Due to the unique conditions of the space environment and abundant resources, the field investigation and study of the Moon, Earth’s sole natural satellite, represent a crucial milestone in China’s forthcoming deep space endeavors. The successful collection of lunar soil by the Chang’E-5 mission signifies the next phase of the lunar exploration program, which aims to establish a preliminary research station on the lunar south pole. Highly adhesive fine dust particles with an adhesion strength of 0.1 to 1.0 kN/m², which originate from the regolith, disperse throughout the lunar surface. These particles exhibit a lasting attraction and subsequent persistent adhesion to the surface of spacecraft or spacesuits. The accumulation of highly adhesive dust on spacecraft presents a serious issue to hinder long-term extravehicular activity and the establishment of a permanent station on lunar surface. In contrast to the immediate physical damage caused by hypervelocity (> 1.0 km/s) impacts, this adhesion observed at low- velocity (0.01 to 100 m/s) collisions can more unobtrusively and mortally degenerate the performance of equipment. In recent years, many interaction models have been developed to forecast the adhesion and ejection dynamics of lunar dust on various surfaces. Most researches on the electrostatic force applied on dust particles near the surface use point-charge approximation. Although this simplification makes the simulation easier, the error can be large due to the polarization of dust induced by the image charge.

Rapid development of artificial intelligence requires the implementation of hardware systems with bioinspired parallel information processing and presentation and energy efficiency. Electrolyte-gated organic transistors (EGOTs) offer significant advantages as neuromorphic devices due to their ultra-low operation voltages, minimal hardwired connectivity, and similar operation environment as electrophysiology. Meanwhile, ionic–electronic coupling and the relatively low elastic moduli of organic channel materials make EGOTs suitable for interfacing with biology. This review presents an overview of the device architectures based on organic electrochemical transistors and organic field-effect transistors. Furthermore, we review the requirements of low energy consumption and tunable synaptic plasticity of EGOTs in emulating biological synapses and how they are affected by the organic materials, electrolyte, architecture, and operation mechanism. In addition, we summarize the basic operation principle of biological sensory systems and the recent progress of EGOTs as a building block in artificial systems. Finally, the current challenges and future development of the organic neuromorphic devices are discussed.

Researchers at Osaka Metropolitan University developed models that classify X-ray images into specific body regions and simultaneously determine the imaging method and image orientation. Using these models, they successfully classified almost all data for use in deep-learning models.

Discover how space–ground fluid AI is set to revolutionize 6G networks by integrating edge AI with satellite systems, enabling global AI services and overcoming challenges of satellite mobility and communication rates. Explore the innovative techniques proposed in a new study published in Engineering that could shape the future of intelligent connectivity.