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Journal articles on the topic 'Interaction modelling'

To see the other types of publications on this topic, follow the link: Interaction modelling.

Author: Grafiati

Published: 23 September 2022

Last updated: 28 September 2022

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1

Fernández, Raúl Cuevas. "Modelling concrete interaction with a bentonite barrier." European Journal of Mineralogy 21, no. 1 (February 6, 2009): 177–91. http://dx.doi.org/10.1127/0935-1221/2009/0021-1876.

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2

Monk, Andrew. "Modelling cyclic interaction." Behaviour & Information Technology 18, no. 2 (January 1999): 127–39. http://dx.doi.org/10.1080/014492999119165.

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3

Meyer, Diegele, Bruckner-Foit, and Moslang. "Crack interaction modelling." Fatigue Fracture of Engineering Materials and Structures 23, no. 4 (April 2000): 315–23. http://dx.doi.org/10.1046/j.1460-2695.2000.00283.x.

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4

Roy, John R., and Jean-Claude Thill. "Spatial interaction modelling." Papers in Regional Science 83, no. 1 (October 1, 2003): 339–61. http://dx.doi.org/10.1007/s10110-003-0189-4.

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5

Falconer, Ruth E., James L. Bown, Nia A. White, and John W. Crawford. "Modelling interactions in fungi." Journal of The Royal Society Interface 5, no. 23 (October 23, 2007): 603–15. http://dx.doi.org/10.1098/rsif.2007.1210.

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Indeterminate organisms have received comparatively little attention in theoretical ecology and still there is much to be understood about the origins and consequences of community structure. The fungi comprise an entire kingdom of life and epitomize the indeterminate growth form. While interactions play a significant role in shaping the community structure of indeterminate organisms, to date most of our knowledge relating to fungi comes from observing interaction outcomes between two species in two-dimensional arena experiments. Interactions in the natural environment are more complex and further insight will benefit from a closer integration of theory and experiment. This requires a modelling framework capable of linking genotype and environment to community structure and function. Towards this, we present a theoretical model that replicates observed interaction outcomes between fungal colonies. The hypotheses underlying the model propose that interaction outcome is an emergent consequence of simple and highly localized processes governing rates of uptake and remobilization of resources, the metabolic cost of production of antagonistic compounds and non-localized transport of internal resources. The model may be used to study systems of many interacting colonies and so provides a platform upon which the links between individual-scale behaviour and community-scale function in complex environments can be built.

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6

Pécol, Philippe, Stefano Dal Pont, Silvano Erlicher, and Pierre Argoul. "Modelling crowd-structure interaction." Mécanique & Industries 11, no. 6 (November 2010): 495–504. http://dx.doi.org/10.1051/meca/2010057.

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7

Clarke, Martin. "Editorial: Spatial Interaction Modelling." Applied Spatial Analysis and Policy 11, no. 4 (November 21, 2018): 645–46. http://dx.doi.org/10.1007/s12061-018-9283-5.

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8

Revtova, Elena. "Modelling Credit Process." Vestnik Volgogradskogo gosudarstvennogo universiteta. Ekonomika, no. 4 (February 2022): 205–15. http://dx.doi.org/10.15688/ek.jvolsu.2021.4.16.

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The article examines the economic phenomenon of credit. The target is to develop a model for element interaction method in the credit process. Hypothesis: it is assumed that scientific research based on a systematic approach is able to reveal the interacting elements in the credit process detail this process and describe the mechanism of its management. The following universal scientific methods have been used: the “black box” method, the element interaction method and the method of interaction of elements in the system with feedback. A three main components have been obtained. The first one is process model of a “black box” loan. The model contains the necessary resource for transformation in the credit process, the block-converter and the pattern of functional dependence the output has acquired on its input. The second one is a simple universal model of the relationship between the elements of the credit process in the system. The model shows the interacting elements of the credit, the specificity of the interaction of the elements, the result of the interaction of the elements of the credit and the effect on the credit and its external environment. The third one is a universal model of the relationship between the elements of the credit process in a system with feedback. The mechanism of regulating processes in the loan has been observed, identified and shown through feedback. The study has provided evidence that to start credit process in target groups they should be in disposition: one lacks all monetary and non-monetary resources whereas the other has an overflow of them; the interacting elements in the loan are efficiency, capacity to pay and pay back; feedback facility can regulate the processes moving in the credit system. Study results having been discovered through research expand the scientific understanding of the processes taking place inside loan. They can be used for an in-depth study of the loan by detailing the components of the model, and for modelling different scenarios of the credit process and its management modes.

