6 resultados para Hyperelliptic curves

em Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco


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Background: Recently, with the access of low toxicity biological and targeted therapies, evidence of the existence of a long-term survival subpopulation of cancer patients is appearing. We have studied an unselected population with advanced lung cancer to look for evidence of multimodality in survival distribution, and estimate the proportion of long-term survivors. Methods: We used survival data of 4944 patients with non-small-cell lung cancer (NSCLC) stages IIIb-IV at diagnostic, registered in the National Cancer Registry of Cuba (NCRC) between January 1998 and December 2006. We fitted one-component survival model and two-component mixture models to identify short-and long-term survivors. Bayesian information criterion was used for model selection. Results: For all of the selected parametric distributions the two components model presented the best fit. The population with short-term survival (almost 4 months median survival) represented 64% of patients. The population of long-term survival included 35% of patients, and showed a median survival around 12 months. None of the patients of short-term survival was still alive at month 24, while 10% of the patients of long-term survival died afterwards. Conclusions: There is a subgroup showing long-term evolution among patients with advanced lung cancer. As survival rates continue to improve with the new generation of therapies, prognostic models considering short-and long-term survival subpopulations should be considered in clinical research.

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We model the Spanish wholesale market as a multiplant linear supply function competition model. According to the theory, the larger generators should have supply curves for each plant which are to the left of the supply curves of plants owned by smaller generators. We test this prediction for fuel plants using data from the Spanish Market Operator (OMEL) from May 2001 to December 2003. Our results indicate that the prediction of the model holds.

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This paper sets out an optimum synthesis methodology for wheel profiles of railway vehicles in order to secure good dynamic behaviour with different track configurations. Specifically, the optimisation process has been applied to the case of rail wheelsets mounted on double gauge bogies, that move over two different gauges, which also have different types of rail: the Iberian gauge (1668 mm) and the UIC gauge (1435 mm). Optimisation is performed using Genetic Algorithms and traditional optimisation methods in a complementary way. The objective function used is based on an ideal equivalent conicity curve which ensures good stability on straight sections and also proper negotiation of curves. To this end the curve is constructed in such a way that it is constant with a low value for small lateral wheelset displacements (with regard to stability), and increases as the displacements increase (to facilitate negotiation of curved sections). Using this kind of ideal conicity curve also enables a wheel profile to be secured where the contact points have a larger distribution over the active contact areas, making wear more homogeneous and reducing stresses. The result is a wheel profile with a conicity that is closer to the target conicity for both gauges studied, producing better curve negotiation while maintaining good stability on straight sections of track. The paper shows the resultant wheel profile, the contact curves it produces, and a number of dynamic analyses demonstrating better dynamic behaviour of the synthesised wheel on curved sections with respect to the original wheel.