A review paper by scientists from Imperial College introduce embodied cross-domain intelligence, a framework for synergistic coupling across physical, biological, computational, and human intelligence, enabling multifunctional, collaborative, and adaptive micro-robotic behavior in dynamic, safety-critical biological settings.

The new research paper, published on May. 14 in the journal Cyborg and Bionic Systems, introduced a unifying concept called embodied cross‑domain intelligence, which weaves together physical, biological, computational, and human intelligence into a synergistic whole.

Why do some bird songs endure for generations while others fade away? An FAU study suggests the answer may lie in the landscape itself. Researchers found that the most common songs of Bachman’s sparrows travel through natural habitats with greater clarity than rarer songs, raising the possibility that environmental acoustics help shape which vocal traditions are learned, shared and passed down over time.

Chemists at Brown University have shown the first experimental evidence that carbon buckyballs, which launched the nanotechnology revolution, have a cousin made from 80 atoms of the element boron.

A new catalyst strategy may help unlock the full potential of carbon nanotubes (CNTs) for advanced technologies. By combining iron (Fe) with scandium (Sc), the researchers extended catalyst lifetime to approximately 18 minutes at 900 °C—more than twice that achieved with comparable rare-earth cocatalysts. Sc helped maintain Fe in a stable oxidized state and suppressed catalyst degradation, enabling longer CNT growth under demanding conditions. The findings provide valuable new directions for future catalyst design.

Urban railways play a key role in sustainable transportation systems. However, achieving precise and energy-efficient train control under diverse operating conditions of urban railways remains a challenge. Researchers from Japan have now developed a recurrent reinforcement learning framework for urban railway speed control that combines expert-guided learning and safety filtering. The proposed method demonstrated stable, real-time operation and energy-saving performance, highlighting its potential for next-generation automatic train operation systems.

The sensors, which are being developed by soil scientists at Lancaster University and researchers at the University of Colorado Boulder and the University of Manchester, track biological activity in soil by having a degradable substrate that is nibbled on by microbes.

The sensors offer the potential to reveal soil secrets including how they respond to climate events as well as their important role in storing carbon.