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9

JONKER, CATHOLIJN M., and JAN TREUR. "Modelling multiple mind–matter interaction." International Journal of Human-Computer Studies 57, no. 3 (September 2002): 165–214. http://dx.doi.org/10.1016/s1071-5819(02)91023-2.

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10

Campañá Cué, C., A. R. Ruiz Salvador, S. Aguilera Morales, F. L. Falcon Rodriguez, and P. Pérez González. "Raffinose–sucrose crystal interaction modelling." Journal of Crystal Growth 231, no. 1-2 (September 2001): 280–89. http://dx.doi.org/10.1016/s0022-0248(01)01489-0.

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11

Hamaguchi, Satoshi, Masashi Yamashiro, Masaaki Matsukuma, and Hideaki Yamada. "Modelling of Plasma Surface Interaction." Journal of Physics: Conference Series 86 (October 1, 2007): 012016. http://dx.doi.org/10.1088/1742-6596/86/1/012016.

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12

Krawczyk, A., and T. Skoczkowski. "Mathematical modelling of electrobiological interaction." IEEE Transactions on Magnetics 32, no. 3 (May 1996): 725–28. http://dx.doi.org/10.1109/20.497342.

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13

Dyskin, A. V., K. B. Ustinov, and A. M. Korsunsky. "On modelling of defect interaction." International Journal of Fracture 71, no. 4 (1995): R79—R83. http://dx.doi.org/10.1007/bf00037823.

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14

K.G.S. "Modelling of soil-structure interaction." Computers and Geotechnics 9, no. 3 (January 1990): 236–37. http://dx.doi.org/10.1016/0266-352x(90)90017-p.

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15

Dix, Alan, Masitah Ghazali, and Devina Ramduny-Ellis. "Modelling Devices for Natural Interaction." Electronic Notes in Theoretical Computer Science 208 (April 2008): 23–40. http://dx.doi.org/10.1016/j.entcs.2008.03.105.

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16

Simmonds, Sidney H., and David K. Playdon. "Modelling soil-structure interaction construction." Computers & Structures 28, no. 2 (January 1988): 283–88. http://dx.doi.org/10.1016/0045-7949(88)90049-1.

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17

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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18

BAYLOR, KEN J., and JAMES J. HEFFRON. "MOLECULAR MODELLING OF GLUTATHIONE-ISOCYANATE INTERACTION." Biochemical Society Transactions 25, no. 1 (February 1, 1997): 48S. http://dx.doi.org/10.1042/bst025048s.

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19

Pollard, J. H. "Modelling the interaction between the sexes." Mathematical and Computer Modelling 26, no. 6 (September 1997): 11–24. http://dx.doi.org/10.1016/s0895-7177(97)00166-0.

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20

Benaroya, Haym, and Rene D. Gabbai. "Modelling vortex-induced fluid–structure interaction." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 366, no. 1868 (November 5, 2007): 1231–74. http://dx.doi.org/10.1098/rsta.2007.2130.

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The principal goal of this research is developing physics-based, reduced-order, analytical models of nonlinear fluid–structure interactions associated with offshore structures. Our primary focus is to generalize the Hamilton's variational framework so that systems of flow-oscillator equations can be derived from first principles. This is an extension of earlier work that led to a single energy equation describing the fluid–structure interaction. It is demonstrated here that flow-oscillator models are a subclass of the general, physical-based framework. A flow-oscillator model is a reduced-order mechanical model, generally comprising two mechanical oscillators, one modelling the structural oscillation and the other a nonlinear oscillator representing the fluid behaviour coupled to the structural motion. Reduced-order analytical model development continues to be carried out using a Hamilton's principle-based variational approach. This provides flexibility in the long run for generalizing the modelling paradigm to complex, three-dimensional problems with multiple degrees of freedom, although such extension is very difficult. As both experimental and analytical capabilities advance, the critical research path to developing and implementing fluid–structure interaction models entails formulating generalized equations of motion, as a superset of the flow-oscillator models; and developing experimentally derived, semi-analytical functions to describe key terms in the governing equations of motion. The developed variational approach yields a system of governing equations. This will allow modelling of multiple d.f. systems. The extensions derived generalize the Hamilton's variational formulation for such problems. The Navier–Stokes equations are derived and coupled to the structural oscillator. This general model has been shown to be a superset of the flow-oscillator model. Based on different assumptions, one can derive a variety of flow-oscillator models.

