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The authors studied the accuracy of two commonly used explainable AI techniques for detecting pneumonia in chest x-rays.

This study aimed to combat widespread hydrocarbon residue persistently polluting global water sources by developing and testing the ability of modified iron(III) oxide-hydroxide (FeO(OH)) nanoparticles to remove small amounts of motor oil from freshwater

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

The authors used a particle-in-cell simulation to determine the effects on extensive and intensive metrics. They found that preliminary simulations could be run quickly with much lower particle counts before more technically demanding and comprehensive simulations are performed.

The authors looked at how pharmacokinetics changed depending on the use of an in vitro or an in vivo model.

The authors developed a quantum inspired model for stock market fluctuations.

Here the authors present an autonomous drone air filtration system that uses a novel algorithm, the gradient ascent ML particle filter (GA/MLPF), to efficiently locate and mitigate outdoor air pollution. They demonstrate that their GA/MLPF algorithm is significantly more efficient than the conventional gradient ascent algorithm, reducing both the time and number of waypoints needed to find the source of pollution.

The authors looked at the correlation between being able to speak more than one language (multilingualism) and visual imagery. They found multilingual individuals had higher visual imagery as measured by the VVIQ.

The authors investigate the relationship between self-esteem, social desirability, and emotion regulation in children and adolescents.

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.