Relevant bibliographies by topics / Modelling

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Modelling'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 November 2024

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Journal articles on the topic "Modelling"

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Rahnev, Mariyan. "TRANSPORTATION SYSTEMS MODELLING." Journal Scientific and Applied Research 18, no. 1 (March 3, 2020): 28–40. http://dx.doi.org/10.46687/jsar.v18i1.277.

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As it is with every activity, the transport network can be organized in many ways by using different technical means and technologies. Taking certain management decisions is connected with the choice of one of the many possible options for realization of the transportation process. This is done with the help of one or a few indicators which serve as a criterion of efficiency, e.g. exploitation costs, profit, safety, time, etc. The research of the transport objects functioning and the optimization of their parameters is a very complicated task in most of the cases. In such cases the solutions of the problems which concern us are derived by the research and experimentation with a substitute (analogue) of the real object, specially designed for this purpose. This analogue is called a model and the process of its creation is called modelling.
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Qumer Gill, Asif, and Muhammad Atif Qureshi. "Adaptive Enterprise Architecture Modelling." Journal of Software 10, no. 5 (May 2015): 628–38. http://dx.doi.org/10.17706/jsw.10.5.628-638.

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Ivanov, Sabin. "MODELLING OF GEOGRAPHICAL REALITY." Journal Scientific and Applied Research 20, no. 1 (November 15, 2020): 33–39. http://dx.doi.org/10.46687/jsar.v20i1.302.

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From a cartographic point of view, mapping modeling can be defined as a method of creating mathematical models that are similar copies of objects of reality. They are divided into descriptive (visual) and predictive. This is the general classification of cartographic models. Descriptive models show existing objects or phenomena, as well as the links between them, presenting the results in an appropriate form that allows the user to identify them immediately. This type of model is simple and easy to create, and this reason makes it the most commonly used. The second type of cartographic model shows the predictive possibilities of the descriptive model and is called a predictive cartographic model. Predictive models require the introduction of factors and parameters that are important for the functioning of phenomena appearing in a particular territory.
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Y, Kovalyov, and Kalashnikova V. "Human life cycle modelling." Modern problems of modeling 25 (June 19, 2023): 110–22. http://dx.doi.org/10.33842/2313-125x-2023-25-110-122.

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Jørgensen, Sven Erik. "Ecological Modelling by `Ecological Modelling'." Ecological Modelling 100, no. 1-3 (December 1997): 5–10. http://dx.doi.org/10.1016/s0304-3800(97)00158-0.

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Bench-Capon, T. J. M., and A. M. McEnery. "Modelling devices and modelling speakers." Interacting with Computers 1, no. 2 (August 1989): 220–24. http://dx.doi.org/10.1016/0953-5438(89)90029-5.

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Adcock, Chris. "Modelling reality and personal modelling." International Journal of Forecasting 10, no. 3 (November 1994): 466–69. http://dx.doi.org/10.1016/0169-2070(94)90078-7.

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Chiorescu, Dan, Gheorghe Nagîț, Oana Dodun, and Ștefan Mihalache. "The Influence of Dimensional-Type Mathematical Model for Cylindrical Deep Drawing on the Distribution of Extreme Unitary Stresses." Applied Mechanics and Materials 657 (October 2014): 168–72. http://dx.doi.org/10.4028/www.scientific.net/amm.657.168.

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The The 3D modelling, compared to the 2D-symmetrical rotation (axial) modelling, is much more complex and one of the reasons for this, with a view to plastic anisotropy, is that 3D modelling adopts normal anisotropy while 2D modelling adopts symmetrical rotational anisotropy. When the finite element method is used for numerical solving, the number of nodes in the discretization network is much larger in the 3D modelling and even when using last generation PCs the processing time is much longer. For this reason, for expedite calculations, as those needed to establish, in a first approximation, the optimal parameters of a deep drawing process I analysed in this paper the possibility to use the 2D-symmetrical rotation (axial) modelling. In a first phase, we intend to create a representative set of deep drawing problems and then to solve them through numerical simulations using Ansys package, using both the 3D modelling, in cartesian coordinates, and the 2D-symmetrical rotation (axial) modelling, in cylindrical coordinates. Runnings shall be carried out for various refinement levels of discretization network in finite elements. The calculation times shall be compared for these two modellings and following the comparative analysis of the results we can deduce the errors entered in the 2D modelling. These errors shall be assessed by analysing and comparing the extreme unitary stresses as follows: normal stress by the Ox and Oy axes , in the 3D modelling, and in the 2D modelling by the Ox / Oy axis; shear stresses by the XZ and YZ planes, in the 3D modelling and in the 2D modelling by the XZ plane.
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Ketova, K. V., I. G. Rusyak, and D. D. Vavilova. "MATHEMATICAL MODELLING OF WORKFORCE POTENTIAL." European Journal of Natural History, no. 3 2020 (2020): 65–69. http://dx.doi.org/10.17513/ejnh.34088.

