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In this study, the authors use existing mathematical models to how high school athletes pace 800 m, 1600 m, and 3200 m distance track events compared to elite athletes.

Here in an effort to better understand how our brains process and remember different categories of information, the authors assessed working memory capacity using an operation span task. They found that individuals with higher working memory capacity had higher overall higher task accuracy regardless of the type of category or the type of visual distractors they had to process. They suggest this may play a role in how some students may be less affected by distracting stimuli compared to others.

In this study, the authors conduct a series of experiments within an elevator traveling on an angle to determine if Einstein's Equivalency Principle and motion vector decomposition can be used to calculate the angle of inclination.

In this study, the authors address the current climate concern of high CO₂ levels by testing solid forms of hydroxide for CO₂ reduction and designing a drone to fly it in ambient air!

This study strongly illustrates that optimizing 3D printing parameters would enhance a triboelectric nanogenerator's performance. The design improvements substantiate the potential of optimized, 3D-printed TENGs for developing scalable, cost-effective solutions for powering low-energy electronics, particularly in applications such as structural health monitoring.

This study investigates skin tone bias in artificial intelligence models used for dermatological disease classification and evaluates a CycleGAN-based data augmentation approach to improve diagnostic performance on darker skin types. We generated synthetic dark-skinned images to enhance dataset diversity and compared model performance before and after augmentation. The results demonstrate that augmentation with synthetic dermatological images can help reduce disparities in diagnostic performance across skin tones, highlighting a practical strategy for improving fairness in dermatology AI systems.

This study investigates how the hyperparameters epochs and batch size affect the classification accuracy of a convolutional neural network (CNN) trained on pulsar candidate data. Our results reveal that accuracy improves with increasing number of epochs and smaller batch sizes, suggesting that with optimized hyperparameters, high accuracy may be achievable with minimal training. These findings offer insights that could help create more efficient machine learning classification models for pulsar signal detection, with the potential of accelerating pulsar discovery and advancing astrophysical research.

The authors looked at using machine learning to identify skills needed to apply for certain jobs, specifically looking at different techniques to parse apart the text. They found that Bidirectional Encoder Representation of Transforms (BERT) performed best.

Reinforcement learning (RL) is a form of machine learning that can be harnessed to develop artificial intelligence by exposing the intelligence to multiple generations of data. The study demonstrates how reply buffer reward mechanics can inform the creation of new pruning methods to improve RL efficiency.

Here, seeking to understand how the COVID-19 pandemic affected the social interactions of junior high school students, the authors surveyed students, teachers, and parents. Contrary to their initial hypotheses, the authors found positive correlation between increased virtual contact during social isolation and in-person conflict and disregard for social norms after the pandemic. While the authors identified the limitations of their study, they suggest that further research into the effect of online interactions is becoming increasingly important.