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This study investigates skin tone bias in artificial intelligence models used for dermatological disease classification and evaluates a CycleGAN-based data augmentation approach to improve diagnostic performance on darker skin types. We generated synthetic dark-skinned images to enhance dataset diversity and compared model performance before and after augmentation. The results demonstrate that augmentation with synthetic dermatological images can help reduce disparities in diagnostic performance across skin tones, highlighting a practical strategy for improving fairness in dermatology AI systems.

This study investigates the ability of Gymnema sylvestre tea to acutely suppress sweet taste perception across various food groups. Our findings demonstrate how this natural botanical can reduce sugar cravings, offering a potential complementary strategy for managing dietary intake in type 2 diabetes.

This study examined whether findings from laboratory studies of baseball cognition also apply during real games. Using 2024 MLB data from 30 players, researchers analyzed batting performance under different levels of feedback and anxiety.

Iridescent materials reflect different colored depending on viewing angle. This specific effect can be achieved by biomimetic photonic materials. This project models the quantitative relationship between these material’s coloring and its nanostructure to facilitate personalized design of art materials.

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.

The authors compare current machine learning algorithms with a new Explainable AI algorithm that produces a human-comprehensible decision tree alongside predictions.

Pediatric cancers pose unique challenges due to their rarity and distinct biological factors, emphasizing the need for accurate survival prediction to guide treatment. This study integrated generative AI and machine learning, including synthetic data, to analyze 9,184 pediatric cancer patients, identifying age at diagnosis, cancer types, and anatomical sites as significant survival predictors. The findings highlight the potential of AI-driven approaches to improve survival prediction and inform personalized treatment strategies, with broader implications for innovative healthcare applications.

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 explore factors affecting graduation rates of students learning English as a second language across Florida counties.

This study explored the use of graphite's conductivity for circuit boards by creating a conductive paste through exfoliation with organic solvents and sonication. The combination of acetone and sonication was found to be the most effective, producing a high-conductivity paste with desirable properties such as a low boiling point. While not a replacement for wires, this conductive paste has potential applications in electronics and infrastructure, provided that key engineering challenges are addressed.