A 2000 mm long saturated laboratory soil column was used to simulate soil aquifer treatment under saturated conditions to assess the removal of chemical and biochemical oxygen demand (COD and BOD), dissolved organic carbon (DOC), nitrogen and phosphate, using high strength artificial wastewater. The removal rates were determined under a combination of constant hydraulic loading rates (HLR) and variable COD concentrations as well as variable HLR under a constant COD. Within the range of COD concentrations considered (42 mg L(-)(1)-135 mg L(-)(1)) it was found that at fixed hydraulic loading rate, a decrease in the influent concentrations of dissolved organic carbon (DOC), biochemical oxygen demand (BOD), total nitrogen and phosphate improved their removal efficiencies. At the high COD concentrations applied residence times influenced the redox conditions in the soil column. Long residence times were detrimental to the removal process for COD, BOD and DOC as anoxic processes and sulphate reduction played an important role as electron acceptors. It was found that total COD mass loading within the range of 911 mg d(-)(1)-1780 mg d(-)(1) applied as low COD wastewater infiltrated coupled with short residence times would provide better effluent quality than the same mass applied as a COD with higher concentration at long residence times. The opposite was true for organic nitrogen where relatively high concentrations coupled with long residence time gave better removal efficiency.

O objetivo deste trabalho foi desenvolver um modelo para reconhecer e reproduzir padrões de viagens encadeadas a pé. O processo de modelagem foi conduzido através da aplicação das técnicas das Redes Neurais Artificiais (RNAs), utilizando-se de uma rede estática MLP e de rede dinâmica Elman. A análise do desempenho do modelo foi baseada nos dados de uma pesquisa de Origem-Destino realizada, em 1987, pelo METRÔ-SP na Região Metropolitana de São Paulo. Na modelagem foi fixado o modo de viagem a pé, e, na abordagem seqüencial, padrões de viagens individuais foram representados em termos de dois componentes: duração da viagem e tipo de atividades. A análise foi realizada partindo da classificação geral e específica para cada segmento do encadeamento de viagens, o que permitiu a comparação dos resultados entre padrões de viagens observados e os reproduzidos pelas redes. Na classificação geral, cinco dos padrões previstos com maior freqüência pelas RNAs representaram em média 58,9% dos indivíduos no conjunto de dados usado para testar o desempenho do modelo. Para o vetor de duas e quatro viagens, as redes neurais reproduziram 50% das durações de viagem e 90% das atividades, tais como Trabalho e Escola. Embora esses resultados não pareçam muito robustos, não significa que eles estejam errados. As porcentagens acima representam a probabilidade de uma pessoa realizar viagens com aquelas durações ou tipo de atividades. / The main objective of this work was to develop a model for recognizing and reproduzing trip-chaining patterns by walk. The process of modeling was conducted applying the techniques of Artificial Neural Networks (ANNs), by using one of the static networks MLP and the Elman dynamic network. The analysis of the performance of the model was based on the origin-destination home-interview survey carried out by METRÔ-SP in São Paulo Metropolitan Area in 1987. The mode of trip by walk was fixed in the model, and, in the sequential approach, individual travel patterns were represented in terms of two components: trip duration and activity type. The analysis was accomplished starting from the general and specific classifications for each segment of the chained trips, which allowed the comparison of the results between the observed travel patterns and reproduced ones through ANNs. In general classification, 5 of the patterns most frequently predicted by the ANNs represented 58.9% of the individuals in the dataset used for testing the model performance. For the vectors of two and four trips, the neural networks reproduced 50% of trip durations and 90% of the activities, such as work and school. Although those results seem not so robust, it does not mean that they are wrong. The percentages above represent the probability of a person making trips with those durations or type of activities.

