Empirical study of the influence of social groups in evacuation scenarios

The effects of social groups on pedestrian dynamics, especially in evacuation scenarios, have attracted some interest recently. However, due to the lack of reliable empirical data, most of the studies focussed on modelling aspects. It was shown that social groups can have a considerable effect, e.g. on evacuation times. In order to test the model predictions we have performed laboratory experiments of evacuations with different types and sizes of the social groups. The experiments have been performed with pupils of different ages. Parameters that have been considered are (1) group size, (2) strength of intra-group interactions, and (3) composition of the groups (young adults, children, and mixtures). For all the experiments high-quality trajectories for all participants have been obtained using the PeTrack software. This allows for a detailed analysis of the group effects. One surprising observation is a decrease of the evacuation time with increasing group size.Comment: 8 pages, 4 figures, to be published in Traffic and Granular Flow '15 (Springer, 2016

Herramientas digitales para la modelización matemática colaborativa en línea

[EN] To enable collaborative modeling activities online digital tools are essential. In this paper we present a holistic and adaptable concept for the development and implementation of modeling activities – which could especially be fruitful in times of homeschooling and distance learning. The concept is based on two digital tools: Jupyter Notebooks and a communication platform with video conferences.We carried out this concept in the context of two types of modeling activities: guided modeling days, where the students work on previously prepared and didactically developed digital learning material, and modeling weeks, in which the students work on open problems from research and industry very freely. In this paper the usage of Jupyter Notebook in modeling activities is presented and illustrated with the example of the optimization of a solar power plant. On top, we share our experiences in online modeling activities with high-school students in Germany.[ES] Para facilitar las actividades de modelización colaborativa en línea, las herramientas digitales son esenciales. En este trabajo presentamos un concepto holístico y adaptable para el desarrollo y la implementación de actividades de modelización – que podría ser especialmente provechoso en tiempos de educación a distancia. El concepto se basa en dos herramientas digitales: Jupyter Notebooks y una plataforma de comunicación con videoconferencia. Realizamos este concepto en el contexto de dos tipos de actividades de modelización matemática: días de modelización guiada, en los que los alumnos trabajan con material de aprendizaje digital previamente preparado y desarrollado didácticamente, y semanas de modelización, en las que los alumnos trabajan en problemas abiertos de la investigación o de la industria de forma libre. Se presenta el uso de Jupyter Notebook en las actividades de modelización y se ilustra con el ejemplo de la optimización de una planta solar. Además, compartimos nuestras experiencias en actividades de modelización en línea con estudiantes de secundaria en Alemania.Schönbrodt, S.; Wohak, K.; Frank, M. (2022). Digital Tools to Enable Collaborative Mathematical Modeling Online. Modelling in Science Education and Learning. 15(1):151-174. https://doi.org/10.4995/msel.2022.16269OJS151174151Blum, W. (2015). Quality Teaching of Mathematical Modelling: What Do We Know, What Can We Do? In S. J. Cho (Ed.), The Proceedings of the 12th International Congress on Mathematical Education (pp. 73-96). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-12688-3_9Blum, W., & Borromeo Ferri, R. (2009). Mathematical Modelling: Can it Be Taught and Learnt? Journal of Mathematical Modelling and Application, 1 (1), 45-58.Blum, W., Galbraith, P., Henn, H.-W., & Niss, M. (2007). Modelling and Applications in Mathematics Education. New York: Springer. https://doi.org/10.1007/978-0-387-29822-1Blum, W., & Lei, D. (2007). How do students and teachers deal with modelling problems? In C. Haines, P. Galbraith, W. Blum, & S. Khan (Eds.), Mathematical Modelling (ICTMA 12): Education, Engineering and Economics (pp. 222-231). Chichester: Horwood Publishing. https://doi.org/10.1533/9780857099419.5.221Borromeo Ferri, R. (2006, 04). Theoretical and empirical differentiations of phases in the modeling process. ZDM, 38(2), 86-95. doi: 10.1007/BF02655883 https://doi.org/10.1007/BF02655883Bruffee, K. (1995). Sharing Our Toys: Cooperative Learning versus Collaborative Learning. Change, 27 (1), 12-18. https://doi.org/10.1080/00091383.1995.9937722Computer-Based Maths. (n.d.). The Computational Thinking Process Poster. www.computationalthinking.org/helix. (accessed: 