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In a new study, researchers at Osaka University have created the world’s first compact, tunable-wavelength blue semiconductor laser, a significant advancement for far-ultraviolet light technology with promising applications in sterilization and disinfection.

This innovative laser employs a specially-designed periodically slotted structure in nitride semiconductors, making possible a blue wavelength laser that is both practical and adaptable for various disinfection technologies. The work is published in the journal Applied Physics Express.

The research team had previously demonstrated second-harmonic generation at wavelengths below 230 nm by using transverse quasi-phase-matching devices crafted from aluminum nitride and vertical microcavity wavelength conversion devices incorporating SrB4O7 nonlinear optical crystals.

Scientists discovered a way to encode more data into light by creating light vortices with quasicrystals. This method could potentially increase data transmission rates through optic fibers by up to 16 times, marking a significant advancement in telecommunications technology.

Modern life relies heavily on efficiently encoding information for transmission. A common method involves encoding data in laser light and sending it through fiber optic cables. As demand for data capacity grows, finding more advanced encoding methods is essential.

Breakthrough in Light Vortex Creation.

MIT CSAIL researchers have developed a generative AI system, LucidSim, to train robots in virtual environments for real-world navigation. Using ChatGPT and physics simulators, robots learn to traverse complex terrains. This method outperforms traditional training, suggesting a new direction for robotic training.


A team of roboticists and engineers at MIT CSAIL, Institute for AI and Fundamental Interactions, has developed a generative AI approach to teaching robots how to traverse terrain and move around objects in the real world.

The group has published a paper describing their work and possible uses for it on the arXiv preprint server. They also presented their ideas at the recent Conference on Robot Learning (CORL 2024), held in Munich Nov. 6–9.

Getting robots to navigate in the real world at some point involves teaching them to learn on the fly, or by training them with videos of similar robots in a real-world environment. While such training has proven to be effective in limited environments, it tends to fail when a robot encounters something novel. In this new effort, the team at MIT developed virtual training that better translates to the real world.

It turns out that the evolution of the most violent collisions between nuclei, as they are studied at the Large Hadron Collider at CERN, depends on the initial conditions, namely the geometry and shape of the colliding nuclei, which are in their ground state. More surprisingly, this insight also allows us to determine properties of the colliding nuclei that cannot easily be studied by other methods.

The researchers have predicted how the shape changes and fluctuations of the colliding nuclei will influence the outcome of extreme high-energy conditions. This paves the way for further studies which will yield a better understanding of the dynamic behavior of nuclei. An article on the results has been published in Physical Review Letters.

The predictions are theoretical but based on an experiment at the world’s leading physics research center, CERN, Switzerland.