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Journal articles on the topic 'Mathematical and software'

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Published: 30 May 2022
Last updated: 31 May 2022

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1

Argon, E., I. L. Chang, G. Gunaratna, D. K. Kahaner, and M. A. Reed. "Mathematical software: Plod." IEEE Micro 8, no. 4 (August 1988): 56–61. http://dx.doi.org/10.1109/40.7772.

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2

Krogh, Fred T. "On developing mathematical software." Journal of Computational and Applied Mathematics 185, no. 2 (January 2006): 196–202. http://dx.doi.org/10.1016/j.cam.2005.03.005.

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3

Hake, J. Fr. "Mathematical software at KFA." ACM SIGNUM Newsletter 20, no. 2 (April 1985): 20–30. http://dx.doi.org/10.1145/1057941.1057945.

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4

Williams, Donald L. "A mathematical software environment." ACM SIGNUM Newsletter 21, no. 3 (July 1986): 2–12. http://dx.doi.org/10.1145/1057958.1057959.

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5

Gayoso Martínez, Víctor, Luis Hernández Encinas, Agustín Martín Muñoz, and Araceli Queiruga Dios. "Using Free Mathematical Software in Engineering Classes." Axioms 10, no. 4 (October 12, 2021): 253. http://dx.doi.org/10.3390/axioms10040253.

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There are many computational applications and engines used in mathematics, with some of the best-known arguably being Maple, Mathematica, MATLAB, and Mathcad. However, although they are very complete and powerful, they demand the use of commercial licences, which can be a problem for some education institutions or in cases where students desire to use the software on an unlimited number of devices or to access it from several of them simultaneously. In this contribution, we show how GeoGebra, WolframAlpha, Python, and SageMath can be applied to the teaching of different mathematical courses in engineering studies, as they are some of the most interesting representatives of free (and mostly open source) mathematical software. As the best way to show a topic in mathematics is by providing examples, this article explains how to make calculations for some of the main topics associated with Calculus, Algebra, and Coding theories. In addition to this, we provide some results associated with the usage of Mathematica in different graded activities. Moreover, the comparison between the results from students that use Mathematica and students that participate in a “traditional” course, solving problems and attending to master classes, is shown.
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6

Ochkov, Valery, and Elena Bogomolova. "Teaching Mathematics with Mathematical Software." Journal of Humanistic Mathematics 5, no. 1 (January 2015): 265–85. http://dx.doi.org/10.5642/jhummath.201501.15.

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7

Wallis, P. "Mathematical Structures for Software Engineering." Computer Journal 35, no. 1 (February 1, 1992): 80. http://dx.doi.org/10.1093/comjnl/35.1.80.

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8

Dongarra, J., and E. Grosse. "Shopping for mathematical software electronically." IEEE Potentials 8, no. 1 (February 1989): 37–38. http://dx.doi.org/10.1109/45.31582.

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9

Lucks, Michael, and Ian Gladwell. "Automated selection of mathematical software." ACM Transactions on Mathematical Software 18, no. 1 (March 1992): 11–34. http://dx.doi.org/10.1145/128745.128747.

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10

Chatterjee, Samprit, Ronald F. Boisvert, Sally E. Howe, and David K. Kahaner. "Guide to Available Mathematical Software." Journal of the American Statistical Association 80, no. 392 (December 1985): 1082. http://dx.doi.org/10.2307/2288608.

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11

Kant, E. "Synthesis of mathematical-modeling software." IEEE Software 10, no. 3 (May 1993): 30–41. http://dx.doi.org/10.1109/52.210600.

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12

Goldberg, Lisa R. "Mathematical Software: Is It Mathematics or Is It Software?" Notices of the American Mathematical Society 63, no. 11 (December 1, 2016): 1293–96. http://dx.doi.org/10.1090/noti1447.

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13

Bönisch, Sebastian, Michael Brickenstein, Gert-Martin Greuel, and Wolfram Sperber. "swMATH — citations for your mathematical software." Computeralgebra-Rundbrief 26, no. 2 (October 2012): 10–11. http://dx.doi.org/10.1007/bf03345852.

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14

Boisvert, Ronald F. "Mathematical software: past, present, and future." Mathematics and Computers in Simulation 54, no. 4-5 (December 2000): 227–41. http://dx.doi.org/10.1016/s0378-4754(00)00185-3.

