The melting point is one of the most important measurements of material properties, which informs potential applications of materials in various fields. Experimental measurement of the melting point is complex and expensive, ...
A new study led by Griffith University has unveiled a machine learning-based framework to accurately estimate groundwater recharge in the Perth Basin, with a particular focus on the Gnangara groundwater system.
Nonlinear dynamical systems are systems that can undergo sudden shifts not due to changes in their state or stability, but in response to the rate at which external conditions or parameters change. These sudden shifts, known ...
Johns Hopkins researchers have developed a powerful new AI tool called "Splam" that can identify where splicing occurs in genes—an advance that could help scientists analyze genetic data with greater accuracy, offering ...
Street art takes many forms, and the vibrant murals on the Berlin Wall both before and after its fall are expressions of people's opinions. But there was often secrecy around the processes for creating the paintings, which ...
In many Asian regions, especially in China, agricultural fields are typically small, scattered, and lack clear boundaries, which complicates effective crop distribution and agricultural analysis using remote sensing technology.
Researchers from the University of Science and Technology of China (USTC), led by Prof. Liu Qi, in collaboration with Harvard Medical School's Marinka Zitnik lab, have developed a novel deep generative algorithm, PocketGen. ...
Undocumented orphaned wells pose hazards to both the environment and the climate. Scientists are building modern tools to help locate, assess, and pave the way for ultimately plugging these forgotten relics.
The algorithms behind generative AI tools like DallE, when combined with physics-based data, can be used to develop better ways to model the Earth's climate. Computer scientists in Seattle and San Diego have now used this ...
The deep neural network models that power today's most demanding machine-learning applications have grown so large and complex that they are pushing the limits of traditional electronic computing hardware.
