In summary, the proposed multidimensional metasurface offers a novel design approach for next-generation spectro-polarimetric modulation devices, holding great promise for the development of multifunctional, miniaturized, and integrated optical detection systems and their application in platforms such as drones, micro/nano satellites, and mobile electronic devices.

A new study unveils an advanced drone-based system that offers, for the first time, a smarter way to monitor sesame health. By combining hyperspectral, thermal, and RGB imagery with deep learning, researchers have developed a powerful method for detecting simultaneous nitrogen and water deficiencies in field-grown sesame. This innovative approach leverages cutting-edge UAV-imaging technology and artificial intelligence to improve the accuracy of stress detection in crops. The integration of multiple data sources enables identification of combined nutrient and water-related deficiencies. This significant step forward in the field of precision farming not only enhances crop management but also supports more sustainable and efficient use of water and fertilizers, key components in building climate-resilient food systems.

Researchers at the University of Tokyo have demonstrated that the strength of synaptic connections in the cerebral cortex varies during sleep, influenced by synaptic learning rules and neuronal activity. Using simulations, they identified conditions under which “sleep learning” may occur. These findings could deepen our understanding of the relationship between sleep and learning/memory and help elucidate the mechanisms of brain disorders linked to sleep disturbances.