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Journal articles on the topic 'Mathematical modeling'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 July 2024

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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 (March 2, 2024): 168–76. http://dx.doi.org/10.55248/gengpi.5.0324.0604.

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2

Guha, Probal, and Vaishnavi Unde. "Mathematical Modeling of Spiral Heat Exchanger." International Journal of Engineering Research 3, no. 4 (April 1, 2014): 226–29. http://dx.doi.org/10.17950/ijer/v3s4/409.

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3

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 notation (simulation language) on the Ramus platform. It is noted that the effectiveness of the budgeting system based on the use of economic-mathematical modeling tools will allow to improve resource expenditure planning taking into account the opportunities, priorities, needs and limitations of a specific enterprise and its external business environment. The need to implement digitalization tools and economic-mathematical modeling in the budgeting system is substantiated. The purpose of the article is to analyze the possibilities of forming an effective enterprise budgeting system based on the successful implementation of economic and mathematical modeling tools. The authors focus on the potential of using business analytics as a result of using economic and mathematical modeling tools to form an effective budgeting system. The article argues for the possibility of effectively using modeling tools in the budgeting process, which allows enterprises to make high-quality management decisions based on forecasts, scenarios, optimization recommendations, visualization of current problem situations, etc. The scientific novelty of this article lies in the fact that the recommendations and conclusions provided by the authors can be useful for domestic enterprises in the current conditions of severe restrictions on available resources, lack of free funds, existing and potential risks. In general, this article is useful for those who want to learn more about the possibilities of using economic-mathematical modeling tools in the budgeting system.

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4

Longo, R. T. "Mathematical modeling technique." AIP Advances 9, no. 12 (December 1, 2019): 125211. http://dx.doi.org/10.1063/1.5129638.

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5

Gerstenschlager, Natasha E., and Katherine Ariemma Marin. "GPS: Mathematical Modeling." Mathematics Teacher: Learning and Teaching PK-12 115, no. 9 (September 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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6

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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7

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 (January 15, 2012): 174–76. http://dx.doi.org/10.15373/22501991/may2014/53.

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8

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

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9

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 (June 28, 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 effect of designs on mathematical modeling ability. Theory of realistic mathematics education and virtual learning environment literature were integrated. The design model developed was verified by experts to be tested. The pre-test / post-test test method was carried out to see the effectiveness of the design. The sixty-seventh graders from a secondary school in North Sumatera were selected as samples. The instructional process developed consists of four stages, namely (1) purposing contextual problems, (2) defining situations from contextual problems, (3) solving problems individually or in groups, and (4) reviewing and comparing solutions. The developed virtual learning environment consists of 5 components, namely (1) users management, (2) content and activities management, (3) resources management, (4) visualization and communication management, and (5) evaluation and assessment management. The mathematical modeling ability concerning experimental group students is significantly higher after being taught through a realistic mathematics education instructional process via a virtual learning environment. Comparison of the experimental group with the control group also showed the same results.

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10

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 (August 31, 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 published from 2013 to 2022. As a result of examining the trends of the studies by subject, we revealed that studies targeting teachers were very insufficient. Moreover, the findings show that biased research methods and quantitatively simple analysis methods were mainly used. Last, the relational trend between research topics and implications were diverse depending on the theme such as task, lesson, and teacher education. Specifically, although the studies have provided implications on teacher education steadily, research targeting the topic of teacher education for mathematical modeling have been very limited. For the future study on mathematical modeling, mathematics educators and researchers need to recognize that teacher education is significant in implementing mathematical modeling in school classrooms successively, and to try diverse studies on teacher education for mathematical modeling. This paper will contribute to helping foreign scholars to know the research on mathematical modeling being conducted in Korea, which will ultimately contribute to the literature on mathematical modeling.

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11

Çaylan Ergene, Büşra, and Özkan Ergene. "Mathematical Modeling Self-Efficacy of Middle School and High School Students." Participatory Educational Research 11, no. 4 (July 14, 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 were used to determine the effect of gender, school level, grade level and previous engagement in model-eliciting activities on the mathematical modeling self-efficacy beliefs. The results showed that the mathematical modeling self-efficacy beliefs of middle school students were significantly higher than those of high school students. Furthermore, middle school students’ mathematical modeling self-efficacy beliefs did not differ significantly by gender, while at the high school level there was a significant difference in favor of males. Regarding grade levels, only a statistically significant difference was found between the mathematical modeling self-efficacy beliefs of seventh- and eighth-grade students. Moreover, middle and high school students who had previously engaged in model-eliciting activities had significantly higher mathematical modeling self-efficacy beliefs than those who had not. In the accessible literature, there is no study on the mathematical modeling self-efficacy beliefs of middle and high school students. Therefore, we believe this study’s results will contribute to the literature on mathematical modeling.

