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Artículos de revistas sobre el tema "Mathematical model"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 9 de junio de 2025

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1

Kumar, Jitender, and V. K. Kukreja. "Mathematical Model of Pulp Washing Using Mathematica." MATEC Web of Conferences 57 (2016): 05008. http://dx.doi.org/10.1051/matecconf/20165705008.

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2

Połowniak, Piotr, and Mariusz Sobolak. "Mathematical model of globoid worm for use of generating CAD model." Mechanik, no. 2 (February 2015): 145/31. http://dx.doi.org/10.17814/mechanik.2015.2.53.

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3

Stehney, Ann K., Sarah Flannery, and David Flannery. "Mathematical Model." Women's Review of Books 19, no. 1 (October 2001): 7. http://dx.doi.org/10.2307/4023851.

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4

Rifki Taufik, Muhammad, Dwi Lestari, and Tri Wijayanti Septiarini. "Mathematical Model for Vaccinated Tuberculosis Disease with VEIT Model." International Journal of Modeling and Optimization 5, no. 3 (June 2015): 192–97. http://dx.doi.org/10.7763/ijmo.2015.v5.460.

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5

ROTARU, Constantin, Oliver CIUICĂ, Eduard MIHAI, Ionică CÎRCIU, and Radu DINCĂ. "SIMPLIFIED MATHEMATICAL MODEL FOR AIRCRAFTSRESPONSE CHARACTERISTICS." SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE 18, no. 1 (June 24, 2016): 55–60. http://dx.doi.org/10.19062/2247-3173.2016.18.1.7.

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6

Wan, Delong, and Huiping Zeng. "Water environment mathematical model mathematical algorithm." IOP Conference Series: Earth and Environmental Science 170 (July 2018): 032133. http://dx.doi.org/10.1088/1755-1315/170/3/032133.

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7

Bаzhanova, А. Yu, M. G. Suryaninov, and G. B. Shotadze. "Finite elements mathematical model of geometric nonlinearity." Odes’kyi Politechnichnyi Universytet. Pratsi, no. 2 (June 15, 2015): 138–44. http://dx.doi.org/10.15276/opu.2.46.2015.25.

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8

Rani, Shruti. "Mathematical Model on Reliability with Three Units." International Journal of Science and Research (IJSR) 10, no. 9 (September 27, 2021): 1107–11. https://doi.org/10.21275/sr21916213532.

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9

TRIVEDI, PRATIK H. "An Appropriate Mathematical Model for A Product." Global Journal For Research Analysis 3, no. 5 (June 15, 2012): 11–12. http://dx.doi.org/10.15373/22778160/may2014/5.

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10

Ambhore, Shubham A., and Navneet K. Lamba. "Lumpy Skin Disease: A Mathematical Model Application." Indian Journal Of Science And Technology 18, no. 9 (March 3, 2025): 755–62. https://doi.org/10.17485/ijst/v18i9.2894.

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Objectives: The purpose of the study is to decrease the value of reproduction number under the value of 1 for the purpose of eradicating Lumpy Skin Disease (LSD). Method: We provide three cases giving the basic reproduction numbers of few of the original systems’ subsystems to calculate 𝑅0 and provide a clearer grasp of its structure. Finding: The value of basic reproduction number for the Susceptible-Infected (S-I) epidemiological model is determined to establish the fundamental reproduction number by comparing it to the basic reproduction numbers of simplified subsystems in existing S-I models and present some intervention technique for the reduction of this value to control the transmission of the LSD. Novelty: LSD is not zoonotic; people can’t get infected with it. Most of the epidemiological studies of LSD concentrate on curing the disease solely in cattle; however, they neglect to consider the possibility of human infection from contact with infected cattle. But we assume the infection on people through blood-feeding insects, certain species of flies and mosquitoes as well as through infected cattle that server of LSD Virus. Keywords: Lumpy Skin Disease; Mathematical Modelling; Reproduction Number

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11

Bouizem, Nacera, Mohamed Helal, Bedr'Eddine Ainseba, and Abdelkader Lakmeche. "Leukemia mathematical model." ITM Web of Conferences 4 (2015): 01006. http://dx.doi.org/10.1051/itmconf/20150401006.

