When examining the average college campus, it becomes obvious that students feel rushed from one place to another as they try to participate in class, clubs, and extracurricular activities. One way that students can feel more comfortable and relaxed around campus is to introduce the aspect of gaming. Studies show that “Moderate videogame play has been found to contribute to emotional stability” (Jones, 2014). This demonstrates that the stress of college can be mitigated by introducing the ability to interact with video games. This same concept has been applied in the workplace, where studies have shown that “Gaming principles such as challenges, competition, rewards and personalization keep employees engaged and learning” (Clark, 2020). This means that if we manage to gamify the college experience, students will be more engaged which will increase and stabilize the retention rate of colleges which utilize this type of experience. Gaming allows students to connect with their peers in a casual environment while also allowing them to find resources around campus and find new places to eat and relax. We plan to gamify the college experience by introducing augmented reality in the form of an app. Augmented reality is “. . . a technology that combines virtual information with the real world” (Chen, 2019). College students will be able to utilize the resources and amenities available to them on campus while completing quests that help them within the application. This demonstrates the ability for video games to engage students using artificial tasks but real actions and experiences which help them feel more connected to campus. Our Founders Lab team has developed and tested an AR application that can be used to connect students with their campus and the resources available to them.

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.

Glioblastoma (GB) is one of the deadliest cancers and the most common form of adult primary brain tumors. SGEF (ARHGEF26) has been previously shown to be overexpressed in GB tumors, play a role in cell invasion/migration, and increase temozolomide (TMZ) resistance.[3] It was hypothesized parental LN229 cell lines with SGEF knockdown (LN229-SGEFi) will show decreased metabolism in the MTS assay and decreased colony formation in a colony formation assay compared to parental LN229 cells after challenging the two cell lines with TMZ. For WB and co-immunoprecipitation (co-IP), parental LN229 cells with endogenous SGEF and BRCA were expected to interact and stain in the BRCA1:IP WB. LN229-SGEFi cells were expected to show very little SGEF precipitated due to shRNA targeted knockdown of SGEF. In conditions with mutations in the BRCA1 binding site (LN229-SGEFi + AdBRCAm/AdDM), SGEF expression was expected to decrease compared to parental LN229 or LN229-SGEFi cells reconstituted with WT SGEF (LN229-SGEFi + AdWT). LN229 infected with AdSGEF with a mutated nuclear localization signal (LN229-SGEFi + AdNLS12m) were expected to show BRCA and SGEF interaction since whole cell lysates were used for the co-IP. MTS data showed no significant differences in metabolism between the two cell lines at all three time points (3, 5, and 7 days). Western blot analysis was successful at imaging both SGEF and BRCA1 protein bands from whole cell lysate. The CFA showed no significant difference between cell lines after being challenged with 500uM TMZ. The co-IP immunoblot showed staining for BRCA1 and SGEF for all lysate samples, including unexpected lysates such as LN229-SGEFi, LN229-SGEFi + AdBRCAm, and LN229-SGEFi + AdDM. These results suggested either an indirect protein interaction between BRCA1 and SGEF, an additional BRCA binding site not included in the consensus, or possible detection of the translocated SGEF in knockdown cells lines since shRNA cannot enter the nucleus. Further optimization of CO-IP protocol, MTS assay, and CFA will be needed to characterize the SGEF/BRCA1 interaction and its role in cell survival.

Cancer rates vary between people, between cultures, and between tissue types, driven by clinically relevant distinctions in the risk factors that lead to different cancer types. Despite the importance of cancer location in human health, little is known about tissue-specific cancers in non-human animals. We can gain significant insight into how evolutionary history has shaped mechanisms of cancer suppression by examining how life history traits impact cancer susceptibility across species. Here, we perform multi-level analysis to test how species-level life history strategies are associated with differences in neoplasia prevalence, and apply this to mammary neoplasia within mammals. We propose that the same patterns of cancer prevalence that have been reported across species will be maintained at the tissue-specific level. We used a combination of factor analysis and phylogenetic regression on 13 life history traits across 90 mammalian species to determine the correlation between a life history trait and how it relates to mammary neoplasia prevalence. The factor analysis presented ways to calculate quantifiable underlying factors that contribute to covariance of entangled life history variables. A greater risk of mammary neoplasia was found to be correlated most significantly with shorter gestation length. With this analysis, a framework is provided for how different life history modalities can influence cancer vulnerability. Additionally, statistical methods developed for this project present a framework for future comparative oncology studies and have the potential for many diverse applications.

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.

