37 resultados para Explicit method, Mean square stability, Stochastic orthogonal Runge-Kutta, Chebyshev method


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In young adults, improvements in the rate of force development as a result of resistance training are accompanied by increases in neural drive in the very initial phase of muscle activation. The purpose of this experiment was to determine if older adults also exhibit similar adaptations in response to rate of force development (RFD) training. Eight young (21-35 years) and eight older (60-79 years) adults were assessed during the production of maximum rapid contractions, before and after four weeks of progressive resistance training for the elbow flexors. Young and older adults exhibited significant increases (P< 0.01) in peak RFD, of 25.6% and 28.6% respectively. For both groups the increase in RFD was accompanied by an increase in the root mean square (RMS) amplitude and in the rate of rise (RER) in the electromyogram (EMG) throughout the initial 100 ms of activation. For older adults, however, this training response was only apparent in the brachialis and brachioradialis muscles. This response was not observed in surface EMG recorded from the biceps brachii muscle during either RFD testing or throughout training, nor was it observed in the pronator teres muscle. The minimal adaptations observed for older adults in the bifunctional muscles biceps brachii and pronator teres are considered to indicate a compromise of the neural adaptations older adults might experience in response to resistance training.

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SFTI-1 is a novel 14 amino acid peptide comprised of a circular backbone constrained by three proline residues, a hydrogen-bond network, and a single disulfide bond. It is the smallest and most potent known Bowman-Birk trypsin inhibitor and the only one with a cyclic peptidic backbone. The solution structure of [ABA(3,11)]SFTI-1, a disulfide-deficient analogue of SFTI-1, has been determined by H-1 NMR spectroscopy. The lowest energy structures of native SFTI-1 and [ABA(3,11)]SFTI-1 are similar and superimpose with a root-mean-square deviation over the backbone and heavy atoms of 0.26 +/- 0.09 and 1.10 +/- 0.22 Angstrom, respectively. The disulfide bridge in SFTI-1 was found to be a minor determinant for the overall structure, but its removal resulted in a slightly weakened hydrogen-bonding network. To further investigate the role of the disulfide bridge, NMR chemical shifts for the backbone H-alpha protons of two disulfide-deficient linear analogues of SFTI-1, [ABA(3,11)]SFTI-1[6,5] and [ABA(3,11)]SFTI-1[1,14] were measured. These correspond to analogues of the cleavage product of SFTI-1 and a putative biosynthetic precursor, respectively. In contrast with the cyclic peptide, it was found that the disulfide bridge is essential for maintaining the structure of these open-chain analogues. Overall, the hydrogen-bond network appears to be a crucial determinant of the structure of SFTI-1 analogues.

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This research work analyses techniques for implementing a cell-centred finite-volume time-domain (ccFV-TD) computational methodology for the purpose of studying microwave heating. Various state-of-the-art spatial and temporal discretisation methods employed to solve Maxwell's equations on multidimensional structured grid networks are investigated, and the dispersive and dissipative errors inherent in those techniques examined. Both staggered and unstaggered grid approaches are considered. Upwind schemes using a Riemann solver and intensity vector splitting are studied and evaluated. Staggered and unstaggered Leapfrog and Runge-Kutta time integration methods are analysed in terms of phase and amplitude error to identify which method is the most accurate and efficient for simulating microwave heating processes. The implementation and migration of typical electromagnetic boundary conditions. from staggered in space to cell-centred approaches also is deliberated. In particular, an existing perfectly matched layer absorbing boundary methodology is adapted to formulate a new cell-centred boundary implementation for the ccFV-TD solvers. Finally for microwave heating purposes, a comparison of analytical and numerical results for standard case studies in rectangular waveguides allows the accuracy of the developed methods to be assessed. © 2004 Elsevier Inc. All rights reserved.

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We have previously [Phys. Rev. A 65, 043803 (2002)] analyzed adaptive measurements for estimating the continuously varying phase of a coherent beam, and a broadband squeezed beam. A real squeezed beam must have finite photon flux N and hence can be significantly squeezed only over a limited frequency range. In this paper we analyze adaptive phase measurements of this type for a realistic model of a squeezed beam. We show that, provided it is possible to suitably choose the parameters of the beam, a mean-square phase uncertainty scaling as (N/kappa)(-5/8) is possible, where kappa is the linewidth of the beam resulting from the fluctuating phase. This is an improvement over the (N/kappa)(-1/2) scaling found previously for coherent beams. In the experimentally realistic case where there is a limit on the maximum squeezing possible, the variance will be reduced below that for coherent beams, though the scaling is unchanged.

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In various signal-channel-estimation problems, the channel being estimated may be well approximated by a discrete finite impulse response (FIR) model with sparsely separated active or nonzero taps. A common approach to estimating such channels involves a discrete normalized least-mean-square (NLMS) adaptive FIR filter, every tap of which is adapted at each sample interval. Such an approach suffers from slow convergence rates and poor tracking when the required FIR filter is "long." Recently, NLMS-based algorithms have been proposed that employ least-squares-based structural detection techniques to exploit possible sparse channel structure and subsequently provide improved estimation performance. However, these algorithms perform poorly when there is a large dynamic range amongst the active taps. In this paper, we propose two modifications to the previous algorithms, which essentially remove this limitation. The modifications also significantly improve the applicability of the detection technique to structurally time varying channels. Importantly, for sparse channels, the computational cost of the newly proposed detection-guided NLMS estimator is only marginally greater than that of the standard NLMS estimator. Simulations demonstrate the favourable performance of the newly proposed algorithm. © 2006 IEEE.

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Chaotic orientations of a top containing a fluid filled cavity are investigated analytically and numerically under small perturbations. The top spins and rolls in nonsliding contact with a rough horizontal plane and the fluid in the ellipsoidal shaped cavity is considered to be ideal and describable by finite degrees of freedom. A Hamiltonian structure is established to facilitate the application of Melnikov-Holmes-Marsden (MHM) integrals. In particular, chaotic motion of the liquid-filled top is identified to be arisen from the transversal intersections between the stable and unstable manifolds of an approximated, disturbed flow of the liquid-filled top via the MHM integrals. The developed analytical criteria are crosschecked with numerical simulations via the 4th Runge-Kutta algorithms with adaptive time steps.

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A new integration scheme is developed for nonequilibrium molecular dynamics simulations where the temperature is constrained by a Gaussian thermostat. The utility of the scheme is demonstrated by its application to the SLLOD algorithm which is the standard nonequilibrium molecular dynamics algorithm for studying shear flow. Unlike conventional integrators, the new integrators are constructed using operator-splitting techniques to ensure stability and that little or no drift in the kinetic energy occurs. Moreover, they require minimum computer memory and are straightforward to program. Numerical experiments show that the efficiency and stability of the new integrators compare favorably with conventional integrators such as the Runge-Kutta and Gear predictor-corrector methods. (C) 1999 American Institute of Physics. [S0021-9606(99)50125-6].