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12

Norsyela, Muhammad Noor Mathivanan, Ab Ghani Puzziawati, and Azura Md. Ghani Nor. "Tracing Mathematical Function of Age Specific Fertility Rate in Peninsular Malaysia." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 3 (March 1, 2018): 637–42. https://doi.org/10.11591/ijeecs.v9.i3.pp637-642.

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The size, structure, and composition of a population are affected by the fertility rates at any point of time. Many researchers took the opportunity to exploit the fertility rates in obtaining better fertility patterns for their country. The curve for the age specific fertility rate is consistent, and this feature allows the curve to be matched with a mathematical model. This paper aimed to identify the best mathematical model that fits the recent age specific fertility rate in Peninsular Malaysia. This study fitted the fertility data of Peninsular Malaysia from 1996 to 2014 to the four mathematical models, which were Hadwiger, Gamma, Beta, and Gompertz models. From the comparisons of the four models, it was found that the best fitted mathematical model is Hadwiger model. In relation to the data of early 21st century, there was an inclination for the best fitted mathematical model from Hadwiger model to Beta model. Hence, the best mathematical model for each year can be used to convert a fertility schedule classified in a five-year age group into a fertility schedule for a single-year of age in Peninsular Malaysia. This model also can be helpful for population projections by using limited and defective data.

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13

Shrivastava, Rajesh, Deepika Basedia, and Keerty Shrivastava. "Predictive Mathematical Model on Breast Cancer: A Study." international journal of mathematics and computer research 12, no. 03 (March 31, 2024): 4107–13. http://dx.doi.org/10.47191/ijmcr/v12i3.05.

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In the present study, we have designed a mathematical model to analyze whether the cases of breast cancer are maximized or minimized in Madhya Pradesh. Especially to check the age range in which it’s more susceptible to the disease and its means of therapy. The important data collected from Jawaharlal Nehru Cancer Hospital, Bhopal (JLNCH) and Gandhi Medical College, Bhopal (GMC) is from over ten years of reviews of the cases. Actual documentary and analytical methods were used to collect and analyze the data. It is concluded from the results that the number of cancer cases is increasing in both hospitals; its projection may reach up to 97.8% by the year 2023; the age range of 40–50 is more vulnerable to the disease. The line of treatment for breast cancer patients is surgery, chemotherapy, and radiotherapy in both hospitals.

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14

WILCZYNSKI, KRZYSZTOF. "A mathematical model of single-screw extrusion. Part X. Experimental verification of the model." Polimery 45, no. 03 (March 2000): 191–96. http://dx.doi.org/10.14314/polimery.2000.191.

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15

Vasileva, Natalia, Vladimir Grigorev-Golubev, and Irina Evgrafova. "Mathematical programming in Mathcad and Mathematica." E3S Web of Conferences 419 (2023): 02007. http://dx.doi.org/10.1051/e3sconf/202341902007.

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An article generalizes the long-term work of authors with packages of applied mathematical programs. It discusses and demonstrates the features and methods of solution of mathematical tasks in mathematical package Mathcad and Mathematica: from the simplest ones, included in the set of typical problems of mathematical disciplines for training specialists for shipbuilding, to complex computational tasks and applied problems of professional orientation, which require the construction of a mathematical model and analysis of the results obtained. The examples show the solution of mathematical problems in symbolic form, mathematical studies in the Mathcad and Mathematica environment, and mathematical programming with these packages.

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16

Grigoriev-Golubev, Vladimir, Natalia Vasileva, and Margarita Volodicheva. "Using the Mathematica package in teaching mathematical disciplines." SHS Web of Conferences 141 (2022): 03001. http://dx.doi.org/10.1051/shsconf/202214103001.

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This article analyzes the capabilities of the Wolfram Mathematica computer system, examines the feasibility of using its tools in the study of mathematical disciplines. The authors propose and demonstrate by examples a methodology for building a training course based on the integration of the methods of the discipline being studied and their implementation in the Mathematica environment. The paper explores the practical significance of including the Mathematica toolkit in the training course, which makes it possible to mathematically model various processes in modern society, demonstrate the solution of mathematically complex problems using the built-in functions of the package, as well as provide visualization of analytically obtained solutions.

