THE INFLUENCE OF LENGTH CHANGE SPEED AND DIRECTION ON DYNAMIC FUNCTION POTENTIATION IN FAST MOUSE MUSCLE

The purpose of this study was to test the hypothesis that the potentiation of dynamic function was dependent upon both length change speed and direction. Mouse EDL was cycled in vitro (25º C) about optimal length (Lo) with constant peak strain (± 2.5% Lo) at 1.5, 3.3 and 6.9 Hz before and after a conditioning stimulus. A single pulse was applied during shortening or lengthening and peak dynamic (concentric or eccentric) forces were assessed at Lo. Stimulation increased peak concentric force at all frequencies (range: 19 ± 1 to 30 ± 2%) but this increase was proportional to shortening speed, as were the related changes to concentric work/power (range: -15 ± 1 to 39 ± 1 %). In contrast, stimulation did not increase eccentric force, work or power at any frequency. Thus, results reveal a unique hysteresis like effect for the potentiation of dynamic output wherein concentric and eccentric forces increase and decrease, respectively, with work cycle frequency.

THE INFLUENCE OF LENGTH CHANGE SPEED AND DIRECTION ON DYNAMIC FUNCTION POTENTIATION IN FAST MOUSE MUSCLE

The purpose of this study was to test the hypothesis that the potentiation of dynamic function was dependent upon both length change speed and direction. Mouse EDL was cycled in vitro (250 C) about optimal length (Lo) with constant peak strain (± 2.5% Lo) at 1.5,3.3 and 6.9 Hz before and after a conditioning stimulus. A single pulse was applied during shortening or lengthening and peak dynamic (concentric or eccentric) forces were assessed at Lo. Stimulation increased peak concentric force at all frequencies (range: 19±1 to 30 ± 2%) but this increase was proportional to shortening speed, as were the related changes to concentric work/power (range: -15 ± 1 to 39 ± 1 %). In contrast, stimulation did not increase eccentric force, work or power at any frequency. Thus, results reveal a unique hysteresis like effect for the potentiation of dynamic output wherein concentric and eccentric forces increase and decrease, respectively, with work cycle frequency.

Effects of Plyometric and Resistance Training on Muscle Strength and Neuromuscular Function in Young Adolescent Soccer Players

This study examined the effect of 8-weeks of resistance (RT) and plyometric (PLYO) training on maximal strength, power and jump performance compared with no added training (CON), in young male soccer players. Forty-one 11-13 year-old soccer players were divided into three groups (RT, PLYO, CON). All participants completed 5 isometric knee extensions at 90° and 5 isokinetic knee extensions at 240°/s pre- and post-training. Peak torque (PT), peak rate of torque development (pRTD), electromechanical-day (EMD), rate of muscle activation (Q30), muscle cross-sectional area (mCSA) and jump performance were examined. Both RT and PLYO resulted in significant (p < 0.05) increases in PT, pRTD and jump performance. RT resulted in significantly greater increases in both isometric and isokinetic PT, while PLYO resulted in significantly greater increases in isometric pRTD and jump performance compared with CON (p < 0.05). Q30 increased to a greater extent in PLYO (20%) compared with RT (5%) and CON (-5%) (p = 0.1). In conclusion, 8-weeks of RT and PLYO resulted in significant improvements in muscle strength and jump performance. RT appears to be more effective at eliciting increases in maximal strength while PLYO appears to enhance explosive strength, mediated by possible increases in the rate of muscle activation.

The Seemingly Unrelated Dynamic Cointegration Regression Model and Testing for Purching Power Parity.

This paper studies seemingly unrelated linear models with integrated regressors and stationary errors. By adding leads and lags of the first differences of the regressors and estimating this augmented dynamic regression model by feasible generalized least squares using the long-run covariance matrix, we obtain an efficient estimator of the cointegrating vector that has a limiting mixed normal distribution. Simulation results suggest that this new estimator compares favorably with others already proposed in the literature. We apply these new estimators to the testing of purchasing power parity (PPP) among the G-7 countries. The test based on the efficient estimates rejects the PPP hypothesis for most countries.

