Bar-Ilan University researchers develop AI model to predict lightning-induced wildfires with unprecedented accuracy
Peer-Reviewed Publication
Updates every hour. Last Updated: 1-May-2025 11:08 ET (1-May-2025 15:08 GMT/UTC)
A groundbreaking new artificial intelligence (AI) model developed by Israeli researchers promises to revolutionize wildfire prediction, with a particular focus on lightning-induced blazes that are growing increasingly common due to climate change. The new AI model can predict where and when lightning strikes are most likely to cause wildfires, achieving over 90% accuracy—a first in wildfire forecasting.
In a paper published in National Science Review, a research team from the Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, reveal differences in the near and far side lunar space environment from Chang’e-6 sample-based evidence. The Chang’e‑6 samples, from the Moon’s far side, lack the vapor deposited layers commonly generated by micrometeorite impacts, which are typically observed in samples from the Moon’s nearside and asteroids. The differences could mainly be driven by the specific space environment of the near and far side of the Moon.
The review bridges physics and biology by analyzing how stochastic thermodynamics—a framework describing energy exchanges in microscopic systems—helps explain limitations across diverse biological functions.
The electromagnetic responses of metamaterial microstructural units are typically described using classical polarization theory models from dielectric physics, such as the Lorentz and Drude models. However, there has been a notable absence of the Debye model, which holds significant importance in dielectric physics. Chinese scientists have now successfully uncovered a novel broadband electromagnetic response mechanism in metamaterial microstructures based on polarization theory - Debye relaxation.