Relevant bibliographies by topics / Conceptual Modelling

Academic literature on the topic 'Conceptual Modelling'

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Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Conceptual Modelling"

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Thagard, Paul. "Modelling Conceptual Revolutions." Dialogue 35, no. 1 (1996): 155–59. http://dx.doi.org/10.1017/s0012217300008131.

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Maciá-Pérez, Francisco, Iren Lorenzo-Fonseca, Jose Vicente Berná-Martinez, and Jose Manuel Sánchez-Bernabeu. "Conceptual Modelling of Complex Network Management Systems." Journal of Computers 10, no. 5 (2015): 309–20. http://dx.doi.org/10.17706/jcp.10.5.309-320.

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Robinson, S. "Conceptual modelling for simulation Part II: a framework for conceptual modelling." Journal of the Operational Research Society 59, no. 3 (March 2008): 291–304. http://dx.doi.org/10.1057/palgrave.jors.2602369.

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Weber, Ron. "Conceptual Modelling and Ontology." Journal of Database Management 14, no. 3 (July 2003): 1–20. http://dx.doi.org/10.4018/jdm.2003070101.

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Maltsev, Eduard. "Conceptual modelling of organizations." Scientific Papers NaUKMA. Economics 1, no. 1 (December 1, 2016): 118–24. http://dx.doi.org/10.18523/2519-4739112016124825.

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Ågerfalk, Pär J., and Owen Eriksson. "Action-oriented conceptual modelling." European Journal of Information Systems 13, no. 1 (March 2004): 80–92. http://dx.doi.org/10.1057/palgrave.ejis.3000486.

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Madsen, Bodil Nistrup, and Hanne Erdman Thomsen. "Terminological concept modelling and conceptual data modelling." International Journal of Metadata, Semantics and Ontologies 4, no. 4 (2009): 239. http://dx.doi.org/10.1504/ijmso.2009.029228.

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Guizzardi, Giancarlo, and Terry Halpin. "Ontological foundations for conceptual modelling." Applied Ontology 3, no. 1-2 (2008): 1–12. http://dx.doi.org/10.3233/ao-2008-0049.

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Papadimitriou, Fivos. "Conceptual Modelling of Landscape Complexity." Landscape Research 35, no. 5 (September 27, 2010): 563–70. http://dx.doi.org/10.1080/01426397.2010.504913.

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Paton, N. W., S. A. Khan, A. Hayes, F. Moussouni, A. Brass, K. Eilbeck, C. A. Goble, S. J. Hubbard, and S. G. Oliver. "Conceptual modelling of genomic information." Bioinformatics 16, no. 6 (June 1, 2000): 548–57. http://dx.doi.org/10.1093/bioinformatics/16.6.548.

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Dissertations / Theses on the topic "Conceptual Modelling"

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McGinnes, Simon. "Conceptual modelling : a psychological perspective." Thesis, London School of Economics and Political Science (University of London), 2000. http://etheses.lse.ac.uk/2290/.

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This thesis describes the formulation and experimental use of psychological principles that apply to conceptual modelling as practised during information systems development. The principles address cognition (perception, memory and mental models) and group dynamics. The aim is to determine whether application of fundamental psychological principles can help to make modellers, especially those who are relatively inexperienced, more effective. An experimental graphical modelling technique (method 'X') is presented that conforms to the psychological principles, together with a supporting software tool for visual construction of models in the design of typical business database systems. The effectiveness of both inexperienced and expert modellers using method 'X' in real business situations was compared with that of modellers using conventional object modelling. Data was gathered in a series of field experiments using participant observation, questionnaires, and interviews and by analysing the resulting models. With conventional object modelling, untrained modellers produced results that were grossly incomplete and incorrect (22-35%, on average). Using method 'X', untrained modellers produced models that were almost complete and correct (better than 82%). Significant productivity gains were observed with method 'X' (approximately 150% for expert modeller and over 450% for untrained modellers). For an expert modeller no measurable differences in quality were observed between methods, but the modeller regarded the quality of method 'X' models as better and expressed a preference method 'X' over the conventional approach. The results appear to support the idea of re-engineering conceptual modelling practice according to psychological first principles. The fact that more dramatic performance improvements were observed for inexperienced modellers suggests that modelling need not require a high degree of expertise, if methods and tools are adapted appropriately. The results could be exploited to empower untrained modellers, such as end users, who wish to develop large software systems but lack access to the skills of trained IT professionals.
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Wang, Wang. "Empirical Investigations of Conceptual Modelling and the Modelling Process." Thesis, Lancaster University, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.518147.

