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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.

Robot-assisted minimally invasive surgery (RMIS) benefits from increased precision and faster recovery, with force feedback from the surgical tool being critical for control. Researchers tested the use of neural networks for detecting the vanishing point of the tool, a key element for force feedback.

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.

Here the authors sought to use three machine learning models to predict poverty levels in Cambodia based on available household data. They found teat multilayer perceptron outperformed the other models, with an accuracy of 87 %. They suggest that data-driven approaches such as these could be used more effectively target and alleviate poverty.

The study explored converting Gracilaria seaweed waste—known for releasing toxic hydrogen sulfide when decomposed—into biochar as a sustainable solution for waste management and soil improvement.

Analysis of the Sloan green and red photometry of the Type Ia supernova 2024neh

Investigation of the potential of waxworm saliva, the secretion of Galleria mellonella, for plastic degradation.

This study explored how green spaces, climate, traffic, and air quality (GCTA) collectively influence asthma-related emergency department visits in the U.S using machine learning models and explainable AI.

The authors looked the ability to predict future stock prices using various machine learning models.

The authors evaluated the effect of concentration and variant of N-Acetylcysteine in hydrolyzing mucus.