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Taking the example of conspiracy-related communication online as one form of contentious politics, this study examines the data collection challenges for multidimensional comparative research across platforms, time, and cultural embeddings. It compares the architectures and features relevant to data collection, access regimes, and use cultures for a set of digital platforms and communication venues. Differentiating between actor- and content-based strategies, this study discusses the potentials and limitations of these approaches, considering differences in platforms, temporal dynamics, and cultural embeddings as well as several layers of equivalence. The discussion highlights crucial insights into designing data collection strategies in multidimensional comparative studies. [ABSTRACT FROM AUTHOR]

The transient rainfall infiltration and grid-based regional slope-stability (TRIGRS)-Scoops three-dimensional (3D) coupling model combines the influences of rainfall infiltration and 3D topography on slope safety. However, the Scoops 3D model defines the physical and mechanical parameters of underground materials with a uniform or layered method, which cannot describe the variability in the horizontal direction. To address this limitation, a method of creating a 3D distribution file of physical and mechanical parameters for underground materials was proposed. This method was applied to a mountainous area in Chishang Town, China. Based on the TRIGRS-Scoops 3D coupling model, the stability of slopes in the study area was assessed under different rainfall conditions. The dangerous slopes were located where the proportion of unstable grids exceeded 1%. The TRIGRS-Scoops 3D coupling model was repeatedly used to determine the curve of the rainfall threshold for landslide activation. This curve is based on the intensity (I) and duration (D) of rainfall. The results show that the 3D distribution file can effectively reflect the variability of parameters in the horizontal direction. The landslide activating rainfall threshold curves for the 13 dangerous slopes in the study area exhibit a linear relationship between lgI and lgD. This study can reveal slope stability under different rainfall conditions and provide a theoretical basis for landslide warnings. [ABSTRACT FROM AUTHOR]

In view of the low identification accuracy of the crack-detection technology of asphalt pavement under the current conditions of complex pavement (subject to strong light, water on the road, debris, and so on), an asphalt pavement crack-detection algorithm based on improved YOLOv5s was proposed by building the data set for asphalt pavement cracks. The first step was to make the following improvements to the original YOLOv5s model according to the characteristics of the asphalt pavement crack data set: The k -means++ algorithm was used to recluster the anchor points of the crack data set, and the initial anchor frame matching the crack characteristics of the asphalt pavement was obtained to replace the default initial anchor frame in the YOLOv5 original model. In the prediction part of the original model, the Convolutional Block Attention Module (CBAM) was added in the order of first channel and then space to improve the detection ability of the model to small cracks. The CIoU_Loss function was used as the regression loss function of the model to replace the GIoU_Loss function in the original model to improve the positioning accuracy of the anchor frame. The second step was to perform an ablation experiment on the improved YOLOv5s model. This would prove that each improvement scheme could increase detection ability without conflict. The final step was to compare the improved YOLOv5s model with various classic target detection models in the data set of this paper: the Crack Forest Data set (CFD), the Crack500 data set, and the Crack200 data set. The results showed that the detection of the improved YOLOv5s model on each data set was better than other target detection models. The mAP@0.5 and mAP@[0.5:0.95] of this model on the data set of this paper were 90.58% and 56.08%, respectively, which were much higher than other target detection models. These findings indicate that the improved YOLOv5s model had better detection under complex pavement conditions and could provide a theoretical basis for the automatic detection of asphalt pavement cracks. [ABSTRACT FROM AUTHOR]

Due to the great differences in geological environment characteristics and landslide disaster mechanism in different regions, the logical structure of each mathematical model is also different. It can only be determined through comparative research. Four improved algorithms based on Bayesian networkwere verified, and the error index was introduced to determine the algorithm with the best modeling effect. The landslide susceptibility probability of 774570 grids in Boshan District was calculated, and the landslide susceptibility distribution map of Boshan District was plotted. Based on the spatial superposition and grid calculator function of GIS, the landslide susceptibility assessment results of each model were compared. [ABSTRACT FROM AUTHOR]

Many liberal democracies have witnessed the rise of radical right parties and movements that threaten liberal values of tolerance and inclusion. Extremist movement factions may promote inflammatory ideas that engage broader publics, but party leaders face dilemmas of endorsing content from extremist origins. However, when that content is shared over larger intermediary networks of aligned supporters and media sites, it may become laundered or disconnected from its original sources so that parties can play it back as official communication. With a dynamic network analysis and various-time series analysis we tracked content flows from the German version of a global far-right anti-immigration campaign across different media platforms, including YouTube, Twitter, and collections of far-right and mainstream media sites. The analysis shows how content from the small extremist Identitarian Movement spread over expanding networks of low-level activists of the Alternative for Germany party and far-right alternative media sites. That network bridging enabled party leadership to launder the source of the content and roll out its own version of the campaign. As a result, national attention became directed to extremist ideas. [ABSTRACT FROM AUTHOR]