Gluon Vortices and Induced Magnetic Field in Compact Stars

The natural candidates for the realization of color superconductivity are the extremely dense cores of compact stars, many of which have very large magnetic fields, especially the so-called magnetars. In this paper we discuss how a color superconducting core can serve to generate and enhance the stellar magnetic field without appealing to a magnetohydrodynamic dynamo mechanism.Comment: To appear in the Proceedings of the VII Latin American Symposium on Nuclear Physics and Applications. Cusco (Peru) June 200

Biomonitorization of iron accumulation in the substantia nigra from Lewy body disease patients

Iron levels in the healthy human brain are known to be high in certain areas such as the substantia nigra (SN), and increase further with age. In addition, there is some evidence for a further increase in iron load in the SN of Parkinsons disease (PD) patients as compared to controls, which correlates with motor disability. Here, we have analyzed total iron levels in cells as well as mouse and human brain samples by atomic absorption spectroscopy (AAS). Our data indicate that iron load is more pronounced in cells with dopaminergic features. Moreover, region-specific differences in iron load reflecting those in the human brain were detected in rodent brains as well. Whilst altered iron load was not observed in other regions also affected in PD patients, we report a significant increase in iron load in the SN of Lewy body disease patients as compared to Alzheimers disease (AD) patients or controls, which correlates with neurodegeneration in this brain area

Diseño de una propuesta de cadena de abastecimiento para una unidad estratégica de negocio dedicada a la producción del servicio de alquiler maquinaria pesada para MC S.A.S.

Ingeniero (a) IndustrialPregrad

Diseño y puesta en marcha de un banco de medidas de dispersión a frecuencias ópticas

Estudio de la dispersión a frecuencias ópticas, de los conceptos relacionados y de los métodos existentes para su medida y caracterización. Estudio de las características de los dispositivos que forman el montaje básico del método de medida MPSM (Modulation Phase Shift Method) y de otros métodos derivados de éste. Diseño a partir de los dispositivos disponibles en el laboratorio del D3 de un banco de pruebas de métodos de medida de dispersión a frecuencias ópticas. Determinación del espectro de retardo de grupo y del coeficiente de dispersión de dispositivos característicos a partir del banco de medidas diseñado, usando MPSM u otros métodos

Demo 95. Patrones de interferencia

Objetivo: Comprender la formación de patrones de interferencia entre dos ondas esféricas

La predicción del fracaso empresarial en el sector hotelero

The main goal of this study is to carry out an analysis about the bankrupt of companies in the hospitality sector in Spain from 2007 to 2017, applying prediction formulas of business failure. The development of models for insolvency prediction began in the United States with the pioneering works of Beaver and Altman in the 1960s. After years of methodological improvements and studies applied to different sectors, it has not been possible to unify amethod applicable bythe Academia. Therefore, the realization of this study has been using the formula Z of Altmanof 1968 and1983and compare them with the formula Z of Amat et al. of 2017. The two methods of Z-Score applied to the companies of the hospitality sector in Spain in bankruptcy process show us a highly percentage of matches in the exercises before the official bankruptcy process, being Altman the one that obtains a better approach to the total number of bankruptcy companies.El objetivo de este estudio es analizar las empresas concursadas del sector hotelero en España,desde el año 2007 hasta el 2017, empleando fórmulas de predicción del fracaso empresarial que sean aplicables a este tipo de actividad económica. La elaboración de modelos de predicción de la insolvencia se inició en Estados Unidos con los trabajos pioneros de Beaver y Altman en la década de los años sesenta. Después de años de mejoras metodológicas y de estudios aplicados a diferentes sectores, no se ha conseguido unificar un método por la comunidad científica, por lo que para la realización de este estudio se utilizarán las fórmulas Z de Altman de los años 1968 y 1983 y se compararán con la fórmula de Amat et al. de 2017. Los dos métodos de Z-Score aplicados a las empresas del sector hotelero en concurso de acreedores en España nos muestran un alto porcentaje de coincidencias en los ejercicios previos a la declaración oficial del concurso, siendo el de Altman el que obtiene una mejor aproximación al número total de empresas concursadas

Accelerating high order discontinuous Galerkin solvers using neural networks: 3D compressible Navier-Stokes equations

We propose to accelerate a high order discontinuous Galerkin solver using neural networks. We include a corrective forcing to a low polynomial order simulation to enhance its accuracy. The forcing is obtained by training a deep fully connected neural network, using a high polynomial order simulation but only for a short time frame. With this corrective forcing, we can run the low polynomial order simulation faster (with large time steps and low cost per time step) while improving its accuracy. We explored this idea for a 1D Burgers' equation in (Marique and Ferrer, CAF 2022), and we have extended this work to the 3D Navier-Stokes equations, with and without a Large Eddy Simulation closure model. We test the methodology with the turbulent Taylor Green Vortex case and for various Reynolds numbers (30, 200 and 1600). In addition, the Taylor Green Vortex evolves with time and covers laminar, transitional, and turbulent regimes, as time progresses. The proposed methodology proves to be applicable to a variety of flows and regimes. The results show that the corrective forcing is effective in all Reynolds numbers and time frames (excluding the initial flow development). We can train the corrective forcing with a polynomial order of 8, to increase the accuracy of simulations from a polynomial order 3 to 6, when correcting outside the training time frame. The low order correct solution is 4 to 5 times faster than a simulation with comparable accuracy (polynomial order 6). Additionally, we explore changes in the hyperparameters and use transfer learning to speed up the training. We observe that it is not useful to train a corrective forcing using a different flow condition. However, an already trained corrective forcing can be used to initialise a new training (at the correct flow conditions) to obtain an effective forcing with only a few training iterations