65 resultados para Semi-simbolism


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Conventional differential scanning calorimetry (DSC) techniques are commonly used to quantify the solubility of drugs within polymeric-controlled delivery systems. However, the nature of the DSC experiment, and in particular the relatively slow heating rates employed, limit its use to the measurement of drug solubility at the drug's melting temperature. Here, we describe the application of hyper-DSC (HDSC), a variant of DSC involving extremely rapid heating rates, to the calculation of the solubility of a model drug, metronidazole, in silicone elastomer, and demonstrate that the faster heating rates permit the solubility to be calculated under non-equilibrium conditions such that the solubility better approximates that at the temperature of use. At a heating rate of 400 degrees C/min (HDSC), metronidazole solubility was calculated to be 2.16 mg/g compared with 6.16 mg/g at 20 degrees C/min. (C) 2005 Elsevier B.V. All rights reserved.

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A neural network based tool has been developed to assist in the process of code transformation. The tool offers advice on appropriate transformations within a knowledge-driven, semi-automatic parallelisation environment. We have identified the essential characteristics of codes relevant to loop transformations. A Kohonen network is used to discover structure in the characterised codes thus revealing new knowledge that may be brought to bear on the mapping between codes and transformations or transformation sequences. A transform selector based on this process has been developed and successfully applied to the parallelisation of sequential codes.

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Purpose. This study examined the mechanical characteristics and release of tetracycline from bioadhesive, semi-solid systems which were designed for the treatment of periodontal diseases.

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Modelling Joule heating is a difficult problem because of the need to introduce correct correlations between the motions of the ions and the electrons. In this paper we analyse three different models of current induced heating (a purely classical model, a fully quantum model and a hybrid model in which the electrons are treated quantum mechanically and the atoms are treated classically). We find that all three models allow for both heating and cooling processes in the presence of a current, and furthermore the purely classical and purely quantum models show remarkable agreement in the limit of high biases. However, the hybrid model in the Ehrenfest approximation tends to suppress heating. Analysis of the equations of motion reveals that this is a consequence of two things: the electrons are being treated as a continuous fluid and the atoms cannot undergo quantum fluctuations. A means for correcting this is suggested.

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In this paper we concentrate on the direct semi-blind spatial equalizer design for MIMO systems with Rayleigh fading channels. Our aim is to develop an algorithm which can outperform the classical training based method with the same training information used, and avoid the problems of low convergence speed and local minima due to pure blind methods. A general semi-blind cost function is first constructed which incorporates both the training information from the known data and some kind of higher order statistics (HOS) from the unknown sequence. Then, based on the developed cost function, we propose two semi-blind iterative and adaptive algorithms to find the desired spatial equalizer. To further improve the performance and convergence speed of the proposed adaptive method, we propose a technique to find the optimal choice of step size. Simulation results demonstrate the performance of the proposed algorithms and comparable schemes.

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