Michael Schratz / Johanna F. Schwarz / Tanja Westfall-Greiter: Lernen als bildende Erfahrung, Vignetten in der Praxisforschung, Mit einem Vorwort von Käte Meyer-Drawe und Beiträgen von Horst Rumpf, Carol Ann Tomlinson, Mike Rose u.a. Innsbruck: Studienverlag 2012 [Rezension]

Rezension von: Michael Schratz / Johanna F. Schwarz / Tanja Westfall-Greiter: Lernen als bildende Erfahrung, Vignetten in der Praxisforschung, Mit einem Vorwort von Käte Meyer-Drawe und Beiträgen von Horst Rumpf, Carol Ann Tomlinson, Mike Rose u.a., Innsbruck: Studienverlag 2012 (161 S.; ISBN 978-3706551182)

Estudio filológico-literario comparativo del relato del funeral de Augusto contado por el biógrafo Suetonio ("Aug."100) y por el historiador Tácito ("Ann."1.8-10)

[ES] Este trabajo se propone el análisis comparativo, bajo el punto de vista filológico y literario, del relato del funeral de Augusto en el biógrafo Suetonio ("Aug."100) y en el historiador Tácito ("Ann."1.8-10). En primer lugar se remarca la importancia política y social del "funus publicum" en Roma, objeto del relato. Sigue un detenido y minucioso análisis en el que se trata de poner de relieve las semejanzas y, sobre todo, las diferencias entre el relato de Suetonio y el de Tácito en cuanto a los datos aportados, la secuenciación de los mismos y, especialmente, en cuanto a la focalización que sobre ellos y sobre el conjunto del relato muestran uno y otro autor. Finalmente se trata de explicar las diferencias, importantes, entre ambos relatos en virtud del género literario (biografía-historia), de la intención y propósito concretos de cada una de las dos obras ("Vita Augusti" de Suetonio y "Annales" de Tácito) y de la distinta personalidad intelectual de cada uno de los dos autores.

Rolf Werning / Ann-Katrin Arndt (Hrsg.): Inklusion: Kooperation und Unterricht entwickeln. Bad Heilbrunn: Klinkhardt 2013 (248 S.) [Rezension]

Rezension von: Rolf Werning/ Ann-Katrin Arndt (Hrsg.): Inklusion: Kooperation und Unterricht entwickeln. Bad Heilbrunn: Klinkhardt 2013 (248 S.; ISBN 978-3-7815-1898-8)

Ludolphus Chartusianus In Christi seruatoris vitam, ex intimis sacrosancti Euangelij collectam visceribus, cui adiecta quoq[ue] est Ann[a]e vita summa vigiliq[ue] diligentia concinnata, no[n] tacitis castissimi coniugis Ioachim encomiis ...

Sign.: []2, A10, a-z8, A-V8, X12

Lecture by Ann Hamilton, given at Florida International University, April 3, 2009

Ann Hamilton discusses her work. Introduction by Carol Damian.

Improvement of the prediction accuracy and efficiency of hot strength of austenitic steels with optimised ANN training schemes

The hot strength of austenitic steels of different carbon contents was modelled using an artificial neural network (ANN) model with optimum training data. As training data employed in a traditional neural network model were randomly selected from experimental data, they were not representative and the prediction accuracy and efficiency were therefore significantly affected. In this work, only representatively experimental data were used for training and during the procedure, one tenth of the training data extracted from experiment were used for testing the training model and terminating the modelling. The effects of the carbon con tent on flow stress, peak strains and peak stresses observed from the experiment for both training and test data were accurately represented with the ANN scheme reported in this work.

GA-ANN Short-Term Electricity Load Forecasting

This paper presents a methodology for short-term load forecasting based on genetic algorithm feature selection and artificial neural network modeling. A feed forward artificial neural network is used to model the 24-h ahead load based on past consumption, weather and stock index data. A genetic algorithm is used in order to find the best subset of variables for modeling. Three data sets of different geographical locations, encompassing areas of different dimensions with distinct load profiles are used in order to evaluate the methodology. The developed approach was found to generate models achieving a minimum mean average percentage error under 2 %. The feature selection algorithm was able to significantly reduce the number of used features and increase the accuracy of the models.