Dental segmentation is a critical step in computer-aided orthodontic treatment planning, but accurate segmentation still faces numerous challenges due to complex tooth morphology, ambiguous gingival boundaries, and clinical issues such as malformed teeth, crowding, and malocclusion. This paper proposes GFACNet, a network that integrates geometric features and anatomical constraints for 3D dental segmentation from intraoral scan data. Our method comprises three key innovations: 1) a morphology-aware graph construction (MAGC) mechanism that adaptively constructs graph structures based on dental geometric characteristics, 2) a multi scale transformer (MST) feature integration module that processes features at different scales while capturing both local and global context, and 3) a hierarchical anatomical constraint loss (HACL) that incorporates multi level anatomical features to guide anatomically consistent segmentation. Experiments on real intraoral scanning datasets demonstrate that GFACNet significantly outperforms existing methods in handling complex dental morphologies, particularly in cases of malformed and missing teeth. Additionally, our method requires reduced computational resources while providing a more practical solution for clinical applications.

Managing massive electric power data is a typical big data application because electric power systems generate millions or billions of status, debugging, and error records every single day. To guarantee the safety and sustainability of electric power systems, massive electric power data need to be processed and analyzed quickly to make real-time decisions. Traditional solutions typically use relational databases to manage electric power data. However, relational databases cannot efficiently process and analyze massive electric power data when the data size increases significantly. In this paper, we show how electric power data can be managed by using HBase, a distributed database maintained by Apache. Our system consists of clients, HBase database, status monitors, data migration modules, and data fragmentation modules. We evaluate the performance of our system through a series of experiments. We also show how HBase’s parameters can be tuned to improve the efficiency of our system.