Dielectric spectra analysis: reliable parameter estimation using interval analysis

Dielectric spectroscopy is an extremely versatile method for characterizing the molecular dynamics over a large range of time scales. Unfortunately, the extraction of model parameters by data fitting is still a crucial problem which is now solved by our program S.A.D.E. S.A.D.E. is based on the algorithm S.I.V.I.A. which was proposed and implemented by Jaulin in order to solve constraint satisfaction problems. The problem of dielectric data analysis is reduced to a problem of choosing the appropriate physical model. In this article, Debye relaxations were used and validated to fit the relaxations of a DGEBA prepolymer and the polarization of the spectrometer electrodes. The conductivity was evaluated too

Stochastic optimization methods for extracting cosmological parameters from CMBR power spectra

The reconstruction of the CMBR power spectrum from a map represents a major computational challenge to which much effort has been applied. However, once the power spectrum has been recovered there still remains the problem of extracting cosmological parameters from it. Doing this involves optimizing a complicated function in a many dimensional parameter space. Therefore efficient algorithms are necessary in order to make this feasible. We have tested several different types of algorithms and found that the technique known as simulated annealing is very effective for this purpose. It is shown that simulated annealing is able to extract the correct cosmological parameters from a set of simulated power spectra, but even with such fast optimization algorithms, a substantial computational effort is needed.Comment: 7 pages revtex, 3 figures, to appear in PR

Theory of band gap bowing of disordered substitutional II-VI and III-V semiconductor alloys

For a wide class of technologically relevant compound III-V and II-VI semiconductor materials AC and BC mixed crystals (alloys) of the type A(x)B(1-x)C can be realized. As the electronic properties like the bulk band gap vary continuously with x, any band gap in between that of the pure AC and BC systems can be obtained by choosing the appropriate concentration x, granted that the respective ratio is miscible and thermodynamically stable. In most cases the band gap does not vary linearly with x, but a pronounced bowing behavior as a function of the concentration is observed. In this paper we show that the electronic properties of such A(x)B(1-x)C semiconductors and, in particular, the band gap bowing can well be described and understood starting from empirical tight binding models for the pure AC and BC systems. The electronic properties of the A(x)B(1-x)C system can be described by choosing the tight-binding parameters of the AC or BC system with probabilities x and 1-x, respectively. We demonstrate this by exact diagonalization of finite but large supercells and by means of calculations within the established coherent potential approximation (CPA). We apply this treatment to the II-VI system Cd(x)Zn(1-x)Se, to the III-V system In(x)Ga(1-x)As and to the III-nitride system Ga(x)Al(1-x)N.Comment: 14 pages, 10 figure

Foreground removal from CMB temperature maps using an MLP neural network

One of the main obstacles in extracting the Cosmic Microwave Background (CMB) signal from observations in the mm-submm range is the foreground contamination by emission from galactic components: mainly synchrotron, free-free and thermal dust emission. Due to the statistical nature of the intrinsic CMB signal it is essential to minimize the systematic errors in the CMB temperature determinations. Following the available knowledge of the spectral behavior of the galactic foregrounds simple, power law-like spectra have been assumed. The feasibility of using a simple neural network for extracting the CMB temperature signal from the combined CMB and foreground signals has been investigated. As a specific example, we have analysed simulated data, like that expected from the ESA Planck Surveyor mission. A simple multilayer perceptron neural network with 2 hidden layers can provide temperature estimates, over more than 80 percent of the sky, that are to a high degree uncorrelated with the foreground signals. A single network will be able to cover the dynamic range of the Planck noise level over the entire sky.Comment: Accepted for publication in Astrophysics and Space Scienc

Estructura agraria y dinámica de pobreza rural en el Perú

A partir de un panel provincial para el periodo entre los censos agropecuarios de 1994 y el 2012, este estudio pretende esclarecer el signo de la relación entre estructura agraria y dinámicas de pobreza rural en el Perú. Los resultados descriptivos revelan que las provincias con reducciones importantes en las tasas de pobreza rural son aquellas cuyas unidades agropecuarias tenían, al inicio del periodo, una mayor cantidad de tierra agrícola - en equivalente de riego -, una estructura de propiedad menos fragmentada, una distribución de la tierra más equitativa y una mayor proporción de productores con capacidad de innovación tecnológica. Por otro lado, los resultados econométricos sugieren que un importante determinante de la dinámica de pobreza rural observada es el tamaño de la propiedad, y no la estructura agraria. Asimismo, se muestra que las provincias cuya tasa de emigración es más alta y cuya tasa de inmigración es más baja son las que sufren un mayor aumento de la pobreza rural. Por último, junto con variables que pueden estar determinando un acceso diferenciado a los mercados, persiste un impacto positivo del grado de diversificación de la actividad productiva sobre las posibilidades de generar dinámicas de reducción de la pobreza en áreas rurales

Neural networks in petroleum geology as interpretation tools

Abstract Three examples of the use of neural networks in analyses of geologic data from hydrocarbon reservoirs are presented. All networks are trained with data originating from clastic reservoirs of Neogene age located in the Croatian part of the Pannonian Basin. Training always included similar reservoir variables, i.e. electric logs (resistivity, spontaneous potential) and lithology determined from cores or logs and described as sandstone or marl, with categorical values in intervals. Selected variables also include hydrocarbon saturation, also represented by a categorical variable, average reservoir porosity calculated from interpreted well logs, and seismic attributes. In all three neural models some of the mentioned inputs were used for analyzing data collected from three different oil fields in the Croatian part of the Pannonian Basin. It is shown that selection of geologically and physically linked variables play a key role in the process of network training, validating and processing. The aim of this study was to establish relationships between log-derived data, core data, and seismic attributes. Three case studies are described in this paper to illustrate the use of neural network prediction of sandstone-marl facies (Case Study # 1, Okoli Field), prediction of carbonate breccia porosity (Case Study # 2, Beničanci Field), and prediction of lithology and saturation (Case Study # 3, Kloštar Field). The results of these studies indicate that this method is capable of providing better understanding of some clastic Neogene reservoirs in the Croatian part of the Pannonian Basin