2021-01-23)Frank, M., Richter, P., Roeckerath, C., & Schönbrodt, S. (2018). Wie funktioniert eigentlich GPS? - Ein Computergestützter Modellierungsworkshop [How does GPS actually work? - A Computer-Supported Modeling Workshop]. In Greefrath, G. and Siller, S. (Ed.), Digitale Werkzeuge, Simulationen und mathematisches Modellieren [Digital tools, simulations and mathematical modeling] (pp. 137-163). Wiesbaden: Springer-Verlag. https://doi.org/10.1007/978-3-658-21940-6_7Frey, K. (2012). Die Projektmethode: Der Weg zum bildenden Tun [The project method: the path to educational action] (12th ed.; U. Schäfer, Ed.). Weinheim: Beltz.Gerhard, M., Hattebuhr, M., Schönbrodt, S., & Wohak, K. (2021). Aufbau und Einsatzmöglichkeiten des Lehr- und Lernmaterials [Structure and possible applications of the teaching and learning material]. In M. Frank & C. Roeckerath (Eds.), Neue Materialien für einen realitätsbezogenen Mathematikunterricht 9 [New materials for reality-based mathematics teaching 9]. Springer Spektrum.Greefrath, G., & Siller, H.-S. (2018). Digitale Werkzeuge, Simulationen und mathematisches Modellieren [Digital tools, simulations and mathematical modeling]. In Greefrath, G. and Siller, S. (Ed.), Digitale Werkzeuge, Simulationen und mathematisches Modellieren [Digital tools, simulations and mathematical modeling] (pp. 3-22). Wiesbaden: Springer-Verlag. https://doi.org/10.1007/978-3-658-21940-6_1Golub, J. (1988). Focus on Collaborative Learning. Urbana, Illinois: National Council of Teachers of English.Johnson, D., & Johnson, R. (1989). Cooperation and Competition: Theory and Research. Interaction Book Company.Johnson, D., & Johnson, R. (2014). Using technology to revolutionize cooperative learning: An opinion. Frontiers in Psychology, 5 , 1-3. https://doi.org/10.3389/fpsyg.2014.01156Panitz, T. (1999a). Collaborative versus cooperative learning: A comparison of the two concepts which will help us understand the underlying nature of interactive learning. ERIC Document Reproduction Service No. ED448443.Panitz, T. (1999b). The Motivational Benefits of Cooperative Learning. New directions for teaching and learning, 78. https://doi.org/10.1002/tl.7806Roberts, T. (2004). Preface. In T. Robert (Ed.), Online Collaborative Learning. Hershey, London: Information Science Publishing.Nason R. and Woodruff E. (2004). Online Collaborative Learning in Mathematics: Some Necessary Innovations. Online Collaborative Learning. Robert T.S (Ed.) pp 103-131 Information Science Publishing, Hershey (London) https://doi.org/10.4018/978-1-59140-174-2.ch005Siller, H.-S., & Greefrath, G. (2010). Mathematical Modelling in Class regarding to Technology. In Proceedings of the 6th CERME conference (pp. 2136-2145). (CERME-Proceedings)Greefrath G.and Siller H-St (2018). Digitale Werkzeuge, Simulationen und mathematisches Modellieren (Digital tools, simulations and mathematical modeling). Digitale Werkzeuge, Simulationen und mathematisches Modellieren (Digital tools, simulations and mathematical modeling). Greefrath G. and Siller S. (Eds.) pp. 3-22. Springer-Verlag (Wiesbaden) https://doi.org/10.1007/978-3-658-21940-6_1Hänze, M., Schmidt-Weigand, F., & Staudel, L. (2010). Gestufte Lernhilfen [Staggered learning aids]. In S. Boller & R. Lau (Eds.), Innere Differenzierung in der Sekundarstufe II. Ein Praxishandbuch für Lehrer/innen [Inner differentiation in upper secondary education. A practical handbook for teachers] (pp. 63-73). Weinheim: Beltz.Kaiser, G., & Schwarz, B. (2010). Authentic Modelling Problems in Mathematics Education - Examples and Experiences. Journal fur Mathematik-Didaktik, 31 , 51-76. https://doi.org/10.1007/s13138-010-0001-3Krajcik J.S. and Blumenfeld Ph.C. (2005). Project-Based Learning. The Cambridge Handbook of the Learning Sciences. Sawyer, R. Keith (Ed.) pp 317-334. Cambridge Handbooks in Psychology. Cambridge University Press (Cambridge) doi:10.1017/CBO9780511816833.020 https://doi.org/10.1017/CBO9780511816833.020Ludwig, M. (1997). Projekte im Mathematikunterricht des Gymnasiums [Projects in mathematics lessons of the high school] (phdthesis). Julius-Maximilians-Universitöt Würzburg. https://doi.org/10.1007/BF03338857Maaß, K. (2010). Classifiation Scheme for Modelling Tasks. Journal fur Mathematik-Didaktik, 31 (2), 285-311. doi: 10.1007/s13138-010-0010-2 https://doi.org/10.1007/s13138-010-0010-2Bock, W., & Bracke, M. (2015). Applied School Mathematics - Made in Kaiserslautern. In H. Neuntzer & D. Prätzel-Wolters (Eds.), Currents in industrial mathematics: From concepts to research to education (pp. 403-437). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-662-48258-2Kronberg, R., York-Barr, J., Arnold, K., Gombos, S., Truex, S., Vallejo, B., & Stevenson, J. (1997). Differentiated Teaching & Learning in Heterogeneous Classrooms: Strategies for Meeting the Needs of All Students. Washington D.C.: ERIC Clearinghouse. Retrieved from https://eric.ed.gov/?id=ED418538Stahl, G., Koschmann, T., & Suthers, D. (2006). Computer-supported collaborative learning: An historical perspective. In R. Sawyer (Ed.), Cambridge handbook of the learning sciences (pp. 409-426). Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511816833.025Niss, M. (1992). Applications and modelling in school mathematics - directions for future development. Roskilde: IMFUFA Roskilde Universitetscenter.Schmidt, L. (2019). Machine Learning: automatische Bilderkennung mit Mathematik?! - Ein Lehr-Lern- Modul im Rahmen eines mathematischen Modellierungstages für Schülerinnen und Schüler der Sekundarstufe II [Machine Learning: automatic image recognition with mathematics?! - A teaching-learning module in the context of a mathematical modeling day for high school students]. www.cammp.online Masterthesis4druck.pdf. (Master's thesis, RWTH Aachen, accessed: 2021-02-23)Schönbrodt, S., & Frank, M. (2020). Schüler/innen forschen zu erneuerbaren Energien - Optimierung eines Solarkraftwerks [Students research on renewable energies - Optimization of a solar power plant]. In H.-S. Siller, W. Weigel, & J. F. Worler (Eds.), Beiträge zum Mathematikunterricht [Contributions to mathematics education] (pp. 1534-1534). Münster: WTM-Verlag.Schönbrodt, S. (2019). Maschinelle Lernmethoden für Klassifizierungsprobleme - Perspektiven für die mathematische Modellierung mit Schülerinnen und Schülern [Machine learning methods for classification problems - perspectives for mathematical modeling with students]. Wiesbaden: Springer Spektrum. https://doi.org/10.1007/978-3-658-25137-6Vos, P. (2011). What is 'authentic' in the teaching and learning of mathematical modelling? In G. Kaiser, W. Blum, R. Borromeo Ferri, & G. Stillman (Eds.), Trends in Teaching and Learning of Mathematical Modelling, ICTMA 14 (pp. 713-722). Dordrecht: Springer. https://doi.org/10.1007/978-94-007-0910-2_68Winter, H. (1995). Mathematikunterricht und Allgemeinbildung [Mathematics education and general education]. Mitteilungen der Gesellschaft für Didaktik der Mathematik, 61 , 37-46. Retrieved 23 January, 2021, from https://ojs.didaktik-der-mathematik.de/index.php/mgdm/article/view/69/80Wohak, K., & Frank, M. (2019). Complex Modeling: Insights into our body through computer tomography - perspectives of a project day on inverse problems. In U. T. Jankvist, M. van den Heuvel-Panhuizen, & M. Veldhuis (Eds.), Eleventh Congress of the European Society for Research in Mathematics Education (pp. 4815-4822). Utrecht: Freudenthal Group.Wohak, K., Sube, M., Schönbrodt, S., Frank, M., & Roeckerath, C. (2021). Authentische und relevante Modellierung mit Schülerinnen und Schülern an nur einem Tag?! [Authentic and relevant modeling with students in just one day?!]. In M. Bracke, M. Ludwig, & K. Vorhölter (Eds.), Modellierungsprojekte mit Schülerinnen und Schülern. 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Differentiation-associated urothelial cytochrome P450 oxidoreductase predicates the xenobiotic-metabolising activity of “luminal” muscle-invasive bladder cancers

Extra-hepatic metabolism of xenobiotics by epithelial tissues has evolved as a self-defence mechanism but has potential to contribute to the local activation of carcinogens. Bladder epithelium (urothelium) is bathed in excreted urinary toxicants and pro-carcinogens. This study reveals how differentiation affects cytochrome P450 (CYP) activity and the role of NADPH:P450 oxidoreductase (POR). CYP1A1 and CYP1B1 transcripts were inducible in normal human urothelial (NHU) cells maintained in both undifferentiated and functional barrier-forming differentiated states in vitro. However, ethoxyresorufin O-deethylation (EROD) activity, the generation of reactive BaP metabolites and BaP-DNA adducts, were predominantly detected in differentiated NHU cell cultures. This gain-of-function was attributable to the expression of POR, an essential electron donor for all CYPs, which was significantly upregulated as part of urothelial differentiation. Immunohistology of muscle-invasive bladder cancer (MIBC) revealed significant overall suppression of POR expression. Stratification of MIBC biopsies into luminal and basal groups, based on GATA3 and cytokeratin 5/6 labeling, showed POR over-expression by a subgroup of the differentiated luminal tumors. In bladder cancer cell lines, CYP1-activity was undetectable/low in basal PORlo T24 and SCaBER cells and higher in the luminal POR over-expressing RT4 and RT112 cells than in differentiated NHU cells, indicating that CYP-function is related to differentiation status in bladder cancers. This study establishes POR as a predictive biomarker of metabolic potential. This has implications in bladder carcinogenesis for the hepatic versus local activation of carcinogens and as a functional predictor of the potential for MIBC to respond to prodrug therapies