From Pettson and Findus to Festus and Mercury...and Back Again: A Comparison of Four Translations of Sven Nordqvist's Picture Books

In order to examine how children's literature might be translated, two different English translations of two Swedish picture books have been analyzed. The original Swedish books are Rävjakten and Pannkakstårtan by Sven Nordqvist. Rävjakten was translated as The Fox Hunt in 1988 and as The Fox Hunt in 2000. Pannkakstårtan was translated as Pancake Pie in 1985 and as The Birthday Cake in 1999. Literary translation in general, specific translation issues for children's literature, and trends in international English style have been considered. Analysis of the four texts has been made, with consideration given to the following areas: changes in illustrations, layout, or format; text changes; lexical choices; and retention, deletion, or modification of names and culturally specific references. The analysis revealed that the following tendencies were true for the later translations: foreignization of the text, word-for-word translation of the text, and a neutral international English variety.

Financial time series analysis : Chaos and neurodynamics approach

This work aims at combining the Chaos theory postulates and Artificial Neural Networks classification and predictive capability, in the field of financial time series prediction. Chaos theory, provides valuable qualitative and quantitative tools to decide on the predictability of a chaotic system. Quantitative measurements based on Chaos theory, are used, to decide a-priori whether a time series, or a portion of a time series is predictable, while Chaos theory based qualitative tools are used to provide further observations and analysis on the predictability, in cases where measurements provide negative answers. Phase space reconstruction is achieved by time delay embedding resulting in multiple embedded vectors. The cognitive approach suggested, is inspired by the capability of some chartists to predict the direction of an index by looking at the price time series. Thus, in this work, the calculation of the embedding dimension and the separation, in Takens‘ embedding theorem for phase space reconstruction, is not limited to False Nearest Neighbor, Differential Entropy or other specific method, rather, this work is interested in all embedding dimensions and separations that are regarded as different ways of looking at a time series by different chartists, based on their expectations. Prior to the prediction, the embedded vectors of the phase space are classified with Fuzzy-ART, then, for each class a back propagation Neural Network is trained to predict the last element of each vector, whereas all previous elements of a vector are used as features.