A research team led by Dr. Jeong Min Park of the Nano Materials Research Division at the Korea Institute of Materials Science (KIMS), in collaboration with Dr. Jaemin Wang and Prof. Dierk Raabe of the Max Planck Institute in Germany, has developed an artificial intelligence (AI)-based model capable of assessing the likelihood and characteristics of internal defects during process design. This research achievement is expected to significantly enhance the reliability of metal additive manufacturing parts and greatly expand their applicability for mass production in industrial settings.

Indigenous peoples have used forest thinning to protect against wildfires for millennia. These traditional methods – including cultural burning – have often been neglected in modern times, which is thought to have contributed to wildfires in the US and elsewhere. Now, researchers have shown for the first time in a regional hotspot for wildfire risk and drought risk that forest thinning with modern tools has an additional benefit: it increases the snowpack in winter by 16 to 30%, thus recovering lost water and helping to safeguard its supply for natural and human needs.

A 10-year investigation of two Beijing rivers shows that wastewater treatment plant upgrades significantly alter nitrogen-cycling microbial communities and viral ecology despite stable overall diversity. Metagenomic analysis revealed that while the α-diversity of nitrogen-cycling microbes remained unchanged, their community composition shifted significantly, with the nitrifier-to-denitrifier ratio decreasing by 70%. In contrast, the DNA viral community structure was largely stable, but its functional gene profile changed: genes related to viral replication increased while auxiliary metabolic genes decreased. The findings highlight the necessity of integrating microbial and viral indicators into water quality assessments following treatment plant upgrades.

A newly proposed biochemical cycle explains the function of photorespiration in plants, long thought to be a 'wasteful' energy consuming process. 

A new review highlights how machine learning is transforming the way scientists detect and measure organic pollutants in the environment, offering powerful new tools to overcome long standing analytical challenges.

A new international study finds that artificial intelligence development is increasingly splitting into three distinct global systems led by the United States, China, and the European Union, each shaped by different policy priorities, innovation models, and governance philosophies. The research suggests that this divergence may permanently reshape the technological landscape and complicate global cooperation on AI safety, standards, and innovation.

A newly launched scientific journal is calling for a deeper integration of artificial intelligence into environmental research, arguing that AI may be one of the most powerful tools available to address climate change, pollution, and resource sustainability.