Browse Articles

The study examined whether high school boys’ water polo teams located near strong programs had similar season performances during the 2024–2025 season.

In this study, the authors examine the relationship between ESG (Environmental, Social, and Governance) factors on financial performance of Vietnamese media companies, as measured by ROA and ROE. They find that ESG is positively correlated with financial performance, as is sales growth. They also find that financial leverage is negatively correlated with ROA. Overall, this is a quality study, although the methods section requires more detail.

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.

This study examined whether findings from laboratory studies of baseball cognition also apply during real games. Using 2024 MLB data from 30 players, researchers analyzed batting performance under different levels of feedback and anxiety.

The authors studied optimized delivery routes to reduce travel times using different traveling salesman algorithms.

The authors looked at how different factors related to practicing and playing chess would impact a player's rating.

The authors looked at which measurements from the NFL combine were the most predictive of success for defensemen in the NFL.

The authors looked at the importance of different point breakdowns needed to win in a game of tennis.

The authors looked at the feasibility to predict wind speeds that will have less reliance on using historical data.

Recent advances in generative AI have made it increasingly hard to distinguish real images from AI-generated ones. Traditional detection models using CNNs or U-net architectures lack precision because they overlook key spatial and frequency domain details. This study introduced a hybrid model combining Convolutional Neural Networks (CNN) with Fast Fourier Transform (FFT) to better capture subtle edge and texture patterns.