In-shoe plantar pressure distribution during running on natural grass and asphalt in recreational runners

The type of surface used for running can influence the load that the locomotor apparatus will absorb and the load distribution could be related to the incidence of chronic injuries. As there is no consensus on how the locomotor apparatus adapts to loads originating from running Surfaces with different compliance, the objective of this study was to investigate how loads are distributed over the plantar surface while running on natural grass and on a rigid surface-asphalt. Forty-four adult runners with 4 3 years of running experience were evaluated while running at 12 km/h for 40 m wearing standardised running shoes and Pedar insoles (Novel). Peak pressure, contact time and contact area were measured in six regions: lateral, central and medial rearfoot, midfoot, lateral and media] forefoot. The Surfaces and regions were compared by three ANOVAS (2 x 6). Asphalt and natural grass were statistically different in all variables. Higher peak pressures were observed on asphalt at the central (p < 0.001) [grass: 303.8(66.7) kPa; asphalt: 342.3(76.3) kPa] and lateral rearfoot (p < 0.001) [grass: 312.7(75.8) kPa: asphalt: 350.9(98.3) kPa] and lateral forefoot (p < 0.001) [grass: 221.5(42.9) kPa asphalt: 245.3(55.5) kPa]. For natural grass, contact time and contact area were significantly greater at the central rearfoot (p < 0.001). These results suggest that natural grass may be a Surface that provokes lighter loads on the rearfoot and forefoot in recreational runners. (C) 2008 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Photoelastic Study of the Support Structures of Distal-Extension Removable Partial Dentures

Purpose: The double system of support, in which the distal-extension removable partial denture adapts, causes inadequate stress around abutment teeth, increasing the possibility of unequal bone resorption. Several ways to reduce or more adequately distribute the stress between abutment teeth and residual ridges have been reported; however, there are no definitive answers to the problem. The purpose of this study was to analyze, by means of photoelasticity, the most favorable stress distribution using three retainers: T bar, rest, proximal plate, I bar (RPI), and circumferential with mesialized rest. Materials and Methods: Three photoelastic models were made simulating a Kennedy Class II inferior arch. Fifteen dentures with long saddles, five of each design, were adjusted to the photoelastic patterns and submitted first to uniformly distributed load, and then to a load localized on the last artificial tooth. The saddles were then shortened and the tests repeated. The quantitative and qualitative analyses of stress intensity were done manually and by photography, respectively. For intragroup analyses the Wilcoxon test for paired samples was used, while for intergroup analyses Friedman and Wilcoxon tests were used to better identify the differences (p < 0.05). Results: The RPI retainer, followed by the T bar, demonstrated the best distribution of load between teeth and residual ridge. The circumferential retainer caused greater concentration of stress between dental apexes. Stress distribution was influenced by the type of retainer, the length of the saddle, and the manner of load application. Conclusions: The long saddles and the uniformly distributed loads demonstrated better distribution of stress on support structures.

Random number generators for the generalized Birnbaum-Saunders distribution

The generalized Birnbaum-Saunders distribution pertains to a class of lifetime models including both lighter and heavier tailed distributions. This model adapts well to lifetime data, even when outliers exist, and has other good theoretical properties and application perspectives. However, statistical inference tools may not exist in closed form for this model. Hence, simulation and numerical studies are needed, which require a random number generator. Three different ways to generate observations from this model are considered here. These generators are compared by utilizing a goodness-of-fit procedure as well as their effectiveness in predicting the true parameter values by using Monte Carlo simulations. This goodness-of-fit procedure may also be used as an estimation method. The quality of this estimation method is studied here. Finally, through a real data set, the generalized and classical Birnbaum-Saunders models are compared by using this estimation method.