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15

Haigh, Thomas. "John R. Rice: Mathematical Software Pioneer." IEEE Annals of the History of Computing 32, no. 4 (October 2010): 72–81. http://dx.doi.org/10.1109/mahc.2010.64.

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16

Hinov, Nikolay, George Kraev, Bogdan Gilev, and Dimitar Vakovsky. "Parallel inverter analysis using mathematical software." Facta universitatis - series: Electronics and Energetics 19, no. 1 (2006): 99–107. http://dx.doi.org/10.2298/fuee0601099h.

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This paper examines the transient processes at turning on of the parallel current source inverter and parallel resonant inverters, using the program MatLab. The obtained results allow the transient process to be assessed and its design to be done to obtain favorable turn on transient process.
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17

Henderson-Sellers, B. "The mathematical validity of software metrics." ACM SIGSOFT Software Engineering Notes 21, no. 5 (September 1996): 89–94. http://dx.doi.org/10.1145/235969.235994.

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18

Pruess, Steven, and Charles T. Fulton. "Mathematical software for Sturm-Liouville problems." ACM Transactions on Mathematical Software 19, no. 3 (September 1993): 360–76. http://dx.doi.org/10.1145/155743.155791.

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19

Di Felice, P. "Reusability of mathematical software: a contribution." IEEE Transactions on Software Engineering 19, no. 8 (1993): 835–43. http://dx.doi.org/10.1109/32.238586.

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20

Mitola, J. "Software radio architecture: a mathematical perspective." IEEE Journal on Selected Areas in Communications 17, no. 4 (April 1999): 514–38. http://dx.doi.org/10.1109/49.761033.

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21

Henderson, Peter B. "Mathematical reasoning in software engineering education." Communications of the ACM 46, no. 9 (September 2003): 45–50. http://dx.doi.org/10.1145/903893.903919.

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22

Mirotznik, M. S. "Tools for schools [mathematical software packages]." IEEE Spectrum 33, no. 9 (September 1996): 41–45. http://dx.doi.org/10.1109/6.535257.

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23

Dmitriev, V. I., and A. I. Bezruk. "Mathematical software for optoelectronic system design." Computational Mathematics and Modeling 1, no. 3 (July 1990): 317–25. http://dx.doi.org/10.1007/bf01126584.

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24

Adey, R. A. "Sources and development of mathematical software." Advances in Engineering Software (1978) 7, no. 2 (April 1985): 101–2. http://dx.doi.org/10.1016/0141-1195(85)90016-6.

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25

Xu, Hui. "Using Mathematical Software in High School Math Class: A Case Study." International Journal of Information and Education Technology 6, no. 12 (2016): 966–71. http://dx.doi.org/10.7763/ijiet.2016.v6.826.

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26

Murray, Megan, Jan Mokros, and Andee Rubin. "Mathematically Rich, Equitable Game Software." Mathematics Teaching in the Middle School 5, no. 3 (November 1999): 180–86. http://dx.doi.org/10.5951/mtms.5.3.0180.

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IT IS NO SECRET THAT MIDDLE SCHOOL STUdents spend significant amounts of free time playing software games, many of which fall into the “edutainment” category and many of which engage children in practicing mathematical skills. Middle school children and their parents spend a great deal of money on such software, often in the hopes of raising levels of mathematical achievement. The reasoning is as follows: If mathematical mastery can be accomplished in an entertaining and engaging manner, it is a win-win situation. Drill-and-practice games are especially popular, and in some instances they lead to modest increases in computational speed and accuracy (Becker 1990). But is it possible for children to go beyond drill and practice while using computer games? What are the ingredients of mathematical games that help students deepen their mathematical thinking? And to what extent are effective mathematical games also accessible to a range of children?
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27

Murtianto, Yanuar Hery, Sutrisno Sutrisno, Nizaruddin Nizaruddin, and Muhtarom Muhtarom. "EFFECT OF LEARNING USING MATHEMATICA SOFTWARE TOWARD MATHEMATICAL ABSTRACTION ABILITY, MOTIVATION, AND INDEPENDENCE OF STUDENTS IN ANALYTIC GEOMETRY." Infinity Journal 8, no. 2 (September 30, 2019): 219. http://dx.doi.org/10.22460/infinity.v8i2.p219-228.

