62 resultados para Horizontal cephalic index


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Transmission terahertz time-domain spectroscopy (THz-TDS) measurements of carbon nanotube arrays are presented. A relatively thin film with vertically aligned multi-walled carbon nanotubes has been prepared and measured using THz-TDS. Experimental results were obtained from 80GHz to 2.5THz, and the sample has been characterized by extracting the relative permittivity of the carbon nanotubes. A combination of the Maxwell-Garnett and Drude models within the frequency range provide a good fit to the measured permittivity.

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This theoretical paper examines a non-normal and non-linear model of a horizontal Rijke tube. Linear and non-linear optimal initial states, which maximize acoustic energy growth over a given time from a given energy, are calculated. It is found that non-linearity and non-normality both contribute to transient growth and that, for this model, linear optimal states are only a good predictor of non-linear optimal states for low initial energies. Two types of non-linear optimal initial state are found. The first has strong energy growth during the first period of the fundamental mode but loses energy thereafter. The second has weaker energy growth during the first period but retains high energy for longer. The second type causes triggering to self-sustained oscillations from lower energy than the first and has higher energy in the fundamental mode. This suggests, for instance, that low frequency noise will be more effective at causing triggering than high frequency noise.

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In the field of motor control, two hypotheses have been controversial: whether the brain acquires internal models that generate accurate motor commands, or whether the brain avoids this by using the viscoelasticity of musculoskeletal system. Recent observations on relatively low stiffness during trained movements support the existence of internal models. However, no study has revealed the decrease in viscoelasticity associated with learning that would imply improvement of internal models as well as synergy between the two hypothetical mechanisms. Previously observed decreases in electromyogram (EMG) might have other explanations, such as trajectory modifications that reduce joint torques. To circumvent such complications, we required strict trajectory control and examined only successful trials having identical trajectory and torque profiles. Subjects were asked to perform a hand movement in unison with a target moving along a specified and unusual trajectory, with shoulder and elbow in the horizontal plane at the shoulder level. To evaluate joint viscoelasticity during the learning of this movement, we proposed an index of muscle co-contraction around the joint (IMCJ). The IMCJ was defined as the summation of the absolute values of antagonistic muscle torques around the joint and computed from the linear relation between surface EMG and joint torque. The IMCJ during isometric contraction, as well as during movements, was confirmed to correlate well with joint stiffness estimated using the conventional method, i.e., applying mechanical perturbations. Accordingly, the IMCJ during the learning of the movement was computed for each joint of each trial using estimated EMG-torque relationship. At the same time, the performance error for each trial was specified as the root mean square of the distance between the target and hand at each time step over the entire trajectory. The time-series data of IMCJ and performance error were decomposed into long-term components that showed decreases in IMCJ in accordance with learning with little change in the trajectory and short-term interactions between the IMCJ and performance error. A cross-correlation analysis and impulse responses both suggested that higher IMCJs follow poor performances, and lower IMCJs follow good performances within a few successive trials. Our results support the hypothesis that viscoelasticity contributes more when internal models are inaccurate, while internal models contribute more after the completion of learning. It is demonstrated that the CNS regulates viscoelasticity on a short- and long-term basis depending on performance error and finally acquires smooth and accurate movements while maintaining stability during the entire learning process.

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Methane hydrate, which is usually found under deep seabed or permafrost zones, is a potential energy resource for future years. Depressurization of horizontalwells bored in methane hydrate layer is considered as one possible method for hydrate dissociation and methane extraction from the hosting soil. Since hydrate is likely to behave as a bonding material to sandy soils, supported well construction is necessary to avoid wellcollapse due to the loss of the apparent cohesion during depressurization. This paper describes both physical and numerical modeling of such horizontal support wells. The experimental part involves depressurization of small well models in a large pressure cell, while the numerical part simulates the corresponding problem. While the experiment models simulate only gas saturated initial conditions, the numerical analysis simulates both gas-saturated and more realistic water-saturated conditions based on effective stress coupled flow-deformation formulation of these three phases. © 2006 Taylor & Francis Group, London.

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We report a novel utilization of periodic arrays of carbon nanotubes in the realization of diffractive photonic crystal lenses. Carbon nanotube arrays with nanoscale dimensions (lattice constant 400 nm and tube radius 50 nm) displayed a negative refractive index in the optical regime where the wavelength is of the order of array spacing. A detailed computational analysis of band gaps and optical transmission through the nanotubes based planar, convex and concave shaped lenses was performed. Due to the negative-index these lenses behaved in an opposite fashion compared to their conventional counter parts. A plano-concave lens was established and numerically tested, displaying ultra-small focal length of 1.5 μm (∼2.3 λ) and a near diffraction-limited spot size of 400 nm (∼0.61 λ). © 2012 Elsevier B.V. All rights reserved.