New mathematical tools shed light on the fluctuations of living matter

Fluctuations in such energy-consuming systems cannot be assessed by traditional physics due to the influence of the arrow of time on their behavior

Quantitative predictions on the behavior of active matter can facilitate the experimental design of such systems

In this study, we proposed a novel Knowledge-Informed Deep Learning (KIDL) paradigm that, to the best of our knowledge, is the first to unify behavioral generalization and traffic flow stability by systematically integrating high-level knowledge distillation from LLMs with physically grounded stability constraints in car-following modeling. Generalization is enhanced by distilling car-following knowledge from LLMs into a lightweight and efficient neural network, while local and string stability are achieved by embedding physically grounded constraints into the distillation process. Experimental results on real-world traffic datasets validate the effectiveness of the KIDL paradigm, showing its ability to replicate and even surpass the LLM's generalization performance. It also outperforms traditional physics-based, data-driven, and hybrid CFMs by at least 10.18% in terms of trajectory simulation error RMSE. Furthermore, the resulting KIDL model is proven through theoretical and numerical analysis to ensure local and string stability at all equilibrium states, offering a strong foundation for advancing AV technologies.

Practically, KIDL offers a deployable solution for AV control, serving as a high-level motion reference that ensures realistic and stable car-following in mixed traffic environments. Moreover, this framework provides a promising pathway for integrating LLM-derived knowledge into traffic modeling by distilling it into a lightweight model with embedded physical constraints, balancing generalization with real-world feasibility.

The CNRS, Institut d’Optique Graduate School and the scaleup Pasqal inaugurated the AtomIQ-Lab associated research laboratory on 26 November 2025. This collaboration between academic research and business aims to push back the technical limits of laser-cooled atom manipulation. The goal is to considerably increase the performance of quantum processors based on this technology, which are marketed by Pasqal, as well as to extend the range of accessible phenomena in the fundamental physics experiments conducted by the team from the Charles Fabry Laboratory (CNRS/Institut d’Optique).

Plants absorb not only nutrients but also toxic metals such as cadmium through their roots. It was previously unclear whether the toxic metals came from the soil or the fertilisers applied. Under the leadership of the Helmholtz Centre for Environmental Research (UFZ) and Duke University in Durham, North Carolina, a research team has examined wheat grains to clarify the question. Using a special isotope signature, they found that most of the toxic metals come from the mineral fertiliser. A combination of mineral and organic fertilisation would not only reduce the concentration of toxic metals but also increase the concentration of metals important for human nutrition. The researchers reach this conclusion in their study published in Environment International.