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Rapid development of technology for the past two decades has greatly influenced mathematic learning system. Mathematica software is one of the most advanced technology that helps learn math especially in Geometry. Therefore this research aims at investigating the effectiveness of analytic geometry learning by using Mathematica software on the mathematical abstraction ability, motivation, and independence of students. This research is a quantitative research with quasi-experimental method. The independent variable is learning media, meanwhile the dependent variables are students’ mathematical abstraction ability, motivation, and independence in learning. The population in this research was the third semester students of mathematics education program and the sample was selected using cluster random sampling. The samples of this research consisted of two distinct classes, with one class as the experimental class was treated using Mathematica software and the other is the control class was treated without using it. Data analyzed using multivariate, particularly Hotelling’s T2 test. The research findings indicated that learning using Mathematica software resulted in better mathematical abstraction ability, motivation, and independence of students, than that conventional learning in analytic geometry subject.
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28

Lomaev, Yu S., I. A. Ivanov, A. V. Tolstykh, and E. V. Islent'ev. "APPLYING SOFTWARE-MATHEMATICAL MODELS OF ONBOARD EQUIPMENT TO DEVELOP ONBOARD SOFTWARE." Siberian Journal of Science and Technology 20, no. 2 (2019): 166–73. http://dx.doi.org/10.31772/2587-6066-2019-20-2-166-173.

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29

Steingartner, William, and Iskender Yar-Muhamedov. "Learning software for handling the mathematical expressions." Journal of Applied Mathematics and Computational Mechanics 17, no. 2 (June 2018): 77–91. http://dx.doi.org/10.17512/jamcm.2018.2.07.

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30

Chizhukhin, G. N., Yu G. Bochkareva, and O. V. Kulagin. "Mathematical Problems of Information Protection in Software." Telecommunications and Radio Engineering 68, no. 13 (2009): 1161–68. http://dx.doi.org/10.1615/telecomradeng.v68.i13.40.

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31

Melton, Austin C., David A. Gustafson, James M. Bieman, and Albert L. Baker. "A mathematical perspective for software measure research." Software Engineering Journal 5, no. 5 (1990): 246. http://dx.doi.org/10.1049/sej.1990.0027.

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32

Baturin, Kirill. "MATHEMATICAL SOFTWARE FOR DEFINITIONS ASSORTMENT STAND COMPOSITION." Актуальные направления научных исследований XXI века: теория и практика 2, no. 5 (November 11, 2014): 161–63. http://dx.doi.org/10.12737/6370.

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33

Dongarra, Jack J., and Eric Grosse. "Distribution of mathematical software via electronic mail." Communications of the ACM 30, no. 5 (May 1987): 403–7. http://dx.doi.org/10.1145/22899.22904.

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34

Kahaner, David, Jeffrey Horlick, and Webb Wyman. "Mathematical Software in Basic Dint: Data Integration." IEEE Micro 5, no. 2 (April 1985): 76–82. http://dx.doi.org/10.1109/mm.1985.304455.

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35

Mitrofanov, Sergey, Nikolay Novikov, Vasily Nikitin, and Sergey Belykh. "Mathematical models and soil fertility management software." E3S Web of Conferences 210 (2020): 04008. http://dx.doi.org/10.1051/e3sconf/202021004008.

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The article presents the results of studies on parametric approximation in spaces R2 (functions of one variable), R3 (functions of two variables) and Rn(n>3) (functions of three or more variables). Various classes of functions satisfying a priori conditions were studied: f(0, 0, 0)=0, $\mathop {\lim 1}\limits_{{x_i} \to + \infty } \,\,({x_1},\, \ldots ,\,{x_n}) = {c_i}$, ci = cont. Working algorithms and C/C++ software functioning in Microsoft Visual Studio 2019 system in Microsoft Windows 10 environment were developed. The main studies of the authors were aimed at developing effective computational algorithms for constructing approximating functions of two variables from various given classes of three-dimensional data samples (three-dimensional interconnected time series). The article provides a detailed description of the problem statement, introduces classes of approximating functions, provides algorithms for estimating the parameters of approximating functions and a description of the software. The estimation algorithm considered in the article is constructed according to the scheme of the coordinate descent method with optimization of the step length (Gauss-Seidel method).
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36

Aladjem, M. A., and M. D. Mikhailov. "Preprocessor for the generation of mathematical software." Communications in Applied Numerical Methods 1, no. 6 (November 1985): 339–42. http://dx.doi.org/10.1002/cnm.1630010614.

