Artykuły w czasopismach: „Mathematical models”

1

Gardiner, Tony, i Gerd Fischer. "Mathematical Models". Mathematical Gazette 71, nr 455 (marzec 1987): 94. http://dx.doi.org/10.2307/3616334.

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Denton, Brian, Pam Denton i Peter Lorimer. "Making Mathematical Models". Mathematical Gazette 78, nr 483 (listopad 1994): 364. http://dx.doi.org/10.2307/3620232.

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Pavankumari, V. "Mathematical and Stochastic Growth Models". International Journal for Research in Applied Science and Engineering Technology 9, nr 11 (30.11.2021): 1576–82. http://dx.doi.org/10.22214/ijraset.2021.39055.

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Abstract: Many statistical and mathematical models of growth are developed in the literature and effectively applied to various conditions in the existent world that involve many research problems in the different fields of applied statistics. Nevertheless, still, there is an equally large number of conditions, which have not yet been mathematically or statistically modeled, due to the complex situations or formed models are mathematically or statistically inflexible. The present study is based on mathematical and stochastic growth models. The specification of both the growth models is depicted. A detailed study of newly modified growth models is mentioned. This research will give substantial information on growth models, such as proposed modified exponential growth models and their specifications clearly motioned which gives scope for future research.

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4

Kumari, V. Pavan, Venkataramana Musala i M. Bhupathi Naidu. "Mathematical and Stochastic Growth Models". International Journal for Research in Applied Science and Engineering Technology 10, nr 5 (31.05.2022): 987–89. http://dx.doi.org/10.22214/ijraset.2022.42330.

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Abstract: Many statistical and mathematical models of growth are developed in the literature and effectively applied to various conditions in the existent world involves many research problems in the different fields of applied statistics. Nevertheless, still, there are an equally a large number of conditions, which have not yet been mathematically or statistically modeled, due to the complex situations or formed models are mathematically or statistically inflexible. The present study is based on mathematical and stochastic growth models. The specification of both the growth models is depicted. A details study of newly modified growth models are mentioned. This research will give substantial information on growth models, such as proposed modified exponential growth models and it’s specifications clearly motioned which gives scope for future research.

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5

Suzuki, Takashi. "Mathematical models of tumor growth systems". Mathematica Bohemica 137, nr 2 (2012): 201–18. http://dx.doi.org/10.21136/mb.2012.142866.

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6

Kogalovsky, M. R. "Digital Libraries of Economic-Mathematical Models: Economic-Mathematical and Information Models". Market Economy Problems, nr 4 (2018): 89–97. http://dx.doi.org/10.33051/2500-2325-2018-4-89-97.

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Banasiak, J. "Kinetic models – mathematical models of everything?" Physics of Life Reviews 16 (marzec 2016): 140–41. http://dx.doi.org/10.1016/j.plrev.2016.01.005.

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8

Kleiner, Johannes. "Mathematical Models of Consciousness". Entropy 22, nr 6 (30.05.2020): 609. http://dx.doi.org/10.3390/e22060609.

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In recent years, promising mathematical models have been proposed that aim to describe conscious experience and its relation to the physical domain. Whereas the axioms and metaphysical ideas of these theories have been carefully motivated, their mathematical formalism has not. In this article, we aim to remedy this situation. We give an account of what warrants mathematical representation of phenomenal experience, derive a general mathematical framework that takes into account consciousness’ epistemic context, and study which mathematical structures some of the key characteristics of conscious experience imply, showing precisely where mathematical approaches allow to go beyond what the standard methodology can do. The result is a general mathematical framework for models of consciousness that can be employed in the theory-building process.

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9

Byrne, Patrick, S. D. Howison, F. P. Kelly i P. Wilmott. "Mathematical Models in Finance." Statistician 45, nr 3 (1996): 389. http://dx.doi.org/10.2307/2988481.

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Kozhanov, V. S., S. O. Ustalkov i A. O. Khudoshina. "TOW CABLES MATHEMATICAL MODELS". Mathematical Methods in Technologies and Technics, nr 5 (2022): 62–68. http://dx.doi.org/10.52348/2712-8873_mmtt_2022_5_62.

