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Journal articles on the topic 'Meta-modelling'

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Published: 4 June 2021
Last updated: 14 March 2023

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1

Sèdes, Florence, and Franck Jeveme Panta. "(Meta-)data modelling." SIGSPATIAL Special 9, no. 1 (July 13, 2017): 35–42. http://dx.doi.org/10.1145/3124104.3124111.

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2

Cuadrado, Jesús Sánchez, and Juan de Lara. "Open meta-modelling frameworks via meta-object protocols." Journal of Systems and Software 145 (November 2018): 1–24. http://dx.doi.org/10.1016/j.jss.2018.07.023.

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3

Krell, Moritz, and Dirk Krüger. "University students’ meta-modelling knowledge." Research in Science & Technological Education 35, no. 3 (January 20, 2017): 261–73. http://dx.doi.org/10.1080/02635143.2016.1274724.

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4

Sreenivas, A., R. Venkatesh, and M. Joseph. "Meta-Modelling for Formal Software Development." Electronic Notes in Theoretical Computer Science 42 (January 2001): 1–11. http://dx.doi.org/10.1016/s1571-0661(04)80875-2.

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5

Tritchler, David. "Modelling study quality in meta-analysis." Statistics in Medicine 18, no. 16 (August 30, 1999): 2135–45. http://dx.doi.org/10.1002/(sici)1097-0258(19990830)18:16<2135::aid-sim183>3.0.co;2-5.

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6

Clark, Tony, and Jens Gulden. "Model Driven Software Engineering Meta-Workbenches: An XTools Approach." JUCS - Journal of Universal Computer Science 26, no. 9 (September 28, 2020): 1148–76. http://dx.doi.org/10.3897/jucs.2020.060.

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Model Driven Software Engineering aims to provide a quality assured process for designing and generating software. Modelling frameworks that offer technologies for domain specific language and associated tool construction are called language workbenches. Since modelling is itself a domain, there are benefits to applying a workbenchbased approach to the construction of modelling languages and tools. Such a framework is a meta-modelling tool and those that can generate themselves are reflective metatools. This article reviews the current state of the art for modelling tools and proposes a set of reflective meta-modelling tool requirements. The XTools framework has been designed as a reflective meta-tool and is used as a benchmark.
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7

Mora Segura, Ángel, and Juan de Lara. "Extremo: An Eclipse plugin for modelling and meta-modelling assistance." Science of Computer Programming 180 (July 2019): 71–80. http://dx.doi.org/10.1016/j.scico.2019.05.003.

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8

Gomaa, H., and M. E. Shin. "Multiple-view modelling and meta-modelling of software product lines." IET Software 2, no. 2 (2008): 94. http://dx.doi.org/10.1049/iet-sen:20060059.

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9

de Lara Jaramillo, Juan, Hans Vangheluwe, and Manuel Alfonseca Moreno. "Using Meta-Modelling and Graph Grammars to Create Modelling Environments." Electronic Notes in Theoretical Computer Science 72, no. 3 (February 2003): 36–50. http://dx.doi.org/10.1016/s1571-0661(04)80610-8.

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10

de Lara, Juan, Hans Vangheluwe, and Manuel Alfonseca. "Meta-modelling and graph grammars for multi-paradigm modelling in AToM3." Software & Systems Modeling 3, no. 3 (August 2004): 194–209. http://dx.doi.org/10.1007/s10270-003-0047-5.

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11

Banerjee, Shreya, and Anirban Sarkar. "Ontology driven meta-modelling of clinical documents." International Journal of Healthcare Technology and Management 16, no. 3/4 (2017): 271. http://dx.doi.org/10.1504/ijhtm.2017.088869.

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Sarkar, Anirban, and Shreya Banerjee. "Ontology driven meta-modelling of clinical documents." International Journal of Healthcare Technology and Management 16, no. 3/4 (2017): 271. http://dx.doi.org/10.1504/ijhtm.2017.10009750.

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13

Kajero, Olumayowa T., Tao Chen, Yuan Yao, Yao-Chen Chuang, and David Shan Hill Wong. "Meta-modelling in chemical process system engineering." Journal of the Taiwan Institute of Chemical Engineers 73 (April 2017): 135–45. http://dx.doi.org/10.1016/j.jtice.2016.10.042.