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17

Teresa Reginska. "REGULARIZATION METHODS FOR MATHEMATICAL MODEL OF LASER BEAMS." Eurasian Journal of Mathematical and Computer Applications 1, no. 1 (2013): 39–49. http://dx.doi.org/10.32523/2306-3172-2013-1-2-39-49.

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18

CORCAU, Jenica-Ileana, and Liviu DINCA. "MATHEMATICAL MODEL AND NUMERICAL SIMULATIONS FOR PHOTOVOLTAIC PANELS." Review of the Air Force Academy 15, no. 3 (December 14, 2017): 47–56. http://dx.doi.org/10.19062/1842-9238.2017.15.3.5.

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19

ul Rahman, Jamshaid, Muhammad Raheel Mohyuddin, S. V. Satyanarayana, Syed Zahoor, and Amira Al-Ghareebi. "Mathematical and Simulink Model for Paint Industry Effluent." DJ Journal of Engineering Chemistry and Fuel 1, no. 2 (February 23, 2016): 1–8. http://dx.doi.org/10.18831/djchem.org/2016021001.

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20

MARTYNYUK, V. V., G. I. RADELCHUK, and O. V. SHPAK. "IMPROVED IMPEDANCE MATHEMATICAL MODEL OF A SOLAR CELL." Measuring and computing devices in technological processes 63, no. 1 (January 2019): 5–9. http://dx.doi.org/10.31891/2219-9365-2019-63-1-5-9.

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21

Sivanpheng, Ounla. "A Mathematical Model for Estimating Varied Irrigation Efficiency." International Journal of Science and Research (IJSR) 11, no. 7 (July 5, 2022): 717–21. http://dx.doi.org/10.21275/sr22709145853.

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22

PEDYASH, Volodymyr. "INVESTIGATION OF THE MATHEMATICAL MODEL OF OPTICAL FIBER." Herald of Khmelnytskyi National University. Technical sciences 317, no. 1 (February 23, 2023): 167–73. http://dx.doi.org/10.31891/2307-5732-2023-317-1-167-173.

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Fiber optic transmission systems are the main type of systems for building telecommunication networks. The transponders of modern optical transmission systems implement efficient modulation and coding methods, which can significantly increase the receiver sensitivity and increase the length of the regeneration area. The task to define the quality characteristics of optical channels occurs during the design of such systems. To solve this problem, it is necessary to develop a mathematical model of the transmission system and perform a simulation using it. One of the main blocks of the system is the propagation medium (optical fiber). The optical fiber model is based on the nonlinear Schrödinger differential equation. Several groups of modeling methods have been developed on its basis. The most widespread is the Fourier method of splitting by physical factors, as it has a simple algorithm structure, high computing speed, and high accuracy. This method is recursive, so to reduce the number of iterations, it is reasonable to use the well-known method of nonlinear phase rotation. To check the functionality of the developed program code, it is recommended to perform testing in several stages. At the first stage, it is proposed to check the accuracy of the distortion of chromatic dispersion. Single pulses of simple shape, such as Gaussian pulses, should be used as a test signal. By calculating the pulse shape using theoretical expressions and comparing them with the modeling results, the accuracy of the optical fiber modeling algorithm can be verified. The next step in verifying the fiber model is to evaluate the accuracy of the simultaneous introduction of dispersion and nonlinear distortions. For this purpose, it is proposed to analyze the propagation of pulses of optical solitons of the first and higher orders.

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23

Kruszewski, Robert. "Equilibrium and business cycle. Mathematical model." Studia i Prace WNEiZ 51 (2018): 197–211. http://dx.doi.org/10.18276/sip.2018.51/3-16.

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24

Щетинина, Ирина Владимировна, Екатерина Алексеевна Москалева, Марина Евгеньевна Волкова, and Валерий Константинович Власов. "COVID-INFODEMY MATHEMATICAL MODEL." ИНФОРМАЦИЯ И БЕЗОПАСНОСТЬ, no. 4(-) (December 15, 2021): 521–30. http://dx.doi.org/10.36622/vstu.2021.24.4.004.

