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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.

The authors looked at whether CBD and THC would decrease reproductive rates in a Drosophila melanogaster model. They found that CBD had a greater impact on reducing hatching rates than THC, and that THC resulted in unexpected mortalities.

As AI becomes more integrated into healthcare, public trust in AI-developed treatment plans remains a concern, especially for emotionally charged health decisions. In a study of 81 community college students, AI-created treatment plans received lower trust ratings compared to physician-developed plans, supporting the hypothesis. The study found no significant differences in AI trust levels across demographic factors, suggesting overall skepticism toward AI-driven healthcare.

Authors emphasize the challenges of manual tumor segmentation and the potential of deep learning models to enhance accuracy by automatically analyzing MRI scans.

Here, recognizing the difficulty that visually impaired people may have differentiating United States currency, the authors sought to use artificial intelligence (AI) models to identify US currencies. With a one-stage AI they reported a test accuracy of 89%, finding that multi-level deep learning models did not provide any significant advantage over a single-level AI.