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14

Boella, Guido, Dov M. Gabbay, Leendert van der Torre, and Serena Villata. "Meta-Argumentation Modelling I: Methodology and Techniques." Studia Logica 93, no. 2-3 (November 14, 2009): 297–355. http://dx.doi.org/10.1007/s11225-009-9213-2.

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15

Krause, F. L., and U. Kaufmann. "Meta-Modelling for Interoperability in Product Design." CIRP Annals 56, no. 1 (2007): 159–62. http://dx.doi.org/10.1016/j.cirp.2007.05.038.

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16

Delfmann, Patrick, Hanns-Alexander Dietrich, Matthias Steinhorst, and Jörg Becker. "Comprehensive Tool Support for Enterprise Modeling and Evaluation." International Journal of Information System Modeling and Design 5, no. 3 (July 2014): 26–54. http://dx.doi.org/10.4018/ijismd.2014070102.

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Enterprise modelling and evaluating subsequently created models is manifold. Enterprise modelling means capturing an organization's business processes, its organizational structure, its corporate strategy, or its supporting information systems in graphical conceptual models. Evaluating these models means assuring their structural and semantic quality as well as their user group adequacy. Enterprise modelling and model evaluation requires comprehensive tool support. Recently, a number of modelling tools have been proposed each addressing particular aspects of enterprise modelling or model evaluation. For example, meta-modelling tools allow for defining new and altering existing modelling languages. Meta-modelling tools are thus well suited for enterprise modelling as this requires a large number of different modelling languages that may need to be adapted to the particular enterprise. In contrast, some modelling tools provide mechanisms to structurally or semantically evaluate models of a predefined language. However, comprehensive tool support for enterprise modelling and model evaluation is missing up to now. With this paper, the authors aim at closing this research gap by proposing a tool combining meta-modelling capabilities with features to structurally and semantically evaluate models as well as to manage model perspectives. The tool thus covers the entire model lifecycle from defining a problem-suitable modelling language, creating models, configuring them for different user groups, and evaluating them in terms of structure and semantics.
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17

Roque, M., B. Vallespir, and G. Doumeingts. "Interoperability in enterprise modelling: Translation, elementary constructs, meta-modelling and UEML development." Computers in Industry 59, no. 7 (September 2008): 672–81. http://dx.doi.org/10.1016/j.compind.2007.12.017.

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18

Fill, Hans-Georg, and Dimitris Karagiannis. "On the Conceptualisation of Modelling Methods Using the ADOxx Meta Modelling Platform." Enterprise Modelling and Information Systems Architectures 8, no. 1 (January 2013): 4–25. http://dx.doi.org/10.1007/bf03345926.

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19

Misiak, Zbigniew, Michał Kossowski, and Wilfrid Utz. "Meta-Model Flexibility as a Significant Factor for Increasing End-User Involvement." International Journal of Technology and Educational Marketing 4, no. 2 (July 2014): 66–74. http://dx.doi.org/10.4018/ijtem.2014070106.

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This paper describes experiences of using meta-modelling tools supporting technology enhanced learning in European Commission's project NEXT-TELL. Due to a target group of the project (teachers with limited experience in modelling) it was very important to provide them with easy to use environment. This resulted in trying to identify least best fit of features needed by teachers while removing “distractions” - both in regards to tool functionality and modelling method applied. Such adjustments of the modelling tool were possible thanks to the underlying ADOxx® meta-modelling platform that allowed adjustment to feedback received from end users/subject matter experts in terms of functionality and usability.
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20

Lamharhar, Hind, Imane Zaoui, Adil Kabbaj, and Dalila Chiadmi. "A Semantic Meta-Modelling Approach for Smart Government." International Journal of Conceptual Structures and Smart Applications 4, no. 2 (July 2016): 72–93. http://dx.doi.org/10.4018/ijcssa.2016070105.

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The main objective of many e-government solutions is establishing smart government through developing user oriented, integrated and interoperable services. However, the heterogeneous and distributed nature of public administrations and the limit of traditional service discovery approaches does not enable achieving efficiently this objective. Rather achieving semantic interoperability through the development of shared e-government knowledge can be the key to smart government. As its main contribution, this paper presents a semantic modelling approach of data and public services, and a service discovery approach for building smart government applications. Our approach uses both semantic web services and conceptual structures technologies. Applying these technologies was shown to have an extremely high potential impact in smart government development. They enabled the improvement of the data and services description, of multiple resources, with additional semantic information, which allowed an automatic processing of information and services.
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21

Sutton, A. J., F. Song, S. M. Gilbody, and K. R. Abrams. "Modelling publication bias in meta-analysis: a review." Statistical Methods in Medical Research 9, no. 5 (October 1, 2000): 421–45. http://dx.doi.org/10.1191/096228000701555244.