Non-negative matrix decomposition approaches to frequency domain analysis of music audio signals

On étudie l’application des algorithmes de décomposition matricielles tel que la Factorisation Matricielle Non-négative (FMN), aux représentations fréquentielles de signaux audio musicaux. Ces algorithmes, dirigés par une fonction d’erreur de reconstruction, apprennent un ensemble de fonctions de base et un ensemble de coef- ficients correspondants qui approximent le signal d’entrée. On compare l’utilisation de trois fonctions d’erreur de reconstruction quand la FMN est appliquée à des gammes monophoniques et harmonisées: moindre carré, divergence Kullback-Leibler, et une mesure de divergence dépendente de la phase, introduite récemment. Des nouvelles méthodes pour interpréter les décompositions résultantes sont présentées et sont comparées aux méthodes utilisées précédemment qui nécessitent des connaissances du domaine acoustique. Finalement, on analyse la capacité de généralisation des fonctions de bases apprises par rapport à trois paramètres musicaux: l’amplitude, la durée et le type d’instrument. Pour ce faire, on introduit deux algorithmes d’étiquetage des fonctions de bases qui performent mieux que l’approche précédente dans la majorité de nos tests, la tâche d’instrument avec audio monophonique étant la seule exception importante.

Photosensitivity of Laser Dye Mixtures in Polymer Matrix – A Photoacoustic Study

Laser induced photoacoustic (PA) technique is used in the study of photostability of polymethyl methacrylate (PMMA) films doped with Rhodamine 6G -Rhodamine B dye system. Energy transfer from a donor molecule to an acceptor molecule in a dye mixture affects the output of the dye system. Details of investigations on the role of laser power, modulation frequency and the irradiation wavelength on the photosensitivity of the dye mixture doped PMMA films are presented.

Modeling and Analysis of Some Heavy Tailed Time Series

The thesis has covered various aspects of modeling and analysis of finite mean time series with symmetric stable distributed innovations. Time series analysis based on Box and Jenkins methods are the most popular approaches where the models are linear and errors are Gaussian. We highlighted the limitations of classical time series analysis tools and explored some generalized tools and organized the approach parallel to the classical set up. In the present thesis we mainly studied the estimation and prediction of signal plus noise model. Here we assumed the signal and noise follow some models with symmetric stable innovations.We start the thesis with some motivating examples and application areas of alpha stable time series models. Classical time series analysis and corresponding theories based on finite variance models are extensively discussed in second chapter. We also surveyed the existing theories and methods correspond to infinite variance models in the same chapter. We present a linear filtering method for computing the filter weights assigned to the observation for estimating unobserved signal under general noisy environment in third chapter. Here we consider both the signal and the noise as stationary processes with infinite variance innovations. We derived semi infinite, double infinite and asymmetric signal extraction filters based on minimum dispersion criteria. Finite length filters based on Kalman-Levy filters are developed and identified the pattern of the filter weights. Simulation studies show that the proposed methods are competent enough in signal extraction for processes with infinite variance.Parameter estimation of autoregressive signals observed in a symmetric stable noise environment is discussed in fourth chapter. Here we used higher order Yule-Walker type estimation using auto-covariation function and exemplify the methods by simulation and application to Sea surface temperature data. We increased the number of Yule-Walker equations and proposed a ordinary least square estimate to the autoregressive parameters. Singularity problem of the auto-covariation matrix is addressed and derived a modified version of the Generalized Yule-Walker method using singular value decomposition.In fifth chapter of the thesis we introduced partial covariation function as a tool for stable time series analysis where covariance or partial covariance is ill defined. Asymptotic results of the partial auto-covariation is studied and its application in model identification of stable auto-regressive models are discussed. We generalize the Durbin-Levinson algorithm to include infinite variance models in terms of partial auto-covariation function and introduce a new information criteria for consistent order estimation of stable autoregressive model.In chapter six we explore the application of the techniques discussed in the previous chapter in signal processing. Frequency estimation of sinusoidal signal observed in symmetric stable noisy environment is discussed in this context. Here we introduced a parametric spectrum analysis and frequency estimate using power transfer function. Estimate of the power transfer function is obtained using the modified generalized Yule-Walker approach. Another important problem in statistical signal processing is to identify the number of sinusoidal components in an observed signal. We used a modified version of the proposed information criteria for this purpose.

Semiclassical evaluation of average nuclear one- and two-body matrix elements

Thomas-Fermi theory is developed to evaluate nuclear matrix elements averaged on the energy shell, on the basis of independent particle Hamiltonians. One- and two-body matrix elements are compared with the quantal results, and it is demonstrated that the semiclassical matrix elements, as function of energy, well pass through the average of the scattered quantum values. For the one-body matrix elements it is shown how the Thomas-Fermi approach can be projected on good parity and also on good angular momentum. For the two-body case, the pairing matrix elements are considered explicitly.

