Researchers have introduced a novel canonical correlation guided deep neural network (CCDNN) to improve multi-source data acquisition and fusion. The model is designed to integrate heterogeneous data more effectively, addressing limitations of existing fusion methods in handling diverse and high-dimensional inputs. By leveraging advanced neural network structures, CCDNN enhances feature representation and decision-making accuracy. Experimental results demonstrate superior performance across benchmark tasks, highlighting its potential for applications in intelligent control, automation, and data-driven engineering systems.

New research led by the University of Oxford and University College London (UCL) has revealed that pollution from coal-fired power plants is significantly reducing the energy output of solar photovoltaic (solar PV) installations, particularly where these are expanding side by side. The findings have been published today (15 May) in Nature Sustainability.

Researchers at Tampere University, Finland, have developed a groundbreaking 3D‑printed ceramic implant material that closely mimics real human bone. The findings advance the development of personalised bone regeneration and may lead to more effective and accessible treatments for bone defects.

Traditional computing architectures struggle to support the complex, real-time demands of modern autonomous robots. A team of researchers has published a comprehensive review in SmartBot, detailing how neuromorphic electronic devices that mimic the biological nervous system can provide the necessary hardware foundation for the next generation of "embodied intelligence".