Influence of pulse repetition rate of Er : YAG laser and dentin depth on tensile bond strength of dentin-resin interface

The study evaluated the in vitro influence of pulse-repetition rate of Er:YAG laser and dentin depth on tensile bond strength of dentin-resin interface. Dentin surfaces of buccal or lingual surfaces from human third molars were submitted to tensile test in different depths (superficial, 1.0 and 1.5 mm) of the same dental area, using the same sample. Surface treatments were acid conditioning solely (control) and Er:YAG laser irradiation (80 mJ) followed by acid conditioning, with different pulse-repetition rates (1, 2, 3, or 4 Hz). Single bond/Z-250 system was used. The samples were stored in distilled water at 37 degrees C for 24 h, and then the first test (superficial dentine) was performed. The bond failures were analyzed. Following, the specimens were identified, grounded until 1.0- and 1.5-mm depths, submitted again to the treatments and to the second and, after that, to third-bond tests on a similar procedure and failure analysis. ANOVA and Tukey test demonstrated a significant difference (p < 0.001) for treatment and treatment X depth interaction (p < 0.05). The tested depths did not show influence (p > 0.05) on the bond strength of dentin-resin interface. It may be concluded that Er:YAG laser with 1, 2, 3, or 4 Hz combined with acid conditioning did not increase the resin tensile bond strength to dentin, regardless of dentin depth. (C) 2007 Wiley Periodicals, Inc.

PARALLEL ALGORITHMS FOR MAXIMAL CLIQUES IN CIRCLE GRAPHS AND UNRESTRICTED DEPTH SEARCH

We present parallel algorithms on the BSP/CGM model, with p processors, to count and generate all the maximal cliques of a circle graph with n vertices and m edges. To count the number of all the maximal cliques, without actually generating them, our algorithm requires O(log p) communication rounds with O(nm/p) local computation time. We also present an algorithm to generate the first maximal clique in O(log p) communication rounds with O(nm/p) local computation, and to generate each one of the subsequent maximal cliques this algorithm requires O(log p) communication rounds with O(m/p) local computation. The maximal cliques generation algorithm is based on generating all maximal paths in a directed acyclic graph, and we present an algorithm for this problem that uses O(log p) communication rounds with O(m/p) local computation for each maximal path. We also show that the presented algorithms can be extended to the CREW PRAM model.