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Petrounias, Ilias. "Conceptual modelling for temporal information systems." Thesis, University of Manchester, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.488408.

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Wu, Zhichao. "Modelling collective learning in conceptual design." Thesis, University of Strathclyde, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.405494.

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Krishna, Aneesh. "Practical methodologies for agent-oriented conceptual modelling." Access electronically, 2006. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20060815.122954/index.html.

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Champion, R. E. M. "Modelling for requirements engineering using conceptual structures." Thesis, University of Manchester, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.334615.

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Harasani, W. "Aircraft conceptual design decision through operational modelling." Thesis, Cranfield University, 2005. http://dspace.lib.cranfield.ac.uk/handle/1826/11093.

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Aircraft manufacturing is not only a difficult business but also a very competitive one, the consequences of any drop in sales would cost billions, loss of jobs, and maybe an economical failure. Therefore, concentrating on just flight performance and adding new technologies just because they exit is not enough to win the airlines attention, especially the flow cost carriers. Manufactures must be able to convince operators that the application of a new design or technology will produce a favourable change in the bottom line of their balance sheets and not just a reduction in fuel burn. Aircraft designers must put more emphasis on what happens to the aircraft after it leaves the assembly line, through the designed life operation cycle of the aircraft with the airline customer, quality should be built in to the aircraft. Knowing what are the airline's concerns, how the aircraft with a given design behaves, and the issues that the airline has, is vital. Firstly, it is important to know what are the issues that the airline has, the costumer (airlines) needs are identified, and, since fleet planning is the top level decision making department in the airline in which a decision is made to buy one aircraft over the other, it is important to understand the process and the elements that are involved in fleet planning. So fleet planning was studied. Second different technologies for the design have been looked at and selected. Then the aircraft, airline, airport, and air traffic control are studied, as well as the interaction between them. A key element of the research is a simulation program DEBOS that has been built to see the impact of the different design technologies and concepts through the operation of a simulation fleet size of 23 aircraft. The Boeing777 aircraft has been chosen to be the base line of the study. Finally, it was found that a given technology with improved performance, or a new concept, would improve the aircraft attractiveness only if it has better life cycle behaviour characteristics.
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Clementini, Eliseo. "A conceptual framework for modelling spatial relations." Lyon, INSA, 2009. http://theses.insa-lyon.fr/publication/2009ISAL0028/these.pdf.

