New AI-physics model predicts air pollution more accurately than human forecasters
Peer-Reviewed Publication
Updates every hour. Last Updated: 21-Oct-2026 11:16 ET (21-Oct-2026 15:16 GMT/UTC)
Traditional air quality models struggle with changing emissions, while pure AI systems operate as opaque "black boxes" that can break the laws of physics. To solve this, researchers developed ProNet, a framework that embeds AI directly into atmospheric equations. Tested across 338 cities, ProNet outperformed experienced human forecasters and accurately predicted extreme events—like severe dust storms—without producing "AI hallucinations." Crucially, it also acts as a scientific diagnostic tool, using routine hourly data to uncover and correct hidden flaws in existing chemical models.
Using satellite observations, researchers tracked water storage recovery after 468 severe droughts in large river basins worldwide. Most events were followed by recovery, but persistent deficits clustered in drylands and some inland basins. Recovery trajectories varied across basins and events, reflecting water availability after drought. Recurrent droughts and intensive human water use were associated with prolonged deficits.
Researchers have developed a multimode band-engineering framework in one-dimensional grating-based photonic crystal waveguides designed to meet the growing demand for scalable and flexible filtering in mode-division and hybrid multiplexing systems. Published in PhotoniX Synergy, the work combines multimode photonic band engineering, scalable higher-order-mode filtering, and reusable filter building blocks to enable broadband manipulation of spatial modes, polarizations, and wavelengths, providing a unified route toward compact and multifunctional silicon photonic devices.
Survey of 1,426 administrators in Guangxi finds positive attitudes toward digital technologies but weaker practical capability, and identifies four distinct digital leadership profiles
A new study argues that artificial intelligence is reshaping the foundations of human capital theory and the economics of education. It introduces "Intelligence Surplus" as a new analytical framework, proposing that education's role is shifting from building individual productivity to creating the intelligent infrastructure needed for human judgment, meaning-making, and responsible action in human-AI societies.
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