In the current age of global climate crisis, corporations must confront the rising pressure to mitigate their environmental impacts. The goal of this research paper is to provide corporations with a resource to manage waste through the implementation of a circular economy and by increasing Corporate Social Responsibility (CSR). Navigating this large and complex system required the use of various methodologies including: the investigation of the relationships between waste management systems and sustainable development across major companies; literature reviews of scholarly articles about CSR, circular economies, recycling, and releases of company reports on sustainable development and financials. Lastly, interviews and a survey were conducted to gain deeper insight into the problems that make circular economies so difficult to achieve at scale.

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.

The PPP Loan Program was created by the CARES Act and carried out by the Small Business Administration (SBA) to provide support to small businesses in maintaining their payroll during the Coronavirus pandemic. This program was approved for $350 billion, but this amount was expanded by an additional $320 billion to meet the demand by struggling businesses, since initial funding was exhausted under two weeks.<br/><br/>Significant controversy surrounds the program. In December 2020, the Department of Justice reported 90 individuals were charged for fraudulent use of funds, totaling $250 million. The loans, which were intended for small business, were actually approved for 450 public companies. Furthermore, the methods of approval are<br/>shrouded in mystery. In an effort to be transparent, the SBA has released information about loan recipients. Conveniently, the SBA has released information of all recipients. Detailed information was released for 661,218 recipients who have received a PPP loan in excess of $150,000. These recipients are the central point of this research.<br/><br/>This research sought to answer two primary questions: how did the SBA determine which loans, and therefore which industries are approved, and did the industries most affected by the pandemic receive the most in PPP loans, as intended by Congress? It was determined that, generally, PPP Loans were approved on the basis of employment percentages relative to the individual state. Furthermore, in general, the loans approved were approved fairly, with respect to the size of the industry. The loans, when adjusted for GDP and Employment factors, yielded a clear ranking that prioritized vulnerable industries first.<br/><br/>However, significant questions remain. The effectiveness of the PPP has been hindered by unclear incentives and negative outcomes, characterized by a government program that has essentially been rushed into service. Furthermore, limitations of available data to regress and compare the SBA's approved loans are not representative of small business.

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.

In this paper, our Founders Lab team members — Jacob Benevento, Sydney Evans, and Alec Whiteley — recount the year-long entrepreneurial journey that led to the creation and launch of our venture, Certified Circular. Certified Circular is a program that certifies on-campus events for implementing circular practices into their activities as well as off-campus businesses. The venture was formed in response to our group’s propelling question and industry selection, which called on us to create and market a venture within the ethical circular economy.

The Covid-19 pandemic has made a significant impact on both the stock market and the<br/>global economy. The resulting volatility in stock prices has provided an opportunity to examine<br/>the Efficient Market Hypothesis. This study aims to gain insights into the efficiency of markets<br/>based on stock price performance in the Covid era. Specifically, it investigates the market’s<br/>ability to anticipate significant events during the Covid-19 timeline beginning November 1, 2019<br/><br/>and ending March 31, 2021. To examine the efficiency of markets, our team created a Stay-at-<br/>Home Portfolio, experiencing economic tailwinds from the Covid lockdowns, and a Pandemic<br/><br/>Loser Portfolio, experiencing economic headwinds from the Covid lockdowns. Cumulative<br/>returns of each portfolio are benchmarked to the cumulative returns of the S&P 500. The results<br/>showed that the Efficient Market Hypothesis is likely to be valid, although a definitive<br/>conclusion cannot be made based on the scope of the analysis. There are recommendations for<br/>further research surrounding key events that may be able to draw a more direct conclusion.