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Journal articles on the topic 'Migration modelling'

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1

Raymer, James, and Peter W. F. Smith. "Modelling migration flows." Journal of the Royal Statistical Society: Series A (Statistics in Society) 173, no. 4 (2010): 703–5. http://dx.doi.org/10.1111/j.1467-985x.2010.00660.x.

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2

PEZIC, ANGELA. "MODELLING REGIONAL MIGRATION." Bulletin of the Australian Mathematical Society 80, no. 1 (2009): 173–76. http://dx.doi.org/10.1017/s0004972709000483.

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3

P, Giampaolo. "Modelling Supercritical CO2 Migration and Storage in Fractured Reservoirs." Petroleum & Petrochemical Engineering Journal 7, no. 1 (2023): 1–3. http://dx.doi.org/10.23880/ppej-16000334.

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4

Butler, George, Jonathan Rudge, and Philip R. Dash. "Mathematical modelling of cell migration." Essays in Biochemistry 63, no. 5 (2019): 631–37. http://dx.doi.org/10.1042/ebc20190020.

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Abstract The complexity of biological systems creates challenges for fully understanding their behaviour. This is particularly true for cell migration which requires the co-ordinated activity of hundreds of individual components within cells. Mathematical modelling can help understand these complex systems by breaking the system into discrete steps which can then be interrogated in silico. In this review, we highlight scenarios in cell migration where mathematical modelling can be applied and discuss what types of modelling are most suited. Almost any aspect of cell migration is amenable to ma
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5

Brillinger, David R., and Brent S. Stewart. "Elephant-seal movements: Modelling migration." Canadian Journal of Statistics 26, no. 3 (1998): 431–43. http://dx.doi.org/10.2307/3315767.

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6

Finkel, M., R. Liedl, and G. Teutsch. "Modelling surfactant influenced PAH migration." Physics and Chemistry of the Earth 23, no. 2 (1998): 245–50. http://dx.doi.org/10.1016/s0079-1946(98)00020-2.

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7

Welte, D. H., T. Hantschel, B. P. Wygrala, K. S. Weissenburger, and D. Carruthers. "Aspects of petroleum migration modelling." Journal of Geochemical Exploration 69-70 (June 2000): 711–14. http://dx.doi.org/10.1016/s0375-6742(00)00105-9.

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8

Doweidar, M. H., S. J. Mousavi, and M. Doblaré. "COMPUTATIONAL MODELLING OF CELL MIGRATION." Journal of Biomechanics 45 (July 2012): S431. http://dx.doi.org/10.1016/s0021-9290(12)70432-3.

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9

APLIN, J., and A. CHARLTON. "Modelling of intravascular trophoblast migration." Cell Biology International Reports 11, no. 3 (1987): 243. http://dx.doi.org/10.1016/0309-1651(87)90057-9.

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10

Qi, Haodong, and Tuba Bircan. "Modelling and predicting forced migration." PLOS ONE 18, no. 4 (2023): e0284416. http://dx.doi.org/10.1371/journal.pone.0284416.

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Migration models have evolved significantly during the last decade, most notably the so-called flow Fixed-Effects (FE) gravity models. Such models attempt to infer how human mobility may be driven by changing economy, geopolitics, and the environment among other things. They are also increasingly used for migration projections and forecasts. However, recent research shows that this class of models can neither explain, nor predict the temporal dynamics of human movement. This shortcoming is even more apparent in the context of forced migration, in which the processes and drivers tend to be hete
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11

Marcoux, M. O., L. Chernis, C. Harris, and P. Whiting. "Velocity Modelling for Depth Migration Using Exact-Time Migration." Exploration Geophysics 26, no. 2-3 (1995): 461–67. http://dx.doi.org/10.1071/eg995461.

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12

Steffens, Bastian, Quentin Corlay, Nathan Suurmeyer, Jessica Noglows, Dan Arnold, and Vasily Demyanov. "Can Agents Model Hydrocarbon Migration for Petroleum System Analysis? A Fast Screening Tool to De-Risk Hydrocarbon Prospects." Energies 15, no. 3 (2022): 902. http://dx.doi.org/10.3390/en15030902.

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Understanding subsurface hydrocarbon migration is a crucial task for petroleum geoscientists. Hydrocarbons are released from deeply buried and heated source rocks, such as shales with a high organic content. They then migrate upwards through the overlying lithologies. Some hydrocarbon becomes trapped in suitable geological structures that, over a geological timescale, produce viable hydrocarbon reservoirs. This work investigates how intelligent agent models can mimic these complex natural subsurface processes and account for geological uncertainty. Physics-based approaches are commonly used in
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13

Aufhauser, E., and M. M. Fischer. "Log-Linear Modelling and Spatial Analysis." Environment and Planning A: Economy and Space 17, no. 7 (1985): 931–51. http://dx.doi.org/10.1068/a170931.

