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Articoli di riviste sul tema "Mathematical Modelling"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 25 luglio 2025

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1

Ketova, K. V., I. G. Rusyak, and D. D. Vavilova. "MATHEMATICAL MODELLING OF WORKFORCE POTENTIAL." European Journal of Natural History, no. 3 2020 (2020): 65–69. http://dx.doi.org/10.17513/ejnh.34088.

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2

Smith, D. "Mathematical modelling." Teaching Mathematics and its Applications 15, no. 1 (1996): 37–41. http://dx.doi.org/10.1093/teamat/15.1.37.

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3

Campbell, P. "Mathematical modelling." Manufacturing Engineer 77, no. 4 (1998): 187–89. http://dx.doi.org/10.1049/me:19980407.

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4

Ziegel, Eric R. "Mathematical Modelling." Technometrics 32, no. 2 (1990): 240. http://dx.doi.org/10.1080/00401706.1990.10484666.

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5

Ramos, J. I. "Mathematical Modelling." Applied Mathematical Modelling 14, no. 8 (1990): 444. http://dx.doi.org/10.1016/0307-904x(90)90102-b.

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6

Rawson, H. "Mathematical modelling." Journal of Non-Crystalline Solids 73, no. 1-3 (1985): 551–63. http://dx.doi.org/10.1016/0022-3093(85)90374-6.

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7

Rawson, H. "Mathematical modelling." Journal of Non-Crystalline Solids 80, no. 1-3 (1986): 92. http://dx.doi.org/10.1016/0022-3093(86)90381-9.

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8

Cundy, H. Martyn, J. S. Berry, D. N. Burghes, I. D. Huntley, D. J. G. James, and A. O. Moscardini. "Mathematical Modelling Courses." Mathematical Gazette 72, no. 460 (1988): 152. http://dx.doi.org/10.2307/3618954.

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9

Reyniers, Diane, J. S. Berry, D. N. Hughes, I. D. Huntley, D. J. G. James, and A. O. Moscardini. "Mathematical Modelling Courses." Journal of the Operational Research Society 39, no. 12 (1988): 1181. http://dx.doi.org/10.2307/2583605.

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10

SMITH, D. N. "Independent Mathematical Modelling." Teaching Mathematics and its Applications 16, no. 3 (1997): 101–6. http://dx.doi.org/10.1093/teamat/16.3.101.

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11

Jones, Mark S. "Teaching mathematical modelling." International Journal of Mathematical Education in Science and Technology 28, no. 4 (1997): 553–60. http://dx.doi.org/10.1080/0020739970280409.

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12

Reyniers, Diane. "Mathematical Modelling Courses." Journal of the Operational Research Society 39, no. 12 (1988): 1181. http://dx.doi.org/10.1057/jors.1988.197.

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13

Lingefjärd, Thomas. "LEARNING MATHEMATICAL MODELLING." Far East Journal of Mathematical Education 16, no. 2 (2016): 149–67. http://dx.doi.org/10.17654/me016020149.

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14

Sodré, Gleison De Jesus Marinho, Raquel Soares do Rêgo Ferreira, and Renato Borges Guerra. "Reverse Mathematical Modelling." Acta Scientiae 24, no. 6 (2022): 552–83. http://dx.doi.org/10.17648/acta.scientiae.7372.

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Abstract (sommario):

Background: Research in mathematics education shows that mathematical modelling is a practice that seeks to “translate” problem situations into possible mathematical models without, however, explaining the complexity involved in the reverse formulation, starting from the mathematical model to delimit the type of situation. Objective: To highlight the problem of reverse mathematical modelling, in the sense of a reverse formulation that goes from the mathematical model to the situation. Design: For this, a course of study and research was carried out guided by the investigative cycle of mathemat

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15

Park, Eun young, and Oh Nam Kwon. "Comparison and Analysis of Middle School Trigonometry Textbook Tasks and Teacher Design Tasks: From the Perspective of Mathematical Modelling." Korean Association For Learner-Centered Curriculum And Instruction 23, no. 7 (2023): 817–38. http://dx.doi.org/10.22251/jlcci.2023.23.7.817.

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Objectives In this study, the triangulation task actually used in the middle school class is analyzed from the perspective of the mathematical modelling tasks. Methods Various triangulation tasks were collected from middle school trigonometry textbooks and activity sheets produced and used by teachers, and classified based on whether connectivity and openness, which are the core characteristics of mathematical modelling tasks, were met, and cognitive activities required. Then, mathematical modelling tasks is selected from among the teacher-designed tasks, and the activities of the tasks

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16

Corrêa, Priscila Dias. "The Mathematical Proficiency Promoted by Mathematical Modelling." Journal of Research in Science Mathematics and Technology Education 4, no. 2 (2021): 107–31. http://dx.doi.org/10.31756/jrsmte.424.

