Modelo de predicción electoral: el caso de la elección municipal 2015 de León de los Aldama, Guanajuato

El artículo analiza los cambios político electorales en León, Guanajuato, a partir de cómo se fue configurando el desplazamiento del Partido Revolucionario Institucional (PRI) por el Partido Acción Nacional (PAN) en este ayuntamiento en el año 1988, hasta el cambio de correlación de fuerzas en el año 2012. Ello da pauta para analizar los escenarios que podrían caracterizar las próximas elecciones de este año. Con este objetivo se propone un modelo estadístico para dicho estudio: el modelo de regresión Dirichlet, el cual permite considerar la naturaleza de los datos electorales.
The article analyzes the electoral changes in León, Guanajuato, based on how it was setting the displacement of the Institutional Revolutionary Party (PRI) by the National Action Party (PAN) in this council in 1988, until the change of correlation forces in 2012, which gives guidelines to analyze the scenarios that could characterize the upcoming elections this year. With this aim the authors proposed a statistical model for the study: the Dirichlet regression model, which allows to consider the nature of electoral data.

The Mass of Kepler-93b and The Composition of Terrestrial Planets

Kepler-93b is a 1.478 ± 0.019 R ⊕ planet with a4.7 day period around a bright (V = 10.2), astroseismicallycharacterized host star with a mass of 0.911 ± 0.033 M⊙ and a radius of 0.919 ± 0.011 R⊙. Based on 86 radial velocity observations obtainedwith the HARPS-N spectrograph on the Telescopio Nazionale Galileo and 32archival Keck/HIRES observations, we present a precise mass estimate of4.02 ± 0.68 M ⊕. The corresponding high densityof 6.88 ± 1.18 g cm-3 is consistent with a rockycomposition of primarily iron and magnesium silicate. We compareKepler-93b to other dense planets with well-constrained parameters andfind that between 1 and 6 M ⊕, all dense planetsincluding the Earth and Venus are well-described by the same fixed ratioof iron to magnesium silicate. There are as of yet no examples of suchplanets with masses >6 M ⊕. All known planets inthis mass regime have lower densities requiring significant fractions ofvolatiles or H/He gas. We also constrain the mass and period of theouter companion in the Kepler-93 system from the long-term radialvelocity trend and archival adaptive optics images. As the sample ofdense planets with well-constrained masses and radii continues to grow,we will be able to test whether the fixed compositional model found forthe seven dense planets considered in this paper extends to the fullpopulation of 1-6 M ⊕ planets.Based on observations made with the Italian Telescopio Nazionale Galileo(TNG) operated on the island of La Palma by the Fundación GalileoGalilei of the INAF (Istituto Nazionale di Astrofisica) at the SpanishObservatorio del Roque de los Muchachos of the Instituto de Astrofisicade Canarias.

Characterising granuloma regression and liver recovery in a murine model of schistosomiasis japonica

For hepatic schistosomiasis the egg-induced granulomatous response and the development of extensive fibrosis are the main pathologies. We used a Schistosoma japonicum-infected mouse model to characterise the multi-cellular pathways associated with the recovery from hepatic fibrosis following clearance of the infection with the anti-schistosomal drug, praziquantel. In the recovering liver splenomegaly, granuloma density and liver fibrosis were all reduced. Inflammatory cell infiltration into the liver was evident, and the numbers of neutrophils, eosinophils and macrophages were significantly decreased. Transcriptomic analysis revealed the up-regulation of fatty acid metabolism genes and the identification of Peroxisome proliferator activated receptor alpha as the upstream regulator of liver recovery. The aryl hydrocarbon receptor signalling pathway which regulates xenobiotic metabolism was also differentially up-regulated. These findings provide a better understanding of the mechanisms associated with the regression of hepatic schistosomiasis.

Compositional data analysis of Holocene sediments from the West Bengal Sundarbans, India: Geochemical proxies for grain-size variability in a delta environment

This paper is part of a special issue of Applied Geochemistry focusing on reliable applications of compositional multivariate statistical methods. This study outlines the application of compositional data analysis (CoDa) to calibration of geochemical data and multivariate statistical modelling of geochemistry and grain-size data from a set of Holocene sedimentary cores from the Ganges-Brahmaputra (G-B) delta. Over the last two decades, understanding near-continuous records of sedimentary sequences has required the use of core-scanning X-ray fluorescence (XRF) spectrometry, for both terrestrial and marine sedimentary sequences. Initial XRF data are generally unusable in ‘raw-format’, requiring data processing in order to remove instrument bias, as well as informed sequence interpretation. The applicability of these conventional calibration equations to core-scanning XRF data are further limited by the constraints posed by unknown measurement geometry and specimen homogeneity, as well as matrix effects. Log-ratio based calibration schemes have been developed and applied to clastic sedimentary sequences focusing mainly on energy dispersive-XRF (ED-XRF) core-scanning. This study has applied high resolution core-scanning XRF to Holocene sedimentary sequences from the tidal-dominated Indian Sundarbans, (Ganges-Brahmaputra delta plain). The Log-Ratio Calibration Equation (LRCE) was applied to a sub-set of core-scan and conventional ED-XRF data to quantify elemental composition. This provides a robust calibration scheme using reduced major axis regression of log-ratio transformed geochemical data. Through partial least squares (PLS) modelling of geochemical and grain-size data, it is possible to derive robust proxy information for the Sundarbans depositional environment. The application of these techniques to Holocene sedimentary data offers an improved methodological framework for unravelling Holocene sedimentation patterns.

A Generalized Fuzzy Linguistic Model for Predicting Component Concentrations in an Optical Gas Sensing System

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NOx, SO2 etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

New Method Based on the UNIFAC–VISCO Model for the Estimation of Ionic Liquids Viscosity Using the Experimental Data Recommended by Mathematical Gnostics

The viscosity of ionic liquids (ILs) has been modeled as a function of temperature and at atmospheric pressure using a new method based on the UNIFAC–VISCO method. This model extends the calculations previously reported by our group (see Zhao et al. J. Chem. Eng. Data 2016, 61, 2160–2169) which used 154 experimental viscosity data points of 25 ionic liquids for regression of a set of binary interaction parameters and ion Vogel–Fulcher–Tammann (VFT) parameters. Discrepancies in the experimental data of the same IL affect the quality of the correlation and thus the development of the predictive method. In this work, mathematical gnostics was used to analyze the experimental data from different sources and recommend one set of reliable data for each IL. These recommended data (totally 819 data points) for 70 ILs were correlated using this model to obtain an extended set of binary interaction parameters and ion VFT parameters, with a regression accuracy of 1.4%. In addition, 966 experimental viscosity data points for 11 binary mixtures of ILs were collected from literature to establish this model. All the binary data consist of 128 training data points used for the optimization of binary interaction parameters and 838 test data points used for the comparison of the pure evaluated values. The relative average absolute deviation (RAAD) for training and test is 2.9% and 3.9%, respectively.

A generalized fuzzy linguistic model for predicting component concentrations in an optical gas sensing system

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NO_x, SO₂ etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.