1,755 research outputs found

    An integrated platform for intuitive mathematical programming modeling using LaTeX

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    This paper presents a novel prototype platform that uses the same LaTeX mark-up language, commonly used to typeset mathematical content, as an input language for modeling optimization problems of various classes. The platform converts the LaTeX model into a formal Algebraic Modeling Language (AML) representation based on Pyomo through a parsing engine written in Python and solves by either via NEOS server or locally installed solvers, using a friendly Graphical User Interface (GUI). The distinct advantages of our approach can be summarized in (i) simplification and speed-up of the model design and development process (ii) non-commercial character (iii) cross-platform support (iv) easier typo and logic error detection in the description of the models and (v) minimization of working knowledge of programming and AMLs to perform mathematical programming modeling. Overall, this is a presentation of a complete workable scheme on using LaTeX for mathematical programming modeling which assists in furthering our ability to reproduce and replicate scientific work

    An integrated platform for intuitive mathematical programming modeling using LaTeX

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    This paper presents a novel prototype platform that uses the same LaTeX mark-up language, commonly used to typeset mathematical content, as an input language for modeling optimization problems of various classes. The platform converts the LaTeX model into a formal Algebraic Modeling Language (AML) representation based on Pyomo through a parsing engine written in Python and solves by either via NEOS server or locally installed solvers, using a friendly Graphical User Interface (GUI). The distinct advantages of our approach can be summarized in (i) simplification and speed-up of the model design and development process (ii) non-commercial character (iii) cross-platform support (iv) easier typo and logic error detection in the description of the models and (v) minimization of working knowledge of programming and AMLs to perform mathematical programming modeling. Overall, this is a presentation of a complete workable scheme on using LaTeX for mathematical programming modeling which assists in furthering our ability to reproduce and replicate scientific work

    The MESSAGEix Integrated Assessment Model and the ix modeling platform (ixmp)

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    The MESSAGE Integrated Assessment Model (IAM) developed by IIASA has been a central tool of energy-environment-economy systems analysis in the global scientific and policy arena. It played a major role in the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC); it provided marker scenarios of the Representative Concentration Pathways (RCPs) and the Shared Socio-Economic Pathways (SSPs); and it underpinned the analysis of the Global Energy Assessment (GEA). Alas, to provide relevant analysis for current and future challenges, numerical models of human and earth systems need to support higher spatial and temporal resolution, facilitate integration of data sources and methodologies across disciplines, and become open and transparent regarding the underlying data, methods, and the scientific workflow. In this manuscript, we present the building blocks of a new framework for an integrated assessment modeling platform; the \ecosystem" comprises: i) an open-source GAMS implementation of the MESSAGE energy++ system model integrated with the MACRO economic model; ii) a Java/database backend for version-controlled data management, iii) interfaces for the scientific programming languages Python & R for efficient input data and results processing workflows; and iv) a web-browser-based user interface for model/scenario management and intuitive \drag-and-drop" visualization of results. The framework aims to facilitate the highest level of openness for scientific analysis, bridging the need for transparency with efficient data processing and powerful numerical solvers. The platform is geared towards easy integration of data sources and models across disciplines, spatial scales and temporal disaggregation levels. All tools apply best-practice in collaborative software development, and comprehensive documentation of all building blocks and scripts is generated directly from the GAMS equations and the Java/Python/R source code

    Engaging undergraduate students in the Philippines in photonics research with a novel publication-driven online mentoring approach

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    This paper discusses a publication-driven, education-by-research model of engaging undergraduate students in Photonics/Optics/Physics research by requiring that all activities lead to journal publication within a year. It is based on a non-traditional university-industry collaboration arrangement that has the following unique features. First, it utilizes offshore-based alumni (in the USA) who are active in science and technology research as mentors for the students (in the Philippines) in conjunction with the university professor advisers. We adopt inexpensive online collaboration technologies to support their virtual presence. Second, our research topic “fuses” uniquely two separate courses of study, namely: (i) the traditional Special Relativity in Physics and (ii) the fast emerging Photonic Integrated Circuits (PICs), into an innovative research area we have called “Special-Relativity-on-a-Chip”. It has the flavor of physics education combined with a taste of the latest in PICs technology. It enhances the textbook understanding of Physics/Engineering while providing potential publication opportunities. Third, the research activities are focused mainly on modelling, simulations and design. These activities strengthen students’ learning since they concentrate on and master specific mathematical/Physics/programming skills. The goal of the project is to publish 5 journal papers, and so far, (i) 3 journal papers have been published, (ii) 1 international conference paper has been accepted for oral presentation, and (iii) 3 national conference papers have been submitted for publication. We also examine the existing research structure in the university to ensure the project’s success and share best practices

    Herramientas digitales para la modelización matemática colaborativa en línea

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    [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. Realitätsbezüge im Mathematikunterricht [Modeling projects with students. Reality references in mathematics lessons] (pp. 37-50). Wiesbaden: Springer Spektrum. https://doi.org/10.1007/978-3-658-33012-5_4Vorholter K. and Freiwald J. (2022). Concept and structure of the Hamburg Modeling Days Modelling in Science Education and Learning. (In this issue).Hattebuhr M. and Frank M. (2022). Compartment models to study human impact on climate change Modelling in Science Education and Learning. (In this issue)

    Digital Tools to Enable Collaborative Mathematical Modeling Online

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    State diagram design application for EDAG Production Solutions

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    En este trabajo de fin de grado se presenta el desarrollo realizado de una aplicación de escritorio para la empresa EDAG Production Solutions GmbH & Co. KG, dedicada a implementar soluciones de producción para sus clientes. Dentro de la empresa, los desarrolladores de software del departamento de producción IT emplean distintas herramientas software al trabajar en las diferentes fases que un proyecto requiere, y, como le ocurre a otras empresas, para ciertos casos el mercado no ofrece soluciones accesibles cuando se trata de resolver problemas concretos. En concreto, EDAG Production Solutions GmbH & Co. KG necesita para el departamento de producción IT una aplicación de escritorio para diseñar diagramas que representan el comportamiento de un sistema y que, a partir de este diseño, genere automáticamente una plantilla del diagrama escrito en Java, ya que esta tarea se realiza manualmente y el tiempo de desarrollo de un proyecto se reducirá notablemente con esta herramienta. Actualmente, la empresa no ve adecuadas las soluciones existentes ya que o no son software libre o no permiten generar código a partir de un diagrama de la forma que necesitan. Además, estas herramientas no son intuitivas y demasiado complejas para lo que realmente necesita la empresa, por ello buscan una solución más adecuada a sus necesidades. La aplicación desarrollada como trabajo final de grado permite esta funcionalidad que EDAG Production Solutions GmbH & Co. KG necesita. Además de la importación y exportación de diagramas en formato GraphML. Esta aplicación se ha desarrollado utilizando Angular, un framework para desarrollar aplicaciones web, sobre Electron, un framework de código abierto que permite ejecutar componentes para aplicaciones web en una aplicación de escritorio compatible con los sistemas operativos para ordenador más utilizados

    Mathematical Modeling Course Creation and Implementation Using Cocalc

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    Being commonly used worldwide, e-learning courses are only starting to evolve in regional Russian universities. Unlike the most Moodle-using colleagues, the authors created mathematical modeling e-course with not so well-known in Russia learning management system Cocalc. The biggest advantage of Cocalc is the ability to write and compile program code inside, with a lot of programming languages supported. Using statistical methods, the authors assessed the results of the course implementation, revealing that students began to perform tasks faster, learned to program fluently in R and Latex and increased their English level knowledge
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