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Инфодемия представляет собой стремительное и неконтролируемое распространение в медиа необоснованной и ложной информации о кризисных событиях. Во время пандемии коронавируса возникла новая разновидность сетевой дезинформации, связанная с распространением различных слухов о заболевании, вакцинации и т. п., которая стремительно развиваясь, охватила все страны, став таким образом ковид-инфодемией. Инфодемия наносит большой вред работе систем здравохранения, правительств стран, существенно снижая уровень доверия к ним граждан. В статье представлена математическая модель инфодемии, основанная на данных по статистике о слухах, посвященных коронавирусной пандемии. За основу взяты эпидемические SEIR и SEIR-D модели. Результаты моделирования показали применимость предлагаемых моделей. Предлагаемые в статье модели можно использовать для прогнозирования развития и моделирования угроз ковид-инфодемии, в задачах определения ущерба, наносимого ковид-инфодемией экономике и здравохранению. Infodemia is the rapid and uncontrolled dissemination of unreasonable and false information about crisis events in the media. During the coronavirus pandemic, a new type of online misinformation has emerged, associated with the spread of various rumors about the disease, vaccinations, etc., which is rapidly developing, spreading across all countries, thus becoming a covid infodemic. Infodemia causes great harm to the work of health care systems, governments of countries, significantly reducing the level of citizens' trust in them. The article presents a mathematical model of infodemic, based on statistics on rumors about the coronavirus pandemic. Epidemic SEIR and SEIR-D models are taken as a basis. The simulation results showed the applicability of the proposed models. The models proposed in the article can be used to predict the development and modeling of the threats of covid-infodemia, in the tasks of determining the damage caused by covid-infodemy to the economy and health care.

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25

Brumercik, Frantisek, Michal Lukac, and Aleksander Nieoczym. "Mechanical Differential Mathematical Model." Communications - Scientific letters of the University of Zilina 17, no. 3 (August 31, 2015): 88–91. http://dx.doi.org/10.26552/com.c.2015.3.88-91.

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26

Khoroshaylo, Yurii, Vladyslav Shlyakhov, Sergii Yefymenko, Svetlana Sotnik, and Aleksandr Kagramanyan. "MATHEMATICAL MODEL OF COLORIMETRY." Bulletin of Kyiv Polytechnic Institute. Series Instrument Making, no. 54(2) (December 25, 2017): 27–32. http://dx.doi.org/10.20535/1970.54(2).2017.119512.

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27

Ivanov, Konstantin. "Mathematical Model of Adaptation." Journal of Modern Mathematics Frontier 5 (2016): 1. http://dx.doi.org/10.14355/jmmf.2016.05.001.

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28

Toumasis, C. "A mathematical diet model." Teaching Mathematics and its Applications 23, no. 4 (December 1, 2004): 165–71. http://dx.doi.org/10.1093/teamat/23.4.165.

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29

Krakhmalev, O. N. "MATHEMATICAL MODEL MANIPULATOR ROBOTS." International Journal of Advanced Studies 5, no. 4 (December 19, 2015): 31. http://dx.doi.org/10.12731/2227-930x-2015-4-4.

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30

Polyakov, S., and M. Borodin. "MATHEMATICAL MODEL DISPENSING MATERIALS." Актуальные направления научных исследований XXI века: теория и практика 2, no. 5 (November 11, 2014): 134–40. http://dx.doi.org/10.12737/6364.

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31

Koenders, M. A., and J. B. Sellmeijer. "Mathematical Model for Piping." Journal of Geotechnical Engineering 118, no. 6 (June 1992): 943–46. http://dx.doi.org/10.1061/(asce)0733-9410(1992)118:6(943).

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32

Bonsu, Bema K., and Marvin B. Harper. "Explanation of Mathematical Model." Pediatric Infectious Disease Journal 23, no. 9 (September 2004): 893. http://dx.doi.org/10.1097/01.inf.0000137586.42248.00.

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33

Hao, Wenrui, Elliott D. Crouser, and Avner Friedman. "Mathematical model of sarcoidosis." Proceedings of the National Academy of Sciences 111, no. 45 (October 27, 2014): 16065–70. http://dx.doi.org/10.1073/pnas.1417789111.

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34

Jafarov, M. A., V. O. Yevdokymenko, D. S. Kamenskyh, K. A. Rustamov, and Z. A. Jafarov. "Mathematical Model Desublimation Conditions." Asian Journal of Chemical Sciences 13, no. 2 (April 13, 2023): 1–6. http://dx.doi.org/10.9734/ajocs/2023/v13i2234.

