The statistical analysis, based on the Wilcoxon test with a significance level of p < 0.05, confirmed substantial improvements across all participant groups. Overall, the ability to distinguish between hypocenter and epicenter increased by 45.39%; understanding of seismic magnitude improved by 39.01%; and the visual identification of structural vulnerabilities increased between 25% and 38%, with moderate to large effect sizes.

Semitransparent thin-film photovoltaics (PV) have been increasing popular for urban structures, vehicles, and applications requiring visible transparency. However, these materials suffer from reduced light absorption, owing to limited absorber volume, leading to low efficiency. In a new study, researchers utilized geometric optics principles to create a novel microprism sheet that leverages light trapping effect to significantly enhance light absorption of thin-film PV sheets.

Korean researchers have developed, for the first time in Korea, a 200Gbps-class photodetector device that can be used in hyperscale AI data centers and 5G/6G mobile communications infrastructure. This technology enables ultrahigh-speed data reception at a level capable of transmitting five 5GB full HD movies per second. Electronics and Telecommunications Research Institute (ETRI) announced that it has developed a photodetector device capable of processing 200Gbps-class optical signals per channel. A photodetector is a key semiconductor component that converts optical signals into electrical signals and is an essential element that determines data reception performance in data centers and communication networks.

Prof. Yingxiang Liu's team at the State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, developed a unique tripodal piezoelectric robot driven by a high-stiffness ring-shaped elastic body. By combining axisymmetric vibration modes with an asymmetric foot arrangement, the proposed robot achieves both planar agile motions and high load capacity.

A new study published in Big Earth Data proposes a large language model (LLM)-based multi-agent framework for remote sensing analysis that integrates data resources, analytical tools, and AI-driven reasoning to assist non-expert users in conducting complex remote sensing tasks. Using the ExpertsRS prototype built on AutoGen and DeepSeek-V3, experiments demonstrate that the system can transform ambiguous queries into expert-level analytical workflows and outperform baseline LLM and single-agent approaches in planning efficiency and result accuracy. The Sentinel-2 dataset used in this study is openly available from the European Space Agency at https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-2. The code repository for this work is accessible at https://github.com/zhelunSun/URSA.

Mississippi State scientists are building on two decades of irrigation research to identify production practices that help growers save water while improving crop yields.