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22

Ashford, Derek, Keith Davids, and Simon J. Bennett. "Developmental effects influencing observational modelling: A meta-analysis." Journal of Sports Sciences 25, no. 5 (March 2007): 547–58. http://dx.doi.org/10.1080/02640410600947025.

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23

Sutton, Alexander J., Fujian Song, Simon M. Gilbody, and Keith R. Abrams. "Modelling publication bias in meta-analysis: a review." Statistical Methods in Medical Research 9, no. 5 (October 2000): 421–45. http://dx.doi.org/10.1177/096228020000900503.

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24

Motz, Regina, Edelweis Rohrer, and Paula Severi. "The description logicSHIQwith a flexible meta-modelling hierarchy." Journal of Web Semantics 35 (December 2015): 214–34. http://dx.doi.org/10.1016/j.websem.2015.05.002.

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25

Dietrich, Hanns-Alexander, Dominic Breuker, Matthias Steinhorst, Patrick Delfmann, and Jörg Becker. "Developing Graphical Model Editors for Meta-Modelling Tools." Enterprise Modelling and Information Systems Architectures 8, no. 2 (October 2013): 42–78. http://dx.doi.org/10.1007/s40786-013-0004-x.

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26

Ventura Martins, Paula, and Marielba Zacarias. "Applying the Business Process and Practice Alignment Meta-model: Daily Practices and Process Modelling." Business Systems Research Journal 8, no. 1 (March 28, 2017): 1–16. http://dx.doi.org/10.1515/bsrj-2017-0001.

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AbstractBackground: Business Process Modelling (BPM) is one of the most important phases of information system design. Business Process (BP) meta-models allow capturing informational and behavioural aspects of business processes. Unfortunately, standard BP meta-modelling approaches focus just on process description, providing different BP models. It is not possible to compare and identify related daily practices in order to improve BP models. This lack of information implies that further research in BP meta-models is needed to reflect the evolution/change in BP. Considering this limitation, this paper introduces a new BP meta-model designed by Business Process and Practice Alignment Meta-model (BPPAMeta-model). Our intention is to present a meta-model that addresses features related to the alignment between daily work practices and BP descriptions. Objectives: This paper intends to present a metamodel which is going to integrate daily work information into coherent and sound process definitions. Methods/Approach: The methodology employed in the research follows a design-science approach. Results: The results of the case study are related to the application of the proposed meta-model to align the specification of a BP model with work practices models. Conclusions: This meta-model can be used within the BPPAM methodology to specify or improve business processes models based on work practice descriptions.
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27

Biniek, Zenon. "Application of the meta-modelling method in Business Intelligence." Informatyka Ekonomiczna 2020, no. 4 (2020): 50–66. http://dx.doi.org/10.15611/ie.2020.4.04.

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28

Chau, Kwok-wing. "Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling." Water 9, no. 3 (March 6, 2017): 186. http://dx.doi.org/10.3390/w9030186.

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29

Attenni, Martina, Carlo Bianchini, Marika Griffo, and Luca James Senatore. "HBIM Meta-Modelling: 50 (and More) Shades of Grey." ISPRS International Journal of Geo-Information 11, no. 9 (August 30, 2022): 468. http://dx.doi.org/10.3390/ijgi11090468.

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The paper aims at investigating modelling strategies in HBIM context to identify at what extent the final use of the model might affects, or should affect, the modelling approach itself. Moreover, the discussion wants to shed light on the possibility of connecting in just one digital environment several instances connected to the building. These aims will be discussed presenting and evaluating two different modelling approaches: the “black box” modelling and the “white box” model-ling. The two terms are partially borrowed from computer science to explain two types of testing. The “black box” testing is performed without any preliminary knowledge about the system functionality and internal components; on the contrary, the “white box” testing, implies a full knowledge of the system. These two approaches will be compared to two ways of conceiving a building information model. In conclusion, the paper will investigate the possibility to integrate in just one model, the grey box model, the two ones previously discussed.
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30

Bellini, Emanuele, Stefano Marrone, and Fiammetta Marulli. "Cyber Resilience Meta-Modelling: The Railway Communication Case Study." Electronics 10, no. 5 (March 2, 2021): 583. http://dx.doi.org/10.3390/electronics10050583.

