SEM analysis and push-out bond strength of fiberglass posts luted with different cements of glass-ionomer in humid environment: pilot test

The objective of this study was to evaluate the push-out bond strength of fiberglass resin reinforced bonded with five ionomer cements. Also, the interface between cement and dentin was inspected by means of SEM. Fifty human canines were chose after rigorous scrutiny process, endodontically treated and divided randomly into five groups (n = 3) according to cement tested: Group I – Ionoseal (VOCO), Group II – Fugi I (GC), Group III – Fugi II Improved (GC), Group IV – Rely X Luting 2 (3M ESPE), Group V – Ketac Cem (3M ESPE). The post-space was prepared to receive a fiberglass post, which was tried before cementation process. No dentin or post surface pretreatment was carried out. After post bonding, all roots were cross-sectioned to acquire 3 thin-slices (1 mm) from three specific regions of tooth (cervical, medium and apical). A Universal test machine was used to carry out the push-out test with cross-head speed set to 0.5mm/mim. All failed specimens were observed under optical microscope to identify the failure mode. Representative specimens from each group was inspected under SEM. The data were analyzed by Kolmogorov-Smirnov and Levene’s tests and by two-way ANOVA, and Tukey’s port hoc test at a significance level of 5%. It was compared the images obtained for determination of types of failures more occurred in different levels. SEM inspection displayed that all cements filled the space between post and dentin, however, some imperfections such bubles and voids were noticed in all groups in some degree of extension. The push-out bond strength showed that cement Ketac Cem presented significant higher results when compared to the Ionoseal (P = 0.02). There were no statistical significant differences among other cements.

Reasoning about shadows in a mobile robot environment

This paper describes a logic-based formalism for qualitative spatial reasoning with cast shadows (Perceptual Qualitative Relations on Shadows, or PQRS) and presents results of a mobile robot qualitative self-localisation experiment using this formalism. Shadow detection was accomplished by mapping the images from the robot’s monocular colour camera into a HSV colour space and then thresholding on the V dimension. We present results of selflocalisation using two methods for obtaining the threshold automatically: in one method the images are segmented according to their grey-scale histograms, in the other, the threshold is set according to a prediction about the robot’s location, based upon a qualitative spatial reasoning theory about shadows. This theory-driven threshold search and the qualitative self-localisation procedure are the main contributions of the present research. To the best of our knowledge this is the first work that uses qualitative spatial representations both to perform robot self-localisation and to calibrate a robot’s interpretation of its perceptual input.