With human-robot collaboration at the core of Industry 5.0, a research team at The Hong Kong Polytechnic University (PolyU) has made significant progress in this field, developing a new generation of “human-machine symbiotic” collaborative manufacturing systems. In addition to perceiving complex environments in real time and accurately interpreting operators’ intentions, the system can achieve skill transfer and self-learning via simple demonstration, while carrying out autonomous process code generation and automatic adjustment for highly accurate task execution. It has been successfully applied to high-end manufacturing tasks such as autonomous drilling on large aircraft and the disassembly of electric vehicle batteries, laying a solid foundation for a new model of human-centric smart manufacturing.

A new study published in Engineering by Xin Wang, Jian Yao, Jin Zhang and their colleagues proposes a machine-learning-guided strategy that combines data-driven descriptor screening with three-dimensional multiphysics and lattice Boltzmann simulations. The authors elucidate how the PA content and its non-uniform distribution within the catalyst layer govern proton transport, platinum utilization, and local current generation. Guided by these insights, they introduce an EDTMPA–PA dual-proton-conducting system and construct a gradient dual-proton-conductor catalyst layer, enabling single cells to achieve record power densities at 160 °C while maintaining long-term operational stability under aerospace-relevant conditions.

JMIR Publications today released a new analysis in its "News & Perspectives" section, examining a potential turning point for artificial intelligence in mental health care.