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21

Hayes, Sean T., James H. Steiger, and Julie A. Adams. "Modelling touch-interaction time on smartphones." Behaviour & Information Technology 35, no. 12 (September 12, 2016): 1022–43. http://dx.doi.org/10.1080/0144929x.2016.1221460.

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22

Phillips, Ryan, and Ken Chi. "Modelling ice rubble–rock berm interaction." International Journal of Physical Modelling in Geotechnics 15, no. 1 (March 2015): 35–43. http://dx.doi.org/10.1680/ijpmg.14.00014.

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23

Tsuprik, V. G., V. G. Zanegin, and L. V. Kim. "Mathematical Modelling of Ice-Structure Interaction." IOP Conference Series: Earth and Environmental Science 272 (June 21, 2019): 022063. http://dx.doi.org/10.1088/1755-1315/272/2/022063.

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24

Willekens, Frans. "LOG-LINEAR MODELLING OF SPATIAL INTERACTION." Papers in Regional Science 52, no. 1 (January 14, 2005): 187–205. http://dx.doi.org/10.1111/j.1435-5597.1983.tb01658.x.

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25

Claussen, Martin, Victor Brovkin, Andrey Ganopolski, Claudia Kubatzki, and Vladimir Petoukhov. "Modelling global terrestrial vegetation–climate interaction." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 353, no. 1365 (January 29, 1998): 53–63. http://dx.doi.org/10.1098/rstb.1998.0190.

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By coupling an atmospheric general circulation model asynchronously with an equilibrium vegetation model, manifold equilibrium solutions of the atmosphere–biosphere system have been explored. It is found that under present–day conditions of the Earth's orbital parameters and sea–surface temperatures, two stable equilibria of vegetation patterns are possible: one corresponding to present–day sparse vegetation in the Sahel, the second solution yielding savannah which extends far into the south–western part of the Sahara. A similar picture is obtained for conditions during the last glacial maximum (21 000 years before present (BP)). For the mid–Holocene (6000 years BP), however, the model finds only one solution: the green Sahara. We suggest that this intransitive behaviour of the atmosphere–biosphere is related to a westward shift of the Hadley–Walker circulation. A conceptual model of atmosphere–vegetation dynamics is used to interpret the bifurcation as well as its change in terms of stability theory.

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26

Alfi, S., and S. Bruni. "Mathematical modelling of train–turnout interaction." Vehicle System Dynamics 47, no. 5 (May 2009): 551–74. http://dx.doi.org/10.1080/00423110802245015.

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27

Tett, Simon. "Ocean-Atmosphere interaction and climate modelling." Journal of Experimental Marine Biology and Ecology 194, no. 2 (December 1995): 287–89. http://dx.doi.org/10.1016/0022-0981(95)90099-3.

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28

Euerby, Melvin R., Jennifer Hulse, Patrik Petersson, Andrey Vazhentsev, and Karim Kassam. "Retention modelling in hydrophilic interaction chromatography." Analytical and Bioanalytical Chemistry 407, no. 30 (November 13, 2015): 9135–52. http://dx.doi.org/10.1007/s00216-015-9079-2.

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29

Halpin, Peter F., and Paul De Boeck. "Modelling Dyadic Interaction with Hawkes Processes." Psychometrika 78, no. 4 (February 22, 2013): 793–814. http://dx.doi.org/10.1007/s11336-013-9329-1.

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30

Hughes, Tertia M. C. "Ocean-atmosphere interaction and climate modelling." Dynamics of Atmospheres and Oceans 25, no. 4 (May 1997): 273–75. http://dx.doi.org/10.1016/0377-0265(95)00464-5.

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31

Bottoni, P., M. F. Costabile, and S. Levialdi. "Analyzing, modelling, and specifying visual interaction." Soft Computing 7, no. 1 (November 2002): 9–19. http://dx.doi.org/10.1007/s00500-002-0168-8.

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32

Cacciabue, P. C. "Understanding and modelling man-machine interaction." Nuclear Engineering and Design 165, no. 3 (September 1996): 351–58. http://dx.doi.org/10.1016/0029-5493(96)01206-x.

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33

Palmeira, Ennio Marques. "Soil–geosynthetic interaction: Modelling and analysis." Geotextiles and Geomembranes 27, no. 5 (October 2009): 368–90. http://dx.doi.org/10.1016/j.geotexmem.2009.03.003.