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37

Bellomonte, L., and R. M. Sperandeo-Mineo. "Mathematical modelling of data: Software for pedagogy." Computers & Education 21, no. 3 (October 1993): 263–69. http://dx.doi.org/10.1016/0360-1315(93)90020-j.

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38

Cox, M. G. "A classification of mathematical software for metrology." ISA Transactions 33, no. 4 (December 1994): 383–89. http://dx.doi.org/10.1016/0019-0578(94)90021-3.

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39

Dongarra, Jack J., and Eric Grosse. "Distribution of mathematical software via electronic mail." ACM SIGNUM Newsletter 20, no. 3 (July 1985): 45–47. http://dx.doi.org/10.1145/1057947.1057951.

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40

Rakhmatov, Dilmurod. "PROBLEMS IN MATHEMATICAL THEOREMS AND SOFTWARE TECHNOLOGY." PHYSICAL AND MATHEMATICAL SCIENCES 3, no. 1 (January 30, 2020): 19–22. http://dx.doi.org/10.26739/2181-0656-2020-3-4.

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41

Зимин, М. И., О. А. Кумукова, and М. М. Зимин. "Mathematical Model and Software for Avalanche Forecasting." Успехи кибернетики / Russian Journal of Cybernetics, no. 1(1) (March 31, 2020): 68–86. http://dx.doi.org/10.51790/2712-9942-2020-1-1-9.

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Описано математическое и программное обеспечение для прогнозирования возможности схода снежных лавин. Учитываются данные о возникновении этих склоновых процессов с конкретных склонов. Описана база данных. The study presents mathematical models and software for avalanche forecasting. They take into account the avalanche occurrence rate for specific slopes. The database is also presented.
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42

Hayati, Zikra, and Khairatul Ulya. "DEVELOPING STUDENTS’ MATHEMATICAL UNDERSTANDING USING GEOGEBRA SOFTWARE." JURNAL ILMIAH DIDAKTIKA: Media Ilmiah Pendidikan dan Pengajaran 22, no. 2 (February 28, 2022): 134. http://dx.doi.org/10.22373/jid.v22i2.11451.

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Technology is an essential reference in all aspects. Technology provides significant changes in education part, especially in learning mathematics. Technology has an impact on the innovation space in mathematics learning, the problems so far tend to be less attractive, the environment is boring and very tied to reading materials, which results in students still not being able to develop their mathematical thinking process optimally, namely students' mathematical understanding is still low. Students' mathematical understanding is one of the most important capabilities that students should have to solve various problems, both mathematical problems for instance problems related to concepts, operations, principles, and in daily life. So that innovation in learning with the influence of learning technology is important to apply. The aim of this study was to develop students' conceptual understanding of transformation geometry using Geogebra software. This study used exploratory mixed methods designs that aimed to describe students' understanding of mathematics by learning using Geogebra software. The results of the study indicate that usage Geogebra software can develop students' understanding of mathematical concepts in the transformation geometry, and assisted teachers create an interesting and fun learning process, teachers find the right procedures in using applications such as the use of different software and techniques in learning mathematics, making it easier for students to understand the abstract material of transformation geometry. The results of this study are focused on test analysis, namely exploring students' mathematical understanding, data can also be obtained from interview techniques that explore test answers, in the form of 2 problems related to transformation geometry. The results of the study indicate that are 5 criteria that have met the students' understanding of mathematics from 7 indicators.
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43

Purwasih, Ratni, Ratna Sariningsih, and Indah Puspita Sari. "SELF EFFICACY TERHADAP KEMAMPUAN HIGH ORDER THINKING MATHEMATICS SISWA MELALUI PEMBELAJARAN BERBANTUAN SOFTWERE GEOGEBRA." AKSIOMA: Jurnal Program Studi Pendidikan Matematika 9, no. 1 (March 31, 2020): 166. http://dx.doi.org/10.24127/ajpm.v9i1.2663.