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Recent times have demonstrated how much the modern critical infrastructures (e.g., energy, essential services, people and goods transportation) depend from the global communication networks. However, in the current Cyber-Physical World convergence, sophisticated attacks to the cyber layer can provoke severe damages to both physical structures and the operations of infrastructure affecting not only its functionality and safety, but also triggering cascade effects in other systems because of the tight interdependence of the systems that characterises the modern society. Hence, critical infrastructure must integrate the current cyber-security approach based on risk avoidance with a broader perspective provided by the emerging cyber-resilience paradigm. Cyber resilience is aimed as a way absorb the consequences of these attacks and to recover the functionality quickly and safely through adaptation. Several high-level frameworks and conceptualisations have been proposed but a formal definition capable of translating cyber resilience into an operational tool for decision makers considering all aspects of such a multifaceted concept is still missing. To this end, the present paper aims at providing an operational formalisation for cyber resilience starting from the Cyber Resilience Ontology presented in a previous work using model-driven principles. A domain model is defined to cope with the different aspects and “resilience-assurance” processes that it can be valid in various application domains. In this respect, an application case based on critical transportation communications systems, namely the railway communication system, is provided to prove the feasibility of the proposed approach and to identify future improvements.
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31

Meier, Sarah K., Kimberly L. Ray, Juliana C. Mastan, Savannah R. Salvage, and Donald A. Robin. "Meta-analytic connectivity modelling of deception-related brain regions." PLOS ONE 16, no. 8 (August 25, 2021): e0248909. http://dx.doi.org/10.1371/journal.pone.0248909.

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Brain-based deception research began only two decades ago and has since included a wide variety of contexts and response modalities for deception paradigms. Investigations of this sort serve to better our neuroscientific and legal knowledge of the ways in which individuals deceive others. To this end, we conducted activation likelihood estimation (ALE) and meta-analytic connectivity modelling (MACM) using BrainMap software to examine 45 task-based fMRI brain activation studies on deception. An activation likelihood estimation comparing activations during deceptive versus honest behavior revealed 7 significant peak activation clusters (bilateral insula, left superior frontal gyrus, bilateral supramarginal gyrus, and bilateral medial frontal gyrus). Meta-analytic connectivity modelling revealed an interconnected network amongst the 7 regions comprising both unidirectional and bidirectional connections. Together with subsequent behavioral and paradigm decoding, these findings implicate the supramarginal gyrus as a key component for the sociocognitive process of deception.
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32

Moffat, James, and Susan Witty. "Phase changes in Meta-modelling using the Fractal Dimension." Information & Security: An International Journal 8 (2002): 52–67. http://dx.doi.org/10.11610/isij.0802.

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33

Tu, Jian, and Yi Pen Cheng. "Stochastic design using cross-validated nonparametric meta-modelling techniques." International Journal of Materials and Product Technology 25, no. 1/2/3 (2006): 182. http://dx.doi.org/10.1504/ijmpt.2006.008281.

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34

Doi, Suhail A. R., and Jan J. Barendregt. "A generalized pairwise modelling framework for network meta-analysis." International Journal of Evidence-Based Healthcare 16, no. 4 (December 2018): 187–94. http://dx.doi.org/10.1097/xeb.0000000000000140.

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35

Basirat, Pegah, Hamed Fazlollahtabar, and Iraj Mahdavi. "System dynamics meta-modelling for reliability considerations in maintenance." International Journal of Process Management and Benchmarking 3, no. 2 (2013): 136. http://dx.doi.org/10.1504/ijpmb.2013.057723.

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36

Thevenon, Luc, and Jean-Marie Flaus. "A Hybrid Meta Modelling Approach Suited for Process Supervision." IFAC Proceedings Volumes 33, no. 17 (July 2000): 1155–60. http://dx.doi.org/10.1016/s1474-6670(17)39568-x.

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37

Kritikos, Kyriakos, and Philippe Massonet. "An Integrated Meta-model for Cloud Application Security Modelling." Procedia Computer Science 97 (2016): 84–93. http://dx.doi.org/10.1016/j.procs.2016.08.283.

