Researchers from the Optics Group at the Universitat Jaume I in Castellón have managed to correct in real time problems related to image aberrations in single-pixel microscopy using a recent technology: programmable deformable lenses. The new method was described by the research team in an open-access article recently published in Nature Communications and is part of the development of the European CONcISE project.

The solution proposed by this team combines an adaptive lens (which “shapes” the light wavefront in real time) with a sensor-less method that evaluates image sharpness directly from the data, without complex algorithms. This approach corrects distortions caused both by the system and by the sample itself, producing sharper images, close to the physical resolution limit, without adding complexity to the microscope.

This adaptive lens is known as a “multi-actuator adaptive lens” (M-AL), which can be easily integrated into the system without significantly modifying the traditional configuration of a single-pixel microscope based on structured illumination. These types of lenses consist of an optically transparent and deformable membrane (similar to a thin sheet of glass or polymer) that can change shape via actuators distributed around or behind it.

How quickly we reply, how active we really are in chats – many people misjudge their own behavior. Researchers at Bielefeld University have, for the first time, used anonymized WhatsApp metadata to make such misperceptions visible. 

Energy-efficient buildings are promising for sustainable development and energy consumption as per environmental, social, and economic criteria. Recently, researchers from Hanbat National University, and Kongju National University, Republic of Korea, have proposed polymer-dispersed liquid crystal-impregnated switchable thermochromic transparent woods that demonstrate excellent ultraviolet blocking performance for smart windows, promoting indoor illumination, privacy, and human health. The novel innovation can help pave the way for next-generation energy-efficient buildings.

Despite rapid robotic automation advancements, most systems struggle to adapt their pre-trained movements to dynamic environments with objects of varying stiffness or weight. To tackle this challenge, researchers from Japan have developed an adaptive motion reproduction system using Gaussian process regression. By learning the relationship between human motion and object properties, their method enables robots to accurately replicate human grasping behaviors using small training datasets and manipulate unfamiliar objects with remarkable precision and efficiency.