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Logan, J. David, Elizabeth S. Allman i John A. Rhodes. "Mathematical Models in Biology". American Mathematical Monthly 112, nr 9 (1.11.2005): 847. http://dx.doi.org/10.2307/30037621.

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Zhuk, Tetyana. "Mathematical Models of Reinsurance". Mohyla Mathematical Journal 3 (29.01.2021): 31–37. http://dx.doi.org/10.18523/2617-70803202031-37.

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Insurance provides financial security and protection of the independence of the insured person. Its principles are quite simple: insurance protects investments, life and property. You regularly pay a certain amount of money in exchange for a guarantee that in case of unforeseen circumstances (accident, illness, death, property damage) the insurance company will protect you in the form of financial compensation.Reinsurance, in turn, has a significant impact on ensuring the financial stability of the insurer. Because for each type of insurance there is a possibility of large and very large risks that one insurance company can not fully assume. In the case of a portfolio with very high risks, the company may limit their acceptance, or give part of the reinsurance. The choice of path depends entirely on the company’s policy and type of insurance.This paper considers the main types of reinsurance and their mathematical models. An analysis of the probability of bankruptcy and the optimal use of a particular type of reinsurance are provided.There are also some examples and main results of research on this topic. After all, today the insurance industry is actively gaining popularity both in Ukraine and around the world. Accordingly, with a lot of competition, every insurer wants to get the maximum profit with minimal e↵ort.

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13

Schneider, B., i G. I. Marchuk. "Mathematical Models in Immunology." Biometrics 42, nr 4 (grudzień 1986): 1003. http://dx.doi.org/10.2307/2530721.

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Knapp, David, i Richard Bellman. "Mathematical Models in Medicine". Mathematical Gazette 70, nr 451 (marzec 1986): 79. http://dx.doi.org/10.2307/3615870.

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15

Buikis, A., J. Cepitis, H. Kalis, A. Reinfelds, A. Ancitis i A. Salminš. "Mathematical Models of Papermaking". Nonlinear Analysis: Modelling and Control 6, nr 1 (1.04.2001): 9–19. http://dx.doi.org/10.15388/na.2001.6.1.15221.

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The mathematical model of wood drying based on detailed transport phenomena considering both heat and moisture transfer have been offered in article. The adjustment of this model to the drying process of papermaking is carried out for the range of moisture content corresponding to the period of drying in which vapour movement and bound water diffusion in the web are possible. By averaging as the desired models are obtained sequence of the initial value problems for systems of two nonlinear first order ordinary differential equations.

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16

Soong, S. J. "Mathematical models of prognosis". Melanoma Research 3, nr 1 (marzec 1993): 24. http://dx.doi.org/10.1097/00008390-199303000-00081.

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Dilão, Rui. "Mathematical models of morphogenesis". ITM Web of Conferences 4 (2015): 01001. http://dx.doi.org/10.1051/itmconf/20150401001.

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18

Mayergoyz, I. "Mathematical models of hysteresis". IEEE Transactions on Magnetics 22, nr 5 (wrzesień 1986): 603–8. http://dx.doi.org/10.1109/tmag.1986.1064347.

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19

Protter, Philip. "Mathematical models of bubbles". Quantitative Finance Letters 4, nr 1 (styczeń 2016): 10–13. http://dx.doi.org/10.1080/21649502.2015.1165863.

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20

Hamblin, C. L. "Mathematical models of dialogue1". Theoria 37, nr 2 (11.02.2008): 130–55. http://dx.doi.org/10.1111/j.1755-2567.1971.tb00065.x.

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21

Jacob, C., F. Charras, X. Trosseille, J. Hamon, M. Pajon i J. Y. Lecoz. "Mathematical models integral rating". International Journal of Crashworthiness 5, nr 4 (styczeń 2000): 417–32. http://dx.doi.org/10.1533/cras.2000.0152.