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12

Köhler, Angela D. A. "The Dangers of Mathematical Modeling." Mathematics Teacher 95, no. 2 (February 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 students are expected to analyze algebraically or graphically. They do not learn how the equation was originally derived, and they can often solve such problems without giving any thought to the application. To truly connect their mathematical skills with the outside world, we need to confront them with problems that have not yet been translated into the language of mathematics. We should just say “Now what?” and give them the necessary time to try out several mathematical models for the given situation.

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13

Taşpinar Şener, Zehra, and Yüksel Dede. "Mathematical Modeling From The Eyes Of Preservice Teachers." Revista Latinoamericana de Investigación en Matemática Educativa 24, no. 2 (July 31, 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 themes: (i) opinions regarding activities, (ii) opinions regarding preservice teachers, (iii) opinions regarding students, and (iv) opinions regarding mathematics teachers. Discussion of these findings revolved around both teacher training and mathematical modeling, which then led to several recommendations being made.

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14

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 (May 7, 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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15

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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16

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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17

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 (December 31, 2015): 154–59. http://dx.doi.org/10.23939/mmc2015.02.154.

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18

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 on MATLAB software. Meanwhile the key-factors affecting the hydraulic effective density and fluid bulk modulus were discussed. The study results provide computing foundation for designing quickly responsive, running steady and highly accurate hydraulic transmission system, which have important theory and engineering significance.

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19

Hirano, Tohru, and Kenji Wakashima. "Mathematical Modeling and Design." MRS Bulletin 20, no. 1 (January 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 shows the inverse design procedure for FGM, in which the final structure to be developed, as well as the boundary conditions, are specified first. After the fabrication method and an allowable material combination are selected from the FGM database, the estimation rules for the material properties of the intermediate compositions are determined based upon the micro-structure. Then, the temperature distribution and the thermal-stress distribution are calculated with the assumed profiles of the distribution functions for the constituents. Other possible combinations and different profiles are also investigated until the optimum is obtained.

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20

Kislitsyna, Irina A., and Galina F. Malykhina. "Mathematical modeling of altimeter." ACTA IMEKO 4, no. 4 (December 23, 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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21

Turko, S. Y., and K. Y. Trubakova. "MATHEMATICAL MODELING FOR PASTURING." VESTNIK OF THE BASHKIR STATE AGRARIAN UNIVERSITY 42, no. 2 (June 19, 2017): 30–34. http://dx.doi.org/10.31563/1684-7628-2017-42-2-30-34.

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22

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

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23

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

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24

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

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25

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

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26

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

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27

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 (September 2000): 1017. http://dx.doi.org/10.2307/2669502.

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28

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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29

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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30

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

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31

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

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32

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

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33

Solov'eva, OE, and VS Markhasin. "Mathematical modeling in physiology." Fiziolohichnyĭ zhurnal 5, no. 57 (August 18, 2011): 78–79. http://dx.doi.org/10.15407/fz57.05.078.

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34

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

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35

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

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36

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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37

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

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38

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

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39

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 (May 2007): 219–24. http://dx.doi.org/10.1007/s10553-007-0038-2.

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40

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

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41

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

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42

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

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43

Roekaerts, Dirk J. E. M. "Stefan Heinz, Mathematical modeling." Theoretical and Computational Fluid Dynamics 27, no. 6 (January 30, 2013): 903–4. http://dx.doi.org/10.1007/s00162-012-0282-x.

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44

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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45

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

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46

Sravanthi, A. Ch, K. Venkata Lakshmi, and K. T. N. Jyothi. "Mathematical modeling in Epidemiology." Research Journal of Science and Technology 9, no. 3 (2017): 353. http://dx.doi.org/10.5958/2349-2988.2017.00061.4.

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47

Dibdin, George H. "Mathematical Modeling of Biofilms." Advances in Dental Research 11, no. 1 (April 1997): 127–32. http://dx.doi.org/10.1177/08959374970110010301.

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A set of mathematical equations constitutes a mathematical model if it aims to represent a real system and is based on some theory of that system's operation. On this definition, mathematical models, some very simple, are everywhere in science. A complex system like a biofilm requires modeling by numerical methods and, because of inevitable uncertainties in its theoretical basis, may not be able to make precise predictions. Nevertheless, such models almost always give new insight into the mechanisms involved, and stimulate further investigation. The way in which diffusion coefficients are measured for use in a model, particularly whether they include effects of reversible reaction, is a key element in the modeling. Reasons are given for separating diffusion from reversible reaction effects and dealing with them in a separate subroutine of the model.

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48

Wiedemann, Kenia, Jie Chao, Benjamin Galluzzo, and Eric Simoneau. "Mathematical modeling with R." ACM Inroads 11, no. 1 (February 13, 2020): 33–42. http://dx.doi.org/10.1145/3380956.

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49

N.T. "Mathematical Modeling in Chemistry." Journal of Molecular Structure: THEOCHEM 279 (February 1993): 321. http://dx.doi.org/10.1016/0166-1280(93)90080-u.

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50

Sveshnikov, A. G. "Mathematical modeling in electrodynamics." Computational Mathematics and Modeling 6, no. 4 (1995): 254–63. http://dx.doi.org/10.1007/bf01128947.

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