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This article describes the classification of medical text data using vector databases and text embedding. Various large language models were used to generate this medical data for the classification task.

Here, the authors wanted to explore mathematical paradoxes in which there are multiple contradictory interpretations or analyses for a problem. They used ChatGPT to generate a novel dataset of fairy tales. They found statistical differences between the artificially generated text and human produced text based on the distribution of parts of speech elements.

Here, the researchers investigated how exposure to active versus passive music affects a teenager's ability to perform a challenging task, namely a Sudoku puzzle, under stressful conditions. Following testing 75 high school teenagers split into two group, the researchers found that singing in a choir (active music) yielded a greater improvement in performance compared to passive listening for brief time periods.

The authors use machine learning to analyze electroencephalogram data and identify slowing patterns that can indicate undetected disorders like epilepsy or dementia

Here the authors investigated the effects of natural coagulants on reducing the turbidity of water samples from the Tennessee River Watershed. They found that turbidity reduction was higher at lower temperatures for eggshells. They then projected and mapped turbidity reactions under two climate change scenarios and three future time spans for eggshells. They found site-specific and time-vary turbidity reactions using natural coagulants could be useful for optimal water treatment plans.

Additive manufacturing (AM) is transforming the production of complex metal parts, but challenges like internal cracking can arise, particularly in critical sectors such as aerospace and automotive. Traditional methods to assess cracking susceptibility are costly and time-consuming, prompting the use of machine learning (ML) for more efficient predictions. This study developed a multi-model ML pipeline that predicts solidification cracking susceptibility (SCS) more accurately by considering secondary alloy properties alongside composition, with Random Forest models showing the best performance, highlighting a promising direction for future research into SCS quantification.

Adolescence is a critical period for self-identity formation, heavily influenced by feedback from social networks. This research examined the interplay between social support from parents and peers and self-concept development in adolescents using data from the National Longitudinal Study of Adolescent to Adult Health. While individual support from parents and peers did not directly impact self-concept, their combined interaction significantly influenced it, highlighting the importance of various social supports in fostering healthy self-concept development and overall adolescent well-being.

Pulmonary diseases like lung cancer and valley fever pose serious health challenges, making accurate and rapid diagnostics essential. This study developed a MATLAB-based software tool that uses computer vision techniques to differentiate between these diseases by analyzing features of lung nodules in CT scans, achieving higher precision than traditional methods.

The authors looked at different models of semantic segmentation to determine which may be best used in the future for segmentation of CT scans to help diagnose certain conditions.

Fungi that attack and kill insects have promise for targeting mosquitoes without the harmful environmental impacts of chemicals like DDT. To find out whether fungi might be effective in controlling mosquitoes in Hawaii, Jiang and Chan test the effects of Hawaiian fungal isolates on mosquito larvae.