An international team of scientists, led by the University of Oxford, has achieved a world-first by creating plasma "fireballs" using the Super Proton Synchrotron accelerator at CERN, Geneva, to study the stability of plasma jets emanating from blazars. The results, published today (3 November) in PNAS, could shed new light on a long-standing mystery about the Universe’s hidden magnetic fields and missing gamma rays.

Five-axis machining plays a pivotal role in manufacturing complex industrial components. In a recent study, researchers introduced a surface-partitioning and iso-scallop field-based strategy tailored for non-spherical cutting tools. By adaptively dividing machining surfaces and generating smooth tool orientations, the method significantly shortens tool paths, reduces machining time, and improves surface quality. This breakthrough not only enhances industrial productivity but also provides a foundation for integrating intelligent optimization in smart manufacturing.

Teacher emotion recognition (TER) is crucial for classroom dynamics, yet limited by scarce multimodal data and feature modeling. We present a multimodal TER dataset with 102 lessons and 2,170 video segments, annotated with interaction-based emotional tags. We propose an Emotion Dual-Space Network (EDSN) with a Commonality Space (ECSC) using central moment differences to align modalities, and a Discrimination Space (EDSC) using gradient reversal and orthogonal projection to extract unique features. EDSN achieves 77.0% accuracy and 0.769 F1-score on the dataset, outperforming state-of-the-art models, demonstrating its effectiveness in educational emotion recognition.