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38

Brinkkemper, Sjaak, Motoshi Saeki, and Frank Harmsen. "Meta-modelling based assembly techniques for situational method engineering." Information Systems 24, no. 3 (May 1999): 209–28. http://dx.doi.org/10.1016/s0306-4379(99)00016-2.

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39

Duarte, Daniel J., Rik Oldenkamp, and Ad M. J. Ragas. "Modelling environmental antibiotic-resistance gene abundance: A meta-analysis." Science of The Total Environment 659 (April 2019): 335–41. http://dx.doi.org/10.1016/j.scitotenv.2018.12.233.

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40

de Lara, Juan, Esther Guerra, and Jesús Sánchez Cuadrado. "Model-driven engineering with domain-specific meta-modelling languages." Software & Systems Modeling 14, no. 1 (July 28, 2013): 429–59. http://dx.doi.org/10.1007/s10270-013-0367-z.

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41

Brinkkemper, S., M. de Lange, R. Looman, and F. H. G. C. van der Steen. "On the derivation of method companionship by meta-modelling." ACM SIGSOFT Software Engineering Notes 15, no. 1 (January 1990): 49–58. http://dx.doi.org/10.1145/382294.382716.

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42

de Lara, J., E. Guerra, R. Cobos, and J. Moreno-Llorena. "Extending Deep Meta-Modelling for Practical Model-Driven Engineering." Computer Journal 57, no. 1 (November 5, 2012): 36–58. http://dx.doi.org/10.1093/comjnl/bxs144.

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43

Bowden, Jack, Dan Jackson, and Simon G. Thompson. "Modelling multiple sources of dissemination bias in meta-analysis." Statistics in Medicine 29, no. 7-8 (March 8, 2010): 945–55. http://dx.doi.org/10.1002/sim.3813.

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44

Dokur, Emrah, Cihan Karakuzu, Uğur Yüzgeç, and Mehmet Kurban. "Using optimal choice of parameters for meta-extreme learning machine method in wind energy application." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 40, no. 3 (February 8, 2021): 390–401. http://dx.doi.org/10.1108/compel-07-2020-0246.

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Purpose This paper aims to deal with the optimal choice of a novel extreme learning machine (ELM) architecture based on an ensemble of classic ELM called Meta-ELM structural parameters by using a forecasting process. Design/methodology/approach The modelling performance of the Meta-ELM architecture varies depending on the network parameters it contains. The choice of Meta-ELM parameters is important for the accuracy of the models. For this reason, the optimal choice of Meta-ELM parameters is investigated on the problem of wind speed forecasting in this paper. The hourly wind-speed data obtained from Bilecik and Bozcaada stations in Turkey are used. The different number of ELM groups (M) and nodes (Nh) are analysed for determining the best modelling performance of Meta-ELM. Also, the optimal Meta-ELM architecture forecasting results are compared with four different learning algorithms and a hybrid meta-heuristic approach. Finally, the linear model based on correlation between the parameters was given as three dimensions (3D) and calculated. Findings It is observed that the analysis has better performance for parameters of Meta-ELM, M = 15 − 20 and Nh = 5 − 10. Also considering the performance metric, the Meta-ELM model provides the best results in all regions and the Levenberg–Marquardt algorithm -feed forward neural network and adaptive neuro fuzzy inference system -particle swarm optimization show competitive results for forecasting process. In addition, the Meta-ELM provides much better results in terms of elapsed time. Originality/value The original contribution of the study is to investigate of determination Meta-ELM parameters based on forecasting process.
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45

Zhang, Xia, Youchao Sun, and Yanjun Zhang. "Ontology modelling of intelligent HCI in aircraft cockpit." Aircraft Engineering and Aerospace Technology 93, no. 5 (June 10, 2021): 794–808. http://dx.doi.org/10.1108/aeat-11-2020-0255.