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34

Derand, Pierre. "Ocean-atmosphere interaction and climate modelling." Atmospheric Research 39, no. 4 (December 1995): 355–56. http://dx.doi.org/10.1016/0169-8095(95)90012-8.

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35

Dix, Alan, Masitah Ghazali, Steve Gill, Joanna Hare, and Devina Ramduny-Ellis. "Physigrams: modelling devices for natural interaction." Formal Aspects of Computing 21, no. 6 (December 6, 2008): 613–41. http://dx.doi.org/10.1007/s00165-008-0099-y.

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36

Kolar, V., and I. Nemec. "Efficient modelling of soil-structure interaction." International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts 26, no. 2 (March 1989): 86. http://dx.doi.org/10.1016/0148-9062(89)90294-5.

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37

Alukaidey, K. A. S., and E. H. T. El-Shirbeeny. "Interaction factors in synchronous machine modelling." Mathematical and Computer Modelling 11 (1988): 969–74. http://dx.doi.org/10.1016/0895-7177(88)90637-1.

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38

Lara, Javier L., Inigo J. Losada, Gabriel Barajas, Maria Maza, and Benedetto Di Paolo. "RECENT ADVANCES IN 3D MODELLING OF WAVE-STRUCTURE INTERACTION WITH CFD MODELS." Coastal Engineering Proceedings, no. 36 (December 30, 2018): 91. http://dx.doi.org/10.9753/icce.v36.waves.91.

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Numerical modelling of the interaction of water waves with coastal structures has continuously been among the most relevant challenges in coastal engineering research and practice. During the last years, 3D modelling based on RANS-type equations, has been the dominant methodology to address the mathematical modelling of wave and coastal structure interaction. However, the three-dimensionality of many flowstructure interactions processes demands overcoming existing modelling limitations. Under some circumstances relevant three-dimensional processes are still tackled using physical modelling. It has been shown that beyond numerical implementation of the well-known mathematical 3-D formulation of the Navier-Stokes equations, the application of 3-D codes to standard coastal engineering problems demands some additional steps to be taken. These steps could be classified into three main groups relevant to: a) the modelling of the physical processes; b) the use of the tool and c) the applicability of the codes. This work presents an analysis of the use of three-dimensional flow models to analyze wave interaction with coastal structures focusing on recent developments overcoming existing limitations. Last modelling advances, including the implementation of new physics and pre-and postprocessing tools will be shown with the aim of extending the use of three-dimensional modelling of wavestructure interaction in both coastal and offshore fields.

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39

Wolf, Alexander, Jörg Miehling, and Sandro Wartzack. "Challenges in interaction modelling with digital human models – A systematic literature review of interaction modelling approaches." Ergonomics 63, no. 11 (July 8, 2020): 1442–58. http://dx.doi.org/10.1080/00140139.2020.1786606.

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40

Brunner, J. D., and N. Chia. "Metabolite-mediated modelling of microbial community dynamics captures emergent behaviour more effectively than species–species modelling." Journal of The Royal Society Interface 16, no. 159 (October 23, 2019): 20190423. http://dx.doi.org/10.1098/rsif.2019.0423.

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Personalized models of the gut microbiome are valuable for disease prevention and treatment. For this, one requires a mathematical model that predicts microbial community composition and the emergent behaviour of microbial communities. We seek a modelling strategy that can capture emergent behaviour when built from sets of universal individual interactions. Our investigation reveals that species–metabolite interaction (SMI) modelling is better able to capture emergent behaviour in community composition dynamics than direct species–species modelling. Using publicly available data, we examine the ability of species–species models and species–metabolite models to predict trio growth experiments from the outcomes of pair growth experiments. We compare quadratic species–species interaction models and quadratic SMI models and conclude that only species–metabolite models have the necessary complexity to explain a wide variety of interdependent growth outcomes. We also show that general species–species interaction models cannot match the patterns observed in community growth dynamics, whereas species–metabolite models can. We conclude that species–metabolite modelling will be important in the development of accurate, clinically useful models of microbial communities.

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41

Godsoe, William, Nathaniel J. Holland, Chris Cosner, Bruce E. Kendall, Angela Brett, Jill Jankowski, and Robert D. Holt. "Interspecific interactions and range limits: contrasts among interaction types." Theoretical Ecology 10, no. 2 (November 29, 2016): 167–79. http://dx.doi.org/10.1007/s12080-016-0319-7.

