The development of connected and autonomous vehicle (CAV) technology has received increasing attention in recent years. Although car-following behavior in mixed traffic with CAVs and human-driven vehicles (HDVs) is a core component of microscopic traffic simulation, intelligent traffic systems, etc., the current study of car-following behavior in mixed traffic has some limitations. Furthermore, actual data do not support its applicability to the Chinese traffic environment. To address this gap, this paper designs and organizes a car-following experiment in mixed traffic in Beijing, extracts the trajectory data of CAVs and HDVs based on video recognition, and reconstructs the extracted trajectory data using the Lagrangian theory and Kalman filter theory to ensure the accuracy of the data. Based on this data set, this paper develops an extended car-following model. The model considers the cooperation between drivers by reformulating the prospect theory (PT). The root mean square percentage error (RMSPE) is selected to calibrate and validate the parameters of the proposed model, and the results show that there is significant heterogeneity between CAVs and HDVs in mixed traffic, and the proposed model captures this heterogeneity well. The model presented in this paper provides theoretical support for microscopic traffic simulation in mixed traffic.
Chimeric antigen receptor (CAR) T cells have an unprecedented positive curative effect for hematological malignances. Most notably, cluster of differentiation 19 (CD19) CAR T-cell therapy for pediatric acute lymphoblastic leukemia is associated with a high complete remission rate and has aroused considerable attention in the medical field. However, it also causes a series of adverse reactions and increases the risk of recurrence. The present review examines the results of CD19 CAR T-cell therapy and lists its adverse effects. In addition, some of the mechanisms of recurrence are characterized and applicable strategies to address this challenging problem are proposed.
To improve the efficiency and accuracy of digital protection of burial sites, the digital protection of burial sites was studied by the collaborative observation technology of LiDAR and unmanned aerial vehicle (UAV) mapping. Multi-feature constrained iterative global registration algorithm is used to realize fast registration of multi-site cloud, the Structure from motion (SFM)-based algorithm is used to generate dense point cloud from UAV image, and then the Iterative Closest Point(ICP) algorithm is used to realize the fusion of point cloud data on-site and image. According to the fused data, a variety of measurable results, such as high-precision burial model and contour map, are produced, and an orthophoto generation system is written independently, which can fully automatically generate high-precision orthophoto map and realize the digitalization of the burial sites. Taking the protection application of the burial site in Yaoheyuan, Ningxia, one of the top 10 archeological discoveries in China in 2017, with guaranteed data accuracy and integrity, the surveying and mapping data of the tomb site was obtained quickly. The global registration accuracy of the proposed algorithm is within 1 cm, and the registration efficiency was superior to the current mainstream open source algorithm, which verified the feasibility and efficiency of this method. It provides an effective scheme for the digital protection of burial cultural heritage, and is also suitable for the digital protection of other heritage sites in the World Heritage List such as Panlongcheng Site and Sanxingdui.
The monitoring of wooden pagodas is a very important task in the restoration of wooden pagodas. Traditionally, this labor has always been carried out by surveying personnel, who manually check all parts of the pagoda, which not only consumes huge manpower, but also suffers from low efficiency and measurement errors. This article evaluates the feasibility of combining portable 3D light detection and ranging (LiDAR) scanning and unmanned aerial vehicle (UAV) photogrammetry to perform these inspection tasks easily and accurately. The wooden pagoda's exterior picture and inside point cloud are acquired using a UAV and a LiDAR scanner, respectively. We propose a feature−based global alignment method to register the site point cloud. The error equation of the column of observed values is utilized as the beginning value of the feature constraint for global leveling. The beam method leveling model solves the spatial transformation parameters and the unknown point leveling values. Then, the Structure from Motion (SfM) algorithm of computer vision is used to realize the fusion of the dense point cloud of the exterior of the wooden pagoda generated from multiple non−measured images by global optimization and the LiDAR point cloud of the interior of the wooden pagoda to obtain the complete point cloud of the wooden pagoda, which makes the deformation monitoring of the pagoda more detailed and comprehensive. After experimental verification, the overall registration accuracy of the Yingxian wooden pagoda reaches 0.006 m. Compared with the scanning point cloud data in 2018, the model is more accurate and complete. By analyzing and comparing the data of the second floor of the wooden pagoda, we knew that the inclination of a second bright layer and a second dark layer is still developing steadily. Overall, the western outer trough inclines thoughtfully, and the column frame slopes from southwest to northeast. Some internal columns showed a negative offset in 2020, and the deformation analysis of a single column was realized by comparing it with the standard column model. The main contribution of this method lies in the effective integration of UAV images and point cloud data to provide accurate data sources for good modeling. This research will provide theoretical and methodological support for the digital protection of architectural heritage and GIS data modeling. The analysis results can provide a scientific basis for the restoration scheme design.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.