Hardness of FRHC-Cu determined by statistical analysis

A statistical indentation method has been employed to study the hardness value of fire-refined high conductivity copper, using nanoindentation technique. The Joslin and Oliver approach was used with the aim to separate the hardness (H) influence of copper matrix, from that of inclusions and grain boundaries. This approach relies on a large array of imprints (around 400 indentations), performed at 150 nm of indentation depth. A statistical study using a cumulative distribution function fit and Gaussian simulated distributions, exhibits that H for each phase can be extracted when the indentation depth is much lower than the size of the secondary phases. It is found that the thermal treatment produces a hardness increase, due to the partly re-dissolution of the inclusions (mainly Pb and Sn) in the matrix.

Regression-based techniques for statistical decision making in single-case designs

The present study evaluates the performance of four methods for estimating regression coefficients used to make statistical decisions regarding intervention effectiveness in single-case designs. Ordinary least squares estimation is compared to two correction techniques dealing with general trend and one eliminating autocorrelation whenever it is present. Type I error rates and statistical power are studied for experimental conditions defined by the presence or absence of treatment effect (change in level or in slope), general trend, and serial dependence. The results show that empirical Type I error rates do not approximate the nominal ones in presence of autocorrelation or general trend when ordinary and generalized least squares are applied. The techniques controlling trend show lower false alarm rates, but prove to be insufficiently sensitive to existing treatment effects. Consequently, the use of the statistical significance of the regression coefficients for detecting treatment effects is not recommended for short data series.

A statistical shape model of the human second cervical vertebra.

PURPOSE: Statistical shape and appearance models play an important role in reducing the segmentation processing time of a vertebra and in improving results for 3D model development. Here, we describe the different steps in generating a statistical shape model (SSM) of the second cervical vertebra (C2) and provide the shape model for general use by the scientific community. The main difficulties in its construction are the morphological complexity of the C2 and its variability in the population. METHODS: The input dataset is composed of manually segmented anonymized patient computerized tomography (CT) scans. The alignment of the different datasets is done with the procrustes alignment on surface models, and then, the registration is cast as a model-fitting problem using a Gaussian process. A principal component analysis (PCA)-based model is generated which includes the variability of the C2. RESULTS: The SSM was generated using 92 CT scans. The resulting SSM was evaluated for specificity, compactness and generalization ability. The SSM of the C2 is freely available to the scientific community in Slicer (an open source software for image analysis and scientific visualization) with a module created to visualize the SSM using Statismo, a framework for statistical shape modeling. CONCLUSION: The SSM of the vertebra allows the shape variability of the C2 to be represented. Moreover, the SSM will enable semi-automatic segmentation and 3D model generation of the vertebra, which would greatly benefit surgery planning.

Energetics and mechanics of walking in patients with chronic low back pain and healthy matched controls.

PURPOSE: Walking in patients with chronic low back pain (cLBP) is characterized by motor control adaptations as a protective strategy against further injury or pain. The purpose of this study was to compare the preferred walking speed, the biomechanical and the energetic parameters of walking at different speeds between patients with cLBP and healthy men individually matched for age, body mass and height. METHODS: Energy cost of walking was assessed with a breath-by-breath gas analyser; mechanical and spatiotemporal parameters of walking were computed using two inertial sensors equipped with a triaxial accelerometer and gyroscope and compared in 13 men with cLBP and 13 control men (CTR) during treadmill walking at standard (0.83, 1.11, 1.38, 1.67 m s(-1)) and preferred (PWS) speeds. Low back pain intensity (visual analogue scale, cLBP only) and perceived exertion (Borg scale) were assessed at each walking speed. RESULTS: PWS was slower in cLBP [1.17 (SD = 0.13) m s(-1)] than in CTR group [1.33 (SD = 0.11) m s(-1); P = 0.002]. No significant difference was observed between groups in mechanical work (P ≥ 0.44), spatiotemporal parameters (P ≥ 0.16) and energy cost of walking (P ≥ 0.36). At the end of the treadmill protocol, perceived exertion was significantly higher in cLBP [11.7 (SD = 2.4)] than in CTR group [9.9 (SD = 1.1); P = 0.01]. Pain intensity did not significantly increase over time (P = 0.21). CONCLUSIONS: These results do not support the hypothesis of a less efficient walking pattern in patients with cLBP and imply that high walking speeds are well tolerated by patients with moderately disabling cLBP.