Oscillatory perturbations with varying amplitudes and frequencies have been found to significantly affect human standing balance. However, previous studies have only applied perturbation in either the anterior-posterior (AP) or the medio-lateral (ML) directions. Little is currently known about the impacts of 2D oscillatory perturbations on postural stability, which are more commonly seen in daily life (i.e., while traveling on trains, ships, etc.). This study investigated the effects of applying 2D perturbations vs 1D perturbations on standing stability, and how increasing the frequency and amplitude of perturbation impacts postural stability. A dual-axis robotic platform was utilized to simulate various oscillatory perturbations and evaluate standing postural stability. Fifteen young healthy subjects were recruited to perform quiet stance on the platform. Impacts of perturbation direction (i.e., 1D versus 2D), amplitude, and frequency on postural stability were investigated by analyzing different stability measures, specifically AP/ML/2D Center-of-Pressure (COP) path length, AP/ML/2D Time-to-Boundary (TtB), and sway area. Standing postural stability was compromised more by 2D perturbations than 1D perturbations, evidenced by a significant increase in COP path length and sway area and decrease in TtB. Further, the stability decreased as 2D perturbation amplitude and frequency increased. A significant increase in COP path length and decrease in TtB were consistently observed as the 2D perturbation amplitude and frequency increased. However, sway area showed a considerable increase only with increasing perturbation amplitude but not with increasing frequency.

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.

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.

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.

This thesis project will be investigating the interactions and organizational theory within the student housing market at Arizona State University. The focus of the project will be around the partnership that makes up many of the communities, the public company known as American Campus Communities, and the auxiliary of Arizona State University Housing. The paper will analyze the organization through the four frames outlined by Bolman and Deal’s Reframing Organizations. These four are the structural, human resource, political, and symbolic frames. The paper will confront two main issues found in the organization. The first is the frequent turnover of staff. The second will be the separation between the departments, leading to unstable communication. Solutions will be proposed that could take some pressure off the problems that are identified. Compensation for staff and adjustments to summer living may allow retention to improve. Adjusted training and top-level management communication and interaction may improve the stark separation between areas of the organization. Analyzing these issues and solutions through the organizational frames allows us to better understand the reasoning behind and possible effects of any decision. This project has been very insightful, and I learned a lot with my studies and am proud to be a part of this organization and its mission to serve the students.

Currently, recycling is a major issue found throughout the world; however, one of the main issues, small format recycling, is still yet to be solved. The main objective of this paper is to discuss the issues surrounding recycling in general and more specifically small format recycling in order to develop a solution that can solve the problem. Working with InnovationSpace and people in industry, interviews were conducted in order to determine the best course of action to address the need of the sponsor, The Sustainability Consortium. After extensive research and interviews, it was determined that implementing a new MRF attachment to circulate small format back to the main residual stream would be the best course of action. This attachment would be modular for a MRF and could be implemented in order to gather more material while also producing higher quality recycled goods. This has major implications for the recycling industry and could help in making recycling profitable once again.