Forecasting Elections with High Volatility

This article uses data from the social survey Allbus 1998 to introduce a method of forecasting elections in a context of electoral volatility. The approach models the processes of change in electoral behaviour, exploring patterns in order to model the volatility expressed by voters. The forecast is based on the matrix of transition probabilities, following the logic of Markov chains. The power of the matrix, and the use of the mover-stayer model, is debated for alternative forecasts. As an example of high volatility, the model uses data from the German general election of 1998. The unification of two German states in 1990 caused the incorporation of around 15 million new voters from East Germany who had limited familiarity and no direct experience of the political culture in West Germany. Under these circumstances, voters were expected to show high volatility.

La movilidad social de los emigrantes españoles en Europa

La migración española de la segunda mitad del siglo XX se ha caracterizado en un primer momento por su carácter masivo y poco cualificado, seguido por un interregno de procesos de retorno y finalmente por una migración estable, no masiva pero altamente cualificada. La atención prestada a la inmigración masiva que recibe España a finales del siglo XX relegó a un segundo plano esta emigración cualificada de españoles. En este artículo se considera la relación entre movilidad espacial (migración de españoles) y su posible consecuencia sobre la movilidad social ascendente que experimentan. Para ello se utilizan los datos procedentes de la encuesta internacional EIMSS (European Internal Migrations Social Survey) y los procedimientos de escalamiento de clase social basados en la ocupación de Goldthorpe. El análisis se complementa con una simulación sobre la movilidad de clase, con la finalidad de visualizar y comparar los efectos sobre la movilidad social de la emigración de españoles a Francia, Alemania, Italia y Gran Bretaña.

Sequential and parallel synchronous alternating iterative methods

The so-called parallel multisplitting nonstationary iterative Model A was introduced by Bru, Elsner, and Neumann [Linear Algebra and its Applications 103:175-192 (1988)] for solving a nonsingular linear system Ax = b using a weak nonnegative multisplitting of the first type. In this paper new results are introduced when A is a monotone matrix using a weak nonnegative multisplitting of the second type and when A is a symmetric positive definite matrix using a P -regular multisplitting. Also, nonstationary alternating iterative methods are studied. Finally, combining Model A and alternating iterative methods, two new models of parallel multisplitting nonstationary iterations are introduced. When matrix A is monotone and the multisplittings are weak nonnegative of the first or of the second type, both models lead to convergent schemes. Also, when matrix A is symmetric positive definite and the multisplittings are P -regular, the schemes are also convergent.

Ocular residual astigmatism and topographic disparity vector indexes in normal healthy eyes

Purpose: To define a range of normality for the vectorial parameters Ocular Residual Astigmatism (ORA) and topography disparity (TD) and to evaluate their relationship with visual, refractive, anterior and posterior corneal curvature, pachymetric and corneal volume data in normal healthy eyes. Methods: This study comprised a total of 101 consecutive normal healthy eyes of 101 patients ranging in age from 15 to 64 years old. In all cases, a complete corneal analysis was performed using a Scheimpflug photography-based topography system (Pentacam system Oculus Optikgeräte GmbH). Anterior corneal topographic data were imported from the Pentacam system to the iASSORT software (ASSORT Pty. Ltd.), which allowed the calculation of the ocular residual astigmatism (ORA) and topography disparity (TD). Linear regression analysis was used for obtaining a linear expression relating ORA and posterior corneal astigmatism (PCA). Results: Mean magnitude of ORA was 0.79 D (SD: 0.43), with a normality range from 0 to 1.63 D. 90 eyes (89.1%) showed against-the-rule ORA. A weak although statistically significant correlation was found between the magnitudes of posterior corneal astigmatism and ORA (r = 0.34, p < 0.01). Regression analysis showed the presence of a linear relationship between these two variables, although with a very limited predictability (R2: 0.08). Mean magnitude of TD was 0.89 D (SD: 0.50), with a normality range from 0 to 1.87 D. Conclusion: The magnitude of the vector parameters ORA and TD is lower than 1.9 D in the healthy human eye.

