Mandibular skeletal growth and modelling between 10 and 15 years of age

This study pertains to a random sample of untreated French-Canadian adolescents (79 females and 107 males) evaluated at 10 and again at 15 years of age. Superimpositions on natural reference structures were performed to describe condylar growth and modelling of 11 mandibular landmarks. Superimpositions on natural cranial/cranial base reference structures were performed to describe mandibular displacement and true rotation.The results showed significant superior and posterior growth/modelling of the condyle and ramus. Males underwent significantly (P < 0.01) greater condylar growth and ramus modelling than females. With the exception of point B, which showed significant superior drift, modelling changes for the corpus landmarks were small and variable. The mandible rotated forward 2-3.3 degrees and was displaced 9.6-12.7 mm inferiorly and 1.9-2.7 mm anteriorly. Individual differences in ramus growth and modelling, both amount and direction, can be explained by mandibular rotation and displacements. Multivariate assessments revealed that superior condylar growth and ramus modelling were most closely associated with forward rotation and inferior mandibular displacement. Posterior growth and modelling were most closely correlated with anterior mandibular displacement and forward rotation. Modelling of the lower anterior border was independent of rotation and displacement.

Analytic element modelling for strip aquifers

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A genetic neuro-model reference adaptive controller for petroleum wells drilling operations

Motivated by rising drilling operation costs, the oil industry has shown a trend towards real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated to parameters modeling. One of the drill-bit performance evaluators, the Rate of Penetration (ROP), has been used in the literature as a drilling control parameter. However, the relationships between the operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on the Auto-Regressive with Extra Input Signals model, or ARX model, to accomplish the system identification and on a Genetic Algorithm (GA) to provide a robust control for the ROP. Results of simulations run over a real offshore oil field data, consisted of seven wells drilled with equal diameter bits, are provided. © 2006 IEEE.