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Artykuły w czasopismach na temat „Mathematical modeling”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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1

Mitra, Novelyn L., Ma Jobelle R. David, and Rommel Pariñas Deus Gleena P. Pascual. "Predictive Modeling for Criminology Licensure Examination Success Through Mathematical Modelling." International Journal of Research Publication and Reviews 5, no. 3 (2024): 168–76. http://dx.doi.org/10.55248/gengpi.5.0324.0604.

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Khurazovna, Aymatova Farida, and Shamsiyev Damin Najmiddinovich. "Mathematical Modeling of Company Activity." American Journal of Applied Science and Technology 5, no. 3 (2025): 38–41. https://doi.org/10.37547/ajast/volume05issue03-07.

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The article discusses the tasks of mathematical modeling of economic processes, particularly focusing on the mathematical modeling of tourism company activities. Under given conditions, a mathematical model is constructed, an optimal solution is found, and the results are analyzed.

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Guha, Probal, and Vaishnavi Unde. "Mathematical Modeling of Spiral Heat Exchanger." International Journal of Engineering Research 3, no. 4 (2014): 226–29. http://dx.doi.org/10.17950/ijer/v3s4/409.

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Latysheva, O., and Yu Chupryna. "Economic and Mathematical Modeling in Budgeting." Economic Herald of the Donbas, no. 4 (74) (2023): 32–36. http://dx.doi.org/10.12958/1817-3772-2023-4(74)-32-36.

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The article is devoted to an overview of modern modeling approaches for effective management of enterprise budgets. The article examines the toolkit of economic and mathematical modeling that can be used in the budgeting system. It is proposed to increase the efficiency of the budgeting process by applying the tools of economic and mathematical modeling at the stages of budget development and resource allocation, as well as in the process of budget control and monitoring. To increase the clarity of the simulation procedure and results, a visualization of the TO BE model is presented in IDF0 no

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5

Ibroximov, Baxtiyor Toyirjon o'g'li, Rejabboy Valijon oʻgʻli Maʼrufjonov, and Saidakbar Oybek oʻgʻli Gʻulomov. "MODELING OF MATHEMATICAL PHYSICS EQUATIONS." Academic Research Journal 2, no. 2 (2023): 112–18. https://doi.org/10.5281/zenodo.7836068.

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Many processes in modern physics are explained by solving the equations of mathematical physics. These equations are nonlinear differential equations that cannot always be solved analytically. In this work, we solved the equations of mathematical physics using the software package "wolfram mathematica" and analyzed the results.

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6

Longo, R. T. "Mathematical modeling technique." AIP Advances 9, no. 12 (2019): 125211. http://dx.doi.org/10.1063/1.5129638.

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Gerstenschlager, Natasha E., and Katherine Ariemma Marin. "GPS: Mathematical Modeling." Mathematics Teacher: Learning and Teaching PK-12 115, no. 9 (2022): 668–73. http://dx.doi.org/10.5951/mtlt.2022.0128.

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Growing Problem Solvers provides four original, related, classroom-ready mathematical tasks, one for each grade band. Together, these tasks illustrate the trajectory of learners’ growth as problem solvers across their years of school mathematics.

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Haris, Denny. "USING VIRTUAL LEARNING ENVIRONMENT ON REALISTIC MATHEMATICS EDUCATION TO ENHANCE SEVENTH GRADERS’ MATHEMATICAL MODELING ABILITY." SCHOOL EDUCATION JOURNAL PGSD FIP UNIMED 12, no. 2 (2022): 152–59. http://dx.doi.org/10.24114/sejpgsd.v12i2.35387.

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Many research studied that realistic mathematics education (RME) can be an alternative solution to students’ difficulties in learning mathematics. Various forms of technology additionally are further employed to support students' mathematical achievements. However, research on the implementation of virtual learning environments (VLE) with the RME approach is still lacking. The main goals of this research were to create an instructional process of virtual learning environments on realistic mathematics education to improve seventh graders' mathematical modeling abilities and to examine the effec

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9

Zarubin, V. S., and E. S. Sergeeva. "Mathematical modeling of structural-sensitive nanocomposites deformation." Computational Mathematics and Information Technologies 2, no. 1 (2018): 17–24. http://dx.doi.org/10.23947/2587-8999-2018-2-1-17-24.

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Kumar, Rakesh, Bharti Saxena, Ritu Shrivastava, and Ramakant Bhardwaj. "Mathematical Modeling of Dengue Disease Transmission Dynamics." Indian Journal Of Science And Technology 17, no. 39 (2024): 4101–10. http://dx.doi.org/10.17485/ijst/v17i39.1526.