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22

Naydenov, Nikolay Dmitriyevich, Vasily Igorevich Spiryagin i Elena Nikolayevna Novokshonova. "ECONOMIC-MATHEMATICAL CLUSTER’S MODELS". Sovremennye issledovaniya sotsialnykh problem, nr 9 (15.11.2015): 415. http://dx.doi.org/10.12731/2218-7405-2015-9-31.

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23

Pollard, John. "Mathematical Models of Population". Population Studies 47, nr 2 (1.07.1993): 369. http://dx.doi.org/10.1080/0032472031000147136.

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24

Gavaghan, David, Alan Garny, Philip K. Maini i Peter Kohl. "Mathematical models in physiology". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 364, nr 1842 (22.03.2006): 1099–106. http://dx.doi.org/10.1098/rsta.2006.1757.

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Computational modelling of biological processes and systems has witnessed a remarkable development in recent years. The search-term ( modelling OR modeling ) yields over 58 000 entries in PubMed, with more than 34 000 since the year 2000: thus, almost two-thirds of papers appeared in the last 5–6 years, compared to only about one-third in the preceding 5–6 decades. The development is fuelled both by the continuously improving tools and techniques available for bio-mathematical modelling and by the increasing demand in quantitative assessment of element inter-relations in complex biological systems. This has given rise to a worldwide public domain effort to build a computational framework that provides a comprehensive theoretical representation of integrated biological function—the Physiome. The current and next issues of this journal are devoted to a small sub-set of this initiative and address biocomputation and modelling in physiology, illustrating the breadth and depth of experimental data-based model development in biological research from sub-cellular events to whole organ simulations.

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25

Mayergoyz, I. D. "Mathematical Models of Hysteresis". Physical Review Letters 56, nr 15 (14.04.1986): 1518–21. http://dx.doi.org/10.1103/physrevlett.56.1518.

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Traykov, M., i Iv Trenchev. "Mathematical models in genetics". Russian Journal of Genetics 52, nr 9 (wrzesień 2016): 985–92. http://dx.doi.org/10.1134/s1022795416080135.

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27

Scherer, Almut, i Angela McLean. "Mathematical models of vaccination". British Medical Bulletin 62, nr 1 (1.07.2002): 187–99. http://dx.doi.org/10.1093/bmb/62.1.187.

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28

Huheey, James E. "Mathematical Models of Mimicry". American Naturalist 131 (czerwiec 1988): S22—S41. http://dx.doi.org/10.1086/284765.

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29

Raup, David M. "Mathematical models of cladogenesis". Paleobiology 11, nr 1 (1985): 42–52. http://dx.doi.org/10.1017/s0094837300011386.

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The evolutionary pattern of speciation and extinction in any biologic group may be described by a variety of mathematical models. These models provide a framework for describing the history of taxonomic diversity (clade shape) and other aspects of larger evolutionary patterns. The simplest model assumes time homogeneity: that is, speciation and extinction probabilities are constant through time and within taxonomic groups. In some cases the homogeneous model provides a good fit to real world paleontological data, but in other cases the model serves only as a null hypothesis that must be rejected before more complex models can be applied. In cases where the homogeneous model does not fit the data, time-inhomogeneous models can be formulated that specify change, regular or episodic, in speciation and extinction probabilities. An appendix provides a list of the most useful equations based on the homogeneous model.

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30

Huggett, R. J. "Mathematical models in agriculture". Applied Geography 5, nr 2 (kwiecień 1985): 172. http://dx.doi.org/10.1016/0143-6228(85)90042-6.

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31

Skogerboe, Gaylord V. "Mathematical models in agriculture". Ecological Modelling 32, nr 4 (lipiec 1986): 317–19. http://dx.doi.org/10.1016/0304-3800(86)90099-2.

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32

Traykov, M., i Iv Trenchev. "Mathematical Models in Genetics". Генетика 52, nr 9 (2016): 1089–96. http://dx.doi.org/10.7868/s0016675816080130.