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The developed and software-implemented simplified three-dimensional mathematical model of the unsteady-state process of HFS desublimation is described. The study of regularities in the HFS desublimation process is performed by numerical modeling.

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35

Алимов, Х. Т., Ф. Х. Дзамихова, and Р. И. Паровик. "Fractional Mathematical Model McSherry." Вестник КРАУНЦ. Физико-математические науки, no. 1 (April 17, 2023): 164–79. http://dx.doi.org/10.26117/2079-6641-2023-42-1-164-179.

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В статье предложено обобщение математической модели Макшерри для моделирования искусственной электрокардиограммы — изменяющегося во времени сигнала, отражающий ионный ток, который заставляет сердечные волокна сокращаться, а затем расслабляться. Обобщение математической модели Макшерри заключается в учете свойства наследственности (памяти) динамического процесса, которое можно описать с помощью дробных производных в смысле Герасимова-Капуто. Эффект памяти динамической системы определяет возможность зависимости ее состояний от предыстории и может указывает на диссипативный характер, рассматриваемого процесса. Далее в работе с помощью теории конечно-разностных схем строится явная конечно-разностная схема первого порядка точности для нахождения численного решения предложенной модели. С помощью алгоритма проводится визуализация результатов моделирования: строятся осциллограммы и фазовые траектории при различных значениях параметров модели для здорового человека. Проводится интерпретация результатов моделирования. Показано, что порядки дробных производных влияют на динамические режимы, рассматриваемой дробной динамической системы. В случае соизмеримой дробной динамической системы предельный цикл начинает разрушаться при значениях порядков дробных производных меньше 0,5. В этом случае роль диссипации имеет значительную роль. В случае несоизмеримой дробной динамической системы могут возникать различные режимы от предельных циклов до затухающих, возможны и хаотические режимы. В работе было показано, что при достаточно больших значениях угловой скорости возникает хаотический режим. Исследование хаотических режимов заслуживает отдельного внимания и будет рассмотрено с следующих статьях. Также порядки дробных производных можно рассматривать как дополнительные степени для параметризации сигналов ЭКГ. The article proposes a generalization of the McSherry mathematical model for modeling an artificial electrocardiogram — a time-varying signal that reflects the ion current that causes the heart fibers to contract and then relax. The generalization of the McSherry mathematical model consists in taking into account the property of heredity (memory) of the dynamic process, which can be described using fractional derivatives in the sense of Gerasimov-Caputo. The memory effect of a dynamic system determines the possibility of dependence of its states on the prehistory and may indicate the dissipative nature of the process under consideration. Further, using the theory of finite-difference schemes, an explicit finite-difference scheme of the first order of accuracy is constructed to find a numerical solution of the proposed model. With the help of the algorithm, the simulation results are visualized: oscillograms and phase trajectories are built for different values of the model parameters for a healthy person. The simulation results are interpreted. It is shown that the orders of fractional derivatives affect the dynamic modes of the considered fractional dynamical system. In the case of a commensurate fractional dynamical system, the limit cycle begins to collapse when the orders of the fractional derivatives are less than 0.5. In this case, the role of dissipation plays a significant role. In the case of an incommensurable fractional dynamical system, various regimes can arise from limit cycles to damped ones, and chaotic regimes are also possible. It was shown in the work that a chaotic regime arises at sufficiently large values of the angular velocity. The study of chaotic regimes deserves special attention and will be considered in the following articles. Also, the orders of fractional derivatives can be considered as additional degrees for the parameterization of ECG signals.

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36

Kézi, Csaba. "Teaching the Analysis of Newton’s Cooling Model to Engineering Students." International Journal of Engineering and Management Sciences 8, no. 2 (June 30, 2023): 63–68. http://dx.doi.org/10.21791/ijems.2023.2.7.

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To apply mathematical methods to physical or other real life problem, we have to formulate the problem in mathematical terms. It means that, we have to construct the mathematical model for the problem. Many physical problems shows the relationships between changing quantities. The rates of change are represented mathematically by derivatives. In this case the mathematical models involve equations relating an unknown function and one or more of its derivatives. These equations are the differential equations. In this article, teaching the analysis of Newton's cooling model to engineering students is presented as one of the applications of separable differential equations.

