Neural and Wavelet Network Models for Financial Distress Classification

This work analyzes the use of linear discriminant models, multi-layer perceptron neural networks and wavelet networks for corporate financial distress prediction. Although simple and easy to interpret, linear models require statistical assumptions that may be unrealistic. Neural networks are able to discriminate patterns that are not linearly separable, but the large number of parameters involved in a neural model often causes generalization problems. Wavelet networks are classification models that implement nonlinear discriminant surfaces as the superposition of dilated and translated versions of a single “mother wavelet” function. In this paper, an algorithm is proposed to select dilation and translation parameters that yield a wavelet network classifier with good parsimony characteristics. The models are compared in a case study involving failed and continuing British firms in the period 1997–2000. Problems associated with over-parameterized neural networks are illustrated and the Optimal Brain Damage pruning technique is employed to obtain a parsimonious neural model. The results, supported by a re-sampling study, show that both neural and wavelet networks may be a valid alternative to classical linear discriminant models.     

Bakery products enriched with phytosterols, α-tocopherol and β-carotene. Sensory evaluation and chemical comparison with market products

A sensory evaluation of croissants and magdalenas (Spanish muffins) has been carried out to compare them with the same products with phytosterol esters, α-tocopherol and β-carotene added. The subjects were habitual consumers and no differences were detected between the two types of product. Furthermore, the chemical composition (53 parameters) of croissants (n = 9) and muffins (n = 8) marketed in Spain, has been analysed and a nutritional evaluation performed on these products with regard to their suitability as carriers of the functional ingredients mentioned above. The chemical compositions are not similar and the main difference is related to fatty acids. Croissants are characterized by a high percentage of saturated fatty acids (SFA) (44.0 ± 3.8%), and the presence of trans fatty acids (TFA) (4.29 ± 1.48%). This contrasted with the high content of polyunsaturated fatty acids (PUFA) (62.1 ± 1.5%) in the muffins and the near absence of TFA (0.16 ± 0.23%). Furthermore, the large differences between the compositions of the commercial croissants and muffins (magdalenas) and, therefore, between their nutritional values, makes the use of phytosterols recommendable in muffins and, in general, in all bakery products in which the SFA + TFA represent ?20% of the fatty acid fraction.     

Combining multiway principal component analysis (MPCA) and clustering for efficient data mining of historical data sets of SBR processes.

A methodology based on Principal Component Analysis (PCA) and clustering is evaluated for process monitoring and process analysis of a pilot-scale SBR removing nitrogen and phosphorus. The first step of this method is to build a multi-way PCA (MPCA) model using the historical process data. In the second step, the principal scores and the Q-statistics resulting from the MPCA model are fed to the LAMDA clustering algorithm. This procedure is iterated twice. The first iteration provides an efficient and effective discrimination between normal and abnormal operational conditions. The second iteration of the procedure allowed a clear-cut discrimination of applied operational changes in the SBR history. Important to add is that this procedure helped identifying some changes in the process behaviour, which would not have been possible, had we only relied on visually inspecting this online data set of the SBR (which is traditionally the case in practice). Hence the PCA based clustering methodology is a promising tool to efficiently interpret and analyse the SBR process behaviour using large historical online data sets.     

More than meets the eye: traditions,nucleus and peripheries of the biographical research field

Scientometrics - The long and established roots of biographical research have been well documented, over the last decades, in a rich body of reflections about the main trends, traditions and...     

Factors influencing the scientific performance of Momentum grant holders: an evaluation of the first 117 research groups

Scientometrics - The Momentum program launched in 2009 provides funding of up to 1 million Euro to establish new, independent research groups at Hungarian academic institutions. Here, our aim was...