Forests play a pivotal role within the ecological milieu, with alterations to forested areas not only impacting the local environment but also serving as a significant contributing factor to the occurrence of landslide disasters. However, the significance of forests in connection with deep seated landslides is diminished. In instances of extreme events, heavy rainfall, highly weak slopes, or seismic events, it is improbable for forest cover to exert any influence. Due to extensive agricultural irrigation, the Heifangtai terrace of China has experienced a significant rise in groundwater levels, leading to the occurrence of numerous landslides. Through field investigations and remote sensing interpretation, this study has revealed dramatical geomorphological changes in the Heifangtai terrace due to the occurrence of widespread landslides. These changes are likely to disrupt the pre-existing hydrological balance within the study area, thereby altering the conditions for landslides. Therefore, understanding how the evolution patterns of loess landslides can be assessed is a crucial scientific issue for predicting the conditions of landslide occurrence. Taking the evolution of landslides along the edge of the Heifangtai terraces as an example, this study first classified different types of loess landslides. Subsequently, the temporal and spatial distribution characteristics of these landslides were analyzed. Finally, utilizing the FLAC3D software, the stability of the loess slopes at the edge of the Heifangtai terrace was examined under the lateral evolution mode. The research findings indicate that the current loess landslides in the Heifangtai terrace exhibits longitudinal multi-periodicity and lateral group-occurring behavior, which is significant for assessing loess landslides along the edge of Heifangtai terraces.

Loess is a special soil with high water sensitivity, which covers a large area in northwest China. Cracks are prone to generate in loess under the arid and semi-arid climate, which will provide the preferential channel for water and reduce the mechanical properties of soils. It is of great significance to understand the evolution characteristics and mechanisms of the cracks in loess areas. At present, the researches on cracks in soils are mainly concentrated in the characteristics of different cracking patterns. However, the spacing cracks are mostly discussed in materials like rock and concrete, rather than soils. The cracking characteristics and mechanisms of spacing cracks in loess is still inadequate. In this research, the drying tests of loess are carried out with different sizes of specimens. The parameters of spacing cracks and local strain distributions of the loess samples are obtained via PCAS and DIC methods during desiccation, respectively. The cracking modes, space cracking laws like insertion and saturation of spacing cracks are revealed. Finally, the size effect on the characteristics of spacing cracks is verified with a discrete element software MatDEM.

Human pose estimation is a complex detection task in which the network needs to capture the rich information contained in the images. In this paper, we propose MSTPose (Multi-Scale Transformer for human Pose estimation). Specifically, MSTPose leverages a high-resolution Convolution neural network (CNN) to extract texture information from images. For the feature maps from three different scales produced by the backbone network, each branch performs the coordinate attention operations. The feature maps are then spatially and channel-wise flattened, combined with keypoint tokens generated through random initialization, and fed into a parallel Transformer structure to learn spatial dependencies between features. As the Transformer outputs one-dimensional sequential features, the mainstream two-dimensional heatmap method is abandoned in favor of one-dimensional coordinate vector regression. The experiments show that MSTPose outperforms other CNN-based pose estimation models and demonstrates clear advantages over CNN + Transformer networks of similar types.