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In the past decade the social sciences have seen an upsurge of interest in analysing multidimensional contingency tables using log-linear models. Two broad families of log-linear models may be distinguished: the family of conventional models and the family of unconventional models (that is, quasi-log-linear and hybrid models). In this paper a brief review of such models is presented and some linkage to the class of generalised linear models suggested by Nelder and Wedderburn is provided. The great potential of log-linear models for spatial analysis is illustrated in applying conventional and u
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14

Śleszyński, Przemysław, Krystian Heffner, Brygida Solga, and Rafał Wiśniewski. "Geograficzne badania migracji w Polsce po 1989 r. = Geographical research on migration in Poland after 1989." Przegląd Geograficzny 93, no. 4 (2022): 605–32. http://dx.doi.org/10.7163/przg.2021.4.6.

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This article summarises a review of geographical studies on migration published in Poland after 1989, as also included in the Report of the Polish Academy of Sciences’ Migration Research Committee (Komitet Badań nad Migracjami) (Horolets et al., 2018). The work identifies around 750 bibliographical items from the years 1990‑2017, which it proves possible to assign to the categories of: the theory and methodology of geographical research into migration (including modelling and forecasting), internal migrations, external migrations (emigration and immigration), studies concerning foreign territo
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15

Giniūnaitė, Rasa, Ruth E. Baker, Paul M. Kulesa, and Philip K. Maini. "Modelling collective cell migration: neural crest as a model paradigm." Journal of Mathematical Biology 80, no. 1-2 (2019): 481–504. http://dx.doi.org/10.1007/s00285-019-01436-2.

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Abstract A huge variety of mathematical models have been used to investigate collective cell migration. The aim of this brief review is twofold: to present a number of modelling approaches that incorporate the key factors affecting cell migration, including cell–cell and cell–tissue interactions, as well as domain growth, and to showcase their application to model the migration of neural crest cells. We discuss the complementary strengths of microscale and macroscale models, and identify why it can be important to understand how these modelling approaches are related. We consider neural crest
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16

Graham, Elspeth. "Problems of modelling intra-urban migration." Espace, populations, sociétés 3, no. 1 (1985): 215–22. http://dx.doi.org/10.3406/espos.1985.1031.

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17

Vasiev, Bakhtier, and Cornelis J. Weijer. "Modelling of Dictyostelium discoideum slug migration." Journal of Theoretical Biology 223, no. 3 (2003): 347–59. http://dx.doi.org/10.1016/s0022-5193(03)00103-6.

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18

Hunt, Gary L. "Equilibrium and Disequilibrium in Migration Modelling." Regional Studies 27, no. 4 (1993): 341–49. http://dx.doi.org/10.1080/00343409312331347605.

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19

Mukhina, E. D., A. Yu Kolesnikov, A. Yu Serovaiskii, and V. G. Kutcherov. "Experimental Modelling of Hydrocarbon Migration Processes." Journal of Physics: Conference Series 950 (October 2017): 042040. http://dx.doi.org/10.1088/1742-6596/950/4/042040.

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20

Bosa, Silvia, Marco Petti, and Sara Pascolo. "Numerical Modelling of Cohesive Bank Migration." Water 10, no. 7 (2018): 961. http://dx.doi.org/10.3390/w10070961.

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River morphological evolution is a challenging topic, involving hydrodynamic flow, sediment transport and bank stability. Lowland rivers are often characterized by the coexistence of granular and cohesive material, with significantly different behaviours. This paper presents a bidimensional morphological model to describe the evolution of the lower course of rivers, where there are both granular and cohesive sediments. The hydrodynamic equations are coupled with two advection–diffusion equations, which consider the transport of granular and cohesive suspended sediment concentration separately.
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21

Helmroth, Erika, Chris Varekamp, and Matthijs Dekker. "Stochastic modelling of migration from polyolefins." Journal of the Science of Food and Agriculture 85, no. 6 (2005): 909–16. http://dx.doi.org/10.1002/jsfa.2153.

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22

Razzhevaikin, V. N. "Interregional migration." Ecological Modelling 53 (January 1991): 164–65. http://dx.doi.org/10.1016/0304-3800(91)90154-s.

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23

Kurniawan, Husni Cahyadi, Adi Susilo, and Teguh Suroso. "STRUCTURE MODELLING OF SUBSURFACE BY USING KIRCHHOFF MIGRATION METHOD AND FINITE DIFFERENCE ANISOTROPY METHOD." INDONESIAN JOURNAL OF APPLIED PHYSICS 6, no. 02 (2017): 79. http://dx.doi.org/10.13057/ijap.v6i02.1422.