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The models of spatial relations have stimulated great interest among researchers in spatial databases from the nineties. We can distinguish three different levels of representation where we can identify spatial relations: a purely geometric one, where objects are represented as point-sets and relations can be formally defined in mathematical terms; a computational level, where objects are represented as spatial data types and relations are calculated using spatial operators; a user level, where objects and relations correspond to the concepts of user context. >From a geometric point of view, we can consider a categorization of spatial relations in three groups: topological, projective, and metric. This dissertation proposes both a general framework for modelling qualitative spatial relations and presents new developments for projective relations. By providing a formal mathematical framework to a qualitative description of relations, the models are of great research interest in geographic information science. The most important geometric property that has been taken into account is the collinearity of three points. The importance of this property is such that it influences the whole approach, making ternary relations the formal basis of the models. We have developed algorithms to calculate the relations from a vector data structure and a reasoning system on ternary projective relations. We also extended the model to the three-dimensional space and the sphere. >From a user perspective, projective relations should consider context information, combining them with reference systems to avoid ambiguities in the meaning of the relation
Les modèles pour les relations spatiales ont stimulé un grand intérêt parmi les chercheurs dans les bases de données spatiales des années 90. On peut distinguer trois niveaux différents de représentation où nous pouvons identifier des relations spatiales : un niveau purement géométrique, où des objets sont représentés comme ensembles de points et les relations peuvent être formellement définies en termes mathématiques ; un niveau informatique, où les objets sont représentés en tant que types de données spatiaux et les relations sont calculées au moyen d'opérateurs spatiaux ; un niveau utilisateur, où les objets et les relations correspondent aux concepts du contexte d'utilisateur. Du point de vue géométrique, on peut considérer une catégorisation des relations spatiales dans trois groupes : topologique, projectif, et métrique. Ce mémoire de thèse propose à la fois un cadre général pour la modélisation des relations spatiales qualitatives, et présente des nouveaux développements pour les relations projectives. En offrant un cadre mathématique formel à une description qualitative des relations, les modèles envisagés se révèlent d’un grand intérêt dans la recherche en sciences de l’information géographique. La propriété géométrique la plus importante qui a été prise en considération est la colinéarité entre trois points. L’importance de cette propriété est telle qu’elle conditionne toute l’approche, en faisant des relations ternaires la base formelle des modèles envisagés. Nous avons développé les algorithmes pour calculer les relations à partir d'une structure de données en format vectoriel et un système de raisonnement sur les relations projectives ternaires. On a aussi étendu les modèles vers l’espace trois-dimensionnelles et la sphère. Du point de vue de l’utilisateur, les relations projectives doivent envisager l’information sur le contexte, en les combinant avec des systèmes de référence pour ôter les ambiguïtés du sens de la relation
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Lim, Sungwoo. "An approach to design sketch modelling." Thesis, University of Strathclyde, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248862.

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Sincock, Andrew Michael. "Conceptual river water quality modelling under dynamic conditions." Thesis, Imperial College London, 2002. http://hdl.handle.net/10044/1/11912.

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Books on the topic "Conceptual Modelling"

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Gregory, Frank. Inductions, modality & conceptual modelling. Coventry: University of Warwick. Warwick Business School Research Bureau, 1992.

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Petrounias, Ilias. Conceptual modelling for temporal informationsystems. Manchester: UMIST, 1994.

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Eide, Øyvind. Media Boundaries and Conceptual Modelling. London: Palgrave Macmillan UK, 2015. http://dx.doi.org/10.1057/9781137544582.

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Düsterhöft, Antje, Meike Klettke, and Klaus-Dieter Schewe, eds. Conceptual Modelling and Its Theoretical Foundations. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28279-9.

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Krogstie, John, Andreas Lothe Opdahl, and Sjaak Brinkkemper, eds. Conceptual Modelling in Information Systems Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72677-7.

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Glaser, Norbert. Conceptual Modelling of Multi-Agent Systems. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0973-8.

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Arne, Sølvberg, Krogstie John, Opdahl Andreas, and Brinkkemper Sjaak, eds. Conceptual modelling in information systems engineering. Berlin: Springer, 2007.

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Commission, India Planning, and N.I.U.A. (Organization : India), eds. Modelling rental housing market: A conceptual framework. New Delhi: National Institute of Urban Affairs, 1989.

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Tu, Yong. Modelling household housing choice behaviour: Some conceptual discussions. Glasgow: University of Glasgow, Centre for Housing Research and Urban Studies, 1994.

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Sobiesiak, Richard. A hypertext authoring framework based on conceptual modelling. Ottawa: National Library of Canada, 1992.

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Book chapters on the topic "Conceptual Modelling"

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Robinson, Stewart. "Conceptual Modelling." In Simulation, 77–95. London: Macmillan Education UK, 2014. http://dx.doi.org/10.1007/978-1-137-32803-8_5.

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Greasley, Andrew. "Conceptual Modelling (Abstraction)." In Simulation Modelling, 35–59. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003124092-3.

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Ma, Hui, and Klaus-Dieter Schewe. "Conceptual Geometric Modelling." In Handbook of Conceptual Modeling, 421–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-15865-0_13.

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Taber, Keith S. "Modelling Conceptual Learning." In Modelling Learners and Learning in Science Education, 277–323. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7648-7_15.

