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FeCo2O4/MoS2 binary composite catalysts were prepared by the hydrothermal method and calcination method. In this paper, the morphology and structure of the materials were characterized by means of SEM, EDS, XRD, and XPS. It was found that MoS2 has high activity and good stability in HER, and and it has more prospect than noble metal catalysts. In oxygen evolution chemical kinetics, its rich redox potential allowed it to adsorb OH− on (Co2+/Co3+, Fe2+/Fe3+) and enhanced the activity of OER. The cross-nanosheet structure of the FeCo2O4/MoS2 composite catalyst exposed more catalytic sites and accelerated charge transfer to achieve more efficient mass transfer. FeCo2O4/MoS2 as an anode and cathode was assembled into a two-electrode system in overall water splitting, which showed good catalytic activity. When the composite ratio of FeCo2O4 to MoS2 was 1:0.3, the composite catalyst had the best catalytic activity. The results show that when FeCo2O4/MoS2 is used as a cathode and anode to assemble an alkaline cell, respectively, the voltage for total water electrolysis is 1.59 V at a current density of 10 mA cm−2 in a 1 M KOH electrolyte, it can keep good stability in a 10 h test with electrolyzed water, and its current retention rate is 98.5%.

The interactions between economic development, environmental sustainability, population growth, and urbanization are vital in assessing the ecological dynamics of urban agglomerations. This study explores the relationship between economic development, environmental sustainability, population growth, and urbanization within the Xia-Zhang-Quan urban agglomeration in Fujian Province from 1989 to 2022. Utilizing Landsat remote sensing images, we calculated the Remote Sensing Ecological Index (RSEI) to evaluate changes in ecological quality. The results show that the average RSEI values for 1989, 2000, 2010, and 2022 were 0.5829, 0.5607, 0.5827, and 0.6195, respectively, indicating an initial decline followed by a significant increase, culminating in an overall upward trend. The spatial distribution of RSEI classification shows that the study area has the largest proportion of mainly “good” ecological quality. The proportion of areas with “excellent” ecological environmental quality has increased (13.41% in 1989 and 25.12% in 2022), while those with “general” quality has decreased (28.03% in 1989 and 21.21% in 2022). Over the past three decades, Xiamen experienced substantial ecological degradation (RSEI change of −0.0897), Zhangzhou showed marked improvement (RSEI change of 0.0519), and Quanzhou exhibited slight deterioration (RSEI change of −0.0396). Central urban areas typically had poorer ecological conditions but showed signs of improvement, whereas non-central urban regions demonstrated significant environmental enhancement. The factor detector analysis identified land use as the dominant factor influencing ecological environmental quality, with precipitation having a relatively minor impact. Interaction analysis revealed that all other factors demonstrated bi-variable enhancement or nonlinear enhancement, suggesting that the interactive effects of these factors are greater than the effects of individual factors alone. Land use consistently showed solid explanatory power. Temperature also exhibited significant influence in 2022 when interacting with other factors. Due to urban planning that can plan for land use, these findings suggest that effective urban planning can harmonize economic development with ecological protection within the Xia-Zhang-Quan urban agglomeration.

With the rapid advancement of urban intelligence, there is an increasingly urgent demand for technological innovation in traffic management. License plate recognition technology can achieve high accuracy under ideal conditions but faces significant challenges in complex traffic environments and adverse weather conditions. To address these challenges, we propose the enhanced attention network and decoding for license plate recognition model (EAND-LPRM). This model leverages an encoder to extract features from image sequences and employs a self-attention mechanism to focus on critical feature information, enhancing its capability to handle complex traffic scenarios such as rainy weather and license plate distortion. We have curated and utilized publicly available datasets that closely reflect real-world scenarios, ensuring transparency and reproducibility. Experimental evaluations conducted on these datasets, which include various complex scenarios, demonstrate that the EAND-LPRM model achieves an accuracy of 94%, representing a 6% improvement over traditional license plate recognition algorithms. The main contributions of this research include the development of a novel attention-mechanism-based architecture, comprehensive evaluation on multiple datasets, and substantial performance improvements under diverse and challenging conditions. This study provides a practical solution for automatic license plate recognition systems in dynamic and unpredictable environments.

