This thesis attempts to explain Everettian quantum mechanics from the ground up, such that those with little to no experience in quantum physics can understand it. First, we introduce the history of quantum theory, and some concepts that make up the framework of quantum physics. Through these concepts, we reveal why interpretations are necessary to map the quantum world onto our classical world. We then introduce the Copenhagen interpretation, and how many-worlds differs from it. From there, we dive into the concepts of entanglement and decoherence, explaining how worlds branch in an Everettian universe, and how an Everettian universe can appear as our classical observed world. From there, we attempt to answer common questions about many-worlds and discuss whether there are philosophical ramifications to believing such a theory. Finally, we look at whether the many-worlds interpretation can be proven, and why one might choose to believe it.

Obesity rates among adults have steadily grown in recent decades all the way up to<br/>42.4% in 2018. This is a 12% increase from the turn of the century (Center for Disease Control<br/>and Prevention, 2021). A major reason for this rise is increased consumption of processed,<br/>high-calorie foods. People eat these foods at a young age and develop bad eating habits that can<br/>last for the rest of their lives. It is essential to intervene early and help adolescents form<br/>balanced, healthy eating habits before bad habits are already formed. Our solution to this<br/>problem is Green Gamers. Green Gamers combines adolescent’s passion for gaming with<br/>healthy eating via in-game rewards for healthy eating. People will be able to purchase healthy<br/>food items, such as a bag of carrots, and on the packaging there will be a QR code. They will<br/>then be able to scan the code on our website, and earn points which will unlock in-game items<br/>and other rewards. Video game rewards act as effective motivators for you people to eat more<br/>healthy foods. After the solution was formulated, a preliminary survey was conducted to<br/>confirm that video game related rewards would inspire children to eat more healthy foods.<br/>Based on those results, we are currently in the process of running a secondary market research<br/>campaign to learn if gift card rewards are a stronger motivator. Our end goal for Green Gamers<br/>would be to partner with large gaming studios and food producers. This would allow us access to<br/>many gaming franchises, so that rewards are available from a wide variety of games: making the<br/>platform appealing to a diverse audience of gamers. Similarly, a relationship with large food<br/>producers would give us the ability to place QR codes on a greater assortment of healthy food<br/>items. Although no relationships with large companies have been forged yet, we plan to utilize<br/>funding to test our concept on small focus groups in schools

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.

Breast cancer is one of the most common types of cancer worldwide. Early detection and diagnosis are crucial for improving the chances of successful treatment and survival. In this thesis, many different machine learning algorithms were evaluated and compared to predict breast cancer malignancy from diagnostic features extracted from digitized images of breast tissue samples, called fine-needle aspirates. Breast cancer diagnosis typically involves a combination of mammography, ultrasound, and biopsy. However, machine learning algorithms can assist in the detection and diagnosis of breast cancer by analyzing large amounts of data and identifying patterns that may not be discernible to the human eye. By using these algorithms, healthcare professionals can potentially detect breast cancer at an earlier stage, leading to more effective treatment and better patient outcomes. The results showed that the gradient boosting classifier performed the best, achieving an accuracy of 96% on the test set. This indicates that this algorithm can be a useful tool for healthcare professionals in the early detection and diagnosis of breast cancer, potentially leading to improved patient outcomes.

This research paper explores the effects of data variance on the quality of Artificial Intelligence image generation models and the impact on a viewer's perception of the generated images. The study examines how the quality and accuracy of the images produced by these models are influenced by factors such as size, labeling, and format of the training data. The findings suggest that reducing the training dataset size can lead to a decrease in image coherence, indicating that AI models get worse as the training dataset gets smaller. Moreover, the study makes surprising discoveries regarding AI image generation models that are trained on highly varied datasets. In addition, the study involves a survey in which people were asked to rate the subjective realism of the generated images on a scale ranging from 1 to 5 as well as sorting the images into their respective classes. The findings of this study emphasize the importance of considering dataset variance and size as a critical aspect of improving image generation models as well as the implications of using AI technology in the future.