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.

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

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.

The authors access a panel of leaf traits across ten deciduous tree species to explore adaptive strategies between large and small trees.

The authors test the antioxidant content of Taiwanese high mountain teas grown in or processed under differing conditions.

This manuscript explores the performance of five different machine learning models in classifying brain tumors from a dataset of MRI scans. The authors find that several of the models showed >90% accuracy. Thus, the authors suggest that machine learning models demonstrate potential for effective implementation in clinical settings, including as a diagnostic tool that can be used to complement the expertise of neuroradiologists.

The authors studied the impact of drying blood as a storage solution for space travel.

This study analyzes genetic alterations and expression patterns of protein kinases involved in phagocytosis across multiple cancers using TCGA data.