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The authors looked at ways to provide better forecasting on website traffic. They found that deep learning models performed better than statistical models.

The authors studied the chemoreception of moon jellyfish in response to food, and developed an AI tool to identify track and quantify the pulsation of swimming jellyfish.

Here the authors investigated air quality forecasting in India, comparing traditional time series models like SARIMA with deep learning models like LSTM. The research found that SARIMA models, which capture seasonal variations, outperform LSTM models in predicting Air Quality Index (AQI) levels across multiple Indian cities, supporting the hypothesis that simpler models can be more effective for this specific task.

The effect of other neuroendocrine hormones in the regulation of adipose tissue thermogenesis.

The authors use the unique movements of the jumping gall wasp to study momentum transfer with potential applications in robotics and extraterrestrial research.

The authors found that SARIMAX-GARCH is more accurate than SARIMAX for load forecasting with respect to energy consumption.

In a common high school experiment to measure friction coefficients, a weighted mass attached to a spring scale is dragged across a surface at a constant velocity. While the constant velocity is necessary for an accurate measurement, it can be difficult to maintain and this can lead to large errors. Here, the authors designed a new experiment to measure friction coefficients in the classroom using only static force and show that their method has a lower standard deviation than the traditional experiment.

The use of gamification in cybersecurity education, particularly through capture-the-flag competitions, involves scoring challenges based on their difficulty and the number of teams that solve them. The study investigated how changing the scoring formulas affects competition outcomes, predicting that different formulas would alter score distributions.

The diagnosis of malaria remains one of the major hurdles to eradicating the disease, especially among poorer populations. Here, the authors use machine learning to improve the accuracy of deep learning algorithms that automate the diagnosis of malaria using images of blood smears from patients, which could make diagnosis easier and faster for many.