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Edge-assisted digital twin (DT) is an efficient way to monitor, analyze, and guide connected vehicles by creating a virtual representation of the vehicle at the edge. This allows for real-time monitoring of vehicle performance, location, and condition, as well as providing guidance and edge services to optimize the vehicle's performance and ensure safe operation. Current traffic digital twinning methods primarily rely on data-driven approaches for predicting and replicating vehicle behavior, which do not adequately capture the complex dynamics and interactions between vehicles. In this paper, we propose a proximity-aware longitudinal lane changing (PALLC) model that simply and efficiently models longitudinal lane changing behavior to develop a DT system. More specifically, we first observe vehicle behavior, and find that the lane-changing behavior of the target vehicle is mainly influenced by neighboring vehicles, and ignoring the influence of distant vehicles can improve the accuracy of the PALLC model. Next, we theoretically analyze the convergence properties of the PALLC model for forecasting and assessing collision risks. Uncertainty factors originating from DT modeling, including model parameter biases, measurement deviations, and environmental noise, are fully considered to explore their effects on the DT system. In the experiments of different types of vehicles and different indicators, the results show that the PALLC model all have higher accuracy compared with the traditional method. Moreover, the PALLC model is more stable under the influence of parameter deviation and measurement deviation. These findings highlight its potential to provide DT edge services for connected vehicles.

Yttria-stabilized zirconia (YSZ), which exhibits a tetragonal phase, finds widespread application in the realm of high-temperature solid electrolytes. In the context of this research endeavor, a concerted effort was undertaken to procure a stable and controlled tetragonal phase structure within 5 mol% yttria-stabilized zirconia (5 YSZ) by modulating the precipitation rate of YSZ sols in three distinct precipitation environments, namely acidic (AC), near to neutral (NE), and alkaline (AL) conditions. Through the characterization of 5 YSZ compounds synthesized in these various environments, encompassing an assessment of their elemental composition, phase structure, local structure, and microscale morphology, it was discerned that under acidic conditions, the product exhibited non-uniform elemental distribution, pronounced agglomeration, and an abundance of hydroxyl groups, all of which proved detrimental to the stability of the tetragonal phase, resulting in the presence of 40.63% monoclinic phase. Conversely, under alkaline conditions, substantial agglomeration and hydroxyl group presence were also noted, with 34.7% monoclinic phase in the product. In stark contrast, under the condition which is near to neutral, the product demonstrated uniform elemental distribution, excellent dispersion characteristics, and a virtual absence of hydroxyl groups. The tetragonal phase structure remained stable and homogeneous, with almost negligible monoclinic phase content. Subsequent ceramic processing resulted in the highest attainable density (99.7%), coupled with the highest ionic conductivity at 850 °C (35.2 mS/cm). This study underscores that pH control during coprecipitation reactions stands as a robust and effective methodology for achieving a stable tetragonal phase structure.

As the technology of autonomous vehicle develops, online hailing autonomous taxi system is regarded as one of the most popular public transportation services in the future. Studies related to demand forecasting, ride matching, path planning, relocation, and pricing strategy for shared online hailing and autonomous taxi services have emerged in recent years. In this study, we conducted a survey based on 141 representative literatures from 1995 to 2022 to understand the state-of-the-art of the key problems of operating autonomous taxi service. First, a comprehensive review of the components of the shared autonomous taxi modelling is presented. Then, how the emerging technologies such as internet of vehicles, big data, cloud and edge computing, and blockchain can be used to enhance the autonomous taxi service is discussed. Last, the current research challenges and the concern or hurdle in public’s adoption of autonomous taxi services are identified.

•First report of chromosome-encoded blaNDM-9in S.Indiana isolated from human.•Multiple resistance genes were carried by a novel transposon Tn7540.•IS26-flanked Tn7540 was transferable with circular intermediate.•S. Indiana QT6365 was closely related to S. Indiana C629 isolated from chicken.

In vehicular edge computing networks, edge service caching has emerged as a promising technology that supports delay sensitive applications. When the vehicles pass the coverage areas of roadside units (RSUs), the vehicles can offload part/all of their tasks to the RSUs that had cached the related service data in advance. However, it is difficult to utilize the service caching resources in RSUs efficiently, for the high mobility, variable task computing requests of vehicles and the limited storage and computation capacity of RSUs. In this paper, we propose a collaborative task offloading and service caching replacement scheme from a vehicle perspective, named CoOR, the task processing cooperation between the adjacent RSUs and the service caching replacement for vehicle are mainly considered. We formulate the system computing cost and delay minimization as a mixed integer programming problem, it faces challenges of task offloading and service caching coupling, the heterogeneous computation requests and dynamic data transmission conditions. Thus, we develop an iterative algorithm combined with the Gibbs sampling and deep reinforcement learning (DRL) to find the optimal decisions. Extensive simulation results show that the proposed schemes have good convergence and better performance than the traditional baselines.

In this study, a microscopic simulation model is developed for pedestrian behavior analysis at signalized crosswalks. It takes into account the special characteristics of pedestrian crossing behavior, such as group evasion with surrounding pedestrians, collision avoidance with conflicting vehicles, response to signal control, and response to crosswalk boundary. The classical social force model is modified to reproduce repulsive and attractive phenomena. Furthermore, a generic calibration approach is proposed based on maximum log-likelihood estimation, which enables to identify significant parameters in a statistical way. Last, a rigorous validation is conducted to confirm the model performance in terms of collision avoidance behavior with conflicting pedestrians and vehicles.