Electrochemical and Theoretical Studies of 1-Alkyl-2-substituted Benzimidazoles as Corrosion Inhibitors for Carbon Steel Surface in HCl Medium

The inhibition efficiencies of newly synthesized four 1-alkyl-2-substituted benzimidazole compounds（a^d） have been studied for the corrosion of carbon steel in 1.0 M HCl by using potentiodynamic polarization measurement. The four inhibitors act as mixed-type inhibitors,which mainly inhibit cathodes. The inhibition efficiency of these compounds enhanced when the concentration of the inhibitors increased. A theoretical study of the corrosion inhibition efficiency of these compounds was carried out by using the B3 LYP level with the 6-31＋G＊ basis set. Considering the solvent effect,the IEFPCM model was selected. Furthermore,the adsorption energies of the inhibitors with the iron（001） surface were studied by using molecular dynamic（MD） simulations. The theoretical results show that these inhibitors all exhibit several adsorption active-centers. Meanwhile,the MD simulations indicate that the adsorption occurs mostly through benzene ring and the lone pair electrons of the nitro atoms. These results demonstrated that the theoretical studies and MD simulations are reliable and promising methods for analyzing the inhibition efficiency of organic inhibitors.

Assessing supply chain performance using genetic algorithm and support vector machine

The rough set-genetic support vector machine(SVM) model is applied to supply chain performance evaluation. First, the rough set theory is used to remove the redundant factors that affect the performance evaluation of supply chain to obtain the core influencing factors. Then the support vector machine is used to extract the core influencing factors to predict the level of supply chain performance. In the process of SVM classification, the genetic algorithm is used to optimize the parameters of the SVM algorithm to obtain the best parameter model, and then the supply chain performance evaluation level is predicted. Finally, an example is used to predict this model, and compared with the result of using only rough set-support vector machine to predict. The results show that the method of rough set-genetic support vector machine can predict the level of supply chain performance more accurately and the prediction result is more realistic, which is a scientific and feasible method.

Multi-user reinforcement learning based task migration in mobile edge computing

Mobile Edge Computing(MEC)is a promising approach.Dynamic service migration is a key technology in MEC.In order to maintain the continuity of services in a dynamic environment,mobile users need to migrate tasks between multiple servers in real time.Due to the uncertainty of movement,frequent migration will increase delays and costs and non-migration will lead to service interruption.Therefore,it is very challenging to design an optimal migration strategy.In this paper,we investigate the multi-user task migration problem in a dynamic environment and minimizes the average service delay while meeting the migration cost.In order to optimize the service delay and migration cost,we propose an adaptive weight deep deterministic policy gradient(AWDDPG)algorithm.And distributed execution and centralized training are adopted to solve the high-dimensional problem.Experiments show that the proposed algorithm can greatly reduce the migration cost and service delay compared with the other related algorithms.