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Deformation is the most direct indicator of structural state changes in arch dams. Therefore, numerous deformation monitoring points are typically arranged on arch dams to obtain deformation data from each point. Considering the complex relationships between the deformation at each monitoring point, this study focuses on the internal structural relationships and information fusion within the dam. The Pearson correlation coefficient is used as a similarity index to determine significant linear correlations between the measuring points. Ward’s cluster analysis method is then applied to group these points based on their similarities. To identify measuring points with strong nonlinear correlations, the Maximum Information Coefficient (MIC) method is employed. By integrating these linear and nonlinear correlations, a model is constructed to characterize the deformation at specific measurement points using data from strongly correlated points. The effectiveness of this model is verified through a concrete engineering case study, offering a novel approach for analyzing arch dam deformations.

This paper proposed a method for reconstructing floorplans from indoor point clouds. Unlike existing corner and line primitive detection algorithms, this method uses a generative adversarial network to learn the complex distribution of indoor layout graphics, and repairs incomplete room masks into more regular segmentation areas. Automatic learning of the structure information of layout graphics can reduce the dependence on geometric priors, and replacing complex optimization algorithms with Deep Neural Networks (DNN) can improve the efficiency of data processing. The proposed method can retain more shape information from the original data and improve the accuracy of the overall structure details. On this basis, the method further used an edge optimization algorithm to eliminate pixel-level edge artifacts that neural networks cannot perceive. Finally, combined with the constraint information of the overall layout, the method can generate compact floorplans with rich semantic information. Experimental results indicated that the algorithm has robustness and accuracy in complex 3D indoor datasets; its performance is competitive with those of existing methods.

We measure the 3D kinematic structures of the young stars within the central 0.5 pc of our Galactic Center using the 10 m telescopes of the W. M. Keck Observatory over a time span of 25 yr. Using high-precision measurements of positions on the sky and proper motions and radial velocities from new observations and the literature, we constrain the orbital parameters for each young star. Our results show two statistically significant substructures: a clockwise stellar disk with 18 candidate stars, as has been proposed before, but with an improved disk membership; and a second, almost edge-on plane of 10 candidate stars oriented east–west on the sky that includes at least one IRS 13 star. We estimate the eccentricity distribution of each substructure and find that the clockwise disk has 〈 e 〉 = 0.39 and the edge-on plane has 〈 e 〉 = 0.68. We also perform simulations of each disk/plane with incompleteness and spatially variable extinction to search for asymmetry. Our results show that the clockwise stellar disk is consistent with a uniform azimuthal distribution within the disk. The edge-on plane has an asymmetry that cannot be explained by variable extinction or incompleteness in the field. The orientation, asymmetric stellar distribution, and high eccentricity of the edge-on plane members suggest that this structure may be a stream associated with the IRS 13 group. The complex dynamical structure of the young nuclear cluster indicates that the star formation process involved complex gas structures and dynamics and is inconsistent with a single massive gaseous disk.

With the development of image recovery models, especially those based on adversarial and perceptual losses, the detailed texture portions of images are being recovered more naturally. However, these restored images are similar but not identical in detail texture to their reference images. With traditional image quality assessment methods, results with better subjective perceived quality often score lower in objective scoring. Assessment methods suffer from subjective and objective inconsistencies. This paper proposes a regional differential information entropy (RDIE) method for image quality assessment to address this problem. This approach allows better assessment of similar but not identical textural details and achieves good agreement with perceived quality. Neural networks are used to reshape the process of calculating information entropy, improving the speed and efficiency of the operation. Experiments conducted with this study’s image quality assessment dataset and the PIPAL dataset show that the proposed RDIE method yields a high degree of agreement with people’s average opinion scores compared with other image quality assessment metrics, proving that RDIE can better quantify the perceived quality of images.

The Nanji Islands, designated as one of China's national nature reserves, are renowned for their abundant microalgal resources. Changes in microalgae can serve as indicators of marine environmental shifts. Despite this significance, the absence of comprehensive historical records onmicroalgal diversity has limited the evaluation ofmarine environmental protection efforts. This study addresses this gap by analyzing surface sediments and sediment cores fromthe Nanji Islands, utilizing 210Pb dating and employing environmental DNA (eDNA) technology to trace historical shifts in the molecular diversity of microalgae. Chloroplast gene fragments were amplified using rbcL primers, and 210Pb dating determined the deepest sediment layer (78-80cm) todateback to1994. Fromthe 70-72cmlayer to the 60-62cmlayer, the number ofmicroalgal reads increased rapidly from27,716 to 65,143, signifying enhanced abundance over the 10 years following the establishment of the nature reserve. This was followed by a deceleration in microalgal abundance growth over the subsequent 20 years, potentially reflecting variations in primary production ofmicroalgae. Concurrently, the rise in operational taxonomic units (OTUs) and the Margalef index suggest a boost in microalgal diversity, which may be attributed to improvements in the marine environment. The microalgal community composition has shifted from a dominance of Dinoflagellata and Streptophyta to a predominance of Bacillariophyta, the change in phosphate would be one of the impact factors worth noting. This study provides foundational data on the historical changes in microalgae in the Nanji Islands and serves as a reference for exploring the relationship between environmental conditions andmicroalgal dynamics. [ABSTRACT FROM AUTHOR]