Ceramic fiber aerogels are ultralight and thermally insulating, but their porous skeletons suffer rapid failure under high-energy laser irradiation. Now, researchers have grown TiO2 nanowires in-situ on mullite nanofiber aerogels, creating a hierarchical structure that dramatically improves laser protection. The new composite raises surface reflectivity from 94.68% to 97.25%, reduces light absorption by 48.3%, and lowers high-temperature thermal conductivity by 30.8%. Under 300 W/cm2 laser ablation, conventional aerogels perforate within 3 seconds, while the TiO2-decorated version withstands 30 seconds and repeated impacts. Even at 500 W/cm2 for 30 seconds, no significant damage occurs.

3D CAD, even with the assistance of software, can be a laborious and time-consuming process. A group of researchers recently wrote a comprehensive review article outlining the current AI-driven 3D CAD state of the art and described challenges that must be overcome to increase the practicality of AI model use in 3D CAD workflows.

Researchers summarized how friction and electrical current interact to wear down moving components in electrified systems, such as railways and aerospace devices. They also evaluated predictive models for wear and highlighted the need for more realistic testing and improved AI-based forecasting.

Neural radiance fields (NeRFs) are a powerful form of artificial intelligence (AI) that utilizes 2D images to synthesize a 3D scene and “learn” what a realistic 3D representation of the base information would look like. While NeRFs initially revolutionized computer vision in the early 2020s, a new method called Gaussian Splatting quickly overtook NeRFs. In this comprehensive review, researchers explore some of the differences between these two methods and their applications in the hopes of better informing future computer vision practitioners.

Advances in foundation models (FM) have opened the gates to an advanced use of artificial intelligence for healthcare. FMs are an AI system with a great ability to perform accurately on new sets of data or tasks, instead of just memorizing the material it was trained with. Vision-language foundation models (VLFM) are particularly of interest to clinicians as a means to aid in report generation and question answering, with vision-language agents (VLAs) being representative of the next frontier for AI acting in a healthcare space.