Adhesion of cast metal alloy and lithium disilicate copings luted to different core build-up materials with self-adhesive resin cement

This study evaluated the shear bond strength of two coping materials (non-nickel chrome-based cast alloy and lithium disilicate ceramic (IPS Empress) to four different core foundation materials (resin composite, cast metal alloy, lithium disilicate, and dentin), luted with adhesive resin cement (RelyX Unicem). Specimens (N = 56) were fabricated and divided into eight groups (n = 7 per group). Each coping material was luted with self-adhesive resin cement (RelyX Unicem) to the core materials. Bond strength was measured in a Universal Testing Machine (0.5 mm/min). Data were statistically analyzed using a two-way analysis of variance (ANOVA) and Tukey’s HSD tests (alpha = 0.05). Both core (p = 0.000) and coping material type (p = 0.000) significantly affected the mean bond strength (MPa) values. Interaction terms were also significant (p = 0.001). The highest bond strength results were obtained when lithium disilicate was bonded to lithium disilicate (21.48) with the resin cement tested. Lithium disilicate in general presented the highest bond results when bonded to all core materials tested (16.55–21.38) except dentin (3.56). Both cast alloy (2.9) and lithium disilicate (3.56) presented the lowest bond results on dentin followed by cast-alloy-cast alloy combination (3.82).     

Embedding neural networks for semantic association in content based image retrieval

Content based image retrieval (CBIR) systems provide potential solution of retrieving semantically similar images from large image repositories against any query image. The research community are competing for more effective ways of content based image retrieval, so they can be used in serving time critical applications in scientific and industrial domains. In this paper a Neural Network based architecture for content based image retrieval is presented. To enhance the capabilities of proposed work, an efficient feature extraction method is presented which is based on the concept of in-depth texture analysis. For this wavelet packets and Eigen values of Gabor filters are used for image representation purposes. To ensure semantically correct image retrieval, a partial supervised learning scheme is introduced which is based on K-nearest neighbors of a query image, and ensures the retrieval of images in a robust way. To elaborate the effectiveness of the presented work, the proposed method is compared with several existing CBIR systems, and it is proved that the proposed method has performed better then all of the comparative systems.