Advances in precision oncology necessitate tools capable of effectively imaging and treating deep-seated tumours. Radioactive quantum dots (RQDs) fulfil this requirement by creating self-luminescent probes through the unique fusion of quantum dots’ (QDs) tunable optical properties with the radiological characteristics of radioactive isotopes. This design enables multimodal imaging and synergistic therapy within deep tissues, overcoming the penetration depth limitations of conventional light sources.

SJTU researchers propose a dynamic MCPV model and CZone mechanism, slashing memory footprint by 90%.

In a paper published in SCIENCE CHINA Earth Sciences, a team led by Professor Chuanfeng Zhao from Peking University presents a comprehensive review of global sea-land breeze research. The study systematically synthesizes recent advances in identification methods, key observational benchmarks, and the complex meteorological and non-meteorological factors that govern these coastal wind systems. The authors highlight that future breakthroughs will depend on improving spatiotemporal precision and clarifying how aerosols influence sea-land breeze dynamics through micro-physical mechanisms.

Waveguide-based electro-optic (E-O) frequency combs are prized for their flexible pump wavelength, tunable repetition rate and near-100% conversion efficiency, but their spectral span is usually limited to a few nanometers. Researchers now report a broadband waveguide E-O comb based on "mode circulation": a single beam is recirculated through the same phase-modulation region eight times as four distinct waveguide modes (TE0–TE3). Enabled by a Z-propagation mode multiplexer that suppresses mode hybridization in anisotropic lithium-niobate-on-insulator waveguides, the device boosts the modulation index by ~7.5×, generating 128 comb lines across 25.6 nm at 25 GHz with an effective half-wave voltage of just 1.24 V and only 7 dB on-chip loss.

A new study published in the Chinese Journal of Aeronautics （https://doi.org/10.1016/j.cja.2025.103943） tries to break this deadlock. Researchers from Northwestern Polytechnical University and Tsinghua University have developed a novel modeling framework called differential equation model long-short term memory neural network (DEM-LSTM). This hybrid approach integrates the theoretical rigor of differential equations with the nonlinear mapping capabilities of LSTM neural network, offering a solution that is both accurate and physically interpretable.