Water scarcity affects upwards of a billion people worldwide today. This project leverages the potential of capturing humidity to build a high-efficiency water condensation device that can generate water and be used for personal and commercial purposes. This compact environment-friendly device would have low power requirements, which would potentially allow it to utilize renewable energy sources and collect water at the most needed location.

Plastic pollution in the ocean is a major global concern. Remotely Operated Vehicles (ROVs) have promise for removing debris from the ocean, but more research is needed to achieve full effectiveness of the ROV technology. Wahlig and Gonzales tackle this issue by developing a deep learning model to distinguish trash from the environment in ROV images.

The authors examine a relationship between tweet sentiment and stock market behavior during the early weeks of the COVID-19 pandemic.

Here, the authors investigated engagement with #GMOFOODS, a hashtag on TikTok. They hypothesized that content focused on the negative effects of genetically modified organisms would receive more interaction driven by consumers. They found that the most common cateogry focused on the disadvantages of GMOs related to nutrition and health with the number of views determining if the video would be provided to users.

Plant diseases can cause up to 50% crop yield loss for the popular tomato plant. A mobile device-based method to identify diseases from photos of symptomatic leaves via computer vision can be more effective due to its convenience and accessibility. To enable a practical mobile solution, a “shallow” convolutional neural networks (CNNs) with few layers, and thus low computational requirement but with high accuracy similar to the deep CNNs is needed. In this work, we explored if such a model was possible.

The Tor network allows individuals to secure their online identities by encrypting their traffic, however it is vulnerable to fingerprinting attacks that threaten users' online privacy. In this paper, the authors develop a new video fingerprinting model to explore how well video streaming can be fingerprinted in Tor. They found that their model could distinguish which one of 50 videos a user was hypothetically watching on the Tor network with 85% accuracy, demonstrating that video fingerprinting is a serious threat to the privacy of Tor users.

In this study, the authors developed a model named DNA Sequence Embedding Network (DNA-SEnet) to classify DNA-asthma associations using their genomic patterns.

Chess engines, or computer programs built to play chess, outperform even the best human players. Kaushikan and Park investigate the inner workings of these chess engines by studying popular chess engines' evaluations of which side of a chess match is most likely to win, and how this is affected by the number of pieces and controlled squares on each side.

Here, seeking to identify a possible explanation for the more frequent diagnosis of autism spectrum disorder (ASD) in males than females, they sought to investigate a potential sex bias in the expression of ASD-associated genes. Based on their analysis, they identified 17 ASD-associated candidate genes that showed stronger collective sex-dependent expression.

In this study the authors looked at the ability of artificial intelligence to detect tempo, rhythm, and intonation of a piece played on violin. Technology such as this would allow for students to practice and get feedback without the need of a teacher.