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37

Rajić, Dušan. "Mathematical-physical model of solving inventive problems." FME Transactions 49, no. 3 (2021): 726–33. http://dx.doi.org/10.5937/fme2103726r.

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The spatial-temporal LT-contradiction matrix is an inventology tool that enables exact calculations of certain parameters in an engineering system through mathematical-physical modeling. It objectifies the decision-making process and creates the preconditions to finding an adequate resource (X-element) with a higher probability, and thus to reach a higher degree of ideality solution (HDIS) of an inventive problem as well. Any engineering system that generates an inventive problem can be described using the LT-contradiction matrix. By crossing the appropriate parameters in the LT-contradiction matrix, with the help of the differential geometry of the tensor, a qualitative-quantitative analysis and calculation of relevant degree all contradictions that exist in the inventive problem can be performed. After that, the path to finding the physical characteristics of the X-element in the mathematical-physical model is facilitated, i.e. finding a real resource that will enable a HDIS of the inventive problem in an engineering system.

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38

Klyachin, Alеksеy. "Mathematical Model for Reconstructing a Damaged Bitmap." Vestnik Volgogradskogo gosudarstvennogo universiteta. Serija 1. Mathematica. Physica, no. 1 (March 2016): 45–56. http://dx.doi.org/10.15688/jvolsu1.2016.1.5.

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39

Jabar, H. Yousif, and k. Saini Dinesh. "Fuzzy and Mathematical Effort Estimation Models for Web Applications Development." Applied Computing Journal 1, no. 1 (January 28, 2021): 10–24. https://doi.org/10.5281/zenodo.4477174.

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This paper proposed an Effort Estimation Model for optimizing the deployment of Web Applications Based Fuzzy and Practical Models. This paper presented the effort estimation model that involves two levels—the first level estimates by Project Managers, and the second level estimates by Project Leaders or Developers. The model considers the classification of each task as either Low or Medium or High complexity. Efforts are estimated to design, code, and test tasks and take a new requirement as a case study, compared with the practical efforts model using historical data for the existing functionalities. The fuzzy logic model verifies the claims made in effort estimation, which proposed a new relation between data and effort value membership for actual data. It converts it into a crisp function in the range [0…1], which helps classify the task's complexity and subtask in the design, coding, and testing phases quickly, low cost, and high accuracy. The proposed effort estimation model would allow the project managers to efficiently control the project, manage the resources effectively, improve the software development process, and trade-off analyses time.

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Khanna, Aditya, and Andrei Kotousov. "A Mathematical Model for Interfacial Defects in Snow Layers." International Journal of Materials, Mechanics and Manufacturing 4, no. 3 (2015): 200–203. http://dx.doi.org/10.7763/ijmmm.2016.v4.256.

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Kumar, Deepak. "A Mathematical Model of Chemotherapeutic Drug for Tumor Treatment." Indian Journal of Applied Research 4, no. 2 (October 1, 2011): 7–10. http://dx.doi.org/10.15373/2249555x/feb2014/101.

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42

Grytsay, V. I. "A mathematical model of the metabolic process of atherosclerosis." Ukrainian Biochemical Journal 88, no. 4 (August 31, 2016): 75–84. http://dx.doi.org/10.15407/ubj88.04.075.

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Kanchanarat, Siwaphorn, and Settapat Chinviriyasit. "A Mathematical Study of an Influenza Model with Vaccination." International Journal of Applied Physics and Mathematics 4, no. 1 (2014): 22–26. http://dx.doi.org/10.7763/ijapm.2014.v4.248.

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44

Vsevolod Horyachko, Vsevolod, Oksana Hoholyuk, Taras Ryzhyi, and Serhiy Rendzinyak. "Mathematical model of electrical activity of biological network areas." Computational Problems of Electrical Engineering 9, no. 2 (November 10, 2019): 8–12. http://dx.doi.org/10.23939/jcpee2019.02.008.