Das Internet wird zunehmend zur IuK-Plattform für Lehre und Forschung. Vom Wandel der Hochschulen sind auch die Hochschulbibliotheken und deren Strukturen sowie Dienstleistungen betroffen. Doch sind sie für diesen Wandel gerüstet? In Deutschland ist eine regional verteilte Informationsinfrastruktur im Bereich der Film- und Medienwissenschaft anzutreffen. Verteilte Ressourcen, seien es Medien oder Internetangebote, sind zwar vielfältig vorhanden, jedoch nur vereinzelt nachgewiesen. Ziel der in 2001 durchgeführten Exploration war es, ein Konzept für eine Virtuelle Film- und Medienbibliothek (Virtual Library Film and Media Studies) zu erstellen. So wurde der differenzierten Informationsbedarf von Dozenten. Es erwies sich als sinnvoll, als ersten Analysepunkt einzelne Internetangebote von vorhandenen ausgewählten virtuellen Fachportalen (DFG gefördert) zu untersuchen. Experteninterviews mit der Fokussierung auf die Lehrenden von den zwei größten Filmhochschulen in Deutschland (Potsdam und München) waren dabei der zentrale Kern. Bei dieser Exploration wurde anhand der Interview-Auswertungen mittels Transkription und anschließender Inhaltsanalyse ein genaues Bedarfsprofil der wissenschaftlichen Zielgruppe herausgearbeitet. Abgerundet wurden diese Untersuchungen durch die quantitative Methode der Fragebogen, die an Filmbibliothekare versendet wurden. Es sollte herausgefunden werden, wie diese Intermediäre den Informationsbedarf der Filmdozenten einschätzten. Sechs Portalsegmente wurden auf diesen Ergebnissen entwickelt. Bei der rasanten Entwicklung der digitalen Fachinformation, gerade im Internet, müssen Bibliothekare die Veränderungen permanent beobachten, um Ihre Dienstleistungen zu aktualisieren. / More and more the internet becomes the information and communication platform for teachings and research. University libraries and their structures as well as their services are concerned with the changes of universities in general. But are the university libraries prepared for this change? In Germany a regionally distributed infrastructure of information within the range of film and media science to be found. Distributed resources in the form of media or offers in internet are variously present, but only occasionally detectable. One of the aims of the exploration accomplished in 2001 was to provide a concept for a VIRTUAL LIBRARY FILM AND MEDIA STUDIES. For this reason the different demands of lectures for informations were investigated. Therefore a mix of qualitative and quantitative methods were used. As the first point of analysis a comparison of the individual internet offers was used on available selected virtual specialized portals (DFG promoted). Expert interviews to bring into focus the lecturers of the two largest film universities in Germany (Potsdam and Munich) formed the central intention. With this exploration analysing the interviews by means of transcription and following analysis of contents an exact need profile has been worked out. These investigations were rounded off by the quantitative method of the questionnaires, which were sent to film librarians. It had to be found out, how the intermedians assess the lecturers need of media information. With the rapid development of digitally specialized information, also in the internet, librarians must permanently observe the changes, in order to update their services.

O objetivo deste trabalho foi desenvolver um modelo para reconhecer e reproduzir padrões de viagens encadeadas a pé. O processo de modelagem foi conduzido através da aplicação das técnicas das Redes Neurais Artificiais (RNAs), utilizando-se de uma rede estática MLP e de rede dinâmica Elman. A análise do desempenho do modelo foi baseada nos dados de uma pesquisa de Origem-Destino realizada, em 1987, pelo METRÔ-SP na Região Metropolitana de São Paulo. Na modelagem foi fixado o modo de viagem a pé, e, na abordagem seqüencial, padrões de viagens individuais foram representados em termos de dois componentes: duração da viagem e tipo de atividades. A análise foi realizada partindo da classificação geral e específica para cada segmento do encadeamento de viagens, o que permitiu a comparação dos resultados entre padrões de viagens observados e os reproduzidos pelas redes. Na classificação geral, cinco dos padrões previstos com maior freqüência pelas RNAs representaram em média 58,9% dos indivíduos no conjunto de dados usado para testar o desempenho do modelo. Para o vetor de duas e quatro viagens, as redes neurais reproduziram 50% das durações de viagem e 90% das atividades, tais como Trabalho e Escola. Embora esses resultados não pareçam muito robustos, não significa que eles estejam errados. As porcentagens acima representam a probabilidade de uma pessoa realizar viagens com aquelas durações ou tipo de atividades. / The main objective of this work was to develop a model for recognizing and reproduzing trip-chaining patterns by walk. The process of modeling was conducted applying the techniques of Artificial Neural Networks (ANNs), by using one of the static networks MLP and the Elman dynamic network. The analysis of the performance of the model was based on the origin-destination home-interview survey carried out by METRÔ-SP in São Paulo Metropolitan Area in 1987. The mode of trip by walk was fixed in the model, and, in the sequential approach, individual travel patterns were represented in terms of two components: trip duration and activity type. The analysis was accomplished starting from the general and specific classifications for each segment of the chained trips, which allowed the comparison of the results between the observed travel patterns and reproduced ones through ANNs. In general classification, 5 of the patterns most frequently predicted by the ANNs represented 58.9% of the individuals in the dataset used for testing the model performance. For the vectors of two and four trips, the neural networks reproduced 50% of trip durations and 90% of the activities, such as work and school. Although those results seem not so robust, it does not mean that they are wrong. The percentages above represent the probability of a person making trips with those durations or type of activities.

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