Mammal-like tails most promising for acrobatic robots
Peer-Reviewed Publication
Updates every hour. Last Updated: 19-Jun-2025 05:10 ET (19-Jun-2025 09:10 GMT/UTC)
While exploring how best to design robots that use tails to reorient their bodies in midair, a team of researchers at the University of Michigan and University of California San Diego found that mammals had already figured out how to do more with less.
Joerg Niessing, an INSEAD marketing professor and digital strategy and transformation expert, has been named the winner of The Case Centre’s Outstanding Case Teacher Competition 2025. This global honour recognises his ability to bring business challenges to life using interactive technology and an innovative and creative teaching approach.
Researchers have designed an alternative, autonomous observational method to monitor the Arctic’s melting ice, which holds promise for improving the autonomy of marine vehicles, aiding in maritime missions, and gaining a deeper understanding of how melting Arctic sea ice affects marine ecosystems. Their conceptual design features a small waterplane area twin hull vessel that acts as a docking and charging station for autonomous underwater vehicles and unmanned aerial vehicles, using solar and turbine energy to enable continuous monitoring.
Inflammatory eye diseases can be challenging to treat, with medications like corticosteroids causing unwanted complications such as glaucoma. However, a study led by researchers from Fujita Health University, Japan, offers hope. The team discovered that local injections of mesenchymal stem cells into periocular tissue can significantly reduce inflammation in conditions such as chronic graft-versus-host disease. These findings offer hope for a safer, more targeted alternative to conventional treatments for ocular inflammatory diseases.
Artificial Intelligence of Things (AIoT) is becoming immensely popular because of its widespread applications. In a groundbreaking study, researchers from Incheon National University present a new AIoT framework called MSF-Net for accurately recognizing human activities using WiFi signals. The framework utilizes a novel approach that combines different signal processing techniques and a deep learning architecture to overcome challenges like environmental interference and achieve high recognition accuracy.