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In the paper, the mathematical model describing the generation of action potential and propagation of an impulse in the neuron's filaments on the basis of the analysis of parametric electriс circuits with distributed parameters and the mathematical model of synaptic interneuron connections are proposed. Developed models allow taking into account the influence of such factors as geometric, physical and chemical parameters of the neuron's filaments and the presence of different neurotransmitters in chemical synapses on transmitting a neural impulse. Further, such models can be used for investigating the conditions of neuron firing at spatial and time integration of input signals, as well as for the simulation of neuromuscular junctions.

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45

Neetu, Sharma, Kumar Saurabh, and Mani Naveen. "Analysis on Present Mathematical Model for Predicting the Crop Production." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 9, no. 12 (October 30, 2020): 168–70. https://doi.org/10.35940/ijitee.L7946.1091220.

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India is a worldwide agriculture business powerhouse. Future of agriculture-based products depends on the crop production. A mathematical model might be characterized as a lot of equations that speak to the conduct of a framework. By using mathematical model in agriculture field, we can predict the production of crop in particular area. There are various factors affecting crops such as Rainfall, GHG Emissions, Temperature, Urbanization, climate, humidity etc. A mathematical model is a simplified representation of a real-world system. It forms the system using mathematical principles in the form of a condition or a set of conditions. Suppose we need to increase the crop production, at that time the mathematical model plays a major role and our work can be easier, more significant by using the mathematical model. Through the mathematical model we predict the crop production in upcoming years. .AI, ML, IOT play a major role to predict the future of agriculture, but without mathematical models it is not possible to predict crop production accurately. To solve the real-world agriculture problem, mathematical models play a major role for accurate results. Correlation Analysis, Multiple Regression analysis and fuzzy logic simulation standards have been utilized for building a grain production benefit depending model from crop production. Prediction of crop is beneficiary to the farmer to analyze the crop management. By using the present agriculture data set which is available on the government website, we can build a mathematical model.

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LIU, Yusheng. "A mathematical model of symmetry based on mathematical definition." Journal of Zhejiang University SCIENCE 3, no. 1 (2002): 24. http://dx.doi.org/10.1631/jzus.2002.0024.

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Abdullah, Saeed Ahmad, Saud Owyed, Abdel-Haleem Abdel-Aty, Emad E. Mahmoud, Kamal Shah, and Hussam Alrabaiah. "Mathematical analysis of COVID-19 via new mathematical model." Chaos, Solitons & Fractals 143 (February 2021): 110585. http://dx.doi.org/10.1016/j.chaos.2020.110585.

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48

Yusheng, Liu, Yang Jiangxin, Wu Zhaotong, and Gao Shuming. "A mathematical model of symmetry based on mathematical definition." Journal of Zhejiang University-SCIENCE A 3, no. 1 (January 2002): 24–29. http://dx.doi.org/10.1631/bf02881837.

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Tlustý, T., and P. Tlustý. "Ryder cup, history and strategy - mathematical model." Studia Kinanthropologica 19, no. 3 (September 30, 2018): 287–90. http://dx.doi.org/10.32725/sk.2018.057.

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Agárdi, Anita, László Kovács, and Tamás Bányai. "Mathematical Model for the Generalized VRP Model." Sustainability 14, no. 18 (September 16, 2022): 11639. http://dx.doi.org/10.3390/su141811639.

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The Vehicle Routing Problem (VRP) is a highly investigated logistics problem. VRP can model in-plant and out-plant material handling or a whole supply chain. The first Vehicle Routing Problem article was published in 1959 by Dantzig and Ramser, and many varieties of VRP have appeared since then. Transport systems are becoming more and more customized these days, so it is necessary to develop a general system that covers many transport tasks. Based on the literature, several components of VRP have appeared, but the development of an integrated system with all components has not yet been completed by the researchers. An integrated system can be useful because it is easy to configure; many transportation tasks can be easily modeled with its help. Our purpose is to present a generalized VRP model and show, in the form of case studies, how many transport tasks the system can model by including (omitting) each component. In this article, a generalized system is introduced, which covers the main VRP types that have appeared over the years. In the introduction, the basic Vehicle Routing Problem is presented, where the most important Vehicle Routing Problem components published so far are also detailed. The paper also gives the mathematical model of the generalization of the Vehicle Routing Problem and some case studies of the model are presented.

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