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The effect of Mo addition on microstructure, impact property, and wear resistance of high boron Fe-4.5 wt% B surfacing alloy, which was developed by the three-arc double-wire welding method was investigated in this work. The phase structure and microstructure of the surfacing alloy were studied by X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and Electron Backscattered Diffraction (EBSD). Besides, a two-body abrasive wear test and impact fracture test was carried out to study the wear behavior and fracture mechanism of the surfacing alloy. The results show, hypereutectic Fe-4.5 wt% B surfacing alloys with different Mo additions are mainly composed of primary (Fe, Mo)2B boride, eutectic matrix, and FeMo2B2. With the increase of Mo content in Fe-4.5 wt% B surfacing alloy, the continuous growth of the eutectic structure is blocked, which can inhibit the formation and expansion of cracks in the surfacing layer. With the increase of Mo, the wear resistance of Fe-4.5 wt% B alloy increases from 16.70 g-1 to 22.72 g-1. In the impact fracture experiment, Fe-4.5 wt% B surfacing alloy has high brittleness, the primary Fe2B is a transgranular fracture, and the interface between primary Fe2B boride and eutectic structure is an intergranular fracture. With the addition of Mo, the fracture mechanism of the surfacing alloy changes from transgranular fracture to transgranular fracture + ductile fracture, which reduces the fragmentation and spalling during the wear process, thereby improving its wear resistance.

Vehicular edge computing is a new computing paradigm. By introducing edge computing into the Internet of Vehicles (IoV), service providers are able to serve users with low-latency services, as edge computing deploys resources (e.g., computation, storage, and bandwidth) at the side close to the IoV users. When mobile nodes are moving and generating structured tasks, they can connect with the roadside units (RSUs) and then choose a proper time and several suitable Mobile Edge Computing (MEC) servers to offload the tasks. However, how to offload tasks in sequence efficiently is challenging. In response to this problem, in this paper, we propose a time-optimized, multi-task-offloading model adopting the principles of Optimal Stopping Theory (OST) with the objective of maximizing the probability of offloading to the optimal servers. When the server utilization is close to uniformly distributed, we propose another OST-based model with the objective of minimizing the total offloading delay. The proposed models are experimentally compared and evaluated with related OST models using simulated data sets and real data sets, and sensitivity analysis is performed. The results show that the proposed offloading models can be efficiently implemented in the mobile nodes and significantly reduce the total expected processing time of the tasks.