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Peregudov, Dmitriy, Anatoly Soloviev, Igor Yashin, and Victor Shutenko. "GALACTIC COSMIC RAY ANISOTROPY MODELLING." Solar-Terrestrial Physics 6, no. 1 (April 1, 2020): 29–34. http://dx.doi.org/10.12737/stp-61202003.

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We calculate the angular distribution of cosmic rays at a given point of the heliosphere under the assumption that the incoming flux from outer space is isotropic. The static magnetic field is shown to cause no anisotropy provided that the observation point is situated out of the trapped particle area. We consider a coronal ejection model in the form of a static cylinder with an axial homogeneous magnetic field inside. We calculate angular distribution samples in the trapped particle area (inside the cylinder) and show that there is a certain cone of directions with a reduced flux. For the same model with the moving cylinder, the angular distribution samples are calculated for different positions of the observation point outside the cylinder. Anisotropy of order of the ejection to light velocity ratio is shown to arise. The calculated samples are in qualitative agreement with URAGAN muon hodoscope data.
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Dissertations / Theses on the topic "Modelling"

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Singh, Peterson Lila Physics Faculty of Science UNSW. "Modelling secondary organic aerosol formation :from chemical mechanistic modelling to empirical modelling." Awarded by:University of New South Wales. Physics, 2007. http://handle.unsw.edu.au/1959.4/40466.

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The work presented in this thesis is primarily concerned with modelling the formation of secondary organic aerosols (SOAs). SOAs cannot easily be measured with direct analytical chemical methods; indirect methods like applying organic carbon to elemental carbon ratios and utilising computer models have been employed to provide an estimate of the SOA mass concentrations in ambient air. The five models presented in this work were either developed or assessed using environmental chamber data. Chamber experiments were undertaken using initial isoprene concentrations in the range of 22 ppb to 343 ppb, with the reactive organic carbon (ROC) to NOx ratios in the range of 2.0 to about 18. Chamber experiments were also performed for the a-pinene / NOx system with initial a-pinene concentrations ranging from 79 ppb to 225 ppb, with ROC/NOx ratios varying from 5.5 to about 41. All of the experiments were performed without the addition of propene or seed aerosol. Background aerosol levels were very low for the experiments presented in the thesis and so homogeneous nucleation processes were considered to occur in the chamber in addition to absorption and oligomerisation formation processes. Initial nucleation events resulting from the photooxidation of isoprene could be detected once the aerosol diameter was greater than 12 nm. In the a-pinene system,new particles formed via homogeneous nucleation processes were detectable in the 100-200nm diameter range. The models presented range in complexity from the near explicit Master Chemical Mechanism to an empirical model whose key feature is its simplicity. The mechanistic model provides an insight into the SOA formation pathways and the influence of varying the initial experimental conditions and the duration of photooxidation on the simulated SOA composition. The aim of the empirical model is to simulate the SOA mass concentration produced during a chamber experiment. The development of the model is intentionally simple so that it can be applied to any hydrocarbon and has been applied successfully to isoprene and a-pinene chamber experiments. In this way, the empirical model is presented as an alternative approach to predicting the temporal variation in SOA mass concentrations. An analysis of the partitioning absorption models developed by Odum et al. (1996) and Hoffmann et al. (1997) has informed the development of the SOA module which has been coupled to a 3D atmospheric model. Embodied within the SOA module is the gas / aerosol partitioning theory which includes the model proposed initially by Pankow et al. (1994) and by Odum et al. (1996).
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Romanowska, Izabela Anna. "Modelling hominin dispersals with agent-based modelling." Thesis, University of Southampton, 2017. https://eprints.soton.ac.uk/420372/.

