Student academic performance has far-reaching implications not only on individual students but also the universities and colleges they attend. Student academic performance can affect their time in school as well as their future earning potential, and colleges and universities have a shared interest in the academic performance and retention of their students as many state and federal funding opportunities consider these metrics when allocating taxpayer dollars. To assist in the mutual desire for students to succeed, the Calm Connection start-up venture formed with the goal of integrating biofeedback therapy with a student’s unique education needs. For students, one of the largest barriers to effective learning is issues of focus and information retention, and the repeated use of biofeedback therapy trains students to overcome these focus issues and works in conjunction with our app’s study aid and scheduling ability.

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.

Consider Steven Cryos’ words, “When disaster strikes, the time to prepare has passed.” Witnessing domestic water insecurity in events such as Hurricane Katrina, the instability in Flint, Michigan, and most recently the winter storms affecting millions across Texas, we decided to take action. The period between a water supply’s disruption and restoration is filled with anxiety, uncertainty, and distress -- particularly since there is no clear indication of when, exactly, restoration comes. It is for this reason that Water Works now exists. As a team of students from diverse backgrounds, what started as an honors project with the Founders Lab at Arizona State University became the seed that will continue to mature into an economically sustainable business model supporting the optimistic visions and tenants of humanitarianism. By having conversations with community members, conducting market research, competing for funding and fostering progress amid the COVID-19 pandemic, our team’s problem-solving traverses the disciplines. The purpose of this paper is to educate our readers about a unique solution to emerging issues of water insecurity that are nested across and within systems who could benefit from the introduction of a personal water reclamation system, showcase our team’s entrepreneurial journey, and propose future directions that will this once pedagogical exercise to continue fulfilling its mission: To heal, to hydrate, and to help bring safe water to everyone.

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.

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.

When you are sitting at the terminal waiting for your flight or taking the bus to get to work, have you ever thought about who used your seat last? More importantly, have you ever thought about the last time that seat was cleaned? Sadly, it is uncertain to see if it was properly sanitized in the last hour, yesterday, in the last week, or even last month. Especially during these tough times, everyone wants to be assured that they are always in a safe and healthy environment. Through the Founders Lab, our team is collaborating with an engineering capstone team to bring automated seat cleaning technology into the market. This product is a custom-designed seat cover that is tear-resistant and provides a sanitary surface for anyone to sit on. When someone leaves the seat, a pressure sensor is triggered, and the cover is replaced with a secondary cover that was stored in a UV radiated container. The waterproof fabric and internal filters prevent spills and food crumbs from remaining when the user changes. The reason for bringing this product into the market is due to the unsanitary conditions in many high traffic areas. This technology can be implemented in public transportation, restaurants, sports stadiums, and much more. It will instantly improve the efficiency of sanitation for many businesses and keep a promise to its users that they will never bring something they sat on back home. #Safeseating

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.