Predictive modelling of human walking over a complete gait cycle

An inverse dynamics multi-segment model of the body was combined with optimisation techniques to simulate normal walking in the sagittal plane on level ground. Walking is formulated as an optimal motor task subject to multiple constraints with minimisation of mechanical energy expenditure over a complete gait cycle being the performance criterion. All segmental motions and ground reactions were predicted from only three simple gait descriptors (inputs): walking velocity, cycle period and double stance duration. Quantitative comparisons of the model predictions with gait measurements show that the model reproduced the significant characteristics of normal gait in the sagittal plane. The simulation results suggest that minimising energy expenditure is a primary control objective in normal walking. However, there is also some evidence for the existence of multiple concurrent performance objectives. Keywords: Gait prediction; Inverse dynamics; Optimisation; Optimal motor tas

The Coulomb interaction and the inverse Faddeev-Popov operator in QCD

We give a proof of a local relation between the inverse Faddeev-Popov operator and the non-Abelian Coulomb interaction between color charges

Scaling behavior of temperature-dependent thermopower in CeAu2Si2 under pressure

We report a combined study of in-plane resistivity and thermopower of the pressure-induced heavy fermion superconductor CeAu2Si2 up to 27.8 GPa. It is found that thermopower follows a scaling behavior in T/T* almost up to the magnetic critical pressure pc ~ 22 GPa. By comparing with resistivity results, we show that the magnitude and characteristic temperature dependence of thermopower in this pressure range are governed by the Kondo coupling and crystal-field splitting, respectively. Below pc, the superconducting transition is preceded by a large negative thermopower minimum, suggesting a close relationship between the two phenomena. Furthermore, thermopower of a variety of Ce-based Kondo-lattices with different crystal structures follows the same scaling relation up to T/T* ~ 2.Comment: 6 pages, 4 figures. Supplementary Material available on reques

Smoothing dynamic positron emission tomography time courses using functional principal components

A functional smoothing approach to the analysis of PET time course data is presented. By borrowing information across space and accounting for this pooling through the use of a nonparametric covariate adjustment, it is possible to smooth the PET time course data thus reducing the noise. A new model for functional data analysis, the Multiplicative Nonparametric Random Effects Model, is introduced to more accurately account for the variation in the data. A locally adaptive bandwidth choice helps to determine the correct amount of smoothing at each time point. This preprocessing step to smooth the data then allows Subsequent analysis by methods Such as Spectral Analysis to be substantially improved in terms of their mean squared error