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 market for searching for food online is exploding. According to one expert at Google, “there are over 1 billion restaurant searches on Google every month” (Kelso, 2020). To capture this market and ride the general digital trend of internet personalization (as evidenced by Google search results, ads, YouTube and social media algorithms, etc), we created Munch to be an algorithm meant to help people find food they’ll love. <br/><br/>Munch offers the ability to search for food by restaurant or even as specific as a menu item (ex: search for the best Pad Thai). The best part? It is customized to your preferences based on a quiz you take when you open the app and from that point continuously learns from your behavior.<br/><br/>This thesis documents the journey of the team who founded Munch, what progress we made and the reasoning behind our decisions, where this idea fits in a competitive marketplace, how much it could be worth, branding, and our recommendations for a successful app in the future.

Humans use emotions to communicate social cues to our peers on a daily basis. Are we able to identify context from facial expressions and match them to specific scenarios? This experiment found that people can effectively distinguish negative and positive emotions from each other from a short description. However, further research is needed to find out whether humans can learn to perceive emotions only from contextual explanations.

Waste pickers are the victims of harsh economic and social factors that have hurt many<br/>developing countries and billions of people around the world. Due to the rise of industrialization<br/>since the 19th century, waste and disposable resources have been discarded around the world to<br/>provide more resources, products, and services to wealthy countries. This has put developing<br/>countries in a precarious position where people have had very few economic opportunities<br/>besides taking on the role of waste pickers, who not only face physical health consequences due<br/>to the work they do but also face exclusion from society due to the negative views of waste<br/>pickers. Many people view waste pickers as scavengers and people who survive off of doing<br/>dirty work, which creates tensions between waste pickers and others in society. This even leads<br/>to many countries outlawing waste picking and has led to the brutal treatment of waste pickers<br/>throughout the world and has even led to thousands of waste pickers being killed by anti-waste<br/>picker groups and law enforcement organizations in many countries.<br/>Waste pickers are often at the bottom of supply chains as they take resources that have<br/>been used and discarded, and provide them to recyclers, waste management organizations, and<br/>others who are able to turn these resources into usable materials again. Waste pickers do not have<br/>many opportunities to rise above the situation they are in as waste picking has become the only<br/>option for many people who need to provide for themselves and their families. They are not<br/>compensated very well for the work they do, which also contributes to the situation where waste<br/>pickers are forced into a position of severe health risks, backlash from society and governments,<br/>not being able to seek better opportunities due to a lack of earning potential, and not being<br/>connected with end-users. Now is the time to create new business models that solve these large<br/>problems in our global society and create a sustainable way to ensure that waste pickers are<br/>treated properly around the world.

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.

Since the inception of what is now known as the Behavioral Analysis Unit (BAU) at the Federal Bureau of Investigation (FBI) in the 1970s, criminal profiling has become an increasingly prevalent entity in both forensic science and the popular imagination. The fundamental idea of which profiling is premised – behavior as a reflection of personality – has been the subject of a great deal of misunderstanding, with professionals and nonprofessionals alike questioning whether profiling represents an art or a science and what its function in forensic science should be. To provide a more thorough understanding of criminal profiling’s capabilities and its efficacy as a law enforcement tool, this thesis will examine the application of criminal profiling to investigations, various court rulings concerning profiling’s admissibility, and the role that popular media plays in the perception and function of the practice. It will also discuss how future research and regulatory advancements may strengthen criminal profiling’s scientific merit and legitimacy.

The Founders lab is a year-long program that gives its students an opportunity to participate in a unique team-based, experiential Barrett honors thesis project to design and apply marketing and sales strategies, as well as business and financial models to start up and launch a new business. This honors thesis project focuses on increasing the rate of vaccination outcomes in a country where people are increasingly busy (less time) and unwilling to get a needle through a new business venture that provides a service that brings vaccinations straight to businesses, making them available for their employees. Through our work with the Founders Lab, our team was able to create this pitch deck.

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.

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.