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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 ability of Spirulina platensis to reduce contaminants in wastewater in order to develop a more accessible treatment option. They found that S platensis did reduce the concentration of pollutants present within simulated agricultural wastewater.

Here the authors investigated the relationship between offensive play-calling styles and scoring success within the NFL's red zone by analyzing play-by-play data and expected points metrics. Their findings suggest that a conservative approach to play design and execution is more strongly associated with maximizing efficiency and point-value gains than aggressive strategies.

The authors looked the ability of sound sensors to predict clogged pipes when the sound intensity data is run through a machine learning algorithm.

The authors looked at how the length of an audio clip used of a song impacted the ability to properly classify it by musical genre.

The authors compare current machine learning algorithms with a new Explainable AI algorithm that produces a human-comprehensible decision tree alongside predictions.

The authors looked at different factors, such as age, pre-existing conditions, and geographic region, and their ability to predict what an individual's health insurance premium would be.

In this study the authors looked at the ability of the Discrete Fourier Transform (DFT) to analyze different musical elements. They found that DFT is a powerful method to analyze recorded music.

The authors assessed the degree of women participation in India's economy as a way to estimate woman's participation in India's economic growth.

Authors examine the effectiveness of Large Language Models (LLMs) like BERT, MathBERT, and OpenAI GPT-3.5 in assisting middle school students with math word problems, particularly following the decline in math performance post-COVID-19.