Scientists track evolution of pumice rafts after 2021 underwater eruption in Japan
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Updates every hour. Last Updated: 7-Jul-2025 12:10 ET (7-Jul-2025 16:10 GMT/UTC)
Groundbreaking study shows machine learning can decode emotions in seven ungulate species. A game-changer for animal welfare?
Can artificial intelligence help us understand what animals feel? A pioneering study suggests the answer is yes. Researchers from the Department of Biology at the University of Copenhagen have successfully trained a machine-learning model to distinguish between positive and negative emotions in seven different ungulate species, including cows, pigs, and wild boars. By analysing the acoustic patterns of their vocalisations, the model achieved an impressive accuracy of 89.49%, marking the first cross-species study to detect emotional valence using AI.
Professor Can Wang from Tianjin University and Professor Zhurui Shen from Nankai University have achieved significant results in their collaborative research. In this study, monolayer Ti3C2Tx was prepared by etching and exfoliating Ti₃AlC₂, and then TiO2/monolayer Ti3C2Tx (T/mT) was synthesized. The surface functional groups enhance the hydrophilicity and surface energy, and a Schottky heterojunction is formed with TiO2, which improves the photocatalytic activity. Meanwhile, the hybrid material can closely bind to Escherichia coli cells and has a high affinity for cell membrane proteins. Experiments show that it has a high charge separation and transfer efficiency, a strong photocurrent signal, and low impedance. In the photocatalytic reaction device, the sterilization efficiency of T/mT reaches 3.3 log in only 12.8 seconds, far exceeding that of TiO2. The various components and chemical bonds of cells have been damaged to varying degrees by active substances. This achievement points the way for the molecular structure design of photocatalytic air disinfection technology and is of far-reaching significance for promoting the progress of air disinfection technology.