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Recently emerging perovskite nanocrystals (PNCs) have drawn considerable attention due to the superior optoelectronic performances. However, the terrible stability of red emissive PNCs (r‐PNCs) limits their broader application in aqueous media. Despite various encapsulation methods are proposed, the preparation of aqueous r‐PNCs with facile operation and excellent durability is still challenging. Herein, aqueous green emissive PNCs (g‐PNCs) by employing water as an external stimulus to treat oleyamine‐capped CsPbBr3 PNCs are synthesized initially, and then aqueous‐stable pure red emissive PNCs by anion‐exchange reaction and subsequently thiocyanate (SCN‐) passivation treatment (SCN‐r‐PNCs) are prepared. The obtained aqueous‐stable SCN‐r‐PNCs display high brightness, outstanding stability, and extended photoluminescence lifetime. Moreover, by combining aqueous r‐PNCs and portable smartphones, a fluorescence chemosensor for the detection of aqueous SCN− ions is constructed, which exhibits simple operation, high sensitivity, high selectivity, low detection limit, and visualized sensing. This work not only provides a new and facile strategy to prepare aqueous‐stable red emissive PNCs, but also further extends the application of red emissive PNCs in aqueous‐based fields. [ABSTRACT FROM AUTHOR]

This study investigates the structural characteristics of SFRC with different amounts of steel fibers following exposure to freeze–thaw cycles, while taking into account various levels of confinement pressure and rates of deformation. The focus of the research is to examine the dynamic mechanical properties of SFRC exposed to freeze–thaw cycles. The inclusion of steel fibers improves the strength of concrete during freeze–thaw cycles, with 1% steel fiber content being the most effective. Strain rate and confining pressure significantly impact the strength and failure mode of SFRC. The strength of concrete increases linearly with the strain rate. With no confining pressure, the cracks in the concrete specimen align with the direction of the applied stress. And with confining pressure, concrete exhibits diagonal shear failure. Microstructural analysis results from scanning electron microscopy are consistent with the macroscopic properties. [ABSTRACT FROM AUTHOR]

To investigate the impact of freeze–thaw damage on the mechanical properties of concrete, this study utilized Python in combination with ABAQUS 2016 to generate a two-dimensional meso-scale model of concrete. Uniaxial compression tests were conducted on the concrete after freeze–thaw cycles to study the evolution of its mechanical properties. Using "relative compressive strength" as a variable, the relationships between this variable and the parameters of the freeze–thaw damage model were determined, leading to the establishment of the freeze–thaw damage model and the simulation of compressive tests on concrete after freeze–thaw cycles. This study also explored the changes in the mechanical properties of concrete with variations in ITZ parameters and coarse aggregate content. The conclusions drawn are as follows: A comparison with experimental data showed that the model ensures that the relative error of each mechanical property parameter does not exceed 7%, verifying the model's rationality. Increasing the ratio of ITZ parameters improved the mechanical properties of the ITZ, enhancing the overall mechanical performance, but had almost no effect on the elastic modulus. Compared to ratios of 0.7 and 0.8, concrete with a ratio of 0.9 showed slower rates of decrease in compressive strength and elastic modulus and slower rates of increase in peak compressive strain after freeze–thaw cycles. The increase in coarse aggregate content had a similar effect on the strength and freeze–thaw resistance of concrete as the ratio of ITZ parameters. Concrete with a coarse aggregate content of 60% exhibited slower rates of change in mechanical properties after freeze–thaw cycles. [ABSTRACT FROM AUTHOR]

LiDAR-unmanned aerial system (LiDAR-UAS) technology can accurately and efficiently obtain detailed and accurate three-dimensional spatial information of objects. The classification of objects in estuarine areas is highly important for management, planning, and ecosystem protection. Owing to the presence of slopes in estuarine areas, distinguishing between dense vegetation (lawns and trees) on slopes and the ground at the tops of slopes is difficult. In addition, the imbalance in the number of point clouds also poses a challenge for accurate classification directly from point cloud data. A multifeature-assisted and multilayer fused neural network (MLF-PointNet++) is proposed for LiDAR-UAS point cloud classification in estuarine areas. First, the 3D shape features that characterize the geometric characteristics of targets and the visible-band difference vegetation index (VDVI) that can characterize vegetation distribution are used as auxiliary features to enhance the distinguishability of dense vegetation (lawns and trees) on slopes and the ground at the tops of slopes. Second, to enhance the extraction of target spatial information and contextual relationships, the feature vectors output by different layers of set abstraction in the PointNet++ model are fused to form a combined feature vector that integrates low and high-level information. Finally, the focal loss function is adopted as the loss function in the MLF-PointNet++ model to reduce the effect of imbalance in the number of point clouds in each category on the classification accuracy. A classification evaluation was conducted using LiDAR-UAS data from the Moshui River estuarine area in Qingdao, China. The experimental results revealed that MLF-PointNet++ had an overall accuracy (OA), mean intersection over union (mIOU), kappa coefficient, precision, recall, and F1-score of 0.976, 0.913, 0.960, 0.953, 0.953, and 0.953, respectively, for object classification in the three representative areas, which were better than the corresponding values for the classification methods of random forest, BP neural network, Naive Bayes, PointNet, PointNet++, and RandLA-Net. The study results provide effective methodological support for the classification of objects in estuarine areas and offer a scientific basis for the sustainable development of these areas. [ABSTRACT FROM AUTHOR]

Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has excellent potential for obtaining water depth information around islands and reefs. Combining the density-based spatial clustering of applications with noise algorithm (DBSCAN) and multiscale quadtree analysis, we propose a new photon-counting lidar denoising method to discard the large amount of noise in ICESat-2 data. First, the kernel density estimation (KDE) is used to preprocess the point cloud data, and a threshold is set to remove the noise photons on the sea surface. Next, the DBSCAN algorithm is used to preliminarily remove underwater noise photons. Then, the quadtree segmentation and Otsu algorithm are used for fine denoising to extract accurate bottom signal photons. Based on ICESat-2 pho-ton-counting data from six typical islands and reefs worldwide, the proposed method outperforms other algorithms in terms of denoising effect. Compared to in situ data, the determination coefficient (R2) reaches 94.59%, and the root mean square error (RMSE) is 1.01 m. The proposed method can extract accurate underwater terrain information, laying a foundation for offshore bathymetry. [ABSTRACT FROM AUTHOR]