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. 

Human health is the Achilles heel of space travel. Researchers at ETH Zurich have now succeeded in printing complex muscle tissue in zero gravity, via parabolic flight experiments. Their work paves the way for beyond-Earth health solutions and testing for future space missions.

Recently, a research team led by Gengjie Jia at the Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, in collaboration with Yu Li at The Chinese University of Hong Kong, Xin Gao at King Abdullah University of Science and Technology, and Andrey Rzhetsky at The University of Chicago, published an article in Quantitative Biology titled “An effective encoding of human medical conditions in disease space provides a versatile framework for deciphering disease associations.” The study presents an embedding-based approach that encodes medical records into a high-dimensional disease space, providing a versatile framework for uncovering disease associations, facilitating genetic parameter estimation, and enabling data-driven disease classification. The authors also discuss the key challenges and future prospects of this emerging paradigm.

A practical, evidence-based checklist developed by scientists at the University of Surrey is helping everyone from keen gardeners to local councils plan their next greening project with confidence.