Benzoic acid and its derivative benzoate are widely utilized as food preservatives. This paper presents a highly sensitive method for detecting benzoic acid using a cross-shaped aluminum resonator integrated on top of a polyimide substrate. The resonance dip for benzoic acid was designed to be at 0.93 THz. The terahertz (THz) transmission spectrum was simulated using a designed structure with varying thicknesses of benzoic acid. Analysis of the spectral response of the THz resonance dips to the thickness of the benzoic acid layer shows that the amplitude of the resonance dips generally increases with an increase in the benzoic acid concentration. The spectral frequency shifts and transmission intensities both demonstrate high sensitivity of the metasurface sensor to trace benzoic acid thicknesses. The sensor exhibits a sensitivity of approximately 0.247 THz/RIU (Refractive Index Unit) and FOM (figure of merit) of 3.927/RIU. The THz metasurface sensor is a fast and accurate sensing tool that can detect trace substances, making it crucial in the field of food safety.

We present a technique that utilizes cascaded resonant cylindrical piezoelectric ceramics and multimode optical fibers wound around them to effectively mitigate laser speckle. By precisely driving the ceramics at their resonant frequencies and inducing comprehensive mode scrambling within the multimode fiber, we achieve a remarkable speckle suppression efficiency of up to 94%. To the best of our knowledge, this sets a new benchmark among various methods aimed at suppressing the speckle of a coherent light. Our study thoroughly explores variables influencing efficiency, including the cascading number of piezoelectric ceramics, driving voltage, fiber core diameter, and more. This method has significant promise for diverse applications that require efficient and fast control of speckle contrast.

High-sensitivity microwave sensing plays a vital role in material characterization and nondestructive testing, with its performance being largely determined by the quality factor (Q factor) of the sensing structure. In this work, a high-Q microwave metasurface sensor based on the mechanism of bound states in the continuum (BIC) is designed and realized to overcome the intrinsic Q-factor limitations of conventional microwave resonators. By introducing a controlled asymmetric perturbation into the meta-atom, a quasi-BIC mode is successfully excited, and its sensing performance is systematically investigated through frequency-domain simulations. The results indicate that the proposed metasurface achieves an exceptionally high radiation Q factor of up to 4599.7 in the microwave band, along with a refractive index sensitivity of 31.267 GHz/RIU. These findings not only demonstrate the significant potential of the BIC mechanism for achieving ultra-high-Q microwave resonators but also provide an effective and promising approach for the development of high-performance microwave sensing systems.

Hyperspectral image classification (HSI) requires a model to distinguish subtle spectral differences while preserving the spatial structure of land-cover regions. CNN-based methods are effective for local spectral–spatial extraction, but their limited receptive fields can weaken broader context modelling. Transformer-based methods improve long-range dependency modelling, yet fixed patch partitioning may reduce their sensitivity to fine local structures. To address these limitations, this study proposes the Dual-Stream Spatial–Spectral Network with Nested Attention (DSSN), which separates local spectral–spatial feature extraction from multi-scale spatial-context modelling before adaptive fusion. The DSSN combines a cascaded 3D-CNN spectral stream, a nested Transformer spatial stream with pixel-level and patch-level interactions, and a channel-attention-based adaptive fusion module. Experiments on Indian Pines, Pavia University and Salinas show DSSN achieves overall accuracies of 98.11%%, 99.88% and 99.82%, respectively, outperforming other baselines. The ablation experiments confirm that each major component contributes to the final performance. Although the model requires more parameters and longer inference time than several compared baselines, its inference time remains at the millisecond level. These results suggest that decoupled spatial–spectral representation and adaptive multi-scale fusion can improve hyperspectral image classification under the evaluated benchmark settings.