Objective. Sparse-view computed tomography (SVCT), which can reduce the radiation doses administered to patients and hasten data acquisition, has become an area of particular interest to researchers. Most existing deep learning-based image reconstruction methods are based on convolutional neural networks (CNNs). Due to the locality of convolution and continuous sampling operations, existing approaches cannot fully model global context feature dependencies, which makes the CNN-based approaches less efficient in modeling the computed tomography (CT) images with various structural information. Approach. To overcome the above challenges, this paper develops a novel multi-domain optimization network based on convolution and swin transformer (MDST). MDST uses swin transformer block as the main building block in both projection (residual) domain and image (residual) domain sub-networks, which models global and local features of the projections and reconstructed images. MDST consists of two modules for initial reconstruction and residual-assisted reconstruction, respectively. The sparse sinogram is first expanded in the initial reconstruction module with a projection domain sub-network. Then, the sparse-view artifacts are effectively suppressed by an image domain sub-network. Finally, the residual assisted reconstruction module to correct the inconsistency of the initial reconstruction, further preserving image details. Main results . Extensive experiments on CT lymph node datasets and real walnut datasets show that MDST can effectively alleviate the loss of fine details caused by information attenuation and improve the reconstruction quality of medical images. Significance. MDST network is robust and can effectively reconstruct images with different noise level projections. Different from the current prevalent CNN-based networks, MDST uses transformer as the main backbone, which proves the potential of transformer in SVCT reconstruction.

In this work, a novel polarization-maintaining hollow-core fiber structure featuring a semi-circular nested dual-ring geometry is proposed. To simultaneously optimize two inherently conflicting performance metrics, namely, birefringence and confinement loss, a multi objective genetic algorithm is employed for geometric parameter tuning, resulting in a set of Pareto-optimal solutions. At the target wavelength of 1550 nm, the first optimal design achieves birefringence exceeding $1\times {10}^{-4}$ over a 1275 nm bandwidth while maintaining confinement loss around ${10}^0$ dB/m; the second design maintains birefringence above $1\times {10}^{-4}$ across a 1000 nm spectral range, with confinement loss on the order of ${10}^{-1}$ dB/m. These optimized designs offer a promising approach for improving the performance of polarization-sensitive applications such as interferometric sensing and high coherence laser systems. The results confirm the suitability of multi-objective genetic algorithms for integrated multi-objective fiber optimization and provide a new strategy for designing low-loss and high-birefringence fiber devices.

Wireless sensor networks provide a solution for structural health monitoring of aviation pipelines. In the installation environment of aviation pipelines, widespread vibrations can be utilized to extract energy through vibration energy harvesting technology to achieve self-powering of sensors. This study analyzed the vibration characteristics of aviation pipeline structures. The vibration characteristics and influencing factors of typical aviation pipeline structures were obtained through simulations and experiments. An N-shaped symmetric vibration energy harvester was designed considering the limited space in aviation pipeline structures. To improve the efficiency of electrical energy extraction from the vibration energy harvester, expand its operating frequency band, and achieve efficient vibration energy harvesting, this study first analyzed its natural frequency characteristics through theoretical analysis. Finite element simulation software was then used to analyze the effects of the external excitation acceleration direction, mass and combination of counterweights, piezoelectric sheet length, and piezoelectric material placement on the output power of the energy harvester. The structural parameters of the vibration energy harvester were optimized, and the optimal working conditions were determined. The experimental results indicate that the N-shaped symmetric vibration energy harvester designed and optimized in this study improves the efficiency of vibration energy harvesting and can be arranged in the limited space of aviation pipeline structures. It achieves efficient energy harvesting under multi-modal conditions, different excitation directions, and a wide operating frequency band, thus meeting the practical application requirement and engineering feasibility of aircraft design.

This work proposes a novel polarization-maintaining hollow-core anti-resonant fiber structure characterized by high birefringence and low transmission loss. To address the inherent trade-off between birefringence and confinement loss, a Pareto-front-based multi-objective optimization algorithm is introduced into the geometrical design of the ARF. The optimal fiber design achieves a birefringence exceeding $1\times {10}^{-4}$ and a confinement loss of approximately 1 dB/m at the telecommunication wavelength of 1.55 $\mathsf{\mu }$m. In particular, the asymmetric wall thickness configuration further improves the trade-off, enabling confinement loss as low as 0.15 dB/m while maintaining birefringence on the order of $1\times {10}^{-4}$. This approach significantly reduces computational cost and exhibits strong potential for applications in polarization-maintaining communications, precision sensing, and high-power laser delivery.