Qiyou Liu | Search arXiv

arXiv2026

Measurement of Nanoscale Surface Disturbances on Liquid Jets Using the Mie Scattering Method

The Plateau--Rayleigh instability of liquid jets governs a wide range of natural and industrial processes. While the classical linear stability theory accurately predicts disturbance growth rates and dominant wavelengths, it cannot quantify the amplitude and frequency spectrum of nanoscale initial surface disturbances. This critical missing information prevents accurate forecasting of breakup lengths and droplet generation. Conventional optical imaging is constrained by the diffraction limit and lacks the capability to resolve such nanoscale surface fluctuations. Here we introduce a Mie-scattering-based measurement technique to characterize nanoscale surface disturbances on liquid jets. The method employs a thin laser sheet to illuminate the jet, with scattered light captured at designated azimuthal observation angles. Based on Lorenz--Mie theory, we develop an inversion algorithm incorporating cosine similarity and cross-correlation to reconstruct jet surface disturbances from measured scattering fringes, achieving a sub-nanometer resolution for detecting diameter variations, with an overall measurement precision limited by a background noise floor of 0.18 nm RMS. Comparative analyses of forward and rainbow scattering signals further confirm that the surface disturbances become axially symmetric as the amplitude reaches a few nanometers, and thus satisfies the assumptions of the scattering model. Synchronous validations against high--speed imaging and scanning electron microscopy corroborate the quantitative accuracy of our scattering method. Applied in the near-nozzle region, the technique identifies two intrinsic signatures of initial perturbations: stochastic pulse-like fluctuations and low-frequency oscillations.

physics.flu-dyn↗

arXiv2026

Boosting Knowledge-based Visual Question Answering with Structured Context Reasoning

Knowledge-based Visual Question Answering aims to answer questions about an image by integrating external knowledge with visual and textual information. Recent approaches often rely on in-context learning to prompt Large Language Models (LLMs) with multimodal context in a zero-shot or few-shot manner. However, we observe that directly concatenating heterogeneous visual descriptions and retrieved knowledge into long, unstructured prompts often degrades reasoning performance, due to both excessive irrelevant context and the lack of explicit relational structure. In this paper, we propose an LLM-based Structured Context Reasoning (SCoRe) framework that infers both explicit and implicit relationships for prediction. SCoRe consists of three stages: Context Acquisition, which generates diverse visual notes and retrieves explicit knowledge via an efficient two-stage multimodal retrieval strategy; Context Selection, which filters relevant visual, explicit, and implicit knowledge using LLM-guided selection; and Context Compression, which performs Relational Logic Distillation (RLD) to transform raw text into explicit entity-relation triplets. These relational triplets serve as a concise and structured prompt for final answer prediction. Extensive experiments on the OK-VQA and A-OKVQA benchmarks demonstrate that SCoRe consistently outperforms state-of-the-art methods.

cs.CV↗