Polymerization shrinkage: effects of boundary conditions and filling technique of resin composite restorations

Objective: To evaluate the linear polymerization shrinkage (LPS) and the effect of polymerization shrinkage of a resin composite and resin-dentin bond strength under different boundary conditions and filling techniques.Methods: Two cavities (4 x 4 x 2 MM) were prepared in bovine incisors (n = 30). The teeth were divided into three groups, according to boundary conditions: In group TE, the total-etch technique was used. In group EE, only enamel was conditioned, and in group NE, none of the watts of the cavities were conditioned. A two-step adhesive system was applied to all cavities. The resin composite was inserted in one (B) or three increments (1), and tight-cured with 600 mW/cm(2) (80 s). The LPS (%) was measured in the top-bottom direction, by placing a probe in contact with resin composite during curing. Enamel and total mean gap widths were measured (400 x) in three slices obtained after sectioning the restorations. Then, the slices were sectioned again, either to obtain sticks from the adhesive interface from the bottom of the cavity or to obtain resin composite sticks (0.8 mm(2)) to be tested for tensile strength (Kratos machine, 0.5 mm/min). The data was subjected to a two-way repeated measures ANOVA and Tukey's test for comparison of the means (alpha = 0.05).Results: the highest percentage of LPS was found for the TE when bulk fitted, and the lowest percentage of LPS was found in the Hand NE when incrementally fitted. The resin dentin bond strength was higher and the total mean gap width was tower for TE group; no significant effect was detected for the main factor fitting techniques. No difference was detected for the tensile strength of resin composite among the experimental groups.Conclusions: the filling technique is not able to minimize effects of the polymerization shrinkage, and bonding to the cavity watts is necessary to assure reduced mean gap width and high bond strength values. (C) 2004 Elsevier Ltd. All rights reserved.

An orthogonal forward regression technique for sparse kernel density estimation

Using the classical Parzen window (PW) estimate as the desired response, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density (SKD) estimates. The proposed algorithm incrementally minimises a leave-one-out test score to select a sparse kernel model, and a local regularisation method is incorporated into the density construction process to further enforce sparsity. The kernel weights of the selected sparse model are finally updated using the multiplicative nonnegative quadratic programming algorithm, which ensures the nonnegative and unity constraints for the kernel weights and has the desired ability to reduce the model size further. Except for the kernel width, the proposed method has no other parameters that need tuning, and the user is not required to specify any additional criterion to terminate the density construction procedure. Several examples demonstrate the ability of this simple regression-based approach to effectively construct a SKID estimate with comparable accuracy to that of the full-sample optimised PW density estimate. (c) 2007 Elsevier B.V. All rights reserved.