Background: X-ray computed tomography (CT) imaging technology provides high-precision anatomical visualization of patients and has become a standard modality in clinical diagnostics. A widely adopted strategy to mitigate radiation exposure is sparse-view scanning. However, traditional iterative approaches require manual design of regularization priors and laborious parameter tuning, while deep learning methods either heavily depend on large datasets or fail to capture global image correlations. Methods: Therefore, this paper proposes a combination of model-driven and data-driven methods, using the ADMM iterative algorithm framework to constrain the network to reduce its dependence on data samples and introducing the CNN and Transformer model to increase the ability to learn the global and local representation of images, further improving the accuracy of the reconstructed image. Results: The quantitative and qualitative results show the effectiveness of our method for sparse-view reconstruction compared with the current most advanced reconstruction algorithms, achieving a PSNR of 42.036 dB, SSIM of 0.979, and MAE of 0.011 at 32 views. Conclusions: The proposed algorithm has effective capability in sparse-view CT reconstruction. Compared with other deep learning algorithms, the proposed algorithm has better generalization and higher reconstruction accuracy.

CsI(Tl) scintillation screens have been widely applied in X-ray nondestructive testing (NDT) because of their low cost, high scintillation efficiency, and fluorescence guide columnar microstructures. The effect of humidity exposure on the microstructure and photoluminescence properties of polycrystalline CsI(Tl) screens was investigated in this work. The results indicate that the grain diameter of the columnar microstructure increased with increased exposure time in a humid environment. The degree of anisotropy of polycrystalline screens changed from (211) and (310) to (110) orientation with exposure time. FT-IR Spectra of the exposed screens peaked at 3734 cm⁻¹, 3710 cm⁻¹, 3676 cm⁻¹, 3647 cm⁻¹, 3627 cm⁻¹, 3598 c⁻¹, and 3566 cm⁻¹ due to the symmetrical and asymmetrical stretching vibrations of water molecules. In addition, the photoluminescence properties of exposed screens decreased with increased humidity exposure time due to the deliquescence caused by water molecules. After hours of humidity exposure, the photoluminescence and imaging properties of the screens decrease obviously and tend to reduce slowly. The moisture absorption and deliquescence would directly affect the service reliability and the storage lifetime of polycrystalline CsI(Tl) screens.

With the rapid development of multispectral detection technology, realizing the synergistic camouflage and thermal management of materials in multi-band has become a major challenge. In this paper, a multifunctional radiation-selective hierarchical metamaterial (RSHM) is designed to realize the modulation of optical properties in a wide spectral range through the delicate design of microstructures and nanostructures. In the atmospheric windows of 3–5 μm and 8–14 μm, the emissivity of the material is as low as 0.14 and 0.25, which can effectively suppress the radiation characteristics of the target in the infrared band, thus realizing efficient infrared stealth. Simultaneously, it exhibits high emissivity in the 2.5–3 μm (up to 0.80) and 5–8 μm (up to 0.98) bands, significantly improving thermal radiation efficiency and enabling active thermal management. Notably, RSHM achieves low reflectivity at 1.06 μm (0.13) and 1.55 μm (0.005) laser wavelengths, as well as in the 8–14 μm (0.06) band, substantially improving laser stealth performances. Additionally, it maintains high transmittance in the visible light range, ensuring excellent visual camouflage effects. Furthermore, the RSHM demonstrates exceptional incident angle and polarization stability, maintaining robust performances even under complex detection conditions. This design is easy to expand relative to other frequency bands of the electromagnetic spectrum and holds significant potential for applications in military camouflage, energy-efficient buildings, and optical devices.

Traditional evolutionary algorithms require a large number of computations for fiber optimization. This paper proposes an enhanced radial basis function-based global and local optimization algorithm to improve efficiency. The algorithm incorporates a surrogate model-based pre-screening module, which integrates both global and local models to improve the exploration and exploitation of the search space. It also employs a local model-assisted search strategy to refine promising regions. A comparative analysis with several other optimization methods is provided. To optimize two low-loss, polarization-maintaining fiber structures, the algorithm needs just 100 evaluations per model, decreasing the computational effort by at least an order of magnitude compared to conventional methods. The best-achieved birefringence is 1 × 10⁻⁴, with a loss of 0.3 dB/m.

This paper employs five different general-purpose optimization methods to perform parameter optimization on single-layer hollow antiresonant fibers. It provides guidance on the establishment of hyperparameters for various optimization methods, with the aim of further defining and standardizing the necessary conditions and convergence criteria for applying optimization algorithms to specialty optical fibers. Through numerical experiments, the study ultimately obtains the converged optimal performance and the range of optimized parameter guidance for single-layer, double-layer, and triple-layer antiresonant fibers with different topological structures.