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Heterogeneous synthetic aperture radar (SAR) images contain more complementary information compared with homologous SAR images; thus, the comprehensive utilization of heterogeneous SAR images could potentially improve performance for the monitoring of sea surface objects, such as sea ice and enteromorpha. Image registration is key to the application of monitoring sea surface objects. Heterogeneous SAR images have intensity differences and resolution differences, and after the uniform resolution, intensity differences are one of the most important factors affecting the image registration accuracy. In addition, sea surface objects have numerous repetitive and confusing features for feature extraction, which also limits the image registration accuracy. In this paper, we propose an improved L2Net network for image registration with intensity differences and repetitive texture features, using sea ice as the research object. The deep learning network can capture feature correlations between image patch pairs, and can obtain the correct matching from a large number of features with repetitive texture. In the SAR image pair, four patches of different sizes centered on the corner points are proposed as inputs. Thus, local features and more global features are fused to obtain excellent structural features, to distinguish between different repetitive textural features, add contextual information, further improve the feature correlation, and improve the accuracy of image registration. An outlier removal strategy is proposed to remove false matches due to repetitive textures. Finally, the effectiveness of our method was verified by comparative experiments.

The quality governance level of an industry is a multi-index evaluation problem that must consider multiple dimensions and factors. This study is the first to construct a comprehensive quality governance evaluation model for the civil aircraft industry of China (CAIC). The index system for the quality governance evaluation of CAIC was established using a literature review, enterprise investigation, expert interviews, and questionnaire surveys. An Analytic Hierarchy Process (AHP) was employed to determine index weights. Based on the evaluation model, data from 53 aviation manufacturing enterprises were collected, and the quality governance level of the CAIC was empirically evaluated; thus, quantitative and qualitative evaluation results were obtained. This empirical study shows that the quality governance of the CAIC is currently at a “medium to low” level. Furthermore, critical factors and bottleneck indices restricting the quality governance level of the CAIC were identified.

Plastic straws are well-known tools to assist human beings in drinking fluid, but most of them have micro-defects including black spot defects, head problems, pressure tube defects, and sealing wrinkles. The manual detection of these defects has drawbacks such as low efficiency, a high false detection rate, and excessive labor. This paper proposed machine vision-based detection with self-adaption and high-accuracy characteristics. A serial synthesis of algorithms including homomorphic filtering, Nobuyuki Otsu, and morphological opening operations is proposed to obtain plastic straws with binary images with good performance, and it was further found that the convolutional neural network can be designed to realize the real-time recognition of black spot defects, where the corner detection algorithm demonstrates the linear fitting of the edge point of the straw with the effective detection of sealing wrinkle defects. We also demonstrated that the multi-threshold classification algorithm is used to detect defects effectively for head problems and pressure tube defects. The detection system based on machine vision successfully overcomes shortcomings of manual inspection, which has high inspection efficiency and adaptively detects multiple defects with 96.85% accuracy. This research can effectively help straw companies achieve high-quality automated production and promotes the application of machine vision in plastic straw defects with the aid of machine learning.

Volatile organic compounds (VOCs) are the main precursors of pollution from ground ozone (O3) and PM2.5, which cause the deterioration of urban air quality. The emissions of VOCs from industrialized areas are significant and their characteristics are complex, which nowadays contribute significantly to the challenges of investigating the emission inventory. Taking a typical industrialized area in Tianjin as a case study, the anthropogenic VOCs emission inventory for 2020 was established in this study by using the activity data from a large-scale survey and the latest emission factors. The impact of VOCs on the environment was analyzed from the perspective of the combined control of PM2.5 and O3. The results showed that the total emission of VOCs in 2020 was about 1.68 Gg, mainly from industrial processes and mobile sources, which accounted for 38.4% and 36.5% of the total emissions, respectively. The top 10 emitted VOCs were toluene, acetone, ethylbenzene, m/p-xylene, i-pentane, n-hexane, formaldehyde, benzene, ethyl acetate and ethylene. The dominant species of O3 formation potential (OFP) were almost all aromatic hydrocarbons and alkenes, with m/p-xylene contributing the most to the OFP emissions (8.90%). The top 10 secondary organic aerosols formation potential (SOAP) emission species were aromatic hydrocarbons and long-chain alkanes, and the largest emission came from toluene (39.9%). An analysis of an ADMS diffusion model showed that VOCs emitted from traffic-heavy main roads and industrialized central areas had the greatest impact on the air quality in the surrounding areas. The VOCs concentration was higher in winter due to unfavorable meteorological conditions. Our research updated the VOC inventory of industrialized areas and evaluated VOCs species reactivity and their impact on ambient air quality.

Green and low-carbon strategies represent governance orientations for resource-based cities to respond to global changes and achieve sustainable development. Designating the Yellow River Basin (YRB), an important ecological functional area and an ecologically fragile area, as the research area, this study used the super-efficiency SBM model while considering undesirable outputs, including carbon emissions, to analyze green land use efficiency (GLUE) and its temporal and spatial differentiation, then used the Tobit regression model to analyze the influencing factors. The results were as follows: (1) The GLUE of the YRB presented a spatial pattern of “high in the west and low in the east”. (2) Overall, the efficiency values of all areas and types increased annually, but differences occurred in various areas and types of resource-based cities. (3) Overall, the efficiency values of the Yellow River Basin showed a “high-low” polarization. (4) Economic development and population growth factors substantially impacted the GLUE of resource-based cities in this region. It is concluded that increasing the efficiency improvement of low-efficiency regions or cities can improve regional GLUE. To ensure regional green and low-carbon transformation and development, it is essential to enhance urban economic vitality, promote an orderly population flow, and strive to improve social and public services.

This study investigates the reflectance of the defective mode (DM) and the lateral shift of reflected beam in defective photonic crystals incorporated with single-layer graphene by the transfer matrix method (TMM). Graphene, treated as an equivalent dielectric with a thickness of 0.34 nm, was embedded in the center of a defect layer. The reflectance of the DM was greatly enhanced as the intraband transition of electrons was converted to an interband transition in graphene. The reflectance of the DM could be further enhanced by increasing the Bragg periodic number. Furthermore, a large lateral shift of the reflected beam could also be induced around the DM. This study may find great applications in highly sensitive sensors.