Point clouds provide an efficient way for 3D geometric object representation. In order to deal with the classification and segmentation of point cloud, it is very important to design an efficient and intelligent model that can directly affect point cloud. Due to the irregularity of the data format, traditional convolutional neural networks cannot be applied to point clouds processing directly. Graph convolution network (GCN) has attracted more and more attention in recent years, especially in the field of non-Euclidean data processing. Point clouds processing with GCN models is an efficient and suitable method, a lot of GCN models have achieved state-of-the-art performance on irregular data processing challenges. In this paper, we propose a Multi-scale Dynamic GCN model for point clouds classification, a Farthest Point Sampling method is applied in our model firstly to efficiently cover the entire point set, it uses different scale k-NN group method to locate on k nearest neighborhood for each central node, Edge Convolution (EdgeConv) operation is used to extract and aggregate local features between neighbor connected nodes and central node. We use ModelNet40, ModelNet10 and ShapeNet part dataset to classify point clouds and segment them semantically. Experiments show that our model achieves a better performance on classification accuracy and model complexity than other state-of-the-art models.

The basin water cycle affected by human activities presents the duality of natural and human characteristics. The effect of human activities to hydrology is becoming point of focus in the world with the development of society. As a physically based distributed parameter model, soil and water assessment tool is deficient when applied to the area with more human activities. In areas of increased activities, monthly changes in irrigation and consumptive water use has been observed over the years. In the past, this module was applied to a system with very little human influence. In this study we revised the theory and made appropriate improvement on irrigation module and consumptive water use module in order to apply it to the water system in Tianjin City, China, where water cycle is strongly affected by the human activities. The watershed model has been calibrated and validated using measured data available for the main river. By comparing the simulation results between initial model and improved model, the Nash-Sutcliffe efficiency is adjusted from 0.62 to 0.89 and correlation coefficient is improved from 0.79 to 0.91. The resulting validated model improves the simulation precision, especially amends the peak value of runoff, and adequately describes the impact of consumptive water use and irrigation on the natural water cycle. The model developed in this paper can be used to study water resource and water environment management in area of high human activities.

Underwater object detection is significant for the practical research of mastering existing marine biological resources. In response to the challenges posed by complex underwater environments such as water scattering and variations in object scales, researchers have developed YOLOv8 for object detection, driven by the rising popularity and iteration of deep learning. Building upon this model, we propose an enhanced underwater object detection model named YOLOv8-DGF. Firstly, we replace the convolutional layers of Spatial Pyramid Pooling Fusion (SPPF) with Invertible Neural Networks to further augment the fusion capacity of detailed features, facilitating the preservation of pivotal information while mitigating the impact of noise. Additionally, we introduce a global attention mechanism into Convolution to Fully Connected (C2f), which weights the input features, thereby emphasizing or suppressing feature information from different locations. Through our ‘Detail to Global’ strategy, the model achieved mAP@0.5 scores of 87.7% and 84.8% on the RUOD and URPC2020 datasets, respectively, with improved processing speed. Extensive ablation experiments on the Pascal VOC dataset demonstrate that YOLOv8-DGF outperforms other methods, achieving the best overall performance.