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Artikel ini merupakan hasil penelitian terkait kemampuan self efficacy matematis siswa SMP pada dua kelas.  Penelitian ini bertujuan untuk mengetahui interaksi antara penerapan pembelajaran worksheet berbasis softwere Geogebra terhadap kemampuan self efficacy matematis siswa ditinjau kemampuan awal matematis (KAM) siswa. Melalui metode quasi eksperimen dengan desain pre test-post test, penelitian ini melibatkan 72 siswa SMP. Hasil penelitian menunjukkan adanya perbedaan peningkatan rata-rata kemampuan self efficacy matematis ditinjau dari kemampuan awal matematis (KAM). Hasil penelitian juga menunjukkan adanya peranan kemampuan self efficacy matematis antara kelas konvensional dan kelas eksperimen dari kemampuan awal siswa. Hal ini menunjukan bahwa peranan pembelajaran matematika berbasis softwere Geogebra mampu meningkatkan self efficacy siswa dibandingkan pembelajaran tanpa menggunakan softwere Geogebra. AbstractThis article is the result of research related to the mathematical self efficacy of junior high school students in two classes. This study aims to determine the interaction between the application of Geogebra softwares-based worksheet learning on students 'mathematical self efficacy abilities in terms of students' initial mathematical abilities. Through the quasi-experimental method with the pre-post-test design, this study involved 72 middle school students. The results showed a difference in the average increase in mathematical self efficacy abilities in terms of initial mathematical abilities (KAM). The results of the study also showed the role of mathematical self efficacy abilities between conventional classes and experimental classes from students' initial abilities. This shows that the role of Geogebra software-based mathematics learning can improve students' self-efficacy compared to learning without using Geogebra software.
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44

Tong, T. O., M. C. Kekana, M. Y. Shatalov, and S. P. Moshokoa. "Numerical Investigation of Brusselator Chemical Model by Residual Function Using Mathematica Software." Journal of Computational and Theoretical Nanoscience 17, no. 7 (July 1, 2020): 2947–54. http://dx.doi.org/10.1166/jctn.2020.9324.

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In recent years, mathematical models have been developed to illustrate some physical phenomena in science and engineering. One of those systems of nonlinear differential equations is Brusselator chemical model. A mathematical template of checking accuracy of from black-boxes has been developed and investigated. Brusselator model is used as case study as its analytical solution is non-existence. The algorithms investigated from Mathematica software includes Adams method, Backward differential formula (BDF) and Implicit Runge-Kutta method which works well on stiff systems. The graphical results are on interval of 0 ≤ t ≤ 30.
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45

KRASNOZHON, O. B., and V. V. MATSIUK. "КОМП’ЮТЕРНО-ОРІЄНТОВАНІ ЕЛЕМЕНТИ НАВЧАННЯ МАТЕМАТИЧНИХ ДИСЦИПЛІН МАЙБУТНІХ УЧИТЕЛІВ МАТЕМАТИКИ." Scientific papers of Berdiansk State Pedagogical University Series Pedagogical sciences 1, no. 2 (October 4, 2021): 255–62. http://dx.doi.org/10.31494/2412-9208-2021-1-2-255-262.

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The article is devoted to the issues of constructing effective computer-oriented components of the methodological system of teaching the disciplines «Linear Algebra» and «Probability Theory with Elements of Mathematical Statistics» provided for in the educational and professional program «Secondary Education (Mathematics)» of the first level of higher education in the specialty 014 Secondary Education (Mathematics). The article analyzes the methodological aspects of the effective organization of computations when finding the angle between a given vector and a nonzero subspace of Euclidean space, as well as using the least squares method for processing experimental data. The theoretical and practical information known to students-mathematicians from the corresponding sections of these academic disciplines is briefly presented. Analyzed educational, methodological and scientific literature used in teaching linear algebra and probability theory with elements of mathematical statistics; the expediency of using computer-oriented elements of teaching mathematical disciplines of future mathematics teachers has been substantiated. The authors proposed the use of computer-oriented learning elements in the processing of the content of disciplines and the development of test tasks of different levels of complexity in linear algebra and probability theory with elements of mathematical statistics in order to objectively assess the level of students' knowledge and timely correct individual educational trajectories. The article provides examples of the application of computer-oriented elements of teaching linear algebra and probability theory with elements of mathematical statistics, and also analyzes the methodological features of the organization of calculations in the software mathematical environment Mathcad. The methodological and practical materials presented in the article can be useful for students to organize and activate independent scientific and pedagogical activities, teachers of secondary educational institutions, heads of optional and circle work of students, teachers of linear algebra and probability theory courses with elements of mathematical statistics of pedagogical higher educational institutions. Key words: methods of teaching mathematics, computer-oriented elements of teaching mathematics, linear algebra, probability theory, mathematical statistics, Euclidean space, non-zero subspace of Euclidean space, least squares method.
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46

