Matlab en las aplicaciones de la matemática
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El Matlab es un software que ayuda a promover la investigación mediante el procesamiento de imágenes; es por ello, que el objetivo de esta indagación fue analizar las aplicaciones del Matlab en el aprendizaje de la matemática, geometría, álgebra y ecuaciones diferenciales. En la revisión sistemática de enfoque cualitativo, se utilizó el análisis documental de artículos científicos; la búsqueda se realizó en la base de datos Scopus, para lo cual se realizó el filtrado respetando la declaración PRISMA; además, se consideró los criterios de inclusión y exclusión; el período de búsqueda fue en el rango 2013-2022. Se obtuvieron 30 artículos que cumplieron con las características para el estudio. Se determinó la efectividad del software MATLAB en el aprendizaje de la matemática y otras áreas del conocimiento. Se concluye su utilidad en el campo académico para realizar gráficas, cálculo de matrices grandes y la solución de diferentes ecuaciones diferenciales.
Matlab is a software that helps to promote research through image processing; therefore, the objective of this research was to analyze the applications of Matlab in the learning of mathematics, geometry, algebra and differential equations. In the qualitative approach systematic review, the documentary analysis of scientific articles was used; the search was performed in the Scopus database, for which the filtering was performed respecting the PRISMA statement; in addition, the inclusion and exclusion criteria were considered; the search period was in the range 2013-2022. Thirty articles were obtained that met the characteristics for the study. The effectiveness of MATLAB software in the learning of mathematics and other areas of knowledge was determined. Its usefulness in the academic field for graphing, calculation of large matrices and the solution of different differential equations was concluded.
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Alloqmani, A., Alsaedi, O., Bahatheg, N., Alnanih, R., y Elrefaei, L. (2021). Design Principles-Based Interactive Learning Tool for Solving Nonlinear Equations. Computer Systems Science & Engineering, 40(3), 1023–1042. https://doi.org/10.32604/csse.2022.019704
Asad, J., Jarrar, R., Khalilia, H., Mallak, S., y Shanak, H. (2021). The mechanics of the vibrating triangle system. Mathematics and Computer Science, 1(63), 27–38. https://doi.org/10.31926/but.mif.2021.1.63.1.3
Bornstein, N. (2020). Teaching Transformations of Trigonometric Functions with Technology. Journal of Interactive Media in Education, 1(15), 1–9. https://doi.org/https://doi.org/10.5334/jime.503
Brahma, J. (2021). Mathematical modelling and simulation for one dimensional-two-phase flow equation in petroleum reservoir: A matlab algorithm approach. WSEAS Transactions on Applied and Theoretical Mechanics, 16, 213–221. https://doi.org/10.37394/232011.2021.16.24
Caglayan, G. (2018). Linear Algebra Students’ Understanding of Similar Matrices and Matrix Representations of Linear Transformations in a MATLAB-Assisted Learning Environment. Computers in the Schools, 35(3), 204–225. https://doi.org/10.1080/07380569.2018.1491774
Chamorro, O., Ortega, O., Morales, G., Quispe, A., Trinidad, N., Gamarra, S., y León, C. (2021). Online Education and Engineering Students’ Perception of Pedagogical Quality, in Learning the Process Control Course with MATLAB. International Journal of Emerging Technologies in Learning, 16(21), 193–200. https://doi.org/10.3991/IJET.V16I21.25235
Cruz, E., y Jurado, F. (2020). Boundary Control for a Certain Class of Reaction-Advection-Diffusion System. Mathematics. https://doi.org/10.3390/math8111854
