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Abstract
Fierce competition as well as the recent financial crisis in financial and banking industries made credit scoring gain importance. An accurate estimation of credit risk helps organizations to decide whether or not to grant credit to potential customers. Many classification methods have been suggested to handle this problem in the literature. This paper proposes a model for evaluating credit risk based on binary quantile regression, using Bayesian estimation. This paper points out the distinct advantages of the latter approach: that is (i) the method provides accurate predictions of which customers may default in the future, (ii) the approach provides detailed insight into the effects of the explanatory variables on the probability of default, and (iii) the methodology is ideally suited to build a segmentation scheme of the customers in terms of risk of default and the corresponding uncertainty about the prediction. An often studied dataset from a German bank is used to show the applicability of the method proposed. The results demonstrate that the methodology can be an important tool for credit companies that want to take the credit risk of their customer fully into account.

Abstract
We have obtained spectral broadening by pumping a nonmicrostructured highly nonlinear fiber with a continuous wave signal from a Raman fiber laser. The experiment was simulated using a generalized Schrodinger equation containing the actual Raman response of the fiber as calculated from the experimental Raman gain. A different input-noise model, that reproduces well the power spectral density of the laser, was used and compared with others previously proposed. (C) 2013 Optical Society of America

Abstract
This paper presents an Android application to help in the teaching of Fourier series expansions in undergraduate Electrical Engineering. Consequently, it discusses the teaching of Fourier series concepts in connection with undergraduate Electrical Engineering education; some of the basic Fourier series theory is briefly reviewed. The presented Android application has been found useful in this context. As expected, the application has an easy-to-use, friendly interface, and can be viewed as a tool to help undergraduate students test and assess the Fourier series expansions on a typical set of signals, whose analytical Fourier series coefficients were found during the theoretical lectures. Additionally, some of its main characteristics include the ability for the students to control the total approximation error and the number of terms/harmonics used in the expansion.

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