As climate change continues to escalate natural hazards around the globe, certain communities feel the impacts of these disasters more so than others. After Hurricane Maria devastated communities in 2017, Puerto Rico struggled to respond to the needs of its citizens, particularly those in rural areas. Many of the regions affected did not have resilient community structures in place to be able to withstand the systemic ripple effects of the hurricane. However, various community endeavors have developed post-Hurricane Maria to foster community collaboration and resiliency, including the development of agricultural tourism, otherwise known as agritourism. <br/>Although agritourism has begun to develop in rural regions of Puerto Rico, including the municipalities of Utuado, Ciales, Florida, and Jayuya, a systems-understanding is lacking of the current agritourism situation in the region and its related capacities, limitations, and opportunities of agritourism. To address this gap, a spatially explicit understanding and map of the underlying tourism infrastructure is needed to support the development of sustainable agritourism in Utuado, Jayuya, Ciales, and Florida municipalities in Puerto Rico. <br/>This report spatially represents the current state of tourism opportunities in the region as a result of asking “What are the spatial networks of gastronomy, accommodations, farms, and attractions that support the development of agritourism in Utuado, Jayuya, Ciales and Florida municipalities in Puerto Rico?” Three steps lead to the spatial representation starting with developing a comprehensive inventory. Second, we visualize the spatial map through Google Maps. Lastly, we explore the larger context of the report through an ArcGIS Storymap. The inventory will help with better understanding the number and variety of tourism resources available. The spatial visualization will help with understanding the distribution of resources and explore potential connections between resources and what relationships could be fostered in the future. Lastly, the ArcGIS Storymap will serve as a framework for outlining the future development of the SARE project. Overall, this report outlines the spatial maps of tourism resources and provides a tool to be used by community partners, tourists, and project partners.

Food insecure populations suffer from the ability to access affordable and nutritious foods as a result of financial and transportation needs. Often these populations are concentrated in areas referenced as food deserts. A food desert is an area that does not have a supermarket or large grocery store within a mile and often is saturated with small non-traditional food stores and fast- food establishments. In this study, 21 food deserts along Grand Avenue in Downtown Phoenix were analyzed to better understand their access to food, population statistics and barriers to being food secure. The research question analyzed is the impact food insecurity has on communities in Phoenix, Arizona. The findings are presented in the form of a research paper, as well as 15 black and white film photographs accompanied by descriptions. There is primary qualitative data presented through photographs and observations, as well as secondary quantitative data analyzed from Census data. The food deserts studied consist of communities that are low-income and majority minority with little to no access to nutritious food in their area. The economics of food insecurity and grocery stores, racial discrimination, access to transportation, impacts on health and education and the sustainability of food deserts are all aspects of food insecurity discussed in the research. Possible solutions such as community gardens and subsidized grocery stores are also presented. The study revealed that food insecurity has several negative impacts on the affected populations and communities and disproportionately impacts low-income and minority communities.

Bioluminescent algae has long fascinated humans as a beautiful natural phenomenon. This creative project uses bioluminescent algae to push the limit of biomimicry, using the algae not as a model but as a technology. Through experimentation with algae samples and industrial design, two potential applications of bioluminescent algae as a sustainable lighting technology were generated. One design focuses on indoor, private lighting, while the other focuses on outdoor, public lighting. Both outcomes attempt to solve problems generated by nighttime lighting including light pollution, wasted electricity usage, and negative impacts on human and environmental health while retaining the benefits of safety and convenience.

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 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.

In an age of crisis, division, and ideological representation, it is vital to understand the representative and leadership qualities that made past presidents successful, not in terms of policy, but in terms of character. This interpretation of the American presidency reflects the nation as a whole, not as a political or personal allegiance, but as a symbol of Americanism in the current age. Through the use of scholarly literature and historical accounts of highlighted American Presidents, (Washington, Lincoln, Roosevelt, FDR, and more), insight can be utilized to create a new model of presidential representation that addresses the faults of current methodologies. This thesis aims to identify the critical successful characteristics and strategies enacted by American presidents to relate with the American people, especially in times of hardship, when understanding and connection are needed the most. These attributes can then formulate a blueprint for positive personal relationships and identify qualities for future Presidential leadership. Once determined, these traits can be formatted into a new model of representation to analyze the representative power and ability of the American presidency in order to establish a baseline for successful representation.