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Wilmont, Ilona, Sytse Hengeveld, Erik Barendsen, and Stijn Hoppenbrouwers. "Cognitive Mechanisms of Conceptual Modelling." In Conceptual Modeling, 74–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41924-9_7.

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Greasley, Andrew. "Conceptual Modelling (Descriptive Model)." In Simulation Modelling, 71–86. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003124092-5.

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Greasley, Andrew. "Conceptual Modelling (Explanatory Model)." In Simulation Modelling, 60–70. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003124092-4.

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Furtado, Antonio L., Marco A. Casanova, and Simone Diniz Junqueira Barbosa. "A Semiotic Approach to Conceptual Modelling." In Conceptual Modeling, 1–12. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12206-9_1.

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López, Lidia, Dolors Costal, Claudia P. Ayala, Xavier Franch, Ruediger Glott, and Kirsten Haaland. "Modelling and Applying OSS Adoption Strategies." In Conceptual Modeling, 349–62. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12206-9_29.

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Maganlal, Atish, and Duncan Coulter. "Atomically True Ontology Modelling: Residential Buildings." In Conceptual Modeling, 82–91. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17995-2_6.

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Conference papers on the topic "Conceptual Modelling"

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"Session 3: Conceptual modelling." In 2017 11th International Conference on Research Challenges in Information Science (RCIS). IEEE, 2017. http://dx.doi.org/10.1109/rcis.2017.7956521.

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Yu Han, Shufen Liu, Xiaoyan Wang, and Bin Li. "Design of a metamodel-based telecoms modelling language." In Conceptual Design (CAID/CD). IEEE, 2008. http://dx.doi.org/10.1109/caidcd.2008.4730787.

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Al-Fedaghi, Sabah S., and Fadi M. Al-Turjman. "Conceptual Modelling: A Privacy Perspective." In 2007 Inaugural IEEE-IES Digital EcoSystems and Technologies Conference. IEEE, 2007. http://dx.doi.org/10.1109/dest.2007.372009.

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"Conceptual modelling for water quality management." In 22nd International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2017. http://dx.doi.org/10.36334/modsim.2017.l21.fu.

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Barra, Carlos, Enrique Canessa, and Sergio E. Chaigneau. "Modeling Inferential Minds In Conceptual Space." In 30th Conference on Modelling and Simulation. ECMS, 2016. http://dx.doi.org/10.7148/2016-0131.

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Oliveira, Bruno, and Orlando Belo. "Using REO on ETL conceptual modelling." In the sixteenth international workshop. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2513190.2513202.

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Quartela, Dick, Maarten W. A. Steen, Stanislav Pokraev, and Marten van Sinderena. "A conceptual framework for service modelling." In 2006 10th IEEE International Enterprise Distributed Object Computing Conference. IEEE, 2006. http://dx.doi.org/10.1109/edoc.2006.5.

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Matula, Jiri, Roman Belunek, and Frantisek Hunka. "OWL references in ORM conceptual modelling." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2016). Author(s), 2017. http://dx.doi.org/10.1063/1.4992242.

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"Session 12: Conceptual modelling and ontologies." In 2016 IEEE Tenth International Conference on Research Challenges in Information Science (RCIS). IEEE, 2016. http://dx.doi.org/10.1109/rcis.2016.7549296.

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Dunmade, I. "A Conceptual Model for Lifecycle Management based Soil and Water Remediation." In Modelling and Simulation. Calgary,AB,Canada: ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.699-044.

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Reports on the topic "Conceptual Modelling"

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Simon, Tony. Modelling Conceptual Development: A computational Account of Conservation Learning. Fort Belvoir, VA: Defense Technical Information Center, February 1990. http://dx.doi.org/10.21236/ada225800.

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Saptsin, Vladimir, and Володимир Миколайович Соловйов. Relativistic quantum econophysics – new paradigms in complex systems modelling. [б.в.], July 2009. http://dx.doi.org/10.31812/0564/1134.