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The early dispersals of hominins have been a major focus of Palaeolithic research in the last 50 years. In this dissertation I have applied a formal modelling technique, simulation, to a selection of archaeological research topics concerning hominin dispersals in order to test the suitability of this tool for formal theory development and hypothesis testing. Therefore, the aims of this research are twofold. The archaeological aim is to improve our understanding of hominin dispersals in the Pleistocene, whilst the methodological contribution was achieved by employing a relatively unknown and underused (in archaeology) computational tool. An overview of the existing data pertinent to the topic of Pleistocene hominin dispersals demonstrated that the current methodology used for researching hominin dispersals, that is data analysis paired with conceptual modelling, is unlikely to solve many of the existing research questions highlighting the need to assess a wider range of scientific tools in order to progress. To that end, two case studies were developed using a specific simulation technique: agent-based modelling. The first case study was used to evaluate if, and under what conditions, the early ‘Out of Africa’ dispersal could lead to a specific demographic pattern reflected in a disparity between two regions with different stone tool traditions known as the Movius line. The model comprises a dynamic environmental reconstruction of Old World vegetation in the timeframe 2.5-0.25 Ma, coupled with standard biological models of population growth and dispersal. It is demonstrated that, under a wide sweep of both environmental and behavioural parameter values, the demographic consequence of dispersal is not a gradual attenuation of the population size away from the point of origin but a pattern of ecologically driven local variation in population density. The second case study looks at the relationships between climate change, migration and the evolution of behavioural plasticity or versatility among hominins. The agent-based model investigates the dynamics between individuals with different adaptations (including ‘versatilist’ individuals) within a non-homogenous population. The results show that dispersal accelerates the evolution of versatilism in the population, therefore promoting a more flexible range of adaptations. In addition, a set of scenarios was tested in which a dispersal wave crosses an environmental barrier. The results do not support the common intuition that such barriers shape the composition of hominin populations. The methodology presented here opens a new route to understanding large-scale spatiotemporal patterns in the archaeological record as well as to testing many of the previously proposed factors affecting early hominins’ lives. It has the potential to provide new insights into a number of ongoing debates, in particular on the relationship between different processes involved in shaping the past such as demographics and cultural complexity. This study also highlights the potential of simulation studies for testing complex conceptual models and the importance of building reference frameworks based on known proxies in order to achieve more rigorous model development in Palaeolithic archaeology and beyond.
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Griesser, Martin Thomas. "The modelling of fabric behaviour for automated modelling." Thesis, University of Hull, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262413.

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Tilgner, Hagen 1980. "Modelling splicing." Doctoral thesis, Universitat Pompeu Fabra, 2011. http://hdl.handle.net/10803/85056.

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L’Splicing de les molècules d’ARN és el procés pel qual les seqüències interposades (“introns”) s’eliminen, i les seqüències restants es concatenen per a formar l’ARN madur. La investigació recent mostra que gairebé tots els gens amb splicing es veuen afectats per splicing alternatiu. Aquí, en primer lloc definim la longitud mínima d’un oligomer d’ARN per a funcionar com a lloc d’unió d’un factor d’splicing. A continuació, explorem la capacitat d’aquests oligomers per a predir estructures completes exó-intró. Destaquem els oligomers que són més informatius per a això, i demostrem que la mateixa precisió com en enfocaments anteriors es pot aconseguir amb menys oligomers. L’observació de que aquest enfocament és lluny de predir amb exactitud tota l’estructura exó-intró ens va portar a investigar els factors que juguen un paper en l’splicing co-transcripcional. Demostrem que els nucleosomes es col.loquen preferentment en els exons i plantegem la hipòtesi que juguen un paper en les decisions de l’splicing. A continuació, introduïm el “completed splicing index” i concluem que l’splicing co-transcripcional és molt generalitzat. A més, l’splicing co-transcripcional mostra vincles amb l’organització de la cromatina. A la llum d’aquests resultats, es van supervisar els canvis de la cromatina en exons diferencialment inclosos en dos teixits. Hem descobert una varietat de marques de les histones, però no totes, mostrant un comportament significativament diferent en els exons més inclosos i més exclosos. Las marques més destacades que apareixen són H3K9ac i dos estats de metilació de lisina 4.
Splicing of RNA molecules is the process, by which intervening sequences (“introns”) in the primary transcript are excised, and the remaining sequences (“exons”) are concatenated to form the mature RNA. Recent evidence shows that almost all spliced genes are affected by alternative splicing. Here, we define the minimal length of RNA oligomers that can sensibly be called splicing factor binding sites. Then, we explore the capacity of these oligomers to predict complete exon-intron structures. We highlight those oligomers that are most informative for this and show, that equal accuracy as in previous approaches can be achieved with less RNA oligomers. The observation, that this approach falls short of accurately predicting the entire exon-intron structure, led us to investigate determinants linked to co-transcriptional splicing. We show that nucleosomes are preferentially positioned on exons and hypothesize that they play a role in splicing decisions. We then introduce the “completed splicing index” and conclude that co-transcriptional splicing is very wide-spread in humans. Furthermore co-transcriptional splicing exhibits links to chromatin organization. In the light of these results, we go on to monitor chromatin changes on differentially included exons in pair-wise tissue comparisons. We find a variety of histone marks, but not all, showing significantly different behavior on up- and downregulated exons. The most prominently appearing marks are H3K9ac and two lysine 4 methylation states.
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Spackman, Roger A. "Electrochemical modelling." Thesis, University of Oxford, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.316899.

