Browse Articles

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 investigates how caffeine and melatonin affect learning in adolescent zebrafish, serving as a model for human teens. Using an automated system to track behavior, we found that melatonin slowed learning while caffeine caused erratic, inconsistent responses, suggesting both substances can negatively impact adolescent learning patterns. These findings highlight the need for further research into their physiological effects and potential implications for human adolescents.

This paper discusses how the conditions at the center of stars affects the nuclear reactions that happen inside these stars. We focused on the effect of temperature and density and found that these two properties interacted to create a greater effect when combined than when separate.

Online review platforms often provide different reviews on the same product, potentially confusing consumers. In this study, we found that the number of raters on Amazon is lower for the same book, while ratings on Amazon were higher than those on Goodreads. Furthermore, these differences in ratings and rater counts were larger for fiction books than for non‑fiction books.

The authors studied optimized delivery routes to reduce travel times using different traveling salesman algorithms.

The authors looked at the ability of a convolutional neural network (CNN) to sort contaminated recycling, with varying levels of contamination. They found as contamination levels increased, the CNN faced more difficulty correctly classifying items.

Recent observations by the “Extremely Metal-Poor Representatives Explored by the Subaru Survey” (EMPRESS) collaboration found normal deuterium levels but unexpectedly low helium-4, challenging current cosmological theories. This study used simulations with the PRyMordial package to test whether dark matter particles interacting with neutrinos in the early universe could explain the discrepancy.

The study investigates the antibacterial properties of household spices on bacteria isolated from everyday objects, aiming to address the limited understanding of bacterial resilience on surfaces and the potential of spices as antibacterial agents. Researchers hypothesized that bacteria would grow faster on some surfaces than others and that spices like honey, chili powder, turmeric, and sumac would inhibit bacterial growth at varying rates. The findings suggest that household spices possess significant antibacterial properties and could be used as emergency disinfectants, particularly in under-resourced settings. However, they cannot replace medical treatments but offer insights into alternative health solutions using common ingredients.

This study evaluates the potential of natural language processing (NLP) models in an emotion-driven bibliotherapy framework to improve mental health challenges.

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