Industrial anomaly detection is crucial for maintaining quality control and reducing production errors, but traditional supervised models require extensive datasets. While embedding-based methods are promising for unsupervised anomaly detection, they are highly memory-intensive and unsuited to low-light conditions. In a new study, researchers developed a new unsupervised model that utilizes both well-lit and low-light images to achieve computationally efficient and memory-friendly industrial anomaly detection.

A research team led by Professor Jie Zeng and Associate Researcher Han Yan from the University of Science and Technology of China, in collaboration with Professor Chao Ma from Hunan University, has developed a novel ceria-supported platinum bilayer cluster catalyst. This breakthrough material demonstrates exceptional catalytic activity and stability in alkene hydrosilylation reactions while achieving atomic-level precision structural identification of the catalyst.

Fluorescent markers are extremely useful in science as tools to track molecules or processes as they carry out their unique activities, revealing unknown facts along the way. However, physically introducing fluorescent markers into targets can result in strong background signals, and even when chemically bound, the target’s hydrophobicity may increase, making the process far from straightforward. Moreover, fluorescent markers are often affected by the properties of the solvent in which they operate. To address these challenges, researchers have developed a method to track the behavior of cellulose nanofibers (CNFs) by conjugating water-compatible fluorescent amino acids to the CNFs. As a result, observers can now microscopically visualize CNFs by following the blue fluorescence emitted from them.

Researchers demonstrate a current-induced magnetization switching originated from weakly asymmetric topological surface states, without the need for injected spin current, in epitaxial MnSb2Te4 thin films. Additionally, they observe field-free switching in MnSb2Te4/FeTe0.9 heterostructures.