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Mackie, Jo. "Situation modelling." Thesis, Lancaster University, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.527167.

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Cox, Sander. "Dynamical modelling." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-262477.

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Guruprasad, Kunchur. "Protein modelling." Thesis, Birkbeck (University of London), 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283204.

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Vaidya, A. M. "Ultrafiltration modelling." Thesis, University of Manchester, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.237688.

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Diamantopoulos, Panayiotis. "Biomedical modelling." Thesis, University of Sussex, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391860.

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Books on the topic "Modelling"

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Flavell, Richard, ed. Modelling Reality and Personal Modelling. Heidelberg: Physica-Verlag HD, 1993. http://dx.doi.org/10.1007/978-3-642-95900-4.

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Springborg, Michael, and Jan-Ole Joswig, eds. Chemical Modelling. Cambridge: Royal Society of Chemistry, 2014. http://dx.doi.org/10.1039/9781782620112.

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Springborg, Michael, and Jan-Ole Joswig, eds. Chemical Modelling. Cambridge: Royal Society of Chemistry, 2015. http://dx.doi.org/10.1039/9781782622703.

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Hinchliffe, Alan, ed. Chemical Modelling. Cambridge: Royal Society of Chemistry, 2007. http://dx.doi.org/10.1039/9781847553317.

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Hinchliffe, Alan, ed. Chemical Modelling. Cambridge: Royal Society of Chemistry, 2002. http://dx.doi.org/10.1039/9781847553324.

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Hinchliffe, Alan, ed. Chemical Modelling. Cambridge: Royal Society of Chemistry, 2007. http://dx.doi.org/10.1039/9781847553331.

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Springborg, Michael, and Jan-Ole Joswig, eds. Chemical Modelling. Cambridge: Royal Society of Chemistry, 2013. http://dx.doi.org/10.1039/9781849737241.

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Skrondal, Anders, and Sophia Rabe-Hesketh. Multilevel Modelling. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications Ltd, 2010. http://dx.doi.org/10.4135/9781446262573.

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Springborg, Michael, and Jan-Ole Joswig, eds. Chemical Modelling. Cambridge: Royal Society of Chemistry, 2021. http://dx.doi.org/10.1039/9781839162657.

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Heiliö, Matti, Timo Lähivaara, Erkki Laitinen, Timo Mantere, Jorma Merikoski, Seppo Pohjolainen, Kimmo Raivio, et al. Mathematical Modelling. Edited by Seppo Pohjolainen. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27836-0.

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Book chapters on the topic "Modelling"

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Chiesa, Giacomo. "Modelling Reality, Modelling Virtuality." In Technological Paradigms and Digital Eras, 39–63. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26199-3_2.

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Steen, W. M. "Modelling." In Handbook of the EuroLaser Academy, 621–60. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5297-0_12.

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Cortes Garcia, Idoia. "Modelling." In Springer Theses, 9–38. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63273-1_2.

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Fernández-Merodo, José Antonio. "Modelling." In Encyclopedia of Earth Sciences Series, 1–10. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-12127-7_203-1.

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Sherwin, Keith, and Michael Horsley. "Modelling." In Thermofluids, 17–40. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4899-4433-7_2.

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Sherwin, Keith, and Michael Horsley. "Modelling." In Thermofluids, 3–5. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4899-6870-8_2.

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Sherwin, Keith. "Modelling." In Introduction to Thermodynamics, 21–39. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1514-8_2.

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North, Chris J. "Modelling." In Self-Study of Teaching and Teacher Education Practices, 97–121. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2176-8_5.

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Friedler, Eran. "Modelling." In Hypertrophic Reservoirs for Wastewater Storage and Reuse, 105–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60112-5_7.

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Bohlin, Torsten. "Modelling." In Interactive System Identification: Prospects and Pitfalls, 111–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-48618-0_5.

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Conference papers on the topic "Modelling"

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Ronot, C., L. Szafran, R. Lencrerot, and A. Barnola. "Geological Modelling for Seismic Modelling." In 80th EAGE Conference and Exhibition 2018. Netherlands: EAGE Publications BV, 2018. http://dx.doi.org/10.3997/2214-4609.201801621.

