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.

As political campaigning becomes increasingly digital and data-driven, data privacy has become a question of democratic governance. Yet, Congress has yet to pass a comprehensive federal data privacy law and even the strongest subnational data privacy laws exempt political campaigns from regulation. <br/><br/>This thesis examines how data privacy laws impact data-driven and digital political campaigning. Specifically, it investigates what information is incorporated into the political data ecosystem, how data privacy laws regulate the collection of this data, and how actors in the political data ecosystem respond to these laws. It examines both sector-specific federal law and subnational data protection regulation through a case study of California. This research suggests that although the California Consumer Privacy Act and California Privacy Rights Act are landmark steps in American data protection, subnational data privacy law remains inhibited by the federal market-based approach.

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.

Disinformation has long been a tactic used by the Russian government to achieve its goals. Today, Vladimir Putin aims to achieve several things: weaken the United States’ strength on the world stage, relieve Western sanctions on himself and his inner circle, and reassert dominant influence over Russia’s near abroad (the Baltics, Ukraine, etc.). This research analyzed disinformation in English, Spanish, and Russian; noting the dominant narratives and geopolitical goals Russia hoped to achieve by destabilizing democracy in each country/region.

This thesis explores the potential for software to act as an educational experience for engineers who are learning system dynamics and controls. The specific focus is a spring-mass-damper system. First, a brief introduction of the spring-mass-damper system is given, followed by a review of the background and prior work concerning this topic. Then, the methodology and main approaches of the system are explained, as well as a more technical overview of the program. Lastly, a conclusion and discussion of potential future work is covered. The project was found to be useful by several engineers who tested it. While there is still plenty of functionality to add, it is a promising first attempt at teaching engineers through software development.

The thesis analyzes the apathetic youth turnout myth and researches to see if voter suppression can explain the reason behind low youth turnout. This thesis is a study done with Arizona State University students to assess their level of voter turnout, their levels of political engagement, and if they have experienced voter suppression. Respondents were also asked about the support given by ASU in terms of helping with the voting process. Results indicate that Arizona State students have high levels of political engagement, and that 1 in 5 ASU students have experienced voter suppression. Furthermore, ASU students on a whole are uncertain about the role ASU should play in supporting students with the voting process.

Water quality and accessibility can impact most aspects of life such as hygiene, medicine,<br/>thermal comfort, sewage disposal, and health, to name a few. Rising concerns related to the<br/>quality of drinking water in the United States caused by municipal water utility failures such as<br/>in Texas or in Michigan has led to an inquiry into the root cause of how a supply-chain for a<br/>basic necessity such as water can run into issues. After initial research and investigation, one<br/>hypothesis for this was the nature of how recyclable materials in a linear economy eventually run<br/>into production or storage problems as exhaustible resources (or space) become less accessible<br/>over time. To remedy this issue, LifeGear360 is introduced to allow individual users the liberty<br/>to treat their water directly if needed, while also remaining in a circular economy for the<br/>lifecycle of the product. As a backpack with water treatment capabilities, natural plant fibers are<br/>used to ensure a renewable cycle of production while also redefining the traditional<br/>“plastic-taste” characteristics many people associate with water pouches to a smoother, cleaner<br/>taste. Engineering, sustainability, and business and public service practice have been used in an<br/>interdisciplinary way to prepare this product for its intended use such as in school, for travel, and<br/>for the outdoors. According to the collected outreach, many indicated that they feel as though<br/>there is a need for a product that allows for the feeling of water security which can include<br/>carrying any personal belongings as well. Marketing strategies such as logo creating and online<br/>outreach continually influence product design, up until production would take place following<br/>the finalized design.

Dreadnought is a free-to-play multiplayer flight simulation in which two teams of 8 players each compete against one another to complete an objective. Each player controls a large-scale spaceship, various aspects of which can be customized to improve a player’s performance in a game. One such aspect is Officer Briefings, which are passive abilities that grant ships additional capabilities. Two of these Briefings, known as Retaliator and Get My Good Side, have strong synergy when used together, which has led to the Dreadnought community’s claiming that the Briefings are too powerful and should be rebalanced to be more in line with the power levels of other Briefings. This study collected gameplay data with and without the use of these specific Officer Briefings to determine the precise impact on gameplay. Linear correlation matrices and inference on two means were used to determine performance impact. It was found that, although these Officer Briefings do improve an individual player’s performance in a game, they do not have a consistent impact on the player’s team performance, and that these Officer Briefings are therefore not in need of rebalancing.