From smartphones to medical devices, computer chips power nearly everything we use today. But hidden deep inside these chips, there’s a little-known threat: hardware trojans — malicious modifications to a chip’s design that can steal data, weaken security and sabotage systems. Traditionally, detecting hardware trojans has been an expensive, time-consuming and complicated process. Now, University of Missouri researchers are introducing a new artificial intelligence-driven method to find these threats faster and more easily than before, said Ripan Kumar Kundu, a doctoral candidate in Mizzou’s College of Engineering. In a project led by Kundu, Mizzou’s team is leveraging existing large language models — the same type of AI that powers popular chatbots — to scan chip designs for hidden threats. The method doesn’t just identify suspicious lines of code with 97% accuracy; it also explains why it’s malicious, making the process more transparent.

New research employing advanced machine learning techniques reveals that China's collateral monetary policy has significantly stimulated shadow banking growth while increasing bank risks. The study finds this policy creates liquidity distribution distortions, with non-primary banks being particularly vulnerable. Importantly, the 2018 New Asset Management Regulation effectively mitigated these effects, demonstrating the value of targeted regulatory interventions.

A Curtin University-led international study has solved the mystery of how the skin of a fossilised fish was able to be preserved for 52 million years, extending our understanding of how even the most delicate of biological material can survive deep time.