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This study aims to investigate the mathematical proficiency promoted by mathematical modelling tasks that require students to get involved in the processes of developing mathematical models, instead of just using known or given models. The research methodology is grounded on design-based research, and the classroom design framework is supported by complexity science underpinnings. The research intervention consists of high-school students, from a grade 11 mathematics course, aiming to solve four different modelling tasks in four distinct moments. Data was collected during the intervention from

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17

Schleicher, Jörg, Lei Wang, and Jin Yun Yuan. "Mathematical Modelling and Mathematical Methods in Energy." Numerical Linear Algebra with Applications 14, no. 4 (2007): 255. http://dx.doi.org/10.1002/nla.521.

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18

ARALOVA, N. I. "MATHEMATICAL MODELLING OF IMMUNE PROCESSES AND ITS APPLICATION." Biotechnologia Acta 13, no. 5 (2020): 5–18. http://dx.doi.org/10.15407/biotech13.05.005.

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The aim of the study was to develop a mathematical model to research hypoxic states in case of simulation of an organism infectious lesions. The model is based on the methods of mathematical modeling and the theory of optimal control of moving objects. The processes of organism damage are simulated with the mathematical model of immune response developed by G.I. Marchuk and the members of his scientific school, adapted to current conditions. This model is based on Burnet’s clone selection theory of the determining role of antigen. Simulation results using the model are presented. The dependenc

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19

Wilson, D. P. "Mathematical modelling of Chlamydia." ANZIAM Journal 45 (April 13, 2004): 201. http://dx.doi.org/10.21914/anziamj.v45i0.883.

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20

Bunday, Brian D. "Mathematical Modelling of Queues." Mathematical Gazette 79, no. 486 (1995): 499. http://dx.doi.org/10.2307/3618077.

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21

Abram, J., Dilwyn Edwards, and Mike Hamson. "Guide to Mathematical Modelling." Mathematical Gazette 75, no. 472 (1991): 243. http://dx.doi.org/10.2307/3620299.

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22

Kasprowicz, Magdalena, Agnieszka Kazimierska, Arkadiusz Ziółkowski, Afroditi Lalou, Zofia Czosnyka, and Marek Czosnyka. "Mathematical Modelling in Hydrocephalus." Neurology India 69, no. 8 (2021): 275. http://dx.doi.org/10.4103/0028-3886.332259.

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23

Ferrucci, Beverly J., and Jack A. Carter. "Technology-active mathematical modelling." International Journal of Mathematical Education in Science and Technology 34, no. 5 (2003): 663–70. http://dx.doi.org/10.1080/0020739031000148921.

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24

Gammon, Katharine. "Mathematical modelling: Forecasting cancer." Nature 491, no. 7425 (2012): S66—S67. http://dx.doi.org/10.1038/491s66a.

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25

Nuttall, Ben. "Mathematical modelling study group." MSOR Connections 11, no. 3 (2011): 10. http://dx.doi.org/10.11120/msor.2011.11030010.

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26

BAOWAN, DUANGKAMON. "MATHEMATICAL MODELLING OF NANOSTRUCTURES." Bulletin of the Australian Mathematical Society 78, no. 2 (2008): 351–52. http://dx.doi.org/10.1017/s0004972708000786.

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27

Gombert, Andreas Karoly, and Jens Nielsen. "Mathematical modelling of metabolism." Current Opinion in Biotechnology 11, no. 2 (2000): 180–86. http://dx.doi.org/10.1016/s0958-1669(00)00079-3.

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28

Alonso, E. E., N. M. Pinyol, and A. Yerro. "Mathematical Modelling of Slopes." Procedia Earth and Planetary Science 9 (2014): 64–73. http://dx.doi.org/10.1016/j.proeps.2014.06.002.

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29

Giersch, C. "Mathematical modelling of metabolism." Current Opinion in Plant Biology 3, no. 3 (2000): 249–53. http://dx.doi.org/10.1016/s1369-5266(00)00072-8.

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30

Brushlinsky, K. V. "Mathematical modelling in plasmastatics." Computer Physics Communications 126, no. 1-2 (2000): 37–40. http://dx.doi.org/10.1016/s0010-4655(99)00244-1.

