Effect of different surface penetrating sealants on the roughness of a nanofiller composite resin

This study evaluated the effectiveness of different sealants applied to a nanofiller composite resin. Forty specimens of Filtek Z-350 were obtained after inserting the material in a 6x3 mm stainless steel mold followed by light activation for 20 s. The groups were divided (n=10) according to the surface treatment applied: Control group (no surface treatment), Fortify, Fortify Plus and Biscover LV. The specimens were subjected to simulated toothbrushing using a 200 g load and 250 strokes/min to simulate 1 week, 1, 3 and 6 months and 1 and 3 years in the mouth, considering 10,000 cycles equivalent to 1 year of toothbrushing. Oral-B soft-bristle-tip toothbrush heads and Colgate Total dentifrice at a 1:2 water-dilution were used. After each simulated time, surface roughness was assessed in random triplicate readings. The data were submitted to two-way ANOVA and Tukey's test at a 95% confidence level. The specimens were observed under scanning electron microscopy (SEM) after each toothbrushing cycle. The control group was not significantly different (p>0.05) from the other groups, except for Fortify Plus (p<0.05), which was rougher. No significant differences (p>0.05) were observed at the 1-month assessment between the experimental and control groups. Fortify and Fortify Plus presented a rougher surface over time, differing from the baseline (p<0.05). Biscover LV did not differ (p>0.05) from the baseline at any time. None of the experimental groups showed a significantly better performance (p>0.05) than the control group at any time. SEM confirmed the differences found during the roughness testing. Surface penetrating sealants did not improve the roughness of nanofiller composite resin.

Ground-level ozone prediction using a neural network model based on meteorological variables and applied to the metropolitan area of Sao Paulo

A neural network model to predict ozone concentration in the Sao Paulo Metropolitan Area was developed, based on average values of meteorological variables in the morning (8:00-12:00 hr) and afternoon (13:00-17: 00 hr) periods. Outputs are the maximum and average ozone concentrations in the afternoon (12:00-17:00 hr). The correlation coefficient between computed and measured values was 0.82 and 0.88 for the maximum and average ozone concentration, respectively. The model presented good performance as a prediction tool for the maximum ozone concentration. For prediction periods from 1 to 5 days 0 to 23% failures (95% confidence) were obtained.