RESUMEN
Background: Among the main advantages of self-adhesive resin cements comprise good aesthetics, strong restoration-tooth bond and biocompatibility. However, some disadvantages, such as high viscosity level, color limitation and short shelf life should be mentioned. Thus, the aim of the current study was to assess bond strength between fiberglass post and root dentin in teeth subjected to self-adhesive resin cements with expired shelf life and hardness. Material and Methods: Sixty (60) single-rooted human teeth were sectioned and divided into 2 groups of different cements: U200 3M and MaxCem Elite Kerr. Each group was divided into 3 subgroups, based on self-adhesive resin cements' shelf life, namely: Within the use-time recommended by the manufacturer or no expiration date; 6 months after opening the aluminum blister; 12 months after opening the aluminum blister. Bond strength was measured through push-out test conducted in universal testing machine; fracture pattern was analyzed, and microhardness was investigated through Knoop test, based on hardness readings. Data were subjected to Shapiro-Wilk normality test; nonparametric test was applied to hardness data, whereas parametric test was applied to bond strength data. Hardness data were subjected to Kruskal-Wallis test, whereas bond strength data were subjected to analysis of variance, which was followed by Tukey test; both tests were conducted at 5% significance level (α = 0.05). Results: There was no statistically significant difference in knoop hardness values recorded for the material / time / root thirds combination (p=0.483). There was no statistically significant difference in bond strength values recorded for the Material / Time / Thirds combination (p=0.237). Conclusions: It was possible concluding that shelf life did not influence material's hardness and bond strength. Key words:Dental cements, Resin Cements, Shelf Life of Products.
RESUMEN
A wide range of applications based on sequential data, named time series, have become increasingly popular in recent years, mainly those based on the Internet of Things (IoT). Several different machine learning algorithms exploit the patterns extracted from sequential data to support multiple tasks. However, this data can suffer from unreliable readings that can lead to low accuracy models due to the low-quality training sets available. Detecting the change point between high representative segments is an important ally to find and thread biased subsequences. By constructing a framework based on the Augmented Dickey-Fuller (ADF) test for data stationarity, two proposals to automatically segment subsequences in a time series were developed. The former proposal, called Change Detector segmentation, relies on change detection methods of data stream mining. The latter, called ADF-based segmentation, is constructed on a new change detector derived from the ADF test only. Experiments over real-file IoT databases and benchmarks showed the improvement provided by our proposals for prediction tasks with traditional Autoregressive integrated moving average (ARIMA) and Deep Learning (Long short-term memory and Temporal Convolutional Networks) methods. Results obtained by the Long short-term memory predictive model reduced the relative prediction error from 1 to 0.67, compared to time series without segmentation.