A new study evaluates how different three-dimensional crustal velocity models influence strong ground motion simulations in the seismically active Sichuan–Yunnan region of southwest China. Using the 2022 Mw 6.6 Luding earthquake as a case study, researchers compared ground motion predictions from nine representative velocity models based on peak ground velocity (PGV), a key engineering parameter. The results show that different models can systematically overestimate or underestimate shaking intensity, while averaging results from multiple models significantly improves prediction stability. The findings provide practical guidance for post-earthquake ground motion assessment and seismic hazard mitigation.

Scientists have pinpointed, for the first time, exactly when key oil- and gas-forming rocks developed in northwest China. By precisely dating tiny zircon crystals preserved in ancient volcanic ash, researchers built a high-resolution timeline for Carboniferous–Permian source rocks in the Junggar Basin and nearby regions. The study shows that these source rocks formed during three distinct time windows and that the shift from marine to land-based environments occurred at different times across the region. These findings resolve long-standing geological debates, support a step-by-step, “scissor-like” closure of the Paleo-Asian Ocean, and provide a crucial time guide for future energy exploration.

Digital Planet at The Fletcher School at Tufts University launches the American AI Jobs Risk Index, the first comprehensive framework ranking U.S. occupations, industries, metro areas, and states by their actual vulnerability — not just exposure — to AI-driven job displacement. The Index projects that 9.3 million jobs are at risk over the next 2–5 years, representing up to $757 billion in annual household income. Silicon Valley, the birthplace of AI, faces the highest displacement of any major metro. High-skill, high-income knowledge workers — writers, programmers, analysts — are at greatest risk. This has implications for local economies but will also have ground-shifting political consequences. The window for making preparations and pre-emptive action is narrow. Learn what can be done to anticipate and head off an economic and political disruption.

Neither radiologists nor multimodal large language models (LLMs) are able to easily distinguish artificial intelligence (AI)-generated “deepfake” X-ray images from authentic ones, according to a new study. The findings highlight the potential risks associated with AI-generated X-ray images, along with the need for tools and training to protect the integrity of medical images and prepare health care professionals to detect deepfakes.

Self-driving laboratories (SDLs) powered by artificial intelligence (AI) are rapidly accelerating materials discovery, but can they also explain their results? Researchers from the Theory Department of the Fritz Haber Insitute, in collaboration with BASF, and BasCat – UniCat BASF JointLab, show that they can. Their new AI-driven strategy works hand-in-hand with SDLs to identify better catalysts while revealing the chemistry behind their performance. The approach was validated on the industrially crucial conversion of propane into propylene.