A research team has developed a novel approach combining refined spatial competition mapping with powerful ensemble learning to predict maximum crown width height (HMCW) in Chinese fir with unprecedented accuracy.

The American Chemical Society (ACS) Fall 2025 meeting, held from August 17 to 21 in Washington, DC, has proven to be a monumental event in the scientific community. As the largest international academic event in the field of chemistry, it has drawn researchers, academics, and industry leaders from across the globe, all converging to discuss the latest advancements and challenges in chemistry and its multidisciplinary applications.

A weak physical force, like that which allows spiders to walk on a ceiling, could hold the key to a new revolution in optical communications, thanks to a discovery by University of Melbourne researchers and their collaborators at Hanyang University in South Korea.

In order to improve the diagnostic accuracy of deep-learning AI algorithms, models require larger amounts of high-quality training data, which presents a significant burden for pathologists or radiologists that diagnose disease based on images composed of billions of pixels. Researchers have developed a method to imitate the expertise of pathologists by tracking their eye movements while diagnosing whole slide images. This data helps scientists train AI models to more accurately identify regions of interest and better classify tissue samples based on the behavior of highly experienced and trained professionals with little to no additional burden placed on these providers.