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33

Macki, Jack W., Paolo Nistri i Pietro Zecca. "Mathematical Models for Hysteresis". SIAM Review 35, nr 1 (marzec 1993): 94–123. http://dx.doi.org/10.1137/1035005.

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34

Pritchard, W. G. "Mathematical Models of Running". SIAM Review 35, nr 3 (wrzesień 1993): 359–79. http://dx.doi.org/10.1137/1035088.

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35

Rust, Roland T., i Richard Metters. "Mathematical models of service". European Journal of Operational Research 91, nr 3 (czerwiec 1996): 427–39. http://dx.doi.org/10.1016/0377-2217(95)00316-9.

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36

Othmer, Hans G. "Mathematical models in biology". Mathematical Biosciences 96, nr 1 (wrzesień 1989): 131–33. http://dx.doi.org/10.1016/0025-5564(89)90088-6.

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37

Po Wen Hu i Anupkumar M. Deshmukh. "Applications of mathematical models". Computers & Industrial Engineering 15, nr 1-4 (styczeń 1988): 364–68. http://dx.doi.org/10.1016/0360-8352(88)90113-1.

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38

Liu, Liyuan. "Mathematical models of SARS". Theoretical and Natural Science 14, nr 1 (30.11.2023): 154–57. http://dx.doi.org/10.54254/2753-8818/14/20240924.

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The COVID-19 pandemic ignited renewed efforts in quantitative analysis of the impact of pathogens on human health. The mathematical models were critical in understanding the outbreaks for future preventive and reactive approaches to similar outbreaks. This paper intends to explore and provide an understanding of the SARS-CoV-2 infection process, especially its kinetics. Additionally, the paper aims to provide an overview of the immune systems response to infection, especially the immune systems response to infected cells. This paper relates the symptoms of influenza to the observed symptoms of COVID-19, a disease caused by the coronavirus. The research method employed in this study involves the utilization of differential equations and computational simulations to model infection dynamics. This assumption provides the basis for the majority of the models. The paper also highlights some of the benefits of modelling SARS-CoV-2 and makes recommendations for future studies. Mathematical models provide insights into the dynamics of SARS-CoV-2 infection, aiding in the development of more effective preventive and therapeutic strategies. Further research should explore the integration of real-world data into models to enhance their accuracy.

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39

Nastasi, Giovanni. "Mathematical Models and Simulations". Axioms 13, nr 3 (25.02.2024): 149. http://dx.doi.org/10.3390/axioms13030149.

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40

Staribratov, Ivaylo, i Nikol Manolova. "Application of Mathematical Models in Graphic Design". Mathematics and Informatics LXV, nr 1 (28.02.2022): 72–81. http://dx.doi.org/10.53656/math2022-1-5-app.

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The article shares the practical experience in creating graphic design in the implementation of projects in the field of applied information technology. The creation of digital art is largely based on mathematical models and concepts that give a good perception of graphics, and it is scientifically justified. The STEAM approach is considered with the idea of the transdisciplinary level of integration between mathematics, graphic design and production practice in student education. For the development of projects like logo design, magazine cover and others, we use software specialized in the field of graphic design and computer graphics. For the realization of the considered projects, among which there are also awarded ones, we use CorelDRAW, Adobe InDesign and Desmos.

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41

LEVKIN, Dmytro. "ARCHITECTONICS OF CALCULATED MATHEMATICAL MODELS UNDER UNCERTAINTY". Herald of Khmelnytskyi National University. Technical sciences 309, nr 3 (26.05.2022): 135–37. http://dx.doi.org/10.31891/2307-5732-2022-309-3-135-137.