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Carson, John S. "Modelling." In the 24th conference. New York, New York, USA: ACM Press, 1992. http://dx.doi.org/10.1145/167293.167306.

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"Modelling." In 2012 International Semiconductor Conference (CAS 2012). IEEE, 2012. http://dx.doi.org/10.1109/smicnd.2012.6400750.

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"Modelling." In 2018 International Semiconductor Conference (CAS). IEEE, 2018. http://dx.doi.org/10.1109/smicnd.2018.8539788.

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Simo, Freddy Kamdem, Dominique Ernadote, and Dominique Lenne. "Towards modelling of modelling in SE." In 2016 IEEE International Symposium on Systems Engineering (ISSE). IEEE, 2016. http://dx.doi.org/10.1109/syseng.2016.7753177.

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Medoh, C., and A. Telukdarie. "Modelling Water Network Behaviors: Agent-based Modelling." In 2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2021. http://dx.doi.org/10.1109/ieem50564.2021.9672976.

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Muthuswamy, B., J. Jevtic, H. H. C. Iu, C. K. Subramaniam, K. Ganesan, V. Sankaranarayanan, K. Sethupathi, H. Kim, M. Pd Shah, and L. O. Chua. "Memristor modelling." In 2014 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2014. http://dx.doi.org/10.1109/iscas.2014.6865179.

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Ney, Hermann. "Stochastic modelling." In the workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2001. http://dx.doi.org/10.3115/1118037.1118042.

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Brossard, Arnaud, Mourad Abed, Christophe Kolski, and Guillaume Uster. "User modelling." In the 6th International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1710035.1710079.

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Terzopoulos, Demetri. "Visual Modelling." In British Machine Vision Conference 1991. Springer-Verlag London Limited, 1991. http://dx.doi.org/10.5244/c.5.2.

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Reports on the topic "Modelling"

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Phillips, C. K. ICRF modelling. Office of Scientific and Technical Information (OSTI), December 1985. http://dx.doi.org/10.2172/6245971.

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VanLehn, Kurt. Student Modelling. Fort Belvoir, VA: Defense Technical Information Center, March 1987. http://dx.doi.org/10.21236/ada222394.

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Ashby, M. F. Wear-mechanism modelling. Office of Scientific and Technical Information (OSTI), March 1993. http://dx.doi.org/10.2172/6447701.

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Schetselaar, E. 3D geological modelling. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2014. http://dx.doi.org/10.4095/296306.

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Santos, Jr, and Eugene. Modelling Temporal Abductive Explanation. Fort Belvoir, VA: Defense Technical Information Center, March 1993. http://dx.doi.org/10.21236/ada263096.

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Flato, G., N. Gillett, V. Arora, A. Cannon, and J. Anstey. Modelling future climate change. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2019. http://dx.doi.org/10.4095/327808.

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Attansio, Orazio, and Debbie Blair. Structural modelling in policymaking. Centre for Excellence and Development Impact and Learning (CEDIL), November 2018. http://dx.doi.org/10.51744/cip9.

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Structural modelling, that is the use of behavioural models to add a framework to the decision problem of an agent, is a useful yet underused tool in evaluation. This paper provides a general introduction to structural modelling, as well as an overview of other commonly used evaluation techniques in Economics and other social sciences. It then goes on to show with three key case studies, how structural models can be used to enrich the findings from randomised control trials. The case studies cover a wide range of policy questions: examining demand for health products in Kenya, incentivising teachers to attend school in India, and evaluating conditional cash transfers for education in Mexico. The case studies show how structural models add to our understanding of the mechanisms behind a given treatment effect, how the findings may change when the policy is rolled out under different circumstances, as well as allowing for the evaluation of different policies that were not originally trialled. The common pitfalls of structural models are discussed, with guidance provided throughout on how to conduct sensitivity analysis and model validation. It is hoped that this paper will persuade other researchers to use structural models, in conjunction with randomised control trials, that will lead to improved evaluation results, a deeper understanding of important problems, and better informed policymaking in the future.
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Sharma, Shreya. Customer Lifetime Value Modelling. Ames (Iowa): Iowa State University, January 2021. http://dx.doi.org/10.31274/cc-20240624-467.

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Wray, Chris, Josephine Musango, Kavesha Damon, and Koech Cheruiyot. Modelling urban spatial change: a review of international and South African modelling initiatives. Gauteng City-Region Observatory, August 2013. http://dx.doi.org/10.36634/dfua2650.

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10

Yuen, David A. Physical Modelling of Sedimentary Basin. Office of Scientific and Technical Information (OSTI), April 2003. http://dx.doi.org/10.2172/899950.

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