Researchers from the SNI network have discovered a novel way to fuse lipid vesicles at neutral pH. By harnessing a fragment of the diphtheria toxin, the team achieved vesicle membrane fusion without the need for pre-treatment or harsh conditions. Their work, recently published in Communications Chemistry, opens the door to new applications in lab-on-a-chip technologies, biosensors, and artificial cell prototypes.

Researchers have developed a graph-based expert system that improves the accuracy and prediction stability of automated bank reconciliation. By modelling historical transactional data as a network graph, the system can learn complex one-to-many matching scenarios that existing tools often fail to predict correctly. The findings point to more reliable automation for high-risk domains such as finance and accounting.

When labelled scans are scarce and hospitals collect images in different ways, a new training recipe developed by SUTD researchers helps segmentation AI keep its bearings across domains without needing more annotations.

Kyoto, Japan -- Predicting earthquakes has long been an unattainable fantasy. Factors like odd animal behaviors that have historically been thought to forebode earthquakes are not supported by empirical evidence. As these factors often occur independently of earthquakes and vice versa, seismologists believe that earthquakes occur with little or no warning. At least, that's how it appears from the surface.

Earthquake-generating zones lie deep within the Earth's crust and thus cannot be directly observed, but scientists have long proposed that faults may undergo a precursory phase before an earthquake during which micro-fracturing and slow slip occur. Yet, despite their obvious potential, exactly how these processes could enable prediction of a main shock remains unclear. Furthermore, observational studies have suggested that small and large earthquakes appear indistinguishable during the beginning of their rupture, raising doubts about the usefulness of short-term precursors.

These difficulties have prompted interest in the use of machine learning to search for potentially predictive fault signals. Machine learning models have demonstrated an ability to predict stick-slip laboratory earthquakes  in small, centimeter-scale experiments, but this approach has not yet been applied to larger, more complex systems that more closely mimic natural faults.