Clinical utility of ocular residual astigmatism and topographic disparity vector indexes in subclinical and clinical keratoconus

Purpose. We aimed to characterize the distribution of the vector parameters ocular residual astigmatism (ORA) and topography disparity (TD) in a sample of clinical and subclinical keratoconus eyes, and to evaluate their diagnostic value to discriminate between these conditions and healthy corneas. Methods. This study comprised a total of 43 keratoconic eyes (27 patients, 17–73 years) (keratoconus group), 11 subclinical keratoconus eyes (eight patients, 11–54 years) (subclinical keratoconus group) and 101 healthy eyes (101 patients, 15–64 years) (control group). In all cases, a complete corneal analysis was performed using a Scheimpflug photography-based topography system. Anterior corneal topographic data was imported from it to the iASSORT software (ASSORT Pty. Ltd), which allowed the calculation of ORA and TD. Results. Mean magnitude of the ORA was 3.23 ± 2.38, 1.16 ± 0.50 and 0.79 ± 0.43 D in the keratoconus, subclinical keratoconus and control groups, respectively (p < 0.001). Mean magnitude of the TD was 9.04 ± 8.08, 2.69 ± 2.42 and 0.89 ± 0.50 D in the keratoconus, subclinical keratoconus and control groups, respectively (p < 0.001). Good diagnostic performance of ORA (cutoff point: 1.21 D, sensitivity 83.7 %, specificity 87.1 %) and TD (cutoff point: 1.64 D, sensitivity 93.3 %, specificity 92.1 %) was found for the detection of keratoconus. The diagnostic ability of these parameters for the detection of subclinical keratoconus was more limited (ORA: cutoff 1.17 D, sensitivity 60.0 %, specificity 84.2 %; TD: cutoff 1.29 D, sensitivity 80.0 %, specificity 80.2 %). Conclusion. The vector parameters ORA and TD are able to discriminate with good levels of precision between keratoconus and healthy corneas. For the detection of subclinical keratoconus, only TD seems to be valid.

Successful functionalization of superporous zeolite templated carbon using aminobenzene acids and electrochemical methods

A novel and selective electrochemical functionalization of a highly reactive superporous zeolite templated carbon (ZTC) with two different aminobenzene acids (2-aminobenzoic and 4-aminobenzoic acid) was achieved. The functionalization was done through potentiodynamic treatment in acid media under oxidative conditions, which were optimized to preserve the unique ZTC structure. Interestingly, it was possible to avoid the electrochemical oxidation of the highly reactive ZTC structure by controlling the potential limit of the potentiodynamic experiment in presence of aminobenzene acids. The electrochemical characterization demonstrated the formation of polymer chains along with covalently bonded functionalities to the ZTC surface. The functionalized ZTCs showed several redox processes, producing a capacitance increase in both basic and acid media. The rate performance showed that the capacitance increase is retained at scan rates as high as 100 mV s−1, indicating that there is a fast charge transfer between the polymer chains formed inside the ZTC porosity or the new surface functionalities and the ZTC itself. The success of the proposed approach was also confirmed by using other characterization techniques, which confirmed the presence of different nitrogen groups in the ZTC surface. This promising method could be used to achieve highly selective functionalization of highly porous carbon materials.

Improving drug discovery using hybrid softcomputing methods

Virtual screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface in order to find new hotspots, where ligands might potentially interact with, and which is implemented in last generation massively parallel GPU hardware, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods and concretely BINDSURF is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of the scoring functions used in BINDSURF we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, being this information exploited afterwards to improve BINDSURF VS predictions.

Improvement of Virtual Screening Predictions using Computational Intelligence Methods

Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. However, the accuracy of most VS methods is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of scoring functions used in most VS methods we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, this information being exploited afterwards to improve VS predictions.