Adelaide University researchers have developed an innovative wearable glove that uses heat, touch and physical objects to transform data into a sensory experience.

A publication led by researchers from the Max Planck Institute for Marine Microbiology in Bremen, Germany, shows that microscopic partnerships between ciliates and bacteria play a role in the nitrogen cycle of lakes. The study, published in The ISME Journal, investigates what determines the ecological niche of the remarkable symbiosis, and how strongly the host depends on its microbial partners.

A research team led by Senior Researcher Hyeong-U Kim of the Semiconductor Manufacturing Research Center at the Korea Institute of Machinery and Materials (President Seok-Hyeon Ryu, hereinafter referred to as KIMM) developed synthesis and etching processes for 6-inch next-generation 2D semiconductors (MoS₂ and WS₂) using low-temperature plasma-based PECVD and RIE equipment and implemented them into an AI-based intelligent system.

An RMIT innovation uses ultrasound to extract leaf protein from discarded cauliflower leaves, identifying a potential new use for vegetable scraps.

Idiopathic pulmonary fibrosis remains a hard-to-treat lung disease with limited effective drugs. A recent study in Engineering used machine learning to screen natural compounds and found dihydromyricetin, a flavonoid from herbs, can block the TGF-β/ALK5 signaling pathway. It alleviates fibrosis and inflammation in cell and mouse models, shows good safety, and offers a new natural-product-based direction for pulmonary fibrosis treatment.

Cardiac hypertrophy often progresses to heart failure, but the underlying link between lysosomes and mitochondria remains unclear. A new study in Engineering finds that the lysosomal protein TRPML1 is significantly reduced in hypertrophic hearts. It directly binds VDAC1 to maintain mitochondrial calcium balance and energy production, providing a potential target for treating pathological cardiac remodeling.

Hydraulic fracturing is moving from experience-based operations to intelligent control. A new study in Engineering introduces an AI-driven framework for subsurface hydraulic fracturing, featuring data-driven analysis, dynamic optimization, and autonomous decision-making. It uses dedicated deep learning models to predict fractures, warn of risks, and forecast production, forming a closed-loop intelligent system. The work also points out current challenges and future directions, supporting the digital shift in unconventional oil and gas development.