Water body extraction techniques from remotely sensed images are crucial in water resources distribution studies, climate change studies and other work. The traditional remote sensing water body extraction has the problems of low accuracy and being time-consuming and laborious, and the water body recognition technique based on deep learning is more efficient and accurate than the traditional threshold method; however, there is the problem that the basic model of semantic segmentation is not well-adapted to complex remote sensing images. Based on this, this study adopts an OCNet feature extraction network to modify the base model of semantic segmentation, and the resulting model achieves excellent performance on water body remote sensing images. Compared with the traditional water body extraction method and the base network, the OCNet modified model has obvious improvement, and is applicable to the extraction of water bodies in true-color remote sensing images such as high-score images and unmanned aerial vehicle remote sensing images. The results show that the model in this study can realize automatic and fast extraction of water bodies from remote sensing images, and the predicted water body image accuracy (ACC) can reach 85%. This study can realize fast and accurate extraction of water bodies, which is of great significance for water resources acquisition and flood disaster prediction.

Identifying the spatial changes in farming–pastoral ecotone (FPE) is of utmost importance for the development of strategies for ecological protection in ecologically fragile areas. This study employed spatial autocorrelation and spatial clustering techniques to map FPE at the pixel scale using CLCD data with a spatial resolution of 30 × 30 m in the years 1985, 2000, and 2021, and then analyzed the changes of the FPE in northern China. The results showed that the FPE is mainly located at the border between the Inner Mongolia Autonomous Region and the adjacent provinces, which is along the Hu-line and the 400-mm isohyetal line. The area of the FPE was 63.94 × 104 km2, 62.90 × 104 km2, and 53.81 × 104 km2 in 1985, 2000, and 2021, respectively, accounting for 6.7%, 6.6%, 5.6% of the total land area in China. The FPE boundary moved northwestward during 1985–2021, demonstrating retreating, fragmenting, and shrinking tendencies. The decreased areas and the moving distances of the gravity center are six times and four times greater during 2000–2021 than that during 1985–2000, respectively. Moreover, the discontinuous change in FPE was mainly due to the increase in forest land, especially for the conversion of grassland to forest land. Our findings provide guidance for the construction of ecological civilization and the optimization of ecosystem structure in the farming–pastoral ecotone.

Auxin/indoleacetic acid (Aux/IAA) proteins play an important regulatory role in the developmental process of plants and their responses to stresses. A previous study has shown that constitutive expression of OsIAA18, an Aux/IAA transcription factor gene of rice improved salt and osmotic tolerance in transgenic Arabidopsis plants. However, little work is known about the regulatory functions of the OsIAA18 gene in regulating the abiotic stress tolerance of rice. In this study, the OsIAA18 gene was introduced into the rice cultivar, Zhonghua 11 and the OsIAA18 overexpression in rice plants exhibited significantly enhanced salt and drought tolerance compared to the wild type (WT). Moreover, overexpression of OsIAA18 in rice increased endogenous levels of abscisic acid (ABA) and the overexpression of OsIAA18 in rice plants showed hypersensitivity to exogenous ABA treatment at both the germination and postgermination stages compared to WT. Overexpression of OsIAA18 upregulated the genes involved in ABA biosynthesis and signaling pathways, proline biosynthesis pathway, and reactive oxygen species (ROS)-scavenging system in the overexpression of OsIAA18 in rice plants under salt and drought stresses. Proline content, superoxide dismutase (SOD), and peroxidase (POD) activities were significantly increased, whereas malonaldehyde (MDA), hydrogen peroxide (H2O2), and superoxide anion radical (O2–) content were significantly decreased in the transgenic plants under salt and drought stresses. Taken together, we suggest that OsIAA18 plays a positive role in drought and salt tolerance by regulating stress-induced ABA signaling. The OsIAA18 gene has a potential application in genetically modified crops with enhanced tolerance to abiotic stresses.