Dual technological development in Botswana agriculture: A Stochastic input distance function approach

To improve the welfare of the rural poor and keep them in the countryside, the government of Botswana has been spending 40% of the value of agricultural GDP on agricultural support services. But can investment make smallholder agriculture prosperous in such adverse conditions? This paper derives an answer by applying a two-output six-input stochastic translog distance function, with inefficiency effects and biased technical change to panel data for the 18 districts and the commercial agricultural sector, from 1979 to 1996 This model demonstrates that herds are the most important input, followed by draft power. land and seeds. Multilateral indices for technical change, technical efficiency and total factor productivity (TFP) show that the technology level of the commercial agricultural sector is more than six times that of traditional agriculture and that the gap has been increasing, due to technological regression in traditional agriculture and modest progress in commercial agriculture. Since the levels of efficiency are similar, the same patient is repeated by the TFP indices. This result highlights the policy dilemma of the trade-off between efficiency and equity objectives.

Moderate Champagne consumption promotes an acute improvement in acute endothelial-independent vascular function in healthy human volunteers

Epidemiological studies have suggested an inverse correlation between red wine consumption and the incidence of CVD. However, Champagne wine has not been fully investigated for its cardioprotective potential. In order to assess whether acute and moderate Champagne wine consumption is capable of modulating vascular function, we performed a randomised, placebo-controlled, cross-over intervention trial. We show that consumption of Champagne wine, but not a control matched for alcohol, carbohydrate and fruit-derived acid content, induced an acute change in endothelium-independent vasodilatation at 4 and 8 h post-consumption. Although both Champagne wine and the control also induced an increase in endothelium-dependent vascular reactivity at 4 h, there was no significant difference between the vascular effects induced by Champagne or the control at any time point. These effects were accompanied by an acute decrease in the concentration of matrix metalloproteinase (MMP-9), a significant decrease in plasma levels of oxidising species and an increase in urinary excretion of a number of phenolic metabolites. In particular, the mean total excretion of hippuric acid, protocatechuic acid and isoferulic acid were all significantly greater following the Champagne wine intervention compared with the control intervention. Our data suggest that a daily moderate consumption of Champagne wine may improve vascular performance via the delivery of phenolic constituents capable of improving NO bioavailability and reducing matrix metalloproteinase activity.

A tunable radial basis function model for nonlinear system identification using particle swarm optimisation

A tunable radial basis function (RBF) network model is proposed for nonlinear system identification using particle swarm optimisation (PSO). At each stage of orthogonal forward regression (OFR) model construction, PSO optimises one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is computationally more efficient.

Monte Carlo methods for matrix computations on the grid

Many scientific and engineering applications involve inverting large matrices or solving systems of linear algebraic equations. Solving these problems with proven algorithms for direct methods can take very long to compute, as they depend on the size of the matrix. The computational complexity of the stochastic Monte Carlo methods depends only on the number of chains and the length of those chains. The computing power needed by inherently parallel Monte Carlo methods can be satisfied very efficiently by distributed computing technologies such as Grid computing. In this paper we show how a load balanced Monte Carlo method for computing the inverse of a dense matrix can be constructed, show how the method can be implemented on the Grid, and demonstrate how efficiently the method scales on multiple processors. (C) 2007 Elsevier B.V. All rights reserved.

Nonlinear system identification using particle swarm optimisation tuned radial basis function models

A novel particle swarm optimisation (PSO) tuned radial basis function (RBF) network model is proposed for identification of non-linear systems. At each stage of orthogonal forward regression (OFR) model construction process, PSO is adopted to tune one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is often more efficient in model construction. The effectiveness of the proposed PSO aided OFR algorithm for constructing tunable node RBF models is demonstrated using three real data sets.

Construction of tunable radial basis function networks using orthogonal forward selection

An orthogonal forward selection (OFS) algorithm based on leave-one-out (LOO) criteria is proposed for the construction of radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines an RBF node, namely, its center vector and diagonal covariance matrix, by minimizing the LOO statistics. For regression application, the LOO criterion is chosen to be the LOO mean-square error, while the LOO misclassification rate is adopted in two-class classification application. This OFS-LOO algorithm is computationally efficient, and it is capable of constructing parsimonious RBF networks that generalize well. Moreover, the proposed algorithm is fully automatic, and the user does not need to specify a termination criterion for the construction process. The effectiveness of the proposed RBF network construction procedure is demonstrated using examples taken from both regression and classification applications.

A fast identification algorithm for Box-Cox transformation based radial basis function neural network

In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.