Biografieforschung: theoretische Perspektiven und methodologische Konzepte für eine re-konstruktive Geschlechterforschung

Die Biografieforschung bezeichnet einen komplexen Forschungsansatz, der auf eine lange Geschichte des wissenschaftlichen Interesses an "persönlichen Dokumenten" verweisen kann. Sie ist eine voraussetzungsvolle Forschungsperspektive, die sich in zentralen Aspekten ihres Vorgehens auf Biografien als theoretisches Konzept, als historisch-empirischen Gegenstand und als komplexe methodologische Strategie bezieht. Andere Begriffe, welche oftmals synonym gebraucht, in der Biografieforschung aber systematisch unterschieden werden, sind "Lebensgeschichte" und "Lebenslauf". Die Autorin skizziert die Perspektiven einer rekonstruktiven Geschlechterforschung innerhalb der Biografieforschung, wozu sie auf die Differenzierungen empirischer Forschung, die methodologischen Prinzipien sowie auf Datenerhebung und Datenanalyse eingeht. Sie hebt insbesondere drei Kontextrelationen bei der Interpretation eines biografischen Textes hervor: Biografie, Interaktion, kulturelle Muster und soziale Regeln. Das skizzierte Konzept von Biografieforschung begreift sie als ein offenes Programm, das vielfältige Anknüpfungspunkte zu aktuellen theoretischen Diskussionen in der Geschlechterforschung aufweist. (ICI2

Polyvinylidene fluoride dense membrane for the pervaporation of methyl acetate-methanol mixtures

In the context of pervaporative separation of methyl acetate-methanol binary mixtures, polyvinylidene fluoride (PVDF) pervaporation membranes were prepared in order to selectively separate methyl acetate by pervaporation.The PVDF membranes were compared to chlorinated polypropylene and polyvinyl alcohol dense membranes (developed for the same application) by pervaporation of a quaternary equimolar methyl acetate-methanol-. n-butyl acetate-. n-butanol reference feed. PVDF membranes resulted in a permeate richer in methyl acetate than the corresponding quaternary feed, and in a selectivity methyl acetate/methanol higher than one for the same mixture. Chlorinated polypropylene and polyvinyl alcohol membranes gave a permeate richer of both methanol and methyl acetate than the corresponding feed and were thus not applicable during the extensive study on the binary methyl acetate-methanol mixture.These preliminary results performances were also assessed with the Hansen solubility parameters theory, which resulted inadequate for predicting the behavior of the two glassy-state and the rubbery-state (PVDF) polymeric membranes during pervaporation.Thus, pervaporation of methyl acetate-methanol binary mixtures by PVDF membranes was studied experimentally using feed concentrations in the range 11-78mol% methyl acetate, and temperatures in the range 30-44°C, resulting in separation factors methyl acetate/methanol above 1 (up to 2.1 at 11mol% methyl acetate in the feed), in the whole feed concentration range. High total fluxes up to 35kgm-2h-1 (at 78mol% methyl acetate and 44°C) were also observed.Interestingly, when removing the contribution of the driving force to the separation, for concentrations below 60. mol% methyl acetate in the feed the membrane was selective for methanol, while for higher concentrations it was selective for methyl acetate (values up to 1.44).This work shows that methyl acetate selective membranes (starting from the improvement of PVDF membranes) are realistic and can be employed in order to concentrate low content methyl acetate-methanol industrial waste streams

Predicting subsurface soil layering and landslide risk with Artificial Neural Networks: a case study from Iran

This paper is concerned principally with the application of ANN model in geotechnical engineering. In particular the application for subsurface soil layering and landslide analysis is discussed in more detail. Three ANN models are trained using the required geotechnical data obtained from the investigation of study area. The quality of the modeling is further improved by the application of some controlling techniques involved in ANN. Based on the obtained results and considering that the test data were not presented to the network in the training process, it can be stated that the trained neural networks are capable of predicting variations in the soil profile and assessing the landslide hazard with an acceptable level of confidence