Clues contained in tree rings have identified mid-14^th-century volcanic activity as the first domino to fall in a sequence that led to the devastation of the Black Death in Europe.

Terahertz (THz) communication has emerged as one of the key technologies for sixth-generation (6G) wireless networks. Nevertheless, the transition to higher operational frequencies poses various challenges including high-speed digital-to-analog conversion (DACs) and analog-to-digital conversion (ADCs), heterogeneous integration of optoelectronic devices, resulting in an urgent need for solutions. In this paper, we demonstrate a groundbreaking THz analog differential operator driven by diffractive neural networks (DNN), implementing ultra-fast and high-throughput analog domain differential operations. The designed multilayer all-optical DNN composed of compact dielectric metasurfaces is trained with trigonometric functions to perform analog differential computing of complex input signals by approximating the differentiation of finite decompositions of time-domain function based on the Fourier transform theory, significantly improving integration, throughput, and processing speed. Our design has been experimentally validated to successfully implement single-direction differential operation on one- and two-dimensional signals with superior structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR), providing a promising path for the development of integrated and ultrafast THz communication systems.

As a data-driven analysis and decision-making tool, student portraits have gained significant attention in education management and personalized instruction. This research systematically explores the construction process of student portraits by integrating knowledge graph technology with advanced data analytics, including clustering, predictive modelling, and natural language processing. It then examines the portraits’ applications in personalized learning, such as student-centric adaptation of content and paths, and personalized teaching, especially the educator-driven instructional adjustments. Through case studies and quantitative analysis of multimodal datasets, including structured academic records, unstructured behavioural logs, and socio-emotional assessments, the research demonstrates how student portraits enable academic early warnings, adaptive learning path design, and equitable resource allocation. The findings provide actionable insights and technical frameworks for implementing precision education.