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Objectives: This study developed a compartmental ordinary differential equation model to investigate dengue transmission dynamics within a human population. The model stratified the population into susceptible, exposed, infected, and recovered classes, incorporating key epidemiological factors. Methods: Model equilibrium analysis was conducted to determine the stability of disease-free and endemic states. The basic reproduction number (R₀) was calculated to quantify the potential for disease spread. Additionally, sensitivity analysis was performed to assess the impact of key parameters on mode

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11

VASHISHTHA, PUSHPENDRA KUMAR, ROHIT GOEL, PRIYANKA SAHNI, and ASHWINI KUMAR. "A Mathematical Modeling of University Examination System." Paripex - Indian Journal Of Research 3, no. 5 (2012): 174–76. http://dx.doi.org/10.15373/22501991/may2014/53.

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12

Klyuchko, O. M. "SOME TRENDS IN MATHEMATICAL MODELING FOR BIOTECHNOLOGY." Biotechnologia Acta 11, no. 1 (2018): 39–57. http://dx.doi.org/10.15407/biotech11.01.039.

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13

Çaylan Ergene, Büşra, and Özkan Ergene. "Mathematical Modeling Self-Efficacy of Middle School and High School Students." Participatory Educational Research 11, no. 4 (2024): 99–114. http://dx.doi.org/10.17275/per.24.51.11.4.

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Mathematical modeling is a cyclical process involving the competencies of understanding the problem, simplifying, mathematizing, working mathematically, interpreting, and validating. Mathematical modeling self-efficacy beliefs are essential to students’ mathematical modeling performance. This study examined middle and high school students’ mathematical modeling self-efficacy beliefs. The participants consisted of 1091 middle school students and 974 high school students. The data were collected through the “Mathematical Modeling Self-Efficacy Scale [MMSS]”. T-tests and ANOVA test statistics wer

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14

Hwang, Seonyoung, and Sunyoung Han. "A Study on Mathematical Modeling Trends in Korea." Korean Society of Educational Studies in Mathematics - Journal of Educational Research in Mathematics 33, no. 3 (2023): 639–66. http://dx.doi.org/10.29275/jerm.2023.33.3.639.

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Mathematical modeling refers to a core competency and teaching-learning method that is being treated as important worldwide. This study aimed at examining trends of previous studies on mathematical modeling, which were published in Korean journals. This study was conducted with the aim of introducing domestic studies on mathematical modeling to both domestic and foreign researchers. Fifty-four studies from 2013 to 2022 were selected for the current trend study and classified in terms of years, research subjects, and research methods. By year, at least one paper and up to 10 papers were publish

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15

Köhler, Angela D. A. "The Dangers of Mathematical Modeling." Mathematics Teacher 95, no. 2 (2002): 140–45. http://dx.doi.org/10.5951/mt.95.2.0140.

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When setting long-term goals, mathematics teachers face a constant dilemma. Most of us realize that our students will need to be mathematically literate in their future jobs, be able to see the real world through mathematical eyes, and be ready to handle the huge quantities of numbers that will be presented to them in their company's reports and in the news. During most of the school year, however, we give our students problems that are already written in mathematical language. Even the socalled real-life applications often consist of just an equation from physics, medicine, or economics that

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16

Nurfitriani, Cut Devy, and Abd Qohar. "ANALISIS KEMAMPUAN KONEKSI MATEMATIS SISWA SMP DALAM MENYELESAIKAN MASALAH KONTEKSTUAL HIMPUNAN." Jurnal Kajian Pembelajaran Matematika 5, no. 2 (2021): 38. http://dx.doi.org/10.17977/um076v5i22021p38-45.

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Mathematical connections are connecting mathematical concepts and mathematical concepts with other sciences and problems of everyday life. Mathematical contextual problems can be used to view and build students' mathematical connections. The purpose of this study was to describe how the mathematical connection abilities of junior high school students when solving contextual problems on set material. The type of research used is qualitative descriptive research. Research data obtained through mathematical connection tests and interviews. Analysis of mathematical connection ability is divided in

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17

Taşpinar Şener, Zehra, and Yüksel Dede. "Mathematical Modeling From The Eyes Of Preservice Teachers." Revista Latinoamericana de Investigación en Matemática Educativa 24, no. 2 (2021): 121–50. http://dx.doi.org/10.12802/relime.21.2421.

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Using preservice teachers’ (PTs) opinions as its base, this study seeks to shed light on the process followed by PTs in teaching mathematical modeling to middle school students. The study group was composed of 18 middle school mathematics PTs, each of whom was selected using purposeful sampling. During the research period, PTs travelled in groups to the schools where they were to perform their practicum. Lessons were video recorded, and PTs shared these recordings and their classroom experiences with their peers. As a result of the analysis, the study’s findings were grouped into four main the

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18

Akramovich, Qosimov Axtam, Abdullayev Ulug’bek To’lanbayevich, Abdulazizov Shokirjon Abdurashid O’g’li, and Hasanboyev Abdurasul Hasanboy O’g’li. "Mathematical Modeling Of Moisture Properties Of Terry Tissue." American Journal of Interdisciplinary Innovations and Research 03, no. 05 (2021): 94–99. http://dx.doi.org/10.37547/tajiir/volume03issue05-17.