Wang, Yingxu. "Software Science: On the General Mathematical Models and Formal Properties of Software." Journal of Advanced Mathematics and Applications 3, no. 2 (December 1, 2014): 130–47. http://dx.doi.org/10.1166/jama.2014.1060.

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47

Palotás, Béla. "Mathematical Modeling on Welding Phenomena." Materials Science Forum 659 (September 2010): 435–40. http://dx.doi.org/10.4028/www.scientific.net/msf.659.435.

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The development of computer technology has been followed by welding as well, in the last 15 years. It is true for software development and it is true for hardware application. The paper gives a summary of computer technology application and mathematical modeling in welding, with a demonstration of software development and modeling activities carried out in Hungary. After some statistical data about software application in welding, this paper demonstrates the differences between handling databases, calculation software and elements of Artificial Intelligence. Some of the most important results of software development carried out at the Budapest University of Technology and Economics (BUTE) are also introduced in this paper. Mathematical modeling is a very important part of information technology development in the case of welding as well.
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48

Lyashenko, Viktor, Elena Kobilskaya, and Tetiana Nabok. "APPLICATION OF MATHEMATICS SOFTWARE FOR SOLVING APPLIED PROBLEMS." Transactions of Kremenchuk Mykhailo Ostrohradskyi National University, no. 3(128) (June 11, 2021): 11–16. http://dx.doi.org/10.30929/1995-0519.2021.3.11-16.

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Purpose. To show the possibilities of using mathematics software in solving problems arising in the construction of mathematical models of various processes and, thus, to reveal the importance of realizing the professional orientation of the mathematical training of students of natural and engineering specialties. Methodology. A number of mathematical models that are presented in the form of a Cauchy problem for a common first-order differential equation are considered in this paper. Mathematical models considered in this paper describe chemical and ecological processes. Euler's numerical method is used to solve the problems that describe the proposed models. This method is implemented in special mathematical software (Mathcad, Matlab). Findings. It is shown how the use of special functions designed to solve the Cauchy problem solves the problems proposed in mathematical models. Graphs are built in Mathcad and Matlab, including graphs that can be used to compare analytical and numerical solutions of problems obtained by Euler's method. Originality. The paper concludes that mathematical models of many technological and physical processes in different industries can be represented by the Cauchy problem for a common differential equation. Practical value. The considered mathematical models show the importance of realization of professional orientation of mathematical training of students of natural and engineering specialties. Conclusions. Mathematical modeling allows you to study and evaluate various processes (physical, economic, environmental), and therefore knowledge of the basics of modeling and special mathematical software is very important in the training of modern specialists in various specialties. The problems presented in the article are used in teaching the courses of numerical methods and computer mathematics in the study of the topic «Numerical Solution of Differential Equations». References 10, tables 2, figures 9.
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49

Lutovac, Miroslav, and Dejan Tosic. "Symbolic signal processing and system analysis." Facta universitatis - series: Electronics and Energetics 16, no. 3 (2003): 423–31. http://dx.doi.org/10.2298/fuee0303423l.

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We present new software in MATLAB and Mathematica for symbolic signal processing and system analysis. Our mission is to encapsulate high-tech engineering and sophisticated mathematical knowledge into easy-to-use software that effectively solves practical problems.
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Poetzel, Adam, Joseph Muskin, Anne Munroe, and Craig Russell. "Three-Dimensional Printing: A Journey in Visualization." Mathematics Teacher 106, no. 2 (September 2012): 102–7. http://dx.doi.org/10.5951/mathteacher.106.2.0102.

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