Esguerra, B., González, N., y Acosta, A. (2018). Mathematical software tools for teaching of complex numbers. Revista Facultad de Ingeniería, 27(48), 79–89. https://doi.org/10.19053/01211129.v27.n48.2018.8403
Gao, N. (2019). Visualization teaching of deformation monitoring and data processing based on MATLAB. International Journal of Emerging Technologies in Learning, 14(2), 42–53. https://doi.org/10.3991/IJET.V14I02.9983
Gaspar, J. M. (2018). Bridging the Gap between Economic Modelling and Simulation: A Simple Dynamic Aggregate Demand-Aggregate Supply Model with Matlab. Hindawi Journal of Applied Mathematics, 2018. https://doi.org/https://doi.org/10.1155/2018/3193068
Gayoso, V., Hernández, L., Martín, A., y Queiruga, A. (2021). Using free mathematical software in engineering classes. Axioms, 10(4), 1–18. https://doi.org/10.3390/axioms10040253
Georgilakis, P. S., Member, S., Orfanos, G. A., y Hatziargyriou, N. D. (2014). Computer-Assisted Interactive Learning for Teaching Transmission Pricing Methodologies. IEEE Transactions on Power Systems, 29(4), 1972–1980. https://doi.org/10.1109/TPWRS.2013.2295197
Gil, P. (2017). Short Project-Based Learning with MATLAB Applications to Support the Learning of Video-Image Processing. Journal of Science Education and Technology, 26(5), 508–518. https://doi.org/10.1007/S10956-017-9695-Z
Hamad, Z., y Abdulrahman, I. (2022). Deep learning-based load forecasting considering data reshaping using MATLABSimulink. International Journal of Energy and Environmental Engineering, 13(2), 853–869. https://doi.org/10.1007/s40095-022-00480-x
Higham, C. F., y Higham, D. J. (2019). Deep Learning: An Introduction for Applied Mathematicians. SIAM Review, 61(4), 860–891. https://doi.org/10.1137/18M1165748
Ibáñez, J., Alonso, J. M., Alonso-Jordá, P., Defez, E., y Sastre, J. (2022). Two Taylor Algorithms for Computing the Action of the Matrix Exponential on a Vector. Algorithms, 15(2), 1–17. https://doi.org/10.3390/a15020048
Ilmavirta, J., Koskela, O., y Railo, J. (2020). Torus computed tomography. SIAM Journal on Applied Mathematics, 80(4), 1947–1976. https://doi.org/10.1137/19m1268070
Jia, J., y Li, S. (2017). An efficient numerical algorithm for the determinant of a cyclic pentadiagonal Toeplitz matrix. Computers and Mathematics with Applications, 74(12), 2992–2999. https://doi.org/10.1016/j.camwa.2017.07.035
Jia, J. T. (2018). Numerical algorithms for the determinant evaluation of general Hessenberg matrices. Journal of Mathematical Chemistry, 56(1), 247–256. https://doi.org/10.1007/S10910-017-0794-0
Korzec, M. D., y Ahnert, T. (2013). Time-stepping methods for the simulation of the self-assembly of nano-crystals in MATLAB on a GPU. Revista de Física Computacional, 251, 396–413. https://doi.org/10.1016/j.jcp.2013.05.040
Li, L. (2017). Partial differential equation calculation and visualization. Journal of Discrete Mathematical Sciences and Cryptography, 20(1), 217–229. https://doi.org/10.1080/09720529.2016.1178918
Massei, S., Robol, L., y Kressner, D. (2020). Hm-toolbox: Matlab software for hodlr and HSS matrices. SIAM Journal on Scientific Computing, 42(2), C43–C68. https://doi.org/10.1137/19M1288048
Matyushkin, I. V, Rubis, P. D., y Zapletina, M. A. (2021). Estudio experimental de la dinámica. Mapeos de valores complejos simples y relacionados con celosías: arquitectura e interfaz del programa del autor para modelado la arquitectura y la interfaz del software del autor. Investigación y modelado informático, 13(6), 1101–1124. https://doi.org/10.20537/2076-7633-2021-13-6-1101-1124
Nasser, M. M. S. (2020). PlgCirMap: A MATLAB toolbox for computing conformal mappings from polygonal multiply connected domains onto circular domains. SoftwareX, 11, 100464. https://doi.org/10.1016/j.softx.2020.100464