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This article concerns the improvement of calculated mathematical models of technological, biotechnological, and economic systems. It is necessary to increase the number of considered parameters to increase the accuracy of calculating the parameters of complex systems during mathematical modeling. This leads to the need to solve nonlocal boundary value problems with non-stationary differential equations, to prove the correctness of which it is impossible to apply the traditional theory of existence and unity of solution. Note that after the architecture of boundary value problems assumes the existence of their solution, it is only necessary to prove its uniqueness. To prove the correctness of calculated mathematical models requires neither generalizing the parameters of the goal function and using approximate constraints, which, in turn, will reduce the boundary value problem to a standard form and its correctness will not be in doubt, nor propose a method to prove the correctness of boundary value certain differential equations, which will consider the specific features of the modeled processes. A separate technique must substantiate the correctness of boundary value problems depending on the type of differential equation that describes the physical and economic processes in the simulated systems. This article studied the conditions for the correctness of boundary value problems for differential equations with constant coefficients. It is proved that there is a corresponding boundary value problem for arbitrary homogeneous differential equations. It is defined the parabolic boundary value problems in terms that use constraints from above on the fundamental solution function. The conditions were obtained under which the parabolic boundary value problem exists and cannot exist, respectively. The obtained results will increase the accuracy of the main optimization task of improving the quality of simulated processes.

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42

Vinayak Kishan Nirmale. "Mathematical Models for Infectious Disease Dynamics and Control Strategies". Communications on Applied Nonlinear Analysis 32, nr 1s (25.10.2024): 54–62. http://dx.doi.org/10.52783/cana.v32.2101.

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The study explores mathematical models related to the dynamics of infectious diseases and approaches to manage them. These models are essential resources for comprehending how illnesses spread throughout people and assessing how well control strategies work. The study of infectious disease epidemiology heavily relies on mathematical modeling and analysis. Here, we give a clear overview of the spread of disease, explain how to mathematically model this stochastic process, and show how to utilize this mathematical representation to analyze the emergent dynamics of real-world epidemics. The advancement of mathematical analysis and modeling is crucial to our expanding comprehension of the evolution and ecology of pathogens. This study emphasizes how important mathematical modeling is to improving our knowledge of the dynamics of infectious diseases and how crucial it is to creating efficient management plans to lessen the burden of infectious diseases on public health.

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43

Leangarun, Teema, Poj Tangamchit i Suttipong Thajchayapong. "Stock Price Manipulation Detection Based on Mathematical Models". International Journal of Trade, Economics and Finance 7, nr 3 (czerwiec 2016): 81–88. http://dx.doi.org/10.18178/ijtef.2016.7.3.503.

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44

Ojha, Pratima, i R. K. Dubey R.K.Dubey. "Mathematical Properties of Homogeneous and Isotropic Cosmological Models". International Journal of Scientific Research 2, nr 2 (1.06.2012): 83–84. http://dx.doi.org/10.15373/22778179/feb2013/30.

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45

ZAVGORODNIY, OLEXIY, DMYTRO LEVKIN, YANA KOTKO i OLEXANDER MAKAROV. "RESEARCH OF COMPUTATIONAL MATHEMATICAL MODELS FOR TECHNICAL SYSTEMS". Herald of Khmelnytskyi National University. Technical sciences 319, nr 2 (27.04.2023): 108–12. http://dx.doi.org/10.31891/2307-5732-2023-319-1-108-112.

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In the theory of analysis and synthesis of technical systems, mathematical modelling and optimization of multilayer systems containing sources of physical fields occupy an important place. This is due to the fact that their state is described by means of boundary value problems with multidimensional differential equations. To solve the boundary value problems and implement the process of optimizing the technical parameters of the modelled systems, it is necessary to conduct interdisciplinary studies of computational and applied optimization mathematical models. Fulfilment of the conditions for the existence of a single solution to boundary value problems by default is possible only when the object of study is a single-layer material under the action of load sources. If it is necessary to calculate and optimize the technical parameters of a multilayer material subjected to load sources, then it is impossible to immediately guarantee the correctness of the calculated and applied optimization mathematical models, since it is necessary to obtain the conditions for the existence and uniqueness of solutions to boundary value problems with systems of differential equations. Maximizing the technical parameters of load sources and averaging the characteristics of material layers will lead to approximate values of the objective function and technical parameters of the modelled system, which leads to irrational consumption of energy and heat resources and uncontrolled losses, and useless losses of the test material in the technological process. The article presents the conditions for the correctness of multipoint boundary value problems with multidimensional differential equations describing the state of a multilayer material under thermal action. It is advisable to use these studies to substantiate the correctness of other technical and biotechnological systems, which will increase the accuracy of the implementation of applied optimization problems of economic and mathematical modelling.

