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The authors assess the effect of ER stress on sleep quality in fruit flies.

When granular materials are poured onto a surface, they form conical piles whose slopes increase before reaching a stable angle of repose. We found that this angle evolution follows a previously unrecognized two-phase exponential growth pattern that is conserved across granular materials with diverse particle properties. The parameters of this model correlate with particle friction and are influenced by deposition conditions, providing a quantitative framework for describing pile formation.

This manuscript tackles a major social issue in the health news sector, with social media being one of the primary sources of information and a prime spot to propagate fake news. The author proposes X-HND , which is a unique architecture that combines emotional and contextual analysis in a Graph Neural Network to accurately detect fake news. This was a multi-step process which involved the creation of a custom health news dataset (HNDataset), and an emotional variant that uses RoBERTa to extract emotion. These dataset were then used to prove the hypothesis that accuracy increases when the custom dataset is used to train the model and that with the integration of emotion capture, the detection accuracy increases further.

This study used surveys, interviews, and XGBoost analysis to examine how experienced and novice entrepreneurs differ in their thinking and decision-making.

The authors looked at how the focus of someone's attention impacted what information was retained in their working memory.

The authors looked at using computer vision to evaluate the degree of exotropia in individuals with strabismus.

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.

Investigation of the largest rigid shape that can be moved through a circular hallway of unit width with an arbitrary turn angle

This study compares the performance of two deep learning models, U-Net and DeconvNet, for segmenting gliomas from MRI scans.