The product our team is commercializing is a NASA designed technology designed to store waste in space. This product works on Earth as well and has applicable multi-use capabilities. Throughout the last several months, the team has identified different markets to determine which of them would experience the most value from this product. The team conducted 25 interviews to grasp the landscape of the different markets related to this product. After a thorough analysis, it was found that vendors who support the disposal of different types of waste and sludge would be the best fit for this product. Vendors like Waste Management, Sharps, Stericycle, Sludge USA, etc.,” have large contracts with hospitals, biotech firms, labs, and cities to manage a wide spectrum of waste. The companies bring value to their clients by making a difficult process easier. However, the process is not seamless and, with certain types of waste, there are significant costs associated with not following an exact process. Throughout this process and interviews with companies like Sludge USA and Waste Management, the team identified a niche market in supporting sludge processes. Caked: Sludge Management is designed to bring value to this market by making their waste disposal process seamless, and saving these institutions significant costs in the long run, while creating additional value.

Though about 75 percent of American waste is recyclable, only 30 percent of it is actually recycled and less than ten percent of plastics disposed of in the United States in 2015 were recycled. A statistic like this demonstrates the immense need to increase recycling rates in order to move towards cultivating a circular economy and benefiting the environment. With Arizona State University’s (ASU) extensive population of on-campus students and faculty, our team was determined to create a solution that would increase recycling rates. After conducting initial market research, our team incentives or education. We conducted market research through student surveys to determine the level of knowledge of our target audience and barriers to entry for local recycling and composting resources. Further, we gained insight into the medium of recycling and sustainability programs they would be interested in participating in. Overall, the results of our surveys demonstrated that a majority of students were interested in participating in these programs, if they were not already involved, and most students on-campus already had access to these resources. Despite having access to these sustainable practices, we identified a knowledge gap between students and their information on how to properly execute sustainable practices such as composting and recycling. In order to address this audience, our team created Circulearning, an educational program that aims to bridge the gap of knowledge and address immediate concerns regarding circular economy topics. By engaging audiences through our quick, accessible educational modules and teaching them about circular practices, we aim to inspire everyone to implement these practices into their own lives. Though our team began the initiative with a focus on implementing these practices solely to ASU campus, we decided to expand our target audience to implement educational programs at all levels after discovering the interest and need for this resource in our community. Our team is extremely excited that our Circulearning educational modules have been shared with a broad audience including students at Mesa Skyline High School, ASU students, and additional connections outside of ASU. With Circulearning, we will educate and inspire people of all ages to live more sustainably and better the environment in which we live.

Human beings have long sought to conquer the unconquerable and to push the boundaries of human endurance. There are few such endeavors more challenging than venturing into the coldest and harshest environments on the planet. The challenges these adventurers face are nearly countless, but one that is often underestimated is the massive risk of dehydration in high mountains and the lack of sufficient technology to meet this important need. Astronauts and mountaineers of NASA's Johnson Space Center have created a technology that solves this problem: a freeze-resistant hydration system that helps stop water from freezing at sub-zero temperatures by using cutting-edge technology and materials science to insulate and heat enough water to prevent dehydration over the course of the day, so that adventurers no longer need to worry about their equipment stopping them. This patented technology is the basis of the founding of Aeropak, an advanced outdoor hydration brand developed by three ASU students (Kendall Robinson, Derek Stein, and Thomas Goers) in collaboration with W.P. Carey’s Founder’s Lab. The primary goal was to develop traction among winter sport enthusiasts to create a robust customer base and evaluate the potential for partnership with hydration solution companies as well as direct sales through online and brick-and-mortar retail avenues. To this end, the Aeropak team performed market research to determine the usefulness and need for the product through a survey sent out to a number of outdoor sporting clubs on Arizona State University’s campus. After determining an interest in a potential product, the team developed a marketing strategy and business model which was executed through Instagram as well as a standalone website, with the goal of garnering interest and traction for a future product. Future goals of the project will be to bring a product to market and expand Aeropak’s reach into a variety of winter sport subcommunities, as well as evaluate the potential for further expansion into large-scale retailers and collaboration with established companies.

Colorimetric assays are an important tool in point-of-care testing that offers several advantages to traditional testing methods such as rapid response times and inexpensive costs. A factor that currently limits the portability and accessibility of these assays are methods that can objectively determine the results of these assays. Current solutions consist of creating a test reader that standardizes the conditions the strip is under before being measured in some way. However, this increases the cost and decreases the portability of these assays. The focus of this study is to create a machine learning algorithm that can objectively determine results of colorimetric assays under varying conditions. To ensure the flexibility of a model to several types of colorimetric assays, three models were trained on the same convolutional neural network with different datasets. The images these models are trained on consist of positive and negative images of ETG, fentanyl, and HPV Antibodies test strips taken under different lighting and background conditions. A fourth model is trained on an image set composed of all three strip types. The results from these models show it is able to predict positive and negative results to a high level of accuracy.