A team of scientists from Korea and Japan has discovered a new type of crystal that can "breathe"—releasing and absorbing oxygen repeatedly at relatively low temperatures. This unique ability could transform the way we develop clean energy technologies, including fuel cells, energy-saving windows, and smart thermal devices.

An AI system capable of pinpointing a device’s location in dense urban areas without relying on GPS has been developed by researchers at the University of Surrey. Narrowing down localisation errors from 734 metres to within 22 metres, the innovation could be a significant step forward for technologies such as self-driving cars and aid delivery vehicles. 

A new study led by researchers at the National Institute for Environmental Studies, Japan Agency for Marine-Earthe Science and Technology and The University of Tokyo published in Communications Earth & Environment, analyzed long-term projections from state-of-the-art Earth System Models and identified large-scale atmospheric circulation and ecosystem changes that lead to the degradation of the Amazon rainforest. The research reveals that if no efforts are made to stop climate change, the dieback of the Amazon rainforest may start within the 21st century.

Researchers have developed a novel method to stimulate and mature human brain organoids using graphene, a one-atom-thick sheet of carbon.

This study published in Robot Learning has been focused on water analysis using the combination of decision making and machine learning for a recently developed robotic system. The unique procedure the researchers have applied has significant impact on the improvement of robots’ performance that will be able to detect, analyze and distinguish drinking water on Earth and other planets.

An international, interdisciplinary research team led by Prof. Jakob N. Kather from the Else Kröner Fresenius Center (EKFZ) for Digital Health at TU Dresden analyzed seven independent patient cohorts from Europe and the USA using their newly developed AI model. The model detects genetic alterations and resulting tissue changes in colorectal cancer directly from tissue section images. This could enable faster and more cost-effective diagnostics in the future. For the development, validation, and data analysis of the model, experts in data and computer science, epidemiology, pathology, and oncology worked closely together. The study has been published in the journal “The Lancet Digital Health”.