Method for effectively maintaining the thickness of mouthguards fabricated using EVA sheets and a circle tray: influence of grooved sheet and heating conditions.
The shapes of ethylene vinyl acetate (EVA) sheets are mainly square or round. The aim of this study was to elucidate a fabrication method that effectively maintains the thickness of the round sheet. Mouthguards were fabricated using EVA sheets (diameter 125 mm, thickness 4.0 mm) and a vacuum-forming machine. The sheet was pinched at the top and bottom and stabilized by the circle tray. Two heating conditions were compared: (i) the sheet was molded when it sagged 10 mm below the level of the sheet frame at the top of the post under normal conditions (N); and (ii) the sheet frame was lowered to and heated at 50 mm from the level of ordinary use and molded when it sagged 10 mm from the sheet frame (L). Two EVA sheet shapes were compared: an ordinary sheet (O) and a sheet with a horizontal v-shaped groove 30 mm from the anterior end (G). The height of the working model was 20 mm at the incisor point and 15 mm at the first molar. The sheet temperatures of the heating and non-heated surface were measured by the radiation thermometer. Post-molding thickness was determined for the incisal and molar portion. Differences in the thickness were analyzed using two-way anova. The temperature difference among points was smaller under condition L than under condition N. Thickness after formation was higher in condition L than in condition N, and was higher in condition G than in condition O. At the labial surface and the cusp, L-G was thickest. With the present techniques, uneven softening during heating can be improved by lowering the sheet frame and consequently reducing the reduction in the thickness of the sheet. Additionally, the thickness reduction is reduced by creating a horizontal groove on the sheet, establishing the clinical efficacy of this method.
Thickness and fit of mouthguards according to a vacuum-forming process.
The purpose of this study was to examine the difference in the thickness and the fit of mouthguards fabricated with a vacuum-forming method of the mouthguard sheet material. The material used in this study was Sports Mouthguard (3.8 mm thickness). Two forming conditions were performed. In the first condition, the sheet was lowered over the working model after the vacuum was applied, and in the other trial, the sheet was lowered over the working model before the vacuum was applied. The sheets were formed using a vacuum former when the heated sheets hung 1.5 cm from the baseline. We measured the thickness and the fit of the mouthguard at the areas of the central incisor and first molar in both conditions. The difference of the thickness at the areas of the central incisor and first molar and the forming condition was analyzed by Two-way anova. The difference of the fit according to the forming conditions was analyzed by the Mann-Whitney U test. The results showed that the thickness of the mouthguard differed at the areas of the central incisor and first molar, but the thickness of the mouthguard did not differ according to the forming conditions. The fit of the mouthguard at the central incisor and first molar was significantly different between the forming conditions (P < 0.01 and P < 0.05). These results suggested that the fit of the mouthguard was the best without any deficiency of thickness when the vacuum was applied first and then the sheet was pressed onto the working model. These results may be useful in fabricating proper mouthguards.
A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization
This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of this paper.