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31

Ramos, J. I. "Mathematical modelling of fires." Applied Mathematical Modelling 14, no. 1 (1990): 52. http://dx.doi.org/10.1016/0307-904x(90)90164-z.

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32

Rodin, ErvinY. "Educational mathematical modelling modules." Mathematical and Computer Modelling 10, no. 1 (1988): 65. http://dx.doi.org/10.1016/0895-7177(88)90122-7.

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33

Rachev, S. "Mathematical and Computer Modelling." Mathematical and Computer Modelling 36, no. 7-8 (2002): 949. http://dx.doi.org/10.1016/s0895-7177(02)00239-x.

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34

El Khatib, N., O. Kafi, A. Sequeira, S. Simakov, Yu Vassilevski, and V. Volpert. "Mathematical modelling of atherosclerosis." Mathematical Modelling of Natural Phenomena 14, no. 6 (2019): 603. http://dx.doi.org/10.1051/mmnp/2019050.

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Abstract (sommario):

The review presents the state of the art in the atherosclerosis modelling. It begins with the biological introduction describing the mechanisms of chronic inflammation of artery walls characterizing the development of atherosclerosis. In particular, we present in more detail models describing this chronic inflammation as a reaction-diffusion wave with regimes of propagation depending on the level of cholesterol (LDL) and models of rolling monocytes initializing the inflammation. Further development of this disease results in the formation of atherosclerotic plaque, vessel remodelling and possi

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35

Rodin, ErvinY. "Mathematical and computer modelling." Mathematical and Computer Modelling 12, no. 12 (1989): I—II. http://dx.doi.org/10.1016/0895-7177(89)90345-2.

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36

Middleton, A., M. Owen, M. Bennett, and J. King. "Mathematical modelling of gibberellinsignalling." Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology 150, no. 3 (2008): S46. http://dx.doi.org/10.1016/j.cbpa.2008.04.023.

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37

Leng, G., and D. J. MacGregor. "Mathematical Modelling in Neuroendocrinology." Journal of Neuroendocrinology 20, no. 6 (2008): 713–18. http://dx.doi.org/10.1111/j.1365-2826.2008.01722.x.

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38

Hickman, F. R. "Mathematical modelling in physics." Physics Education 21, no. 3 (1986): 173–80. http://dx.doi.org/10.1088/0031-9120/21/3/311.

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39

Pescetti, D. "Mathematical modelling of hysteresis." Il Nuovo Cimento D 11, no. 8 (1989): 1191–216. http://dx.doi.org/10.1007/bf02459024.

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40

M, Chamuchi Nyaboe, Johana Sigey K, and Kangethe Giterere. "Mathematical Modelling of HIV/AIDS and Transmission Dynamics." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 06, no. 06 (2018): 01–08. http://dx.doi.org/10.9756/sijcsea/v6i6/06050090101.

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41

Ural, Alattin. "A Classification of Mathematical Modeling Problems of Prospective Mathematics Teachers." Journal of Educational Issues 6, no. 1 (2020): 98. http://dx.doi.org/10.5296/jei.v6i1.16566.

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The purpose of this research is to classify the mathematical modelling problems produced by pre-service mathematics teachers in terms of the number of variables and to determine the mathematical modelling skills and mathematical skills used in solving the problems in each class. The current study is a qualitative research and the data was analyzed using descriptive analysis. The data of the study was obtained from the mathematical modelling problem written by 59 senior mathematics teachers. They were given a 1-week period to write the problems and solutions. The participants took mathematical

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42

Alexander, R. McN. "Modelling approaches in biomechanics." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 358, no. 1437 (2003): 1429–35. http://dx.doi.org/10.1098/rstb.2003.1336.

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Conceptual, physical and mathematical models have all proved useful in biomechanics. Conceptual models, which have been used only occasionally, clarify a point without having to be constructed physically or analysed mathematically. Some physical models are designed to demonstrate a proposed mechanism, for example the folding mechanisms of insect wings. Others have been used to check the conclusions of mathematical modelling. However, others facilitate observations that would be difficult to make on real organisms, for example on the flow of air around the wings of small insects. Mathematical m

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43

Lei, Jinzhi. "Viewpoints on modelling: Comments on "Achilles and the tortoise: Some caveats to mathematical modelling in biology"." Mathematics in Applied Sciences and Engineering 1, no. 1 (2020): 85–90. http://dx.doi.org/10.5206/mase/10267.

