A Korean research team has developed the “intelligent core” of a 6G network capable of autonomous control through artificial intelligence (AI). This achievement marks the first realization of an “intelligent 6G core” in Korea and represents a significant milestone in advancing next-generation network infrastructure. Electronics and Telecommunications Research Institute (ETRI) announced that it has successfully developed an “intelligent, service-programmable mobile core network,” a key enabling technology for the 6G era. This technology aims to realize an autonomous 6G core in which AI continuously learns and controls network operations, enabling dynamic adaptation to diverse service requirements.

This study reveals the spatial heterogeneity and temporal dynamics of vegetation net primary production (NPP) across the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) outer ecological barrier, and clarifies the joint impacts of vegetation features, climate and human activities on regional ecological functions.

A research team from HKU Engineering has pioneered a fundamentally new imaging strategy known as AIMED (Arbitrary illumination microscopy with encoded depth), which utilizes a sub-sampling approach. By integrating innovations in axial optical encoding with advanced computational image reconstruction, the AIMED technology enables a substantial increase in 3D imaging speed while enhancing photon safety, all with minimal additional system complexity. This breakthrough demonstrates significant advantages across efficiency, image quality, and system compatibility.

A research team observes negative bend resistance in single-crystalline copper thin films for the first time.

A new study reveals that for the first time, areas of Canada’s boreal peatlands damaged by oil and gas exploration have failed to recover as scientists and companies predicted and instead led to a tripling of their methane emissions, with global implications. 

A new study published in Big Earth Data proposes GeoJSON agents, a novel multi-agent large language model (LLM) framework for geospatial analysis that transforms natural language instructions into structured GeoJSON operations through function calling and code generation. Experiments on a hierarchical benchmark of 70 spatial tasks show that the code generation–based agent achieved 97.14% accuracy and the function calling–based agent achieved 85.71%, both significantly outperforming general-purpose models, while highlighting the trade-off between flexibility and execution stability in GeoAI applications. The data that support the findings of this study are openly available in figshare at https://doi.org/10.6084/m9.figshare.29921492.