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The purpose of the investigation was to research the corrosion resistance of water‐cooled rebar quenched in a novel agent (CQ) named CQ‐cooled rebar.

Water‐cooled rebar was quenched in CQ about 1 s, then cooled in air. The corrosion resistance of water‐cooled rebar and CQ‐cooled rebar was evaluated by atmospheric exposure (AE) and wet/dry cyclic accelerated corrosion tests (CCT). The electrochemical properties of the two rebar scales were researched using electrochemical tests, and their compositions and structure were examined using XRD, SEM and FT‐IR.

The corrosion tests showed that the corrosion resistance of CQ‐cooled rebar was better than that of water‐cooled rebar. The electrochemical tests indicated that the CQ‐cooled rebar scale had a higher corrosion potential, a lower corrosion current density and a higher polarization resistance. The thickness of the scale was 56 μm for CQ‐cooled rebar, and 29 μm for water‐cooled rebar. The phase constitution of the two scales comprised Fe₂O₃, Fe₃O₄, 2FeO · SiO₂ and FeO, but the mass ratio of Fe₂O₃ and Fe₃O₄ to 2FeO · SiO₂ and FeO, called protective ability index of the scale (PAIS), changed from 0.45 for water‐cooled rebar to 24 for CQ‐cooled rebar.

The results clarified the role of CQ‐quenching in improving the corrosion resistance of water‐cooled rebar, which was to generate thick and compact Fe₃O₄ and Fe₂O₃ layers over the rebar substrate, and retard the anodic dissolution and cathodic hydrogen evolution reaction.

With the advent of the web computing era, the transmission mode of the Internet of Everything has caused an explosion in data volume, which has brought severe challenges to traditional routing protocols. The limitations of the existing routing protocols under the condition of rapid data growth are elaborated, and the routing problem is remodeled as a Markov decision process. this paper aims to solve the problem of high blocking probability due to the increase in data volume by combining deep reinforcement learning. Finally, the correctness of the proposed algorithm in this paper is verified by simulation.

The limitations of the existing routing protocols under the condition of rapid data growth are elaborated and the routing problem is remodeled as a Markov decision process. Based on this, a deep reinforcement learning method is used to select the next-hop router for each data transmission task, thereby minimizing the length of the data transmission path while avoiding data congestion.

Simulation results show that the proposed method can significantly reduce the probability of data congestion and increase network throughput.

This paper proposes an intelligent routing algorithm for the network congestion caused by the explosive growth of data volume in the future of the big data era. With the help of deep reinforcement learning, it is possible to dynamically select the transmission jump router according to the current network state, thereby reducing the probability of congestion and improving network throughput.

Field robots can surmount or avoid some obstacles when operating on rough ground. However, cable-climbing robots can only surmount obstacles because their moving path is completely restricted along the cables. This paper aims to analyse the dynamic obstacle-surmounting models for the driving and driven wheels of the climbing mechanism, and design a mechanical structure for a bilateral-wheeled cable-climbing robot to improve the obstacle crossing capability.

A mechanical structure of the bilateral-wheeled cable-climbing robot is designed in this paper. Then, the kinematic and dynamic obstacle-surmounting of the driven and driving wheels are investigated through static-dynamic analysis and Lagrangian mechanical analysis, respectively. The climbing and obstacle-surmounting experiments are carried out to improve the obstacle crossing capability. The required motion curve, speed and driving moment of the robot during obstacle-surmounting are generated from the experiments results.

The presented method offers a solution for dynamic obstacle-surmounting analysis of a bilateral-wheeled cable-climbing robot. The simulation, laboratory testing and field experimental results prove that the climbing capability of the robot is near-constant on cables with diameters between 60 and 205 mm.

The dynamic analysis method presented in this paper is found to be applicable to rod structures with large obstacles and improved the stability of the robot at high altitude. Simulations and experiments are also conducted for performance evaluation.