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Abstract (sommario):

Mathematical modelling has been proven to be useful in understanding some problems from biological science, provided that it is used properly. However, it has also attracted some criticisms as partially presented in a recent opinion article \cite{Gilbert2018} from biological community. This note intends to clarify some confusion and misunderstanding in regard to mathematically modelling by commenting on those critiques raised in \cite{Gilbert2018}, with a hope of initiating some further discussion so that both applied mathematicians and biologist can better use mathematical modelling and bette

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44

Pockett, Susan. "The Unreasonable Political Effectiveness of Mathematical Modelling." Journal of NeuroPhilosophy 2, no. 2 (2023): 276–88. https://doi.org/10.5281/zenodo.10200253.

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Ever since Thomas Malthus used simple mathematics to model the evolution of food production vs the increasing human population of planet Earth and declared that we would very soon eat ourselves out of house and home, politicians have been falling hook, line and sinker for mathematical models.  This paper examines how and why the mathematical modelling of complex systems has proved unreasonably effective in convincing mathematically illiterate politicians to take practical measures that have turned out to be disastrous. It then suggests that mathematical modelling of the human brain is not

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45

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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46

Weinhandl, Robert, and Zsolt Lavicza. "Real-World Modelling to Increase Mathematical Creativity." Journal of Humanistic Mathematics 11, no. 1 (2021): 265–99. http://dx.doi.org/10.5642/jhummath.202101.13.

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Modelling could be characterised as one of the core activities in mathematics education. However, when learning and teaching mathematics, mathematical modelling is mostly used to apply and deepen mathematical knowledge and competencies. Our educational study aims to explore how mathematical modelling, using real objects and high-quality mathematical technologies, could be utilised to acquire mathematical knowledge and competencies, and how learners could creatively use their existing knowledge. To discover the potential of mathematical modelling using real objects and high-quality mathematical

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47

Wilson, Peter D. G., and Jack R. Dainty. "Modelling in nutrition: An introduction." Proceedings of the Nutrition Society 58, no. 1 (1999): 133–38. http://dx.doi.org/10.1079/pns19990018.

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The purpose of the present paper is to provide an introduction to modelling, particularly mathematical modelling, for nutritional researchers with little or no experience of the modelling process. It aims to outline the function of modelling, and to give some guidance on factors to consider when designing protocols to generate data as part of the modelling process. It is not intended in any way to be a comprehensive guide to mathematical modelling. The paper discusses the uses of modelling, and presents a ‘hydrodynamic analogy’ to compartmental modelling, to explain the process to the non-math

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48

Kumar, Prakash. "Mathematical Modelling and Simulating Applications." International Journal for Research in Applied Science and Engineering Technology 12, no. 1 (2024): 1188–93. http://dx.doi.org/10.22214/ijraset.2024.58132.

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Abstract: This paper reveals the importance of mathematical modelling, its growing role and its applications. It is a myth that modelling projects progress easily from working throughto utilizing, this is scarcely ever the situation. In computer science, the use of modelling and simulating a computer is utilized to fabricate a mathematical model which contains key boundaries of the actual model. Thus, the study aims to give a basic idea of mathematical modelling, its uses, and its role in recent scenarios.

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49

Wei, Yicheng, Qiaoping Zhang, and Jing Guo. "Can Mathematical Modelling Be Taught and Learned in Primary Mathematics Classrooms: A Systematic Review of Empirical Studies." Education Sciences 12, no. 12 (2022): 923. http://dx.doi.org/10.3390/educsci12120923.

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STEM education has been promoted in schools worldwide to cultivate students’ 21st-century skills. Mathematical modelling is a valuable method for developing STEM education. However, in this respect, more attention is given to secondary level or above compared with kindergarten or primary level. Teaching mathematics at the primary level is closely related to authentic problems, which is a crucial characteristic of mathematical modelling activities. After screening 239 publications from various databases, we reviewed 10 empirical studies on mathematical modelling at the primary level. In this sy

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50

Fajri, Hardian Mei, Arita Marini, and Suyono Suyono. "A bibliometric study on mathematical modelling in elementary schools in the Scopus database between 1990-2024." Eurasia Journal of Mathematics, Science and Technology Education 21, no. 2 (2025): em2577. https://doi.org/10.29333/ejmste/15916.

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Mathematical modelling is an approach to bridge real-world problems into mathematics as an effort to improve students’ mathematical literacy. The purpose of this study is to conduct a bibliometric analysis of published articles related to mathematical modelling in elementary school. This research uses bibliometric analysis method. This study used the Scopus database scanned with the keywords “mathematical modelling” and “elementary school” with a time span of 1990-2024 obtained as many as 78 articles. The data collected was then analyzed using R-software and VOSviewer applications. The results

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