Like people, materials evolve over time. They also behave differently when they are stressed and relaxed. Scientists looking to measure the dynamics of how materials change have developed a new technique that leverages X-ray ...
A collaborative team of researchers led by Ben Weinstein of the University of Florida, Oregon, US, used machine learning to generate highly detailed maps of over 100 million individual trees from 24 sites across the U.S., ...
Amidst the challenges posed by climate change in predicting typhoons, a team of researchers has developed a technology that leverages real-time satellite data and deep learning capabilities to predict typhoons with greater ...
Scientists propose a new way of implementing a neural network with an optical system which could make machine learning more sustainable in the future. The researchers at the Max Planck Institute for the Science of Light have ...
Urban spaces are particularly susceptible to the effects of climate change, such as heat waves, floods, and storms. But which areas of a city are affected, and how can city planners respond?
A research team used synchrotron-based X-ray microcomputed tomography (SR-μCT) to non-invasively obtain high-quality 3D images of fresh soybean root nodules, quantifying the volumes of the central infected zone (CIZ) and ...
A research team has developed LGNet, a dual-branch network that combines convolutional neural networks (CNNs) and visual transformers (VTs) for plant disease identification. LGNet effectively fuses local and global features, ...
In a study published in iScience, researchers at Queen Mary University of London have taken major steps in better understanding some key questions about learning and intelligence. Led by Dr. Elisabetta Versace, the team used ...
UCLA researchers have conducted an in-depth analysis of nonlinear information encoding strategies for diffractive optical processors, offering new insights into their performance and utility. Their study, published in Light: ...
Researchers from the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), TU Chemnitz, TU Dresden and Forschungszentrum Jülich have been the first to demonstrate that not just individual bits, but entire bit sequences can be stored ...