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42

Di Battista, Andrew, Christos Nicolaides, and Orestis Georgiou. "Modelling disease transmission from touchscreen user interfaces." Royal Society Open Science 8, no. 7 (July 2021): 210625. http://dx.doi.org/10.1098/rsos.210625.

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The extensive use of touchscreens for all manner of human–computer interactions has made them plausible instruments of touch-mediated disease transmission. To that end, we employ stochastic simulations to model human–fomite interaction with a distinct focus on touchscreen interfaces. The timings and frequency of interactions from within a closed population of infectious and susceptible individuals was modelled using a queuing network. A pseudo-reproductive number R was used to compare outcomes under various parameter conditions. We then apply the simulation to a specific real-world scenario; namely that of airport self-check-in and baggage drop. A counterintuitive result was that R decreased with increased touch rates required for touchscreen interaction. Additionally, as one of few parameters to be controlled, the rate of cleaning/disinfecting screens plays an essential role in mitigating R , though alternative technological strategies could prove more effective. The simulation model developed provides a foundation for future advances in more sophisticated fomite disease-transmission modelling.

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43

Hernandez, Maria-Josefina. "Spatiotemporal dynamics in variable population interactions with density-dependent interaction coefficients." Ecological Modelling 214, no. 1 (June 2008): 3–16. http://dx.doi.org/10.1016/j.ecolmodel.2008.01.007.

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44

Duke, David, Philip Barnard, David Duce, and Jon May. "Syndetic Modelling." Human-Computer Interaction 13, no. 4 (December 1, 1998): 337–93. http://dx.doi.org/10.1207/s15327051hci1304_1.

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45

Dennett, Adam. "Modelling population flows using spatial interaction models." Australian Population Studies 2, no. 2 (November 11, 2018): 33–58. http://dx.doi.org/10.37970/aps.v2i2.38.

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Background Spatial Interaction Models have been used for decades to explain and predict flows (of migrants, capital, traffic, trade etc.) between geographic locations.Aims This paper will guide users through the process of fitting and calibrating spatial interaction models in order to understand, explain and predict internal migration flows in Australia. Data and methods Internal migration data from the Australian 2011 Census of Population and Housing, which records people who have moved between Greater Capital City Statistical Areas over 5-year periods, is used to exemplify the modelling process. The R statistical software is used to process and visualise the data as well as run the models. Results The full suite of Wilson’s family of spatial interaction models is fitted to the internal migration data, revealing that distance and origin/destination populations are some of the most important influencing factors affecting internal migration flows. We see whether constraining the model to known flows about origins and/or destinations will improve the fits and model estimates. Conclusions Spatial interaction modelling has been a tool in the box of some population geographers for a number of decades. However, recent advances in more forgiving programming languages such as R and Python now mean that this powerful modelling methodology is no longer only available to those who also possess advanced computer programming skills. This guide has exemplified the process of fitting and calibrating spatial interaction models on Australian internal migration data, but the methods could easily be applied to other flow data sets in other contexts.

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46

Yang, Jin, Sanyi Tang, and Robert A. Cheke. "Modelling pulsed immunotherapy of tumour–immune interaction." Mathematics and Computers in Simulation 109 (March 2015): 92–112. http://dx.doi.org/10.1016/j.matcom.2014.09.001.

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47

van de Vooren, F. W. C. J. "Modelling transport in interaction with the economy." Transportation Research Part E: Logistics and Transportation Review 40, no. 5 (September 2004): 417–37. http://dx.doi.org/10.1016/j.tre.2003.11.001.

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48

Challamel, N., and P. de Buhan. "Mixed modelling applied to soil–pipe interaction." Computers and Geotechnics 30, no. 3 (April 2003): 205–16. http://dx.doi.org/10.1016/s0266-352x(03)00011-9.

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49

Ansari, Yousef, George Kouretzis, and Scott William Sloan. "Physical modelling of lateral sand–pipe interaction." Géotechnique 71, no. 1 (January 2021): 60–75. http://dx.doi.org/10.1680/jgeot.18.p.119.

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50

Soyaslan, I. I., A. Dogan, and R. Karaguzel. "Modelling of lake—groundwater interaction in Turkey." Proceedings of the Institution of Civil Engineers - Water Management 161, no. 5 (October 2008): 277–87. http://dx.doi.org/10.1680/wama.2008.161.5.277.

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