. VirusFreeSports was the brainchild of three ASU Honors students who sought to remedy these devastating consequences by creating environments where children can participate in sports and exercise safely, free of the threat COVID-19 or other transmissible illnesses. The ultimate goal for the project team was to build traction for their idea, which culminated in a video pitch sent to potential investors. Although largely created as an exercise and we did not create a full certification course, merely a prototype through a website with sample questions to gauge interest, the project was a success as a large target market for this product was identified that showed great promise. Our team believes that early entrance to the market, as well as the lack of any other competitors would give the team a tremendous advantage in creating an impactful and influential service.

The sports universe has been around for over a century and yet the at home sports viewing experience has seen little change. Even though our society has seen monumental innovative, technological advances, watching sports at home has not undergone any changes and may soon find itself a thing of the past unless something changes. When the COVID-19 pandemic arose, a problem surfaced of revenue loss and decreasing fan retention leaving teams and leagues stumbling for solutions. RYZE offers a never before seen product that can revolutionize how sports fans watch and engage in sporting events. By taking the lucrative concept of “battle passes” from the video game industry and placing it in the sports industry, RYZE hopes to increase overall fan revenue, retention, and engagement. A clear market size and wide range of potential customers, RYZE looks to help fans stay engaged while also earning prizes. With competition ranging from fantasy sports to virtual reality, RYZE has competitive advantages that give it potential to become a sports fan’s go to product when thinking about their favorite team. RYZE has gone from a propelling question to a bright idea and then to a prototype along with a full pitch deck and hopes to engrain itself in college athletics, MLB, and other professional sports leagues.