COLOR STABILITY OF DENTAL CERAMICS SUBMITTED TO ARTIFICIAL ACCELERATED AGING AFTER REPEATED FIRINGS

Statement of problem. Color stability is an important factor to ensure the long-term clinical success of ceramic restorations. There is a lack of information on how color is affected by fabrication procedures, such as the number of firings. Purpose. The purpose of this study was to evaluate the effects that the number of firings and type of substrate have on the color stability of dental ceramic submitted to artificial accelerated aging. Material and methods. Sixty specimens were fabricated: 30 metal ceramic (Verabond II + IPS d.SIGN) and 30 all-ceramic (IPS d.SIGN). Specimens were divided into 3 groups (n=10), and submitted to 2, 3, or 4 firings (+/- 900 degrees C), respectively, according to the manufacturer`s instructions. Color readings were obtained with a spectro photometer before and after artificial accelerated aging, and L*, a*, and b* coordinates and total color variation (Delta E) were analyzed (2-way ANOVA, Bonferroni, (alpha=05). Results. For metal ceramic specimens, differences for the L* coordinates were significant (P<.05) only for the group submitted to 3 firings. With respect to the all-ceramic specimens, smaller L* coordinates were obtained for greater a* and b* coordinates, indicating that the greater the number of firings, the darker and more reddish/yellowish the specimen. All Delta E values, for all groups, were below 1.0. All-ceramic specimens submitted to 3 and 4 firings presented Delta E means differing statistically (P<.05) from those of the metal ceramic group. Conclusions. The type of substrate and number of firings affected the color stability of the ceramic material tested. Artificial accelerated aging did not produce perceptible color stability changes (Delta E<1.0). (J Prosthet Dent 2009-101:13-18)

Efficient solutions to factored MDPs with imprecise transition probabilities

When modeling real-world decision-theoretic planning problems in the Markov Decision Process (MDP) framework, it is often impossible to obtain a completely accurate estimate of transition probabilities. For example, natural uncertainty arises in the transition specification due to elicitation of MOP transition models from an expert or estimation from data, or non-stationary transition distributions arising from insufficient state knowledge. In the interest of obtaining the most robust policy under transition uncertainty, the Markov Decision Process with Imprecise Transition Probabilities (MDP-IPs) has been introduced to model such scenarios. Unfortunately, while various solution algorithms exist for MDP-IPs, they often require external calls to optimization routines and thus can be extremely time-consuming in practice. To address this deficiency, we introduce the factored MDP-IP and propose efficient dynamic programming methods to exploit its structure. Noting that the key computational bottleneck in the solution of factored MDP-IPs is the need to repeatedly solve nonlinear constrained optimization problems, we show how to target approximation techniques to drastically reduce the computational overhead of the nonlinear solver while producing bounded, approximately optimal solutions. Our results show up to two orders of magnitude speedup in comparison to traditional ""flat"" dynamic programming approaches and up to an order of magnitude speedup over the extension of factored MDP approximate value iteration techniques to MDP-IPs while producing the lowest error of any approximation algorithm evaluated. (C) 2011 Elsevier B.V. All rights reserved.