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The globally increasing frequency of extreme drought events exacerbates the contradiction between the supply of water and the demand for high-quality urban greening. However, the mechanism of the response of urban shrubs to drought stress remains unclear. In this study, three typical urban shrubs (Euonymus japonicus, golden vicary [Ligustrum × vicaryi], and Japanese purple barberry [Berberis thunbergii var. atropurpurea]) that are used for greening in northern China were exposed to three levels of water (full irrigation, natural rain-fed, and extreme drought) in different periods of the growing season (April to May, June to July, and August to September) to investigate the responses of leaf water potential and photosynthetic parameters. The main results were as follows: (1) all the leaf water potentials (Ψ) and photosynthetic parameters (Pn) showed a typical linear relationship along the water gradient in the middle of the growing season. Extreme drought decreased the photosynthetic rates by 1.26~11.03 μmol·m−2·s−1 compared with the irrigated groups. However, the responses were less pronounced in the early and late growing seasons. (2) Different shrubs responded with different intensities and mechanisms. B. thunbergii var. atropurpurea showed clear anisohydric behavior throughout the whole growing season, while L. × vicaryi and E. japonicus showed stronger isohydric behavior during the early and late growing seasons. These findings are important to improve the sustainability of maintenance of ornamental plants from the scope of the efficient utilization of urban water resources. [ABSTRACT FROM AUTHOR]

The development of green spaces in urban parks can significantly enhance the quality of the urban and ecological environment. This paper utilizes 2021 Gaofen-7 (GF-7) satellite remote sensing images as its primary data source and uses deep learning algorithms for the precise extraction of the green space coverage within Beijing's fifth ring road. It also incorporates the park points of interest (POI) information, road data, and other auxiliary data to extract green park space details. The analysis focuses on examining the relationship between supply and demand in the spatial allocation of green park spaces from an accessibility perspective. The main findings are as follows: (1) The application of deep learning algorithms improves the accuracy of green space extraction by 10.68% compared to conventional machine methods. (2) The distribution of parks and green spaces within the fifth ring road of Beijing is uneven, showing a clear pattern of "more in the north and less in the south". The accessibility within a five-minute service radius achieves a coverage rate of 46.65%, with a discernible blind zone in the southeast. (3) There is an imbalance in the per capita green space location entropy within the fifth ring road of Beijing, there is a big difference in per capita green space location entropy (44.19), and social fairness needs to be improved. The study's outcomes unveil the intricate relationship between service capacity and spatial allocation, shedding light on the supply and demand dynamics of parks and green spaces within Beijing's fifth ring road. This insight will contribute to the construction of ecologically sustainable and aesthetically pleasing living spaces in modern megacities. [ABSTRACT FROM AUTHOR]

The composite bolted joints are the weak points of overall structure, lacking an effective and accurate monitoring method. In this paper, a novel MXene/CNTs film sensor with high sensitivity and stability was synthesized for the damage evolution monitoring of composite bolted joints. The correlation between the response signals of the sensors and the mechanical behaviors of the composite structures is established. Based on this research, the damage evolution monitoring and failure level evaluation of composite bolted joints were studied. The damage properties of the composite bolted joints can be reflected in real‐time by the resistance change rates of the sensors. The experimental results show that the failure level of composite bolted joints can be divided into four levels (I‐II‐III‐IV) according to the resistance change rates of sensors. Especially when the damage extended to the III stage, the ∆R/R0 surged from 0.3 to 0.75. The time point of this step signal occurred about 1 min earlier than the time point of max bearing strength, which can effectively warn the occurrence of structural failure. Highlights: Research used high‐sensitivity MXene/CNTs sensor to monitor bolt structureSensor array can be integrated with the composite bolted structureStructural damage of composite bolted joints was accurately monitoredFailure level evaluation of composite bolted structure was achieved [ABSTRACT FROM AUTHOR]