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This study, designed to design and predict the water-related properties of pili tissue, mainly analyzed the linear density, pili height, and density of pili properties using a mathematical model using a full-factor experimental method. The article developed a mathematical model of the water absorption and construction properties of piliy textiles and correlated them with the results of practical experiments.

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19

Filatov, Evgeni, Leonid Kotlyar, Anna Voronkova, and Tadeusz Zaborowski. "Mathematical modeling of cavitation-free electrochemical machining process." Mechanik, no. 4 (April 2015): 328/129–328/133. http://dx.doi.org/10.17814/mechanik.2015.4.186.

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20

J,, Venkatesan, Nagarajan G, Seeniraj R. V, and Kumar S. "Mathematical Modeling of Water Cooled Automotive Air Compressor." International Journal of Engineering and Technology 1, no. 1 (2009): 50–56. http://dx.doi.org/10.7763/ijet.2009.v1.9.

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21

Kostrobij, P., I. Grygorchak, F. Ivaschyshyn, B. Markovych, O. Viznovych, and M. Tokarchuk. "Mathematical modeling of subdiffusion impedance in multilayer nanostructures." Mathematical Modeling and Computing 2, no. 2 (2015): 154–59. http://dx.doi.org/10.23939/mmc2015.02.154.

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22

Shi, Pei Cheng, and Yang Min Sun. "Hydraulic Fluid Mathematical Modeling." Applied Mechanics and Materials 432 (September 2013): 127–32. http://dx.doi.org/10.4028/www.scientific.net/amm.432.127.

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In hydraulic system, the property of hydraulic fluid will have non-linear behavior change when the temperature and pressure of fluid and the amount of dissolved and undissolved air vary, thus influencing the hydraulic system performance. The authors first studied the property of hydraulic fluid, and then constructed the mathematical expression theoretically, which shows the hydraulic fluid effective density using the fluid pressure, temperature and air content parameters. The computation simulations of the hydraulic fluid non-linear property were carried out using the simulation program based

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23

Hirano, Tohru, and Kenji Wakashima. "Mathematical Modeling and Design." MRS Bulletin 20, no. 1 (1995): 40–42. http://dx.doi.org/10.1557/s0883769400048922.

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For the design of functionally gradient materials (FGMs), necessary material properties, such as thermal-expansion-coefficient and Young's modulus in the specific region, are optimized by controlling the distribution profiles of composition and microstructures, as well as micropores in the materials. For this purpose, our research team employs the inverse design procedure in which both the basic material combination and the optimum profile of the composition and microstructures are determined with respect to the objective structural shape and the thermomechanical boundary conditions. Figure 1

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24

Kislitsyna, Irina A., and Galina F. Malykhina. "Mathematical modeling of altimeter." ACTA IMEKO 4, no. 4 (2015): 16. http://dx.doi.org/10.21014/acta_imeko.v4i4.263.

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The aim of the survey is to simulate photon altimeter designed for a soft landing on the lunar surface. Simulation of the process of scattering of gamma rays from the lunar surface with a typical composition of the lunar soil was implemented.

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25

Turko, S. Y., and K. Y. Trubakova. "MATHEMATICAL MODELING FOR PASTURING." VESTNIK OF THE BASHKIR STATE AGRARIAN UNIVERSITY 42, no. 2 (2017): 30–34. http://dx.doi.org/10.31563/1684-7628-2017-42-2-30-34.

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Page, Warren. "Mathematical Competition in Modeling." College Mathematics Journal 17, no. 1 (1986): 32. http://dx.doi.org/10.2307/2686868.

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27

Sun, Jiayang. "Mathematical Modeling of Traffic." Journal of Physics: Conference Series 2012, no. 1 (2021): 012060. http://dx.doi.org/10.1088/1742-6596/2012/1/012060.

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Tran, Dung, and Barbara J. Dougherty. "Authenticity of Mathematical Modeling." Mathematics Teacher 107, no. 9 (2014): 672–78. http://dx.doi.org/10.5951/mathteacher.107.9.0672.

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Curry, Evans W., and Derald Walling. "Mathematical modeling in psychophysics." International Journal of Mathematical Education in Science and Technology 16, no. 4 (1985): 543–46. http://dx.doi.org/10.1080/0020739850160411.

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Givi, Peyman. "Review of "Mathematical Modeling"." AIAA Journal 50, no. 12 (2012): 2943. http://dx.doi.org/10.2514/1.j052335.

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31

Dykstra, Richard L., Judah Rosenblatt, Stoughton Bell, Douglas Mooney, and Randall Swift. "Mathematical Analysis for Modeling." Journal of the American Statistical Association 95, no. 451 (2000): 1017. http://dx.doi.org/10.2307/2669502.

