With the objective to evaluate the bonding effi cacy of a new self-adhesive resin cement (RelyX Unicem, 3M ESPE) to enamel and dentin using a shear bonding strengths test with or without acid etching pretreatment, fl at buccal dentin surface and mesial/distal enamel surface were made using a high-speed diamond bur. Copper rings were luted using Rely X Unicem (RU; 3M ESPE), Panavia F (PF; Kuraray) or Vitique (VI; DMG). For RU, the shear bonding strengths using GL (Gluma Etch, Heraeus) acid etching pretreatment were also tested. The teeth were placed into copper rings (inner diameter: 16mm, height: 4mm) and embedded in methylmethacrylate resin. The specimens were stored for 24h in distilled water at 37 ℃ prior to shear bonding strengths testing. In addition, bond failures were examined by optical microscope and categorized as 4 models such as different adhesive, cohesive, or mixed. Shear bonding strengths were calculated by dividing the maximum debonding force over the cross sectional area of each specimen. The Kruskal-Wallis test was used to determine pairwise statistical differences (P < 0.05) in SBS between the experimental groups. For dentin bonding strength, statistical analysis showed that there was no signifi cant difference among RU (12.84 MPa), PF (14.93 MPa) and VI (11.03 MP); and the bonding strengths of them were higher than RU with acid etching pretreatment (9.12 MP). When bonded to enamel, PF (17.99 MP) and VI (17.58 MP) scored signifi cantly higher than RU effi cacy.The use of self-adhesive cement RelyX Unicem can obtain the bonding strengths to dentin similar to traditional resin cements. Phosphoric acid etching can improve the bonding strengths of the selfadhesive resin cement to enamel, but was negative for dentin.
As plug-in electric vehicles (PEVs) become more and more popular, there is a growing interest in the management of their charging power. Many models exist nowadays to manage the charging of plug-in electric vehicles, and it is important that these models are implemented in a better way. This paper investigates a price-driven charging management model in which all plug-in electric vehicles are informed of the charging strategies of neighboring plug-in electric vehicles and adjust their own strategies to minimize the cost, while an aggregator determines the unit price based on overall electricity consumption to coordinate the charging strategies of the plug-in electric vehicles. In this article, we used an asynchronous distributed generalized Nash game algorithm to investigate a charging management model for plug-in electric vehicles in a smart charging station (SCS). In a charging management model, we need to consider constraints on the charge and discharge rates of plug-in electric vehicles, the battery capacity, the amount of charge per plug-in electric vehicle, and the maximum electrical load that the whole system can allow. Meeting the constraints of plug-in electric vehicles and smart charging stations, the model coordinates the charging strategy of each plug-in electric vehicle to ultimately reduce the cost of smart charging stations, which is the cost that the smart charging station should pay to the higher-level power supply facility. To the best of our knowledge, this algorithm used in this paper has not been used to solve this model, and it has better performance than the generalized Nash equilibria (GNE) seeking algorithm originally used for this model, which is called a fast alternating direction multiplier method (Fast-ADMM). In the simulation results, the asynchronous algorithm we used showed a correlation error of 0.0076 at the 713th iteration, compared to 0.0087 for the synchronous algorithm used for comparison, and the cost of the smart charging station was reduced to USD 4800.951 after coordination using the asynchronous algorithm, which was also satisfactory. We used an asynchronous algorithm to better implement a plug-in electric vehicle charging management model; this also demonstrates the potential advantages of using an asynchronous algorithm for solving the charging management model for plug-in electric vehicles.
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