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Purwanto, Burhan Eko, Icha Jusmalisa, Indah Permata Sari, Agus Jatmiko i Andika Eko Pasetiyo. "Learning Models to Improved Mathematical Communication Skills". Desimal: Jurnal Matematika 3, nr 1 (23.01.2020): 7–16. http://dx.doi.org/10.24042/djm.v3i1.5650.

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The problem faced by students in slow learning is in terms of communication. The use of Auditory, Intellectually, Repetition (AIR) and Cooperative Think Pair Share (TPS) types is needed to help students communicate mathematically in expressing mathematical ideas. This study aims to determine whether or not there are differences in mathematical communication skills of students using the Auditory, Intellectually, Repetition (AIR) learning model with students who use Think Pair Share (TPS) Cooperative learning models. This research uses a quantitative approach using the Quasi Experiment method. The research design is in the form of posttest only, non-equivalent group design. Testing data using the T test with Independent Samples T-Test. Based on the results of hypothesis testing obtained p-value> α 0.05. So it was concluded that there were indications of differences between Auditory, Intellectually, Repetition (AIR) learning models with (TPS) Cooperative learning models and superior (AIR) learning models compared to Cooperative learning models of (TPS) Type in influencing students' mathematical communication skills.

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47

Toxirjonovich, Orinov Nodirbek, i Mirzaaxmedov Muhammadbobur Karimberdiyevich. "Mathematical models of technical systems". ACADEMICIA: An International Multidisciplinary Research Journal 10, nr 11 (2020): 221–25. http://dx.doi.org/10.5958/2249-7137.2020.01343.9.

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Tedeschi, Luís Orlindo, Danny Gene Fox, Roberto Daniel Sainz, Luís Gustavo Barioni, Sérgio Raposo de Medeiros i Celso Boin. "Mathematical models in ruminant nutrition". Scientia Agricola 62, nr 1 (styczeń 2005): 76–91. http://dx.doi.org/10.1590/s0103-90162005000100015.

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Mathematical models can be used to improve performance, reduce cost of production, and reduce nutrient excretion by accounting for more of the variation in predicting requirements and feed utilization in each unique production situation. Mathematical models can be classified into five or more categories based on their nature and behavior. Determining the appropriate level of aggregation of equations is a major problem in formulating models. The most critical step is to describe the purpose of the model and then to determine the appropriate mix of empirical and mechanistic representations of physiological functions, given development and evaluation dataset availability, inputs typically available and the benefits versus the risks of use associated with increased sensitivity. We discussed five major feeding systems used around the world. They share common concepts of energy and nutrient requirement and supply by feeds, but differ in structure and application of the concepts. Animal models are used for a variety of purposes, including the simple description of observations, prediction of responses to management, and explanation of biological mechanisms. Depending upon the objectives, a number of different approaches may be used, including classical algebraic equations, predictive empirical relationships, and dynamic, mechanistic models. The latter offer the best opportunity to make full use of the growing body of knowledge regarding animal biology. Continuing development of these types of models and computer technology and software for their implementation holds great promise for improvements in the effectiveness with which fundamental knowledge of animal function can be applied to improve animal agriculture and reduce its impact on the environment.

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Dolganova, Olga, i Valeriy Lokhov. "Mathematical models of growth deformation". PNRPU Mechanics Bulletin 1 (30.03.2014): 126–41. http://dx.doi.org/10.15593/2224-9893/2014.1.06.

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Eggermont, J. J. "Mathematical Models for Developmental Changes". Acta Oto-Laryngologica 99, sup421 (styczeń 1985): 102–7. http://dx.doi.org/10.3109/00016488509121763.

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