In this article, a multiple diffuse coding metasurface (MDCM) of independent polarization is designed to control the propagation direction of diffuse reflections under different polarizations and to improve the monostatic and bistatic RCS (radar cross section) reduction effect. First, a method for controlling the distribution range and propagation direction of the diffuse field is studied, and the diffuse field distribution of the random phase metasurface is optimized by a genetic algorithm to improve the uniformity of the diffuse scattering distribution. Then, the random phase distribution is superimposed on the periodic gradient phase distributions of the linear and hedge types in the orthogonal direction so that the main propagation direction of the diffuse metasurface deviates from the specular reflection region under different polarizations, showing single and two diffuse beams. Finally, the anisotropic unit cell with a rectangle inside and an improved Jerusalem cross on the outside is employed as the basic coding element of the MDCM due to its independent polarization phase response. The numerical and experimental results show that the MDCM features multiple diffuse scattering, independent polarization and angle insensitivity and can efficiently improve the monostatic and bistatic RCS reduction effect simultaneously. Because the scattered energies are redirected away from the specular reflection direction, the specular scattering reduction effect is better than the isotropic diffuse metasurface. The proposed method increases the difficulty of detection by single or netted radar and has the potential for the applications of stealth techniques.

Aero-engine blades are an integral part of the aero-engine, and the integrity of these blades affects the flight performance and safety performance of an aircraft. The traditional manual detection method is time-consuming, labor-intensive, and inefficient. Hence, it is particularly important to use intelligent detection methods to detect and identify damage. In order to quickly and accurately identify the damage of the aero-engine blades, the present study proposes a network based on the Improved Cascade Mask R-CNN network—to establish the damage related to the aero-engine blades and detection models. The model can identify the damage type and locate and segment the area of damage. Furthermore, the accuracy rate can reach up to 98.81%, the Bbox-mAP is 78.7%, and the Segm-mAP is 77.4%. In comparing the Improved Cascade Mask R-CNN network with the YOLOv4, Cascade R-CNN, Res2Net, and Cascade Mask R-CNN networks, the results revealed that the network used in the present is excellent and effective.

Ultrasonic non-destructive testing can effectively detect damage in aircraft composite materials, but traditional manual testing is time-consuming and labor-intensive. To realize the intelligent recognition of aircraft composite material damage, this paper proposes a 1D-YOLO network, in which intelligent fusion recognizes both the ultrasonic C-scan image and ultrasonic A-scan signal of composite material damage. Through training and testing the composite material damage data on aircraft skin, the accuracy of the model is 94.5%, the mean average precision is 80.0%, and the kappa value is 97.5%. The use of dilated convolution and a recursive feature pyramid effectively improves the feature extraction ability of the model. The effectively used Cascade R-CNN (Cascade Region-Convolutional Neural Network) improves the recognition effect of the model, and the effectively used one-dimensional convolutional neural network excludes non-damaged objects. Comparing our network with YOLOv3, YOLOv4, cascade R-CNN, and other networks, the results show that our network can identify the damage of composite materials more accurately.

Type II nuclear hormone receptors, including farnesoid X receptors (FXR), liver X receptors (LXR), and peroxisome proliferator-activated receptors (PPAR), which serve as drug targets for metabolic diseases, are permanently positioned in the nucleus and thought to be bound to DNA regardless of the ligand status. However, recent genome-wide location analysis showed that LXRα and PPARα binding in the liver is largely ligand-dependent. We hypothesized that pioneer factor Foxa2 evicts nucleosomes to enable ligand-dependent binding of type II nuclear receptors and performed genome-wide studies to test this hypothesis. ATAC-Seq was used to profile chromatin accessibility; ChIP-Seq was performed to assess transcription factors (Foxa2, FXR, LXRα, and PPARα) binding; and RNA-Seq analysis determined differentially expressed genes in wildtype and Foxa2 mutants treated with a ligand (GW4064 for FXR, GW3965, and T09 for LXRα). We reveal that chromatin accessibility, FXR binding, LXRα occupancy, and ligand-responsive activation of gene expression by FXR and LXRα require Foxa2. Unexpectedly, Foxa2 occupancy is drastically increased when either receptor, FXR or LXRα, is bound by an agonist. In addition, co-immunoprecipitation experiments demonstrate that Foxa2 interacts with either receptor in a ligand-dependent manner, suggesting that Foxa2 and the receptor, bind DNA as an interdependent complex during ligand activation. Furthermore, PPARα binding is induced in Foxa2 mutants treated with FXR and LXR ligands, leading to the activation of PPARα targets. Our model requires pioneering activity for ligand activation that challenges the existing ligand-independent binding mechanism. We also demonstrate that Foxa2 is required to achieve activation of the proper receptor - one that binds the added ligand - by repressing the activity of a competing receptor.