Niu, B. (2016). Codimension-two Bifurcations Induce Hysteresis Behavior and Multistabilities in Delay-coupled Kuramoto Oscillators. Dinámica No Lineal, 87(2), 803–814. https://arxiv.org/pdf/1608.03349.pdf
Pavón, P., Portillo, G., Rincón, A., y Rodríguez, L. (2021). Influence of the fractal geometry on the mechanical resistance of cantilever beams designed through topology optimization. Applied Sciences (Switzerland), 11(22). https://doi.org/10.3390/app112210554
Rios, V., Mollinedo, R., y Quispitupa, M. (2017). Influencia del software Matlab en el aprendizaje de sistemas de ecuaciones Diferenciales Ordinarias De Primer Orden En los estudiantes de ingeniería Universidad Alas Peruanas Puerto Maldonado. CEPROSIMAD, 05(2), 24–38. file:///C:/Users/DAVID/Downloads/46-Article Text-98-2-10-20180515 (4).pdf
Roberts, K. J., Pringle, W. J., y Westerink, J. J. (2019). OceanMesh2D 1.0: MATLAB-based software for two-dimensional unstructured mesh generation in coastal ocean modeling. Geoscientific Model Development, 12(5), 1847–1868. https://doi.org/10.5194/GMD-12-1847-2019
Saharizan, N. S., y Zamri, N. (2019). Numerical solution for a new fuzzy transform of hyperbolic goursat partial differential equation. Indonesian Journal of Electrical Engineering and Computer Science, 16(1), 292–298. https://doi.org/10.11591/ijeecs.v16.i1.pp292-298
Sangwine, S. J., y Hitzer, E. (2017). Clifford Multivector Toolbox (for MATLAB). Advances in Applied Clifford Algebras, 27(1), 539–558. https://doi.org/10.1007/s00006-016-0666-x
Sastre, J., Ibáñez, J., Alonso, P., Peinado, J., y Defez, E. (2017). Two algorithms for computing the matrix cosine function. Applied Mathematics and Computation, 312, 66–77. https://doi.org/10.1016/j.amc.2017.05.019
Shah, K., Naz, H., Sarwar, M., y Abdeljawad, T. (2022). On spectral numerical method for variable-order partial differential equations. AIMS Mathematics, 7(6), 10422–10438. https://doi.org/10.3934/MATH.2022581
Smith, H., y Norato, J. A. (2020). A MATLAB code for topology optimization using the geometry projection method. Optimización Estructural y Multidisciplinar, 1579–1594. https://doi.org/10.1007/s00158-020-02552-0
Song, S. H., Antonelli, M., Fung, T. W. K., Armstrong, B. D., Chong, A., Lo, A., y Shi, B. E. (2019). Developing and assessing MATLAB exercises for active concept learning. IEEE Transactions on Education, 62(1), 2–10. https://doi.org/10.1109/TE.2018.2811406
Thomas, J. I. (2021). Multiple slit interference: a hyperbola based analysis. European Journal of Physics. https://doi.org/10.1088/1361-6404/ac05d3
Wang, K. (2021). A Theoretical Analysis Method of Spatial Analytic Geometry and Mathematics under Digital Twins. Advances in Civil Engineering, 2021(2018). https://doi.org/10.1155/2021/8910274
Yang, M., Hirt, C., y Pail, R. (2020). TGF: A new MATLAB-based software for terrain-related gravity field calculations. Remote Sensing, 12(7). https://doi.org/10.3390/rs12071063
Yassein, S. y Aswhad, A. (2019). Efficient Iterative Method for solving Korteweg-de vries equations. Iraqi Journal of Science, 60(7), 1575–1583. https://doi.org/10.24996/ijs.2019.60.7.17
Ye, K., y Lim, L. H. (2018). Fast Structured Matrix Computations: Tensor Rank and Cohn–Umans Method. Foundations of Computational Mathematics, 18(1), 45–95. https://doi.org/10.1007/S10208-016-9332-X
You, K., y Park, H. J. (2021). Re-visiting Riemannian geometry of symmetric positive definite matrices for the analysis of functional connectivity. NeuroImage, 225(August 2020), 117464. https://doi.org/10.1016/j.neuroimage.2020.117464
Zhao, N., y Chen, X. (2022). Exploring the integration of mathCAD-assisted mathematics experiments into higher mathematics teaching. Computer-Aided Design and Applications, 19(S1), 117–127. https://doi.org/10.14733/cadaps.2022.S1.117-127