This paper proposes a novel two-level route layout mode for the autonomous modular bus (AMB) based urban public transportation corridor. The two-level route consists of two components: mainline and feeder routes. The mainline covers all stations along the corridor, while feeder routes serve only specific consecutive stations. The departure and terminal stations of the feeder route are set up as special stations where AMBs can combine and split. The feeder route layout scheme remains fixed during an operating period but can be adjusted across different periods. We establish a mixed-integer programming model to concurrently determine the optimal special station deployment scheme, AMB timetable, and vehicle formation plan under the two-level route layout mode. The objectives are to minimize the average passenger waiting time, AMB energy consumption, and the number of special stations. Subsequently, the model is addressed by employing the multi-objective boxing match algorithm. A case study is conducted utilizing a real-world urban transportation corridor, comparing the proposed method against an optimization approach that concentrates on AMB timetabling and vehicle formation planning without considering feeder routes. The results underscore the superiority of our method, which reduces average passenger waiting time by 13.00% and vehicle energy consumption by 2.53%, without increasing the number of required special stations.

Instance segmentation in medical imaging plays a crucial role in clinical diagnostic tasks, and have shown promising performance in practical applications. In this paper, we discuss a more fine-grained instance segmentation task: dental structured instance segmentation based on panoramic radiographs. However, direct segmentation of tooth structures encounters inherent challenges. Traditional instance segmentation networks often fall short in capturing intricate internal features, and exacerbated by the frequent blurring found in medical imaging, which can result in the deficiency of anatomical details. To deal with these problems, we propose a novel framework called DSISDPR, which combines a dental structured instance segmentation (DSIS) network with an enhanced diffusion prior refinement (DPR) method. Specifically, our innovatively designed structureaware network leverages fine-grained feature fusion, acquiring a richer representation of internal anatomical structures. With the integration of adversarial learning, the model is primed to deliver holistic and subtle predictions of tooth structures. Furthermore, taking inspiration from dentists’ inherent ability to utilize prior knowledge, such as understanding dental structures to label invisible anatomical structures, we propose a diffusion inpainting to refine the results of DSIS without additional annotations. Equipped with built-in structure learning, DPR is capable of modifying anomalies within each predicted segmentation, resulting in a more robust and complete structured segmentation result. Meanwhile, we ensure rigorous oversight over the reconstruction of areas affected by abnormalities, ensuring that any introduced adjustments minimally disrupt the wellpredicted structured segmentation results. Extensive experiments have demonstrated that our DSIS-DPR outperforms all existing classical instance segmentation networks. The collected dataset is available:https://github.com/Zzz512/TSD.