A review by researchers from Shandong University and collaborating institutions argues that making virtual objects look real is no longer enough; the next advance in virtual reality (VR) and augmented reality (AR) will require making them feel real as well. Published in Materials Futures, the article proposes a closed-loop "human-machine-environment" haptic interaction framework that links tactile sensing, intelligent processing, and physical feedback into a single system for more realistic digital interaction.A review by researchers from Shandong University and collaborating institutions argues that making virtual objects look real is no longer enough; the next advance in virtual reality (VR) and augmented reality (AR) will require making them feel real as well. Published in Materials Futures, the article proposes a closed-loop "human-machine-environment" haptic interaction framework that links tactile sensing, intelligent processing, and physical feedback into a single system for more realistic digital interaction.

A new study led by Flinders University has flipped the historic narrative about Australia’s thylacine, finding the native apex predator known as the ‘Tasmanian tiger’ was significantly different from the wolves and wild dogs it was likened to before being hunted to extinction.   

In-depth investigations on historic thylacine (Thylacinus cynocephalus) skull specimens indicate their jaws were probably unsuited to holding down large animals like sheep – instead their long, deep jaw would have been able to subdue relatively small prey with powerful snapping bites.

A new review brings together research on geopolitical tensions, meteorological disasters, regional environmental degradation, supply chain disruption, systemic financial instability, and technology-mediated societal and institutional risks. It shows how shocks can spread through interconnected political, economic, ecological, technological, and social systems, and outlines a flexible cycle of risk identification, assessment, management, and adaptive monitoring supported by international cooperation.

Medical imaging datasets are essential for training artificial intelligence systems, but sharing brain magnetic resonance imaging (MRI) scans can expose patient-specific anatomical patterns. Anonymization must therefore do more than remove identifiers: it must make the overall image harder to link back to a patient while retaining the lesion information needed for segmentation and classification. Existing generative approaches often emphasize global image quality or facial de-identification, leaving lesion-centric utility and re-identification risk insufficiently addressed.