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In this study, the impact of shutting down schools on the emotional aspects of high school students was analyzed using survey responses.

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 experiment, the authors used C. elegans as a simple model organism to observe the impact of probiotics on the human digestive system. The results of the experiments showed that the C. elegans were, on average, most present in Chobani cultures over other tested yogurts. While not statistically significant, these results still demonstrated that C. elegans might prefer Chobani cultures over other probiotic yogurts, which may also indicate greater gut benefits from Chobani over the other yogurt brands tested.

The authors investigated adoption rates of solar photovoltaic power at K-12 schools in California.

In this study, the authors hypothesized that closed-circuit television images could be stored with improved resolution by using enhanced deep residual (EDSR) networks.

Improving the classifier of the difficulty level of Sudoku.

In this study, the authors explored whether students' test scores were significantly higher on online exams during the COVID-19 school lockdown when compared to those of the in-person exams before the lockdown.

This study evaluates the potential of natural language processing (NLP) models in an emotion-driven bibliotherapy framework to improve mental health challenges.

Classical financial forecasting models often fail to capture the complex, nonlinear dynamics of the stock market. This study demonstrates that incorporating a single variable to represent the 'geometric curvature' of a time series dramatically improves the accuracy of standard econometric forecasts. Our findings highlight that geometric properties are a significant predictive factor, opening new avenues for more powerful financial modeling.

Domestic waste classification using convolutional neural network