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This work deals with the new, relativistic direction in quantum econophysics, within the bounds of which a change of the classical paradigms in mathematical modelling of socio-economic system is offered. Classical physics proceeds from the hypothesis that immediate values of all the physical quantities, characterizing system’s state, exist and can be accurately measured in principle. Non-relativistic quantum mechanics does not reject the existence of the immediate values of the classical physical quantities, nevertheless not each of them can be simultaneously measured (the uncertainty principle). Relativistic quantum mechanics rejects the existence of the immediate values of any physical quantity in principle, and consequently the notion of the system state, including the notion of the wave function, which becomes rigorously nondefinable. The task of this work consists in econophysical analysis of the conceptual fundamentals and mathematical apparatus of the classical physics, relativity theory, non-relativistic and relativistic quantum mechanics, subject to the historical, psychological and philosophical aspects and modern state of the socio-economic modeling problem. We have shown that actually and, virtually, a long time ago, new paradigms of modeling were accepted in the quantum theory, within the bounds of which the notion of the physical quantity operator becomes the primary fundamental conception(operator is a mathematical image of the procedure, the action), description of the system dynamics becomes discrete and approximate in its essence, prediction of the future, even in the rough, is actually impossible when setting aside the aftereffect i.e. the memory. In consideration of the analysis conducted in the work we suggest new paradigms of the economical-mathematical modeling.
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Russell, H. A. J., and S. K. Frey. Canada One Water: integrated groundwater-surface-water-climate modelling for climate change adaptation. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329092.

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Canada 1 Water is a 3-year governmental multi-department-private-sector-academic collaboration to model the groundwater-surface-water of Canada coupled with historic climate and climate scenario input. To address this challenge continental Canada has been allocated to one of 6 large watershed basins of approximately two million km2. The model domains are based on natural watershed boundaries and include approximately 1 million km2 of the United States. In year one (2020-2021) data assembly and validation of some 20 datasets (layers) is the focus of work along with conceptual model development. To support analysis of the entire water balance the modelling framework consists of three distinct components and modelling software. Land Surface modelling with the Community Land Model will support information needed for both the regional climate modelling using the Weather Research & Forecasting model (WRF), and input to HydroGeoSphere for groundwater-surface-water modelling. The inclusion of the transboundary watersheds will provide a first time assessment of water resources in this critical international domain. Modelling is also being integrated with Remote Sensing datasets, notably the Gravity Recovery and Climate Experiment (GRACE). GRACE supports regional scale watershed analysis of total water flux. GRACE along with terrestrial time-series data will serve provide validation datasets for model results to ensure that the final project outputs are representative and reliable. The project has an active engagement and collaborative effort underway to try and maximize the long-term benefit of the framework. Much of the supporting model datasets will be published under open access licence to support broad usage and integration.
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Perdigão, Rui A. P. New Horizons of Predictability in Complex Dynamical Systems: From Fundamental Physics to Climate and Society. Meteoceanics, October 2021. http://dx.doi.org/10.46337/211021.

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Discerning the dynamics of complex systems in a mathematically rigorous and physically consistent manner is as fascinating as intimidating of a challenge, stirring deeply and intrinsically with the most fundamental Physics, while at the same time percolating through the deepest meanders of quotidian life. The socio-natural coevolution in climate dynamics is an example of that, exhibiting a striking articulation between governing principles and free will, in a stochastic-dynamic resonance that goes way beyond a reductionist dichotomy between cosmos and chaos. Subjacent to the conceptual and operational interdisciplinarity of that challenge, lies the simple formal elegance of a lingua franca for communication with Nature. This emerges from the innermost mathematical core of the Physics of Coevolutionary Complex Systems, articulating the wealth of insights and flavours from frontier natural, social and technical sciences in a coherent, integrated manner. Communicating thus with Nature, we equip ourselves with formal tools to better appreciate and discern complexity, by deciphering a synergistic codex underlying its emergence and dynamics. Thereby opening new pathways to see the “invisible” and predict the “unpredictable” – including relative to emergent non-recurrent phenomena such as irreversible transformations and extreme geophysical events in a changing climate. Frontier advances will be shared pertaining a dynamic that translates not only the formal, aesthetical and functional beauty of the Physics of Coevolutionary Complex Systems, but also enables and capacitates the analysis, modelling and decision support in crucial matters for the environment and society. By taking our emerging Physics in an optic of operational empowerment, some of our pioneering advances will be addressed such as the intelligence system Earth System Dynamic Intelligence and the Meteoceanics QITES Constellation, at the interface between frontier non-linear dynamics and emerging quantum technologies, to take the pulse of our planet, including in the detection and early warning of extreme geophysical events from Space.
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Daudelin, Francois, Lina Taing, Lucy Chen, Claudia Abreu Lopes, Adeniyi Francis Fagbamigbe, and Hamid Mehmood. Mapping WASH-related disease risk: A review of risk concepts and methods. United Nations University Institute for Water, Environment and Health, December 2021. http://dx.doi.org/10.53328/uxuo4751.

