Institute of Fundamental Technological Research

Shape optimization computational technology is used in order to maximize life time of notched machine/structural components in low cycle fatigue regime. The present approach is composed of three steps: (i) stress–strain calculation using notch correction and plasticity models, (ii) estimation of critical plane to asses fatigue lives, (iii) formulation of the optimization problem with constraint set on the number of cycles corresponding to crack initiation. The optimal design procedure is a combination of the computer aided geometrical design mathematical methods for the shape definition, the boundary element method used for analysis of the response quantities, assisted by the sequential linear programming method with move limits. Numerical examples display significant increase in the number of cycles corresponding to crack initiation phase in comparison to traditional (regular) notch shapes.

Low cycle fatigue, Notch correction, Plasticity models, Fatigue life prediction, Life time maximization, Shape optimization

Background
A central goal of molecular biology is to understand the regulatory mechanisms of gene transcription and protein synthesis. Because of their solid basis in statistics, allowing to deal with the stochastic aspects of gene expressions and noisy measurements in a natural way, Bayesian networks appear attractive in the field of inferring gene interactions structure from microarray experiments data. However, the basic formalism has some disadvantages, e.g. it is sometimes hard to distinguish between the origin and the target of an interaction. Two kinds of microarray experiments yield data particularly rich in information regarding the direction of interactions: time series and perturbation experiments. In order to correctly handle them, the basic formalism must be modified. For example, dynamic Bayesian networks (DBN) apply to time series microarray data. To our knowledge the DBN technique has not been applied in the context of perturbation experiments.

Results
We extend the framework of dynamic Bayesian networks in order to incorporate perturbations. Moreover, an exact algorithm for inferring an optimal network is proposed and a discretization method specialized for time series data from perturbation experiments is introduced. We apply our procedure to realistic simulations data. The results are compared with those obtained by standard DBN learning techniques. Moreover, the advantages of using exact learning algorithm instead of heuristic methods are analyzed.

Conclusion
We show that the quality of inferred networks dramatically improves when using data from perturbation experiments. We also conclude that the exact algorithm should be used when it is possible, i.e. when considered set of genes is small enough.