Turbulence is ubiquitous and occurs in our everyday lives on Earth, such as when milk is stirred into coffee, or in the stormy motions of the ocean and the atmosphere. Media in space and astrophysical systems can also develop turbulence, but the physics is different because the medium is in an ionized state (known as the plasma state) and the flow motion is strongly coupled to electromagnetic fields. Furthermore, space plasma is collisionless because the medium is so dilute that the particles do not collide with each other. How does plasma develop into turbulence? How is the fluctuation energy distributed in collisionless turbulence? Turbulence in space plasma remains a mystery in physics.  

An international research team from the Technische Universität Braunschweig and University of Tokyo in cooperation with Tohoku University, Kyushu University, Osaka University, and multinational research institutes has now demonstrated, for the first time, a reversal phenomenon of magnetic field rotation in space turbulence. A structure emerges within the unstructured space plasma. The results are published in The Astrophysical Journal Letters. 

In the International Journal of Extreme Manufacturing, Prof. Yang Li at Zhejiang University and his co-workers developed a dual-sided stencil printing technique that packs high-capacity energy storage into electrode tracks just 80 μm wide by giving a tungsten stencil two distinct physical personalities—a dual-sided "Janus mask" named after the two-faced Roman god.

A growing surplus of refinery sulfur could find a new use in asphalt production, reducing reliance on petroleum-based bitumen and lowering costs. But a new academic review highlights unanswered questions about emissions, human exposure and long-term health implications, calling for safer formulations and life-cycle assessment of sulfur-modified asphalt.

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.