Laser-based metal processing enables the automated and precise production of complex components, whether for the automotive industry or for medicine. However, conventional methods require time- and resource-consuming preparations. Researchers at Empa in Thun are using machine learning to make laser processes more precise, more cost-effective and more efficient.

New link between the amygdala and how zebra finches learn their birdsongs by imitating other zebra finches.

The Hong Kong Polytechnic University (PolyU) has a strong research track record in space technology, with its researchers involved in several national and international space exploration projects. A PolyU project has recently been awarded the Innovation and Technology Fund under the Innovation and Technology Support Programme (ITSP) for key space technology capability research and development with application in terrain cameras.

A new study in Engineering introduces ERQA, a medical knowledge retrieval and QA framework driven by an enhanced large language model. It integrates a semantic vector database and a literature repository. Tests on the pandemic and TripClick datasets show its good performance in multiple tasks, but it also has some limitations. The researchers plan to further improve the model.

Researchers at The University of Osaka have developed a groundbreaking energy-efficient and high-precision measurement system leveraging the inherent similarity between waveforms generated by the same type of signal source. Unlike black-box approaches such as generative AI, the system is built on the explicit theoretical framework of compressed sensing. This innovative approach drastically reduces the amount of data required for accurate signal reproduction, leading to significant energy savings. Demonstrated with an electroencephalogram (EEG) measuring system, the technology achieved world-leading energy efficiency using only commercially available electronic components, consuming a mere 72μW. This breakthrough paves the way for long-term, battery-powered wearable devices and self-powered, battery-free IoT devices that can operate on minimal energy harvested from the environment, with broad applications in healthcare, disaster prevention, and environmental monitoring.

For the design of future materials, it is important to understand how the individual atoms inside a material interact with each other quantum mechanically. Previously inexplicable vibrational states between carbon chains (carbyne) and nanotubes have puzzled materials scientists. Researchers from Austria, Italy, France, China and Japan lead by the University of Vienna have now succeeded in getting to the bottom of this phenomenon with the help of Raman spectroscopy, innovative theoretical models and the use of machine learning. The results published in "Nature Communications" show the universal applicability of carbyne as a sensor due to its sensitivity to external influences.