'Poised to disintegrate': Antarctica's 'Doomsday Glacier' is set to lose its ice shelf this year - Live Science

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Neurons are a motley crew, varying widely in connectivity and firing properties. But computational models of the brain usually treat neurons as identical. “The convention in the field is homogeneity,” says Scott Rich, assistant professor at the University of Connecticut. Heterogeneity makes models mathematically complex and unwieldy, so researchers have shied away from incorporating it, he says. “But it shouldn’t be [omitted] if we really want to understand how the brain works.”

We’re sharing this article early to provide faster access to peer-reviewed, accepted research. It is citable and carries a permanent DOI. This version is subject to further edits and will be replaced automatically by the final Version of Record. All legal disclaimers apply. AbstractLost circulation remains one of the most critical operational challenges in drilling engineering, yet predictive modeling in real-field conditions is often constrained by severely imbalanced and limited datasets. While previous studies have primarily relied on single imbalance-handling techniques or large datasets, this study introduces a sequential, hybrid data-centric machine learning framework specifically designed for small, multiclass, and highly skewed drilling datasets. A dataset of 258 operational records from the Buzergan oil field in southern Iraq, including drilling parameters, mud properties, and geological indicators, were used to predict lost circulation severity.

Scientists at the Max Planck Institute for the Science of Light (MPL) have achieved a breakthrough in quantum physics, developing a way to study individual molecules on a surface with extraordinary precision. For the first time, researchers have demonstrated that molecules attached to a surface can reach the ultimate quantum limit of optical coherence, overcoming a major obstacle that has long restricted molecular quantum research. The findings, published in Science, could open new possibilities for investigating how molecules interact with surfaces and developing advanced quantum technologies.

NASA's OSIRIS-REx mission encountered an unexpected mystery when it arrived at asteroid Bennu in 2018. Instead of the relatively smooth terrain that earlier observations had suggested, the spacecraft found a rugged landscape dominated by enormous boulders, with very few areas suitable for collecting samples. "When OSIRIS-REx got to Bennu in 2018, we were surprised by what we saw," said Andrew Ryan, a scientist at the University of Arizona's Lunar and Planetary Laboratory, who led the mission's working group responsible for analyzing the physical and thermal properties of its samples. "We expected some boulders, but we anticipated at least some large regions with smoother, finer regolith that would be easy to collect. Instead, it looked like it was all boulders, and we were scratching our heads for a while. "

NASA's OSIRIS-REx mission encountered an unexpected mystery when it arrived at asteroid Bennu in 2018. Instead of the relatively smooth terrain that earlier observations had suggested, the spacecraft found a rugged landscape dominated by enormous boulders, with very few areas suitable for collecting samples. "When OSIRIS-REx got to Bennu in 2018, we were surprised by what we saw," said Andrew Ryan, a scientist at the University of Arizona's Lunar and Planetary Laboratory, who led the mission's working group responsible for analyzing the physical and thermal properties of its samples. "We expected some boulders, but we anticipated at least some large regions with smoother, finer regolith that would be easy to collect. Instead, it looked like it was all boulders, and we were scratching our heads for a while. "