Fracture strength of machined ceramic crowns as a function of tooth preparation design and the elastic modulus of the cement

Objectives: To determine, by means of static fracture testing the effect of the tooth preparation design and the elastic modulus of the cement on the structural integrity of the cemented machined ceramic crown-tooth complex. 
Methods: Human maxillary extracted premolar teeth were prepared for all-ceramic crowns using two preparation designs; a standard preparation in accordance with established protocols and a novel design with a flat occlusal design. All-ceramic feldspathic (Vita MK II) crowns were milled for all the preparations using a CAD/CAM system (CEREC-3). The machined all-ceramic crowns were resin bonded to the tooth structure using one of three cements with different elastic moduli: Super-Bond C&B, Rely X Unicem and Panavia F 2.0. The specimens were subjected to compressive force through a 4 mm diameter steel ball at a crosshead speed of 1 mm/min using a universal test machine (Loyds Instrument Model LRX.). The load at the fracture point was recorded for each specimen in Newtons (N). These values were compared to a control group of unprepared/unrestored teeth. 
Results: There was a significant difference between the control group, with higher fracture strength, and the cemented samples regardless of the occlusal design and the type of resin cement. There was no significant difference in mean fracture load between the two designs of occlusal preparation using Super-Bond C&B. For the Rely X Unicem and Panavia F 2.0 cements, the proposed preparation design with a flat occlusal morphology provides a system with increased fracture strength. 
Significance: The proposed novel flat design showed less dependency on the resin cement selection in relation to the fracture strength of the restored tooth. The choice of the cement resin, with respect to its modulus of elasticity, is more important in the anatomic design than in the flat design. © 2013 Academy of Dental Materials.

An investigation of the ((3p)(4s) ) resonances in the elastic scattering of electrons by argon atoms

Initial results are presented on the elastic scattering of electrons by argon atoms in the neighbourhood of the well known ((3p) (4s)) resonances. An R-matrix calculation is employed to investigate how the resonance characteristics change as the spin-orbit interaction is switched on.

Low-Power Process-Variation Tolerant Arithmetic Units Using Input-Based Elastic Clocking

In this paper we propose a design methodology for low-power high-performance, process-variation tolerant architecture for arithmetic units. The novelty of our approach lies in the fact that possible delay failures due to process variations and/or voltage scaling are predicted in advance and addressed by employing an elastic clocking technique. The prediction mechanism exploits the dependence of delay of arithmetic units upon input data patterns and identifies specific inputs that activate the critical path. Under iso-yield conditions, the proposed design operates at a lower scaled down Vdd without any performance degradation, while it ensures a superlative yield under a design style employing nominal supply and transistor threshold voltage. Simulation results show power savings of upto 29%, energy per computation savings of upto 25.5% and yield enhancement of upto 11.1% compared to the conventional adders and multipliers implemented in the 70nm BPTM technology. We incorporated the proposed modules in the execution unit of a five stage DLX pipeline to measure performance using SPEC2000 benchmarks [9]. Maximum area and throughput penalty obtained were 10% and 3% respectively.

Decision Making by Credal Nets

Credal nets are probabilistic graphical models which extend Bayesian nets to cope with sets of distributions. This feature makes the model particularly suited for the implementation of classifiers and knowledge-based systems. When working with sets of (instead of single) probability distributions, the identification of the optimal option can be based on different criteria, some of them eventually leading to multiple choices. Yet, most of the inference algorithms for credal nets are designed to compute only the bounds of the posterior probabilities. This prevents some of the existing criteria from being used. To overcome this limitation, we present two simple transformations for credal nets which make it possible to compute decisions based on the maximality and E-admissibility criteria without any modification in the inference algorithms. We also prove that these decision problems have the same complexity of standard inference, being NP^PP-hard for general credal nets and NP-hard for polytrees.

Improving parameter learning of Bayesian nets from incomplete data

This paper addresses the estimation of parameters of a Bayesian network from incomplete data. The task is usually tackled by running the Expectation-Maximization (EM) algorithm several times in order to obtain a high log-likelihood estimate. We argue that choosing the maximum log-likelihood estimate (as well as the maximum penalized log-likelihood and the maximum a posteriori estimate) has severe drawbacks, being affected both by overfitting and model uncertainty. Two ideas are discussed to overcome these issues: a maximum entropy approach and a Bayesian model averaging approach. Both ideas can be easily applied on top of EM, while the entropy idea can be also implemented in a more sophisticated way, through a dedicated non-linear solver. A vast set of experiments shows that these ideas produce significantly better estimates and inferences than the traditional and widely used maximum (penalized) log-likelihood and maximum a posteriori estimates. In particular, if EM is adopted as optimization engine, the model averaging approach is the best performing one; its performance is matched by the entropy approach when implemented using the non-linear solver. The results suggest that the applicability of these ideas is immediate (they are easy to implement and to integrate in currently available inference engines) and that they constitute a better way to learn Bayesian network parameters.