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The report provides a review of how risk is conceived of, modelled, and mapped in studies of infectious water, sanitation, and hygiene (WASH) related diseases. It focuses on spatial epidemiology of cholera, malaria and dengue to offer recommendations for the field of WASH-related disease risk mapping. The report notes a lack of consensus on the definition of disease risk in the literature, which limits the interpretability of the resulting analyses and could affect the quality of the design and direction of public health interventions. In addition, existing risk frameworks that consider disease incidence separately from community vulnerability have conceptual overlap in their components and conflate the probability and severity of disease risk into a single component. The report identifies four methods used to develop risk maps, i) observational, ii) index-based, iii) associative modelling and iv) mechanistic modelling. Observational methods are limited by a lack of historical data sets and their assumption that historical outcomes are representative of current and future risks. The more general index-based methods offer a highly flexible approach based on observed and modelled risks and can be used for partially qualitative or difficult-to-measure indicators, such as socioeconomic vulnerability. For multidimensional risk measures, indices representing different dimensions can be aggregated to form a composite index or be considered jointly without aggregation. The latter approach can distinguish between different types of disease risk such as outbreaks of high frequency/low intensity and low frequency/high intensity. Associative models, including machine learning and artificial intelligence (AI), are commonly used to measure current risk, future risk (short-term for early warning systems) or risk in areas with low data availability, but concerns about bias, privacy, trust, and accountability in algorithms can limit their application. In addition, they typically do not account for gender and demographic variables that allow risk analyses for different vulnerable groups. As an alternative, mechanistic models can be used for similar purposes as well as to create spatial measures of disease transmission efficiency or to model risk outcomes from hypothetical scenarios. Mechanistic models, however, are limited by their inability to capture locally specific transmission dynamics. The report recommends that future WASH-related disease risk mapping research: - Conceptualise risk as a function of the probability and severity of a disease risk event. Probability and severity can be disaggregated into sub-components. For outbreak-prone diseases, probability can be represented by a likelihood component while severity can be disaggregated into transmission and sensitivity sub-components, where sensitivity represents factors affecting health and socioeconomic outcomes of infection. -Employ jointly considered unaggregated indices to map multidimensional risk. Individual indices representing multiple dimensions of risk should be developed using a range of methods to take advantage of their relative strengths. -Develop and apply collaborative approaches with public health officials, development organizations and relevant stakeholders to identify appropriate interventions and priority levels for different types of risk, while ensuring the needs and values of users are met in an ethical and socially responsible manner. -Enhance identification of vulnerable populations by further disaggregating risk estimates and accounting for demographic and behavioural variables and using novel data sources such as big data and citizen science. This review is the first to focus solely on WASH-related disease risk mapping and modelling. The recommendations can be used as a guide for developing spatial epidemiology models in tandem with public health officials and to help detect and develop tailored responses to WASH-related disease outbreaks that meet the needs of vulnerable populations. The report’s main target audience is modellers, public health authorities and partners responsible for co-designing and implementing multi-sectoral health interventions, with a particular emphasis on facilitating the integration of health and WASH services delivery contributing to Sustainable Development Goals (SDG) 3 (good health and well-being) and 6 (clean water and sanitation).
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