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<p class="abstrak">The image of subsurface with a migration method keeps on developing to get an image result which the closest in real condition. The conditions in subsurface are very complex and variables it couses the process of wave propagation which can not be judged as the same in every layers. so it is needed an anisotropy pharameter analysiz (η) in seismic data migration process. The research will compare two kinds of migrations those are Kirchhoff migration and finite difference anisotropy. It is done because not all datas are processed by anisotropy, even with isotropi it will
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24

McKinnell, Skip. "Modelling salmon migration as a mixture problem." Canadian Journal of Fisheries and Aquatic Sciences 76, no. 6 (2019): 856–70. http://dx.doi.org/10.1139/cjfas-2017-0546.

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Pulses of abundance in salmon migrations can arise from single populations arriving at different times, from multiple populations with different timing characteristics, or as a combination of these. Daily observations typically record an aggregate measure of abundance passing some location rather than the abundances of the individual components. An objective method is described that partitions a compound migration into its component parts by exploiting differences in the characteristics of each pulse. Simulated data were used to demonstrate when greater model complexity may be desirable. Three
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25

Searle, C., and J. H. van Vuuren. "Modelling forced migration: A framework for conflict-induced forced migration modelling according to an agent-based approach." Computers, Environment and Urban Systems 85 (January 2021): 101568. http://dx.doi.org/10.1016/j.compenvurbsys.2020.101568.

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26

KAZZAZ, M. Mohanned, and Marek RYCHLÝ. "Ontology-Based Context Modelling and Reasoning in the Web Service Migration Framework." Acta Electrotechnica et Informatica 13, no. 4 (2013): 5–12. http://dx.doi.org/10.15546/aeei-2013-0042.

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27

Zhao, Jieling, Youfang Cao, Luisa A. DiPietro, and Jie Liang. "Dynamic cellular finite-element method for modelling large-scale cell migration and proliferation under the control of mechanical and biochemical cues: a study of re-epithelialization." Journal of The Royal Society Interface 14, no. 129 (2017): 20160959. http://dx.doi.org/10.1098/rsif.2016.0959.

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Computational modelling of cells can reveal insight into the mechanisms of the important processes of tissue development. However, current cell models have limitations and are challenged to model detailed changes in cellular shapes and physical mechanics when thousands of migrating and interacting cells need to be modelled. Here we describe a novel dynamic cellular finite-element model (DyCelFEM), which accounts for changes in cellular shapes and mechanics. It also models the full range of cell motion, from movements of individual cells to collective cell migrations. The transmission of mechan
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28

De Luca, Giancarlo, Patrizio Mariani, Brian R. MacKenzie, and Matteo Marsili. "Fishing out collective memory of migratory schools." Journal of The Royal Society Interface 11, no. 95 (2014): 20140043. http://dx.doi.org/10.1098/rsif.2014.0043.

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Animals form groups for many reasons, but there are costs and benefits associated with group formation. One of the benefits is collective memory. In groups on the move, social interactions play a crucial role in the cohesion and the ability to make consensus decisions. When migrating from spawning to feeding areas, fish schools need to retain a collective memory of the destination site over thousands of kilometres, and changes in group formation or individual preference can produce sudden changes in migration pathways. We propose a modelling framework, based on stochastic adaptive networks, th
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29

De, Luca Giancarlo, Patrizio Mariani, Brian R. MacKenzie, and Matteo Marsili. "Fishing out collective memory of migratory schools." Journal Of The Royal Society Interface 11, no. 95 (2014): 1–29. https://doi.org/10.1098/rsif.2014.0043.

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Animals form groups for many reasons, but there are costs and benefits associated with group formation. One of the benefits is collective memory. In groups on the move, social interactions play a crucial role in the cohesion and the ability to make consensus decisions. When migrating from spawning to feeding areas, fish schools need to retain a collective memory of the destination site over thousands of kilometres, and changes in group formation or individual preference can produce sudden changes in migration pathways. We propose a modelling framework, based on stochastic adaptive networks, th
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30

Macniven, Alan. "Modelling Viking Migration to the Inner Hebrides." Journal of the North Atlantic 2013, sp4 (2013): 3. http://dx.doi.org/10.3721/037.004.sp407.

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31

Sun, Jian, Binliang Lin, and Hongwei Kuang. "Modelling the dynamic processes of channel migration." Proceedings of the Institution of Civil Engineers - Water Management 168, no. 6 (2015): 256–69. http://dx.doi.org/10.1680/wama.14.00081.

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32

Praveen Kumar, R., and G. R. Dodagoudar. "Meshfree modelling of one-dimensional contaminant migration." Géotechnique 58, no. 6 (2008): 523–26. http://dx.doi.org/10.1680/geot.2008.58.6.523.

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33

Oleynik, Elena Borisovna, Natalya Victorovna Ivashina, and Yuriy Davidovich Shmidt. "Migration processes modelling: methods and tools (overview)." Computer Research and Modeling 13, no. 6 (2021): 1205–32. http://dx.doi.org/10.20537/2076-7633-2021-13-6-1205-1232.

