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Pedestrian trajectory prediction holds significant research value in various fields, such as autonomous driving, autonomous service robots, and human flow monitoring. Two key challenges in pedestrian trajectory prediction are the modeling of pedestrian social interactions and movement factors. Previous methods have not utilized interpretable information to explore complex situations when modeling social interactions. These methods also focus too much on temporal interactions at each moment when modeling movement factors and are therefore susceptible to slight motion changes. To solve the above problems, we propose an Interpretable Masked Graph Convolution Network (IMGCN) for pedestrian trajectory prediction. The IMGCN utilizes interpretable information such as the pedestrian view area, distance, and motion direction to intelligently mask interaction features, resulting in more precise modeling of social interaction and movement factors. Specifically, we design a spatial and a temporal branch to model pedestrians' social interaction and movement factors, respectively. Within the spatial branch, the view-distance mask module masks pedestrian social interaction by determining whether the pedestrian is within a certain distance and view area to achieve more accurate interaction modeling. In the temporal branch, the motion offset mask module masks pedestrian temporal interaction according to the offset degree of their motion direction to achieve accurate modeling of movement factors. Ultimately, the 2D Gaussian distribution parameters of future trajectory points are predicted by the temporal convolution networks for multi-modal trajectory prediction. On the ETH, UCY and SDD datasets, our proposed method outperforms the baseline models in terms of average displacement error and final displacement error. The code is publicly available at . [ABSTRACT FROM AUTHOR]

Nanoparticles exhibit excellent molecular adsorption capabilities and facilitate cellular penetration and intercellular molecule transfer by aiding in their movement across cell membranes. Additionally, nanomaterials serve as effective carriers for chemical drugs. Hydrogel, a polymer network system with a high water content that maintains a specific spatial shape, can be utilized to load various materials and drugs for biomedical applications. Periodontitis, one of the most prevalent chronic oral infectious diseases, poses a significant global health concern. However, current treatment modalities for periodontitis have several drawbacks. Nanoparticle-hydrogel composites, comprising nanoparticles embedded within a hydrogel matrix, offer a synergistic combination of controlled drug release from nanoparticles and prolonged retention properties of hydrogels, effectively addressing current treatment limitations. This paper provides an overview of nanoparticle-hydrogel composites, their properties, and their application in periodontitis treatment. Specifically, it delves into the definition, types, and unique qualities of these composites relevant to periodontal therapy. Furthermore, it highlights the computational modeling and simulation techniques utilized to optimize composite design and drug release kinetics. It concludes with a discussion of current challenges in the field and potential areas for future research.