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The authors develop a method for detecting fake AI-generated images from real images.

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.

Semantic segmentation - labelling each pixel in an image to a specific class- models require large amounts of manually labeled and collected data to train.

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.

The authors propose a method to recycle Christmas tree needles into a non-toxic reducing agent for synthesizing copper nanoparticles.

Here, the authors characterize how silver ions nucleate a star-block copolymer to generate nano-sized silver particles.

The authors looked at the ability of synthetic auxin to increase root hair growth in Arabidopsis thaliana. They found that 0.1 µM synthetic auxin significantly increased root hair length, but that 0.01 µM and 1 µM did not have any significant effect.

The authors looked at the feasibility to predict wind speeds that will have less reliance on using historical data.

Irrespective of the final application of a molecule, synthetic accessibility is the rate-determining step in discovering and developing novel entities. However, synthetic complexity is challenging to quantify as a single metric, since it is a composite of several measurable metrics, some of which include cost, safety, and availability. Moreover, defining a single synthetic accessibility metric for both natural products and non-natural products poses yet another challenge given the structural distinctions between these two classes of compounds. Here, we propose a model for synthetic accessibility of all chemical compounds, inspired by the Central Limit Theorem, and devise a novel synthetic accessibility metric assessing the overall feasibility of making chemical compounds that has been fitted to a Gaussian distribution.

The authors test the durability of synthetic versus non-synthetic fabrics though loose thread counts, color fade over time, and shrinkage tests.