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Purpose Semantic modelling is an essential prerequisite for designing the intelligent human–computer interaction in future aircraft cockpit. The purpose of this paper is to outline an ontology-based solution to this issue. Design/methodology/approach The scenario elements are defined considering the cognitive behaviours, system functions, interaction behaviours and interaction situation. The knowledge model consists of a five-tuple array including concepts, relations, functions, axioms and instances. Using the theory of belief-desire-intention, the meta-model of cognitive behaviours is established. The meta-model of system functions is formed under the architecture of sub-functions. Supported by information flows, the meta-model of interaction behaviours is presented. Based on the socio-technical characteristics, the meta-model of interaction situation is proposed. The knowledge representation and reasoning process is visualized with the semantic web rule language (SWRL) on the Protégé platform. Finally, verification and evaluation are carried out to assess the rationality and quality of the ontology model. Application scenarios of the proposed modelling method are also illustrated. Findings Verification results show that the knowledge reasoning based on SWRL rules can further enrich the knowledge base in terms of instance attributes and thereby improve the adaptability and learning ability of the ontology model in different simulations. Evaluation results show that the ontology model has a good quality with high cohesion and low coupling. Practical implications The approach presented in this paper can be applied to model complex human–machine–environment systems, from a semantics-driven perspective, especially for designing future cockpits. Originality/value Different from the traditional approaches, the method proposed in this paper tries to deal with the socio-technical modelling issues concerning multidimensional information semantics. Meanwhile, the constructed model has the ability of autonomous reasoning to adapt to complex situations.
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46

Kim, H., G. Pak, H. Jun, S. Kim, and J. Yoon. "Distributed modelling of urban runoff using a meta-channel concept." Water Science and Technology 61, no. 11 (June 1, 2010): 2707–15. http://dx.doi.org/10.2166/wst.2010.187.

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Hydraulic flood routing is advantageous for computational accuracy; however, it requires individual calculations of an extensive pipe network in the case of a two-dimensional analysis. In this study, a method for considering the characteristics of a two-dimensional pipe network has been developed using the meta-channel concept to simplify the detailed calculations involved in the estimation of runoff from urban catchments. In essence, the meta-channel concept turns a two-dimensional pipe network into a one-dimensional pipe with an effective hydraulic geometry. Once such geometry has been identified, the flood routing can then be performed for an urban drainage system. A nonlinear diffusion wave equation, derived from the Saint-Venant equation, was used for flood routing, with an explicit method used for the numerical solution. The celerity and diffusion coefficients, which are two parameters of the diffusion wave equation, were estimated for the Goonja drainage from a two-dimensional pipe network using the meta-channel concept. A comparison of the results of the meta-channel-based pipe routing with the distributed SWMM simulation and observed data, showed close similarities, and identified the applicability of the meta-channel concept in an urban drainage setting.
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47

Meibody, M. N. P., H. Naseh, and F. Ommi. "Adaptive surrogate modelling algorithm for meta-model-based design optimisation." International Journal of Industrial and Systems Engineering 39, no. 3 (2021): 394. http://dx.doi.org/10.1504/ijise.2021.119721.

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48

Cheng, Minghui, Chao Dang, Dan M. Frangopol, Michael Beer, and Xian-Xun Yuan. "Transfer prior knowledge from surrogate modelling: A meta-learning approach." Computers & Structures 260 (February 2022): 106719. http://dx.doi.org/10.1016/j.compstruc.2021.106719.

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49

Bananmah, Mohammad, Mohammad Reza Nikoo, Banafsheh Nematollahi, and Mojtaba Sadegh. "Optimizing chute-flip bucket system based on meta-modelling approach." Canadian Journal of Civil Engineering 47, no. 5 (May 2020): 584–95. http://dx.doi.org/10.1139/cjce-2018-0534.

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Optimal design of chute-flip bucket (CFB) system depends on various parameters, among which energy dissipation and cavitation prevention are the most important. This study develops a simulation-optimization model based on a calibrated Flow-3D numerical model, multi-layer perceptron artificial neural network (MLP-ANN), and genetic algorithm (GA) optimization approach for determining the optimal geometry of the CFB system. To alleviate the computational time burden of the Flow-3D numerical model, a MLP-ANN meta-model is developed based on some limited simulations of Flow-3D. The meta-model framework is then coupled with GA to provide an efficient design framework for the CFB system. The proposed framework is employed to design optimal geometry of the CFB system of the Jareh dam in Ahvaz, Iran. The results show that the obtained optimal design increases the cavitation index up to 30% and energy dissipation up to 32% compared to the old engineering design already in place.
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50

Abler, D., S. Harris, and J. Davies. "189 META-MODELLING MARKOV MODEL SIMULATIONS FOR COST EFFECTIVENESS ANALYSES." Radiotherapy and Oncology 102 (March 2012): S91. http://dx.doi.org/10.1016/s0167-8140(12)70160-3.

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