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32

Artale, V., C. L. R. Milazzo, and A. Ricciardello. "Mathematical modeling of hexacopter." Applied Mathematical Sciences 7 (2013): 4805–11. http://dx.doi.org/10.12988/ams.2013.37385.

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Kurasov, D. "Mathematical modeling system MatLab." Journal of Physics: Conference Series 1691 (November 2020): 012123. http://dx.doi.org/10.1088/1742-6596/1691/1/012123.

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Moghadas, Seyed M. "Mathematical modeling of tuberculosis." Mathematical Population Studies 24, no. 1 (2017): 1–2. http://dx.doi.org/10.1080/08898480.2015.1054222.

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NACHEMSON, ALF, MALCOLM H. POPE, and Malcolm H. Pope. "Concepts in Mathematical Modeling." Spine 16, no. 6 (1991): 675. http://dx.doi.org/10.1097/00007632-199106000-00021.

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Sotnikov, D. V., A. V. Zherdev, and B. B. Dzantiev. "Mathematical modeling of bioassays." Biochemistry (Moscow) 82, no. 13 (2017): 1744–66. http://dx.doi.org/10.1134/s0006297917130119.

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Solov'eva, OE, and VS Markhasin. "Mathematical modeling in physiology." Fiziolohichnyĭ zhurnal 5, no. 57 (2011): 78–79. http://dx.doi.org/10.15407/fz57.05.078.

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Zinnes, Dina A. "Musings on Mathematical Modeling." Conflict Management and Peace Science 11, no. 2 (1991): 1–16. http://dx.doi.org/10.1177/073889429101100201.

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Antipova, Ekaterina S. "Mathematical modeling of conflicts." Yugra State University Bulletin, no. 4 (August 16, 2024): 41–56. http://dx.doi.org/10.18822/byusu20230441-56.

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Subject of research: conflicts of various origins. Purpose of research: to develop a mathematical model of the conflict, to obtain a single strict definition of the conflict, allowing to formalize any conflict and describe it mathematically. Methods and objects of research: the methods of control theory and methods of the theory of dynamical systems are used in the work. The conflict model is described by difference equations. Main results of research: the definition of conflict is formulated, which allows to formalize any conflict. A mathematical model of conflicts is constructed. The possibl

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40

Montain, Scott J. "Mathematical Modeling of Hyponatremia." Medicine & Science in Sports & Exercise 40, Supplement (2008): 45. http://dx.doi.org/10.1249/01.mss.0000320965.48399.59.

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Schleich, Kolja, and Inna N. Lavrik. "Mathematical modeling of apoptosis." Cell Communication and Signaling 11, no. 1 (2013): 44. http://dx.doi.org/10.1186/1478-811x-11-44.

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Guerrini, Luca. "Mathematical modeling in economics." Physics of Life Reviews 9, no. 4 (2012): 415–17. http://dx.doi.org/10.1016/j.plrev.2012.08.005.

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Page, Warren. "Mathematical Competition in Modeling." College Mathematics Journal 17, no. 1 (1986): 32–33. http://dx.doi.org/10.1080/07468342.1986.11972927.

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Busenna, Ali, I. M. Kolesnikov, S. N. Ovcharov, S. I. Kolesnikov, and V. I. Zuber. "Mathematical modeling of platforming." Chemistry and Technology of Fuels and Oils 43, no. 3 (2007): 219–24. http://dx.doi.org/10.1007/s10553-007-0038-2.

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Lingefjärd, Thomas. "Faces of mathematical modeling." ZDM 38, no. 2 (2006): 96–112. http://dx.doi.org/10.1007/bf02655884.

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Harrington, PeterdeB. "Mathematical modeling in chemistry." Vibrational Spectroscopy 4, no. 2 (1993): 262. http://dx.doi.org/10.1016/0924-2031(93)87048-x.

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Prisnyakov, V. F., and L. M. Prisnyakova. "Mathematical modeling of emotions." Cybernetics and Systems Analysis 30, no. 1 (1994): 142–49. http://dx.doi.org/10.1007/bf02366374.

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Roekaerts, Dirk J. E. M. "Stefan Heinz, Mathematical modeling." Theoretical and Computational Fluid Dynamics 27, no. 6 (2013): 903–4. http://dx.doi.org/10.1007/s00162-012-0282-x.

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Fusaro, B. A. "Mathematical competition in modeling." Mathematical Modelling 6, no. 6 (1985): 473–85. http://dx.doi.org/10.1016/0270-0255(85)90048-x.

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Vardoulakis, Ioannis. "Sand production: Mathematical modeling." Revue européenne de génie civil 10, no. 6-7 (2006): 817–28. http://dx.doi.org/10.3166/regc.10.817-828.

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