Metasurfaces are artificially designed two-dimensional structures composed of a large number of subwavelength-sized units arranged periodically or aperiodically. They can overcome the limitations of natural materials and endow electromagnetic waves with unique manipulation capabilities. The motivation for metasurface research stems from the core challenges of wireless communication: optimizing signal coverage and quality in complex electromagnetic environments.

With the increasing demand for high-frequency bands (millimeter-wave and terahertz) and large-scale connectivity in 5G/6G communications, traditional technologies face challenges such as high hardware complexity, excessive power consumption, and severe channel interference. Due to their low power consumption, low cost, and easy deployment, metasurfaces have emerged as an ideal solution to these problems. 

In particular, the concept of digital-coded metasurfaces, proposed in 2014, introduced programmable elements (such as PIN diodes) to discretize electromagnetic properties into "0/1" digital states. Combined with controllers like FPGAs, these metasurfaces enable real-time dynamic regulation of electromagnetic waves. This breakthrough has transformed metasurfaces from “fixed-function materials” into “intelligent electromagnetic surfaces”, capable of dynamically optimizing wireless channels and even directly participating in signal modulation and transmission.

A single species found in the Alcatrazes Archipelago, brain coral, produces around 170 tons of calcium carbonate annually. This represents the retention of approximately 20 tons of carbon in mineral form, which can last for centuries or millennia. A study by the Federal University of São Paulo highlights the potential ecosystem services provided by subtropical corals.

Panoramic visual-inertial odometry (VIO) systems are crucial for navigating Global Positioning System (GPS)-denied environments, but their potential is hindered by image distortions caused by geometric inconsistent projection models.  

Self-driving cars know their own way in unpredictable traffic, thanks to path planning technology. Among current AI-driven efforts to make path planning more efficient and reliable, a research team has developed an optimization framework proven especially effective in uncertain environments. The results were published June 3 under the title “Action-Curiosity-Based Deep Reinforcement Learning Algorithm for Path Planning in a Nondeterministic Environment” in Intelligent Computing, a Science Partner Journal.

Could robots help those with mobility issues make a pizza? With support from the National Science Foundation, a team of Virginia Tech mechanical engineers are using robotic grippers and artificial intelligence to make that idea a reality.

The targeted engineering of artificial proteins with unique properties – that is possible with the assistance of a novel method developed by a research team led by Prof. Dr Dominik Niopek at the Institute of Pharmacy and Molecular Biotechnology (IPMB) of Heidelberg University. It centers around a new AI model. This allows for forecasting how two proteins have to be fitted together at the molecular level from individual parts – subunits – in order to engineer a functional, adjustable new protein. Called ProDomino, this open-source tool opens up many different possible applications in biotechnology and medicine.