The contemporary world is motivated by data-driven decision-making. Small 501(c)3 nonprofit organizations are often limited in their reach due to their size, lack of funding, and a lack of data analysis expertise. In an effort to increase accessibility to data analysis for such organizations, a Founders Lab team designed a product to help them understand and utilize geographic information systems (GIS) software. This product – You Got GIS – strikes the balance between highly technical documentation and general overviews, benefiting 501(c)3 nonprofits in their pursuit of data-driven decision-making. Through the product’s use of case studies and methodologies, You Got GIS serves as a thought experiment platform to start answering questions regarding GIS. The product aims to continuously build partnerships in an effort to improve curriculum and user engagement.

This paper is centered on the use of generative adversarial networks (GANs) to convert or generate RGB images from grayscale ones. The primary goal is to create sensible and colorful versions of a set of grayscale images by training a discriminator to recognize failed or generated images and training a generator to attempt to satisfy the discriminator. The network design is described in further detail below; however there are several potential issues that arise including the averaging of a color for certain images such that small details in an image are not assigned unique colors leading to a neutral blend. We attempt to mitigate this issue as much as possible.

This paper is centered on the use of generative adversarial networks (GANs) to convert or generate RGB images from grayscale ones. The primary goal is to create sensible and colorful versions of a set of grayscale images by training a discriminator to recognize failed or generated images and training a generator to attempt to satisfy the discriminator. The network design is described in further detail below; however there are several potential issues that arise including the averaging of a color for certain images such that small details in an image are not assigned unique colors leading to a neutral blend. We attempt to mitigate this issue as much as possible.

This paper is centered on the use of generative adversarial networks (GANs) to convert or generate RGB images from grayscale ones. The primary goal is to create sensible and colorful versions of a set of grayscale images by training a discriminator to recognize failed or generated images and training a generator to attempt to satisfy the discriminator. The network design is described in further detail below; however there are several potential issues that arise including the averaging of a color for certain images such that small details in an image are not assigned unique colors leading to a neutral blend. We attempt to mitigate this issue as much as possible.

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.

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.