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

The study explores how music and sports impact cognitive development in young children, particularly in relation to learning disorders like ADHD and dyslexia.

In this article, Choi and Rossitto investigated the limitations of virtual learning by examining in-person dance learning compared to virtual dance learning while wearing EEG headsets. They found that in-person learners outperformed virtual learners and that virtual learners had higher mirror neuron activity as assessed by Mu rhythm power.

Here the authors hypothesized that reducing folliculin (FLCN) might affect p62 protein levels in the dorsal hippocampus of mice, given their potential functional connection and p62's role in neurodegenerative diseases. Their study, using western blots and a two-way ANOVA on young wild-type mice, found that p62 levels correlated with FLCN expression, but ultimately concluded there's no evidence of a functional connection between FLCN and p62 in this specific model.

The authors developed a quantum inspired model for stock market fluctuations.

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

The authors looked at the correlation between being able to speak more than one language (multilingualism) and visual imagery. They found multilingual individuals had higher visual imagery as measured by the VVIQ.

This article investigates differences in gene expression in the brains of patients with and without Parkinson's disease. The authors identify a crucial transcriptional regulator may be a relevant target for future therapeutic treatment for Parkinson's disease.

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

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.