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34

Schneider, F., O. Coussy, and L. Dormieux. "A Mechanical Modelling of the Primary Migration." Revue de l'Institut Français du Pétrole 53, no. 2 (1998): 151–61. http://dx.doi.org/10.2516/ogst:1998015.

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35

Herbert, A. W., D. P. Hodgkinson, D. A. Lever, J. Rae, and P. C. Robinson. "Mathematical modelling of radionuclide migration in groundwater." Quarterly Journal of Engineering Geology and Hydrogeology 19, no. 2 (1986): 109–20. http://dx.doi.org/10.1144/gsl.qjeg.1986.019.02.04.

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36

Krishnaiah, S., and D. N. Singh. "Centrifuge modelling of heat migration in soils." International Journal of Physical Modelling in Geotechnics 4, no. 3 (2004): 39–47. http://dx.doi.org/10.1680/ijpmg.2004.040303.

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37

Neckářa, Bohuslav, Mahinder Kumar Soni, and Dipayan Das. "Modelling of Radial Fiber Migration in Yarns." Textile Research Journal 76, no. 6 (2006): 486–91. http://dx.doi.org/10.1177/0040517506061963.

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38

Richards, Shane A., Hugh P. Possingham, and John Noye. "Diel vertical migration: modelling light-mediated mechanisms." Journal of Plankton Research 18, no. 12 (1996): 2199–222. http://dx.doi.org/10.1093/plankt/18.12.2199.

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39

JANSSEN, L. P. B. M., J. PRINS, C. HOFFMANN, R. J. DE MEIJER, and A. W. L. VEEN. "MODELLING THE MIGRATION OF CONTAMINANTS IN SOIL." Chemical Engineering Communications 89, no. 1 (1990): 37–47. http://dx.doi.org/10.1080/00986449008940557.

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40

Leitch, A. M., and R. F. Weinberg. "Modelling granite migration by mesoscale pervasive flow." Earth and Planetary Science Letters 200, no. 1-2 (2002): 131–46. http://dx.doi.org/10.1016/s0012-821x(02)00596-4.

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41

Stopher, Miles Alexander, Peter Lang, Ernst Kozeschnik, and Pedro E. J. Rivera-Diaz-del-Castillo. "Modelling hydrogen migration and trapping in steels." Materials & Design 106 (September 2016): 205–15. http://dx.doi.org/10.1016/j.matdes.2016.05.051.

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42

Kilo, Martin, Robert A. Jackson, and Günter Borchardt. "Computer modelling of ion migration in zirconia." Philosophical Magazine 83, no. 29 (2003): 3309–25. http://dx.doi.org/10.1080/14786430310001605001.

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43

Majhi, Santosh Kumar, and Sunil Kumar Dhal. "Threat Modelling of Virtual Machine Migration Auction." Procedia Computer Science 78 (2016): 107–13. http://dx.doi.org/10.1016/j.procs.2016.02.018.

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44

Camacho, Carmen. "Migration modelling in the New Economic Geography." Mathematical Social Sciences 66, no. 3 (2013): 233–44. http://dx.doi.org/10.1016/j.mathsocsci.2013.04.006.

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45

Bencheva, Gergana. "Computer modelling of haematopoietic stem cells migration." Computers & Mathematics with Applications 64, no. 3 (2012): 337–49. http://dx.doi.org/10.1016/j.camwa.2012.02.045.

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46

Chen, B. Y., T. E. Tay, S. T. Pinho, and V. B. C. Tan. "Modelling delamination migration in angle-ply laminates." Composites Science and Technology 142 (April 2017): 145–55. http://dx.doi.org/10.1016/j.compscitech.2017.02.010.

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47

Ferreira da Silva, Eduardo Filpo, and Paul Sava. "Modelling and migration with orthogonal isochron rays." Geophysical Prospecting 57, no. 5 (2009): 773–84. http://dx.doi.org/10.1111/j.1365-2478.2008.00754.x.

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48

Congdon, Peter. "Aspects of General Linear Modelling of Migration." Statistician 41, no. 2 (1992): 133. http://dx.doi.org/10.2307/2348246.

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49

Pooler, James. "MODELLING INTERPROVINCIAL MIGRATION USING ENTROPY-MAXIMIZING METHODS." Canadian Geographer/Le Géographe canadien 31, no. 1 (1987): 57–64. http://dx.doi.org/10.1111/j.1541-0064.1987.tb01585.x.

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50

Thompson, Robin N., Christian A. Yates, and Ruth E. Baker. "Modelling Cell Migration and Adhesion During Development." Bulletin of Mathematical Biology 74, no. 12 (2012): 2793–809. http://dx.doi.org/10.1007/s11538-012-9779-0.

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