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.

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.

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.

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.

Stardust grains can provide useful information about the Solar System environment before the Sun was born. Stardust grains show distinct isotopic compositions that indicate their origins, like the atmospheres of red giant stars, asymptotic giant branch stars, and supernovae (e.g., Bose et al. 2010). It has been argued that some stardust grains likely condensed in classical nova outbursts (e.g., Amari et al. 2001). These nova candidate grains contain 13C, 15N and 17O-rich nuclides which are produced by proton burning. However, these nuclides alone cannot constrain the stellar source of nova candidate grains. Nova ejecta is rich in 7Be that decays to 7Li (which has a half-life of ~53 days). I want to measure 6,7Li isotopes in nova candidate grains using the NanoSIMS 50L (nanoscale secondary ion mass spectrometry) to establish their nova origins without ambiguity. Several stardust grains that are nova candidate grains were identified in meteorite Acfer 094 on the basis of their oxygen isotopes. The identified silicate and oxide stardust grains are <500 nm in size and exist in the meteorite surrounded by meteoritic silicates. Therefore, 6,7Li isotopic measurements on these grains are hindered because of the large 300-500 nm oxygen ion beam in the NanoSIMS. I devised a methodology to isolate stardust grains by performing Focused Ion Beam milling with the FIB – Nova 200 NanoLab (FEI) instrument. We proved that the current FIB instrument cannot be used to prepare stardust grains smaller than 1 𝜇m due to lacking capabilities of the FIB. For future analyses, we could either use the same milling technique with the new and improved FIB – Helios 5 UX or use the recently constructed duoplasmatron on the NanoSIMS that can achieve a size of ~75 nm oxygen ion beam.

The majority of drones are extremely simple, their functions include flight and sometimes recording video and audio. While drone technology has continued to improve these functions, particularly flight, additional functions have not been added to mainstream drones. Although these basic functions serve as a good framework for drone designs, it is now time to extend off from this framework. With this Honors Thesis project, we introduce a new function intended to eventually become common to drones. This feature is a grasping mechanism that is capable of perching on branches and carrying loads within the weight limit. This concept stems from the natural behavior of many kinds of insects. It paves the way for drones to further imitate the natural design of flying creatures. Additionally, it serves to advocate for dynamic drone frames, or morphing drone frames, to become more common practice in drone designs.

In a healthcare system already struggling with burnout among its professionals, the COVID-19 pandemic presented a barrage of personal and occupational strife to US healthcare workers. Structural and everyday discrimination contributed to the health inequities of people of color in the US, exacerbated by COVID-19-related racism and xenophobia. There is little research regarding the effects of COVID-19 and related and/or concurring discrimination upon minority nursing staff, despite their importance in supporting the diverse American patient population with culturally competent, tireless care amid the pandemic. This cross-sectional survey study aimed to examine 1) the relationships between discrimination, social support, resilience, and quality of life among minority nursing staff in the US during COVID-19, and 2) the differences of discrimination, social support resilience, and quality of life among minority nursing staff between different racial/ethnic groups during COVID-19. The sample (n = 514) included Black/African American (n = 161, 31.4%), Latinx/Hispanic (n = 131, 25.5%), Asian (n = 87, 17%), Native American/Alaskan Native (n = 69, 13.5%), and Pacific Islander (n = 65, 12.7%) nursing staff from 47 US states. The multiple regression results showed that witnessing discrimination was associated with a lower quality of life score, while higher social support and resilience scores were associated with higher quality of life scores across all racial groups. Furthermore, while participants from all racial groups witnessed and experienced discrimination, Hispanic/Latinx nursing staff experienced discrimination most commonly, alongside having lowest quality of life and highest resilience scores. Native American/Alaskan Native nursing staff had similarly high discrimination and low quality of life, although low resilience scores. Our findings suggest that minority nursing staff who have higher COVID-19 morbidity and mortality rates (Hispanic/Latinx, Native American/Alaskan Native) were left more vulnerable to negative effects from discrimination. Hispanic/Latinx nursing staff reported a relatively higher resilience score than all other groups, potentially attributed to the positive effects of biculturality in the workplace, however, the low average quality of life score suggests a simultaneous erosion of well-being. Compared to all other groups, Native American and Alaskan Native nursing staff’s low resilience and quality of life scores suggest a potential compounding effect of historical trauma affecting their well-being, especially in contrast to Hispanic/Latinx nursing staff. This study has broader implications for research on the lasting effects of COVID-19 on minority healthcare workers’ and communities’ well-being, especially regarding Hispanic/Latinx and Native American/Alaskan Native nursing staff.

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.