The accurate estimation of bearing stratum depth, the subsurface soil or rock layer depth that can support a foundation, is key to preventing soil-related disasters in earthquake-prone areas and ensuring a building’s structural safety. However, traditional methods to assess this depth involve high costs, time, and labor. Now, researchers from Japan have demonstrated improved depth prediction accuracy by employing machine learning algorithms, making it both cost-effective and a generalizable alternative to traditional field surveys.

Solid-oxide fuel cells are a promising material for future green energy infrastructure due to their high efficiency and long lifespan. However, they require operation at high temperatures of around 700-800℃. Now researchers at Kyushu University have succeeded in developing a new SOFC material with an efficient operating temperature of 300℃. The team expects that their new findings will greatly accelerate the practical application of green energy devices.